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Remote Sensing for Land & Resources    2021, Vol. 33 Issue (1) : 191-198     DOI: 10.6046/gtzyyg.2020083
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A study of remote sensing evaluation model and main controlling factors of land ecological quality:A case study of Guang’an City
CHEN Zhen(), XIA Xueqi, CHEN Jianping
School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China
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Abstract  

In order to scientifically evaluate the ecological quality of the land and effectively identify the main controlling factors of the land ecology, the authors established a remote sensing evaluation model based on ideal points in Guang’an which served as a research area, evaluated the ecological quality of the land in Guang’an in 2000, 2005, 2010 and 2015, and analyzed the main controlling factors. A kilometer grid was used as the evaluation unit. The evaluation index system was constructed based on the fourteen evaluation criteria in the four criterion layers, i.e., ecological background, ecological structure, ecological benefits and ecological stress. The evaluation index system was constructed by applying Delphi method and entropy weight method. The weight value of each evaluation index and the ideal point values were calculated by using the ideal point model, and the ideal point level was divided. The principal factor analysis method was used to obtain the main control factors of each year, and then the relationship between the spatial distribution of the ideal point level and the environmental impact factor was performed. Through research, the authors obtained the overall upward trend of land ecological quality in Guang’an City. It is shown that the proportion of land ecological quality at various levels of area and spatial distribution and the proportion of forest land area and temperature factor are the most important main control factors, and the proportion of woodland and the temperature are positively related to the land ecological quality. After analysis, the suggestions on land ecological quality supervision in Guang’an City are put forward. which can provide references for land ecological quality supervision in other areas.
Keywords land ecological quality      ecological evaluation index      ideal point model      main controlling factor      principal component analysis     
ZTFLH:  TP79  
Issue Date: 18 March 2021
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Zhen CHEN
Xueqi XIA
Jianping CHEN
Cite this article:   
Zhen CHEN,Xueqi XIA,Jianping CHEN. A study of remote sensing evaluation model and main controlling factors of land ecological quality:A case study of Guang’an City[J]. Remote Sensing for Land & Resources, 2021, 33(1): 191-198.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020083     OR     https://www.gtzyyg.com/EN/Y2021/V33/I1/191
Fig.1  Administrative map of Guang’an City
年份 森林 草地 水体 荒地 建设用地
2000年 79.07 6.68 2.55 9.75 1.95
2005年 82.99 0.26 3.56 11.43 1.76
2010年 79.33 8.65 2.75 6.42 2.85
2015年 85.79 4.44 4.39 1.84 3.54
Tab.1  Proportion of land use types in 2000, 2005, 2010 and 2015(%)
Fig.2  The land classification map of the study area in 2000, 2005, 2010 and 2015
准则层 评价指标
生态本底 年均降水量、年均气温、植被指数、GPP、地形
生态结构 林地占比、草地占比、水体占比、裸地占比、建设用地占比
生态效益 生态服务价值
生态胁迫 GDP、人口密度、夜间灯光
Tab.2  Remote sensing evaluation index system for land ecological quality
Fig.3  Grade map of ideal points in 2000, 2005, 2010 and 2015
生态等级 2000年 2005年 2010年 2015年
第1级 7.07 1.25 2.76 2.61
第2级 33.79 56.52 28.60 17.04
第3级 54.84 34.74 64.59 72.41
第4级 3.42 6.61 3.22 7.13
第5级 0.81 0.83 0.76 0.71
Tab.3  Proportion of evaluation units of each ecological level in 4 phases(%)
主控因子 第1主成分 第2主成分 第3主成分
地形(-) -0.308 07 -0.026 89 -0.342 47
人口(-) -0.520 77 -0.338 18 -0.716 06
GDP(-) -0.424 16 -0.243 47 -0.569 85
灯光(-) -0.329 16 -0.154 92 -0.482 63
气温(+) 0.595 20 0.562 66 0.544 18
NDVI(+) 0.433 00 0.306 73 -0.123 04
降水(+) 0.482 34 0.426 82 0.288 68
GPP(+) -0.844 80 -0.569 17 -1.084 96
林地占比(+) 2.955 60 -0.599 57 -0.172 50
草地占比(+) -0.600 23 0.175 69 -0.762 56
水体占比(+) -0.695 58 -1.952 64 2.563 03
荒地占比(+) -0.399 69 2.579 52 1.348 87
建筑用地占比(-) -0.343 67 -0.166 58 -0.490 69
Tab.4  Main control factors in 2000
主控因子 第1主成分 第2主成分 第3主成分
地形(-) 0.336 04 -0.333 25 1.884 00
人口(-) -0.336 63 -0.240 46 0.412 78
主控因子 第1主成分 第2主成分 第3主成分
GDP(-) 0.215 26 -0.319 19 1.442 95
灯光(-) -0.071 25 -0.289 97 0.836 87
气温(+) 2.581 45 -0.147 24 -0.860 66
NDVI(+) -0.394 08 -0.349 28 0.012 99
降水(+) 1.390 53 -0.139 22 -0.949 99
GPP(+) -0.883 23 -0.240 13 -1.551 93
林地占比(+) -0.211 71 0.464 05 0.333 61
草地占比(+) -0.862 01 -0.087 26 -0.959 10
水体占占比(+) -0.892 80 -0.247 32 -0.530 70
荒地占比(+) -0.100 58 3.297 76 0.220 05
建筑用地占比(-) -0.770 99 -0.263 21 -0.290 85
Tab.5  Main control factors in 2005
主控因子 第1主成分 第2主成分 第3主成分
地形(-) -0.315 20 -0.063 57 -0.386 22
人口(-) -0.521 95 -0.052 42 -0.896 30
GDP(-) -0.433 11 -0.07 549 -0.723 02
灯光(-) -0.295 67 -0.061 55 -0.671 06
气温(+) 0.585 50 -0.244 60 0.996 77
NDVI(+) 0.602 72 -0.446 48 0.634 94
降水(+) 0.491 46 -0.276 41 0.736 05
GPP(+) -0.849 94 0.093 58 -1.650 53
林地占比(+) 2.906 75 0.445 09 -0.69 235
草地占比(+) -0.503 90 -1.160 84 0.437 99
水体占比(+) -0.682 32 2.986 16 1.131 55
荒地占比(+) -0.614 83 -1.083 00 1.739 95
建筑用地占比(-) -0.369 50 -0.060 47 -0.657 77
Tab.6  Main control factors in 2010
主控因子 第1主成分 第2主成分 第3主成分
地形(-) -0.302 46 -0.247 35 0.037 47
人口(-) -0.443 68 -0.416 52 0.302 54
GDP(-) -0.378 95 -0.363 77 0.244 99
灯光(-) -0.268 11 -0.317 80 0.584 07
气温(+) 0.532 44 0.156 52 -1.461 93
NDVI(+) 0.513 35 -0.067 13 -0.423 64
降水(+) 0.493 81 0.029 23 -1.366 81
GPP(+) -0.804 23 -0.539 67 1.890 78
林地占比(+) 2.937 22 -0.105 74 0.921 57
草地占比(+) -0.719 98 -0.533 02 0.016 33
水体占比(+) -0.477 37 3.247 15 0.346 06
荒地占比(+) -0.764 16 -0.513 73 -1.589 99
建筑用地占比(-) -0.317 88 -0.328 19 0.498 56
Tab.7  Main control factors in 2015
年份 主控因子1 主控因子2 主控因子3
2000年 林地占比 荒地占比 水体占比
2005年 气温 荒地占比 地形
2010年 林地占比 水体占比 荒地占比
2015年 林地占比 水体占比 GPP
Tab.8  Main control factors in 2000, 2005, 2010 and 2015
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