There is increasing interest in developing automatic procedures to segment landscapes into soil spatial entities that replace conventional, expensive manual procedures for delineating and classifying soils. Geographic...There is increasing interest in developing automatic procedures to segment landscapes into soil spatial entities that replace conventional, expensive manual procedures for delineating and classifying soils. Geographic object-based image analysis(GEOBIA)partitions remote sensing imagery or digital elevation models into homogeneous image objects based on image segmentation. We used an object-based methodology for the detailed delineation and classification of soil types using digital maps of topography and vegetation as soil covariates, based on the Random Forests(RF) classifier. We compared the object-based method's results with those of a pixel-based classification using the same classifier. We used 18 digital elevation model derivatives and 5 remote sensing indices that were related to vegetation cover and soil. Using 171 soil profiles with their associated environmental variable values,the RF method was used to identify the most important soil type predictors for use in the segmentation process. A stack of rastergeodatasets corresponding to the selected predictors was segmented using a multi-resolution segmentation algorithm, which resulted in homogeneous objects related to soil types. These objects were further classified as soil types using the same method, RF. We also conducted a pixel-based classification using the same classifier and soil profiles, and the resulting maps were assessed in terms of their accuracy using 30% of the soil profiles for validation. We found that GEOBIA was an effective method for soil type mapping, and was superior to the pixel-based approach. The optimized object-based soil map had an overall accuracy of 58%, which was 10% higher than that of the optimized pixel-based map.展开更多
This research aimed to analyze the possibility to estimate and automatically map large areas of soybean cultivation through the use of MODIS(Moderate-Resolution Imaging Spectroradiometer)images.Two major techniques we...This research aimed to analyze the possibility to estimate and automatically map large areas of soybean cultivation through the use of MODIS(Moderate-Resolution Imaging Spectroradiometer)images.Two major techniques were used:GEOgraphic-Object-Based Image Analysis(GEOBIA)and Data Mining(DM).In order to obtain the images,the segmentation algorithm implemented by Definiens Developer was used.A decision tree(DT)was created from a training set previously prepared.Time-series of images from the MODIS sensor aboard the Terra satellite were acquired in order to represent the wide variation of the vegetation pattern along the soybean crop cycle.The time-series data were used only for the CEI index.Furthermore,to compare the results obtained from GEOBIA,the slicing technique was used at the CEI level.After the training,the DT was applied to the vegetation indices generating the thematic map of the spatial distribution of soybean.In accordance with the error matrix and kappa parameter analysis,tests for statistical significance were created.Results indicate that the classification achieved by Kappa coefficients is 0.76.In short,the obtained results proved that combining vegetation indices and time-series data using GEOBIA return promising results for mapping soybean plantation on a regional scale.展开更多
The aim of this study was to develop a straightforward approach for flood area mapping in a transboundary riverbed using Geographic Object-Based Image Analysis.Weak bilateral/multilateral cooperation among neighboring...The aim of this study was to develop a straightforward approach for flood area mapping in a transboundary riverbed using Geographic Object-Based Image Analysis.Weak bilateral/multilateral cooperation among neighboring countries hampers effective disaster management and mitigation activities over transboundary areas and strengthens the demand for reliable remote-sensing-derived information.Three object-based classification approaches using ENVISAT/ASAR and multi-temporal LANDSAT TM data were developed and validated for flood area delineation.The accuracy assessment of the classification results was based on oblique air photo interpretation and an area-based comparison with the official flood map.The bi-level object-based model using the Normalized Difference Water Index and the original post-flood TM bands attained 92.67%overall accuracy in inundated-areas detection,while the ENVISAT/ASAR classification was the least accurate(85.33%),probably due to the lower spatial resolution of the Synthetic Aperture Radar image.A strong agreement(92.14%)was found between the LANDSAT flood extent and the official flood map,suggesting that the proposed method has the potential to be employed in the future as a standard part of a flood crisis management process.展开更多
基金supported by the Romanian Government through a doctoral scholarship
文摘There is increasing interest in developing automatic procedures to segment landscapes into soil spatial entities that replace conventional, expensive manual procedures for delineating and classifying soils. Geographic object-based image analysis(GEOBIA)partitions remote sensing imagery or digital elevation models into homogeneous image objects based on image segmentation. We used an object-based methodology for the detailed delineation and classification of soil types using digital maps of topography and vegetation as soil covariates, based on the Random Forests(RF) classifier. We compared the object-based method's results with those of a pixel-based classification using the same classifier. We used 18 digital elevation model derivatives and 5 remote sensing indices that were related to vegetation cover and soil. Using 171 soil profiles with their associated environmental variable values,the RF method was used to identify the most important soil type predictors for use in the segmentation process. A stack of rastergeodatasets corresponding to the selected predictors was segmented using a multi-resolution segmentation algorithm, which resulted in homogeneous objects related to soil types. These objects were further classified as soil types using the same method, RF. We also conducted a pixel-based classification using the same classifier and soil profiles, and the resulting maps were assessed in terms of their accuracy using 30% of the soil profiles for validation. We found that GEOBIA was an effective method for soil type mapping, and was superior to the pixel-based approach. The optimized object-based soil map had an overall accuracy of 58%, which was 10% higher than that of the optimized pixel-based map.
文摘This research aimed to analyze the possibility to estimate and automatically map large areas of soybean cultivation through the use of MODIS(Moderate-Resolution Imaging Spectroradiometer)images.Two major techniques were used:GEOgraphic-Object-Based Image Analysis(GEOBIA)and Data Mining(DM).In order to obtain the images,the segmentation algorithm implemented by Definiens Developer was used.A decision tree(DT)was created from a training set previously prepared.Time-series of images from the MODIS sensor aboard the Terra satellite were acquired in order to represent the wide variation of the vegetation pattern along the soybean crop cycle.The time-series data were used only for the CEI index.Furthermore,to compare the results obtained from GEOBIA,the slicing technique was used at the CEI level.After the training,the DT was applied to the vegetation indices generating the thematic map of the spatial distribution of soybean.In accordance with the error matrix and kappa parameter analysis,tests for statistical significance were created.Results indicate that the classification achieved by Kappa coefficients is 0.76.In short,the obtained results proved that combining vegetation indices and time-series data using GEOBIA return promising results for mapping soybean plantation on a regional scale.
文摘The aim of this study was to develop a straightforward approach for flood area mapping in a transboundary riverbed using Geographic Object-Based Image Analysis.Weak bilateral/multilateral cooperation among neighboring countries hampers effective disaster management and mitigation activities over transboundary areas and strengthens the demand for reliable remote-sensing-derived information.Three object-based classification approaches using ENVISAT/ASAR and multi-temporal LANDSAT TM data were developed and validated for flood area delineation.The accuracy assessment of the classification results was based on oblique air photo interpretation and an area-based comparison with the official flood map.The bi-level object-based model using the Normalized Difference Water Index and the original post-flood TM bands attained 92.67%overall accuracy in inundated-areas detection,while the ENVISAT/ASAR classification was the least accurate(85.33%),probably due to the lower spatial resolution of the Synthetic Aperture Radar image.A strong agreement(92.14%)was found between the LANDSAT flood extent and the official flood map,suggesting that the proposed method has the potential to be employed in the future as a standard part of a flood crisis management process.
