E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Remote Sensing of Land Surface Properties, Patterns and Processes"

Quicklinks

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (30 April 2008)

Special Issue Editors

Guest Editor
Prof. Dr. Qihao Weng (Website1, Website2)

Center for Urban and Environmental Change, Department of Geography, Geology, and Anthropology, Indiana State University, Terre Haute, IN 47809, USA
Phone: +1 812 237 2255
Fax: +1 812 237 8029
Interests: remote sensing; GIS; land use and land cover change; urban environment and ecosystem
Guest Editor
Prof. Dr. Dale A. Quattrochi

Earth Science Office, ZP11, Marshall Space Flight Center, NASA, Huntsville, AL 35812, USA
Phone: +1-256-961-7887
Fax: +1-256-961-7788
Interests: thermal remote sensing; urban heat island analysis; geospatial techniques and remote sensing; land use/land cover change
Guest Editor
Dr. George Xian

SAIC, USGS Center for Earth Resources Observation and Science, Sioux Falls, SD 57198, USA
Interests: remote sensing of land cover and urban; surface thermal properties; regional climate change

Special Issue Information

Dear Colleagues,

In this special issue, we wish to explore the current state in using remote sensing technology to understand land surface properties, patterns, and processes. In particular, studies that employ remotely sensed data to derive quantitative measurements of land surface properties, to characterize and quantify land surface ecological and geographical patterns, and to analyze and model land surface processes are encouraged. The virtues and importance of remote sensing data from various ground, aircraft, and satellite platforms will be assessed. Moreover, we wish to explore how improved sensor and analytical techniques can be employed to better define, characterize, quantify, and model land surface forms, patterns, and processes. The topics may include, but are not limited to, the following:

  • Retrieval and analysis of land surface biophysical parameters, temperatures, surface emissivity, surface roughness and anisotropy.
  • Analysis of remote sensed data derived parameters such as land surface temperature, emissivity, vegetation cover, soil type, soil moisture, and surface water content for estimation of surface energy fluxes and investigation of land-atmosphere interactions.
  • Utilization of remote sensing data of various platforms for landscape characterization (e.g., land cover/land use attributes, impervious surfaces, and habitats).
  • Analysis of land surface patterns and their relations to land surface processes and properties.
  • Application of GIS, geospatial statistics, visualization, and landscape ecological approaches to remote sensing of land surfaces.
  • Study of the improvements in spatial, spectral, radiometric, and temporal resolutions of remote sensing data for analysis of land surface properties, patterns, and processes.
  • Investigation of impacts of spatial and temporal scale on analysis of remote sensing data.

Dr. Dale A. Quattrochi
Prof. Dr. Qihao Weng
Dr. George Xian
Guest Editors

Keywords

  • remote sensing of land surface properties
  • patterns and processes

Published Papers (16 papers)

View options order results:
result details:
Displaying articles 1-16
Export citation of selected articles as:

Research

Open AccessArticle Data Base Design with GIS in Ecosystem Based Multiple Use Forest Management in Artvin, Turkey: A Case Study in Balcı Forest Management Planning Unit
Sensors 2009, 9(3), 1644-1661; doi:10.3390/s90301644
Received: 11 December 2008 / Revised: 25 February 2009 / Accepted: 2 March 2009 / Published: 10 March 2009
PDF Full-text (5811 KB) | HTML Full-text | XML Full-text
Abstract
In Turkey, the understanding of planning focused on timber production has given its place on Multiple Use Management (MUM). Because the whole infrastructure of forestry with inventory system leading the way depends on timber production, some cases of bottle neck are expected [...] Read more.
In Turkey, the understanding of planning focused on timber production has given its place on Multiple Use Management (MUM). Because the whole infrastructure of forestry with inventory system leading the way depends on timber production, some cases of bottle neck are expected during the transition period. Database design, probably the most important stage during the transition to MUM, together with the digital basic maps making up the basis of this infrastructure constitute the main point of this article. Firstly, the forest management philosophy of Turkey in the past was shortly touched upon in the article. Ecosystem Based Multiple Use Forest Management (EBMUFM) approaches was briefly introduced. The second stage of the process of EBMUFM, database design was described by examining the classical planning infrastructure and the coverage to be produced and consumed were suggested in the form of lists. At the application stage, two different geographical databases were established with GIS in Balcı Planning Unit of the years 1984 and 2006. Following that the related basic maps are produced. Timely diversity of the planning unit of 20 years is put forward comparatively with regard to the stand parameters such as tree types, age class, development stage, canopy closure, mixture, volume and increment. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)
Open AccessArticle Full Hierarchic Versus Non-Hierarchic Classification Approaches for Mapping Sealed Surfaces at the Rural-Urban Fringe Using High-Resolution Satellite Data
Sensors 2009, 9(1), 22-45; doi:10.3390/s90100022
Received: 4 December 2008 / Revised: 17 December 2008 / Accepted: 23 December 2008 / Published: 5 January 2009
Cited by 8 | PDF Full-text (271 KB) | HTML Full-text | XML Full-text
Abstract
Since 2008 more than half of the world population is living in cities and urban sprawl is continuing. Because of these developments, the mapping and monitoring of urban environments and their surroundings is becoming increasingly important. In this study two object-oriented approaches [...] Read more.
Since 2008 more than half of the world population is living in cities and urban sprawl is continuing. Because of these developments, the mapping and monitoring of urban environments and their surroundings is becoming increasingly important. In this study two object-oriented approaches for high-resolution mapping of sealed surfaces are compared: a standard non-hierarchic approach and a full hierarchic approach using both multi-layer perceptrons and decision trees as learning algorithms. Both methods outperform the standard nearest neighbour classifier, which is used as a benchmark scenario. For the multi-layer perceptron approach, applying a hierarchic classification strategy substantially increases the accuracy of the classification. For the decision tree approach a one-against-all hierarchic classification strategy does not lead to an improvement of classification accuracy compared to the standard all-against-all approach. Best results are obtained with the hierarchic multi-layer perceptron classification strategy, producing a kappa value of 0.77. A simple shadow reclassification procedure based on characteristics of neighbouring objects further increases the kappa value to 0.84. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)
Open AccessArticle Spectral and Spatial-Based Classification for Broad-Scale Land Cover Mapping Based on Logistic Regression
Sensors 2008, 8(12), 8067-8085; doi:10.3390/s8128067
Received: 9 October 2008 / Revised: 5 November 2008 / Accepted: 17 November 2008 / Published: 8 December 2008
Cited by 10 | PDF Full-text (615 KB) | HTML Full-text | XML Full-text
Abstract
Improvement of satellite sensor characteristics motivates the development of new techniques for satellite image classification. Spatial information seems to be critical in classification processes, especially for heterogeneous and complex landscapes such as those observed in the Mediterranean basin. In our study, a [...] Read more.
Improvement of satellite sensor characteristics motivates the development of new techniques for satellite image classification. Spatial information seems to be critical in classification processes, especially for heterogeneous and complex landscapes such as those observed in the Mediterranean basin. In our study, a spectral classification method of a LANDSAT-5 TM imagery that uses several binomial logistic regression models was developed, evaluated and compared to the familiar parametric maximum likelihood algorithm. The classification approach based on logistic regression modelling was extended to a contextual one by using autocovariates to consider spatial dependencies of every pixel with its neighbours. Finally, the maximum likelihood algorithm was upgraded to contextual by considering typicality, a measure which indicates the strength of class membership. The use of logistic regression for broad-scale land cover classification presented higher overall accuracy (75.61%), although not statistically significant, than the maximum likelihood algorithm (64.23%), even when the latter was refined following a spatial approach based on Mahalanobis distance (66.67%). However, the consideration of the spatial autocovariate in the logistic models significantly improved the fit of the models and increased the overall accuracy from 75.61% to 80.49%. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)
Open AccessArticle An Annual Plant Growth Proxy in the Mojave Desert Using MODIS-EVI Data
Sensors 2008, 8(12), 7792-7808; doi:10.3390/s8127792
Received: 7 May 2008 / Revised: 19 November 2008 / Accepted: 24 November 2008 / Published: 3 December 2008
Cited by 15 | PDF Full-text (539 KB) | HTML Full-text | XML Full-text
Abstract
In the arid Mojave Desert, the phenological response of vegetation is largely dependent upon the timing and amount of rainfall, and maps of annual plant cover at any one point in time can vary widely. Our study developed relative annual plant growth [...] Read more.
In the arid Mojave Desert, the phenological response of vegetation is largely dependent upon the timing and amount of rainfall, and maps of annual plant cover at any one point in time can vary widely. Our study developed relative annual plant growth models as proxies for annual plant cover using metrics that captured phenological variability in Moderate-Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) satellite images. We used landscape phenologies revealed in MODIS data together with ecological knowledge of annual plant seasonality to develop a suite of metrics to describe annual growth on a yearly basis. Each of these metrics was applied to temporally-composited MODIS-EVI images to develop a relative model of annual growth. Each model was evaluated by testing how well it predicted field estimates of annual cover collected during 2003 and 2005 at the Mojave National Preserve. The best performing metric was the spring difference metric, which compared the average of three spring MODIS-EVI composites of a given year to that of 2002, a year of record drought. The spring difference metric showed correlations with annual plant cover of R2 = 0.61 for 2005 and R2 = 0.47 for 2003. Although the correlation is moderate, we consider it supportive given the characteristics of the field data, which were collected for a different study in a localized area and are not ideal for calibration to MODIS pixels. A proxy for annual growth potential was developed from the spring difference metric of 2005 for use as an environmental data layer in desert tortoise habitat modeling. The application of the spring difference metric to other imagery years presents potential for other applications such as fuels, invasive species, and dust-emission monitoring in the Mojave Desert. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)
Open AccessArticle Classification Metrics for Improved Atmospheric Correction of Multispectral VNIR Imagery
Sensors 2008, 8(11), 6999-7011; doi:10.3390/s8116999
Received: 18 August 2008 / Revised: 29 September 2008 / Accepted: 30 October 2008 / Published: 5 November 2008
Cited by 4 | PDF Full-text (771 KB) | HTML Full-text | XML Full-text
Abstract
Multispectral visible/near-infrared (VNIR) earth observation satellites, e.g., Ikonos, Quickbird, ALOS AVNIR-2, and DMC, usually acquire imagery in a few (3 – 5) spectral bands. Atmospheric correction is a challenging task for these images because the standard methods require at least one shortwave [...] Read more.
Multispectral visible/near-infrared (VNIR) earth observation satellites, e.g., Ikonos, Quickbird, ALOS AVNIR-2, and DMC, usually acquire imagery in a few (3 – 5) spectral bands. Atmospheric correction is a challenging task for these images because the standard methods require at least one shortwave infrared band (around 1.6 or 2.2 µm) or hyperspectral instruments to derive the aerosol optical thickness. New classification metrics for defining cloud, cloud over water, haze, water, and saturation are presented to achieve improvements for an automatic processing system. The background is an ESA contract for the development of a prototype atmospheric processor for the optical payload AVNIR-2 on the ALOS platform. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)
Figures

Open AccessCommunication Analyzing Land Use/Land Cover Changes Using Remote Sensing and GIS in Rize, North-East Turkey
Sensors 2008, 8(10), 6188-6202; doi:10.3390/s8106188
Received: 19 August 2008 / Revised: 21 September 2008 / Accepted: 19 September 2008 / Published: 1 October 2008
Cited by 27 | PDF Full-text (1154 KB) | HTML Full-text | XML Full-text
Abstract
Mapping land use/land cover (LULC) changes at regional scales is essential for a wide range of applications, including landslide, erosion, land planning, global warming etc. LULC alterations (based especially on human activities), negatively effect the patterns of climate, the patterns of natural [...] Read more.
Mapping land use/land cover (LULC) changes at regional scales is essential for a wide range of applications, including landslide, erosion, land planning, global warming etc. LULC alterations (based especially on human activities), negatively effect the patterns of climate, the patterns of natural hazard and socio-economic dynamics in global and local scale. In this study, LULC changes are investigated by using of Remote Sensing and Geographic Information Systems (GIS) in Rize, North-East Turkey. For this purpose, firstly supervised classification technique is applied to Landsat images acquired in 1976 and 2000. Image Classification of six reflective bands of two Landsat images is carried out by using maximum likelihood method with the aid of ground truth data obtained from aerial images dated 1973 and 2002. The second part focused on land use land cover changes by using change detection comparison (pixel by pixel). In third part of the study, the land cover changes are analyzed according to the topographic structure (slope and altitude) by using GIS functions. The results indicate that severe land cover changes have occurred in agricultural (36.2%) (especially in tea gardens), urban (117%), pasture (-72.8%) and forestry (-12.8%) areas has been experienced in the region between 1976 and 2000. It was seen that the LULC changes were mostly occurred in coastal areas and in areas having low slope values. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)
Figures

Open AccessArticle Detection of Aspens Using High Resolution Aerial Laser Scanning Data and Digital Aerial Images
Sensors 2008, 8(8), 5037-5054; doi:10.3390/s8085037
Received: 5 August 2008 / Revised: 21 August 2008 / Accepted: 22 August 2008 / Published: 25 August 2008
Cited by 10 | PDF Full-text (145 KB) | HTML Full-text | XML Full-text
Abstract
The aim was to use high resolution Aerial Laser Scanning (ALS) data and aerial images to detect European aspen (Populus tremula L.) from among other deciduous trees. The field data consisted of 14 sample plots of 30 m × 30 [...] Read more.
The aim was to use high resolution Aerial Laser Scanning (ALS) data and aerial images to detect European aspen (Populus tremula L.) from among other deciduous trees. The field data consisted of 14 sample plots of 30 m × 30 m size located in the Koli National Park in the North Karelia, Eastern Finland. A Canopy Height Model (CHM) was interpolated from the ALS data with a pulse density of 3.86/m2, low-pass filtered using Height-Based Filtering (HBF) and binarized to create the mask needed to separate the ground pixels from the canopy pixels within individual areas. Watershed segmentation was applied to the low-pass filtered CHM in order to create preliminary canopy segments, from which the non-canopy elements were extracted to obtain the final canopy segmentation, i.e. the ground mask was analysed against the canopy mask. A manual classification of aerial images was employed to separate the canopy segments of deciduous trees from those of coniferous trees. Finally, linear discriminant analysis was applied to the correctly classified canopy segments of deciduous trees to classify them into segments belonging to aspen and those belonging to other deciduous trees. The independent variables used in the classification were obtained from the first pulse ALS point data. The accuracy of discrimination between aspen and other deciduous trees was 78.6%. The independent variables in the classification function were the proportion of vegetation hits, the standard deviation of in pulse heights, accumulated intensity at the 90th percentile and the proportion of laser points reflected at the 60th height percentile. The accuracy of classification corresponded to the validation results of earlier ALS-based studies on the classification of individual deciduous trees to tree species. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)
Open AccessArticle Surface Temperature Mapping of the University of Northern Iowa Campus Using High Resolution Thermal Infrared Aerial Imageries
Sensors 2008, 8(8), 5055-5068; doi:10.3390/s8085055
Received: 25 July 2008 / Revised: 22 August 2008 / Accepted: 22 August 2008 / Published: 25 August 2008
Cited by 7 | PDF Full-text (757 KB) | HTML Full-text | XML Full-text
Abstract
The goal of this project was to map the surface temperature of the University of Northern Iowa campus using high-resolution thermal infrared aerial imageries. A thermal camera with a spectral bandwidth of 3.0-5.0 μm was flown at the average altitude of 600 [...] Read more.
The goal of this project was to map the surface temperature of the University of Northern Iowa campus using high-resolution thermal infrared aerial imageries. A thermal camera with a spectral bandwidth of 3.0-5.0 μm was flown at the average altitude of 600 m, achieving ground resolution of 29 cm. Ground control data was used to construct the pixelto-temperature conversion model, which was later used to produce temperature maps of the entire campus and also for validation of the model. The temperature map then was used to assess the building rooftop conditions and steam line faults in the study area. Assessment of the temperature map revealed a number of building structures that may be subject to insulation improvement due to their high surface temperatures leaks. Several hot spots were also identified on the campus for steam pipelines faults. High-resolution thermal infrared imagery proved highly effective tool for precise heat anomaly detection on the campus, and it can be used by university facility services for effective future maintenance of buildings and grounds. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)
Open AccessArticle Empirical Retrieval of Surface Melt Magnitude from Coupled MODIS Optical and Thermal Measurements over the Greenland Ice Sheet during the 2001 Ablation Season
Sensors 2008, 8(8), 4915-4947; doi:10.3390/s8084915
Received: 3 July 2008 / Revised: 22 July 2008 / Accepted: 25 August 2008 / Published: 22 August 2008
Cited by 3 | PDF Full-text (3372 KB) | HTML Full-text | XML Full-text
Abstract
Accelerated ice flow near the equilibrium line of west-central Greenland Ice Sheet (GIS) has been attributed to an increase in infiltrated surface melt water as a response to climate warming. The assessment of surface melting events must be more than the detection [...] Read more.
Accelerated ice flow near the equilibrium line of west-central Greenland Ice Sheet (GIS) has been attributed to an increase in infiltrated surface melt water as a response to climate warming. The assessment of surface melting events must be more than the detection of melt onset or extent. Retrieval of surface melt magnitude is necessary to improve understanding of ice sheet flow and surface melt coupling. In this paper, we report on a new technique to quantify the magnitude of surface melt. Cloud-free dates of June 10, July 5, 7, 9, and 11, 2001 Moderate Resolution Imaging Spectroradiometer (MODIS) daily reflectance Band 5 (1.230-1.250μm) and surface temperature images rescaled to 1km over western Greenland were used in the retrieval algorithm. An optical-thermal feature space partitioned as a function of melt magnitude was derived using a one-dimensional thermal snowmelt model (SNTHERM89). SNTHERM89 was forced by hourly meteorological data from the Greenland Climate Network (GC-Net) at reference sites spanning dry snow, percolation, and wet snow zones in the Jakobshavn drainage basin in western GIS. Melt magnitude or effective melt (E-melt) was derived for satellite composite periods covering May, June, and July displaying low fractions (0-1%) at elevations greater than 2500m and fractions at or greater than 15% at elevations lower than 1000m assessed for only the upper 5 cm of the snow surface. Validation of E-melt involved comparison of intensity to dry and wet zones determined from QSCAT backscatter. Higher intensities (> 8%) were distributed in wet snow zones, while lower intensities were grouped in dry zones at a first order accuracy of ~ ±2%. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)
Open AccessArticle Object-Based Point Cloud Analysis of Full-Waveform Airborne Laser Scanning Data for Urban Vegetation Classification
Sensors 2008, 8(8), 4505-4528; doi:10.3390/s8084505
Received: 1 July 2008 / Revised: 28 July 2008 / Accepted: 28 July 2008 / Published: 4 August 2008
Cited by 64 | PDF Full-text (6855 KB) | HTML Full-text | XML Full-text
Abstract
Airborne laser scanning (ALS) is a remote sensing technique well-suited for 3D vegetation mapping and structure characterization because the emitted laser pulses are able to penetrate small gaps in the vegetation canopy. The backscattered echoes from the foliage, woody vegetation, the terrain, [...] Read more.
Airborne laser scanning (ALS) is a remote sensing technique well-suited for 3D vegetation mapping and structure characterization because the emitted laser pulses are able to penetrate small gaps in the vegetation canopy. The backscattered echoes from the foliage, woody vegetation, the terrain, and other objects are detected, leading to a cloud of points. Higher echo densities (> 20 echoes/m2) and additional classification variables from full-waveform (FWF) ALS data, namely echo amplitude, echo width and information on multiple echoes from one shot, offer new possibilities in classifying the ALS point cloud. Currently FWF sensor information is hardly used for classification purposes. This contribution presents an object-based point cloud analysis (OBPA) approach, combining segmentation and classification of the 3D FWF ALS points designed to detect tall vegetation in urban environments. The definition tall vegetation includes trees and shrubs, but excludes grassland and herbage. In the applied procedure FWF ALS echoes are segmented by a seeded region growing procedure. All echoes sorted descending by their surface roughness are used as seed points. Segments are grown based on echo width homogeneity. Next, segment statistics (mean, standard deviation, and coefficient of variation) are calculated by aggregating echo features such as amplitude and surface roughness. For classification a rule base is derived automatically from a training area using a statistical classification tree. To demonstrate our method we present data of three sites with around 500,000 echoes each. The accuracy of the classified vegetation segments is evaluated for two independent validation sites. In a point-wise error assessment, where the classification is compared with manually classified 3D points, completeness and correctness better than 90% are reached for the validation sites. In comparison to many other algorithms the proposed 3D point classification works on the original measurements directly, i.e. the acquired points. Gridding of the data is not necessary, a process which is inherently coupled to loss of data and precision. The 3D properties provide especially a good separability of buildings and terrain points respectively, if they are occluded by vegetation. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)
Open AccessArticle Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots
Sensors 2008, 8(5), 3557-3585; doi:10.3390/s8053557
Received: 29 April 2008 / Accepted: 23 May 2008 / Published: 26 May 2008
Cited by 80 | PDF Full-text (1262 KB) | HTML Full-text | XML Full-text
Abstract
This paper outlines how light Unmanned Aerial Vehicles (UAV) can be used in remote sensing for precision farming. It focuses on the combination of simple digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains. [...] Read more.
This paper outlines how light Unmanned Aerial Vehicles (UAV) can be used in remote sensing for precision farming. It focuses on the combination of simple digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains. In 2005, these instruments were fitted to powered glider and parachute, and flown at six dates staggered over the crop season. We monitored ten varieties of wheat, grown in trial micro-plots in the South-West of France. For each date, we acquired multiple views in four spectral bands corresponding to blue, green, red, and near-infrared. We then performed accurate corrections of image vignetting, geometric distortions, and radiometric bidirectional effects. Afterwards, we derived for each experimental micro-plot several vegetation indexes relevant for vegetation analyses. Finally, we sought relationships between these indexes and field-measured biophysical parameters, both generic and date-specific. Therefore, we established a robust and stable generic relationship between, in one hand, leaf area index and NDVI and, in the other hand, nitrogen uptake and GNDVI. Due to a high amount of noise in the data, it was not possible to obtain a more accurate model for each date independently. A validation protocol showed that we could expect a precision level of 15% in the biophysical parameters estimation while using these relationships. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)
Open AccessArticle Temporal Variability Corrections for Advanced Microwave Scanning Radiometer E (AMSR-E) Surface Soil Moisture: Case Study in Little River Region, Georgia, U.S.
Sensors 2008, 8(4), 2617-2627; doi:10.3390/s8042617
Received: 3 December 2007 / Accepted: 8 April 2008 / Published: 14 April 2008
Cited by 14 | PDF Full-text (300 KB) | HTML Full-text | XML Full-text
Abstract
Statistical correction methods, the Cumulative Distribution Function (CDF) matching technique and Regional Statistics Method (RSM) are applied to adjust the limited temporal variability of Advanced Microwave Scanning Radiometer E (AMSR-E) data using the Common Land Model (CLM). The temporal variability adjustment between [...] Read more.
Statistical correction methods, the Cumulative Distribution Function (CDF) matching technique and Regional Statistics Method (RSM) are applied to adjust the limited temporal variability of Advanced Microwave Scanning Radiometer E (AMSR-E) data using the Common Land Model (CLM). The temporal variability adjustment between CLM and AMSR-E data was conducted for annual and seasonal periods for 2003 in the Little River region, GA. The results showed that the statistical correction techniques improved AMSR-E’s limited temporal variability as compared to ground-based measurements. The regression slope and intercept improved from 0.210 and 0.112 up to 0.971 and -0.005 for the non-growing season. The R2 values also modestly improved. The Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) products were able to identify periods having an attenuated microwave brightness signal that are not likely to benefit from these statistical correction techniques. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)
Open AccessArticle Downscaling Thermal Infrared Radiance for Subpixel Land Surface Temperature Retrieval
Sensors 2008, 8(4), 2695-2706; doi:10.3390/s8042695
Received: 2 March 2008 / Accepted: 8 April 2008 / Published: 6 April 2008
Cited by 42 | PDF Full-text (597 KB) | HTML Full-text | XML Full-text
Abstract
Land surface temperature (LST) retrieved from satellite thermal sensors often consists of mixed temperature components. Retrieving subpixel LST is therefore needed in various environmental and ecological studies. In this paper, we developed two methods for downscaling coarse resolution thermal infrared (TIR) radiance [...] Read more.
Land surface temperature (LST) retrieved from satellite thermal sensors often consists of mixed temperature components. Retrieving subpixel LST is therefore needed in various environmental and ecological studies. In this paper, we developed two methods for downscaling coarse resolution thermal infrared (TIR) radiance for the purpose of subpixel temperature retrieval. The first method was developed on the basis of a scale-invariant physical model on TIR radiance. The second method was based on a statistical relationship between TIR radiance and land cover fraction at high spatial resolution. The two methods were applied to downscale simulated 990-m ASTER TIR data to 90-m resolution. When validated against the original 90-m ASTER TIR data, the results revealed that both downscaling methods were successful in capturing the general patterns of the original data and resolving considerable spatial details. Further quantitative assessments indicated a strong agreement between the true values and the estimated values by both methods. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)
Open AccessArticle Satellite-based Flood Modeling Using TRMM-based Rainfall Products
Sensors 2007, 7(12), 3416-3427; doi:10.3390/s7123416
Received: 30 November 2007 / Accepted: 18 December 2007 / Published: 20 December 2007
Cited by 25 | PDF Full-text (422 KB) | HTML Full-text | XML Full-text
Abstract
Increasingly available and a virtually uninterrupted supply of satellite-estimatedrainfall data is gradually becoming a cost-effective source of input for flood predictionunder a variety of circumstances. However, most real-time and quasi-global satelliterainfall products are currently available at spatial scales ranging from 0.25o [...] Read more.
Increasingly available and a virtually uninterrupted supply of satellite-estimatedrainfall data is gradually becoming a cost-effective source of input for flood predictionunder a variety of circumstances. However, most real-time and quasi-global satelliterainfall products are currently available at spatial scales ranging from 0.25o to 0.50o andhence, are considered somewhat coarse for dynamic hydrologic modeling of basin-scaleflood events. This study assesses the question: what are the hydrologic implications ofuncertainty of satellite rainfall data at the coarse scale? We investigated this question onthe 970 km2 Upper Cumberland river basin of Kentucky. The satellite rainfall productassessed was NASA’s Tropical Rainfall Measuring Mission (TRMM) Multi-satellitePrecipitation Analysis (TMPA) product called 3B41RT that is available in pseudo real timewith a latency of 6-10 hours. We observed that bias adjustment of satellite rainfall data canimprove application in flood prediction to some extent with the trade-off of more falsealarms in peak flow. However, a more rational and regime-based adjustment procedureneeds to be identified before the use of satellite data can be institutionalized among floodmodelers. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)
Open AccessArticle Global Distribution and Density of Constructed Impervious Surfaces
Sensors 2007, 7(9), 1962-1979; doi:10.3390/s7091962
Received: 30 August 2007 / Accepted: 13 September 2007 / Published: 21 September 2007
Cited by 129 | PDF Full-text (4904 KB) | HTML Full-text | XML Full-text
Abstract
We present the first global inventory of the spatial distribution and density ofconstructed impervious surface area (ISA). Examples of ISA include roads, parking lots,buildings, driveways, sidewalks and other manmade surfaces. While high spatialresolution is required to observe these features, the new product [...] Read more.
We present the first global inventory of the spatial distribution and density ofconstructed impervious surface area (ISA). Examples of ISA include roads, parking lots,buildings, driveways, sidewalks and other manmade surfaces. While high spatialresolution is required to observe these features, the new product reports the estimateddensity of ISA on a one-km2 grid based on two coarse resolution indicators of ISA – thebrightness of satellite observed nighttime lights and population count. The model wascalibrated using 30-meter resolution ISA of the USA from the U.S. Geological Survey.Nominally the product is for the years 2000-01 since both the nighttime lights andreference data are from those two years. We found that 1.05% of the United States landarea is impervious surface (83,337 km2) and 0.43 % of the world’s land surface (579,703km2) is constructed impervious surface. China has more ISA than any other country(87,182 km2), but has only 67 m2 of ISA per person, compared to 297 m2 per person in theUSA. The distribution of ISA in the world’s primary drainage basins indicates that watersheds damaged by ISA are primarily concentrated in the USA, Europe, Japan, China and India. The authors believe the next step for improving the product is to include reference ISA data from many more areas around the world. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)
Open AccessArticle Airborne Laser Scanning of Forest Stem Volume in a Mountainous Environment
Sensors 2007, 7(8), 1559-1577; doi:10.3390/s7081559
Received: 20 July 2007 / Accepted: 14 August 2007 / Published: 17 August 2007
Cited by 57 | PDF Full-text (1816 KB) | HTML Full-text | XML Full-text
Abstract
Airborne laser scanning (ALS) is an active remote sensing technique that uses the time-of-flight measurement principle to capture the three-dimensional structure of the earth’s surface with pulsed lasers that transmit nanosecond-long laser pulses with a high pulse repetition frequency. Over forested areas [...] Read more.
Airborne laser scanning (ALS) is an active remote sensing technique that uses the time-of-flight measurement principle to capture the three-dimensional structure of the earth’s surface with pulsed lasers that transmit nanosecond-long laser pulses with a high pulse repetition frequency. Over forested areas most of the laser pulses are reflected by the leaves and branches of the trees, but a certain fraction of the laser pulses reaches the forest floor through small gaps in the canopy. Thus it is possible to reconstruct both the three-dimensional structure of the forest canopy and the terrain surface. For the retrieval of quantitative forest parameters such as stem volume or biomass it is necessary to use models that combine ALS with inventory data. One approach is to use multiplicative regression models that are trained with local inventory data. This method has been widely applied over boreal forest regions, but so far little experience exists with applying this method for mapping alpine forest. In this study the transferability of this approach to a 128 km2 large mountainous region in Vorarlberg, Austria, was evaluated. For the calibration of the model, inventory data as operationally collected by Austrian foresters were used. Despite these inventory data are based on variable sample plot sizes, they could be used for mapping stem volume for the entire alpine study area. The coefficient of determination R2 was 0.85 and the root mean square error (RMSE) 90.9 m3ha-1 (relative error of 21.4%) which is comparable to results of ALS studies conducted over topographically less complex environments. Due to the increasing availability, ALS data could become an operational part of Austrian’s forest inventories. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)

Journal Contact

MDPI AG
Sensors Editorial Office
St. Alban-Anlage 66, 4052 Basel, Switzerland
sensors@mdpi.com
Tel. +41 61 683 77 34
Fax: +41 61 302 89 18
Editorial Board
Contact Details Submit to Sensors
Back to Top