Aiming at the problems of high time-consuming, low accuracy and weak versatility of the existing methods of wa- ter extraction based on TM image, this paper combines principal component analysis （PCA） with the modif...Aiming at the problems of high time-consuming, low accuracy and weak versatility of the existing methods of wa- ter extraction based on TM image, this paper combines principal component analysis （PCA） with the modified normalized difference water index （MNDWI） which was improved by XU Han-qiu to construct a false color composite image that could separate water from others easily. This method can realize the water extraction based on TM image by analyzing the spectral characteristics of water in this false color image and establishing a water extraction model. This paper also compares the effi- ciency of this method with MNDWI, （TM2 ＋ TM3） - （TM4 ＋ TM5） and new water index （NWI）, which were applied in the city and mountain of Taiyuan, respectively. The results show that the proposed method can extract water body from TM im- age more rapidly and efficiently and its accuracy is up to 94.03 %. In addition, this method does not require a manual selec- tion threshold, which meets the research reuuirement of high automaticm.展开更多
Glacial lakes in the High Mountain Asia(HMA)are sensitive to global warming and can result in much more severe flood disasters than some largesized lakes.An accurate and robust method for the extraction of glacial lak...Glacial lakes in the High Mountain Asia(HMA)are sensitive to global warming and can result in much more severe flood disasters than some largesized lakes.An accurate and robust method for the extraction of glacial lakes is critical to effective management of these natural water resources.Conventional methods often have limitations in terms of low spectral contrast and heterogeneous backgrounds in an image.This study presents a robust and automated method for the yearly mapping of glacial lake over a large scale,which took advantage of the complementarity between the modified normalized difference water index(MNDWI)and the nonlocal active contour model,required only local homogeneity in reflectance features of lake.The cloud computing approach with the Google Earth Engine(GEE)platform was used to process the intensive amount of Landsat 8 images from 2015 (344 path/rows and approximately 7504 scenes).The experimental results were validated by very high resolution images from Chinese GaoFen-1 (GF-1) panchromatic multi-spectral(PMS)and appeared a general good agreement.This is the first time that information regarding the spatial distribution of glacial lakes over the HMA has been derived automatically within quite a short period of time.By integrating it with the relevant indices,it can also be applied to detect other land cover types such as snow or vegetation with improved accuracy.展开更多
文摘Aiming at the problems of high time-consuming, low accuracy and weak versatility of the existing methods of wa- ter extraction based on TM image, this paper combines principal component analysis （PCA） with the modified normalized difference water index （MNDWI） which was improved by XU Han-qiu to construct a false color composite image that could separate water from others easily. This method can realize the water extraction based on TM image by analyzing the spectral characteristics of water in this false color image and establishing a water extraction model. This paper also compares the effi- ciency of this method with MNDWI, （TM2 ＋ TM3） - （TM4 ＋ TM5） and new water index （NWI）, which were applied in the city and mountain of Taiyuan, respectively. The results show that the proposed method can extract water body from TM im- age more rapidly and efficiently and its accuracy is up to 94.03 %. In addition, this method does not require a manual selec- tion threshold, which meets the research reuuirement of high automaticm.
基金funded by the National Natural Science Foundation Project (Grant Nos. 41701481 and 41401511)
文摘Glacial lakes in the High Mountain Asia(HMA)are sensitive to global warming and can result in much more severe flood disasters than some largesized lakes.An accurate and robust method for the extraction of glacial lakes is critical to effective management of these natural water resources.Conventional methods often have limitations in terms of low spectral contrast and heterogeneous backgrounds in an image.This study presents a robust and automated method for the yearly mapping of glacial lake over a large scale,which took advantage of the complementarity between the modified normalized difference water index(MNDWI)and the nonlocal active contour model,required only local homogeneity in reflectance features of lake.The cloud computing approach with the Google Earth Engine(GEE)platform was used to process the intensive amount of Landsat 8 images from 2015 (344 path/rows and approximately 7504 scenes).The experimental results were validated by very high resolution images from Chinese GaoFen-1 (GF-1) panchromatic multi-spectral(PMS)and appeared a general good agreement.This is the first time that information regarding the spatial distribution of glacial lakes over the HMA has been derived automatically within quite a short period of time.By integrating it with the relevant indices,it can also be applied to detect other land cover types such as snow or vegetation with improved accuracy.
