Showing papers in "Journal of remote sensing in 2013"

Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data

Abstract: We have produced the first 30 m resolution global land-cover maps using Landsat Thematic Mapper TM and Enhanced Thematic Mapper Plus ETM+ data. We have classified over 6600 scenes of Landsat TM data after 2006, and over 2300 scenes of Landsat TM and ETM+ data before 2006, all selected from the green season. These images cover most of the world's land surface except Antarctica and Greenland. Most of these images came from the United States Geological Survey in level L1T orthorectified. Four classifiers that were freely available were employed, including the conventional maximum likelihood classifier MLC, J4.8 decision tree classifier, Random Forest RF classifier and support vector machine SVM classifier. A total of 91,433 training samples were collected by traversing each scene and finding the most representative and homogeneous samples. A total of 38,664 test samples were collected at preset, fixed locations based on a globally systematic unaligned sampling strategy. Two software tools, Global Analyst and Global Mapper developed by extending the functionality of Google Earth, were used in developing the training and test sample databases by referencing the Moderate Resolution Imaging Spectroradiometer enhanced vegetation index MODIS EVI time series for 2010 and high resolution images from Google Earth. A unique land-cover classification system was developed that can be crosswalked to the existing United Nations Food and Agriculture Organization FAO land-cover classification system as well as the International Geosphere-Biosphere Programme IGBP system. Using the four classification algorithms, we obtained the initial set of global land-cover maps. The SVM produced the highest overall classification accuracy OCA of 64.9% assessed with our test samples, with RF 59.8%, J4.8 57.9%, and MLC 53.9% ranked from the second to the fourth. We also estimated the OCAs using a subset of our test samples 8629 each of which represented a homogeneous area greater than 500 m × 500 m. Using this subset, we found the OCA for the SVM to be 71.5%. As a consistent source for estimating the coverage of global land-cover types in the world, estimation from the test samples shows that only 6.90% of the world is planted for agricultural production. The total area of cropland is 11.51% if unplanted croplands are included. The forests, grasslands, and shrublands cover 28.35%, 13.37%, and 11.49% of the world, respectively. The impervious surface covers only 0.66% of the world. Inland waterbodies, barren lands, and snow and ice cover 3.56%, 16.51%, and 12.81% of the world, respectively.

Remote sensing of rice crop areas

Abstract: Rice means life for millions of people and it is planted in many regions of the world. It primarily grows in the major river deltas of Asia and Southeast Asia, such as the Mekong Delta, known as the Rice Bowl of Vietnam, the second-largest rice-producing nation on Earth. However, Latin America, the USA, and Australia have extensive rice-growing regions. In addition, rice is the most rapidly growing source of food in Africa. Rice is therefore of significant importance to food security in an increasing number of low-income food-deficit countries. This review article gives a complementary overview of how remote sensing can support the assessment of paddy rice cultivation worldwide. This article presents and discusses methods for rice mapping and monitoring, differentiating between the results achievable using different sensors of various spectral characteristics and spatial resolution. The remote sensing of rice-growing areas can not only contribute to the precise mapping of rice areas and the assessment of the dynamics in rice-growing regions, but can also contribute to harvest prediction modelling, the analyses of plant diseases, the assessment of rice-based greenhouse gas methane emission due to vegetation submersion, the investigation of erosion-control-adapted agricultural systems, and the assessment of ecosystem services in rice-growing areas.

Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSA

Abstract: Landslide detection from extensive remote-sensing imagery is an important preliminary work for landslide mapping, landslide inventories, and landslide hazard assessment. Aimed at development of an automatic procedure for landslide detection, a new method for automatic landslide detection from remote-sensing imagery is presented in this study. We achieved this objective using a scene classification method based on the bag-of-visual-words BoVW representation in combination with the unsupervised probabilistic latent semantic analysis pLSA model and the k -nearest neighbour k -NN classifier. Given a remote-sensing image, we divided it into equal-sized square sub-images and then described each sub-image as a BoVW representation. The pLSA model was applied to sub-images by using the BoVW representation to discover the object classes depicted in the sub-images, and then a k -NN classifier was used to classify the sub-images into landslide areas and non-landslide areas based on object distribution. We investigated the performance and applicability of the method using remote-sensing imagery from the Ili area. The results show that the method is robust and can produce good performance without the acquisition of three-dimensional 3D topography. We anticipate that these results will be helpful in landslide inventory mapping and landslide hazard assessment in landslide-stricken areas.

Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data

Abstract: Nitrogen N is one of the most important limiting nutrients for sugarcane production. Conventionally, sugarcane N concentration is examined using direct methods such as collecting leaf samples from the field followed by analytical assays in the laboratory. These methods do not offer real-time, quick, and non-destructive strategies for estimating sugarcane N concentration. Methods that take advantage of remote sensing, particularly hyperspectral data, can present reliable techniques for predicting sugarcane leaf N concentration. Hyperspectral data are extremely large and of high dimensionality. Many hyperspectral features are redundant due to the strong correlation between wavebands that are adjacent. Hence, the analysis of hyperspectral data is complex and needs to be simplified by selecting the most relevant spectral features. The aim of this study was to explore the potential of a random forest RF regression algorithm for selecting spectral features in hyperspectral data necessary for predicting sugarcane leaf N concentration. To achieve this, two Hyperion images were captured from fields of 6–7 month-old sugarcane, variety N19. The machine-learning RF algorithm was used as a feature-selection and regression method to analyse the spectral data. Stepwise multiple linear SML regression was also examined to predict the concentration of sugarcane leaf N after the reduction of the redundancy in hyperspectral data. The results showed that sugarcane leaf N concentration can be predicted using both non-linear RF regression coefficient of determination, R 2 = 0.67; root mean square error of validation RMSEV = 0.15%; 8.44% of the mean and SML regression models R 2 = 0.71; RMSEV = 0.19%; 10.39% of the mean derived from the first-order derivative of reflectance. It was concluded that the RF regression algorithm has potential for predicting sugarcane leaf N concentration using hyperspectral data.

Height and biomass of mangroves in Africa from ICESat/GLAS and SRTM

Abstract: The accurate quantification of the three-dimensional 3-D structure of mangrove forests is of great importance, particularly in Africa where deforestation rates are high and the lack of background data is a major problem. The objectives of this study are to estimate 1 the total area, 2 canopy height distributions, and 3 above-ground biomass AGB of mangrove forests in Africa. To derive the 3-D structure and biomass maps of mangroves, we used a combination of mangrove maps derived from Landsat Enhanced Thematic Mapper Plus ETM+, lidar canopy height estimates from ICESat/GLAS Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System, and elevation data from SRTM Shuttle Radar Topography Mission for the African continent. The lidar measurements from the large footprint GLAS sensor were used to derive local estimates of canopy height and calibrate the interferometric synthetic aperture radar InSAR data from SRTM. We then applied allometric equations relating canopy height to biomass in order to estimate AGB from the canopy height product. The total mangrove area of Africa was estimated to be 25,960 km2 with 83% accuracy. The largest mangrove areas and the greatest total biomass were found in Nigeria covering 8573 km2 with 132 × 106 Mg AGB. Canopy height across Africa was estimated with an overall root mean square error of 3.55 m. This error includes the impact of using sensors with different resolutions and geolocation error. This study provides the first systematic estimates of mangrove area, height, and biomass in Africa.

Comparison of PERSIANN and V7 TRMM Multi-satellite Precipitation Analysis TMPA products with rain gauge data over Iran

Abstract: The objective of this research is to evaluate daily rain rates derived from three widely used high-resolution satellite precipitation products PERSIANN, TMPA-3B42V7, and TMPA-3B42RT using rain gauge observations over the entire country of Iran. Evaluations are implemented for 47 comprehensive daily rainfall events during the winter and spring seasons from 2003 to 2006. These events are selected because each encompasses more than 50% of the country’s area. In this study, daily rainfall observations derived from 1180 rain gauges distributed throughout the country are employed as reference surface data. Six statistical indices: bias, multiplicative bias MBias, relative bias RBias, mean absolute error MAE, root mean square error RMSE, and linear correlation coefficient CC, as well as a contingency table are applied to evaluate the satellite rainfall estimates qualitatively. The spatially averaged results over the entire country indicate that 3B42V7, with an average bias value of –1.47 mmd−1, RBias of –13.6%, MAE of 4.5 mmd−1, RMSE of 6.5 mmd−1, and CC of 0.61, leads to better estimates of daily precipitation than those of PERSIANN and 3B42RT. Furthermore, PERSIANN with an average MBias value of 0.56 tends to underestimate precipitation, while 3B42V7 and 3B42RT with average MBias values of 0.86 and 1.02, respectively, demonstrate a reasonable agreement in regard to rainfall estimations with the rain gauge data. With respect to the categorical verification technique implemented in this study, PERSIANN exhibits better results associated with the probability of detection of rainfall events; however, its false alarm ratio is worse than that of 3B42V7 and 3B42RT.

Intercalibration of DMSP-OLS night-time light data by the invariant region method

Abstract: DMSP-OLS Defense Meteorological Satellite Program Operational Linescan System night-time light data can accurately reflect the scope and intensity of human activities. However, the raw data cannot be used directly for temporal analyses due to the lack of inflight calibration. There are three problems that should be addressed in intercalibration. First, because of differences between sensors, the data are not identical even when obtained in the same year. Second, different acquisition times may lead to random or systematic fluctuations in the data obtained by satellites in different orbits. Third, a pixel saturation phenomenon also exists in the urban centres of the image. Therefore, an invariant region method was used in this article, and the relative radiometric calibration and saturation correction achieved the desired results. In the meantime, intercalibration models for each satellite year of DMSP-OLS night-time light data were produced. Finally, intercalibration accuracy was evaluated, and the intercalibration results were tested with the corresponding gross domestic product GDP data.

Barriers to adopting satellite remote sensing for water quality management

Abstract: Sustainable practices require a long-term commitment to creating solutions to environmental, social, and economic issues. The most direct way to ensure that management practices achieve sustainability is to monitor the environment. Remote sensing technology has the potential to accelerate the engagement of communities and managers in the implementation and performance of best management practices. Over the last few decades, satellite technology has allowed measurements on a global scale over long time periods, and is now proving useful in coastal waters, estuaries, lakes, and reservoirs, which are relevant to water quality managers. Comprehensive water quality climate data records have the potential to provide rapid water quality assessments, thus providing new and enhanced decision analysis methodologies and improved temporal/spatial diagnostics. To best realize the full application potential of these emerging technologies an open and effective dialogue is needed between scientists, policy makers, environmental managers, and stakeholders at the federal, state, and local levels. Results from an internal US Environmental Protection Agency qualitative survey were used to determine perceptions regarding the use of satellite remote sensing for monitoring water quality. The goal of the survey was to begin understanding why management decisions do not typically rely on satellite-derived water quality products.

Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: a segmentation-based approach

Abstract: FROM-GLC Fine Resolution Observation and Monitoring of Global Land Cover is the first 30 m resolution global land-cover map produced using Landsat Thematic Mapper TM and Enhanced Thematic Mapper Plus ETM+ data. Due to the lack of temporal features as inputs in producing FROM-GLC, considerable confusion exists among land-cover types e.g. agriculture lands, grasslands, shrublands, and bareland. The Moderate Resolution Imaging Spectrometer MODIS provides high-temporal frequency information on surface cover. Other auxiliary bioclimatic, digital elevation model DEM, and world maps on soil-water conditions are possible sources for improving the accuracy of FROM-GLC. In this article, a segmentation-based approach was applied to Landsat imagery to down-scale coarser-resolution MODIS data 250 m and other 1 km resolution auxiliary data to the segment scale based on TM data. Two classifiers support vector machine SVM and random forest RF and two different strategies for use of training samples global and regional samples based on a spatial temporal selection criterion were performed. Results show that RF based on the global use of training samples achieves an overall classification accuracy of 67.08% when assessed by test samples collected independently. This is better than the 64.89% achieved by FROM-GLC based on the same set of test samples. Accuracies for vegetation cover types are most substantially improved.

Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests

Abstract: Using high-spatial-resolution multispectral imagery alone is insufficient for achieving highly accurate and reliable thematic mapping of urban areas. Integration of lidar-derived elevation information into image classification can considerably improve classification results. Additionally, traditional pixel-based classifiers have some limitations in regard to certain landscape and data types. In this study, we take advantage of current advances in object-based image analysis and machine learning algorithms to reduce manual image interpretation and automate feature selection in a classification process. A sequence of image segmentation, feature selection, and object classification is developed and tested by the data sets in two study areas Mannheim, Germany and Niagara Falls, Canada. First, to improve the quality of segmentation, a range image of lidar data is incorporated in an image segmentation process. Among features derived from lidar data and aerial imagery, the random forest, a robust ensemble classifier, is then used to identify the best features using iterative feature elimination. On the condition that the number of samples is at least two or three times the number of features, a segmentation scale factor has no particular effect on the selected features or classification accuracies. The results of the two study areas demonstrate that the presented object-based classification method, compared with the pixel-based classification, improves by 0.02 and 0.05 in kappa statistics, and by 3.9% and 4.5% in overall accuracy, respectively.

Estimation of nitrogen, phosphorus, and potassium contents in the leaves of different plants using laboratory-based visible and near-infrared reflectance spectroscopy: comparison of partial least-square regression and support vector machine regression methods

Abstract: Nitrogen, phosphorus, and potassium are some of the most important biochemical components of plant organic matter, and hence, estimation of their contents can help monitor the metabolism processes and health of plants. This study, conducted in the Yixing region of China, aimed to compare partial least squares regression PLSR and support vector machine regression SVMR methods for estimating the nitrogen C N, phosphorus C P, and potassium C K contents present in leaves of diverse plants using laboratory-based visible and near-infrared Vis-NIR reflectance spectroscopy. A total of 95 leaf samples taken from rice, corn, sesame, soybean, tea, grass, shrub, and arbour plants were collected, and their C N, C P, C K, and Vis-NIR reflectance data were measured in a laboratory. The PLSR and SVMR methods were calibrated to estimate the C N, C P, and C K contents of the obtained samples from spectral reflectance. Cross-validation with an independent data set was employed to assess the performance of the calibrated models. The calibration results indicated that the PLSR method accounted for 59.1%, 50.9%, and 50.6% of the variation of C N, C P, and C K, whereas the SVMR method accounted for more than 90% of the variation of C N, C P, and C K. According to cross-validation, the SVMR method achieved better estimation accuracies, which had determination coefficients of 0.706, 0.722, and 0.704 for C N, C P, and C K, respectively, than the PLSR method, which had determination coefficients of 0.663, 0.643, and 0.541. It was concluded that the SVMR method combined with laboratory-based Vis-NIR reflectance data has the potential to estimate the contents of biochemical components.

Multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using an object-based support vector machine and a rule-based approach

Abstract: We have investigated multi-temporal polarimetric synthetic aperture radar SAR data for urban land-cover classification using an object-based support vector machine SVM in combinations of rules. Six-date RADARSAT-2 high-resolution polarimetric SAR data in both ascending and descending passes were acquired in the rural–urban fringe of the Greater Toronto Area during the summer of 2008. The major land-use/land-cover classes include high-density residential areas, low-density residential areas, industrial and commercial areas, construction sites, parks, golf courses, forests, pasture, water, and two types of agricultural crops. Various polarimetric SAR parameters were evaluated for urban land-cover mapping and they include the parameters from Pauli, Freeman and Cloude–Pottier decompositions, the coherency matrix, intensities of each polarization, and their logarithm forms. The multi-temporal SAR polarimetric features were classified first using an SVM classifier. Then specific rules were developed to improve the SVM classification results by extracting major roads and streets using shape features and contextual information. For the comparison of the polarimetric SAR parameters, the best classification performance was achieved using the compressed logarithmic filtered Pauli parameters. For the evaluation of the multi-temporal SAR data set, the best classification result was achieved using all six-date data kappa = 0.91, while very good classification results kappa = 0.86 were achieved using only three-date polarimetric SAR data. The results indicate that the combination of both the ascending and the descending polarimetric SAR data with an appropriate temporal span is suitable for urban land-cover mapping.

GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments

Abstract: The latest breed of very high resolution VHR commercial satellites opens new possibilities for cartographic and remote-sensing applications. In fact, one of the most common applications of remote-sensing images is the extraction of land-cover information for digital image base maps by means of classification techniques. The aim of the study was to compare the potential classification accuracy provided by pan-sharpened orthoimages from both GeoEye-1 and WorldView-2 WV2 VHR satellites over urban environments. The influence on the supervised classification accuracy was evaluated by means of an object-based statistical analysis regarding three main factors: i sensor used; ii sets of image object IO features used for classification considering spectral, geometry, texture, and elevation features; and iii size of training samples to feed the classifier nearest neighbour NN. The new spectral bands of WV2 Coastal, Yellow, Red Edge, and Near Infrared-2 did not improve the benchmark established from GeoEye-1. The best overall accuracy for GeoEye-1 close to 89% was attained by using together spectral and elevation features, whereas the highest overall accuracy for WV2 83% was achieved by adding textural features to the previous ones. In the case of buildings classification, the normalized digital surface model computed from light detection and ranging data was the most valuable feature, achieving producer's and user's accuracies close to 95% and 91% for GeoEye-1 and VW2, respectively. Last but not least and regarding the size of the training samples, the rule of ‘the larger the better' was true but, based on statistical analysis, the ideal choice would be variable depending on both each satellite and target class. In short, 20 training IOs per class would be enough if the NN classifier was applied on pan-sharpened orthoimages from both GeoEye-1 and WV2.

Detection of rural electrification in Africa using DMSP-OLS night lights imagery

Abstract: We report on the first systematic ground-based validation of the US Air Force Defense Meteorological Satellite Program’s Operational Linescan System DMSP-OLS night lights imagery to detect rural electrification in the developing world. Drawing upon a unique survey of villages in Senegal and Mali, this study compares night-time light output from the DMSP-OLS against ground-based survey data on electricity use in 232 electrified villages and additional administrative data on 899 unelectrified villages. The analysis reveals that electrified villages are consistently brighter than unelectrified villages across annual composites, monthly composites, and a time series of nightly imagery. Electrified villages appear brighter because of the presence of streetlights, and brighter villages tend to have more streetlights. By contrast, the correlation of light output with household electricity use and access is low. We further demonstrate that a detection algorithm using data on night-time light output and the geographic location of settlements can accurately classify electrified villages. This research highlights the potential to use night lights imagery for the planning and monitoring of ongoing efforts to connect the 1.4 billion people who lack electricity around the world.

Land-use/land-cover change and its influence on surface temperature: a case study in Beijing City

Abstract: Rapid global economic development has resulted in a corresponding intensification of urbanization, which has in turn impacted the ecology of vast regions of the world. A series of problems have thus been introduced, such as changes in land-use/land-cover LULC and changes in local climate. The process of urbanization predominantly represents changes in land-use, and is deemed by researchers to be the chief cause of climate change and ecological change. One of the principal purposes of the research in this field is to find ways to mitigate the influence of land-use change on local or global environments. In the study presented in this article, satellite images were utilized to extract information regarding land-use in Beijing City, and to develop maps of land surface temperature LST during two different periods of time: 2 August 1999 and 8 August 2010. A supervised classification scheme, a support vector machine, was used to derive the land-use change map for the above periods. Maps of surface temperature are derived from the thermal band of Landsat images using the mono-window algorithm. Results from post-classification comparison indicated that an increase in impervious surface areas was found to be dramatic, while the area of farmland decreased rapidly. The changes in LULC were found to have led to a variation in surface temperature, as well as a spatial distribution pattern of the urban heat island phenomenon. This research revealed that the hotspots were mainly located in areas dominated by three kinds of material: bare soil, rooftops, and marble surfaces. Results from the local Moran's I index indicated that the use of lower surface temperature materials will help to mitigate the influence of the urban heat island phenomenon. The results of this research study provide a reference for government departments involved in the process of designing residential regions. Such a reference should enable the development of areas sympathetic to environmental changes and hence mitigate the effects of the growing intensity of urbanization.

Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression

Abstract: Hyperspectral remote sensing enables the large-scale mapping of canopy biochemical properties. This study explored the possibility of retrieving the concentration of nitrogen, phosphorus, potassium, calcium, magnesium, and sodium from mangroves in the Berau Delta, Indonesia. The objectives of the study were to 1 assess the accuracy of foliar chemistry retrieval, 2 compare the performance of models based on support vector regression SVR, i.e. ϵ-SVR, ν-SVR, and least squares SVR LS-SVR, to models based on partial least squares regression PLSR, and 3 investigate which spectral transformations are best suited. The results indicated that nitrogen could be successfully modelled at the landscape level R ² = 0.67, root mean square error RMSE = 0.17, normalized RMSE nRMSE = 15%, whereas estimations of P, K, Ca, Mg, and Na were less encouraging. The developed nitrogen model was applied over the study area to generate a map of foliar N variation, which can be used for studying ecosystem processes in mangroves. While PLSR attained good results directly using all untransformed bands, the highest accuracy for nitrogen modelling was achieved using a combination of LS-SVR and continuum-removed derivative reflectance. All SVR techniques suffered from multicollinearity when using the full spectrum, and the number of independent variables had to be reduced by singling out the most informative wavelength bands. This was achieved by interpreting and visualizing the structure of the PLSR and SVR models.

A hybrid pansharpening approach and multiscale object-based image analysis for mapping diseased pine and oak trees

Abstract: We developed a multiscale object-based classification method for detecting diseased trees Japanese Oak Wilt and Japanese Pine Wilt in high-resolution multispectral satellite imagery. The proposed method involved 1 a hybrid intensity–hue–saturation smoothing filter-based intensity modulation IHS-SFIM pansharpening approach to obtain more spatially and spectrally accurate image segments; 2 synthetically oversampling the training data of the ‘Diseased tree’ class using the Synthetic Minority Over-sampling Technique SMOTE; and 3 using a multiscale object-based image classification approach. Using the proposed method, we were able to map diseased trees in the study area with a user's accuracy of 96.6% and a producer's accuracy of 92.5%. For comparison, the diseased trees were mapped at a user's accuracy of 84.0% and a producer's accuracy of 70.1% when IHS pansharpening was used alone and a single-scale classification approach was implemented without oversampling the ‘Diseased tree’ class.

Airborne photogrammetry and lidar for DSM extraction and 3D change detection over an urban area – a comparative study

Abstract: A digital surface model DSM extracted from stereoscopic aerial images, acquired in March 2000, is compared with a DSM derived from airborne light detection and ranging lidar data collected in July 2009. Three densely built-up study areas in the city centre of Ghent, Belgium, are selected, each covering approximately 0.4 km2. The surface models, generated from the two different 3D acquisition methods, are compared qualitatively and quantitatively as to what extent they are suitable in modelling an urban environment, in particular for the 3D reconstruction of buildings. Then the data sets, which are acquired at two different epochs t 1 and t 2, are investigated as to what extent 3D building changes can be detected and modelled over the time interval. A difference model, generated by pixel-wise subtracting of both DSMs, indicates changes in elevation. Filters are proposed to differentiate ‘real’ building changes from false alarms provoked by model noise, outliers, vegetation, etc. A final 3D building change model maps all destructed and newly constructed buildings within the time interval t 2 – t 1. Based on the change model, the surface and volume of the building changes can be quantified.

Modelling the diurnal variations of urban heat islands with multi-source satellite data

Abstract: Examination of the diurnal variations in surface urban heat islands UHIs has been hindered by incompatible spatial and temporal resolutions of satellite data. In this study, a diurnal temperature cycle genetic algorithm DTC-GA approach was used to generate the hourly 1 km land-surface temperature LST by integrating multi-source satellite data. Diurnal variations of the UHI in ‘ideal’ weather conditions in the city of Beijing were examined. Results show that the DTC-GA approach was applicable for generating the hourly 1 km LSTs. In the summer diurnal cycle, the city experienced a weak UHI effect in the early morning and a significant UHI effect from morning to night. In the diurnal cycles of the other seasons, the city showed transitions between a significant UHI effect and weak UHI or urban heat sink effects. In all diurnal cycles, daytime UHIs varied significantly but night-time UHIs were stable. Heating/cooling rates, surface energy balance, and local land use and land cover contributed to the diurnal variations in UHI. Partial analysis shows that diurnal temperature range had the most significant influence on UHI, while strong negative correlations were found between UHI signature and urban and rural differences in the normalized difference vegetation index, albedo, and normalized difference water index. Different contributions of surface characteristics suggest that various strategies should be used to mitigate the UHI effect in different seasons.

Changes in the glaciers of Chandra–Bhaga basin, Himachal Himalaya, India, between 1980 and 2010 measured using remote sensing

Abstract: This study reports the glacier changes of Chandra–Bhaga basin, northwest Himalaya, India, from 1980 to 2010. Satellite remote-sensing data from the Landsat Multispectral Scanner MSS and Thematic Mapper TM, the Linear Imaging Self Scanning Sensor LISS and Advanced Wide Field Sensor AWiFS of the Indian Remote Sensing IRS series, and the Shuttle Radar Topography Mission SRTM digital elevation model DEM were used to study the changes in glacier parameters such as glacier area, length, snout elevation, and the impact of glacier topographical parameters glacier slope, aspect, and altitude range on the glacier changes. It was found that the total glaciated area had shrunk to 368.2 km2 in 2010 from 377.6 km2 in 1980, a loss of 2.5%. The average position of glacier terminuses retreated by 465.5 ± 169.1 m from 1980 to 2010 with an average rate of 15.5 ± 5.6 m year−1. The decadal scale analysis showed that the average rate of retreat had increased the most in the recent decade. A moraine-dammed lake located in the study region was found to have expanded in area from 0.65 ± 0.01 km2 in 1980 to 1.26 ± 0.03 km2 in 2010. Glaciers with steep slope and less altitude range have lost more area than the glaciers having gentle slope and greater altitude range.

Evaluation of ASTER GDEM using GPS benchmarks and SRTM in China

Abstract: The Advanced Spaceborne Thermal Emission and Reflection Radiometer ASTER Global Digital Elevation Model GDEM has generated one of the most complete high-resolution digital topographic data sets of the world to date. The ASTER GDEM covers land surfaces between 83° N and 83° S at a spatial resolution of 1 arc-second approximately 30 m at the equator. As an improvement over Shuttle Radar Topography Mission SRTM coverage, the ASTER GDEM will be a very useful product for many applications, such as relief analysis, hydrological studies, and radar interferometry. In this article, its absolute vertical accuracy in China was assessed at five study sites using ground control points GCPs from high-accuracy GPS benchmarks and also using a DEM-to-DEM comparison with the Consultative Group on International Agriculture Research Consortium for Spatial Information CGIAR-CSI SRTM DEM Version 4.1. It is demonstrated that the vertical accuracy of ASTER GDEM is 26 m root mean square error RMSE against GPS-GCPs, while for the SRTM DEM it is 23 m. Furthermore, height differences in the GDEM-SRTM comparison appear to be overestimated in the areas with a south or southwest aspect in the five study areas. To a certain extent, the error can be attributed to variations in heights due to land-cover effects and undefined inland waterbodies. But the ASTER GDEM needs further error-mitigating improvements to meet the expected accuracy specification. However, as for its unprecedented detail, it is believed that the ASTER GDEM offers a major alternative in accessibility to high-quality elevation data.

VSDI: a visible and shortwave infrared drought index for monitoring soil and vegetation moisture based on optical remote sensing

Abstract: In this article, a new index, the visible and shortwave infrared drought index VSDI, is proposed for monitoring both soil and vegetation moisture using optical spectral bands. VSDI is defined as , where ρ represents the reflectance of shortwave infrared SWIR red and blue channels, respectively. VSDI is theoretically based on the difference between moisture-sensitive bands SWIR and red and moisture reference band blue, and is expected to be efficient for agricultural drought monitoring over different land-cover types during the plant-growing season. The fractional water index FWI derived from 49 Mesonet stations over nine climate divisions CDs across Oklahoma are used as ground truth data and VSDI is compared with three other drought indices. The results show that VSDI generally presents the highest correlation with FWI among the four indices, either for whole sites or for individual CDs. The NDVI threshold method is applied to demonstrate the satisfactory performance of VSDI over different land-cover types. A time-lag analysis is also conducted and suggests that VSDI can be used as a real-time drought indicator with a time lag of less than 8 days. The VSDI drought maps are produced and compared with the US Drought Monitor USDM maps. A good agreement has been observed between the two products, and finer spatial information is also found in VSDI. In conclusion, VSDI appears to be a real-time drought indicator that is applicable over different land-cover types and is suitable for drought monitoring through the plant-growing season.

Automated cloud and shadow detection and filling using two-date Landsat imagery in the USA

Abstract: A simple, efficient, and practical approach for detecting cloud and shadow areas in satellite imagery and restoring them with clean pixel values has been developed. Cloud and shadow areas are detected using spectral information from the blue, shortwave infrared, and thermal infrared bands of Landsat Thematic Mapper or Enhanced Thematic Mapper Plus imagery from two dates a target image and a reference image. These detected cloud and shadow areas are further refined using an integration process and a false shadow removal process according to the geometric relationship between cloud and shadow. Cloud and shadow filling is based on the concept of the Spectral Similarity Group SSG, which uses the reference image to find similar alternative pixels in the target image to serve as replacement values for restored areas. Pixels are considered to belong to one SSG if the pixel values from Landsat bands 3, 4, and 5 in the reference image are within the same spectral ranges. This new approach was applied to five Landsat path/rows across different landscapes and seasons with various types of cloud patterns. Results show that almost all of the clouds were captured with minimal commission errors, and shadows were detected reasonably well. Among five test scenes, the lowest producer's accuracy of cloud detection was 93.9% and the lowest user's accuracy was 89%. The overall cloud and shadow detection accuracy ranged from 83.6% to 99.3%. The pixel-filling approach resulted in a new cloud-free image that appears seamless and spatially continuous despite differences in phenology between the target and reference images. Our methods offer a straightforward and robust approach for preparing images for the new 2011 National Land Cover Database production.

InSAR time-series analysis of land subsidence in Bangkok, Thailand

Abstract: Land subsidence poses a serious risk to the low-lying coastal city of Bangkok, Thailand; major flooding occurred there in 1983 and again in 2011. Extreme water pumping in the past led to subsidence rates of up to 120 mm year−1. Although water extraction is now controlled, maximum rates measured by levelling today are still up to 20 mm year−1. In this study, we apply interferometric synthetic aperture radar InSAR time-series analysis to study subsidence in Bangkok between October 2005 and March 2010. We validate the InSAR results, by comparing levelling rates and find good agreement between the two techniques. We detect approximately 300,000 coherent pixels overall, with an average density of 120 observations per km2. This is two orders of magnitude greater than the density of levelling benchmarks and reveals subsiding areas that are missed by the levelling network.

Mapping inland lake water quality across the Lower Peninsula of Michigan using Landsat TM imagery

Abstract: The number, size, and distribution of inland freshwater lakes present a challenge for traditional water-quality assessment due to the time, cost, and logistical constraints of field sampling and laboratory analyses. To overcome this challenge, Landsat imagery has been used as an effective tool to assess basic water-quality indicators, such as Secchi depth SD, over a large region or to map more advanced lake attributes, such as cyanobacteria, for a single waterbody. The overarching objective of this research application was to evaluate Landsat Thematic Mapper TM for mapping nine water-quality metrics over a large region and to identify hot spots of potential risk. The second objective was to evaluate the addition of landscape pattern metrics to test potential improvements in mapping lake attributes and to understand drivers of lake water quality in this region. Field-level in situ water-quality measurements were collected across diverse lakes n = 42 within the Lower Peninsula of Michigan. A multicriteria statistical approach was executed to map lake water quality that considered variable importance, model complexity, and uncertainty. Overall, band ratio radiance models performed well R2 = 0.65–0.81 for mapping SD, chlorophyll-a, green biovolume, total phosphorus TP, and total nitrogen TN with weaker R2 = 0.37 ability to map total suspended solids TSS and cyanobacteria levels. In this application, Landsat TM and pattern metrics showed poor ability to accurately map non-purgable organic carbon NPOC and diatom biovolume, likely due to a combination of gaps in temporal overpass and field sampling and lack of signal sensitivity within broad spectral channels of Landsat TM. The composition and configuration of croplands, urban, and wetland patches across the landscape were found to be moderate predictors of lake water quality that can complement lake remote-sensing data. Of the 4071 lakes, over 4 ha in the Lower Peninsula, approximately two-thirds, were identified as mesotrophic n = 2715. This application highlights how an operational tool might support lake decision-making or assessment protocols to identify hot spots of potential risk.

Use of UK-DMC 2 and ALOS PALSAR for studying the age of oil palm trees in southern peninsular Malaysia

Abstract: This article demonstrates some techniques for studying the age of oil palm trees Elaeis guineensis Jacq . using the Disaster Monitoring Constellation 2 from the UK UK-DMC 2 and Advanced Land Observing Satellite phased array L-band synthetic aperture radar ALOS PALSAR remote-sensing data at a private oil palm estate in southern peninsular Malaysia. Several techniques were explored with UK-DMC 2 data, namely 1 radiance, vegetation indices, and fraction of shadow; 2 texture measurement; 3 classifications, namely Iterative Self-Organizing Data Analysis Technique ISODATA classification, maximum-likelihood classification MLC, and random forest RF classification; 4 in terms of ALOS PALSAR data, the correlation of polarizations i.e. horizontal transmitting and horizontal receiving termed HH polarization and horizontal transmitting and vertical receiving termed HV polarization and the ratio of these polarizations to the age of oil palm trees. From the results, band 1 near-infrared of UK-DMC 2, fraction of shadow, and mean filter from the grey-level co-occurrence matrix GLCM demonstrated strong correlation of determination R 2 = 0.76–0.80 with the age of oil palm trees, while the ALOS PALSAR HH polarization could correlate moderately strongly R 2 = 0.49 with the age of oil palm trees. Adding fraction of shadow and UK-DMC 2 data using the RF method further improved the overall accuracy of age classification from 45.3% MLC method to 52.9%. This study concluded that texture measurement GLCM mean and fraction of shadow are useful for studying the age of oil palm trees, although discriminating variation in age between mature oil palm trees is difficult because the leaf area index development of mature oil palm trees stabilizes at about 10 years of age. Future studies should involve height information, because this has the potential to be used as one of the most important variables for studying the age of oil palm trees.

Close-range hyperspectral imaging for geological field studies: workflow and methods

Abstract: Close-range hyperspectral imaging is a new method for geological research, in which imaging spectrometry is applied from the ground, allowing the mineralogy and lithology in near-vertical cliff sections to be studied in detail. Contemporary outcrop studies often make use of photorealistic three-dimensional 3D models, derived from terrestrial laser scanning lidar, that facilitate geological interpretation of geometric features. Hyperspectral imaging provides complementary geochemical information that can be combined with lidar models, enhancing quantitative and qualitative analyses. This article describes a complete workflow for applying close-range hyperspectral imaging, from planning the optimal scan conditions and data acquisition, through pre-processing the hyperspectral imagery and spectral mapping, integration with lidar photorealistic 3D models, and analysis of the geological results. Pre-processing of the hyperspectral images involves the reduction of scanner artefacts and image discontinuities, as well as relative reflectance calibration using empirical line correction, based on two calibrated reflection targets. Signal-to-noise ratios better than 70:1 are achieved for materials with 50% reflectance. The lidar-based models are textured with products such as hyperspectral classification maps. Examples from carbonate and siliciclastic geological environments are presented, with results showing that spectrally similar material, such as different dolomite types or sandstone and siltstone, can be distinguished and spectrally mapped. This workflow offers a novel and flexible technique for applications, in which a close-range instrument setup is required and the spatial distribution of minerals or chemical variations is valuable.

Multispectral derivation of bathymetry in Singapore's shallow, turbid waters

Abstract: In very shallow waters, active sensing determinations of bathymetry are often expensive and unwieldy. Sea depth estimation using passive remote-sensing methods is an attractive alternative, especially using cheap multispectral imagery with high spatial resolution. Three models for the determination of bathymetry from multispectral imagery were utilized with new eight-band images from DigitalGlobe's Worldview-2 satellite platform. All three were trained with electronic navigational chart data and evaluated for accuracy in Singapore's turbid shallow coastal waters. These waters are characterized by high turbidity, suspended sediment, and vehicle traffic. Of the three models, a linear band algorithm performed best, with a root-mean-square error RMSE of 0.48 m. A look-up table classification provided a precision of 0.64 m, but was limited by a training set that did not fully represent variance in water column and benthic properties. Possibly owing to the domination of particle backscatter over pigment absorption in these turbid waters, a linear ratio algorithm did not perform as well as the linear band algorithm, achieving an RMSE of only 0.56 m. Analysis found that the usual relationship between ratios of low-absorption to high-absorption bands and depth does not hold as well for these waters, likely due to backscatter dominating leaving-water signals, masking relative absorption effects. High turbidity, with a Secchi disk depth of 1.9 m, limited analysis to shallow reefs and coastline and likely impacted the sensitivity of the bathymetric algorithms. A larger validation data set containing water quality and benthic data is required for further investigation to determine specific sources of error.

Spatial improvement of human population distribution based on multi-sensor remote-sensing data: an input for exposure assessment

Abstract: A spatial mismatch of hazard data and exposure data e.g. population exists in risk analysis. This article provides an integrated approach for a rapid and accurate estimation of population distribution on a per-pixel basis, through the combined use of medium and coarse spatial resolution remote-sensing data, namely the Defense Meteorological Satellite Program Operational Linescan System DMSP/OLS night-time imagery, enhanced vegetation index EVI, and digital elevation model DEM data. The DMSP/OLS night-time light data have been widely used for the estimation of population distribution because of their free availability, global coverage, and high temporal resolution. However, given its low-radiometric resolution as well as the overglow effects, population distribution cannot be estimated accurately. In the present study, the DMSP/OLS data were combined with EVI and DEM data to develop an elevation-adjusted human settlement index EAHSI image. The model for population density estimation, developed based on the significant linear correlation between population and EAHSI, was implemented in Zhejiang Province in southeast China, and a spatialized population density map was generated at a resolution of 250 m × 250 m. Compared with the results from raw human settlement index 59.69% and single night-time lights 35.89%, the mean relative error of estimated population by EAHSI has been greatly reduced 17.74%, mainly due to the incorporation of elevation information. The accurate estimation of population density can be used as an input for exposure assessment in risk analysis on a regional scale and on a per-pixel basis.

Assessment of land surface temperature in relation to landscape metrics and fractional vegetation cover in an urban/peri-urban region using Landsat data

Abstract: Land surface temperature LST is essentially considered to be one of the most important indicators used for assessment of the urban thermal environment. It is quite evident that land-use/land-cover LULC and landscape patterns have ecological implications at varying spatial scales, which in turn influence the distribution of habitat and material/energy fluxes in the landscape. This article attempts to quantitatively analyse the complex interrelationships between urban LST and LULC landscape patterns with the purpose of elucidating their relation to landscape processes. The study employed an integrated approach involving remote-sensing, geographic information system GIS, and landscape ecology techniques on bi-temporal Landsat Thematic Mapper images of Southwestern Sydney metropolitan region and the surrounding fringe, taken at approximately the same time of the year in July 1993 and July 2006. First, the LULC categories and LST were extracted from the bi-temporal images. The LST distribution and changes and LST of the LULC categories were then quantitatively analysed using landscape metrics and LST zones. The results show that large differences in temperature existed in even a single LULC category, except for variations between different LULC categories. In each LST zone, the regressive function of LST with fractional vegetation cover FVC indicated a significant relationship between LST and FVC. Landscape metrics of LULC categories in each zone in relation to the other zones showed changing patterns between 1993 and 2006. This study also illustrates that a method integrating retrieval of LST and FVC from remote-sensing images combined with landscape metrics provides a novel and feasible way to describe the spatial distribution and temporal variation in urban thermal patterns and associated LULC conditions in a quantitative manner.