Edge detection is an image processing technique for finding the boundaries of objects within images. It is typically used to interpret gravity and magnetic data, and find the horizontal boundaries of geological bodies...Edge detection is an image processing technique for finding the boundaries of objects within images. It is typically used to interpret gravity and magnetic data, and find the horizontal boundaries of geological bodies. Large deviations between model and true edges are common because of the interference of depth and errors in computing the derivatives; thus, edge detection methods cannot provide information about the depth of the source. To simultaneously obtain the horizontal extent and depth of geophysical anomalies, we use normalized edge detection filters, which normalize the edge detection function at different depths, and the maxima that correspond to the location of the source. The errors between model and actual edges are minimized as the depth of the source decreases and the normalized edge detection method recognizes the extent of the source based on the maxima, allowing for reliable model results. We demonstrate the applicability of the normalized edge detection filters in defining the horizontal extent and depth using synthetic and actual aeromagnetic data.展开更多
Accurate wetland delineation is the basis of wetland definition and mapping, and is of great importance for wetland management and research. The Zoige Plateau on the Qinghai-Tibet Plateau was used as a research site f...Accurate wetland delineation is the basis of wetland definition and mapping, and is of great importance for wetland management and research. The Zoige Plateau on the Qinghai-Tibet Plateau was used as a research site for research on alpine wetland delineation. Several studies have analyzed the spatiotemporal pattern and dynamics of these alpine wetlands, but none have addressed the issues of wetland boundaries. The objective of this work was to discriminate the upper boundaries of alpine wetlands by coupling ecological methods and satellite observations. The combination of Landsat 8 images and supervised classification was an effective method for rapid identification of alpine wetlands in the Zoig6 Plateau. Wet meadow was relatively stable compared with hydric soils and wetland hydrology and could be used as a primary indicator for discriminating the upper boundaries of alpine wetlands. A slope of less than 4.5° could be used as the threshold value for wetland delineation. The normalized difference vegetation index （NDVI） in 434 field sites showed that a threshold value of 0.3 could distinguish grasslands from emergent marsh and wet meadow in September. The median normalized difference water index （NDWI） of emergent marsh remained more stable than that of wet meadow and grasslands during the period from September until July of the following year. The index of mean density in wet meadow zones was higher than the emergent and upland zones. Over twice the number of species occurred in the wet meadow zone compared with the emergent zone, and close to the value of upland zone. Alpine wetlands in the three reserves in 2014 covered 1175.19 kin2 with a classification accuracy of 75.6%. The combination of ecological methods and remote sensing technology will play an important role in wetland delineation at medium and small scales. The correct differentiation between wet meadow and grasslands is the key to improving the accuracy of future wetland delineation.展开更多
基金supported by the China Postdoctoral Science Foundation (No.2014M551188)the Deep Exploration in China Sinoprobe-09-01 (No.201011078)
文摘Edge detection is an image processing technique for finding the boundaries of objects within images. It is typically used to interpret gravity and magnetic data, and find the horizontal boundaries of geological bodies. Large deviations between model and true edges are common because of the interference of depth and errors in computing the derivatives; thus, edge detection methods cannot provide information about the depth of the source. To simultaneously obtain the horizontal extent and depth of geophysical anomalies, we use normalized edge detection filters, which normalize the edge detection function at different depths, and the maxima that correspond to the location of the source. The errors between model and actual edges are minimized as the depth of the source decreases and the normalized edge detection method recognizes the extent of the source based on the maxima, allowing for reliable model results. We demonstrate the applicability of the normalized edge detection filters in defining the horizontal extent and depth using synthetic and actual aeromagnetic data.
基金Under the auspices of National Natural Science Foundation of China(No.41201445,41103041)National Science and Technology Support Program(No.2012BAJ24B01)National High Technology Research and Development Program of China(No.2009AA12200307)
文摘Accurate wetland delineation is the basis of wetland definition and mapping, and is of great importance for wetland management and research. The Zoige Plateau on the Qinghai-Tibet Plateau was used as a research site for research on alpine wetland delineation. Several studies have analyzed the spatiotemporal pattern and dynamics of these alpine wetlands, but none have addressed the issues of wetland boundaries. The objective of this work was to discriminate the upper boundaries of alpine wetlands by coupling ecological methods and satellite observations. The combination of Landsat 8 images and supervised classification was an effective method for rapid identification of alpine wetlands in the Zoig6 Plateau. Wet meadow was relatively stable compared with hydric soils and wetland hydrology and could be used as a primary indicator for discriminating the upper boundaries of alpine wetlands. A slope of less than 4.5° could be used as the threshold value for wetland delineation. The normalized difference vegetation index （NDVI） in 434 field sites showed that a threshold value of 0.3 could distinguish grasslands from emergent marsh and wet meadow in September. The median normalized difference water index （NDWI） of emergent marsh remained more stable than that of wet meadow and grasslands during the period from September until July of the following year. The index of mean density in wet meadow zones was higher than the emergent and upland zones. Over twice the number of species occurred in the wet meadow zone compared with the emergent zone, and close to the value of upland zone. Alpine wetlands in the three reserves in 2014 covered 1175.19 kin2 with a classification accuracy of 75.6%. The combination of ecological methods and remote sensing technology will play an important role in wetland delineation at medium and small scales. The correct differentiation between wet meadow and grasslands is the key to improving the accuracy of future wetland delineation.
