TY - GEN
T1 - Analysis of ecological vulnerability based on landscape pattern and ecological sensitivity
T2 - Remote Sensing and Modeling of Ecosystems for Sustainability V
AU - Jiang, Miao
AU - Gao, Wei
AU - Chen, Xiuwan
AU - Zhang, Xianfeng
AU - Wei, Wenxia
PY - 2008
Y1 - 2008
N2 - Ecological vulnerability evaluation has important real significance and scientific value. In this study, under the support of Remote Sensing and Geographic Information System, we use TM images, distribution map of sand desertification and soil salinization, and related geographic information, and adopt a combined landscape pattern and ecosystem sensitivity approach to access the ecological vulnerability of Duerbete County. We consider the following five factors to develop the model: (1) reciprocal of fractal dimension (FD'), (2) isolation (FI), (3) fragmentation (FN), (4) sensitivity of sand desertification (SD), and (5) sensitivity of soil salinization (SA). Then we build the evaluation model and calculate the vulnerability of landscape type of Duerbete. Through Kriging interpolation, we get the regional eco-environment vulnerability of whole county. Then we evaluate this cropping-pastoral interlacing region-Duerbete County. The conclusions are: (1) The vulnerability of all landscape types is in the following decreasing order: grassland > cropland > unused area > water area > construction area > wattenmeer > reed bed > woodland > paddy field; (2) There are significant positive relationships between VI and FN, VI and SD, SD and FN, SA and FN. This suggests that FN and SD have considerable impact on the eco-environmental vulnerability; (3) With the combination of FN, SD and SA, the regional eco-environment vulnerability can be evaluated well. The result is reasonable and can support ecological construction.
AB - Ecological vulnerability evaluation has important real significance and scientific value. In this study, under the support of Remote Sensing and Geographic Information System, we use TM images, distribution map of sand desertification and soil salinization, and related geographic information, and adopt a combined landscape pattern and ecosystem sensitivity approach to access the ecological vulnerability of Duerbete County. We consider the following five factors to develop the model: (1) reciprocal of fractal dimension (FD'), (2) isolation (FI), (3) fragmentation (FN), (4) sensitivity of sand desertification (SD), and (5) sensitivity of soil salinization (SA). Then we build the evaluation model and calculate the vulnerability of landscape type of Duerbete. Through Kriging interpolation, we get the regional eco-environment vulnerability of whole county. Then we evaluate this cropping-pastoral interlacing region-Duerbete County. The conclusions are: (1) The vulnerability of all landscape types is in the following decreasing order: grassland > cropland > unused area > water area > construction area > wattenmeer > reed bed > woodland > paddy field; (2) There are significant positive relationships between VI and FN, VI and SD, SD and FN, SA and FN. This suggests that FN and SD have considerable impact on the eco-environmental vulnerability; (3) With the combination of FN, SD and SA, the regional eco-environment vulnerability can be evaluated well. The result is reasonable and can support ecological construction.
KW - Cropping-pastoral interlacing region
KW - Duerbete County
KW - Ecological sensitivity
KW - Ecological vulnerability
KW - Landscape pattern
UR - https://www.scopus.com/pages/publications/56249091118
U2 - 10.1117/12.794619
DO - 10.1117/12.794619
M3 - 会议稿件
AN - SCOPUS:56249091118
SN - 9780819473035
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Remote Sensing and Modeling of Ecosystems for Sustainability V
Y2 - 13 August 2008 through 13 August 2008
ER -