Modeling spatial and temporal change of soil erosion based on multi-temporal remotely sensed data

In order to monitor the pattern, distribution, and trend of land use/cover change （LUCC） and its impacts on soil erosion, it is highly appropriate to adopt Remote Sensing （RS） data and Geographic Information System （GIS） to analyze, assess, simulate, and predict the spatial and temporal evolution dynamics. In this paper, multi-temporal Landsat TM/ETM＋ re- motely sensed data are used to generate land cover maps by image classification, and the Cellular Automata Markov （CA_Markov） model is employed to simulate the evolution and trend of landscape pattern change. Furthermore, the Re- vised Universal Soil Loss Equation （RUSLE） is used to evaluate the situation of soil erosion in the case study mining area. The trend of soil erosion is analyzed according to total/average amount of soil erosion, and the rainfall （R）, cover man- agement （C）, and support practice （P） factors in RUSLE relevant to soil erosion are determined. The change trends of soil erosion and the relationship between land cover types and soil erosion amount are analyzed. The results demonstrate that the CA_Markov model is suitable to simulate and predict LUCC trends with good efficiency and accuracy, and RUSLE can calculate the total soil erosion effectively. In the study area, there was minimal erosion grade and this is expected to con- tinue to decline in the next few years, according to our prediction results.

Assessing the impact of urbanization on net primary productivity using multi-scale remote sensing data： a case study of Xuzhou, China

An improved Carnegie Ames Stanford Approach （CASA） model based on two kinds of remote sensing （RS） data, Landsat Enhanced Thematic Mapper Plus （ETM ＋） and Moderate Resolution Imaging Spectro- radiometer （MODIS）, and climate variables were applied to estimate the Net Primary Productivity （NPP） of Xuzhou in June of each year from 2001 to 2010. The NPP of the study area decreased as the spatial scale increased. The average NPP of terrestrial vegetation in Xuzhou showed a decreasing trend in recent years, likely due to changes in climate and environment. The study area was divided into four sub-regions, designated as highest, moderately high, moderately low, and lowest in NPR The area designated as the lowest sub-region in NPP increased with expanding scale, indicating that the NPP distribution varied with different spatial scales. The NPP of different vegetation types was also significantly influenced by scale. In particular, the NPP of urban woodland produced lower estimates because of mixed pixels. Similar trends in NPP were observed with different RS data. In addition, expansion of residential areas and reduction of vegetated areas were the major reasons for NPP change. Land cover changes in urban areas reduced NPP, which could chiefly be attributed to human-induced disturbance.

Hyperspectral image classification based on volumetric texture and dimensionality reduction

A novel approach using volumetric texture and reduced-spectral features is presented for hyperspectral image classification. Using this approach, the volumetric textural fea^res were extracted by volumetric gray-level co-occurrence matrices （VGLCM）. The spectral features were extracted by minimum estimated abundance covar- iance （MEAC） and linear prediction （LP）-based band selection, and a semi-supervised k-means （SKM） cluster- ing method with deleting the worst cluster （SKMd） band- clustering algorithms. Moreover, four feature combination schemes were designed for hyperspectral image classifica- tion by using spectral and textural features. It has been proven that the proposed method using VGLCM outper- forms the gray-level co-occurrence matrices （GLCM） method, and the experimental results indicate that the combination of spectral information with volumetric textural features leads to an improved classification performance in hyperspectral imagery.

A labor-free index-guided semantic segmentation approach for urban vegetation mapping from high-resolution true color imagery

Accurate and timely information on urban vegetation(UV)can be used as an important indicator to estimate the health of cities.Due to the low cost of RGB cameras,true color imagery(TCI)has been widely used for high spatial resolution UV mapping.However,the current index-based and classifier-based UV mapping approaches face problems of the poor ability to accurately distinguish UV and the high reliance on massive annotated samples,respectively.To address this issue,an index-guided semantic segmentation(IGSS)framework is proposed in this paper.Firstly,a novel cross-scale vegetation index(CSVI)is calculated by the combination of TCI and Sentinel-2 images,and the index value can be used to provide an initial UV map.Secondly,reliable UV and non-UV samples are automatically generated for training the semantic segmentation model,and then the refined UV map can be produced.The experimental results show that the proposed CSVI outperformed the existingfive RGB vegetation indices in highlighting UV cover and suppressing complex backgrounds,and the proposed IGSS workflow achieved satisfactory results with an OA of 87.72%∼88.16%and an F1 score of 87.73%∼88.37%,which is comparable with the fully-supervised method.

Spatial-temporal variations of natural suitability of human settlement environment in the Three Gorges Reservoir Area A case study in Fengjie County,China

With rapid environmental degeneration and socio-economie development,the human settlement environment (HSE)has experienced dramatic changes and attracted attention from different communities.Consequently,the spatial-temporal evaluation of natural suitability of the human settlement environment (NSHSE)has become essential for understanding the patterns and dynamics of HSE,and for coordinating sustainable development among regional populations,resources,and environments.This study aims to explore the spatialtemporal evolution of NSHSE patterns in 1997,2005,and 2009 in Fengjie County near the Three Gorges Reservoir Area (TGRA).A spatially weighted NSHSE model was established by integrating multi-source data (e.g.,census data,meteorological data,remote sensing images,DEM data,and GIS data)into one framework,where the Ordinary Least Squares (OLS)linear regression model was applied to calculate the weights of indices in the NSHSE model.Results show that the trend of natural suitability has been first downward and then upward, which is evidenced by the disparity of NSHSE existing in the south,north,and central areas of Fengjie County. Results also reveal clustered NSHSE patterns for all 30 townships.Meanwhile,NSHSE has significant influence on population distribution,and 71.49% of the total population is living in moderate and high suitable districts.

Sub-pixel change detection for urban land-cover analysis via multi-temporal remote sensing images

Conventional change detection approaches are mainly based on per-pixel processing,which ignore the sub-pixel spectral variation resulted from spectral mixture.Especially for medium-resolution remote sensing images used in urban landcover change monitoring,land use/cover components within a single pixel are usually complicated and heterogeneous due to the limitation of the spatial resolution.Thus,traditional hard detection methods based on pure pixel assumption may lead to a high level of omission and commission errors inevitably,degrading the overall accuracy of change detection.In order to address this issue and find a possible way to exploit the spectral variation in a sub-pixel level,a novel change detection scheme is designed based on the spectral mixture analysis and decision-level fusion.Nonlinear spectral mixture model is selected for spectral unmixing,and change detection is implemented in a sub-pixel level by investigating the inner-pixel subtle changes and combining multiple composition evidences.The proposed method is tested on multi-temporal Landsat Thematic Mapper and China–Brazil Earth Resources Satellite remote sensing images for the land-cover change detection over urban areas.The effectiveness of the proposed approach is confirmed in terms of several accuracy indices in contrast with two pixel-based change detection methods(i.e.change vector analysis and principal component analysis-based method).In particular,the proposed sub-pixel change detection approach not only provides the binary change information,but also obtains the characterization about change direction and intensity,which greatly extends the semantic meaning of the detected change targets.

Land surface temperature retrieval from Landsat 8 data and validation with geosensor network

A method for the retrieval of land surface temperature （LST） from the two thermal bands of Landsat 8 data is proposed in this paper. The emissivities of vegetation, bare land, buildings, and water are estimated using different features of the wavelength ranges and spectral response functions. Based on the Planck function of the Thermal Infrared Sensor （TIRS） band 10 and band 11, the radiative transfer equation is rebuilt and the LST is obtained using the modified emissivity parameters. A sensitivity analysis for the LST retrieval is also conducted. The LST was retrieved from Landsat 8 data for the city of Zoucheng, Shandong Province, China, using the proposed algorithm, and the LST reference data were obtained at the same time from a geosensor network （GSN）. A comparative analysis was conducted between the retrieved LST and the reference data from the GSN. The results showed that water had a higher LST error than the other land-cover types, of less than 1.2℃, and the LST errors for buildings and vegetation were less than 0.75℃. The difference between the retrieved LST and reference data was about 1℃ on a clear day. These results confirm that the proposed algorithm is effective for the retrieval of LST from the Landsat 8 thermal bands, and a GSN is an effective way to validate and improve the performance of LST retrieval.

Data fusion in data scarce areas using a back-propagation artificial neural network model： a case study of the South China Sea

The cloud cover for the South China Sea andits coastal area is relatively large throughout the year,which limits the potential application of optical remotesensing. A H J-charge-coupled device （HJ-CCD） has theadvantages of wide field, high temporal resolution, andshort repeat cycle. However, this instrument suffers fromits use of only four relatively low-quality bands whichcan＇t adequately resolve the features of long wavelengths.The Landsat Enhanced Thematic Mapper-plus （ETM＋）provides high-quality data, however, the Scan LineCorrector （SLC） stopped working and caused striping ofremote sensed images, which dramatically reduced thecoverage of the ETM＋ data. In order to combine theadvantages of the HJ-CCD and Landsat ETM＋ data, weadopted a back-propagation artificial neural network （BP-ANN） to fuse these two data types for this study. Theresults showed that the fused output data not only have theadvantage of data intactness for the HJ-CCD, but also havethe advantages of the multi-spectral and high radiometricresolution of the ETM＋ data. Moreover, the fused datawere analyzed qualitatively, quantitatively and from apractical application point of view. Experimental studiesindicated that the fused data have a full spatial distribution,multi-spectral bands, high radiometric resolution, a smalldifference between the observed and fused output data, anda high correlation between the observed and fused data.The excellent performance in its practical application is afurther demonstration that the fused data are of highquality.