http://www.mdpi.com/journal/remotesensing Latest open access articles published in Remote Sens. at http://www.mdpi.com/journal/remotesensing/ http://www.mdpi.com/2072-4292/2/3/794/ Satellite observations of the oceans have great potential to improve the quality and predictive power of numerical ocean models and are frequently used in model skill assessment as well as data assimilation. In this study we introduce and compare various measures for the quantitative comparison of satellite images and model output that have not been used in this context before. We devised a series of test to compare their performance, including their sensitivity to noise and missing values, which are ubiquitous in satellite images. Our results show that two of our adapted measures, the Adapted Gray Block distance and the entropic distance D2, perform better than the commonly used root mean square error and image correlation. http://www.mdpi.com/2072-4292/2/3/794/ Fri, 19 Mar 2010 00:00:00 CET Remote Sensing 2010-03-19 2 3 Article 794 818 2072-4292 Introduction and Assessment of Measures for Quantitative Model-Data Comparison Using Satellite Images 2010-03-19 doi: 10.3390/rs2030794 Mattern Fennel Dowd http://www.mdpi.com/2072-4292/2/3/777/ This paper presents the results of snow cover monitoring studies in Liaoning Province, northeastern China, using MODIS data. Snow cover plays an important role in both the regional water balance and soil moisture properties during the early spring in northeastern China. In addition, heavy snowfalls commonly trigger hazards such as flooding, caused by rapid snow melt, or crop failure, resulting from fluctuations in soil temperature associated with changes in the snow cover. The latter is a function of both regional, or global, climatic changes, as well as fluctuations in the albedo resulting from variations in the Snow Covered Area (SCA). These impacts are crucial to human activities, especially to those living in middle-latitude areas such as Liaoning Province. Thus, SCA monitoring is currently an important tool in studies of global climate change, particularly because satellite remote sensing data provide timely and efficient snow cover information for large areas. In this study, MODIS L1B data, MODIS Daily Snow Products (MOD10A1) and MODIS 8-day Snow Products (MOD10A2) were used to monitor the SCA of Liaoning Province over the winter months of November–April, 2006–2008. The effects of cloud masking and forest masking on the snow monitoring results were also assessed. The results show that the SCA percentage derived from MODIS L1B data is relatively consistent, but slightly higher than that obtained from MODIS Snow Products. In situ data from 25 snow stations were used to assess the accuracy of snow cover monitoring from the SCA compared to the results from MODIS Snow Products. The studies found that the SCA results were more reliable than MODIS Snow Products in the study area. http://www.mdpi.com/2072-4292/2/3/777/ Wed, 17 Mar 2010 00:00:00 CET Remote Sensing 2010-03-17 2 3 Article 777 793 2072-4292 Snow Cover Monitoring Using MODIS Data in Liaoning Province, Northeastern China 2010-03-17 doi: 10.3390/rs2030777 Zhang Yan Lu http://www.mdpi.com/2072-4292/2/3/758/ In this study we use long-term satellite, climate, and crop observations to document the spatial distribution of the recent stagnation in food grain production affecting the water-limited tropics (WLT), a region where 1.5 billion people live and depend on local agriculture that is constrained by chronic water shortages. Overall, our analysis shows that the recent stagnation in food production is corroborated by satellite data. The growth rate in annually integrated vegetation greenness, a measure of crop growth, has declined significantly (p < 0.10) in 23% of the WLT cropland area during the last decade, while statistically significant increases in the growth rates account for less than 2%. In most countries, the decade-long declines appear to be primarily due to unsustainable crop management practices rather than climate alone. One quarter of the statistically significant declines are observed in India, which with the world’s largest population of food-insecure people and largest WLT croplands, is a leading example of the observed declines. Here we show geographically matching patterns of enhanced crop production and irrigation expansion with groundwater that have leveled off in the past decade. We estimate that, in the absence of irrigation, the enhancement in dry-season food grain production in India, during 1982–2002, would have required an increase in annual rainfall of at least 30% over almost half of the cropland area. This suggests that the past expansion of use of irrigation has not been sustainable. We expect that improved surface and groundwater management practices will be required to reverse the recent food grain production declines. http://www.mdpi.com/2072-4292/2/3/758/ Thu, 11 Mar 2010 00:00:00 CET Remote Sensing 2010-03-11 2 3 Article 758 776 2072-4292 Decadal Variations in NDVI and Food Production in India 2010-03-11 doi: 10.3390/rs2030758 Cristina Milesi Arindam Samanta Hirofumi Hashimoto K. Krishna Kumar Sangram Ganguly Prasad S. Thenkabail Ashok N. Srivastava Ramakrishna R. Nemani Ranga B. Myneni http://www.mdpi.com/2072-4292/2/3/740/ During the last twenty years, numerous agricultural and farming areas of Cuba have seen a marked increase in invading plants; among the most common species found is the Marabú (Dychrostachys cinerea) and the Aroma (Acacia farnesiana). In the present study, an analysis was carried out of the expansion of these species over the last two decades, in the river basin of the Guanabo (17 km north-east of Havana). This was done by digital processing of satellite images and an analysis of the spatial and statistical data of the Geographical Information System (GIS). The zones most affected by this scrubland were mapped and a study of how natural factors may have influenced land use and the tendency of these species to increase was carried out. http://www.mdpi.com/2072-4292/2/3/740/ Tue, 09 Mar 2010 00:00:00 CET Remote Sensing 2010-03-09 2 3 Article 740 757 2072-4292 An Analysis of the Spatial Colonization of Scrubland Intrusive Species in the Itabo and Guanabo Watershed, Cuba 2010-03-09 doi: 10.3390/rs2030740 Jose Damian Ruiz Sinoga Ricardo Remond Noa Danai Fernandez Perez http://www.mdpi.com/2072-4292/2/3/717/ Near-space, defined as the region between 20 km and 100 km, offers many new capabilities that are not accessible to low earth orbit (LEO) satellites and airplanes, because it is above storm and not constrained by either the orbital mechanics of satellites or the high fuel consumption of airplanes. By placing radar transmitter/receiver in near-space platforms, many functions that are currently performed with satellites or airplanes could be performed in a cheaper way. Inspired by these advantages, this paper introduces several near-space vehicle-based radar configurations, such as near-space passive bistatic radar and high-resolution wide-swath (HRWS) synthetic aperture radar (SAR). Their potential applications, technical challenges and possible solutions are investigated. It is shown that near-space is a satisfactory solution to some specific remote sensing applications. Firstly, near-space passive bistatic radar using opportunistic illuminators offers a solution to persistent regional remote sensing, which is particularly interest for protecting homeland security or monitoring regional environment. Secondly, near-space provides an optimal solution to relative HRWS SAR imaging. Moreover, as motion compensation is a common technical challenge for the described radars, an active transponder-based motion compensation is also described. http://www.mdpi.com/2072-4292/2/3/717/ Tue, 09 Mar 2010 00:00:00 CET Remote Sensing 2010-03-09 2 3 Review 717 739 2072-4292 Near-Space Microwave Radar Remote Sensing: Potentials and Challenge Analysis 2010-03-09 doi: 10.3390/rs2030717 Wen-Qin Wang Jingye Cai Qicong Peng http://www.mdpi.com/2072-4292/2/3/697/ Mediterranean coastal areas are experiencing rapid land cover change caused by human-induced land degradation and extreme climatic events. Vegetation index time series provide a useful way to monitor vegetation phenological variations. This study quantitatively describes Enhanced Vegetation Index (EVI) temporal changes for Mediterranean land-covers from the perspective of vegetation phenology and its relation with climate. A time series from 2001 to 2007 of the MODIS Enhanced Vegetation Index 16-day composite (MOD13Q1) was analyzed to extract anomalies (by calculating z-scores) and frequency domain components (by the Fourier Transform). Vegetation phenology analyses were developed for diverse land-covers for an area in south Alicante (Spain) providing a useful way to analyze and understand the phenology associated to those land-covers. Time series of climatic variables were also analyzed through anomaly detection techniques and the Fourier Transform. Correlations between EVI time series and climatic variables were computed. Temperature, rainfall and radiation were significantly correlated with almost all land-cover classes for the harmonic analysis amplitude term. However, vegetation phenology was not correlated with climatic variables for the harmonic analysis phase term suggesting a delay between climatic variations and vegetation response. http://www.mdpi.com/2072-4292/2/3/697/ Tue, 02 Mar 2010 00:00:00 CET Remote Sensing 2010-03-02 2 3 Article 697 716 2072-4292 Land-Cover Phenologies and Their Relation to Climatic Variables in an Anthropogenically Impacted Mediterranean Coastal Area 2010-03-02 doi: 10.3390/rs2030697 Ignacio Melendez-Pastor Jose Navarro-Pedreño Magaly Koch Ignacio Gómez Encarni I. Hernández http://www.mdpi.com/2072-4292/2/3/673/ Spatial variability in a crop field creates a need for precision agriculture. Economical and rapid means of identifying spatial variability is obtained through the use of geotechnology (remotely sensed images of the crop field, image processing, GIS modeling approach, and GPS usage) and data mining techniques for model development. Higher-end image processing techniques are followed to establish more precision. The goal of this paper was to investigate the strength of key spectral vegetation indices for agricultural crop yield prediction using neural network techniques. Four widely used spectral indices were investigated in a study of irrigated corn crop yields in the Oakes Irrigation Test Area research site of North Dakota, USA. These indices were: (a) red and near-infrared (NIR) based normalized difference vegetation index (NDVI), (b) green and NIR based green vegetation index (GVI), (c) red and NIR based soil adjusted vegetation index (SAVI), and (d) red and NIR based perpendicular vegetation index (PVI). These four indices were investigated for corn yield during 3 years (1998, 1999, and 2001) and for the pooled data of these 3 years. Initially, Back-propagation Neural Network (BPNN) models were developed, including 16 models (4 indices * 4 years including the data from the pooled years) to test for the efficiency determination of those four vegetation indices in corn crop yield prediction. The corn yield was best predicted using BPNN models that used the means and standard deviations of PVI grid images. In all three years, it provided higher prediction accuracies, coefficient of determination (r2), and lower standard error of prediction than the models involving GVI, NDVI, and SAVI image information. The GVI, NDVI, and SAVI models for all three years provided average testing prediction accuracies of 24.26% to 94.85%, 19.36% to 95.04%, and 19.24% to 95.04%, respectively while the PVI models for all three years provided average testing prediction accuracies of 83.50% to 96.04%. The PVI pool model provided better average testing prediction accuracy of 94% with respect to other vegetation models, for which it ranged from 89–93%. Similarly, the PVI pool model provided coefficient of determination (r2) value of 0.45 as compared to 0.31–0.37 for other index models. Log10 data transformation technique was used to enhance the prediction ability of the PVI models of years 1998, 1999, and 2001 as it was chosen as the preferred index. Another model (Transformed PVI (Pool)) was developed using the log10 transformed PVI image information to show its global application. The transformed PVI models provided average corn yield prediction accuracies of 90%, 97%, and 98% for years 1998, 1999, and 2001, respectively. The pool PVI transformed model provided as average testing accuracy of 93% along with r2 value of 0.72 and standard error of prediction of 0.05 t/ha. http://www.mdpi.com/2072-4292/2/3/673/ Mon, 01 Mar 2010 00:00:00 CET Remote Sensing 2010-03-01 2 3 Article 673 696 2072-4292 Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques 2010-03-01 doi: 10.3390/rs2030673 Sudhanshu Sekhar Panda Daniel P. Ames Suranjan Panigrahi http://www.mdpi.com/2072-4292/2/3/665/ Measurements of the near surface horizontal wind field in a hurricane with spatial resolution of order 1–10 km are possible using airborne microwave radiometer imagers. An assessment is made of the information content of the measured winds as a function of the spatial resolution of the imager. An existing algorithm is used which estimates the maximum surface air pressure depression in the hurricane eye from the maximum wind speed. High resolution numerical model wind fields from Hurricane Frances 2004 are convolved with various HIRAD antenna spatial filters to observe the impact of the antenna design on the central pressure depression in the eye that can be deduced from it. http://www.mdpi.com/2072-4292/2/3/665/ Mon, 01 Mar 2010 00:00:00 CET Remote Sensing 2010-03-01 2 3 Article 665 672 2072-4292 Impact of Spatial Resolution on Wind Field Derived Estimates of Air Pressure Depression in the Hurricane Eye 2010-03-01 doi: 10.3390/rs2030665 Ruba Amarin Christopher Ruf Linwood Jones http://www.mdpi.com/2072-4292/2/3/641/ Accurate road environment information is needed in applications such as road maintenance and virtual 3D city modelling. Vehicle-based laser scanning (VLS) can produce dense point clouds from large areas efficiently from which the road and its environment can be modelled in detail. Pole-like objects such as traffic signs, lamp posts and tree trunks are an important part of road environments. An automatic method was developed for the extraction of pole-like objects from VLS data. The method was able to find 77.7% of the poles which were found by a manual investigation of the data. Correctness of the detection was 81.0%. http://www.mdpi.com/2072-4292/2/3/641/ Fri, 26 Feb 2010 00:00:00 CET Remote Sensing 2010-02-26 2 3 Article 641 664 2072-4292 Detection of Vertical Pole-Like Objects in a Road Environment Using Vehicle-Based Laser Scanning Data 2010-02-26 doi: 10.3390/rs2030641 Matti Lehtomäki Anttoni Jaakkola Juha Hyyppä Antero Kukko Harri Kaartinen http://www.mdpi.com/2072-4292/2/3/611/ Recent work has shown that more research is needed in applying fractal analysis to multi-resolution remote sensing data for landscape characterization. The purpose of this study was to closely examine the impacts that spatial and spectral resolutions have on fractal dimensions using real-world multi-resolution remotely sensed data as opposed to the more conventional single resolution and aggregation approach. The study focused on fractal analysis of forested landscapes in the southeastern United States and Central America. Initially, the effects of spatial resolution on the computed fractal dimensions were examined using data from three instruments with different spatial resolutions. Based on the criteria of mean value and variation within the accepted ranges of fractal dimensions, it was determined that 30-m Landsat TM data were best able to capture the complexity of a forested landscape in Central America compared to 4-m IKONOS data and 250-m MODIS data. Also, among the spectral bands of Landsat TM images of four national forests in the southeastern United States, tests showed that the spatial indices of fractal dimensions are much more distinguishable in the visible bands than they are in the near-mid infrared bands. Thus, based solely on the fractal analysis, the fractal dimensions could have relatively higher chances to distinguish forest characteristics (e.g., stand sizes and species) in the Landsat TM visible wavelength bands than in the near-mid infrared bands. This study has focused on a relative comparison between visible and near-mid infrared wavelength bands; however it will be important to study in the future the effect of a combination of those bands such as the Normalized Difference Vegetation Index (NDVI) on fractal dimensions of forested landscapes. http://www.mdpi.com/2072-4292/2/3/611/ Fri, 26 Feb 2010 00:00:00 CET Remote Sensing 2010-02-26 2 3 Article 611 640 2072-4292 Effects of Spatial and Spectral Resolutions on Fractal Dimensions in Forested Landscapes 2010-02-26 doi: 10.3390/rs2030611 Mohammad Al-Hamdan James Cruise Douglas Rickman Dale Quattrochi http://www.mdpi.com/2072-4292/2/2/591/ Forest fires pose a threat more serious than illegal felling in developing countries and are a cause of major concern for environmental security. Fires in tropical forests, though not devastating on a large scale as compared to large and infrequent fires in boreal or Mediterranean systems, still cause loss to biodiversity and economic and monetary value. In India, human-induced forest fires increasingly affect legally protected nature conservation areas. An array of satellite sensors that are now available can be deployed to monitor such events on a global and local scale. The present study uses night-time Advanced Along Track Scanning Radiometer (A)ATSR satellite data from the last nine years to identify high fire-prone zones, fire affected areas in protected zones and the distribution of these incidents in relation to bio-geographic zones. Central India, with its vegetation type that is just right for fire ignition and spread, was observed to be the most severely affected area with maximum fire incidences. The bio-geographic zone comprising this area–such as the Deccan peninsula, which includes provinces like Central Highlands, Eastern Highlands, Central Plateau and Chhota Nagpur–was observed to be the most affected, accounting for approximately 36% of the total fire occurrences during the period 1997–2005. In protected areas, 778 fire incidents were observed within the last eight years. Comparison of (A)ATSR fire locations with MODIS active fire data for the Western Ghats (mainly of tropical evergreen forests and savannahs) and the Eastern Ghats (tropical deciduous) showed a spatial agreement of 72% with a minimum distance between the two products of 100 m. This study focuses on regions in India that are vulnerable to forest fires during specific time-frames and appraises the situation with an aim to minimize such incidents, if not completely stop the fire spread and its consequent destruction and loss. Our main objective is to understand seasonal and spatial variation in fire pattern and to identify zones of frequent burning. http://www.mdpi.com/2072-4292/2/2/591/ Wed, 24 Feb 2010 00:00:00 CET Remote Sensing 2010-02-24 2 2 Article 591 610 2072-4292 Tracking Fires in India Using Advanced Along Track Scanning Radiometer (A)ATSR Data 2010-02-24 doi: 10.3390/rs2020591 Amarnath Giriraj Shilpa Babar Anke Jentsch Singuluri Sudhakar Manchi Sri Ramachandra Murthy http://www.mdpi.com/2072-4292/2/2/579/ Tillage information is crucial for environmental modeling as it directly affects evapotranspiration, infiltration, runoff, carbon sequestration, and soil losses due to wind and water erosion from agricultural fields. However, collecting this information can be time consuming and costly. Remote sensing approaches are promising for rapid collection of tillage information on individual fields over large areas. Numerous regression-based models are available to derive tillage information from remote sensing data. However, these models require information about the complex nature of underlying watershed characteristics and processes. Unlike regression-based models, Artificial Neural Network (ANN) provides an efficient alternative to map complex nonlinear relationships between an input and output datasets without requiring a detailed knowledge of underlying physical relationships. Limited or no information currently exist quantifying ability of ANN models to identify contrasting tillage practices from remote sensing data. In this study, a set of Landsat TM-based ANN models was developed to identify contrasting tillage practices in the Texas High Plains. Observed tillage data from Moore and Ochiltree Counties were used to develop and evaluate the models, respectively. The overall classification accuracy for the 15 models developed with the Moore County dataset varied from 74% to 91%. Statistical evaluation of these models against the Ochiltree County dataset produced results with an overall classification accuracy varied from 66% to 80%. The ANN models based on TM band 5 or indices of TM Band 5 may provide consistent and accurate tillage information when applied to the Texas High Plains. http://www.mdpi.com/2072-4292/2/2/579/ Tue, 23 Feb 2010 00:00:00 CET Remote Sensing 2010-02-23 2 2 Article 579 590 2072-4292 Artificial Neural Network Approach for Mapping Contrasting Tillage Practices 2010-02-23 doi: 10.3390/rs2020579 K. P. Sudheer Prasanna Gowda Indrajeet Chaubey Terry Howell http://www.mdpi.com/2072-4292/2/2/562/ The paper investigates the value of using distinct vegetation indices to quantify and characterize agricultural crop characteristics at different growth stages. Research was conducted on four crops (corn, soybean, wheat, and canola) over eight years grown under different tillage practices and nitrogen management practices that varied rate and timing. Six different vegetation indices were found most useful, depending on crop phenology and management practices: (a) simple ratio for biomass, (b) NDVI for intercepted PAR, (c) SAVI for early stages of LAI, (d) EVI for later stages of LAI, (e) CIgreen for leaf chlorophyll, (f) NPCI for chlorophyll during later stages, and (g) PSRI to quantify plant senescence. There were differences among varieties of corn and soybean for the vegetation indices during the growing season and these differences were a function of growth stage and vegetative index. These results clearly imply the need to use multiple vegetation indices to best capture agricultural crop characteristics. http://www.mdpi.com/2072-4292/2/2/562/ Tue, 23 Feb 2010 00:00:00 CET Remote Sensing 2010-02-23 2 2 Article 562 578 2072-4292 Value of Using Different Vegetative Indices to Quantify Agricultural Crop Characteristics at Different Growth Stages under Varying Management Practices 2010-02-23 doi: 10.3390/rs2020562 Jerry L. Hatfield John H. Prueger http://www.mdpi.com/2072-4292/2/2/545/ Influences of soil line variations on two-band vegetation indices (VIs) and their vegetation isolines in red and near-infrared (NIR) reflectance space are investigated based on recently derived relationships between the relative variations of VIs with variations of the soil line parameters in the accompanying paper by Yoshioka et al. [1]. The soil line influences are first demonstrated numerically in terms of variations of vegetation isolines and VI values along with the isolines. A hypothetical case is then analyzed by assuming the discrepancies between the general and regional soil lines for a Southern Brazil area reported elsewhere. The results indicate the validity of our analytical approach for the evaluation of soil line influences and the applicability for adjustment of VI errors using external data sources of soil reflectance spectra. http://www.mdpi.com/2072-4292/2/2/545/ Fri, 12 Feb 2010 00:00:00 CET Remote Sensing 2010-02-12 2 2 Article 545 561 2072-4292 Soil Line Influences on Two-Band Vegetation Indices and Vegetation Isolines: A Numerical Study 2010-02-12 doi: 10.3390/rs2020545 Hiroki Yoshioka Tomoaki Miura José A. M. Demattê Karim Batchily Alfredo R. Huete http://www.mdpi.com/2072-4292/2/2/526/ This study introduces a new geographic framework, phenological classification, for the conterminous United States based on Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time-series data and a digital elevation model. The resulting pheno-class map is comprised of 40 pheno-classes, each having unique phenological and topographic characteristics. Cross-comparison of the pheno-classes with the 2001 National Land Cover Database indicates that the new map contains additional phenological and climate information. The pheno-class framework may be a suitable basis for the development of an Advanced Very High Resolution Radiometer (AVHRR)-MODIS NDVI translation algorithm and for various biogeographic studies. http://www.mdpi.com/2072-4292/2/2/526/ Thu, 11 Feb 2010 00:00:00 CET Remote Sensing 2010-02-11 2 2 Article 526 544 2072-4292 Phenological Classification of the United States: A Geographic Framework for Extending Multi-Sensor Time-Series Data 2010-02-11 doi: 10.3390/rs2020526 Yingxin Gu Jesslyn F. Brown Tomoaki Miura Willem J. D. van Leeuwen Bradley C. Reed http://www.mdpi.com/2072-4292/2/2/514/ Disturbance plays a fundamental role in determining the vertical structure of vegetation in many terrestrial ecosystems, and knowledge of disturbance histories is vital for developing effective management and restoration plans. In this study, we investigated the potential of using vertical vegetation profiles derived from discrete-return lidar to predict time since fire (TSF) in a landscape of oak scrub in east-central Florida. We predicted that fire influences vegetation structure at the mesoscale (i.e., spatial scales of tens of meters to kilometers). To evaluate this prediction, we binned lidar returns into 1m vertical by 5 × 5 m horizontal cells and averaged the resulting profiles over a range of horizontal window sizes (0 to 500 m on a side). We then performed a series of resampling tests to compare the performance of support vector machine (SVM), k-nearest neighbor (k-NN), logistic regression, and linear discriminant analysis (LDA) classifiers and to estimate the amount of training data necessary to achieve satisfactory performance. Our results indicate that: (1) the SVMs perform significantly better than the other classifiers, (2) SVM classifiers may require relatively small training data sets, and (3) the highest classification accuracies occur with averaging over windows representing sizes in the mesoscale range. http://www.mdpi.com/2072-4292/2/2/514/ Thu, 11 Feb 2010 00:00:00 CET Remote Sensing 2010-02-11 2 2 Communication 514 525 2072-4292 Using Lidar-Derived Vegetation Profiles to Predict Time since Fire in an Oak Scrub Landscape in East-Central Florida 2010-02-11 doi: 10.3390/rs2020514 James J. Angelo Brean W. Duncan John F. Weishampel http://www.mdpi.com/2072-4292/2/2/497/ Urban farming, practiced by about 800 million people globally, has contributed significantly to food security and food safety. The practice has sustained livelihood of the urban and peri-urban low income dwellers in developing countries for many years. Its popularity among the urban low income is largely due to lack of formal jobs and as a means of adding up to household income. There is increasing need to sustainably manage urban farming in developing nations in recent times. Population increase due to rural-urban migration and natural, coupled with infrastructure developments are competing with urban farming for available space and scarce resources such as water for irrigation. Lack of reliable data on the extent of urban/peri-urban areas being used for farming has affected developing sustainable policies to manage urban farming in Accra. Using ground based survey methods to map the urban farmlands are inherently problematic and prohibitively expensive. This has influenced accurate assessment of the future role of urban farming in enhancing food security. Remote sensing, however, allows areas being used as urban farmlands to be rapidly established at relatively low cost. This paper will review advances in the use of remote sensing technology to develop an integrated monitoring technique for urban farmlands in Accra. http://www.mdpi.com/2072-4292/2/2/497/ Tue, 02 Feb 2010 00:00:00 CET Remote Sensing 2010-02-02 2 2 Article 497 513 2072-4292 Urban and Peri-Urban Agriculture in Developing Countries Studied using Remote Sensing and In Situ Methods 2010-02-02 doi: 10.3390/rs2020497 Kwasi Appeaning Addo http://www.mdpi.com/2072-4292/2/2/478/ While high expectations have been raised about the utility of high resolution satellite imagery for biodiversity assessment, there has been almost no empirical assessment of its use, particularly in the biodiverse tropics which represent a very challenging environment for such assessment challenge. This research evaluates the use of high spatial resolution (IKONOS) and medium spatial resolution (Landsat ETM+) satellite imagery for assessing vegetation diversity in a dry tropical forest in central India. Contrary to expectations, across multiple measures of plant distribution and diversity, the resolution of IKONOS data is too fine for the purpose of plant diversity assessment and Landsat imagery performs better. http://www.mdpi.com/2072-4292/2/2/478/ Tue, 02 Feb 2010 00:00:00 CET Remote Sensing 2010-02-02 2 2 Article 478 496 2072-4292 Assessing Plant Diversity in a Dry Tropical Forest: Comparing the Utility of Landsat and Ikonos Satellite Images 2010-02-02 doi: 10.3390/rs2020478 Harini Nagendra Duccio Rocchini Rucha Ghate Bhawna Sharma Sajid Pareeth http://www.mdpi.com/2072-4292/2/2/464/ The student-built Agricultural Camera (AgCam) now onboard the International Space Station observes the Earth surface through two linescan cameras with Charge-Coupled Device (CCD) arrays sensitive to visible and near-infrared wavelengths, respectively. The electro-optical components of the AgCam were characterized using precision calibration equipment; a method for modeling and applying these measurements was derived. Correction coefficients to minimize effects of optical vignetting, CCD non-uniform quantum efficiency, and CCD dark current are separately determined using a least squares fit approach. Application of correction coefficients yields significant variability reduction in flat-field images; comparable results are obtained when applied to ground test images. http://www.mdpi.com/2072-4292/2/2/464/ Mon, 01 Feb 2010 00:00:00 CET Remote Sensing 2010-02-01 2 2 Article 464 477 2072-4292 Radiometric Calibration for AgCam 2010-02-01 doi: 10.3390/rs2020464 Doug Olsen Changyong Dou Xiaodong Zhang Lianbo Hu Hojin Kim Edward Hildum http://www.mdpi.com/2072-4292/2/2/446/ Time series of fire occurrence, derived from MODIS data, have been used to characterise the spatio-temporal distribution of fire events during the 2004–2009 period in 17 protected areas (PAs) of West and Central Africa, with particular attention to those of the SUN network in Senegal, Burkina Faso, Benin and Niger. The temporal distribution of the fire activity and the number of fire occurences are quite different inside the PAs and in their surrounding area. A progressive increase of the length of the burning season is observed in the West Africa PAs. Quantitatively, the general trend over the last five years is an increase of the fire density (+22%) inside the PAs and a decrease (−27%) outside. The results indicate that the capacity of the PAs to maintain the biological diversity of the region is probably decreasing due to the combined effects of the anthropic pressure inside the PAs and of an on-going isolation process. http://www.mdpi.com/2072-4292/2/2/446/ Fri, 29 Jan 2010 00:00:00 CET Remote Sensing 2010-01-29 2 2 Article 446 463 2072-4292 Interannual Changes of Fire Activity in the Protected Areas of the SUN Network and Other Parks and Reserves of the West and Central Africa Region Derived from MODIS Observations 2010-01-29 doi: 10.3390/rs2020446 Jean-Marie Grégoire Dario Simonetti http://www.mdpi.com/2072-4292/2/2/432/ In this study we compared the accuracy of low-pulse airborne laser scanning (ALS) data, multi-temporal high-resolution noninterferometric TerraSAR-X radar data and a combined feature set derived from these data in the estimation of forest variables at plot level. The TerraSAR-X data set consisted of seven dual-polarized (HH/HV or VH/VV) Stripmap mode images from all seasons of the year. We were especially interested in distinguishing between the tree species. The dependent variables estimated included mean volume, basal area, mean height, mean diameter and tree species-specific mean volumes. Selection of best possible feature set was based on a genetic algorithm (GA). The nonparametric k-nearest neighbour (k-NN) algorithm was applied to the estimation. The research material consisted of 124 circular plots measured at tree level and located in the vicinity of Espoo, Finland. There are large variations in the elevation and forest structure in the study area, making it demanding for image interpretation. The best feature set contained 12 features, nine of them originating from the ALS data and three from the TerraSAR-X data. The relative RMSEs for the best performing feature set were 34.7% (mean volume), 28.1% (basal area), 14.3% (mean height), 21.4% (mean diameter), 99.9% (mean volume of Scots pine), 61.6% (mean volume of Norway spruce) and 91.6% (mean volume of deciduous tree species). The combined feature set outperformed an ALS-based feature set marginally; in fact, the latter was better in the case of species-specific volumes. Features from TerraSAR-X alone performed poorly. However, due to favorable temporal resolution, satellite-borne radar imaging is a promising data source for updating large-area forest inventories based on low-pulse ALS. http://www.mdpi.com/2072-4292/2/2/432/ Thu, 28 Jan 2010 00:00:00 CET Remote Sensing 2010-01-28 2 2 Article 432 445 2072-4292 Comparing Accuracy of Airborne Laser Scanning and TerraSAR-X Radar Images in the Estimation of Plot-Level Forest Variables 2010-01-28 doi: 10.3390/rs2020432 Markus Holopainen Reija Haapanen Mika Karjalainen Mikko Vastaranta Juha Hyyppä Xiaowei Yu Sakari Tuominen Hannu Hyyppä http://www.mdpi.com/2072-4292/2/2/416/ Remotely sensed estimates of crop residue cover (fR) are required to assess the extent of conservation tillage over large areas; the impact of decay processes on estimates of residue cover is unknown. Changes in wheat straw composition and spectral reflectance were measured during the decay process and their impact on estimates of fR were assessed. Proportions of cellulose and hemicellulose declined, while lignin increased. Spectral features associated with cellulose diminished during decomposition. Narrow-band spectral residue indices robustly estimated fR, while broad-band indices were inconsistent. Advanced multi-spectral sensors or hyperspectral sensors are required to assess fR reliably over diverse agricultural landscapes. http://www.mdpi.com/2072-4292/2/2/416/ Wed, 27 Jan 2010 00:00:00 CET Remote Sensing 2010-01-27 2 2 Article 416 431 2072-4292 Spectral Reflectance of Wheat Residue during Decomposition and Remotely Sensed Estimates of Residue Cover 2010-01-27 doi: 10.3390/rs2020416 Craig S. T. Daughtry Guy Serbin James B. Reeves III Paul C. Doraiswamy Earle Raymond Hunt Jr. http://www.mdpi.com/2072-4292/2/2/388/ Climate change and variability are expected to impact the synchronicity and interactions between the Sonoran Desert and the forested sky islands which represent steep biological and environmental gradients. The main objectives were to examine how well satellite greenness time series data and derived phenological metrics (e.g., season start, peak greenness) can characterize specific vegetation communities across an elevation gradient, and to examine the interactions between climate and phenological metrics for each vegetation community. We found that representative vegetation types (11), varying between desert scrub, mesquite, grassland, mixed oak, juniper and pine, often had unique seasonal and interannual phenological trajectories and spatial patterns. Satellite derived land surface phenometrics (11) for each of the vegetation communities along the cline showed numerous distinct significant relationships in response to temperature (4) and precipitation (7) metrics. Satellite-derived sky island vegetation phenology can help assess and monitor vegetation dynamics and provide unique indicators of climate variability and patterns of change. http://www.mdpi.com/2072-4292/2/2/388/ Wed, 27 Jan 2010 00:00:00 CET Remote Sensing 2010-01-27 2 2 Article 388 415 2072-4292 Phenological Characterization of Desert Sky Island Vegetation Communities with Remotely Sensed and Climate Time Series Data 2010-01-27 doi: 10.3390/rs2020388 Willem J.D. van Leeuwen Jennifer E. Davison Grant M. Casady Stuart E. Marsh http://www.mdpi.com/2072-4292/2/1/375/ The aim of this study was to combine the FAO-56 dual approach and remotely-sensed data for mapping water use (ETc) in irrigated wheat crops of a semi-arid region. The method is based on the relationships established between Normalized Difference Vegetation Index (NDVI) and crop biophysical variables such as basal crop coefficient, cover fraction and soil evaporation. A time series of high spatial resolution SPOT and Landsat images acquired during the 2002/2003 agricultural season has been used to generate the profiles of NDVI in each pixel that have been related to crop biophysical parameters which were used in conjunction with FAO-56 dual source approach. The obtained results showed that the spatial distribution of seasonal ETc varied between 200 and 450 mm depending to sowing date and the development of the vegetation. The validation of spatial results showed that the ETc estimated by FAO-56 corresponded well with actual ET measured by eddy covariance system over test sites of wheat, especially when soil evaporation and plant water stress are not encountered. http://www.mdpi.com/2072-4292/2/1/375/ Wed, 20 Jan 2010 00:00:00 CET Remote Sensing 2010-01-20 2 1 Article 375 387 2072-4292 Combining Satellite Remote Sensing Data with the FAO-56 Dual Approach for Water Use Mapping In Irrigated Wheat Fields of a Semi-Arid Region 2010-01-20 doi: 10.3390/rs2010375 Salah Er-Raki Abdelghani Chehbouni Benoit Duchemin http://www.mdpi.com/2072-4292/2/1/352/ The Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency (ESA), launched on November 2009, is an unprecedented initiative to globally monitor surface soil moisture using a novel 2-D L-band interferometric radiometer concept. Airborne campaigns and ground-based field experiments have proven that radiometers operating at L-band are highly sensitive to soil moisture, due to the large contrast between the dielectric constant of soil minerals and water. Still, soil moisture inversion from passive microwave observations is complex, since the microwave emission from soils depends strongly on its moisture content but also on other surface characteristics such as soil type, soil roughness, surface temperature and vegetation cover, and their contributions must be carefully de-coupled in the retrieval process. In the present study, different soil moisture retrieval configurations are examined, depending on whether prior information is used in the inversion process or not. Retrievals are formulated in terms of vertical (Tvv) and horizontal (Thh) polarizations separately and using the first Stokes parameter (TI ), over six main surface conditions combining dry, moist and wet soils with bare and vegetation-covered surfaces. A sensitivity analysis illustrates the influence that the geophysical variables dominating the Earth’s emission at L-band have on the precision of the retrievals, for each configuration. It shows that, if adequate constraints on the ancillary data are added, the algorithm should converge to more accurate estimations. SMOS-like brightness temperatures are also generated by the SMOS End-to-end Performance Simulator (SEPS) to assess the retrieval errors produced by the different cost function configurations. Better soil moisture retrievals are obtained when the inversion is constrained with prior information, in line with the sensitivity study, and more robust estimates are obtained using TI than using Tvv and Thh. This paper analyzes key issues to devise an optimal soil moisture inversion algorithm for SMOS and results can be readily transferred to the upcoming SMOS data to produce the much needed global maps of the Earth’s surface soil moisture. http://www.mdpi.com/2072-4292/2/1/352/ Wed, 20 Jan 2010 00:00:00 CET Remote Sensing 2010-01-20 2 1 Article 352 374 2072-4292 Analysis of a Least-Squares Soil Moisture Retrieval Algorithm from L-band Passive Observations 2010-01-20 doi: 10.3390/rs2010352 María Piles Mercè Vall-llossera Adriano Camps Marco Talone Alessandra Monerris http://www.mdpi.com/2072-4292/2/1/333/ Continuous monitoring of extreme environments, such as the European Alps, is hampered by the sparse and/or irregular distribution of meteorological stations, the difficulties in performing ground surveys and the complexity of interpolating existing station data. Remotely sensed Land Surface Temperature (LST) is therefore of major interest for a variety of environmental and ecological applications. But while MODIS LST data from the Terra and Aqua satellites are aimed at closing the gap between data demand and availability, clouds and other atmospheric disturbances often obscure parts or even the entirety of these satellite images. A novel algorithm is presented in this paper, which is able to reconstruct incomplete MODIS LST maps. All nine years of the available daily LST data (2000–2008) have been processed, allowing the original LST map resolution of 1,000 m to be improved to 200 m, which means the resulting LST maps can be applied at a regional level. Extracted time series and aggregated data are shown as examples and are compared to meteorological station time series as an indication of the quality obtained. http://www.mdpi.com/2072-4292/2/1/333/ Mon, 18 Jan 2010 00:00:00 CET Remote Sensing 2010-01-18 2 1 Article 333 351 2072-4292 Estimating Daily Land Surface Temperatures in Mountainous Environments by Reconstructed MODIS LST Data 2010-01-18 doi: 10.3390/rs1020333 Markus Neteler http://www.mdpi.com/2072-4292/2/1/306/ The Cauvery basin of Karnataka State encompasses a range of land cover types, from dense forest areas and plantations in the Western Ghats hills, to fertile agricultural lands in the river valley. Recent demographic changes, rapid economic development and urbanization have led to the conversion of vast stretches of forested land into plantations and permanent agriculture. We examine the human drivers of forest cover change between 2001 and 2006, using MODIS 250 m data at multiple spatial scales of nested administrative units i.e., districts and taluks. Population density does not emerge as a major driver of forest distribution or deforestation. Protected areas and landscape accessibility play a major role in driving the distribution of stable forest cover at different spatial scales. The availability of forested land for further clearing emerges as a major factor impacting the distribution of deforestation, with new deforestation taking place in regions with challenging topography. This research highlights the importance of using a regional approach to study land cover change, and indicates that the drivers of forest change may be very different in long settled landscapes, for which little is known in comparison to frontier forests. http://www.mdpi.com/2072-4292/2/1/306/ Tue, 12 Jan 2010 00:00:00 CET Remote Sensing 2010-01-12 2 1 Article 306 332 2072-4292 Accessibility, Demography and Protection: Drivers of Forest Stability and Change at Multiple Scales in the Cauvery Basin, India 2010-01-12 doi: 10.3390/rs2010306 Nikhil Lele Harini Nagendra Jane Southworth http://www.mdpi.com/2072-4292/2/1/290/ Payload size and weight are critical factors for small Unmanned Aerial Vehicles (UAVs). Digital color-infrared photographs were acquired from a single 12-megapixel camera that did not have an internal hot-mirror filter and had a red-light-blocking filter in front of the lens, resulting in near-infrared (NIR), green and blue images. We tested the UAV-camera system over two variably-fertilized fields of winter wheat and found a good correlation between leaf area index and the green normalized difference vegetation index (GNDVI). The low cost and very-high spatial resolution associated with the camera-UAV system may provide important information for site-specific agriculture. http://www.mdpi.com/2072-4292/2/1/290/ Mon, 11 Jan 2010 00:00:00 CET Remote Sensing 2010-01-11 2 1 Article 290 305 2072-4292 Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring 2010-01-11 doi: 10.3390/rs2010290 E. Raymond Hunt, Jr. W. Dean Hively Stephen J. Fujikawa David S. Linden Craig S. T. Daughtry Greg W. McCarty http://www.mdpi.com/2072-4292/2/1/278/ Leaf area index (LAI) is one of the most commonly used ecological variables in describing forests. Since 2000, 1-km resolution Moderate Resolution Imaging Spectroradiometer (MODIS)-based 8-day composites of LAI have been operationally available from the National Aeronautics and Space Administration (NASA), USA, at no cost to the user. In this paper, we present a simple protocol to enhance the spatial resolution of NASA-produced LAI composites to 250-m resolution. This is done by fusing MODIS-based estimates of enhanced vegetation index (EVI), consisting of 16-day 250-m resolution composites (also from NASA), with estimates of LAI. We apply the protocol to derive 250-m resolution maps of LAI for the boreal forest region of northern Alberta, Canada. Data fusion was possible in this study because of the inherent linear correlation that exists between EVI and LAI for the April to October growing period of 2005–2008, producing r2-values of 0.85–0.95 and p-values < 0.0001. Comparison of MODIS-based LAI with field-based measurements using the Tracing Radiation and Architecture of Canopies (TRAC) sensor and LAI-2000 Plant Canopy Analyzer showed reasonable agreement across values; statistical comparison of LAI data points produced an r2-value of 0.71 and a p-value < 0.0001. Seventy one percent of MODIS-based LAI were within ±20% of field estimates. http://www.mdpi.com/2072-4292/2/1/278/ Fri, 08 Jan 2010 00:00:00 CET Remote Sensing 2010-01-08 2 1 Article 278 289 2072-4292 Spatial Enhancement of MODIS-based Images of Leaf Area Index: Application to the Boreal Forest Region of Northern Alberta, Canada 2010-01-08 doi: 10.3390/rs2010278 Quazi K. Hassan Charles P.-A. Bourque http://www.mdpi.com/2072-4292/2/1/262/ Thinning of the atmospheric ozone layer leads to elevated levels of Ultraviolet-B (UVB) at the Earth's surface, resulting in an increase of health risks to living organisms due to DNA damage. This paper examines the multidecadal changes of total column ozone from 1979 to 2005 with the aid of ground-based UVB stations using the ultraviolet multifilter rotating shadow-band radiometer (UV-MFRSR). For the purpose of demonstration, four USDA ground stations, WA01, CO01, MD01, and AZ01, were selected for detailed comparisons against the satellite data. The major finding of this study is that over the course of the time series, on a monthly scale, the UV index (UVI) has increased at the four selected USDA stations while total ozone has decreased in the continental USA over the past three decades and spatial distributions of UVI and total ozone have shown substantial variations from coastal zones to the Midwest Regions of the USA, yet the tendency toward recovery of ozone layer in the continental USA cannot be fully confirmed. This leads to a conclusion that the UVI changes might have been influenced by other factors in addition to the total ozone in the atmospheric environment across at least 76% of the continental USA. http://www.mdpi.com/2072-4292/2/1/262/ Tue, 05 Jan 2010 00:00:00 CET Remote Sensing 2010-01-05 2 1 Article 262 277 2072-4292 Detection of Multidecadal Changes in UVB and Total Ozone Concentrations over the Continental US with NASA TOMS Data and USDA Ground-Based Measurements 2010-01-05 doi: 10.3390/rs2010262 Zhiqiang Gao Wei Gao Ni-Bin Chang http://www.mdpi.com/2072-4292/2/1/211/ This paper presents an exhaustive review of global croplands and their water use, for the end of last millennium, mapped using remote sensing and non-remote sensing approaches by world’s leading researchers on the subject. A comparison at country scale of global cropland area estimated by these studies had a high R2-value of 0.89–0.94. The global cropland area estimates amongst different studies are quite close and range between 1.47–1.53 billion hectares. However, significant uncertainties exist in determining irrigated areas which, globally, consume nearly 80% of all human water use. The estimates show that the total water use by global croplands varies between 6,685 to 7,500 km3 yr−1 and of this around 4,586 km3 yr−1 is by rainfed croplands (green water use) and the rest by irrigated croplands (blue water use). Irrigated areas use about 2,099 km3 yr−1 (1,180 km3 yr−1 of blue water and the rest from rain that falls over irrigated croplands). However, 1.6 to 2.5 times the blue water required by irrigated croplands is actually withdrawn from reservoirs or pumping of ground water, suggesting an irrigation efficiency of only between 40–62 percent. The weaknesses, trends, and future directions to precisely estimate the global croplands are examined. Finally, the paper links global croplands and their water use to a paradigm for ensuring future food security. http://www.mdpi.com/2072-4292/2/1/211/ Mon, 04 Jan 2010 00:00:00 CET Remote Sensing 2010-01-04 2 1 Article 211 261 2072-4292 A Holistic View of Global Croplands and Their Water Use for Ensuring Global Food Security in the 21st Century through Advanced Remote Sensing and Non-remote Sensing Approaches 2010-01-04 doi: 10.3390/rs2010211 Prasad S. Thenkabail Munir A. Hanjra Venkateswarlu Dheeravath Muralikrishna Gumma http://www.mdpi.com/2072-4292/2/1/191/ Radio-frequency interference (RFI) present in microwave radiometry measurements leads to erroneous radiometric results. Sources of RFI include spurious signals and harmonics from lower frequency bands, spread-spectrum signals overlapping the “protected” band of operation, or out-of-band emissions not properly rejected by the pre-detection filters due to its finite rejection. The presence of RFI in the radiometric signal modifies the detected power and therefore the estimated antenna temperature from which the geophysical parameters will be retrieved. In recent years, techniques to detect the presence of RFI in radiometric measurements have been developed. They include time- and/or frequency domain analyses, or time and/or frequency domain statistical analysis of the received signal which, in the absence of RFI, must be a zero-mean Gaussian process. Statistical analyses performed to date include the calculation of the Kurtosis, and the Shapiro-Wilk normality test of the received signal. Nevertheless, statistical analysis of the received signal could be more extensive, as reported in the Statistics literature. The objective of this work is the study of the performance of a number of normality tests encountered in the Statistics literature when applied to the detection of the presence of RFI in the radiometric signal, which is Gaussian by nature. A description of the normality tests and the RFI detection results for different kinds of RFI are presented in view of determining an omnibus test that can deal with the blind spots of the currently used methods. http://www.mdpi.com/2072-4292/2/1/191/ Thu, 31 Dec 2009 00:00:00 CET Remote Sensing 2009-12-31 2 1 Article 191 210 2072-4292 Normality Analysis for RFI Detection in Microwave Radiometry 2009-12-31 doi: 10.3390/rs2010191 Jose Miguel Tarongi Adriano Camps http://www.mdpi.com/2072-4292/2/1/166/ Passive microwave remote sensing is one of the most promising techniques for soil moisture retrieval. However, the inversion of soil moisture from brightness temperature observations is not straightforward, as it is influenced by numerous factors such as surface roughness, vegetation cover, and soil texture. Moreover, the relationship between brightness temperature, soil moisture and the factors mentioned above is highly non-linear and ill-posed. Consequently, Artificial Neural Networks (ANNs) have been used to retrieve soil moisture from microwave data, but with limited success when dealing with data different to that from the training period. In this study, an ANN is tested for its ability to predict soil moisture at 1 km resolution on different dates following training at the same site for a specific date. A novel approach that utilizes information on the variability of soil moisture, in terms of its mean and standard deviation for a (sub) region of spatial dimension up to 40 km, is used to improve the current retrieval accuracy of the ANN method. A comparison between the ANN with and without the use of the variability information showed that this enhancement enables the ANN to achieve an average Root Mean Square Error (RMSE) of around 5.1% v/v when using the variability information, as compared to around 7.5% v/v without it. The accuracy of the soil moisture retrieval was further improved by the division of the target site into smaller regions down to 4 km in size, with the spatial variability of soil moisture calculated from within the smaller region used in the ANN. With the combination of an ANN architecture of a single hidden layer of 20 neurons and the dual-polarized brightness temperatures as input, the proposed use of variability and sub-region methodology achieves an average retrieval accuracy of 3.7% v/v. Although this accuracy is not the lowest as comparing to the research in this field, the main contribution is the ability of ANN in solving the problem of predicting “out-of-range” soil moisture values. However, the applicability of this method is highly dependent on the accuracy of the mean and standard deviation values within the sub-region, potentially limiting its routine application. http://www.mdpi.com/2072-4292/2/1/166/ Thu, 31 Dec 2009 00:00:00 CET Remote Sensing 2009-12-31 2 1 Article 166 190 2072-4292 Use of Soil Moisture Variability in Artificial Neural Network Retrieval of Soil Moisture 2009-12-31 doi: 10.3390/rs2010166 Soo-See Chai Jeffrey P. Walker Oleg Makarynskyy Michael Kuhn Bert Veenendaal Geoff West http://www.mdpi.com/2072-4292/2/1/151/ Expert assessments for crop and range productivity of very-large arid and semiarid areas worldwide are ever more in demand and these studies require greater sensitivity in delineating the different grades or levels of soil salinity. In conjunction with field study in arid southeastern Oregon, we assess the merit of adding decision-tree analysis (DTA) to a commonly used remote-sensing method. Randomly sampled surface soil horizons were analyzed for saturation percentage, field capacity, pH and electrical conductivity (EC). IFSAR data were acquired for terrain analysis and surficial geological mapping, followed by derivation of layers for analysis. Significant correlation was found between EC values and surface elevation, bands 1, 2, 3 and 4 of the Landsat TM image, and brightness and wetness indices. Maximum-likelihood supervised classification of the Landsat images yields two salinity classes: non-saline soils (EC < 4 dSm–1), prediction accuracy of 97%, and saline soils (EC < 4 dSm–1), prediction accuracy 60%. Addition of DTA results in successful prediction of five classes of soil salinity and an overall accuracy of about 99%. Moreover, the calculated area of salt-affected soil was overestimated when mapped using remote sensing data only compared to that predicted by additionally using DTA. DTA is a promising approach for mapping soil salinity in more productive and accurate ways compared to only using remote-sensing analysis. http://www.mdpi.com/2072-4292/2/1/151/ Wed, 30 Dec 2009 00:00:00 CET Remote Sensing 2009-12-30 2 1 Article 151 165 2072-4292 Application of Remote-sensing Data and Decision-Tree Analysis to Mapping Salt-Affected Soils over Large Areas 2009-12-30 doi: 10.3390/rs2010151 Abdelhamid A. Elnaggar Jay S. Noller http://www.mdpi.com/2072-4292/2/1/124/ A wide range of satellite methods is applied now in seismology. The first applications of satellite data for earthquake exploration were initiated in the ‘70s, when active faults were mapped on satellite images. It was a pure and simple extrapolation of airphoto geological interpretation methods into space. The modern embodiment of this method is alignment analysis. Time series of alignments on the Earth's surface are investigated before and after the earthquake. A further application of satellite data in seismology is related with geophysical methods. Electromagnetic methods have about the same long history of application for seismology. Stable statistical estimations of ionosphere-lithosphere relation were obtained based on satellite ionozonds. The most successful current project "DEMETER" shows impressive results. Satellite thermal infra-red data were applied for earthquake research in the next step. Numerous results have confirmed previous observations of thermal anomalies on the Earth's surface prior to earthquakes. A modern trend is the application of the outgoing long-wave radiation for earthquake research. In ‘80s a new technology—satellite radar interferometry—opened a new page. Spectacular pictures of co-seismic deformations were presented. Current researches are moving in the direction of pre-earthquake deformation detection. GPS technology is also widely used in seismology both for ionosphere sounding and for ground movement detection. Satellite gravimetry has demonstrated its first very impressive results on the example of the catastrophic Indonesian earthquake in 2004. Relatively new applications of remote sensing for seismology as atmospheric sounding, gas observations, and cloud analysis are considered as possible candidates for applications. http://www.mdpi.com/2072-4292/2/1/124/ Wed, 30 Dec 2009 00:00:00 CET Remote Sensing 2009-12-30 2 1 Review 124 150 2072-4292 Satellite Remote Sensing in Seismology. A Review 2009-12-30 doi: 10.3390/rs2010124 Andrew A. Tronin http://www.mdpi.com/2072-4292/2/1/115/ The value of the back-scattering coefficient of soil is dependent on its dielectric constant. An attempt has been made to estimate the scattering coefficient for a slightly rough surface for soil in combination with diesel, using the Perturbation Model. A database of the estimated Cj band (5.3 GHz) scattering coefficients for soil in combination with diesel for both horizontal and vertical polarization and different look angles has been generated. The results show that as the diesel contamination increases, the scattering coefficient decreases in both horizontal and vertical polarization. For active microwave remote sensing the scattering coefficient data for soil in combination with diesel for different weight percentage content is useful for image analysis and its applications. By using this database it is possible to design an active microwave sensor for remote sensing detection of oil, which would be useful in the field of environmental science. The backscattering coefficient for three different look angles (45, 50 and 55) has been calculated, which is desirable for space borne remote sensing sensors. http://www.mdpi.com/2072-4292/2/1/115/ Tue, 29 Dec 2009 00:00:00 CET Remote Sensing 2009-12-29 2 1 Article 115 123 2072-4292 Study of Soil Scattering Coefficients in Combination with Diesel for a Slightly Rough Surface in the Cj Band 2009-12-29 doi: 10.3390/rs2010115 Alireza Taravat Najafabadi http://www.mdpi.com/2072-4292/2/1/76/ During 1996–2006 the Ministry of Agriculture and Forestry in Finland, MTT Agrifood Research Finland and the Finnish Geodetic Institute carried out a joint remote sensing satellite research project. It evaluated the applicability of composite multispectral SAR and optical satellite data for cereal yield estimations in the annual crop inventory program. Three Vegetation Indices models (VGI, Infrared polynomial, NDVI and Composite multispetral SAR and NDVI) were validated to estimate cereal yield levels using solely optical and SAR satellite data (Composite Minimum Dataset). The average R2 for cereal yield (yb) was 0.627. The averaged composite SAR modeled grain yield level was 3,750 kg/ha (RMSE = 10.3%, 387 kg/ha) for high latitude spring cereals (4,018 kg/ha for spring wheat, 4,037 kg/ha for barley and 3,151 kg/ha for oats). http://www.mdpi.com/2072-4292/2/1/76/ Tue, 29 Dec 2009 00:00:00 CET Remote Sensing 2009-12-29 2 1 Article 76 114 2072-4292 Integrating Vegetation Indices Models and Phenological Classification with Composite SAR and Optical Data for Cereal Yield Estimation in Finland (Part I) 2009-12-29 doi: 10.3390/rs2010076 Heikki Laurila Mika Karjalainen Juha Hyyppä Jouko Kleemola http://www.mdpi.com/2072-4292/2/1/52/ The present work aims at identifying favorable locations for groundwater resources harvesting and extraction along the Wadi Feiran basin, SW Sinai Peninsula, Egypt, in an effort to facilitate new development projects in this area. Landsat ETM+, Radarsat-1 and PALSAR images of Wadi Feiran basin were used in this work to perform multisource data fusion and texture analysis, in order to classify the wadi deposits based on grain size distribution and predominant rock composition as this information may lead to the location of new groundwater resources. An unsupervised classification was first performed on two sets of fused images (i.e., ETM+/Radarsat-1 and ETM+/PALSAR) resulting in five classes (hybrid classes) describing the main alluvial sediments in the wadi system. Some variations in the spatial distribution of individual classes were observed, due to the different spectral and spatial resolutions of Radarsat-1 (C-band, 12.5 m) and PALSAR (L-band, 6.25 m) data. Alluvial deposits are mixtures of parent rocks located further upstream often at a great distance. In order to classify the alluvial deposits in terms of individual rock types (endmembers), a spectral linear unmixing of the optical ETM+ image was performed. Subsequently, each class of the fused (hybrid) images was correlated with (1) individual rock type fractions (endmembers) obtained from spectrally unmixing the ETM+ image, (2) the geocoded and calibrated radar images (Radarsat-1 and PALSAR) and, (3) the slope map generated from the SRTM data. The goal was to determine predominant rock composition, mean backscatter and slope values for each of the five hybrid classes. Backscatter coefficient values extracted from both radar data (C- and L-band) were correlated and checked in the field, confirming that both wavelengths produced more or less similar textural classes that correspond to specific grain or fragment sizes of alluvial deposits. However, comparison of the spatial distribution of matching hybrid classes showed some variations due to the greater discrimination power of surface texture by Radarsat-1 C-band despite its lower spatial resolution. Furthermore, both hybrid classification results showed that regardless of elevation, areas that are covered by fine and moderate grains (fine sand to pebble) and are located along gentle terrains are favorable for groundwater recharge; while areas that are covered by very coarse grains (cobble to boulder) and are located along steep terrains are more likely to be affected by flash floods. http://www.mdpi.com/2072-4292/2/1/52/ Mon, 28 Dec 2009 00:00:00 CET Remote Sensing 2009-12-28 2 1 Article 52 75 2072-4292 Textural and Compositional Characterization of Wadi Feiran Deposits, Sinai Peninsula, Egypt, Using Radarsat-1, PALSAR, SRTM and ETM+ Data 2009-12-28 doi: 10.3390/rs2010052 Ahmed Gaber Magaly Koch Farouk El-Baz http://www.mdpi.com/2072-4292/2/1/36/ Recent advances in radar techniques and systems have favoured the development of microwave interferometers, suitable for the non-contact vibration monitoring of large structures. The paper addresses the application of microwave remote sensing to the measurement of the vibration response in the stay-cables of cable-stayed bridges. The reliability and accuracy of the proposed technique were investigated by comparing the natural frequencies (and the cable tensions predicted from natural frequencies) identified from radar data and the corresponding quantities obtained using more conventional techniques. The investigation, carried out on the cables of two different cable-stayed bridges, clearly highlights: (a) the accuracy of the results provided by the microwave remote sensing; (b) the simplicity of use of the radar technique (especially when compared with conventional approaches) and its effectiveness to simultaneously measuring the dynamic response of all the stay-cables of an array. http://www.mdpi.com/2072-4292/2/1/36/ Mon, 28 Dec 2009 00:00:00 CET Remote Sensing 2009-12-28 2 1 Article 36 51 2072-4292 Application of Microwave Remote Sensing to Dynamic Testing of Stay-Cables 2009-12-28 doi: 10.3390/rs2010036 Carmelo Gentile http://www.mdpi.com/2072-4292/2/1/19/ A new method, called Illumination Dependent Colour Channels (IDCC), is presented to improve individual tree species classification. The method is based on tree crown division into illuminated and shaded parts on a digital aerial image. Colour values of both sides of the tree crown are then used in species classification. Tree crown division is achieved by comparing the projected location of an aerial image pixel with its neighbours on a Canopy Height Model (CHM), which is calculated from a synchronized LIDAR point cloud. The sun position together with the mapping aircraft position are also utilised in illumination status detection. The new method was tested on a dataset of 295 trees and the classification results were compared with ones measured with two other feature extraction methods. The results of the developed method gave a clear improvement in overall tree species classification accuracy. http://www.mdpi.com/2072-4292/2/1/19/ Mon, 28 Dec 2009 00:00:00 CET Remote Sensing 2009-12-28 2 1 Article 19 35 2072-4292 Individual Tree Species Classification by Illuminated—Shaded Area Separation 2009-12-28 doi: 10.3390/rs2010019 Eetu Puttonen Paula Litkey Juha Hyyppä http://www.mdpi.com/2072-4292/2/1/1/ Frequent hurricane landfalls along the northern Gulf of Mexico, in addition to causing immediate damage to vegetation, also have long term effects on coastal ecosystem structure and function. This study investigated the utility of using time series enhanced vegetation index (EVI) imagery composited in MODIS product MOD13Q1 for assessing hurricane damage to vegetation and its recovery. Vegetation in four US coastal states disturbed by five hurricanes between 2002 and 2008 were explored by change imagery derived from pre- and post-hurricane EVI data. Interpretation of the EVI changes within months and between years distinguished a clear disturbance pattern caused by Hurricanes Katrina and Rita in 2005, and a recovering trend of the vegetation between 2005 and 2008, particularly within the 100 km coastal zone. However, for Hurricanes Gustav, Ike, and Lili, the disturbance pattern which varied by the change imagery were not noticeable in some images due to lighter vegetation damage. The EVI pre- and post-hurricane differences between two adjacent years and around one month after hurricane disturbance provided the most likely damage area and patterns. The study also revealed that as hurricanes damaged vegetation in some coastal areas, strong precipitation associated with these storms may benefit growth of vegetation in other areas. Overall, the study illustrated that the MODIS product could be employed to detect severe hurricane damage to vegetation, monitor vegetation recovery dynamics, and assess benefits of hurricanes to vegetation. http://www.mdpi.com/2072-4292/2/1/1/ Thu, 24 Dec 2009 00:00:00 CET Remote Sensing 2009-12-24 2 1 Article 1 18 2072-4292 Potential of MODIS EVI in Identifying Hurricane Disturbance to Coastal Vegetation in the Northern Gulf of Mexico 2009-12-24 doi: 10.3390/rs2010001 Fugui Wang Eurico J. D’Sa http://www.mdpi.com/2072-4292/1/4/1380/ A new simple airborne method based on wide optics camera is developed for leaf area index (LAI) estimation in coniferous forests. The measurements are carried out in winter, when the forest floor is completely snow covered and thus acts as a light background for the hemispherical analysis of the images. The photos are taken automatically and stored on a laptop during the flights. The R2 value of the linear regression of the airborne and ground based LAI measurements was 0.89. http://www.mdpi.com/2072-4292/1/4/1380/ Tue, 22 Dec 2009 00:00:00 CET Remote Sensing 2009-12-22 1 4 Article 1380 1394 2072-4292 Leaf Area Index (LAI) Estimation of Boreal Forest Using Wide Optics Airborne Winter Photos 2009-12-22 doi: 10.3390/rs1041380 Terhikki Manninen Lauri Korhonen Pekka Voipio Panu Lahtinen Pauline Stenberg http://www.mdpi.com/2072-4292/1/4/1353/ Urbanization and the resulting changes in land cover have myriad impacts on ecological systems. Monitoring these changes across large spatial extents and long time spans requires synoptic remotely sensed data with an appropriate temporal sequence. We developed a multi-temporal land cover dataset for a six-county area surrounding the Seattle, Washington State, USA, metropolitan region. Land cover maps for 1986, 1991, 1995, 1999, and 2002 were developed from Landsat TM images through a combination of spectral unmixing, image segmentation, multi-season imagery, and supervised classification approaches to differentiate an initial nine land cover classes. We then used ancillary GIS layers and temporal information to define trajectories of land cover change through multiple updating and backdating rules and refined our land cover classification for each date into 14 classes. We compared the accuracy of the initial approach with the landscape trajectory modifications and determined that the use of landscape trajectory rules increased our ability to differentiate several classes including bare soil (separated into cleared for development, agriculture, and clearcut forest) and three intensities of urban. Using the temporal dataset, we found that between 1986 and 2002, urban land cover increased from 8 to 18% of our study area, while lowland deciduous and mixed forests decreased from 21 to 14%, and grass and agriculture decreased from 11 to 8%. The intensity of urban land cover increased with 252 km2 in Heavy Urban in 1986 increasing to 629 km2 by 2002. The ecological systems that are present in this region were likely significantly altered by these changes in land cover. Our results suggest that multi-temporal (i.e., multiple years and multiple seasons within years) Landsat data are an economical means to quantify land cover and land cover change across large and highly heterogeneous urbanizing landscapes. Our data, and similar temporal land cover change products, have been used in ecological modeling of past, present, and likely future changes in ecological systems (e.g., avian biodiversity, water quality). Such data are important inputs for ecological modelers, policy makers, and urban planners to manage and plan for future landscape change. http://www.mdpi.com/2072-4292/1/4/1353/ Tue, 22 Dec 2009 00:00:00 CET Remote Sensing 2009-12-22 1 4 Article 1353 1379 2072-4292 Using Urban Landscape Trajectories to Develop a Multi-Temporal Land Cover Database to Support Ecological Modeling 2009-12-22 doi: 10.3390/rs1041353 Jeffrey Hepinstall-Cymerman Stefan Coe Marina Alberti http://www.mdpi.com/2072-4292/1/4/1338/ This paper presents an approach denominated Group Inversion Approach (GIA) which aims at detecting soil moisture temporal invariants, i.e., the stable temporal soil moisture locations, by using mainly remotely sensed data. The soil moisture temporal invariants are those locations where independently of the absolute value changes, the relative spatial distribution of soil moisture remains almost constant. In this procedure, the soil moisture values estimated from different inversion approaches and sensor configurations are compared among themselves and with the ground data. The procedure has been tested in a watershed of around 7,000 km2 with data collected during the SMEX’02 experiment in Iowa (USA). The results indicate that fields with inversion errors lower than five times the soil moisture variability detected with ground measurements represent well the mean watershed soil moisture values. The GIA technique has been also found in good agreement with the classical technique used to detect the stable soil moisture features, based exclusively on ground measurements. http://www.mdpi.com/2072-4292/1/4/1338/ Mon, 21 Dec 2009 00:00:00 CET Remote Sensing 2009-12-21 1 4 Article 1338 1352 2072-4292 “Group Inversion Approach” for Detection of Soil Moisture Temporal-Invariant Locations 2009-12-21 doi: 10.3390/rs1041338 Claudia Notarnicola http://www.mdpi.com/2072-4292/1/4/1321/ Unlike mobile survey systems, stationary survey systems are given very little direct georeferencing attention. Direct Georeferencing is currently being used in several mobile applications, especially in terrestrial and airborne LiDAR systems. Georeferencing of stationary terrestrial LiDAR scanning data, however, is currently performed indirectly through using control points in the scanning site. The indirect georeferencing procedure is often troublesome; the availability of control stations within the scanning range is not always possible. Also, field procedure can be laborious and involve extra equipment and target setups. In addition, the conventional method allows for possible human error due to target information bookkeeping. Additionally, the accuracy of this procedure varies according to the quality of the control used. By adding a dual GPS antenna apparatus to the scanner setup, thereby supplanting the use of multiple ground control points scattered throughout the scanning site, we mitigate not only the problems associated with indirect georeferencing but also induce a more efficient set up procedure while maintaining sufficient precision. In this paper, we describe a new method for determining the 3D absolute orientation of LiDAR point cloud using GPS measurements from two antennae firmly mounted on the optical head of a stationary LiDAR system. In this paper, the general case is derived where the orientation angles are not small; this case completes the theory of stationary LiDAR direct georeferencing. Simulation and real world field experimentation of the prototype implementation suggest a precision of about 0.05 degrees (~1 milli-radian) for the three orientation angles. http://www.mdpi.com/2072-4292/1/4/1321/ Thu, 17 Dec 2009 00:00:00 CET Remote Sensing 2009-12-17 1 4 Article 1321 1337 2072-4292 Direct Georeferencing of Stationary LiDAR 2009-12-17 doi: 10.3390/rs1041321 Ahmed Mohamed Benjamin Wilkinson http://www.mdpi.com/2072-4292/1/4/1298/ Digital cover photography (DCP) is a high resolution, vertical field-of-view method for ground-based estimation of forest metrics, and has advantages over fisheye sensors owing to its ease of application and high accuracy. We conducted the first thorough technical appraisal of DCP using both single-lens-reflex (DSLR) and point-and-shoot cameras and concluded that differences result primarily from the better quality optics available for the DSLR camera. File compression, image size and ISO equivalence had little or no effect on estimates of forest metrics. We discuss the application of DCP for ground truthing of remotely sensed canopy metrics, and highlight its strengths over fisheye photography for testing and calibration of vertical field-of-view remote sensing. http://www.mdpi.com/2072-4292/1/4/1298/ Tue, 15 Dec 2009 00:00:00 CET Remote Sensing 2009-12-15 1 4 Article 1298 1320 2072-4292 Measurement of Crown Cover and Leaf Area Index Using Digital Cover Photography and Its Application to Remote Sensing 2009-12-15 doi: 10.3390/rs1041298 Burak Pekin Craig Macfarlane http://www.mdpi.com/2072-4292/1/4/1273/ We used the Enhanced Vegetation Index (EVI) from MODIS to scale evapotranspiration (ETactual) over agricultural and riparian areas along the Lower Colorado River in the southwestern US. Ground measurements of ETactual by alfalfa, saltcedar, cottonwood and arrowweed were expressed as fraction of potential (reference crop) ETo (EToF) then regressed against EVI scaled between bare soil (0) and full vegetation cover (1.0) (EVI*). EVI* values were calculated based on maximum and minimum EVI values from a large set of riparian values in a previous study. A satisfactory relationship was found between crop and riparian plant EToF and EVI*, with an error or uncertainty of about 20% in the mean estimate (mean ETactual = 6.2 mm d−1, RMSE = 1.2 mm d−1). The equation for ETactual was: ETactual = 1.22 × ETo-BC × EVI*, where ETo-BC is the Blaney Criddle formula for ETo. This single algorithm applies to all the vegetation types in the study, and offers an alternative to ETactual estimates that use crop coefficients set by expert opinion, by using an algorithm based on the actual state of the canopy as determined by time-series satellite images. http://www.mdpi.com/2072-4292/1/4/1273/ Thu, 10 Dec 2009 00:00:00 CET Remote Sensing 2009-12-10 1 4 Article 1273 1297 2072-4292 An Empirical Algorithm for Estimating Agricultural and Riparian Evapotranspiration Using MODIS Enhanced Vegetation Index and Ground Measurements of ET. I. Description of Method 2009-12-10 doi: 10.3390/rs1041273 Pamela L. Nagler Kiyomi Morino R. Scott Murray John Osterberg Edward P. Glenn http://www.mdpi.com/2072-4292/1/4/1257/ Modification of the original bands and integration of ancillary data in digital image classification has been shown to improve land use land cover classification accuracy. There are not many studies demonstrating such techniques in the context of the mountains of Nepal. The objective of this study was to explore and evaluate the use of modified band and ancillary data in Landsat and IRS image classification, and to produce a land use land cover map of the Galaudu watershed of Nepal. Classification of land uses were explored using supervised and unsupervised classification for 12 feature sets containing the LandsatMSS, TM and IRS original bands, ratios, normalized difference vegetation index, principal components and a digital elevation model. Overall, the supervised classification method produced higher accuracy than the unsupervised approach. The result from the combination of bands ration 4/3, 5/4 and 5/7 ranked the highest in terms of accuracy (82.86%), while the combination of bands 2, 3 and 4 ranked the lowest (45.29%). Inclusion of DEM as a component band shows promising results. http://www.mdpi.com/2072-4292/1/4/1257/ Tue, 08 Dec 2009 00:00:00 CET Remote Sensing 2009-12-08 1 4 Article 1257 1272 2072-4292 Improving Landsat and IRS Image Classification: Evaluation of Unsupervised and Supervised Classification through Band Ratios and DEM in a Mountainous Landscape in Nepal 2009-12-08 doi: 10.3390/rs1041257 Krishna Bahadur K.C. http://www.mdpi.com/2072-4292/1/4/1240/ The Izeh fault zone is a transverse fault zone with dextral strike slip (and some reverse component) in the Zagros Mountains (Iran). It causes some structural deformations. This fault zone is acting as eastern boundary of Dezful Embayment and forms subsidence of the embayment. The fault has been recognized using remote sensing techniques in conjunction with surface and subsurface analyses. The stratigraphic columns have been prepared in 3D form using Geographical Information System (GIS) tools on the basis of structural styles and thickness of lithologic units. Height differences for erosion levels have been calculated in stratigraphic columns with respect to the subsidence in the Dezful Embayment, which is related to Izeh zone. These height differences have been estimated to be 5,430 m in the central part (and 5,844 m in the northern part) from the Eocene to recent times. This study shows that comparison of the same erosion levels in Asmari-Pabdeh formation boundaries for interior and eastern block of the Izeh fault zone with the absolute uplifting due to the fault activity which is about 533 m per million years in the Izeh zone. The present study reveals that subtracting the absolute uplifting from total subsidence; the real subsidence of Dezful embayment from Eocene to Recent is 0.13 mm/year. The mean rate of uplifting along the Izeh fault zone is 0.015 mm/year. http://www.mdpi.com/2072-4292/1/4/1240/ Tue, 08 Dec 2009 00:00:00 CET Remote Sensing 2009-12-08 1 4 Article 1240 1256 2072-4292 Upliftment Estimation of the Zagros Transverse Fault in Iran Using Geoinformatics Technology 2009-12-08 doi: 10.3390/rs1041240 Hojjat Ollah Safari Saeid Pirasteh Biswajeet Pradhan http://www.mdpi.com/2072-4292/1/4/1212/ Altimetry is probably one of the most powerful tools for ice sheet observation. Our vision of the Antarctic ice sheet has been deeply transformed since the launch of the ERS1 satellite in 1991. With the launch of ERS2 and Envisat, the series of altimetric observations now provides 19 years of continuous and homogeneous observations that allow monitoring of the shape and volume of ice sheets. The topography deduced from altimetry is one of the relevant parameters revealing the processes acting on ice sheet. Moreover, altimeter also provides other parameters such as backscatter and waveform shape that give information on the surface roughness or snow pack characteristics. http://www.mdpi.com/2072-4292/1/4/1212/ Mon, 07 Dec 2009 00:00:00 CET Remote Sensing 2009-12-07 1 4 Review 1212 1239 2072-4292 Antarctic Ice Sheet and Radar Altimetry: A Review 2009-12-07 doi: 10.3390/rs1041212 Frédérique Rémy Soazig Parouty http://www.mdpi.com/2072-4292/1/4/1190/ We describe the operation of a bistatic HF radar network and outline analysis methods for the derivation of the elliptical velocity components from the radar echo spectra. Bistatic operation is illustrated by application to a bistatic pair: Both remote systems receive backscattered echo, with one remote system in addition receiving bistatic echoes transmitted by the other. The pair produces elliptical velocity components in addition to two sets of radials. Results are compared with drifter measurements and checks performed on internal consistency in the radar results. We show that differences in drifter/radar current velocities are consistent with calculated radar data uncertainties. Elliptical and radial velocity components are demonstrated to be consistent within the data uncertainties. Inclusion of bistatic operation in radar networks can be expected to increase accuracy in derived current velocities and extend the coverage area. http://www.mdpi.com/2072-4292/1/4/1190/ Tue, 01 Dec 2009 00:00:00 CET Remote Sensing 2009-12-01 1 4 Article 1190 1211 2072-4292 HF Radar Bistatic Measurement of Surface Current Velocities: Drifter Comparisons and Radar Consistency Checks 2009-12-01 doi: 10.3390/rs1041190 Belinda Lipa Chad Whelan Bill Rector Bruce Nyden http://www.mdpi.com/2072-4292/1/4/1171/ In this paper we propose a hybrid classification method, adopting the best features extraction strategy for each land cover class on multidate ASTER data. To enable an effective comparison among images, Multivariate Alteration Detection (MAD) transformation was applied in the pre-processing phase, because of its high level of automation and reliability in the enhancement of change information among different images. Consequently, different features identification procedures, both spectral and object-based, were implemented to overcome problems of misclassification among classes with similar spectral response. Lastly, a post-classification comparison was performed on multidate ASTER-derived land cover (LC) maps to evaluate the effects of change in the study area. http://www.mdpi.com/2072-4292/1/4/1171/ Mon, 30 Nov 2009 00:00:00 CET Remote Sensing 2009-11-30 1 4 Article 1171 1189 2072-4292 A Class-Oriented Strategy for Features Extraction from Multidate ASTER Imagery 2009-11-30 doi: 10.3390/rs1041171 Nicola Crocetto Eufemia Tarantino http://www.mdpi.com/2072-4292/1/4/1139/ Automated, image based methods for the retrieval of vegetation biophysical and biochemical variables are often hampered by the lack of a priori knowledge about land cover and phenology, which makes the retrieval a highly underdetermined problem. This study addresses this problem by presenting a novel approach, called CRASh, for the concurrent retrieval of leaf area index, leaf chlorophyll content, leaf water content and leaf dry matter content from high resolution solar reflective earth observation data. CRASh, which is based on the inversion of the combined PROSPECT+SAILh radiative transfer model (RTM), explores the benefits of combining semi-empirical and physically based approaches. The approach exploits novel ways to address the underdetermined problem in the context of an automated retrieval from mono-temporal high resolution data. To regularize the inverse problem in the variable domain, RTM inversion is coupled with an automated land cover classification. Model inversion is based on a two step lookup table (LUT) approach: First, a range of possible solutions is selected from a previously calculated LUT based on the analogy between measured and simulated reflectance. The final solution is determined from this subset by minimizing the difference between the variables used to simulate the spectra contained in the reduced LUT and a first guess of the solution. This first guess of the variables is derived from predictive semi-empirical relationships between classical vegetation indices and the single variables. Additional spectral regularization is obtained by the use of hyperspectral data. Results show that estimates obtained with CRASh are significantly more accurate than those obtained with a tested conventional RTM inversion and semi-empirical approach. Accuracies obtained in this study are comparable to the results obtained by various authors for better constrained inversions that assume more a priori information. The completely automated and image-based nature of the approach facilitates its use in operational chains for upcoming high resolution airborne and spaceborne imaging spectrometers. http://www.mdpi.com/2072-4292/1/4/1139/ Fri, 27 Nov 2009 00:00:00 CET Remote Sensing 2009-11-27 1 4 Article 1139 1170 2072-4292 Enhanced Automated Canopy Characterization from Hyperspectral Data by a Novel Two Step Radiative Transfer Model Inversion Approach 2009-11-27 doi: 10.3390/rs1041139 Wouter Dorigo Rudolf Richter Frédéric Baret Richard Bamler Wolfgang Wagner http://www.mdpi.com/2072-4292/1/4/1125/ Large quantities of water are consumed by irrigated crops and riparian vegetation in western U.S. irrigation districts. Remote sensing methods for estimating evaporative water losses by soil and vegetation (evapotranspiration, ET) over wide river stretches are needed to allocate water for agricultural and environmental needs. We used the Enhanced Vegetation Index (EVI) from MODIS sensors on the Terra satellite to scale ET over agricultural and riparian areas along the Lower Colorado River in the southwestern U.S., using a linear regression equation between ET of riparian plants and alfalfa measured on the ground, and meteorological and remote sensing data, with an error or uncertainty of about 20%. The algorithm was applied to irrigation districts and riparian areas from Lake Mead to the U.S./Mexico border. The results for agricultural crops were similar to results produced by crop coefficients developed for the irrigation districts along the river. However, riparian ET was only half as great as crop coefficient estimates set by expert opinion, equal to about 40% of reference crop evapotranspiration. Based on reported acreages in 2007, agricultural crops (146,473 ha) consumed 2.2 × 109 m3 yr−1 of water. All riparian shrubs and trees (47,014 ha) consumed 3.8 × 108 m3 yr−1, of which saltcedar, the dominant riparian shrub (25,044 ha), consumed 1.8 × 108 m3 yr−1, about 1% of the annual flow of the river. This method could supplement existing protocols for estimating ET by providing an estimate based on the actual state of the canopy as determined by frequent-return satellite data. http://www.mdpi.com/2072-4292/1/4/1125/ Fri, 20 Nov 2009 00:00:00 CET Remote Sensing 2009-11-20 1 4 Article 1125 1138 2072-4292 An Empirical Algorithm for Estimating Agricultural and Riparian Evapotranspiration Using MODIS Enhanced Vegetation Index and Ground Measurements of ET. II. Application to the Lower Colorado River, U.S. 2009-11-20 doi: 10.3390/rs1041125 R. Scott Murray Pamela L. Nagler Kiyomi Morino Edward P. Glenn http://www.mdpi.com/2072-4292/1/4/1108/ A ten-year data-set descriptive of Italian forest gross primary production (GPP) has been recently constructed by the application of Modified C-Fix, a parametric model driven by remote sensing and ancillary data. That data-set is currently being used to develop multivariate regression models which link the inter-year GPP variations of five forest types (white fir, beech, chestnut, deciduous and evergreen oaks) to seasonal values of temperature and precipitation. The five models obtained, which explain from 52% to 88% of the inter-year GPP variability, are then applied to predict the effects of expected environmental changes (+2 °C and increased CO2 concentration). The results show a variable response of forest GPP to the simulated climate change, depending on the main ecosystem features. In contrast, the effects of increasing CO2 concentration are always positive and similar to those given by a combination of the two environmental factors. These findings are analyzed with reference to previous studies on the subject, particularly concerning Mediterranean environments. The analysis confirms the plausibility of the scenarios obtained, which can cast light on the important issue of forest carbon pool variations under expected global changes. http://www.mdpi.com/2072-4292/1/4/1108/ Thu, 19 Nov 2009 00:00:00 CET Remote Sensing 2009-11-19 1 4 Article 1108 1124 2072-4292 Evaluating the Effects of Environmental Changes on the Gross Primary Production of Italian Forests 2009-11-19 doi: 10.3390/rs1041108 Fabio Maselli Marco Moriondo Marta Chiesi Gherardo Chirici Nicola Puletti Anna Barbati Piermaria Corona http://www.mdpi.com/2072-4292/1/4/1065/ The high-resolution Experimental Advanced Airborne Research LIDAR (EAARL) is a new technology for cross-environment surveys of channels and floodplains. EAARL measurements of basic channel geometry, such as wetted cross-sectional area, are within a few percent of those from control field surveys. The largest channel mapping errors are along stream banks. The LIDAR data adequately support 1D and 2D computational fluid dynamics models and frequency domain analyses by wavelet transforms. Further work is needed to establish the stream monitoring capability of the EAARL and the range of water quality conditions in which this sensor will accurately map river bathymetry. http://www.mdpi.com/2072-4292/1/4/1065/ Thu, 19 Nov 2009 00:00:00 CET Remote Sensing 2009-11-19 1 4 Article 1065 1096 2072-4292 Remote Sensing of Channels and Riparian Zones with a Narrow-Beam Aquatic-Terrestrial LIDAR 2009-11-19 doi: 10.3390/rs1041065 Jim McKean Dave Nagel Daniele Tonina Philip Bailey Charles Wayne Wright Carolyn Bohn Amar Nayegandhi http://www.mdpi.com/2072-4292/1/4/1097/ The timing of the disappearance of the snow cover in spring, or snow melt day (SMD), is a key parameter controlling the carbon dioxide balance between the northern mire and heath ecosystems and the atmosphere. We present a simple method for the determination of the SMD using a satellite-based surface albedo product (SAL). The method is based on the local change of albedo from higher wintertime values towards the lower summertime values. The satellite SMD timing correlates well with the SMD determined from snow depth measurements at Finnish weather stations (r = 0.86, slope 1.05). In 50% of the cases the error was 3.4 days or less and bias less than half a day. This would lead to a moderate uncertainty in the annual CO2 balance of mire and heath ecosystems, if the published SMD—CO2 balance relations are valid. However, due to the limited data sets available a systematic validation is left for the future. http://www.mdpi.com/2072-4292/1/4/1097/ Thu, 19 Nov 2009 00:00:00 CET Remote Sensing 2009-11-19 1 4 Article 1097 1107 2072-4292 A Simple Method to Determine the Timing of Snow Melt by Remote Sensing with Application to the CO2 Balances of Northern Mire and Heath Ecosystems 2009-11-19 doi: 10.3390/rs1041097 Janne Rinne Mika Aurela Terhikki Manninen http://www.mdpi.com/2072-4292/1/4/1055/ Water quality information in the coastal region of Hong Kong and the Pearl River Estuary (PRE) is of great concern to the local community. Due to great landuse-landcover (LULC) changes with rapid industrialization and urbanization in the Pearl River Delta (PRD) region, water quality in the PRE has worsened during the last 20 years. Frequent red tide and harmful algal blooms have occurred in the estuary and its adjacent coastal waters since the 1980s and have caused important economic losses, also possibly threatening to the coastal environment, fishery, and public health in Hong Kong. In addition, recent literature shows that water nutrients in Victoria Harbor of Hong Kong have been proven to be strongly influenced by both the Pearl River and sewage effluent in the wet season (May to September), but it is still unclear how the PRE diluted water intrudes into Victoria Harbor. Due to the cloudy and rainy conditions in the wet season in Hong Kong, ASAR images will be used to monitor the PRE river plumes and track the intruding routes of PRE water nutrients. In this paper, we first review LULC change in the PRD and then show our preliminary results to analyze water quality spatial and temporal information from remote observations with different sensors in the coastal region and estuary. The study will also emphasizes on time series of analysis of LULC trends related to annual sediment yields and critical source areas of erosion for the PRD region since the 1980s. http://www.mdpi.com/2072-4292/1/4/1055/ Tue, 17 Nov 2009 00:00:00 CET Remote Sensing 2009-11-17 1 4 Communication 1055 1064 2072-4292 Investigating the Impacts of Landuse-landcover (LULC) Change in the Pearl River Delta Region on Water Quality in the Pearl River Estuary and Hong Kong’s Coast 2009-11-17 doi: 10.3390/rs1041055 Yuanzhi Zhang Yufei Wang Yunpeng Wang Hongyan Xi http://www.mdpi.com/2072-4292/1/4/1043/ To ensure remote sensing MODIS hotspot (also known as active fire products or hotspots) quality and precision in forest fire control and management in Thailand, an increased level of confidence is needed. Accuracy assessment of MODIS hotspots utilizing field survey data validation is described. A quantitative evaluation of MODIS hotspot products has been carried out since the 2007 forest fire season. The carefully chosen hotspots were scattered throughout the country and within the protected areas of the National Parks and Wildlife Sanctuaries. Three areas were selected as test sites for validation guidelines. Both ground and aerial field surveys were also conducted in this study by the Forest Fire Control Division, National Park, Wildlife and Plant Conversation Department, Ministry of Natural Resources and Environment, Thailand. High accuracy of 91.84 %, 95.60% and 97.53% for the 2007, 2008 and 2009 fire seasons were observed, resulting in increased confidence in the use of MODIS hotspots for forest fire control and management in Thailand. http://www.mdpi.com/2072-4292/1/4/1043/ Tue, 17 Nov 2009 00:00:00 CET Remote Sensing 2009-11-17 1 4 Article 1043 1054 2072-4292 MODIS Hotspot Validation over Thailand 2009-11-17 doi: 10.3390/rs1041043 Veerachai Tanpipat Kiyoshi Honda Prayoonyong Nuchaiya http://www.mdpi.com/2072-4292/1/4/1028/ In this study, multitemporal subpixel analysis was used to identify cypress canopies from Landsat 7 ETM+ imagery. One spring and one fall image were selected for each of two sites, an eastern one centered on Tallahassee, FL and a western one centered on Panama City, FL. Signatures derived from the two eastern images were applied on the two western images that served as the control images for accuracy assessment. Results indicated that multitemporal subpixel analysis greatly improved the classification accuracy and signatures developed from one scene could be used to the subpixel classification of another scene with caution. http://www.mdpi.com/2072-4292/1/4/1028/ Tue, 17 Nov 2009 00:00:00 CET Remote Sensing 2009-11-17 1 4 Article 1028 1042 2072-4292 Detection of Cypress Canopies in the Florida Panhandle Using Subpixel Analysis and GIS 2009-11-17 doi: 10.3390/rs1041028 Jialing Wang Paul A. Lang http://www.mdpi.com/2072-4292/1/4/1009/ Multilayer canopy representations are the most common structural stand representations due to their simplicity. Implementation of recent advances in technology has allowed scientists to simulate geometrically explicit forest canopies. The effect of simplified representations of tree architecture (i.e., multilayer representations) of four Fagus sylvatica (L.) stands, each with different LAI, on the light absorption estimates was assessed in comparison with explicit 3D geometrical stands. The absorbed photosynthetic radiation at stand level was calculated. Subsequently, each geometrically explicit 3D stand was compared with three multilayer models representing horizontal, uniform, and planophile leaf angle distributions. The 3D stands were created either by in situ measured trees or by modelled trees generated with the AMAP plant growth software. The Physically Based Ray Tracer (PBRT) algorithm was used to simulate the irradiance absorbance of the detailed 3D architecture stands, while for the three multilayer representations, the probability of light interception was simulated by applying the Beer-Lambert’s law. The irradiance inside the canopies was characterized as direct, diffuse and scattered irradiance. The irradiance absorbance of the stands was computed during eight angular sun configurations ranging from 10° (near nadir) up to 80° sun zenith angles. Furthermore, a leaf stratification (the number and angular distribution of leaves per LAI layer inside a canopy) analysis between the 3D stands and the multilayer representations was performed, indicating the amount of irradiance each leaf is absorbing along with the percentage of sunny and shadow leaves inside the canopy. The results reveal that a multilayer representation of a stand, using a multilayer modelling approach, greatly overestimated the absorbed irradiance in an open canopy, while it provided a better approximation in the case of a closed canopy. Moreover, the actual stratification of leaves differed significantly between a multilayer representation and a 3D architecture canopy of the same LAI. The deviations in irradiance absorbance were caused by canopy structure, clumping and positioning of leaves. Although it was found that the use of canopy simplifications for modelling purposes in closed canopies is demonstrated as a valid option, special care should be taken when considering forest stands irradiance simulation for sparse canopies and particularly on higher sun zenith angles where the surrounding trees strongly affect the absorbed irradiance and results can highly deviate from the multilayer assumptions. http://www.mdpi.com/2072-4292/1/4/1009/ Mon, 16 Nov 2009 00:00:00 CET Remote Sensing 2009-11-16 1 4 Article 1009 1027 2072-4292 Assessing the Impact of Canopy Structure Simplification in Common Multilayer Models on Irradiance Absorption Estimates of Measured and Virtually Created Fagus sylvatica (L.) Stands 2009-11-16 doi: 10.3390/rs1041009 Dimitrios Biliouris Dimitry Van der Zande Willem W. Verstraeten Bart Muys Pol Coppin http://www.mdpi.com/2072-4292/1/4/992/ The inability to monitor wetland drag coefficients at a regional scale is rooted in the difficulty to determine vegetation structure from remote sensing data. Based on the fact that the backscattering coefficient is sensitive to marsh vegetation structure, this paper presents a methodology to estimate the drag coefficient from a combination of SAR images, interaction models and ancillary data. We use as test case a severe fire event occurred in the Paraná River Delta (Argentina) at the beginning of 2008, when 10% of the herbaceous vegetation was burned up. A map of the reduction of the wetland drag coefficient is presented. http://www.mdpi.com/2072-4292/1/4/992/ Fri, 13 Nov 2009 00:00:00 CET Remote Sensing 2009-11-13 1 4 Article 992 1008 2072-4292 Estimating Flow Resistance of Wetlands Using SAR Images and Interaction Models 2009-11-13 doi: 10.3390/rs1040992 Mercedes Salvia Mariano Franco Francisco Grings Pablo Perna Roman Martino Haydee Karszenbaum Paolo Ferrazzoli http://www.mdpi.com/2072-4292/1/4/971/ Unlike traditional ground-based methodology, remote sensing allows for the rapid estimation of crop residue cover (fR). While the Cellulose Absorption Index (CAI) is ideal for fR estimation, a new index, the Shortwave Infrared Normalized Difference Residue Index (SINDRI), utilizing ASTER bands 6 and 7, is proposed for future multispectral sensors and would be less costly to implement. SINDRI performed almost as well as CAI and better than other indices at five locations in the USA on multiple dates. A minimal upgrade from one broad band to two narrow bands would provide fR data for carbon cycle modeling and tillage verification. http://www.mdpi.com/2072-4292/1/4/971/ Wed, 11 Nov 2009 00:00:00 CET Remote Sensing 2009-11-11 1 4 Article 971 991 2072-4292 An Improved ASTER Index for Remote Sensing of Crop Residue 2009-11-11 doi: 10.3390/rs1040971 Guy Serbin E. Raymond Hunt Jr. Craig S. T. Daughtry Gregory W. McCarty Paul C. Doraiswamy http://www.mdpi.com/2072-4292/1/4/952/ Changes in wildlife conservation areas have serious implications for ecological systems and the distribution of wildlife species. Using the Masai Mara ecosystem as an example, we analyzed long-term land use/cover changes and wildlife population dynamics. Multitemporal satellite images, together with physical and social economic data were employed in a post classification analysis with GIS to analyze outcomes of different land use practices and policies. The results show rapid land use/cover conversions and a drastic decline for a wide range of wildlife species. Integration of land use/cover monitoring data and wildlife resources data can allow for the analysis of changes, and can be used to project trends to provide knowledge about potential land use/cover change scenarios and ecological impacts. http://www.mdpi.com/2072-4292/1/4/952/ Wed, 11 Nov 2009 00:00:00 CET Remote Sensing 2009-11-11 1 4 Article 952 970 2072-4292 Analysis of Land Use/Cover Changes and Animal Population Dynamics in a Wildlife Sanctuary in East Africa 2009-11-11 doi: 10.3390/rs1040952 Charles Ndegwa Mundia Yuji Murayama http://www.mdpi.com/2072-4292/1/4/934/ Applications of LiDAR remote sensing are exploding, while moving from the research to the operational realm. Increasingly, natural resource managers are recognizing the tremendous utility of LiDAR-derived information to make improved decisions. This review provides a cross-section of studies, many recent, that demonstrate the relevance of LiDAR across a suite of terrestrial natural resource disciplines including forestry, fire and fuels, ecology, wildlife, geology, geomorphology, and surface hydrology. We anticipate that interest in and reliance upon LiDAR for natural resource management, both alone and in concert with other remote sensing data, will continue to rapidly expand for the foreseeable future. http://www.mdpi.com/2072-4292/1/4/934/ Wed, 11 Nov 2009 00:00:00 CET Remote Sensing 2009-11-11 1 4 Review 934 951 2072-4292 LiDAR Utility for Natural Resource Managers 2009-11-11 doi: 10.3390/rs1040934 Andrew Thomas Hudak Jeffrey Scott Evans Alistair Matthew Stuart Smith http://www.mdpi.com/2072-4292/1/4/915/ Remote sensing data can provide valuable information about the surface expression of regional geomorphologic and geological features of arid regions. In the present study, several processing techniques were applied to reveal such in the Qatar Peninsula. Those included preprocessing for radiometric and geometric correction, various enhancement methods, classification, accuracy assessment, contrast stretching, color composition, and principal component analyses. Those were coupled with field groundtruthing and lab analyses. Field groundtruthing included one hundred and forty measurements of spectral reflectance for various sediment exposures representing main sand types in the four studied parts in Qatar. Lab investigations included grain size analysis, X-ray diffraction and laboratory measurements of spectral reflectance. During the course of this study three sand types have been identified: (i) sabkha-derived salt-rich, quartz sand, and (ii) beach-derived calcareous sand and (iii) aeolian dune quartz. Those areas are spectrally distinct in the VNIR, suggesting that VNIR spectral data can be used to discriminate them. The study found that the main limitation of the ground spectral reflectance study is the difficulty of covering large areas. The study also found that ground and laboratory spectral radiance are generally higher in reflectance than those of Landsat TM. This is due to several factors such as atmospheric conditions, the low altitude or different scales. Whereas for areas with huge size of dune sand, the Landsat TM spectral has higher reflectance than those from field and laboratory. The study observed that there is a good correspondence or correlation of the wavelengths maximum sensitivity between the three spectral measurements i.e lab, field and space-borne measurements. http://www.mdpi.com/2072-4292/1/4/915/ Wed, 11 Nov 2009 00:00:00 CET Remote Sensing 2009-11-11 1 4 Article 915 933 2072-4292 Remote Sensing and Spectral Characteristics of Desert Sand from Qatar Peninsula, Arabian/Persian Gulf 2009-11-11 doi: 10.3390/rs1040915 Abdulali Sadiq Fares Howari http://www.mdpi.com/2072-4292/1/4/896/ Quaking aspen (Populus tremuloides) is commonly believed to be declining throughout western North America. Using a historical vegetation map and Landsat TM5 imagery, this study detects changes in regional aspen cover over two different time periods of 85 and 18 years and examines aspen change patterns with biophysical variables in the Targhee National Forest of eastern Idaho, USA. A subpixel classification approach was successfully used to classify aspen. The results indicate greater spatial variability in regional aspen change patterns than indicated by local-scale studies. The observed spatial variability appears to be an inherent pattern in regional aspen dynamics, which interacts with biophysical variables, but persists over time. http://www.mdpi.com/2072-4292/1/4/896/ Tue, 10 Nov 2009 00:00:00 CET Remote Sensing 2009-11-10 1 4 Article 896 914 2072-4292 Regional Assessment of Aspen Change and Spatial Variability on Decadal Time Scales 2009-11-10 doi: 10.3390/rs1040896 Temuulen Tsagaan Sankey http://www.mdpi.com/2072-4292/1/4/875/ Nearest neighbor techniques are commonly used in remote sensing, pattern recognition and statistics to classify objects into a predefined number of categories based on a given set of predictors. These techniques are especially useful for highly nonlinear relationship between the variables. In most studies the distance measure is adopted a priori. In contrast we propose a general procedure to find an adaptive metric that combines a local variance reducing technique and a linear embedding of the observation space into an appropriate Euclidean space. To illustrate the application of this technique, two agricultural land cover classifications using mono-temporal and multi-temporal Landsat scenes are presented. The results of the study, compared with standard approaches used in remote sensing such as maximum likelihood (ML) or k-Nearest Neighbor (k-NN) indicate substantial improvement with regard to the overall accuracy and the cardinality of the calibration data set. Also, using MNN in a soft/fuzzy classification framework demonstrated to be a very useful tool in order to derive critical areas that need some further attention and investment concerning additional calibration data. http://www.mdpi.com/2072-4292/1/4/875/ Mon, 09 Nov 2009 00:00:00 CET Remote Sensing 2009-11-09 1 4 Article 875 895 2072-4292 Supervised Classification of Agricultural Land Cover Using a Modified k-NN Technique (MNN) and Landsat Remote Sensing Imagery 2009-11-09 doi: 10.3390/rs1040875 Luis Samaniego Karsten Schulz http://www.mdpi.com/2072-4292/1/4/858/ Apple scab causes significant losses in the production of this fruit. A timely and more site-specific monitoring and spraying of the disease could reduce the number of applications of fungicides in the fruit industry. The aim of this leaf-scale study therefore lies in the early detection of apple scab infections in a non-invasive and non-destructive way. In order to attain this objective, fluorescence- and hyperspectral imaging techniques were used. An experiment was conducted under controlled environmental conditions, linking hyperspectral reflectance and fluorescence imaging measurements to scab infection symptoms in a susceptible apple cultivar (Malus x domestica Borkh. cv. Braeburn). Plant stress was induced by inoculation of the apple plants with scab spores. The quantum efficiency of Photosystem II (PSII) photochemistry was derived from fluorescence images of leaves under light adapted conditions. Leaves inoculated with scab spores were expected to have lower PSII quantum efficiency than control (mock) leaves. However, besides scab-induced, also immature leaves exhibited low PSII quantum efficiency. Therefore, this study recommends the simultaneous use of fluorescence imaging and hyperspectral techniques. A shortwave infrared narrow-waveband ratio index (R1480/R2135) is presented in this paper as a promising tool to identify scab stress before symptoms become visible to the naked eye. Low PSII quantum efficiency attended by low narrow waveband R1480/R2135 index values points out scab stress in an early stage. Apparent high PSII quantum efficiency together with high overall reflectance in VIS and SWIR spectral domains indicate a severe, well-developed scab infection. http://www.mdpi.com/2072-4292/1/4/858/ Fri, 06 Nov 2009 00:00:00 CET Remote Sensing 2009-11-06 1 4 Article 858 874 2072-4292 Hyperspectral Reflectance and Fluorescence Imaging to Detect Scab Induced Stress in Apple Leaves 2009-11-06 doi: 10.3390/rs1040858 Stephanie Delalieux Annemarie Auwerkerken Willem W. Verstraeten Ben Somers Roland Valcke Stefaan Lhermitte Johan Keulemans Pol Coppin http://www.mdpi.com/2072-4292/1/4/842/ This paper introduces derivations of soil line influences on two-band vegetation indices (VIs) and vegetation isolines in the red and near infra-red reflectance space. Soil line variations are described as changes in the soil line parameters (slope and offset) and the red reflectance of the soil surface. A general form of a VI model equation written as a ratio of two linear functions (e.g., NDVI and SAVI) was assumed. It was found that relative VI variations can be approximated by a linear combination of the three soil parameters. The derived expressions imply the possibility of estimating and correcting for soil-induced bias errors in VIs and their derived biophysical parameters, caused by the assumption of a general soil line, through the use of external data sources such as regional soil maps. http://www.mdpi.com/2072-4292/1/4/842/ Tue, 03 Nov 2009 00:00:00 CET Remote Sensing 2009-11-03 1 4 Article 842 857 2072-4292 Derivation of Soil Line Influence on Two-Band Vegetation Indices and Vegetation Isolines 2009-11-03 doi: 10.3390/rs1040842 Hiroki Yoshioka Tomoaki Miura José A. M. Demattê Karim Batchily Alfredo R. Huete http://www.mdpi.com/2072-4292/1/4/829/ In this paper we present a stereo feature-based method using SIFT (Scale-invariant feature transform) descriptors. We use automatic feature extractors, matching algorithms between images and techniques of robust estimation to produce a DTM (Digital Terrain Model) using convergent shots of a rock glacier.The geomorphologic structure observed in this study is the Veleta rock glacier (Sierra Nevada, Granada, Spain). This rock glacier is of high scientific interest because it is the southernmost active rock glacier in Europe and it has been analyzed every year since 2001. The research on the Veleta rock glacier is devoted to the study of its displacement and cartography through geodetic and photogrammetric techniques. http://www.mdpi.com/2072-4292/1/4/829/ Wed, 28 Oct 2009 00:00:00 CET Remote Sensing 2009-10-28 1 4 Article 829 841 2072-4292 Photogrammetric Methodology for the Production of Geomorphologic Maps: Application to the Veleta Rock Glacier (Sierra Nevada, Granada, Spain) 2009-10-28 doi: 10.3390/rs1040829 Javier de Matías José Juan de Sanjosé Gonzalo López-Nicolás Carlos Sagüés José Jesús Guerrero http://www.mdpi.com/2072-4292/1/4/818/ In 2008, Wyoming was ranked 8th in barley production and 20th in hay production in the United States and these crops support Wyoming’s $800 million cattle industry. However, with a mean elevation of 2,040 meters, much of Wyoming has a limited crop growing season (as little as 60 days) and relies on late-summer and early-fall streamflow for agricultural water supply. Wyoming is host to over 80 glaciers with the majority of these glaciers being located in the Wind River Range. These “frozen reservoirs” provide a stable source of streamflow (glacier meltwater) during this critical late-summer and early-fall growing season. Given the potential impacts of climate change (increased temperatures resulting in glacier recession), the quantification of glacier meltwater during the late-summer and early-fall growing seasons is needed. Glacier area changes in the Wind River Range were estimated for 42 glaciers using Landsat data from 1985 to 2005. The total surface area of the 42 glaciers was calculated to be 41.2 ± 11.7 km2 in 1985 and 30.8 ± 8.2 km2 in 2005, an average decrease of 25% over the 21 year period. Small glaciers experienced noticeably more area reduction than large glaciers. Of the 42 glaciers analyzed, 17 had an area of greater than 0.5 km2 in 1985, while 25 were less than 0.5 km2 in 1985. The glaciers with a surface area less than 0.5 km2 experienced an average surface area loss (fraction of 1985 surface area) of 43%, while the larger glaciers (greater than 0.5 km2) experienced an average surface area loss of 22%. Applying area-volume scaling relationships for glaciers, volume loss was estimated to be 409 × 106 m3 over the 21 year period, which results in an estimated 4% to 10% contribution to warm season (July–October) streamflow. http://www.mdpi.com/2072-4292/1/4/818/ Wed, 28 Oct 2009 00:00:00 CET Remote Sensing 2009-10-28 1 4 Article 818 828 2072-4292 Estimated Wind River Range (Wyoming, USA) Glacier Melt Water Contributions to Agriculture 2009-10-28 doi: 10.3390/rs1040818 Kyle Cheesbrough Jake Edmunds Glenn Tootle Greg Kerr Larry Pochop http://www.mdpi.com/2072-4292/1/4/795/ The present paper reports on an investigation to monitor the drought status in Algerian forest covered areas with satellite Earth observations because ground data are scarce and hard to collect. The main goal of this study is to map surface energy fluxes with remote sensing data, based on a simplified algorithm to solve the energy balance equation on each data pixel. Cultivated areas, forest cover and a large water surface were included in the investigated surfaces. The input parameters involve remotely sensed data in the visible, near infrared and thermal infrared. The surface energy fluxes are estimated by expressing the partitioning of energy available at the surface between the sensible heat flux (H) and the latent heat flux (LE) through the evaporative fraction (Λ) according to the S-SEBI (Simplified Surface Energy Balance Index) concept. The method is applicable under the assumptions of constant atmospheric conditions and sufficient wet and dry pixels over a Landsat 7 image. The results are analyzed and discussed considering instantaneous latent heat flux at the data acquisition time. The results confirm the relationships between albedo (r0), the surface temperature (T0) and the evaporative fraction. The method provides estimates of air temperature and LE close to reference measurements. The estimate of latent heat flux and other variables are comparable to those of previous studies. Their comparison with other methods shows reasonable agreement. This approach has demonstrated its simplicity and the fact that remote sensing data alone is sufficient; it could be very promising in areas where data are scarce and difficult to collect. http://www.mdpi.com/2072-4292/1/4/795/ Tue, 27 Oct 2009 00:00:00 CET Remote Sensing 2009-10-27 1 4 Article 795 817 2072-4292 Mapping Latent Heat Flux in the Western Forest Covered Regions of Algeria Using Remote Sensing Data and a Spatialized Model 2009-10-27 doi: 10.3390/rs1040795 Souidi Zahira Hamimed Abderrahmane Khalladi Mederbal Donze Frederic http://www.mdpi.com/2072-4292/1/4/776/ Recent years have seen the progression of light detection and ranging (lidar) from the realm of research to operational use in natural resource management. Numerous government agencies, private industries, and public/private stakeholder consortiums are planning or have recently acquired large-scale acquisitions, and a national U.S. lidar acquisition is likely before 2020. Before it is feasible for land managers to integrate lidar into decision making, resource assessment, or monitoring across the gambit of natural resource applications, consistent standards in project planning, data processing, and user-driven products are required. This paper introduces principal lidar acquisition parameters, and makes recommendations for project planning, processing, and product standards to better serve natural resource managers across multiple disciplines. http://www.mdpi.com/2072-4292/1/4/776/ Tue, 27 Oct 2009 00:00:00 CET Remote Sensing 2009-10-27 1 4 Article 776 794 2072-4292 Discrete Return Lidar in Natural Resources: Recommendations for Project Planning, Data Processing, and Deliverables 2009-10-27 doi: 10.3390/rs1040776 Jeffrey S. Evans Andrew T. Hudak Russ Faux Alistair M. S. Smith http://www.mdpi.com/2072-4292/1/4/758/ The purpose of this research was to evaluate the performance of existing spectral band ratio algorithms and develop a novel algorithm to quantify phycocyanin (PC) in cyanobacteria using hyperspectral remotely-sensed data. We performed four spectroscopic experiments on two different laboratory cultured cyanobacterial species and found that the existing band ratio algorithms are highly sensitive to chlorophylls, making them inaccurate in predicting cyanobacterial abundance in the presence of other chlorophyll-containing organisms. We present a novel spectral band ratio algorithm using 700 and 600 nm that is much less sensitive to the presence of chlorophyll. http://www.mdpi.com/2072-4292/1/4/758/ Mon, 19 Oct 2009 00:00:00 CEST Remote Sensing 2009-10-19 1 4 Article 758 775 2072-4292 A Novel Algorithm for Predicting Phycocyanin Concentrations in Cyanobacteria: A Proximal Hyperspectral Remote Sensing Approach 2009-10-19 doi: 10.3390/rs1040758 Sachidananda Mishra Deepak R. Mishra Wendy M. Schluchter http://www.mdpi.com/2072-4292/1/4/731/ A novel, automatic tertiary classifier is proposed for identifying vegetation, building and non-building objects from a single nadir aerial image. The method is unsupervised, that is, no parameter adjustment is done during the algorithm’s execution. The only assumption the algorithm makes about the building structures is that they have convex rooftop sections. Results are provided for two different actual data sets. http://www.mdpi.com/2072-4292/1/4/731/ Fri, 16 Oct 2009 00:00:00 CEST Remote Sensing 2009-10-16 1 4 Article 731 757 2072-4292 Automatic Vegetation Identification and Building Detection from a Single Nadir Aerial Image 2009-10-16 doi: 10.3390/rs1040731 Nicholas Shorter Takis Kasparis http://www.mdpi.com/2072-4292/1/4/697/ Sun glint, the specular reflection of light from water surfaces, is a serious confounding factor for remote sensing of water column properties and benthos. This paper reviews current techniques to estimate and remove the glint radiance component from imagery. Methods for processing of ocean color images use statistical sea surface models to predict the glint from the sun and sensor positions and wind data. Methods for higher resolution imaging, used in coastal and shallow water mapping, estimate the glint radiance from the near-infrared signal. The effects of some current methods are demonstrated and possibilities for future techniques are briefly addressed. http://www.mdpi.com/2072-4292/1/4/697/ Mon, 12 Oct 2009 00:00:00 CEST Remote Sensing 2009-10-12 1 4 Review 697 730 2072-4292 Sun Glint Correction of High and Low Spatial Resolution Images of Aquatic Scenes: a Review of Methods for Visible and Near-Infrared Wavelengths 2009-10-12 doi: 10.3390/rs1040697 Susan Kay John D. Hedley Samantha Lavender http://www.mdpi.com/2072-4292/1/4/676/ Monitoring of active faults in urban areas is of great importance, providing useful information to assess seismic hazards and risks. The present study concerns the monitoring of the potential ground deformation caused by the active tectonism in the cities of Patras and Pyrgos in Western Greece. A PS interferometric analysis technique was applied using a rich data–set of ERS–1 & 2 SLC images. The results of the interferometric analysis were compared with the tectonic maps of the two cities. Patras show clearer uplift–subsidence results due to the more distinct fault pattern and intense deformation compared to the Pyrgos area, where more diffused deformation is observed, with no significant displacements on the surface. http://www.mdpi.com/2072-4292/1/4/676/ Wed, 30 Sep 2009 00:00:00 CEST Remote Sensing 2009-09-30 1 4 Article 676 696 2072-4292 Detection and Monitoring of Active Faults in Urban Environments: Time Series Interferometry on the Cities of Patras and Pyrgos (Peloponnese, Greece) 2009-09-30 doi: 10.3390/rs1040676 Issaak Parcharidis Sotiris Kokkalas Ioannis Fountoulis Michael Foumelis http://www.mdpi.com/2072-4292/1/4/644/ Two arsenic-accumulating Pteris ferns (Pteris cretica mayii and Pteris multifida), along with a non-accumulating control fern (Nephrolepis exaltata) were grown in greenhouse conditions in clean sand spiked with 0, 20, 50, 100 and 200 ppm sodium arsenate. Spectral data were collected for each of five replicates prior to harvest at 4-week intervals. Fern samples were analyzed for total metals content and Partial Least Squares and Stepwise Linear Regression techniques were used to develop models from the spectral data. Results showed that Pteris cretica mayii and Pteris multifida are confirmed hyperaccumulators of inorganic arsenic and that reasonably accurate predictive models of arsenic concentration can be developed from the first derivative of spectral reflectance of the hyperaccumulating Pteris ferns. Both the arsenic uptake and spectral results indicate that there is some species-specific variability but the results compare favorably with previously published data and additional research is recommended. http://www.mdpi.com/2072-4292/1/4/644/ Wed, 30 Sep 2009 00:00:00 CEST Remote Sensing 2009-09-30 1 4 Article 644 675 2072-4292 Spectroscopic Analysis of Arsenic Uptake in Pteris Ferns 2009-09-30 doi: 10.3390/rs1040644 Terrence Slonecker Barry Haack Susan Price http://www.mdpi.com/2072-4292/1/4/620/ The characterization and evaluation of the recent status of biodiversity in Southern Africa’s Savannas is a major prerequisite for suitable and sustainable land management and conservation purposes. This paper presents an integrated concept for vegetation type mapping in a dry savanna ecosystem based on local scale in-situ botanical survey data with high resolution (Landsat) and coarse resolution (MODIS) satellite time series. In this context, a semi-automated training database generation procedure using object-oriented image segmentation techniques is introduced. A tree-based Random Forest classifier was used for mapping vegetation type associations in the Kalahari of NE Namibia based on inter-annual intensity- and phenology-related time series metrics. The utilization of long-term inter-annual temporal metrics delivered the best classification accuracies (Kappa = 0.93) compared with classifications based on seasonal feature sets. The relationship between annual classification accuracies and bi-annual precipitation sums was conducted using data from the Tropical Rainfall Measuring Mission (TRMM). Increased error rates occurred in years with high rainfall rates compared to dry rainy seasons. The variable importance was analyzed and showed high-rank positions for features of the Enhanced Vegetation Index (EVI) and the blue and middle infrared bands, indicating that soil reflectance was crucial information for an accurate spectral discrimination of Kalahari vegetation types. Time series features related to reflectance intensity obtained increased rank-positions compared to phenology-related metrics. http://www.mdpi.com/2072-4292/1/4/620/ Wed, 30 Sep 2009 00:00:00 CEST Remote Sensing 2009-09-30 1 4 Article 620 643 2072-4292 On the Suitability of MODIS Time Series Metrics to Map Vegetation Types in Dry Savanna Ecosystems: A Case Study in the Kalahari of NE Namibia 2009-09-30 doi: 10.3390/rs1040620 Christian Hüttich Ursula Gessner Martin Herold Ben J. Strohbach Michael Schmidt Manfred Keil Stefan Dech http://www.mdpi.com/2072-4292/1/3/606/ Recent advances in the field of remote sensing have led to an increase in available rainfall data on a regional and global scale. Several NASA sponsored satellite missions provide valuable precipitation data. However, the advantages of the data are limited by complications related to the indirect nature of satellite estimates. This study intends to develop a stochastic model for uncertainty analysis of satellite rainfall fields through simulating error fields and imposing them over satellite estimates. In order to examine reliability and performance of the presented model, ensembles of satellite estimates are simulated for a large area across the North and South Carolina. The generated ensembles are then compared with original satellite estimates with respect to statistical properties and spatial dependencies. The results show that the model can be used to describe the uncertainties associated to TRMM multi-satellite precipitation estimates. The presented model is validated using random sub-samples of the observations based on the bootstrap technique. The results indicate that the model performs reasonably well with different numbers of available rain gauges. http://www.mdpi.com/2072-4292/1/3/606/ Fri, 11 Sep 2009 00:00:00 CEST Remote Sensing 2009-09-11 1 3 Article 606 619 2072-4292 Accounting for Uncertainties of the TRMM Satellite Estimates 2009-09-11 doi: 10.3390/rs1030606 Amir AghaKouchak Nasrin Nasrollahi Emad Habib http://www.mdpi.com/2072-4292/1/3/577/ The transition from film imaging to digital imaging in photogrammetric data capture is opening interesting possibilities for photogrammetric processes. A great advantage of digital sensors is their radiometric potential. This article presents a state-of-the-art review on the radiometric aspects of digital photogrammetric images. The analysis is based on a literature research and a questionnaire submitted to various interest groups related to the photogrammetric process. An important contribution to this paper is a characterization of the photogrammetric image acquisition and image product generation systems. The questionnaire revealed many weaknesses in current processes, but the future prospects of radiometrically quantitative photogrammetry are promising. http://www.mdpi.com/2072-4292/1/3/577/ Thu, 10 Sep 2009 00:00:00 CEST Remote Sensing 2009-09-10 1 3 Article 577 605 2072-4292 Digital Airborne Photogrammetry—A New Tool for Quantitative Remote Sensing?—A State-of-the-Art Review On Radiometric Aspects of Digital Photogrammetric Images 2009-09-10 doi: 10.3390/rs1030577 Eija Honkavaara Roman Arbiol Lauri Markelin Lucas Martinez Michael Cramer Stéphane Bovet Laure Chandelier Risto Ilves Sascha Klonus Paul Marshal Daniel Schläpfer Mark Tabor Christian Thom Nikolaj Veje http://www.mdpi.com/2072-4292/1/3/557/ This paper presents the results of measurements of aerosol optical properties which were made between 2006 and 2008 within the framework of various international projects in different locations such as Spitsbergen, northern Norway and Crete. The investigations were made under different baric topography conditions and in various seasons of the year which facilitated the investigations of spatial and temporal dependencies between upper troposphere mass state and spectral variations of aerosol properties. The results of aerosol optical depth (AOD) measurements showed significant episodes during which jet stream events (300 hPa surface) over the Arctic were present. The mean spectral characteristics of AOD from “before” and “after” the event differ by 0.14 versus the “during” phase of the episode. The macrometeorological relative topography charts shown also the relationships between the 500 hPa, close sea-level pressure SLP (1,000 hPa) charts surfaces and the attenuation caused by aerosol scattering and absorption in vertical profiles during the afternoon hours. http://www.mdpi.com/2072-4292/1/3/557/ Fri, 04 Sep 2009 00:00:00 CEST Remote Sensing 2009-09-04 1 3 Article 557 576 2072-4292 Aerosol Optical Depth Measured at Different Coastal Boundary Layers and Its Links with Synoptic-Scale Features 2009-09-04 doi: 10.3390/rs1030557 Agnieszka Ponczkowska Tymon Zielinski Tomasz Petelski Krzysztof Markowicz Giorgos Chourdakis Giorgos Georgoussis http://www.mdpi.com/2072-4292/1/3/534/ This paper examines the spatiotemporal pattern of urbanization in Kathmandu Valley using remote sensing and spatial metrics techniques. The study is based on 33-years of time series data compiled from satellite images. Along with new developments within the city fringes and rural villages in the valley, shifts in the natural environment and newly developed socioeconomic strains between residents are emerging. A highly dynamic spatial pattern of urbanization is observed in the valley. Urban built-up areas had a slow trend of growth in the 1960s and 1970s but have grown rapidly since the 1980s. The urbanization process has developed fragmented and heterogeneous land use combinations in the valley. However, the refill type of development process in the city core and immediate fringe areas has shown a decreasing trend in the neighborhood distances between land use patches, and an increasing trend towards physical connectedness, which indicates a higher probability of homogenous landscape development in the upcoming decades. http://www.mdpi.com/2072-4292/1/3/534/ Thu, 03 Sep 2009 00:00:00 CEST Remote Sensing 2009-09-03 1 3 Article 534 556 2072-4292 Examining Spatiotemporal Urbanization Patterns in Kathmandu Valley, Nepal: Remote Sensing and Spatial Metrics Approaches 2009-09-03 doi: 10.3390/rs1030534 Rajesh Bahadur Thapa Yuji Murayama http://www.mdpi.com/2072-4292/1/3/519/ In this study, we tested the Maximum Entropy model (Maxent) for its application and performance in remotely sensing invasive Tamarix sp. Six Landsat 7 ETM+ satellite scenes and a suite of vegetation indices at different times of the growing season were selected for our study area along the Arkansas River in Colorado. Satellite scenes were selected for April, May, June, August, September, and October and tested in single-scene and time-series analyses. The best model was a time-series analysis fit with all spectral variables, which had an AUC = 0.96, overall accuracy = 0.90, and Kappa = 0.79. The top predictor variables were June tasselled cap wetness, September tasselled cap wetness, and October band 3. A second time-series analysis, where the variables that were highly correlated and demonstrated low predictive strengths were removed, was the second best model. The third best model was the October single-scene analysis. Our results may prove to be an effective approach for mapping Tamarix sp., which has been a challenge for resource managers. Of equal importance is the positive performance of the Maxent model in handling remotely sensed datasets. http://www.mdpi.com/2072-4292/1/3/519/ Mon, 31 Aug 2009 00:00:00 CEST Remote Sensing 2009-08-31 1 3 Article 519 533 2072-4292 Mapping Invasive Tamarisk (Tamarix): A Comparison of Single-Scene and Time-Series Analyses of Remotely Sensed Data 2009-08-31 doi: 10.3390/rs1030519 Paul H. Evangelista Thomas J. Stohlgren Jeffrey T. Morisette Sunil Kumar http://www.mdpi.com/2072-4292/1/3/496/ The impact of target bidirectional reflectance in dual field of view spectroscopy was described and quantified using field measurements and ray-tracing simulations. A data-driven normalization method was developed to convert reflectance factors under cloud obscured conditions into clear sky reflectance by decomposing the target bidirectional reflectance into an isotropic target-specific component and a group-specific bidirectional component. An evaluation on tree, grass and gravel targets suggests a reduction in relative reflectance error obtained by normalization from 15% to less than 5% between 400 and 1800 nm. At higher wavelengths a decreased signal-to-noise ratio increases the errors. http://www.mdpi.com/2072-4292/1/3/496/ Tue, 25 Aug 2009 00:00:00 CEST Remote Sensing 2009-08-25 1 3 Article 496 518 2072-4292 Evaluation and Normalization of Cloud Obscuration Related BRDF Effects in Field Spectroscopy 2009-08-25 doi: 10.3390/rs1030496 Jan Stuckens Ben Somers Willem W. Verstraeten Rony Swennen Pol Coppin http://www.mdpi.com/2072-4292/1/3/466/ This paper provides an overview of some time-reversal (TR) techniques for remote sensing and imaging using ultrawideband (UWB) electromagnetic signals in the microwave and millimeter wave range. The TR techniques explore the TR invariance of the wave equation in lossless and stationary media. They provide superresolution and statistical stability, and are therefore quite useful for a number of remote sensing applications. We first discuss the TR concept through a prototypal TR experiment with a discrete scatterer embedded in continuous random media. We then discuss a series of TR-based imaging algorithms employing UWB signals: DORT, space-frequency (SF) imaging and TR-MUSIC. Finally, we consider a dispersion/loss compensation approach for TR applications in dispersive/lossy media, where TR invariance is broken. http://www.mdpi.com/2072-4292/1/3/466/ Mon, 24 Aug 2009 00:00:00 CEST Remote Sensing 2009-08-24 1 3 Review 466 495 2072-4292 Ultrawideband Microwave Sensing and Imaging Using Time-Reversal Techniques: A Review 2009-08-24 doi: 10.3390/rs1030466 Mehmet Emre Yavuz Fernando L. Teixeira http://www.mdpi.com/2072-4292/1/3/445/ Agricultural use is by far the largest consumer of fresh water worldwide, especially in the Mediterranean, where it has reached unsustainable levels, thus posing a serious threat to water resources. Having a good estimate of the water used in an agricultural area would help water managers create incentives for water savings at the farmer and basin level, and meet the demands of the European Water Framework Directive. This work presents an integrated methodology for estimating water use in Mediterranean agricultural areas. It is based on well established methods of estimating the actual evapotranspiration through surface energy fluxes, customized for better performance under the Mediterranean conditions: small parcel sizes, detailed crop pattern, and lack of necessary data. The methodology has been tested and validated on the agricultural plain of the river Strimonas (Greece) using a time series of Terra MODIS and Landsat 5 TM satellite images, and used to produce a seasonal water use map at a high spatial resolution. Finally, a tool has been designed to implement the methodology with a user-friendly interface, in order to facilitate its operational use. http://www.mdpi.com/2072-4292/1/3/445/ Thu, 20 Aug 2009 00:00:00 CEST Remote Sensing 2009-08-20 1 3 Article 445 465 2072-4292 Integrated Methodology for Estimating Water Use in Mediterranean Agricultural Areas 2009-08-20 doi: 10.3390/rs1030445 Thomas K. Alexandridis Ines Cherif Yann Chemin George N. Silleos Eleftherios Stavrinos George C. Zalidis http://www.mdpi.com/2072-4292/1/3/418/ Accurate estimates of the magnitude and spatial distribution of both formal and informal economic activity have many useful applications. Developing alternative methods for making estimates of these economic activities may prove to be useful when other measures are of suspect accuracy or unavailable. This research explores the potential for estimating the formal and informal economy for Mexico using known relationships between the spatial patterns of nighttime satellite imagery and economic activity in the United States (U.S.). Regression models have been developed between spatial patterns of nighttime imagery and Adjusted Official Gross State Product (AGSP) for the U.S. states. These regression parameters derived from the regression models of the U.S. were ‘blindly’ applied to Mexico to estimate the Estimated Gross State Income (EGSI) at the sub-national level and the Estimated Gross Domestic Income (EGDI) at the national level. Comparison of the EGDI estimate of Mexico against the official Gross National Income (GNI) estimate suggests that the magnitude of Mexico’s informal economy and the inflow of remittances are 150 percent larger than their existing official estimates in the GNI. http://www.mdpi.com/2072-4292/1/3/418/ Tue, 18 Aug 2009 00:00:00 CEST Remote Sensing 2009-08-18 1 3 Article 418 444 2072-4292 Estimation of Mexico’s Informal Economy and Remittances Using Nighttime Imagery 2009-08-18 doi: 10.3390/rs1030418 Tilottama Ghosh Sharolyn Anderson Rebecca L. Powell L. Powell Paul C. Sutton Christopher D. Elvidge http://www.mdpi.com/2072-4292/1/3/408/ Mapping species composition is a focus of the wetland science community as this information will substantially enhance assessment and monitoring abilities. Hyperspectral remote sensing has been utilized as a cost-efficient approach. While hyperspectral instruments can record hundreds of contiguous narrow bands, much of the data are redundant and/or provide no increase in utility for distinguishing objects. Knowledge of the optimal bands allows users to efficiently focus on bands that provide the most information and several data reduction tools are available. The objective of this Communication was to evaluate Principal Components Analysis (PCA) for identifying optimal bands to discriminate wetland plant species. In-situ hyperspectral reflectance measurements were obtained for thirty-five species in two diverse Great Lakes wetlands. PCA was executed on a suite of categories based on botanical plant/substrate characteristics and spectral configuration schemes. Results showed that the data dependency of PCA makes it a poor, stand alone tool for selecting optimal wavelengths. PCA does not allow diagnostic comparison across sites and wavelengths identified by PCA do not necessarily represent wavelengths that indicate biophysical attributes of interest. Further, narrow bands captured by hyperspectral sensors need to be substantially re-sampled and/or smoothed in order for PCA to identify useful information. http://www.mdpi.com/2072-4292/1/3/408/ Tue, 18 Aug 2009 00:00:00 CEST Remote Sensing 2009-08-18 1 3 Communication 408 417 2072-4292 Evaluating Principal Components Analysis for Identifying Optimal Bands Using Wetland Hyperspectral Measurements From the Great Lakes, USA 2009-08-18 doi: 10.3390/rs1030408 Nathan Torbick Brian Becker http://www.mdpi.com/2072-4292/1/3/393/ In this paper we present a framework for modelling potential species distribution (PSD) of balsam fir [bF; Abies balsamea (L.) Mill.] as a function of landscape-level descriptions of: (i) growing degree days (GDD: a temperature related index), (ii) land-surface wetness, (iii) incident photosynthetically active radiation (PAR), and (iv) tree habitat suitability. GDD and land-surface wetness are derived primarily from remote sensing data acquired with the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Terra satellite. PAR is calculated with an existing spatial model of solar radiation. Raster-based calculations of habitat suitability and PSD are obtained by multiplying normalized values of species environmental-response functions (one for each environmental variable) parameterized for balsam fir. As a demonstration of the procedure, we apply the calculations to a high bF-content area in northwest New Brunswick, Canada, at 250-m resolution. Location of medium-to-high habitat suitability values (i.e., >0.50) and actual forests, with >50% bF, matched on average 92% of the time. http://www.mdpi.com/2072-4292/1/3/393/ Mon, 17 Aug 2009 00:00:00 CEST Remote Sensing 2009-08-17 1 3 Article 393 407 2072-4292 Potential Species Distribution of Balsam Fir Based on the Integration of Biophysical Variables Derived with Remote Sensing and Process-Based Methods 2009-08-17 doi: 10.3390/rs1030393 Quazi K. Hassan Charles P.-A. Bourque http://www.mdpi.com/2072-4292/1/3/375/ This paper presents a Ship Monitoring System (SIMONS) working with Synthetic Aperture Radar (SAR) images. It is able to infer ship detection and classification information, and merge the results with other input channels, such as polls from the Automatic Identification System (AIS). Two main stages can be identified, namely: SAR processing and data dissemination. The former has three independent modules, which are related to Coastline Detection (CD), Ship Detection (SD) and Ship Classification (SC). The later is solved via an advanced web interface, which is compliant with the OpenSource standards fixed by the Open Geospatial Consortium (OGC). SIMONS has been designed to be a modular, unsupervised and reliable system that meets Near-Real Time (NRT) delivery requirements. From data ingestion to product delivery, the processing chain is fully automatic accepting ERS and ENVISAT formats. SIMONS has been developed by GMV Aerospace, S.A. with three main goals, namely: 1) To limit the dependence on the ancillary information provided by systems such as AIS. 2) To achieve the maximum level of automatism and restrict human manipulation. 3) To limit the error sources and their propagation. Spanish authorities have validated SIMONS. The results have been satisfactory and have confirmed that the system is useful for improving decision making. For single-polarimetric images with a resolution of 30 m, SIMONS permits the location of ships larger than 40 m with a classification ratio around 50% of positive matches. These values are expected to be improved with SAR data from new sensors. In the paper, the performance of SD and SC modules is assessed by cross-check of SAR data with AIS reports. http://www.mdpi.com/2072-4292/1/3/375/ Fri, 14 Aug 2009 00:00:00 CEST Remote Sensing 2009-08-14 1 3 Article 375 392 2072-4292 Operational Ship Monitoring System Based on Synthetic Aperture Radar Processing 2009-08-14 doi: 10.3390/rs1030375 Gerard Margarit José A. Barba Milanés Antonio Tabasco http://www.mdpi.com/2072-4292/1/3/355/ The areal extent and spatial distribution of evergreen forests in the tropical zones are important for the study of climate, carbon cycle and biodiversity. However, frequent cloud cover in the tropical regions makes mapping evergreen forests a challenging task. In this study we developed a simple and novel mapping algorithm that is based on the temporal profile analysis of Land Surface Water Index (LSWI), which is calculated as a normalized ratio between near infrared and shortwave infrared spectral bands. The 8-day composites of MODIS Land Surface Reflectance data (MOD09A1) in 2001 at 500-m spatial resolution were used to calculate LSWI. The LSWI-based mapping algorithm was applied to map evergreen forests in tropical Africa, America and Asia (30°N–30°S). The resultant maps of evergreen forests in the tropical zone in 2001, as estimated by the LSWI-based algorithm, are compared to the three global forest datasets [FAO FRA 2000, GLC2000 and the standard MODIS Land Cover Product (MOD12Q1) produced by the MODIS Land Science Team] that are developed through complex algorithms and processes. The inter-comparison of the four datasets shows that the area estimate of evergreen forest from the LSWI-based algorithm fall within the range of forest area estimates from the FAO FRA 2000, GLC2000 and MOD12Q1 at a country level. The area and spatial distribution of evergreen forests from the LSWI-based algorithm is to a large degree similar to those of the MOD12Q1 produced by complex mapping algorithms. The results from this study demonstrate the potential of the LSWI-based mapping algorithm for large-scale mapping of evergreen forests in the tropical zone at moderate spatial resolution. http://www.mdpi.com/2072-4292/1/3/355/ Wed, 12 Aug 2009 00:00:00 CEST Remote Sensing 2009-08-12 1 3 Article 355 374 2072-4292 A Simple Algorithm for Large-Scale Mapping of Evergreen Forests in Tropical America, Africa and Asia 2009-08-12 doi: 10.3390/rs1030355 Xiangming Xiao Chandrashekhar M. Biradar Christina Czarnecki Tunrayo Alabi Michael Keller http://www.mdpi.com/2072-4292/1/3/345/ Global land cover is one of the essential terrestrial baseline datasets available for ecosystem modeling, however uncertainty remains an issue. Tools such as Google Earth offer enormous potential for land cover validation. With an ever increasing amount of very fine spatial resolution images (up to 50 cm × 50 cm) available on Google Earth, it is becoming possible for every Internet user (including non remote sensing experts) to distinguish land cover features with a high degree of reliability. Such an approach is inexpensive and allows Internet users from any region of the world to get involved in this global validation exercise. The Geo-Wiki Project is a global network of volunteers who wish to help improve the quality of global land cover maps. Since large differences occur between existing global land cover maps, current ecosystem and land-use science lacks crucial accurate data (e.g., to determine the potential of additional agricultural land available to grow crops in Africa), volunteers are asked to review hotspot maps of global land cover disagreement and determine, based on what they actually see in Google Earth and their local knowledge, if the land cover maps are correct or incorrect. Their input is recorded in a database, along with uploaded photos, to be used in the future for the creation of a new and improved hybrid global land cover map. http://www.mdpi.com/2072-4292/1/3/345/ Mon, 03 Aug 2009 00:00:00 CEST Remote Sensing 2009-08-03 1 3 Letter 345 354 2072-4292 Geo-Wiki.Org: The Use of Crowdsourcing to Improve Global Land Cover 2009-08-03 doi: 10.3390/rs1030345 Steffen Fritz Ian McCallum Christian Schill Christoph Perger Roland Grillmayer Frédéric Achard Florian Kraxner Michael Obersteiner http://www.mdpi.com/2072-4292/1/3/330/ Classifying remote sensing imageries to obtain reliable and accurate land use and land cover (LULC) information still remains a challenge that depends on many factors such as complexity of landscape, the remote sensing data selected, image processing and classification methods, etc. The aim of this paper is to extract reliable LULC information from Landsat imageries of the Lower Hunter region of New South Wales, Australia. The classical maximum likelihood classifier (MLC) was first applied to classify Landsat-MSS of 1985 and Landsat-TM of 1995 and 2005. The major LULC identified were Woodland, Pasture/scrubland, Vineyard, Built-up and Water-body. By applying post-classification correction (PCC) using ancillary data and knowledge-based logic rules the overall classification accuracy was improved from about 72% to 91% for 1985 map, 76% to 90% for 1995 map and 79% to 87% for 2005 map. The improved overall Kappa statistics due to PCC were 0.88 for the 1985 map, 0.86 for 1995 and 0.83 for 2005. The PCC maps, assessed by McNemar’s test, were found to have much higher accuracy in comparison to their counterpart MLC maps. The overall improvement in classification accuracy of the LULC maps is significant in terms of their potential use for land change modelling of the region. http://www.mdpi.com/2072-4292/1/3/330/ Fri, 31 Jul 2009 00:00:00 CEST Remote Sensing 2009-07-31 1 3 Article 330 344 2072-4292 Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data Using Post-Classification Enhancement 2009-07-31 doi: 10.3390/rs1030330 Ramita Manandhar Inakwu O. A. Odeh Tiho Ancev http://www.mdpi.com/2072-4292/1/3/318/ Tamarisk (Tamarix spp., saltcedar) is a well-known invasive phreatophyte introduced from Asia to North America in the 1800s. This report compares the efficacy of Landsat 5 Thematic Mapper (TM5), QuickBird (QB) and EO-1 Hyperion data in discriminating tamarisk populations near De Beque, Colorado, USA. As a result of highly correlated reflectance among the spectral bands provided by each sensor, relatively standard image analysis methods were employed. Multispectral data at high spatial resolution (QB, 2.5 m Ground Spatial Distance or GSD) proved more effective in tamarisk delineation than either multispectral (TM5) or hyperspectral (Hyperion) data at moderate spatial resolution (30 m GSD). http://www.mdpi.com/2072-4292/1/3/318/ Fri, 31 Jul 2009 00:00:00 CEST Remote Sensing 2009-07-31 1 3 Article 318 329 2072-4292 Remote Sensing and Mapping of Tamarisk along the Colorado River, USA: A Comparative Use of Summer-Acquired Hyperion, Thematic Mapper and QuickBird Data 2009-07-31 doi: 10.3390/rs1030318 Gregory A. Carter Kelly L. Lucas Gabriel A. Blossom Cheryl L. Lassitter Dan M. Holiday David S. Mooneyhan Danielle R. Fastring Tracy R. Holcombe Jerry A. Griffith http://www.mdpi.com/2072-4292/1/3/300/ Surface soil moisture is a key variable needed to understand and predict the climate. L-band microwave radiometry seems to be the best technique to remotely measure the soil moisture content, since the influence of other effects such as surface roughness and vegetation is comparatively small. This work describes a numerical model developed to efficiently compute the four elements of the Stokes emission vector (Th, Tv, TU and TV) of vegetation-covered soils at low microwave frequencies, as well as the single-scattering albedo and the extinction coefficient of the vegetation layer over a wide range of incidence angles. A comparison with L-band (1.400–1.427 MHz) experimental radiometric data gathered during the SMOS REFLEX 2003 field experiment over vines is presented and discussed. The measured and simulated emissivities at vertical polarization agree very well. However, at horizontal polarization there is some disagreement introduced by the soil emission model. Important radiometric parameters, such as the albedo, the attenuation and the transmissivity are computed and analyzed in terms of their values and trends, as well as their dependence on the observation and scene parameters. It is found that the vegetation attenuation is mainly driven by the presence of branches and leaves, while the albedo is mainly driven by the branches. The comparison of the simulated parameters with the values obtained by fitting the experimental data with the t-w model is very satisfactory. http://www.mdpi.com/2072-4292/1/3/300/ Mon, 20 Jul 2009 00:00:00 CEST Remote Sensing 2009-07-20 1 3 Article 300 317 2072-4292 Numerical Simulation of the Full-Polarimetric Emissivity of Vines and Comparison with Experimental Data 2009-07-20 doi: 10.3390/rs1030300 Alberto Martinez-Vazquez Adriano Camps Juan Manuel Lopez-Sanchez Mercedes Vall-llossera Alessandra Monerris http://www.mdpi.com/2072-4292/1/3/278/ Atmospheric correction impacts on the accuracy of satellite image-based land cover classification are a growing concern among scientists. In this study, the principle objective was to enhance classification accuracy by minimizing contamination effects from aerosol scattering in Landsat TM images due to the variation in solar zenith angle corresponding to cloud-free earth targets. We have derived a mathematical model for aerosols to compute and subtract the aerosol scattering noise per pixel of different vegetation classes from TM images of Nicolet in north-eastern Wisconsin. An algorithm in C++ has been developed with iterations to simulate, model, and correct for the solar zenith angle influences on scattering. Results from a supervised classification with corrected TM images showed increased class accuracy for land cover types over uncorrected images. The overall accuracy of the supervised classification was improved substantially (between 13% and 18%). The z-score shows significant difference between the corrected data and the raw data (between 4.0 and 12.0). Therefore, the atmospheric correction was essential for enhancing the image classification. http://www.mdpi.com/2072-4292/1/3/278/ Wed, 15 Jul 2009 00:00:00 CEST Remote Sensing 2009-07-15 1 3 Article 278 299 2072-4292 A Highly Accurate Classification of TM Data through Correction of Atmospheric Effects 2009-07-15 doi: 10.3390/rs1030278 Widad Elmahboub Frank Scarpace Bill Smith http://www.mdpi.com/2072-4292/1/3/266/ Tracking and monitoring ocean features which have short coherent time periods from sequential satellite images requires that the images have both very high spatial resolutions and short temporal sampling intervals (i.e., repeated cycles). Satellite images from a single sensor in a polar-orbiting satellite usually cannot meet these requirements since high spatial resolution of the sensor may result in relatively long temporal sampling interval and vice versa, such as the case of Synthetic Aperture Radar (SAR). This paper presents a new multi-sensor approach to overcome the long temporal sampling interval associated with a single SAR sensor while taking advantage of high spatial resolution of SAR images for the application of ocean feature tracking.Currently, there are two SAR sensors on different satellites, the European Remote Sensing Satellite-2 (ERS-2) and the ENVIronment SATellite (ENVISAT), having acquisition time offset around 28 minutes with almost exactly the same path.That is, ERS-2 is following ENVISAT with a 28-minutes delay, which is a good time-scale for ocean mesoscale feature tracking.A pair of SAR images from ERS-2 and ENVISAT collected on April 27, 2005 has been chosen to track ocean surface features by using wavelet analysis. As demonstrated in the case studies, this technique is robust and capable to derive ocean surface drift near an oil slick and around a big eddy in the South China Sea (SCS). http://www.mdpi.com/2072-4292/1/3/266/ Mon, 13 Jul 2009 00:00:00 CEST Remote Sensing 2009-07-13 1 3 Article 266 277 2072-4292 Deriving Ocean Surface Drift Using Multiple SAR Sensors 2009-07-13 doi: 10.3390/rs1030266 Antony K. Liu Ming-Kuang Hsu http://www.mdpi.com/2072-4292/1/3/243/ This paper focuses on an automated ANN classification system consisting of two modules: an unsupervised Kohonen’s Self-Organizing Mapping (SOM) neural network module, and a supervised Multilayer Perceptron (MLP) neural network module using the Backpropagation (BP) training algorithm. Two training algorithms were provided for the SOM network module: the standard SOM, and a refined SOM learning algorithm which incorporated Simulated Annealing (SA). The ability of our automated ANN system to perform Land-Use/Land-Cover (LU/LC) classifications of a Landsat Thematic Mapper (TM) image was tested using a supervised MLP network, an unsupervised SOM network, and a combination of SOM with SA network. Our case study demonstrated that the ANN classification system fulfilled the tasks of network training pattern creation, network training, and network generalization. The results from the three networks were assessed via a comparison with reference data derived from the high spatial resolution Digital Colour Infrared (CIR) Digital Orthophoto Quarter Quad (DOQQ) data. The supervised MLP network obtained the most accurate classification accuracy as compared to the two unsupervised SOM networks. Additionally, the classification performance of the refined SOM network was found to be significantly better than that of the standard SOM network essentially due to the incorporation of SA. This is mainly due to the SA-assisted classification utilizing the scheduling cooling scheme. It is concluded that our automated ANN classification system can be utilized for LU/LC applications and will be particularly useful when traditional statistical classification methods are not suitable due to a statistically abnormal distribution of the input data. http://www.mdpi.com/2072-4292/1/3/243/ Thu, 09 Jul 2009 00:00:00 CEST Remote Sensing 2009-07-09 1 3 Article 243 265 2072-4292 An Automated Artificial Neural Network System for Land Use/Land Cover Classification from Landsat TM Imagery 2009-07-09 doi: 10.3390/rs1030243 Hui Yuan Cynthia F. Van Der Wiele Siamak Khorram