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[1]ZHAO Yinjun,ZENG Lan,HE Zhong,et al.Interpretation and Landscape Pattern of Karst Landform Based on Multi-Source Remote Sensing Images[J].Research of Soil and Water Conservation,2017,24(04):158-162.

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Interpretation and Landscape Pattern of Karst Landform Based on Multi-Source Remote Sensing Images

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 24 Number of periods: 2017 04 Page number: 158-162 Column: Public date: 2017-08-28

Title:: Interpretation and Landscape Pattern of Karst Landform Based on Multi-Source Remote Sensing Images

Author(s):: ZHAO Yinjun^1,2, ZENG Lan², HE Zhong^3,4, LIANG Qiao²; 1. Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Guangxi Teachers Education University, Ministry of Education, Nanning 530001, China;
2. School of Geography and Planning, Guangxi Teachers Education University, Nanning 530001, China;
3. Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, Shaanxi 712100, China

Keywords:: karst; interpretation keys; GF-1

CLC:: P901;TP79

DOI:: -

Abstract:: Identifying the distribution and patterns of karst landform is fundamental to landform interpretation and subsequent landscape structures analysis. Due to the complexity of landforms and the similarity of their growth environment, it is difficult to understand and interpret karst landform. On the perspective of geography and landscape ecology, we used GF-1 images and TM images supplemented with DEM and contour to build interpretation symbol and classification system. Finally, karst landscape patterns were analyzed combining GIS and Fragstats software. The results shows that: because of the high resolution in GF-1 image, it is more appropriate to differentiate surface morphology with a high accuracy than TM images; less affected by human activities, the karst landform in Mashan County shows low fragmentation and high naturalness, diversity, heterogeneity， integrity, all in favor of karst landscape protection; the peak clusters are dominant landscape types in Mashan County.

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