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Remote Sens., Volume 7, Issue 1 (January 2015), Pages 1-1180

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Editorial

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Open AccessEditorial Acknowledgement to Reviewers of Remote Sensing in 2014
Remote Sens. 2015, 7(1), 627-646; doi:10.3390/rs70100627
Received: 7 January 2015 / Accepted: 7 January 2015 / Published: 7 January 2015
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Abstract
The editors of the Remote Sensing office would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2014:[...] Full article

Research

Jump to: Editorial, Review, Other

Open AccessArticle High-Resolution Imagery of Earth at Night: New Sources, Opportunities and Challenges
Remote Sens. 2015, 7(1), 1-23; doi:10.3390/rs70100001
Received: 12 September 2014 / Accepted: 15 December 2014 / Published: 23 December 2014
Cited by 20 | PDF Full-text (10987 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Images of the Earth at night are an exceptional source of human geographical data, because artificial light highlights human activity in a way that daytime scenes do not. The quality of such imagery dramatically improved in 2012 with two new spaceborne detectors. The
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Images of the Earth at night are an exceptional source of human geographical data, because artificial light highlights human activity in a way that daytime scenes do not. The quality of such imagery dramatically improved in 2012 with two new spaceborne detectors. The higher resolution and precision of the data considerably expands the scope of possible applications. In this paper, we introduce the two new data sources and discuss their potential limitations using three case studies. Data from the Visible Infrared Imaging Radiometer Suite Day-Night Band (VIIRS DNB) is shown to have sufficient resolution to identify major sources of waste light, such as airports, and we find considerable variation in the peak radiance of the world’s largest airports. Nighttime imagery brings “cultural footprints” to light: DNB data reveals that American cities emit many times more light per capita than German cities and that cities in the former East of Germany emit more light per capita than those in the former West. Photographs from the International Space Station, the second new source of imagery, provide some limited spectral information, as well as street-level resolution. These images may be of greater use for epidemiological studies than the lower resolution DNB data. Full article
(This article belongs to the Special Issue Remote Sensing with Nighttime Lights)
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Open AccessArticle A Space View of Radar Archaeological Marks: First Applications of COSMO-SkyMed X-Band Data
Remote Sens. 2015, 7(1), 24-50; doi:10.3390/rs70100024
Received: 10 August 2014 / Accepted: 15 December 2014 / Published: 23 December 2014
Cited by 12 | PDF Full-text (28187 KB) | HTML Full-text | XML Full-text
Abstract
With the development of Synthetic Aperture Radar (SAR) in terms of multi-band, multi-polarization and high-resolution data, space radar remote sensing for archaeology has become a potential field for research. Nevertheless, the archaeological detection capability of this technology has so far not been fully
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With the development of Synthetic Aperture Radar (SAR) in terms of multi-band, multi-polarization and high-resolution data, space radar remote sensing for archaeology has become a potential field for research. Nevertheless, the archaeological detection capability of this technology has so far not been fully assessed. This paper is a pioneering effort to assess the potential of satellite SAR X-band data in the detection of archaeological marks. We focus on the results obtained from a collaborative contribution jointly carried out by archaeologists and remote sensing experts in order to test the use of COSMO-SkyMed data in different contexts and environmental conditions. The methodological approaches we adopted are based on two different feature-enhancement procedures: (i) multi-temporal analysis performed to reduce noise and highlight archaeological marks; (ii) single-date analysis to assess the ability of the single SAR scene to detect archaeological features like with optical remote sensing. Results from multi-temporal data analysis, conducted using 40 scenes from COSMO-SkyMed X-band Stripmap data (27 February to 17 October 2013), enable us to detect unknown archaeological crop, soil, and shadow marks representing Luoyang city, dating from the Eastern-Han to Northern-Wei Dynasties. Single-date analyses were conducted using COSMO-SkyMed Spotlight scenes acquired for Sabratha (Libya) and Metapontum (southern Italy). These case studies were selected because they are characterized by diverse superficial conditions (desert and Mediterranean area) and archaeological marks (crop, soil and shadow). The results we obtained for both of them show that even a single SAR X-band acquisition is a feasible and effective approach for archaeological prospection. Overall, the methodological approach adopted demonstrated that both multi-temporal and single-date analysis are suitable for the enhancement of archaeological and palaeoenvironmental features. Full article
(This article belongs to the Special Issue New Perspectives of Remote Sensing for Archaeology)
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Open AccessArticle New Asia Dust Storm Detection Method Based on the Thermal Infrared Spectral Signature
Remote Sens. 2015, 7(1), 51-71; doi:10.3390/rs70100051
Received: 13 September 2014 / Accepted: 5 December 2014 / Published: 23 December 2014
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Abstract
As hyperspectral instruments can provide the detailed spectral information, a new spectral similarity method for detecting and differentiating dust from non-dust scenes using the Atmospheric Infrared Sounder (AIRS) observations has been developed. The detection is based on a pre-defined Dust Spectral Similarity Index
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As hyperspectral instruments can provide the detailed spectral information, a new spectral similarity method for detecting and differentiating dust from non-dust scenes using the Atmospheric Infrared Sounder (AIRS) observations has been developed. The detection is based on a pre-defined Dust Spectral Similarity Index (DSSI), which was calculated from the accumulated brightness temperature differences between selected 16 AIRS observation channels, in the thermal infrared region of 800–1250 cm−1. It has been demonstrated that DSSI can effectively separate the dust from non-dust by elevating dust signals. For underlying surface covered with dust, the DSSI tends to show values close to 1.0. However, the values of DSSI for clear sky surfaces or clouds (ice and water) are basically lower than those of dust, as their spectrums have significant differences with dust. To evaluate this new simple DSSI dust detection algorithm, several Asia dust events observed in northern China were analyzed, and the results agree favorably with those from the Moderate resolution Imaging Spectro radiometer (MODIS) and Cloud Aerosol LiDAR with Orthogonal Polarization (CALIOP) observations. Full article
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Open AccessArticle Investigation of Slow-Moving Landslides from ALOS/PALSAR Images with TCPInSAR: A Case Study of Oso, USA
Remote Sens. 2015, 7(1), 72-88; doi:10.3390/rs70100072
Received: 5 September 2014 / Accepted: 15 December 2014 / Published: 24 December 2014
Cited by 11 | PDF Full-text (9481 KB) | HTML Full-text | XML Full-text
Abstract
Monitoring slope instability is of great significance for understanding landslide kinematics and, therefore, reducing the related geological hazards. In recent years, interferometric synthetic aperture radar (InSAR) has been widely applied to this end, especially thanks to the prompt evolution of multi-temporal InSAR (MTInSAR)
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Monitoring slope instability is of great significance for understanding landslide kinematics and, therefore, reducing the related geological hazards. In recent years, interferometric synthetic aperture radar (InSAR) has been widely applied to this end, especially thanks to the prompt evolution of multi-temporal InSAR (MTInSAR) algorithms. In this paper, temporarily-coherent point InSAR (TCPInSAR), a recently-developed MTInSAR technique, is employed to investigate the slow-moving landslides in Oso, U.S., with 13 ALOS/PALSAR images. Compared to other MTInSAR techniques, TCPInSAR can work well with a small amount of data and is immune to unwrapping errors. Furthermore, the severe orbital ramps emanated from the inaccurate determination of the ALOS satellite’s state vector can be jointly estimated by TCPInSAR, resulting in an exhaustive separation between the orbital errors and displacement signals. The TCPInSAR-derived deformation map indicates that the riverside slopes adjacent to the North Fork of the Stillaguamish River, where the 2014 mudslide occurred, were active during 2007 and 2011. Besides, Coal Mountain has been found to be experiencing slow-moving landslides with clear boundaries and considerable magnitudes. The Deer Creek River is also threatened by a potential landslide dam due to the creeps detected in a nearby slope. The slope instability information revealed in this study is helpful to deal with the landslide hazards in Oso. Full article
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Open AccessArticle Surface Freshwater Storage Variations in the Orinoco Floodplains Using Multi-Satellite Observations
Remote Sens. 2015, 7(1), 89-110; doi:10.3390/rs70100089
Received: 29 September 2014 / Accepted: 16 December 2014 / Published: 24 December 2014
Cited by 6 | PDF Full-text (2828 KB) | HTML Full-text | XML Full-text
Abstract
Variations in surface water extent and storage are poorly characterized from regional to global scales. In this study, a multi-satellite approach is proposed to estimate the water stored in the floodplains of the Orinoco Basin at a monthly time-scale using remotely-sensed observations of
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Variations in surface water extent and storage are poorly characterized from regional to global scales. In this study, a multi-satellite approach is proposed to estimate the water stored in the floodplains of the Orinoco Basin at a monthly time-scale using remotely-sensed observations of surface water from the Global Inundation Extent Multi-Satellite (GIEMS) and stages from Envisat radar altimetry. Surface water storage variations over 2003–2007 exhibit large interannual variability and a strong seasonal signal, peaking during summer, and associated with the flood pulse. The volume of surface water storage in the Orinoco Basin was highly correlated with the river discharge at Ciudad Bolivar (R = 0.95), the closest station to the mouth where discharge was estimated, although discharge lagged one month behind storage. The correlation remained high (R = 0.73) after removing seasonal effects. Mean annual variations in surface water volume represented ~170 km3, contributing to ~45% of the Gravity Recovery and Climate Experiment (GRACE)-derived total water storage variations and representing ~13% of the total volume of water that flowed out of the Orinoco Basin to the Atlantic Ocean. Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
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Open AccessArticle High-Precision Attitude Post-Processing and Initial Verification for the ZY-3 Satellite
Remote Sens. 2015, 7(1), 111-134; doi:10.3390/rs70100111
Received: 13 October 2014 / Accepted: 5 December 2014 / Published: 24 December 2014
Cited by 5 | PDF Full-text (2368 KB) | HTML Full-text | XML Full-text
Abstract
Attitude data, which is the important data strongly correlated with the geometric accuracy of optical remote sensing satellite images, are generally obtained using a real-time Extended Kalman Filter (EKF) with star-tracker and gyro data for current high-resolution satellites, such as Orb-view, IKONOS, Quickbird,Pleiades,
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Attitude data, which is the important data strongly correlated with the geometric accuracy of optical remote sensing satellite images, are generally obtained using a real-time Extended Kalman Filter (EKF) with star-tracker and gyro data for current high-resolution satellites, such as Orb-view, IKONOS, Quickbird,Pleiades, and ZY-3.We propose a forward-backward Unscented Kalman Filter (UKF) for post-processing, and the proposed method employs UKF to suppress noise by using an unscented transformation (UT) rather than an EKF in a nonlinear attitude system. Moreover, this method makes full use of the collected data in the fixed-interval and computational resources on the ground, and it determines optimal attitude results by forward-backward filtering and weighted smoothing with the raw star-tracker and gyro data collected for a fixed period. In this study, the principle and implementation of the proposed method are described. The post-processed attitude was compared with the on-board attitude, and the absolute accuracy was evaluated by the two methods. One method compares the positioning accuracy of the object space coordinates with the post-processed and on-board attitude data without using ground control points (GCPs). The other method compares the tie-point residuals of the image coordinates after a free net adjustment. In addition, the internal and external parameters of the camera were accurately calibrated before use for an objective evaluation of the attitude accuracy. The experimental results reveal that the accuracy of the post-processed attitude is superior to the accuracy of the on-board processed attitude. This method has been applied to the ZiYuan-3 satellite system for processing the raw star-tracker and gyro data daily. Full article
Open AccessArticle The Performances of MODIS-GPP and -ET Products in China and Their Sensitivity to Input Data (FPAR/LAI)
Remote Sens. 2015, 7(1), 135-152; doi:10.3390/rs70100135
Received: 8 October 2014 / Accepted: 15 December 2014 / Published: 24 December 2014
Cited by 24 | PDF Full-text (2318 KB) | HTML Full-text | XML Full-text
Abstract
The aims are to validate and assess the performances of MODIS gross primary production (MODIS-GPP) and evapotranspiration (MODIS-ET) products in China’s different land cover types and their sensitivity to remote sensing input data. In this study, MODIS-GPP and -ET are evaluated using flux
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The aims are to validate and assess the performances of MODIS gross primary production (MODIS-GPP) and evapotranspiration (MODIS-ET) products in China’s different land cover types and their sensitivity to remote sensing input data. In this study, MODIS-GPP and -ET are evaluated using flux derived/measured data from eight sites of ChinaFLUX. Results show that MODIS-GPP generally underestimates GPP (R2 is 0.58, bias is −6.7 gC/m2/8-day and RMSE is 19.4 gC/m2/8-day) at all sites and MODIS-ET overestimates ET (R2 is 0.36, bias is 6 mm/8-day and RMSE is 11 mm/8-day) when comparing with derived GPP and measured ET, respectively. For evergreen forests, MODIS-GPP gives a poor performance with R2 varying from 0.03 to 0.44; in contrast, MODIS-ET provides more reliable results. In croplands, MODIS-GPP can explain 80% of GPP variance, but it overestimates flux derived GPP in non-growing season and underestimates flux derived GPP in growing season; similar overestimations also presented in MODIS-ET. For grasslands and mixed forests, MODIS-GPP and -ET perform good estimating accuracy. By designing four experimental groups and taking GPP simulation as an example, we suggest that the maximum light use efficiency of croplands should be optimized, and the differences of meteorological data have little impact on GPP estimation, whereas remote sensing leaf area index/fraction of photo-synthetically active radiation (LAI/FPAR) can greatly affect GPP/ET estimations for all land cover types. Thus, accurate remote sensing parameters are important for achieving reliable estimations. Full article
Open AccessArticle Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery
Remote Sens. 2015, 7(1), 153-168; doi:10.3390/rs70100153
Received: 13 October 2014 / Accepted: 15 December 2014 / Published: 24 December 2014
Cited by 23 | PDF Full-text (6470 KB) | HTML Full-text | XML Full-text
Abstract
This study evaluates and compares the performance of four machine learning classifiers—support vector machine (SVM), normal Bayes (NB), classification and regression tree (CART) and K nearest neighbor (KNN)—to classify very high resolution images, using an object-based classification procedure. In particular, we investigated how
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This study evaluates and compares the performance of four machine learning classifiers—support vector machine (SVM), normal Bayes (NB), classification and regression tree (CART) and K nearest neighbor (KNN)—to classify very high resolution images, using an object-based classification procedure. In particular, we investigated how tuning parameters affect the classification accuracy with different training sample sizes. We found that: (1) SVM and NB were superior to CART and KNN, and both could achieve high classification accuracy (>90%); (2) the setting of tuning parameters greatly affected classification accuracy, particularly for the most commonly-used SVM classifier; the optimal values of tuning parameters might vary slightly with the size of training samples; (3) the size of training sample also greatly affected the classification accuracy, when the size of training sample was less than 125. Increasing the size of training samples generally led to the increase of classification accuracies for all four classifiers. In addition, NB and KNN were more sensitive to the sample sizes. This research provides insights into the selection of classifiers and the size of training samples. It also highlights the importance of the appropriate setting of tuning parameters for different machine learning classifiers and provides useful information for optimizing these parameters. Full article
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Open AccessArticle Spatio-Temporal Change of Snow Cover and Its Response to Climate over the Tibetan Plateau Based on an Improved Daily Cloud-Free Snow Cover Product
Remote Sens. 2015, 7(1), 169-194; doi:10.3390/rs70100169
Received: 14 October 2014 / Accepted: 15 December 2014 / Published: 24 December 2014
Cited by 12 | PDF Full-text (9875 KB) | HTML Full-text | XML Full-text
Abstract
Using new, daily cloud-free snow-cover products, this study examines snow cover dynamics and their response to climate change. The results demonstrate that the daily cloud-free snow-cover products not only posses the advantages of the AMSR-E (unaffected by weather conditions) and MODIS (relatively higher
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Using new, daily cloud-free snow-cover products, this study examines snow cover dynamics and their response to climate change. The results demonstrate that the daily cloud-free snow-cover products not only posses the advantages of the AMSR-E (unaffected by weather conditions) and MODIS (relatively higher resolution) products, but are also characterized by high snow and overall classification accuracies (~85% and ~98%, respectively), substantially greater than those of the existing daily snow-cover products for all sky conditions and very similar to, or even slightly greater than, those of the daily MODIS products for clear-sky conditions. Using the snow-cover products, we analyzed the snow cover dynamics over the Tibetan Plateau and determined that the maximum number of snow-covered days (SCD) in a year followed a decreasing tendency from 2003 to 2010, with a decrease in snow-covered area (SCA) equivalent to 55.3% of the total Tibetan Plateau area. There is also a slightly increasing tendency in the maximum snow cover area (SCA), and a slightly decreasing tendency in the persistent snow cover area (i.e., pixels of SCD > 350 days) was observed for the 8-year period, which was characterized by increases in temperature (0.09 °C/year) and in precipitation (0.26 mm/year). This suggests that, on the Tibetan Plateau, changes in temperature and precipitation exert a considerable influence on the regional SCD and SCA, as well as the distribution of persistent snow cover. Full article
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Open AccessArticle Retrieval of a Temporal High-Resolution Leaf Area Index (LAI) by Combining MODIS LAI and ASTER Reflectance Data
Remote Sens. 2015, 7(1), 195-210; doi:10.3390/rs70100195
Received: 27 October 2014 / Accepted: 15 November 2014 / Published: 24 December 2014
Cited by 4 | PDF Full-text (3303 KB) | HTML Full-text | XML Full-text
Abstract
This paper aims to retrieve temporal high-resolution LAI derived by fusing MOD15 products (1 km resolution), field-measured LAI and ASTER reflectance (15-m resolution). Though the inversion of a physically based canopy reflectance model using high-resolution satellite data can produce high-resolution LAI products, the
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This paper aims to retrieve temporal high-resolution LAI derived by fusing MOD15 products (1 km resolution), field-measured LAI and ASTER reflectance (15-m resolution). Though the inversion of a physically based canopy reflectance model using high-resolution satellite data can produce high-resolution LAI products, the obstacle to producing temporal products is obvious due to the low temporal resolution of high resolution satellite data. A feasible method is to combine different source data, taking advantage of the spatial and temporal resolution of different sensors. In this paper, a high-resolution LAI retrieval method was implemented using a dynamic Bayesian network (DBN) inversion framework. MODIS LAI data with higher temporal resolution were used to fit the temporal background information, which is then updated by new, higher resolution data, herein ASTER data. The interactions between the different resolution data were analyzed from a Bayesian perspective. The proposed method was evaluated using a dataset collected in the HiWater (Heihe Watershed Allied Telemetry Experimental Research) experiment. The determination coefficient and RMSE between the estimated and measured LAI are 0.80 and 0.43, respectively. The research results suggest that even though the coarse-resolution background information differs from the high-resolution satellite observations, a satisfactory estimation result for the temporal high-resolution LAI can be produced using the accumulated information from both the new observations and background information. Full article
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Open AccessArticle A Hierarchical Approach to Persistent Scatterer Network Construction and Deformation Time Series Estimation
Remote Sens. 2015, 7(1), 211-228; doi:10.3390/rs70100211
Received: 30 September 2014 / Accepted: 15 December 2014 / Published: 24 December 2014
Cited by 2 | PDF Full-text (5822 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a hierarchical approach to network construction and time series estimation in persistent scatterer interferometry (PSI) for deformation analysis using the time series of high-resolution satellite SAR images. To balance between computational efficiency and solution accuracy, a dividing and conquering algorithm
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This paper presents a hierarchical approach to network construction and time series estimation in persistent scatterer interferometry (PSI) for deformation analysis using the time series of high-resolution satellite SAR images. To balance between computational efficiency and solution accuracy, a dividing and conquering algorithm (i.e., two levels of PS networking and solution) is proposed for extracting deformation rates of a study area. The algorithm has been tested using 40 high-resolution TerraSAR-X images collected between 2009 and 2010 over Tianjin in China for subsidence analysis, and validated by using the ground-based leveling measurements. The experimental results indicate that the hierarchical approach can remarkably reduce computing time and memory requirements, and the subsidence measurements derived from the hierarchical solution are in good agreement with the leveling data. Full article
(This article belongs to the Special Issue Remote Sensing Dedicated to Geographical Conditions Monitoring)
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Open AccessArticle Modeling Forest Aboveground Biomass and Volume Using Airborne LiDAR Metrics and Forest Inventory and Analysis Data in the Pacific Northwest
Remote Sens. 2015, 7(1), 229-255; doi:10.3390/rs70100229
Received: 29 July 2014 / Accepted: 15 December 2014 / Published: 24 December 2014
Cited by 15 | PDF Full-text (6691 KB) | HTML Full-text | XML Full-text
Abstract
The United States Forest Service Forest Inventory and Analysis (FIA) Program provides a diverse selection of data used to assess the status of the nation’s forests using sample locations dispersed throughout the country. Airborne laser scanning (ALS) systems are capable of producing accurate
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The United States Forest Service Forest Inventory and Analysis (FIA) Program provides a diverse selection of data used to assess the status of the nation’s forests using sample locations dispersed throughout the country. Airborne laser scanning (ALS) systems are capable of producing accurate measurements of individual tree dimensions and also possess the ability to characterize forest structure in three dimensions. This study investigates the potential of discrete return ALS data for modeling forest aboveground biomass (AGBM) and gross volume (gV) at FIA plot locations in the Malheur National Forest, eastern Oregon utilizing three analysis levels: (1) individual subplot (r = 7.32 m); (2) plot, comprising four clustered subplots; and (3) hectare plot (r = 56.42 m). A methodology for the creation of three point cloud-based airborne LiDAR metric sets is presented. Models for estimating AGBM and gV based on LiDAR-derived height metrics were built and validated utilizing FIA estimates of AGBM and gV derived using regional allometric equations. Simple linear regression models based on the plot-level analysis out performed subplot-level and hectare-level models, producing R2 values of 0.83 and 0.81 for AGBM and gV, utilizing mean height and the 90th height percentile as predictors, respectively. Similar results were found for multiple regression models, where plot-level analysis produced models with R2 values of 0.87 and 0.88 for AGBM and gV, utilizing multiple height percentile metrics as predictor variables. Results suggest that the current FIA plot design can be used with dense airborne LiDAR data to produce area-based estimates of AGBM and gV, and that the increased spatial scale of hectare plots may be inappropriate for modeling AGBM of gV unless exhaustive tree tallies are available. Overall, this study demonstrates that ALS data can be used to create models that describe the AGBM and gV of Pacific Northwest FIA plots and highlights the potential of estimates derived from ALS data to augment current FIA data collection procedures by providing a temporary intermediate estimation of AGBM and gV for plots with outdated field measurements. Full article
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Open AccessArticle Spectral Slope as an Indicator of Pasture Quality
Remote Sens. 2015, 7(1), 256-274; doi:10.3390/rs70100256
Received: 15 August 2014 / Accepted: 15 December 2014 / Published: 25 December 2014
Cited by 4 | PDF Full-text (1296 KB) | HTML Full-text | XML Full-text
Abstract
In this study, we develop a spectral method for assessment of pasture quality based only on the spectral information obtained with a small number of wavelengths. First, differences in spectral behavior were identified across the near infrared–shortwave infrared spectral range that were indicative
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In this study, we develop a spectral method for assessment of pasture quality based only on the spectral information obtained with a small number of wavelengths. First, differences in spectral behavior were identified across the near infrared–shortwave infrared spectral range that were indicative of changes in chemical properties. Then, slopes across different spectral ranges were calculated and correlated with the changes in crude protein (CP), neutral detergent fiber (NDF) and metabolic energy concentration (MEC). Finally, partial least squares (PLS) regression analysis was applied to identify the optimal spectral ranges for accurate assessment of CP, NDF and MEC. Six spectral domains and a set of slope criteria for real-time evaluation of pasture quality were suggested. The evaluation of three level categories (low, medium, high) for these three parameters showed a success rate of: 73%–96% for CP, 72%–87% for NDF and 60%–85% for MEC. Moreover, only one spectral range, 1748–1764 nm, was needed to provide a good estimation of CP, NDF and MEC. Importantly, five of the six selected spectral regions were not affected by water absorbance. With some modifications, this rationale can be applied to further analyses of pasture quality from airborne sensors. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)
Open AccessArticle Long-Term Distribution Patterns of Chlorophyll-a Concentration in China’s Largest Freshwater Lake: MERIS Full-Resolution Observations with a Practical Approach
Remote Sens. 2015, 7(1), 275-299; doi:10.3390/rs70100275
Received: 9 October 2014 / Accepted: 15 December 2014 / Published: 29 December 2014
Cited by 7 | PDF Full-text (3119 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
A new empirical Chl-a algorithm has been developed and validated for the largest freshwater lake of China (Poyang Lake) using a normalized green-red difference index (NGRDI), where the uncertainty was estimated to be <45% for Chl-a ranging between 1.3 and 10.5 mg·m−3
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A new empirical Chl-a algorithm has been developed and validated for the largest freshwater lake of China (Poyang Lake) using a normalized green-red difference index (NGRDI), where the uncertainty was estimated to be <45% for Chl-a ranging between 1.3 and 10.5 mg·m−3. The combined approach of using the NGRDI algorithm and atmospherically-corrected Medium Resolution Imaging Spectrometer (MERIS) data showed an advantage over other popular approaches. The algorithm was then applied to 325 carefully-selected MERIS full-resolution (300-m) scenes between 2003 and 2012, with pixels of extreme turbidity (NGRDI < 0.06, corresponding to >~25 mg·L−1 total suspended sediments or TSS) masked. The long-term Chl-a distribution showed significant spatial gradient and temporal variability, with Chl-a ranging between 2.4 ± 0.2 mg·m−3 in April and 4.4 ± 1.0 mg·m−3 in July and no significant increasing or decreasing trend during the 10-year period. In waters where Chl-a was retrievable (i.e., where TSS is <25 mg·L−1), Chl-a concentration indicated a significant negative correlation with TSS concentration on a seasonal scale and a significant positive correlation with precipitation over the years. Potential eutrophic regions in the southern and eastern lake, thought to be results of limited water exchange with the main lake, were delineated based on the occurrence frequency of high Chl-a (>10 mg·m−3) in summer. The study not only provides, for the first time, synoptic baseline information on the lake’s Chl-a distributions and potential eutrophic regions, but also demonstrates a practical approach that might be extended to assess eutrophication conditions in other inland waters. Full article
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Open AccessArticle Retrieval of Land Surface Temperature over the Heihe River Basin Using HJ-1B Thermal Infrared Data
Remote Sens. 2015, 7(1), 300-318; doi:10.3390/rs70100300
Received: 20 August 2014 / Accepted: 23 December 2014 / Published: 29 December 2014
Cited by 1 | PDF Full-text (10530 KB) | HTML Full-text | XML Full-text
Abstract
The reliable estimation of spatially distributed Land Surface Temperature (LST) is useful for monitoring regional land surface heat fluxes. A single-channel method is developed to derive the LST over the Heihe River Basin in China using data from the infrared sensor (IRS) onboard
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The reliable estimation of spatially distributed Land Surface Temperature (LST) is useful for monitoring regional land surface heat fluxes. A single-channel method is developed to derive the LST over the Heihe River Basin in China using data from the infrared sensor (IRS) onboard the Chinese “Environmental and Disaster Monitoring and Forecasting with a Small Satellite Constellation” (HJ-1B for short for one of the satellites), with ancillary water vapor information from Moderate Resolution Imaging Spectroradiometer (MODIS) products (MOD05) and in situ automatic sun tracking photometer CE318 data for the first time. In situ LST data for the period from mid-June to mid-September 2012 were acquired from automatic meteorological stations (AMS) that are part of Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project. MOD05-based LST and CE318-based LST are compared with in situ measurements at 16 AMS sites with land cover types of vegetable, maize and orchards. The results show that the use of the MOD05 product could achieve a comparable accuracy in LST retrieval with that achieved using the CE318 data. The largest difference between the MOD05-based LST and CE318-based LST is 0.84 K throughout the study period over the Heihe River Basin. The standard deviation (STD), root mean square error (RMSE), and correlation coefficient (R) of HJ-1B/IRS vs. the in situ measurements are 2.45 K, 2.78 K, and 0.67, respectively, whereas those for the MODIS 1 km LST product vs. the in situ measurements are 4.07 K, 2.98 K, and 0.79, respectively. The spatial pattern of the HJ-1B/LST over the study area in the Heihe River Basin generally agreed well with the MODIS 1 km LST product and contained more detailed spatial textures. Full article
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Open AccessArticle Annual Change Detection by ASTER TIR Data and an Estimation of the Annual Coal Loss and CO2 Emission from Coal Seams Spontaneous Combustion
Remote Sens. 2015, 7(1), 319-341; doi:10.3390/rs70100319
Received: 22 July 2014 / Accepted: 16 December 2014 / Published: 30 December 2014
Cited by 3 | PDF Full-text (2961 KB) | HTML Full-text | XML Full-text
Abstract
Coal fires, including both underground and coal waste pile fires, result in large losses of coal resources and emit considerable amounts of greenhouse gases. To estimate the annual intensity of greenhouse gas emissions and the loss of coal resources, estimating the annual loss
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Coal fires, including both underground and coal waste pile fires, result in large losses of coal resources and emit considerable amounts of greenhouse gases. To estimate the annual intensity of greenhouse gas emissions and the loss of coal resources, estimating the annual loss from fire-influenced coal seams is a feasible approach. This study assumes that the primary cause of coal volume loss is subsurface coal seam fires. The main calculation process is divided into three modules: (1) Coal fire quantity calculations, which use change detection to determine the areas of the different coal fire stages (increase/growth, maintenance/stability and decrease/shrinkage). During every change detections, the amount of coal influenced by fires for these three stages was calculated by multiplying the coal mining residual rate, combustion efficiency, average thickness and average coal intensity. (2) The life cycle estimate is based on remote sensing long-term coal fires monitoring. The life cycles for the three coal fire stages and the corresponding life cycle proportions were calculated; (3) The diurnal burnt rates for different coal fire stages were calculated using the CO2 emission rates from spontaneous combustion experiments, the coal fire life cycle, life cycle proportions. Then, using the fire-influenced quantity aggregated across the different stages, the diurnal burn rates for the different stages and the time spans between the multi-temporal image pairs used for change detection, we estimated the annual coal loss to be 44.3 × 103 tons. After correction using a CH4 emission factor, the CO2 equivalent emissions resulting from these fires was on the order of 92.7 × 103 tons. We also discovered that the centers of these coal fires migrated from deeper to shallower parts of the coal seams or traveled in the direction of the coal seam strike. This trend also agrees with the cause of the majority coal fires: spontaneous combustion of coalmine goafs. Full article
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Open AccessArticle Remote Sensing of Sonoran Desert Vegetation Structure and Phenology with Ground-Based LiDAR
Remote Sens. 2015, 7(1), 342-359; doi:10.3390/rs70100342
Received: 29 May 2014 / Accepted: 2 December 2014 / Published: 30 December 2014
Cited by 6 | PDF Full-text (2285 KB) | HTML Full-text | XML Full-text
Abstract
Long-term vegetation monitoring efforts have become increasingly important for understanding ecosystem response to global change. Many traditional methods for monitoring can be infrequent and limited in scope. Ground-based LiDAR is one remote sensing method that offers a clear advancement to monitor vegetation dynamics
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Long-term vegetation monitoring efforts have become increasingly important for understanding ecosystem response to global change. Many traditional methods for monitoring can be infrequent and limited in scope. Ground-based LiDAR is one remote sensing method that offers a clear advancement to monitor vegetation dynamics at high spatial and temporal resolution. We determined the effectiveness of LiDAR to detect intra-annual variability in vegetation structure at a long-term Sonoran Desert monitoring plot dominated by cacti, deciduous and evergreen shrubs. Monthly repeat LiDAR scans of perennial plant canopies over the course of one year had high precision. LiDAR measurements of canopy height and area were accurate with respect to total station survey measurements of individual plants. We found an increase in the number of LiDAR vegetation returns following the wet North American Monsoon season. This intra-annual variability in vegetation structure detected by LiDAR was attributable to a drought deciduous shrub Ambrosia deltoidea, whereas the evergreen shrub Larrea tridentata and cactus Opuntia engelmannii had low variability. Benefits of using LiDAR over traditional methods to census desert plants are more rapid, consistent, and cost-effective data acquisition in a high-resolution, 3-dimensional context. We conclude that repeat LiDAR measurements can be an effective method for documenting ecosystem response to desert climatology and drought over short time intervals and at detailed-local spatial scale. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)
Open AccessArticle Diverse Responses of Remotely Sensed Grassland Phenology to Interannual Climate Variability over Frozen Ground Regions in Mongolia
Remote Sens. 2015, 7(1), 360-377; doi:10.3390/rs70100360
Received: 2 November 2014 / Accepted: 25 December 2014 / Published: 31 December 2014
Cited by 2 | PDF Full-text (1974 KB) | HTML Full-text | XML Full-text
Abstract
Frozen ground may regulate the phenological shifts of dry and cold grasslands at the southern edge of the Eurasian cryosphere. In this study, an investigation based on the MODIS Collection 5 phenology product and climatic data collected from 2001 to 2009 reveals the
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Frozen ground may regulate the phenological shifts of dry and cold grasslands at the southern edge of the Eurasian cryosphere. In this study, an investigation based on the MODIS Collection 5 phenology product and climatic data collected from 2001 to 2009 reveals the diverse responses of grassland phenology to interannual climate variability over various frozen ground regions in Mongolia. Compared with middle and southern typical steppe and desert steppe, the spring (start of season; SOS) and autumn (end of season; EOS) phenological events of northern forest steppe with lower air temperature tend to be earlier and later, respectively. Both the SOS and EOS are less sensitive to climate variability in permafrost regions than in other regions, whereas the SOS of typical steppe is more sensitive to both air temperature and precipitation over sporadic permafrost and seasonal frozen ground regions. Over various frozen ground regions in Mongolia; the SOS is mainly dominated by the prior autumn precipitation, and frozen ground plays a vital role in storing the precipitation of the previous autumn for the subsequent grass green-up. The EOS is mainly dominated by autumn air temperature. These findings could help to improve phenological models of grasslands in extremely dry and cold regions. Full article
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Open AccessArticle Stand Volume Estimation Using the k-NN Technique Combined with Forest Inventory Data, Satellite Image Data and Additional Feature Variables
Remote Sens. 2015, 7(1), 378-394; doi:10.3390/rs70100378
Received: 18 July 2014 / Accepted: 16 December 2014 / Published: 31 December 2014
Cited by 3 | PDF Full-text (2000 KB) | HTML Full-text | XML Full-text
Abstract
The main objective of this study was to evaluate the effectiveness of adding feature variables, such as forest type information and topographic- and climatic-environmental factors to satellite image data, on the accuracy of stand volume estimates made with the k-nearest neighbor (
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The main objective of this study was to evaluate the effectiveness of adding feature variables, such as forest type information and topographic- and climatic-environmental factors to satellite image data, on the accuracy of stand volume estimates made with the k-nearest neighbor (k-NN) technique in southwestern Japan. Data from the Forest Resources Monitoring Survey—a national plot sampling survey in Japan—was used as in situ data in this study. The estimates obtained from three Landsat Enhanced Thematic Mapper Plus (ETM+) datasets acquired in different seasons with various combinations of additional feature variables were compared. The results showed that although the addition of environmental factors to satellite image data did not always help improve estimation accuracy, the use of summer rainfall (SRF) data had a consistent positive effect on accuracy improvement. Therefore, SRF may be a useful feature variable to consider in stand volume estimation in this study area. Moreover, the use of forest type information is very effective at reducing k-NN estimation errors when using an optimum combination of satellite image data and environmental factors. All of the results indicated that the k-NN technique combined with appropriate feature variables is applicable to nationwide stand volume estimation in Japan. Full article
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Open AccessArticle Severe Wildfires Near Moscow, Russia in 2010: Modeling of Carbon Monoxide Pollution and Comparisons with Observations
Remote Sens. 2015, 7(1), 395-429; doi:10.3390/rs70100395
Received: 6 June 2014 / Accepted: 15 December 2014 / Published: 31 December 2014
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Abstract
The spatial and temporal distributions of the carbon monoxide (CO) concentration were calculated with the Regional Atmospheric Modeling System and Hybrid Particle and Concentration Transport model (RAMS/HYPACT) in the provinces near Moscow during the abnormally hot summer of 2010. The forest, steppe and
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The spatial and temporal distributions of the carbon monoxide (CO) concentration were calculated with the Regional Atmospheric Modeling System and Hybrid Particle and Concentration Transport model (RAMS/HYPACT) in the provinces near Moscow during the abnormally hot summer of 2010. The forest, steppe and meadow hot spots were defined by the satellite data MCD14ML (MODIS Terra and Aqua satellite data). The calculations indicated that the surface CO concentrations from the model were two times less than the experimental data obtained from the Moscow State University (MSU) station and Zvenigorod Scientific Station (ZSS). Conversely, the total column CO concentrations obtained from the model were two to three times larger than the experimental values obtained from the Obukhov Institute of Atmospheric Physics (OIAP) and ZSS stations. The vertical transfer of pollutants was overestimated. Tentatively, it could be assumed that an aerosol influence in the model calculations is a reason for the overestimation. The comparisons between the wind speed, temperature and humidity profiles calculated in the model with the data from the standard balloon sounding exhibited good agreement. The CO total column data of the Measurements of Pollution in the Troposphere (MOPITTv5 NIR and TIR/NIR) obtained from the OIAP and ZSS stations appear more realistic than do the MOPITTv4 data. However, the surface MOPITT values of CO concentration for Moscow have the large distinction from the ground measurements. A careful proposal regarding satellite orbit optimization was made, which could improve future spectrometric measurements, such as the MOPITT, Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI) measurements. Full article
Open AccessArticle Radiometric Non-Uniformity Characterization and Correction of Landsat 8 OLI Using Earth Imagery-Based Techniques
Remote Sens. 2015, 7(1), 430-446; doi:10.3390/rs70100430
Received: 31 July 2014 / Accepted: 15 December 2014 / Published: 31 December 2014
Cited by 2 | PDF Full-text (2075 KB) | HTML Full-text | XML Full-text
Abstract
Landsat 8 is the first satellite in the Landsat mission to acquire spectral imagery of the Earth using pushbroom sensor instruments. As a result, there are almost 70,000 unique detectors on the Operational Land Imager (OLI) alone to monitor. Due to minute variations
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Landsat 8 is the first satellite in the Landsat mission to acquire spectral imagery of the Earth using pushbroom sensor instruments. As a result, there are almost 70,000 unique detectors on the Operational Land Imager (OLI) alone to monitor. Due to minute variations in manufacturing and temporal degradation, every detector will exhibit a different behavior when exposed to uniform radiance, causing a noticeable striping artifact in collected imagery. Solar collects using the OLI’s on-board solar diffuser panels are the primary method of characterizing detector level non-uniformity. This paper reports on an approach for using a side-slither maneuver to estimate relative detector gains within each individual focal plane module (FPM) in the OLI. A method to characterize cirrus band detector-level non-uniformity using deep convective clouds (DCCs) is also presented. These approaches are discussed, and then, correction results are compared with the diffuser-based method. Detector relative gain stability is assessed using the side-slither technique. Side-slither relative gains were found to correct streaking in test imagery with quality comparable to diffuser-based gains (within 0.005% for VNIR/PAN; 0.01% for SWIR) and identified a 0.5% temporal drift over a year. The DCC technique provided relative gains that visually decreased striping over the operational calibration in many images. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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Open AccessArticle On Uncertainties of the Priestley-Taylor/LST-Fc Feature Space Method to Estimate Evapotranspiration: Case Study in an Arid/Semiarid Region in Northwest China
Remote Sens. 2015, 7(1), 447-466; doi:10.3390/rs70100447
Received: 29 September 2014 / Accepted: 26 December 2014 / Published: 31 December 2014
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Abstract
Accurate evapotranspiration (ET) estimation is very crucial for water resource management, particularly for the arid and semi-arid region. The remote sensing-based Priestley-Taylor method (RS-PT method) can estimate ET at regional scale, using the feature space of remotely sensed land surface temperature (LST) and
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Accurate evapotranspiration (ET) estimation is very crucial for water resource management, particularly for the arid and semi-arid region. The remote sensing-based Priestley-Taylor method (RS-PT method) can estimate ET at regional scale, using the feature space of remotely sensed land surface temperature (LST) and vegetation index (VI). This study evaluates the RS-PT feature space method over an arid and semi-arid region in northwest China using satellite data from the moderate-resolution space-borne sensor Advanced Along-Track Scanning Radiometer (AATSR), the observations from the high-resolution airborne sensor Wide-angle Infrared Dual-mode line/area Array Scanner (WiDAS) and ground measurements of heat fluxes collected in summer 2008. The results show that the mean difference for latent heat flux (LE) estimates resulting from different domain sizes is 69.5 W/m2. When using high-resolution images from airborne measurements, the dry boundary is strongly affected by the pixels of impervious surfaces, which lead to a mean difference of 15.36 W/m2 for LE estimates. In addition, the physically based Surface Energy Balance Index (SEBI) model is used to analyze the accuracy of dry/wet boundaries in the RS-PT method. Compared with the SEBI-estimated relative evaporative fraction (Λr), the RS-PT method underestimated Λr by ~0.11. For the RS-PT method, the uncertainty in the determination of the dry/wet boundaries has a significant impact on the accuracy of the ET estimate, not only depending on the size of the area to build the feature space, but also on the land covers. Full article
Open AccessArticle Offshore Wind Resources Assessment from Multiple Satellite Data and WRF Modeling over South China Sea
Remote Sens. 2015, 7(1), 467-487; doi:10.3390/rs70100467
Received: 23 August 2014 / Accepted: 16 December 2014 / Published: 6 January 2015
Cited by 13 | PDF Full-text (17148 KB) | HTML Full-text | XML Full-text
Abstract
Using accurate inputs of wind speed is crucial in wind resource assessment, as predicted power is proportional to the wind speed cubed. This study outlines a methodology for combining multiple ocean satellite winds and winds from WRF simulations in order to acquire the
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Using accurate inputs of wind speed is crucial in wind resource assessment, as predicted power is proportional to the wind speed cubed. This study outlines a methodology for combining multiple ocean satellite winds and winds from WRF simulations in order to acquire the accurate reconstructed offshore winds which can be used for offshore wind resource assessment. First, wind speeds retrieved from Synthetic Aperture Radar (SAR) and Scatterometer ASCAT images were validated against in situ measurements from seven coastal meteorological stations in South China Sea (SCS). The wind roses from the Navy Operational Global Atmospheric Prediction System (NOGAPS) and ASCAT agree well with these observations from the corresponding in situ measurements. The statistical results comparing in situ wind speed and SAR-based (ASCAT-based) wind speed for the whole co-located samples show a standard deviation (SD) of 2.09 m/s (1.83 m/s) and correlation coefficient of R 0.75 (0.80). When the offshore winds (i.e., winds directed from land to sea) are excluded, the comparison results for wind speeds show an improvement of SD and R, indicating that the satellite data are more credible over the open ocean. Meanwhile, the validation of satellite winds against the same co-located mast observations shows a satisfactory level of accuracy which was similar for SAR and ASCAT winds. These satellite winds are then assimilated into the Weather Research and Forecasting (WRF) Model by WRF Data Assimilation (WRFDA) system. Finally, the wind resource statistics at 100 m height based on the reconstructed winds have been achieved over the study area, which fully combines the offshore wind information from multiple satellite data and numerical model. The findings presented here may be useful in future wind resource assessment based on satellite data. Full article
(This article belongs to the Special Issue Earth Observations for the Sustainable Development)
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Open AccessArticle Soil Salinity Retrieval from Advanced Multi-Spectral Sensor with Partial Least Square Regression
Remote Sens. 2015, 7(1), 488-511; doi:10.3390/rs70100488
Received: 21 May 2014 / Accepted: 23 December 2014 / Published: 6 January 2015
Cited by 12 | PDF Full-text (20665 KB) | HTML Full-text | XML Full-text
Abstract
Improper use of land resources may result in severe soil salinization. Timely monitoring and early warning of soil salinity is in urgent need for sustainable development. This paper addresses the possibility and potential of Advanced Land Imager (ALI) for mapping soil salinity. In
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Improper use of land resources may result in severe soil salinization. Timely monitoring and early warning of soil salinity is in urgent need for sustainable development. This paper addresses the possibility and potential of Advanced Land Imager (ALI) for mapping soil salinity. In situ field spectra and soil salinity data were collected in the Yellow River Delta, China. Statistical analysis demonstrated the importance of ALI blue and near infrared (NIR) bands for soil salinity. A partial least square regression (PLSR) model was established between soil salinity and ALI-convolved field spectra. The model estimated soil salinity with a R2 (coefficient of determination), RPD (ratio of prediction to deviation), bias, standard deviation (SD) and root mean square error (RMSE) of 0.749, 3.584, 0.036 g∙kg−1, 0.778 g∙kg−1 and 0.779 g∙kg−1. The model was then applied to atmospherically corrected ALI data. Soil salinity was underestimated for moderately (soil salinity within 2–4 g∙kg−1) and highly saline (soil salinity >4 g∙kg−1) soils. The underestimates increased with the degree of soil salinization, with a maximum value of ~4 g∙kg−1. The major contribution for the underestimation (>80%) may result from data inaccuracy other than model ineffectiveness. Uncertainty analysis confirmed that improper atmospheric correction contributed to a very conservative uncertainty of 1.3 g∙kg−1. Field sampling within remote sensing pixels was probably the major source responsible for the underestimation. Our study demonstrates the effectiveness of PLSR model in retrieving soil salinity from new-generation multi-spectral sensors. This is very valuable for achieving worldwide soil salinity mapping with low cost and considerable accuracy. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)
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Open AccessArticle MODIS-Based Fractional Crop Mapping in the U.S. Midwest with Spatially Constrained Phenological Mixture Analysis
Remote Sens. 2015, 7(1), 512-529; doi:10.3390/rs70100512
Received: 17 September 2014 / Accepted: 23 December 2014 / Published: 6 January 2015
Cited by 7 | PDF Full-text (30844 KB) | HTML Full-text | XML Full-text
Abstract
Since the 2000s, bioenergy land use has been rapidly expanded in U.S. agricultural lands. Monitoring this change with limited acquisition of remote sensing imagery is difficult because of the similar spectral properties of crops. While phenology-assisted crop mapping is promising, relying on frequently
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Since the 2000s, bioenergy land use has been rapidly expanded in U.S. agricultural lands. Monitoring this change with limited acquisition of remote sensing imagery is difficult because of the similar spectral properties of crops. While phenology-assisted crop mapping is promising, relying on frequently observed images, the accuracies are often low, with mixed pixels in coarse-resolution imagery. In this paper, we used the eight-day, 500 m MODIS products (MOD09A1) to test the feasibility of crop unmixing in the U.S. Midwest, an important bioenergy land use region. With all MODIS images acquired in 2007, the 46-point Normalized Difference Vegetation Index (NDVI) time series was extracted in the study region. Assuming the phenological pattern at a pixel is a linear mixture of all crops in this pixel, a spatially constrained phenological mixture analysis (SPMA) was performed to extract crop percent covers with endmembers selected in a dynamic local neighborhood. The SPMA results matched well with the USDA crop data layers (CDL) at pixel level and the Crop Census records at county level. This study revealed more spatial details of energy crops that could better assist bioenergy decision-making in the Midwest. Full article
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Open AccessArticle PSI Deformation Map Retrieval by Means of Temporal Sublook Coherence on Reduced Sets of SAR Images
Remote Sens. 2015, 7(1), 530-563; doi:10.3390/rs70100530
Received: 30 July 2014 / Accepted: 15 December 2014 / Published: 7 January 2015
Cited by 6 | PDF Full-text (26298 KB) | HTML Full-text | XML Full-text
Abstract
Prior to the application of any persistent scatterer interferometry (PSI) technique for the monitoring of terrain displacement phenomena, an adequate pixel selection must be carried out in order to prevent the inclusion of noisy pixels in the processing. The rationale is to detect
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Prior to the application of any persistent scatterer interferometry (PSI) technique for the monitoring of terrain displacement phenomena, an adequate pixel selection must be carried out in order to prevent the inclusion of noisy pixels in the processing. The rationale is to detect the so-called persistent scatterers, which are characterized by preserving their phase quality along the multi-temporal set of synthetic aperture radar (SAR) images available. Two criteria are mainly available for the estimation of pixels’ phase quality, i.e., the coherence stability and the amplitude dispersion or permanent scatterers (PS) approach. The coherence stability method allows an accurate estimation of the phase statistics, even when a reduced number of SAR acquisitions is available. Unfortunately, it requires the multi-looking of data during the coherence estimation, leading to a spatial resolution loss in the final results. In contrast, the PS approach works at full-resolution, but it demands a larger number of SAR images to be reliable, typically more than 20. There is hence a clear limitation when a full-resolution PSI processing is to be carried out and the number of acquisitions available is small. In this context, a novel pixel selection method based on exploiting the spectral properties of point-like scatterers, referred to as temporal sublook coherence (TSC), has been recently proposed. This paper seeks to demonstrate the advantages of employing PSI techniques by means of TSC on both orbital and ground-based SAR (GB-SAR) data when the number of images available is small (10 images in the work presented). The displacement maps retrieved through the proposed technique are compared, in terms of pixel density and phase quality, with traditional criteria. Two X-band datasets composed of 10 sliding spotlight TerraSAR-X images and 10 GB-SAR images, respectively, over the landslide of El Forn de Canillo (Andorran Pyrenees), are employed for this study. For both datasets, the TSC technique has showed an excellent performance compared with traditional techniques, achieving up to a four-fold increase in the number of persistent scatters detected, compared with the coherence stability approach, and a similar density compared with the PS approach, but free of outliers. Full article
Open AccessArticle A One Year Landsat 8 Conterminous United States Study of Cirrus and Non-Cirrus Clouds
Remote Sens. 2015, 7(1), 564-578; doi:10.3390/rs70100564
Received: 11 October 2014 / Accepted: 15 December 2014 / Published: 7 January 2015
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Abstract
The first year of available Landsat 8 data over the conterminous United States (CONUS), composed of 11,296 acquisitions sensed over more than 11 thousand million 30 m pixel locations, was analyzed comparing the spatial and temporal incidence of 30 m cloud and cirrus
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The first year of available Landsat 8 data over the conterminous United States (CONUS), composed of 11,296 acquisitions sensed over more than 11 thousand million 30 m pixel locations, was analyzed comparing the spatial and temporal incidence of 30 m cloud and cirrus states available in the standard Landsat 8 Level 1 product suite. This comprehensive data analysis revealed that on average over a year of CONUS observations (i) 35.9% were detected with high confidence cloud, with spatio-temporal patterns similar to those observed by previous Landsat 5 and 7 cloud analyses; (ii) 28.2% were high confidence cirrus; (iii) 20.1% were both high confidence cloud and high confidence cirrus; and (iv) 6.9% were detected as high confidence cirrus but low confidence cloud. The results illustrate the potential of the 30 m cloud and cirrus states available in the standard Landsat 8 Level 1 product suite but imply that the historical CONUS Landsat archive has about 7% of undetected cirrus contaminated pixels. Systematic cloud detection commission errors over a minority of highly reflective exposed soil/sand surfaces were found and it is recommended that caution be taken when using the currently available Landsat 8 cloud data over similar surfaces. Full article
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Open AccessArticle The Impact of Positional Errors on Soft Classification Accuracy Assessment: A Simulation Analysis
Remote Sens. 2015, 7(1), 579-599; doi:10.3390/rs70100579
Received: 27 October 2014 / Accepted: 25 December 2014 / Published: 7 January 2015
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Abstract
Validating or accessing the accuracy of soft classification maps has rapidly developed over the past few years. This assessment employs a soft error matrix as generalized from the traditional, hard classification error matrix. However, the impact of positional error on the soft classification
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Validating or accessing the accuracy of soft classification maps has rapidly developed over the past few years. This assessment employs a soft error matrix as generalized from the traditional, hard classification error matrix. However, the impact of positional error on the soft classification is uncertain and whether the well-accepted half-pixel registration accuracy is suitable for the soft classification accuracy assessment is unknown. In this paper, a simulation analysis was conducted to examine the influence of positional error on the overall accuracy (OA) and kappa in soft classification accuracy assessment under different landscape conditions (i.e., spatial characteristics and spatial resolutions). Results showed that with positional error ranging from 0 to 3 soft pixels, the OA-error varied from 0 to 44.6 percent while the kappa-error varied from 0 to 93.7 percent. Landscape conditions with smaller mean patch size (MPS) and greater fragmentation produced greater positional error impact on the accuracy measures at spatial resolutions of 1 and 2 unit distances. However, this trend did not hold for spatial resolutions of 5 and 10 unit distances. A half of a pixel was not sufficient to keep the overall accuracy error and kappa error under 10 percent. The results indicate that for soft classification accuracy assessment the requirement for registration accuracy is higher and depends greatly on the landscape characteristics. There is a great need to consider positional error for validating soft classification maps of different spatial resolutions. Full article
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Open AccessArticle The Ground-Based Absolute Radiometric Calibration of Landsat 8 OLI
Remote Sens. 2015, 7(1), 600-626; doi:10.3390/rs70100600
Received: 1 August 2014 / Accepted: 23 December 2014 / Published: 7 January 2015
Cited by 27 | PDF Full-text (3157 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents the vicarious calibration results of Landsat 8 OLI that were obtained using the reflectance-based approach at test sites in Nevada, California, Arizona, and South Dakota, USA. Additional data were obtained using the Radiometric Calibration Test Site, which is a suite
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This paper presents the vicarious calibration results of Landsat 8 OLI that were obtained using the reflectance-based approach at test sites in Nevada, California, Arizona, and South Dakota, USA. Additional data were obtained using the Radiometric Calibration Test Site, which is a suite of instruments located at Railroad Valley, Nevada, USA. The results for the top-of-atmosphere spectral radiance show an average difference of −2.7, −0.8, 1.5, 2.0, 0.0, 3.6, 5.8, and 0.7% in OLI bands 1–8 as compared to an average of all of the ground-based measurements. The top-of-atmosphere spectral reflectance shows an average difference of 1.6, 1.3, 2.0, 1.9, 0.9, 2.1, 3.1, and 2.1% from the ground-based measurements. Except for OLI band 7, the spectral radiance results are generally within ±5% of the design specifications, and the reflectance results are generally within ±3% of the design specifications. The results from the data collected during the tandem Landsat 7 and 8 flight in March 2013 indicate that ETM+ and OLI agree to each other to within ±2% in similar bands in top-of-atmosphere spectral radiance, and to within ±4% in top-of-atmosphere spectral reflectance. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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Open AccessArticle A Practical Split-Window Algorithm for Estimating Land Surface Temperature from Landsat 8 Data
Remote Sens. 2015, 7(1), 647-665; doi:10.3390/rs70100647
Received: 17 October 2014 / Accepted: 4 January 2015 / Published: 8 January 2015
Cited by 19 | PDF Full-text (40796 KB) | HTML Full-text | XML Full-text
Abstract
This paper developed a practical split-window (SW) algorithm to estimate land surface temperature (LST) from Thermal Infrared Sensor (TIRS) aboard Landsat 8. The coefficients of the SW algorithm were determined based on atmospheric water vapor sub-ranges, which were obtained through a modified split-window
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This paper developed a practical split-window (SW) algorithm to estimate land surface temperature (LST) from Thermal Infrared Sensor (TIRS) aboard Landsat 8. The coefficients of the SW algorithm were determined based on atmospheric water vapor sub-ranges, which were obtained through a modified split-window covariance–variance ratio method. The channel emissivities were acquired from newly released global land cover products at 30 m and from a fraction of the vegetation cover calculated from visible and near-infrared images aboard Landsat 8. Simulation results showed that the new algorithm can obtain LST with an accuracy of better than 1.0 K. The model consistency to the noise of the brightness temperature, emissivity and water vapor was conducted, which indicated the robustness of the new algorithm in LST retrieval. Furthermore, based on comparisons, the new algorithm performed better than the existing algorithms in retrieving LST from TIRS data. Finally, the SW algorithm was proven to be reliable through application in different regions. To further confirm the credibility of the SW algorithm, the LST will be validated in the future. Full article
(This article belongs to the Special Issue Recent Advances in Thermal Infrared Remote Sensing)
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Open AccessArticle Impact of Missing Passive Microwave Sensors on Multi-Satellite Precipitation Retrieval Algorithm
Remote Sens. 2015, 7(1), 668-683; doi:10.3390/rs70100668
Received: 10 October 2014 / Accepted: 6 January 2015 / Published: 9 January 2015
Cited by 1 | PDF Full-text (12675 KB) | HTML Full-text | XML Full-text
Abstract
The impact of one or two missing passive microwave (PMW) input sensors on the end product of multi-satellite precipitation products is an interesting but obscure issue for both algorithm developers and data users. On 28 January 2013, the Version-7 TRMM Multi-satellite Precipitation Analysis
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The impact of one or two missing passive microwave (PMW) input sensors on the end product of multi-satellite precipitation products is an interesting but obscure issue for both algorithm developers and data users. On 28 January 2013, the Version-7 TRMM Multi-satellite Precipitation Analysis (TMPA) products were reproduced and re-released by National Aeronautics and Space Administration (NASA) Goddard Space Flight Center because the Advanced Microwave Sounding Unit-B (AMSU-B) and the Special Sensor Microwave Imager-Sounder-F16 (SSMIS-F16) input data were unintentionally disregarded in the prior retrieval. Thus, this study investigates the sensitivity of TMPA algorithm results to missing PMW sensors by intercomparing the “early” and “late” Version-7 TMPA real-time (TMPA-RT) precipitation estimates (i.e., without and with AMSU-B, SSMIS-F16 sensors) with an independent high-density gauge network of 200 tipping-bucket rain gauges over the Chinese Jinghe river basin (45,421 km2). The retrieval counts and retrieval frequency of various PMW and Infrared (IR) sensors incorporated into the TMPA system were also analyzed to identify and diagnose the impacts of sensor availability on the TMPA-RT retrieval accuracy. Results show that the incorporation of AMSU-B and SSMIS-F16 has substantially reduced systematic errors. The improvement exhibits rather strong seasonal and topographic dependencies. Our analyses suggest that one or two single PMW sensors might play a key role in affecting the end product of current combined microwave-infrared precipitation estimates. This finding supports algorithm developers’ current endeavor in spatiotemporally incorporating as many PMW sensors as possible in the multi-satellite precipitation retrieval system called Integrated Multi-satellitE Retrievals for Global Precipitation Measurement mission (IMERG). This study also recommends users of satellite precipitation products to switch to the newest Version-7 TMPA datasets and the forthcoming IMERG products whenever they become available. Full article
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Open AccessArticle Monitoring Groundwater Variations from Satellite Gravimetry and Hydrological Models: A Comparison with in-situ Measurements in the Mid-Atlantic Region of the United States
Remote Sens. 2015, 7(1), 686-703; doi:10.3390/rs70100686
Received: 13 August 2014 / Accepted: 7 January 2015 / Published: 12 January 2015
Cited by 9 | PDF Full-text (5283 KB) | HTML Full-text | XML Full-text
Abstract
Aimed at mapping time variations in the Earth’s gravity field, the Gravity Recovery and Climate Experiment (GRACE) satellite mission is applicable to access terrestrial water storage (TWS), which mainly includes groundwater, soil moisture (SM), and snow. In this study, SM and accumulated snow
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Aimed at mapping time variations in the Earth’s gravity field, the Gravity Recovery and Climate Experiment (GRACE) satellite mission is applicable to access terrestrial water storage (TWS), which mainly includes groundwater, soil moisture (SM), and snow. In this study, SM and accumulated snow water equivalent (SWE) are simulated by the Global Land Data Assimilation System (GLDAS) land surface models (LSMs) and then used to isolate groundwater anomalies from GRACE-derived TWS in Pennsylvania and New York States of the Mid-Atlantic region of the United States. The monitoring well water-level records from the U.S. Geological Survey Ground-Water Climate Response Network from January 2005 to December 2011 are used for validation. The groundwater results from different combinations of GRACE products (from three institutions, CSR, GFZ and JPL) and GLDAS LSMs (CLM, NOAH and VIC) are compared and evaluated with in-situ measurements. The intercomparison analysis shows that the solution obtained through removing averaged simulated SM and SWE of the three LSMs from the averaged GRACE-derived TWS of the three centers would be the most robust to reduce the noises, and increase the confidence consequently. Although discrepancy exists, the GRACE-GLDAS estimated groundwater variations generally agree with in-situ observations. For monthly scales, their correlation coefficient reaches 0.70 at 95% confidence level with the RMSE of the differences of 2.6 cm. Two-tailed Mann-Kendall trend test results show that there is no significant groundwater gain or loss in this region over the study period. The GRACE time-variable field solutions and GLDAS simulations provide precise and reliable data sets in illustrating the regional groundwater storage variations, and the application will be meaningful and invaluable when applied to the data-poor regions. Full article
(This article belongs to the Special Issue Remote Sensing Dedicated to Geographical Conditions Monitoring)
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Open AccessArticle A Simple Fusion Method for Image Time Series Based on the Estimation of Image Temporal Validity
Remote Sens. 2015, 7(1), 704-724; doi:10.3390/rs70100704
Received: 29 August 2014 / Accepted: 5 January 2015 / Published: 12 January 2015
Cited by 7 | PDF Full-text (5931 KB) | HTML Full-text | XML Full-text
Abstract
High-spatial-resolution satellites usually have the constraint of a low temporal frequency, which leads to long periods without information in cloudy areas. Furthermore, low-spatial-resolution satellites have higher revisit cycles. Combining information from high- and low- spatial-resolution satellites is thought a key factor for studies
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High-spatial-resolution satellites usually have the constraint of a low temporal frequency, which leads to long periods without information in cloudy areas. Furthermore, low-spatial-resolution satellites have higher revisit cycles. Combining information from high- and low- spatial-resolution satellites is thought a key factor for studies that require dense time series of high-resolution images, e.g., crop monitoring. There are several fusion methods in the bibliography, but they are time-consuming and complicated to implement. Moreover, the local evaluation of the fused images is rarely analyzed. In this paper, we present a simple and fast fusion method based on a weighted average of two input images (H and L), which are weighted by their temporal validity to the image to be fused. The method was applied to two years (2009–2010) of Landsat and MODIS (MODerate Imaging Spectroradiometer) images that were acquired over a cropped area in Brazil. The fusion method was evaluated at global and local scales. The results show that the fused images reproduced reliable crop temporal profiles and correctly delineated the boundaries between two neighboring fields. The greatest advantages of the proposed method are the execution time and ease of use, which allow us to obtain a fused image in less than five minutes. Full article
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Open AccessArticle Angular Dependency of Hyperspectral Measurements over Wheat Characterized by a Novel UAV Based Goniometer
Remote Sens. 2015, 7(1), 725-746; doi:10.3390/rs70100725
Received: 27 October 2014 / Accepted: 4 January 2015 / Published: 12 January 2015
Cited by 23 | PDF Full-text (1561 KB) | HTML Full-text | XML Full-text
Abstract
In this study we present a hyperspectral flying goniometer system, based on a rotary-wing unmanned aerial vehicle (UAV) equipped with a spectrometer mounted on an active gimbal. We show that this approach may be used to collect multiangular hyperspectral data over vegetated environments.
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In this study we present a hyperspectral flying goniometer system, based on a rotary-wing unmanned aerial vehicle (UAV) equipped with a spectrometer mounted on an active gimbal. We show that this approach may be used to collect multiangular hyperspectral data over vegetated environments. The pointing and positioning accuracy are assessed using structure from motion and vary from σ = 1° to 8° in pointing and σ = 0.7 to 0.8 m in positioning. We use a wheat dataset to investigate the influence of angular effects on the NDVI, TCARI and REIP vegetation indices. Angular effects caused significant variations on the indices: NDVI = 0.83–0.95; TCARI = 0.04–0.116; REIP = 729–735 nm. Our analysis highlights the necessity to consider angular effects in optical sensors when observing vegetation. We compare the measurements of the UAV goniometer to the angular modules of the SCOPE radiative transfer model. Model and measurements are in high accordance (r2 = 0.88) in the infrared region at angles close to nadir; in contrast the comparison show discrepancies at low tilt angles (r2 = 0.25). This study demonstrates that the UAV goniometer is a promising approach for the fast and flexible assessment of angular effects. Full article
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Open AccessArticle Potential of X-Band TerraSAR-X and COSMO-SkyMed SAR Data for the Assessment of Physical Soil Parameters
Remote Sens. 2015, 7(1), 747-766; doi:10.3390/rs70100747
Received: 10 September 2014 / Accepted: 6 January 2015 / Published: 12 January 2015
Cited by 13 | PDF Full-text (3948 KB) | HTML Full-text | XML Full-text
Abstract
The aim of this paper is to analyze the potential of X-band SAR measurements (COSMO-SkyMed and TerraSAR-X) made over bare soils for the estimation of soil moisture and surface geometry parameters at a semi-arid site in Tunisia (North Africa). Radar signals acquired with
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The aim of this paper is to analyze the potential of X-band SAR measurements (COSMO-SkyMed and TerraSAR-X) made over bare soils for the estimation of soil moisture and surface geometry parameters at a semi-arid site in Tunisia (North Africa). Radar signals acquired with different configurations (HH and VV polarizations, incidence angles of 26° and 36°) are statistically compared with ground measurements (soil moisture and roughness parameters). The radar measurements are found to be highly sensitive to the various soil parameters of interest. A linear relationship is determined for the radar signals as a function of volumetric soil moisture, and a logarithmic correlation is observed between the radar signals and three surface roughness parameters: the root mean square height (Hrms), the parameter Zs = Hrms2/l (where l is the correlation length) and the parameter Zg = Hrms × (Hrms/l)α (where α is the power of the surface height correlation function). The highest dynamic sensitivity is observed for Zg at high incidence angles. Finally, the performance of different physical and semi-empirical backscattering models (IEM, Baghdadi-calibrated IEM and Dubois models) is compared with SAR measurements. The results provide an indication of the limits of validity of the IEM and Dubois models, for various radar configurations and roughness conditions. Considerable improvements in the IEM model performance are observed using the Baghdadi-calibrated version of this model. Full article
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Open AccessArticle A GEOBIA Methodology for Fragmented Agricultural Landscapes
Remote Sens. 2015, 7(1), 767-787; doi:10.3390/rs70100767
Received: 10 October 2014 / Accepted: 25 December 2014 / Published: 13 January 2015
Cited by 10 | PDF Full-text (15739 KB) | HTML Full-text | XML Full-text
Abstract
Very high resolution remotely sensed images are an important tool for monitoring fragmented agricultural landscapes, which allows farmers and policy makers to make better decisions regarding management practices. An object-based methodology is proposed for automatic generation of thematic maps of the available classes
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Very high resolution remotely sensed images are an important tool for monitoring fragmented agricultural landscapes, which allows farmers and policy makers to make better decisions regarding management practices. An object-based methodology is proposed for automatic generation of thematic maps of the available classes in the scene, which combines edge-based and superpixel processing for small agricultural parcels. The methodology employs superpixels instead of pixels as minimal processing units, and provides a link between them and meaningful objects (obtained by the edge-based method) in order to facilitate the analysis of parcels. Performance analysis on a scene dominated by agricultural small parcels indicates that the combination of both superpixel and edge-based methods achieves a classification accuracy slightly better than when those methods are performed separately and comparable to the accuracy of traditional object-based analysis, with automatic approach. Full article
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Open AccessArticle Modeling Aboveground Biomass in Dense Tropical Submontane Rainforest Using Airborne Laser Scanner Data
Remote Sens. 2015, 7(1), 788-807; doi:10.3390/rs70100788
Received: 9 September 2014 / Accepted: 7 January 2015 / Published: 14 January 2015
Cited by 25 | PDF Full-text (11229 KB) | HTML Full-text | XML Full-text
Abstract
Successful implementation of projects under the REDD+ mechanism, securing payment for storing forest carbon as an ecosystem service, requires quantification of biomass. Airborne laser scanning (ALS) is a relevant technology to enhance estimates of biomass in tropical forests. We present the analysis and
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Successful implementation of projects under the REDD+ mechanism, securing payment for storing forest carbon as an ecosystem service, requires quantification of biomass. Airborne laser scanning (ALS) is a relevant technology to enhance estimates of biomass in tropical forests. We present the analysis and results of modeling aboveground biomass (AGB) in a Tanzanian rainforest utilizing data from a small-footprint ALS system and 153 field plots with an area of 0.06–0.12 ha located on a systematic grid. The study area is dominated by steep terrain, a heterogeneous forest structure and large variation in AGB densities with values ranging from 43 to 1147 Mg·ha−1, which goes beyond the range that has been reported in existing literature on biomass modeling with ALS data in the tropics. Root mean square errors from a 10-fold cross-validation of estimated values were about 33% of a mean value of 462 Mg·ha−1. Texture variables derived from a canopy surface model did not result in improved models. Analyses showed that (1) variables derived from echoes in the lower parts of the canopy and (2) canopy density variables explained more of the AGB density than variables representing the height of the canopy. Full article
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Open AccessArticle Developing in situ Non-Destructive Estimates of Crop Biomass to Address Issues of Scale in Remote Sensing
Remote Sens. 2015, 7(1), 808-835; doi:10.3390/rs70100808
Received: 11 November 2014 / Accepted: 7 January 2015 / Published: 14 January 2015
Cited by 11 | PDF Full-text (53063 KB) | HTML Full-text | XML Full-text
Abstract
Ground-based estimates of aboveground wet (fresh) biomass (AWB) are an important input for crop growth models. In this study, we developed empirical equations of AWB for rice, maize, cotton, and alfalfa, by combining several in situ non-spectral and spectral predictors. The non-spectral predictors
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Ground-based estimates of aboveground wet (fresh) biomass (AWB) are an important input for crop growth models. In this study, we developed empirical equations of AWB for rice, maize, cotton, and alfalfa, by combining several in situ non-spectral and spectral predictors. The non-spectral predictors included: crop height (H), fraction of absorbed photosynthetically active radiation (FAPAR), leaf area index (LAI), and fraction of vegetation cover (FVC). The spectral predictors included 196 hyperspectral narrowbands (HNBs) from 350 to 2500 nm. The models for rice, maize, cotton, and alfalfa included H and HNBs in the near infrared (NIR); H, FAPAR, and HNBs in the NIR; H and HNBs in the visible and NIR; and FVC and HNBs in the visible; respectively. In each case, the non-spectral predictors were the most important, while the HNBs explained additional and statistically significant predictors, but with lower variance. The final models selected for validation yielded an R2 of 0.84, 0.59, 0.91, and 0.86 for rice, maize, cotton, and alfalfa, which when compared to models using HNBs alone from a previous study using the same spectral data, explained an additional 12%, 29%, 14%, and 6% in AWB variance. These integrated models will be used in an up-coming study to extrapolate AWB over 60 × 60 m transects to evaluate spaceborne multispectral broad bands and hyperspectral narrowbands. Full article
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Open AccessArticle Use of Radarsat-2 and Landsat TM Images for Spatial Parameterization of Manning’s Roughness Coefficient in Hydraulic Modeling
Remote Sens. 2015, 7(1), 836-864; doi:10.3390/rs70100836
Received: 21 March 2014 / Accepted: 23 December 2014 / Published: 14 January 2015
Cited by 6 | PDF Full-text (1595 KB) | HTML Full-text | XML Full-text
Abstract
Vegetation resistance influences water flow in floodplains. Characterization of vegetation for hydraulic modeling includes the description of the spatial variability of vegetation type, height and density. In this research, we explored the use of dual polarized Radarsat-2 wide swath mode backscatter coefficients (σ°)
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Vegetation resistance influences water flow in floodplains. Characterization of vegetation for hydraulic modeling includes the description of the spatial variability of vegetation type, height and density. In this research, we explored the use of dual polarized Radarsat-2 wide swath mode backscatter coefficients (σ°) and Landsat 5 TM to derive spatial hydraulic roughness. The spatial roughness parameterization included four steps: (i) land use classification from Landsat 5 TM; (ii) establishing a relationship between σ° statistics and vegetation parameters; (iii) relative surface roughness (Ks) determination from Synthetic Aperture Radar (SAR) backscatter temporal variability; (iv) derivation of the spatial distribution of the spatial hydraulic roughness both from Manning’s roughness coefficient look up table (LUT) and relative surface roughness. Hydraulic simulations were performed using the FLO-2D hydrodynamic model to evaluate model performance under three different hydraulic modeling simulations results with different Manning’s coefficient parameterizations, which includes SWL1, SWL2 and SWL3. SWL1 is simulated water levels with optimum floodplain roughness (np) with channel roughness nc = 0.03 m−1/3/s; SWL2 is simulated water levels with calibrated values for both floodplain roughness np = 0.65 m−1/3/s and channel roughness nc = 0.021 m−1/3/s; and SWL3 is simulated water levels with calibrated channel roughness nc and spatial Manning’s coefficients as derived with aid of relative surface roughness. The model performance was evaluated using Nash-Sutcliffe model efficiency coefficient (E) and coefficient of determination (R2), based on water levels measured at a gauging station in the wetland. The overall performance of scenario SWL1 was characterized with E = 0.75 and R2 = 0.95, which was improved in SWL2 to E = 0.95 and R2 = 0.99. When spatially distributed Manning values derived from SAR relative surface values were parameterized in the model, the model also performed well and yielding E = 0.97 and R2 = 0.98. Improved model performance using spatial roughness shows that spatial roughness parameterization can support flood modeling and provide better flood wave simulation over the inundated riparian areas equally as calibrated models. Full article
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Open AccessArticle Seasonal Land Cover Dynamics in Beijing Derived from Landsat 8 Data Using a Spatio-Temporal Contextual Approach
Remote Sens. 2015, 7(1), 865-881; doi:10.3390/rs70100865
Received: 16 November 2014 / Accepted: 12 January 2015 / Published: 14 January 2015
Cited by 7 | PDF Full-text (22247 KB) | HTML Full-text | XML Full-text
Abstract
Seasonal dynamic land cover maps could provide useful information to ecosystem, water-resource and climate modelers. However, they are rarely mapped more frequent than annually. Here, we propose an approach to map dynamic land cover types with frequently available satellite data. Landsat 8 data
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Seasonal dynamic land cover maps could provide useful information to ecosystem, water-resource and climate modelers. However, they are rarely mapped more frequent than annually. Here, we propose an approach to map dynamic land cover types with frequently available satellite data. Landsat 8 data acquired from nine dates over Beijing within a one-year period were used to map seasonal land cover dynamics. A two-step procedure was performed for training sample collection to get better results. Sample sets were interpreted for each acquisition date of Landsat 8 image. We used the random forest classifier to realize the mapping. Nine sets of experiments were designed to incorporate different input features and use of spatial temporal information into the dynamic land cover classification. Land cover maps obtained with single-date data in the optical spectral region were used as benchmarks. Texture, NDVI and thermal infrared bands were added as new features for improvements. A Markov random field (MRF) model was applied to maintain the spatio-temporal consistency. Classifications with all features from all images were performed, and an MRF model was also applied to the results estimated with all features. The best overall accuracies achieved for each date ranged from 75.31% to 85.61%. Full article
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Open AccessArticle Estimating Land Development Time Lags in China Using DMSP/OLS Nighttime Light Image
Remote Sens. 2015, 7(1), 882-904; doi:10.3390/rs70100882
Received: 30 June 2014 / Accepted: 6 January 2015 / Published: 14 January 2015
Cited by 2 | PDF Full-text (7841 KB) | HTML Full-text | XML Full-text
Abstract
The Chinese real estate industry has experienced rapid growth since China’s economic reform. Along with a booming industry, a third of purchased lands were left undeveloped in the last decade. Knowledge of real estate development time lags between land being purchased and property
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The Chinese real estate industry has experienced rapid growth since China’s economic reform. Along with a booming industry, a third of purchased lands were left undeveloped in the last decade. Knowledge of real estate development time lags between land being purchased and property being occupied can enable policymakers to produce more effective policies and regulations to guide the real estate industry and sustain economic development and social welfare. This paper presents an innovative method to estimate provincial land development time lags in China using DMSP/OLS NTL imagery and real estate statistical data. The results showed that real estate development time lag was common in China during 2000–2010. More than half of the study sites showed development time lags of three years or longer. An Increment of Developed Pixels (IDP) index was established to outline yearly land development completions in China between 2000 and 2010. A Comprehensive Real Estate Price Index (CREPI) was created to explore the causes of the time lags. A strong and positive correlation was found between the real estate development time lags and CREPI values (with r = 0.619, n = 31, p < 0.0005). The results indicated that the land development time lag during the study period was positively correlated to the activity of the local real estate market, the price trend of land and housing properties, and the local economic situation. The results also proved that with the support of statistical data the DMSP/OLS NTL image could offer an economically efficient and reliable solution to estimate the time lag of real estate development. Full article
(This article belongs to the Special Issue Remote Sensing with Nighttime Lights)
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Open AccessArticle Estimation of Land Surface Temperature under Cloudy Skies Using Combined Diurnal Solar Radiation and Surface Temperature Evolution
Remote Sens. 2015, 7(1), 905-921; doi:10.3390/rs70100905
Received: 4 November 2014 / Accepted: 7 January 2015 / Published: 15 January 2015
Cited by 3 | PDF Full-text (9659 KB) | HTML Full-text | XML Full-text
Abstract
Land surface temperature (LST) is a key parameter in the interaction of the land-atmosphere system. However, clouds affect the retrieval of LST data from thermal-infrared remote sensing data. Thus, it is important to determine a method for estimating LSTs at times when the
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Land surface temperature (LST) is a key parameter in the interaction of the land-atmosphere system. However, clouds affect the retrieval of LST data from thermal-infrared remote sensing data. Thus, it is important to determine a method for estimating LSTs at times when the sky is overcast. Based on a one-dimensional heat transfer equation and on the evolution of daily temperatures and net shortwave solar radiation (NSSR), a new method for estimating LSTs under cloudy skies (Tcloud) from diurnal NSSR and surface temperatures is proposed. Validation is performed against in situ measurements that were obtained at the ChangWu ecosystem experimental station in China. The results show that the root-mean-square error (RMSE) between the actual and estimated LSTs is as large as 1.23 K for cloudy data. A sensitivity analysis to the errors in the estimated LST under clear skies (Tclear) and in the estimated NSSR reveals that the RMSE of the obtained Tcloud is less than 1.5 K after adding a 0.5 K bias to the actual Tclear and 10 percent NSSR errors to the actual NSSR. Tcloud is estimated by the proposed method using Tclear and NSSR products of MSG-SEVIRI for southern Europe. The results indicate that the new algorithm is practical for retrieving the LST under cloudy sky conditions, although some uncertainty exists. Notably, the approach can only be used during the daytime due to the assumption of the variation in LST caused by variations in insolation. Further, if there are less than six Tclear observations on any given day, the method cannot be used. Full article
(This article belongs to the Special Issue Recent Advances in Thermal Infrared Remote Sensing)
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Open AccessArticle Object-Based Crop Species Classification Based on the Combination of Airborne Hyperspectral Images and LiDAR Data
Remote Sens. 2015, 7(1), 922-950; doi:10.3390/rs70100922
Received: 9 November 2014 / Accepted: 6 January 2015 / Published: 15 January 2015
Cited by 14 | PDF Full-text (58828 KB) | HTML Full-text | XML Full-text
Abstract
Identification of crop species is an important issue in agricultural management. In recent years, many studies have explored this topic using multi-spectral and hyperspectral remote sensing data. In this study, we perform dedicated research to propose a framework for mapping crop species by
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Identification of crop species is an important issue in agricultural management. In recent years, many studies have explored this topic using multi-spectral and hyperspectral remote sensing data. In this study, we perform dedicated research to propose a framework for mapping crop species by combining hyperspectral and Light Detection and Ranging (LiDAR) data in an object-based image analysis (OBIA) paradigm. The aims of this work were the following: (i) to understand the performances of different spectral dimension-reduced features from hyperspectral data and their combination with LiDAR derived height information in image segmentation; (ii) to understand what classification accuracies of crop species can be achieved by combining hyperspectral and LiDAR data in an OBIA paradigm, especially in regions that have fragmented agricultural landscape and complicated crop planting structure; and (iii) to understand the contributions of the crop height that is derived from LiDAR data, as well as the geometric and textural features of image objects, to the crop species’ separabilities. The study region was an irrigated agricultural area in the central Heihe river basin, which is characterized by many crop species, complicated crop planting structures, and fragmented landscape. The airborne hyperspectral data acquired by the Compact Airborne Spectrographic Imager (CASI) with a 1 m spatial resolution and the Canopy Height Model (CHM) data derived from the LiDAR data acquired by the airborne Leica ALS70 LiDAR system were used for this study. The image segmentation accuracies of different feature combination schemes (very high-resolution imagery (VHR), VHR/CHM, and minimum noise fractional transformed data (MNF)/CHM) were evaluated and analyzed. The results showed that VHR/CHM outperformed the other two combination schemes with a segmentation accuracy of 84.8%. The object-based crop species classification results of different feature integrations indicated that incorporating the crop height information into the hyperspectral extracted features provided a substantial increase in the classification accuracy. The combination of MNF and CHM produced higher classification accuracy than the combination of VHR and CHM, and the solely MNF-based classification results. The textural and geometric features in the object-based classification could significantly improve the accuracy of the crop species classification. By using the proposed object-based classification framework, a crop species classification result with an overall accuracy of 90.33% and a kappa of 0.89 was achieved in our study area. Full article
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Open AccessArticle Estimation of Daily Air Temperature Based on MODIS Land Surface Temperature Products over the Corn Belt in the US
Remote Sens. 2015, 7(1), 951-970; doi:10.3390/rs70100951
Received: 7 October 2014 / Accepted: 5 January 2015 / Published: 15 January 2015
Cited by 11 | PDF Full-text (7192 KB) | HTML Full-text | XML Full-text
Abstract
Air temperature (Ta) is a key input in a wide range of agroclimatic applications. Moderate Resolution Imaging Spectroradiometer (MODIS) Ts (Land Surface Temperature (LST)) products are widely used to estimate daily Ta. However, only daytime LST (Ts-day) or nighttime LST (Ts-night) data have
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Air temperature (Ta) is a key input in a wide range of agroclimatic applications. Moderate Resolution Imaging Spectroradiometer (MODIS) Ts (Land Surface Temperature (LST)) products are widely used to estimate daily Ta. However, only daytime LST (Ts-day) or nighttime LST (Ts-night) data have been used to estimate Tmax/Tmin (daily maximum or minimum air temperature), respectively. The relationship between Tmax and Ts-night, and the one between Tmin and Ts-day has not been studied. In this study, both the ability of Ts-night data to estimate Tmax and the ability of Ts-day data to estimate Tmin were tested and studied in the Corn Belt during the growing season (May–September) from 2008 to 2012, using MODIS daily LST products from both Terra and Aqua. The results show that using Ts-night for estimating Tmax could result in a higher accuracy than using Ts-day for a similar estimate. Combining Ts-day and Ts-night, the estimation of Tmax was improved by 0.19–1.85, 0.37–1.12 and 0.26–0.93 °C for crops, deciduous forest and developed areas, respectively, when compared with using only Ts-day or Ts-night data. The main factors influencing the Ta estimation errors spatially and temporally were analyzed and discussed, such as satellite overpassing time, air masses, irrigation, etc. Full article
Open AccessArticle Utilization of the Suomi National Polar-Orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band for Arctic Ship Tracking and Fisheries Management
Remote Sens. 2015, 7(1), 971-989; doi:10.3390/rs70100971
Received: 10 June 2014 / Accepted: 25 December 2014 / Published: 16 January 2015
Cited by 9 | PDF Full-text (8253 KB) | HTML Full-text | XML Full-text
Abstract
Maritime ships operating on-board illumination at night appear as point sources of light to highly sensitive low-light imagers on-board environmental satellites. Unlike city lights or lights from offshore gas platforms, whose locations remain stationary from one night to the next, lights from ships
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Maritime ships operating on-board illumination at night appear as point sources of light to highly sensitive low-light imagers on-board environmental satellites. Unlike city lights or lights from offshore gas platforms, whose locations remain stationary from one night to the next, lights from ships typically are ephemeral. Fishing boat lights are most prevalent near coastal cities and along the thermal gradients in the open ocean. Maritime commercial ships also operate lights that can be detected from space. Such observations have been made in a limited way via U.S. Department of Defense satellites since the late 1960s. However, the Suomi National Polar-orbiting Partnership (S-NPP) satellite, which carries a new Day/Night Band (DNB) radiometer, offers a vastly improved ability for users to observe commercial shipping in remote areas such as the Arctic. Owing to S-NPP’s polar orbit and the DNB’s wide swath (~3040 km), the same location in Polar Regions can be observed for several successive passes via overlapping swaths—offering a limited ability to track ship motion. Here, we demonstrate the DNB’s improved ability to monitor ships from space. Imagery from the DNB is compared with the heritage low-light sensor, the Operational Linescan System (OLS) on board the Defense Meteorological Support Program (DMSP) satellites, and is evaluated in the context of tracking individual ships in the Polar Regions under both moonlit and moonless conditions. In a statistical sense, we show how DNB observations of ship lights in the East China Sea can be correlated with seasonal fishing activity, while also revealing compelling structures related to regional fishery agreements established between various nations. Full article
(This article belongs to the Special Issue Remote Sensing with Nighttime Lights)
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Open AccessArticle GRACE Gravity Satellite Observations of Terrestrial Water Storage Changes for Drought Characterization in the Arid Land of Northwestern China
Remote Sens. 2015, 7(1), 1021-1047; doi:10.3390/rs70101021
Received: 11 June 2014 / Accepted: 12 January 2015 / Published: 16 January 2015
Cited by 11 | PDF Full-text (12955 KB) | HTML Full-text | XML Full-text
Abstract
Drought is a complex natural hazard which can have negative effects on agriculture, economy, and human life. In this paper, the primary goal is to explore the application of the Gravity Recovery and Climate Experiment (GRACE) gravity satellite data for the quantitative investigation
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Drought is a complex natural hazard which can have negative effects on agriculture, economy, and human life. In this paper, the primary goal is to explore the application of the Gravity Recovery and Climate Experiment (GRACE) gravity satellite data for the quantitative investigation of the recent drought dynamic over the arid land of northwestern China, a region with scarce hydrological and meteorological observation datasets. The spatiotemporal characteristics of terrestrial water storage changes (TWSC) were first evaluated based on the GRACE satellite data, and then validated against hydrological model simulations and precipitation data. A drought index, the total storage deficit index (TSDI), was derived on the basis of GRACE-recovered TWSC. The spatiotemporal distributions of drought events from 2003 to 2012 in the study region were obtained using the GRACE-derived TSDI. Results derived from TSDI time series indicated that, apart from four short-term (three months) drought events, the study region experienced a severe long-term drought from May 2008 to December 2009. As shown in the spatial distribution of TSDI-derived drought conditions, this long-term drought mainly concentrated in the northwestern area of the entire region, where the terrestrial water storage was in heavy deficit. These drought characteristics, which were detected by TSDI, were consistent with local news reports and other researchers’ results. Furthermore, a comparison between TSDI and Standardized Precipitation Index (SPI) implied that GRACE TSDI was a more reliable integrated drought indicator (monitoring agricultural and hydrological drought) in terms of considering total terrestrial water storages for large regions. The GRACE-derived TSDI can therefore be used to characterize and monitor large-scale droughts in the arid regions, being of special value for areas with scarce observations. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)
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Open AccessArticle Mapping Deciduous Rubber Plantation Areas and Stand Ages with PALSAR and Landsat Images
Remote Sens. 2015, 7(1), 1048-1073; doi:10.3390/rs70101048
Received: 10 September 2014 / Revised: 5 January 2015 / Accepted: 12 January 2015 / Published: 19 January 2015
Cited by 12 | PDF Full-text (47145 KB) | HTML Full-text | XML Full-text
Abstract
Accurate and updated finer resolution maps of rubber plantations and stand ages are needed to understand and assess the impacts of rubber plantations on regional ecosystem processes. This study presented a simple method for mapping rubber plantation areas and their stand ages by
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Accurate and updated finer resolution maps of rubber plantations and stand ages are needed to understand and assess the impacts of rubber plantations on regional ecosystem processes. This study presented a simple method for mapping rubber plantation areas and their stand ages by integration of PALSAR 50-m mosaic images and multi-temporal Landsat TM/ETM+ images. The L-band PALSAR 50-m mosaic images were used to map forests (including both natural forests and rubber trees) and non-forests. For those PALSAR-based forest pixels, we analyzed the multi-temporal Landsat TM/ETM+ images from 2000 to 2009. We first studied phenological signatures of deciduous rubber plantations (defoliation and foliation) and natural forests through analysis of surface reflectance, Normal Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI) and generated a map of rubber plantations in 2009. We then analyzed phenological signatures of rubber plantations with different stand ages and generated a map, in 2009, of rubber plantation stand ages (≤5, 6–10, >10 years-old) based on multi-temporal Landsat images. The resultant maps clearly illustrated how rubber plantations have expanded into the mountains in the study area over the years. The results in this study demonstrate the potential of integrating microwave (e.g., PALSAR) and optical remote sensing in the characterization of rubber plantations and their expansion over time. Full article
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Open AccessArticle UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis
Remote Sens. 2015, 7(1), 1074-1094; doi:10.3390/rs70101074
Received: 31 October 2014 / Accepted: 12 January 2015 / Published: 19 January 2015
Cited by 36 | PDF Full-text (86113 KB) | HTML Full-text | XML Full-text
Abstract
Unmanned aerial vehicle (UAV) remote sensing has great potential for vegetation mapping in complex urban landscapes due to the ultra-high resolution imagery acquired at low altitudes. Because of payload capacity restrictions, off-the-shelf digital cameras are widely used on medium and small sized UAVs.
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Unmanned aerial vehicle (UAV) remote sensing has great potential for vegetation mapping in complex urban landscapes due to the ultra-high resolution imagery acquired at low altitudes. Because of payload capacity restrictions, off-the-shelf digital cameras are widely used on medium and small sized UAVs. The limitation of low spectral resolution in digital cameras for vegetation mapping can be reduced by incorporating texture features and robust classifiers. Random Forest has been widely used in satellite remote sensing applications, but its usage in UAV image classification has not been well documented. The objectives of this paper were to propose a hybrid method using Random Forest and texture analysis to accurately differentiate land covers of urban vegetated areas, and analyze how classification accuracy changes with texture window size. Six least correlated second-order texture measures were calculated at nine different window sizes and added to original Red-Green-Blue (RGB) images as ancillary data. A Random Forest classifier consisting of 200 decision trees was used for classification in the spectral-textural feature space. Results indicated the following: (1) Random Forest outperformed traditional Maximum Likelihood classifier and showed similar performance to object-based image analysis in urban vegetation classification; (2) the inclusion of texture features improved classification accuracy significantly; (3) classification accuracy followed an inverted U relationship with texture window size. The results demonstrate that UAV provides an efficient and ideal platform for urban vegetation mapping. The hybrid method proposed in this paper shows good performance in differentiating urban vegetation mapping. The drawbacks of off-the-shelf digital cameras can be reduced by adopting Random Forest and texture analysis at the same time. Full article
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Open AccessArticle Assessing Handheld Mobile Laser Scanners for Forest Surveys
Remote Sens. 2015, 7(1), 1095-1111; doi:10.3390/rs70101095
Received: 29 August 2014 / Accepted: 12 January 2015 / Published: 19 January 2015
Cited by 11 | PDF Full-text (45226 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
A handheld mobile laser scanning (HMLS) approach to forest inventory surveying allows virtual reconstructions of forest stands and extraction of key structural parameters from beneath the canopy, significantly reducing survey time when compared against static laser scan and fieldwork methods. A proof of
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A handheld mobile laser scanning (HMLS) approach to forest inventory surveying allows virtual reconstructions of forest stands and extraction of key structural parameters from beneath the canopy, significantly reducing survey time when compared against static laser scan and fieldwork methods. A proof of concept test application demonstrated the ability of this technique to successfully extract diameter at breast height (DBH) and stem position compared against a concurrent terrestrial laser scan (TLS) survey. When stems with DBH > 10 cm are examined, an HMLS to TLS modelling success rate of 91% was achieved with the root mean square error (RMSE) of the DBH and stem position being 1.5 cm and 2.1 cm respectively. The HMLS approach gave a survey coverage time per surveyor of 50 m2/min compared with 0.85 m2/min for the TLS instrument and 0.43 m2/min for the field study. This powerful tool has potential applications in forest surveying by providing much larger data sets at reduced operational costs to current survey methods. HMLS provides an efficient, cost effective, versatile forest surveying technique, which can be conducted as easily as walking through a plot, allowing much more detailed, spatially extensive survey data to be collected. Full article
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Open AccessArticle Oil Spill Detection in Glint-Contaminated Near-Infrared MODIS Imagery
Remote Sens. 2015, 7(1), 1112-1134; doi:10.3390/rs70101112
Received: 2 October 2014 / Accepted: 12 January 2015 / Published: 19 January 2015
Cited by 11 | PDF Full-text (6823 KB) | HTML Full-text | XML Full-text
Abstract
We present a methodology to detect oil spills using MODIS near-infrared sun glittered radiance imagery. The methodology was developed by using a set of seven MODIS images (training dataset) and validated using four other images (validation dataset). The method is based on the
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We present a methodology to detect oil spills using MODIS near-infrared sun glittered radiance imagery. The methodology was developed by using a set of seven MODIS images (training dataset) and validated using four other images (validation dataset). The method is based on the ratio image R = L'GN/LGN, where L'GN is the MODIS-retrieved normalized sun glint radiance image and LGN the same quantity, but obtained from the Cox and Munk isotropic (independent of wind direction) sun glint model. We show that in the R image, while clean water pixel values tend to one, oil spills stand out as anomalies. Moreover, we provide a criterion to distinguish between positive and negative oil-water contrast. A pixel in an R image is classified as a potential oil spill or water via a variable threshold Rs as a function of L'GN, where the threshold values are obtained from the slicks of our training dataset. Two different fitting curves are provided for Rs, according to the contrast sign. The selection of the correct fitting curve is based on the contrast type, resulting from the criterion above. Results indicate that the thresholding is able to isolate the spills and that the spills of the validation dataset are successfully detected. Spurious look-alike features, such as clouds, and other non-spill features, e.g., large areas at the glint region border, are also detected as oil spills and must be eliminated. We believe that our methodology represents a novel and promising, though preliminary, approach towards automatic oil spill detection in optical satellite images. Full article
(This article belongs to the Special Issue Remote Sensing of Water Resources)
Open AccessArticle The Thermal Infrared Sensor (TIRS) on Landsat 8: Design Overview and Pre-Launch Characterization
Remote Sens. 2015, 7(1), 1135-1153; doi:10.3390/rs70101135
Received: 21 August 2014 / Accepted: 15 December 2014 / Published: 19 January 2015
Cited by 11 | PDF Full-text (49936 KB) | HTML Full-text | XML Full-text
Abstract
The Thermal Infrared Sensor (TIRS) on Landsat 8 is the latest thermal sensor in that series of missions. Unlike the previous single-channel sensors, TIRS uses two channels to cover the 10–12.5 micron band. It is also a pushbroom imager; a departure from the
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The Thermal Infrared Sensor (TIRS) on Landsat 8 is the latest thermal sensor in that series of missions. Unlike the previous single-channel sensors, TIRS uses two channels to cover the 10–12.5 micron band. It is also a pushbroom imager; a departure from the previous whiskbroom approach. Nevertheless, the instrument requirements are defined such that data continuity is maintained. This paper describes the design of the TIRS instrument, the results of pre-launch calibration measurements and shows an example of initial on-orbit science performance compared to Landsat 7. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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Open AccessArticle Soil Drought Anomalies in MODIS GPP of a Mediterranean Broadleaved Evergreen Forest
Remote Sens. 2015, 7(1), 1154-1180; doi:10.3390/rs70101154
Received: 7 July 2014 / Accepted: 12 January 2015 / Published: 20 January 2015
Cited by 4 | PDF Full-text (9122 KB) | HTML Full-text | XML Full-text
Abstract
The Moderate Resolution Imaging Spectroradiometer (MODIS) yields global operational estimates of terrestrial gross primary production (GPP). In this study, we compared MOD17A2 GPP with tower eddy flux-based estimates of GPP from 2001 to 2010 over an evergreen broad-leaf Mediterranean forest in Southern France
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The Moderate Resolution Imaging Spectroradiometer (MODIS) yields global operational estimates of terrestrial gross primary production (GPP). In this study, we compared MOD17A2 GPP with tower eddy flux-based estimates of GPP from 2001 to 2010 over an evergreen broad-leaf Mediterranean forest in Southern France with a significant summer drought period. The MOD17A2 GPP shows seasonal variations that are inconsistent with the tower GPP, with close-to-accurate winter estimates and significant discrepancies for summer estimates which are the least accurate. The analysis indicated that the MOD17A2 GPP has high bias relative to tower GPP during severe summer drought which we hypothesized caused by soil water limitation. Our investigation showed that there was a significant correlation (R2 = 0.77, p < 0.0001) between the relative soil water content and the relative error of MOD17A2 GPP. Therefore, the relationship between the error and the measured relative soil water content could explain anomalies in MOD17A2 GPP. The results of this study indicate that careful consideration of the water conditions input to the MOD17A2 GPP algorithm on remote sensing is required in order to provide accurate predictions of GPP. Still, continued efforts are necessary to ascertain the most appropriate index, which characterizes soil water limitation in water-limited environments using remote sensing. Full article
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Open AccessReview Mapping Surface Broadband Albedo from Satellite Observations: A Review of Literatures on Algorithms and Products
Remote Sens. 2015, 7(1), 990-1020; doi:10.3390/rs70100990
Received: 8 October 2014 / Accepted: 5 January 2015 / Published: 16 January 2015
Cited by 13 | PDF Full-text (1336 KB) | HTML Full-text | XML Full-text
Abstract
Surface albedo is one of the key controlling geophysical parameters in the surface energy budget studies, and its temporal and spatial variation is closely related to the global climate change and regional weather system due to the albedo feedback mechanism. As an efficient
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Surface albedo is one of the key controlling geophysical parameters in the surface energy budget studies, and its temporal and spatial variation is closely related to the global climate change and regional weather system due to the albedo feedback mechanism. As an efficient tool for monitoring the surfaces of the Earth, remote sensing is widely used for deriving long-term surface broadband albedo with various geostationary and polar-orbit satellite platforms in recent decades. Moreover, the algorithms for estimating surface broadband albedo from satellite observations, including narrow-to-broadband conversions, bidirectional reflectance distribution function (BRDF) angular modeling, direct-estimation algorithm and the algorithms for estimating albedo from geostationary satellite data, are developed and improved. In this paper, we present a comprehensive literature review on algorithms and products for mapping surface broadband albedo with satellite observations and provide a discussion of different algorithms and products in a historical perspective based on citation analysis of the published literature. This paper shows that the observation technologies and accuracy requirement of applications are important, and long-term, global fully-covered (including land, ocean, and sea-ice surfaces), gap-free, surface broadband albedo products with higher spatial and temporal resolution are required for climate change, surface energy budget, and hydrological studies. Full article
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Other

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Open AccessCorrection Correction: Behling, R., et al. Automated Spatiotemporal Landslide Mapping over Large Areas Using RapidEye Time Series Data. Remote Sens. 2014, 6, 8026-8055
Remote Sens. 2015, 7(1), 666-667; doi:10.3390/rs70100666
Received: 9 December 2014 / Accepted: 9 December 2014 / Published: 8 January 2015
PDF Full-text (652 KB) | HTML Full-text | XML Full-text
Abstract
Due to a technical error at the Editorial Office, Figure 7 of manuscript [1] was missing in the published paper. The correct version is reproduced below. We apologize for any inconvenience caused to authors or readers of Remote Sensing.[...] Full article
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Figure 7

Open AccessCorrection Correction: Yu, Q., et al. Narrowband Bio-Indicator Monitoring of Temperate Forest Carbon Fluxes in Northeastern China. Remote Sens. 2014, 6, 8986-9013
Remote Sens. 2015, 7(1), 684-685; doi:10.3390/rs70100684
Received: 29 December 2014 / Accepted: 6 January 2015 / Published: 9 January 2015
PDF Full-text (6502 KB) | HTML Full-text | XML Full-text
Abstract
Due to a technical error at the Editorial Office, Figure 8 of manuscript [1] is missing in the published paper. The correct version of the figure is reproduced below. We apologize for any inconvenience caused to authors or readers of Remote Sensing.[...] Full article
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Figure 8

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