The detection of glacial lake change in the Himalayas, Nepal is extremely significant since the glacial lake change is one of the crucial indicators of global climate change in this area, where is the most sensitive a...The detection of glacial lake change in the Himalayas, Nepal is extremely significant since the glacial lake change is one of the crucial indicators of global climate change in this area, where is the most sensitive area of the global climate changes. In the Hima- layas, some of glacial lakes are covered by the dark mountains＇ shadow because of their location. Therefore, these lakes can not be de- tected by conventional method such as Normalized Difference Water Index （NDWI）, because the reflectance feature of shadowed glacial lake is different comparing to the ones which are located in the open flat area. The shadow causes two major problems： 1） glacial lakes which are covered by shadow completely result in underestimation of the number of glacial lakes; 2） glacial lakes which are partly iden- tified are considered to undervalue the area of glacial lakes. The aim of this study is to develop a new model, named Detection of Shadowed Glacial Lakes （DSGL） model, to identify glacial lakes under the shadow environment by using Advanced Space-borne Ther- mal Emission and Reflection Radiometer （ASTER） data in the Himalayas, Nepal. The DSGL model is based on integration of two dif- ferent modifications of NDWI, namely NDWls model and NDWIshe model. NDWI~ is defined as integration of the NDWI and slope analysis and used for detecting non-shadowed lake in the mountain area. The NDWIshe is proposed as a new methodology to overcome the weakness of NDWI~ on identifying shadowed lakes in highly elevated mountainous area such as the Himalayas. The first step of the NDWIshe is to enhance the data from ASTER 1B using the histogram equalization （HE） method, and its outcome product is named AS- TERho. We used the ASTERhe for calculating the NDWIhc and the NDWIshe. Integrated with terrain analysis using Digital Elevation Model （DEM） data, the NDWIshe can be used to identify the shadowed glacial lakes in the Himalayas. NDWIs value of 0.41 is used to identify the glacier lake （NDWI~ 〉 0.41）, and 0.3 of NDWIshe is used to identify the shadowed glacier lake （NDWIsho 〈 0.3）. The DSGL model was proved to be able to classify the glacial lakes more accurately, while the NDWI model had tendency to underestimate the presence of actual glacial lakes. Correct classification rate regarding the products from NDWI model and DSGL model were 57% and 99%, respectively. The results of this paper demonstrated that the DSGL model is promising to detect glacial lakes in the shadowed en- vironment at high mountains.展开更多
基金Under the auspices of Taikichiro Mori Memorial Research Grants of Keio University (No. 19, 2010)Doctoral Students Research Support Program of Keio University (No. 87, 2010)Academic Frontier Fund's 'Integrated Research for Community Strategic Concept by Construction and Management of Digital Asia' by Ministry of Education, Culture, Sports, Science and Technology (MEXT) (No. 04F003, 2004-2008)
文摘The detection of glacial lake change in the Himalayas, Nepal is extremely significant since the glacial lake change is one of the crucial indicators of global climate change in this area, where is the most sensitive area of the global climate changes. In the Hima- layas, some of glacial lakes are covered by the dark mountains＇ shadow because of their location. Therefore, these lakes can not be de- tected by conventional method such as Normalized Difference Water Index （NDWI）, because the reflectance feature of shadowed glacial lake is different comparing to the ones which are located in the open flat area. The shadow causes two major problems： 1） glacial lakes which are covered by shadow completely result in underestimation of the number of glacial lakes; 2） glacial lakes which are partly iden- tified are considered to undervalue the area of glacial lakes. The aim of this study is to develop a new model, named Detection of Shadowed Glacial Lakes （DSGL） model, to identify glacial lakes under the shadow environment by using Advanced Space-borne Ther- mal Emission and Reflection Radiometer （ASTER） data in the Himalayas, Nepal. The DSGL model is based on integration of two dif- ferent modifications of NDWI, namely NDWls model and NDWIshe model. NDWI~ is defined as integration of the NDWI and slope analysis and used for detecting non-shadowed lake in the mountain area. The NDWIshe is proposed as a new methodology to overcome the weakness of NDWI~ on identifying shadowed lakes in highly elevated mountainous area such as the Himalayas. The first step of the NDWIshe is to enhance the data from ASTER 1B using the histogram equalization （HE） method, and its outcome product is named AS- TERho. We used the ASTERhe for calculating the NDWIhc and the NDWIshe. Integrated with terrain analysis using Digital Elevation Model （DEM） data, the NDWIshe can be used to identify the shadowed glacial lakes in the Himalayas. NDWIs value of 0.41 is used to identify the glacier lake （NDWI~ 〉 0.41）, and 0.3 of NDWIshe is used to identify the shadowed glacier lake （NDWIsho 〈 0.3）. The DSGL model was proved to be able to classify the glacial lakes more accurately, while the NDWI model had tendency to underestimate the presence of actual glacial lakes. Correct classification rate regarding the products from NDWI model and DSGL model were 57% and 99%, respectively. The results of this paper demonstrated that the DSGL model is promising to detect glacial lakes in the shadowed en- vironment at high mountains.
