基于改进遥感生态指数的甘肃省古浪县生态质量评价

doi:10.12118/j.issn.1000-6060.2022.322

Abstract

Abstract:

Arid and semi-arid areas account for approximately 47% of China’s total land area. Most of these areas are deserts, with simple ecological structures and fragile ecosystems. To evaluate the ecological quality of arid and semi-arid areas more objectively and accurately, this study improves the remote sensing ecological index (RSEI), and proposes a drought remote sensing ecological index (DRSEI) suitable for arid areas, which is a coupling of five ecological factors, namely, soil-adjusted vegetation index, wetness, normalized difference built-up and soil index, desertification index, and land surface temperature. DRSEI is more sensitive to vegetation and has a higher resolution to impermeable water surfaces, land, and sandy land than RSEI, which is suitable for ecological quality assessment in arid and semi-arid areas. DRSEI was used to dynamically monitor and evaluate the ecological quality of Gulang County, Gansu Province, China from 1994 to 2020. The results show that the overall ecological quality of Gulang County improved from 1994 to 2020, increasing the vegetation coverage in the central and southeast regions and considerably improving the ecological environment. The northern Tengger Desert has poor ecological quality, whereas the eastern branches of the southern Qilian Mountains have excellent, good, and middle ecological quality. The quantitative evaluation of the ecological quality in the arid and semi-arid areas based on DRSEI is of great practical significance for guiding the ecological environment improvement and sustainable development in the arid and semi-arid areas in China.

Key words: DRSEI, ecological quality evaluation, principal component analysis, coefficient of variation, dynamic monitoring, Gansu Province

LUO Rongji, WANG Hongtao, WANG Cheng. Ecological quality evaluation of Gulang County in Gansu Province based on improved remote sensing ecological index[J].Arid Land Geography, 2023, 46(4): 539-549.

Figures/Tables 10

Fig. 1

Tab. 1

Tab. 2

Tab. 3

Fig. 2

Fig. 3

Tab. 4

Fig. 4

Fig. 5

Fig 6

References 33

[1]	Yang H F, Yao L, Wang Y B, et al. Relative contribution of climate change and human activities to vegetation degradation and restoration in north Xinjiang, China[J]. The Rangeland Journal, 2017, 39: 289-302. doi: 10.1071/RJ16069
[2]	陈亚宁, 李玉朋, 李稚, 等. 全球气候变化对干旱区影响分析[J]. 地球科学进展, 2022, 37(2): 111-119. doi: 10.11867/j.issn.1001-8166.2022.006
	[Chen Yaning, Li Yupeng, Li Zhi, et al. Analysis of the impact of global climate change on dryland areas[J]. Advances in Earth Science, 2022, 37(2): 111-119.] doi: 10.11867/j.issn.1001-8166.2022.006
[3]	孙权, 张显峰, 江淼. 干旱区生态环境敏感参量遥感反演与评价系统研究[J]. 北京大学学报(自然科学版), 2011, 47(6): 1073-1080.
	[Sun Quan, Zhang Xianfeng, Jiang Miao. Eco-environmental variables estimation from remotely sensed data and eco-environmental assessment: Models and system[J]. Acta Scientiarum Naturalium Universitatis Pekinensis (Natural Science Edition), 2011, 47(6): 1073-1080.]
[4]	Geng Q L, Wu P T, Zhao X N. Spatial and temporal trends in climatic variables in arid areas of northwest China[J]. International Journal of Climatology, 2016, 36(12): 4118-4129. doi: 10.1002/joc.4621
[5]	阮永健, 吴秀芹. 基于GRACE和GLDAS的西北干旱区地下水资源量可持续性评价[J]. 干旱区研究, 2022, 39(3): 787-800.
	[Ruan Yongjian, Wu Xiuqin. Evaluation of groundwater resource sustainability based on GRACE and GLDAS in arid region of northwest China[J]. Arid Zone Research, 2022, 39(3): 787-800.]
[6]	Song G, Huang J T, Ning B H, et al. Effects of groundwater level on vegetation in the arid area of western China[J]. China Geology, 2021, 4(3): 527-535.
[7]	田丰收, 刘新平, 原伟鹏. 新疆和田地区耕地面源污染生态风险评价[J]. 干旱区地理, 2019, 42(2): 295-304.
	[Tian Fengshou, Liu Xinping, Yuan Weipeng. Ecological risk assessment of farmland non-point source pollution in Hotan Prefecture,Xinjiang[J]. Arid Land Geography, 2019, 42(2): 295-304.]
[8]	Cai X R, Li Z Q, Liang Y Q. Tempo-spatial changes of ecological vulnerability in the arid area based on ordered weighted average model[J]. Ecological Indicators, 2021, 133, doi: 10.1016/J.ECOLIND.2021.108398. doi: 10.1016/J.ECOLIND.2021.108398
[9]	崔旺来, 奚恒辉, 蔡莉, 等. 基于NDVI的中国海岛县生态系统服务价值时空变化[J]. 经济地理, 2021, 41(9): 184-192, 224. doi: 10.2307/142599
	[Cui Wanglai, Xi Henghui, Cai Li, et al. Spatial and temporal change of ecosystem service value in China’s island counties base on NDVI[J]. Economic Geography, 2021, 41(9): 184-192, 224.] doi: 10.2307/142599
[10]	孙灏, 马立茹, 蔡创创, 等. 干旱区地表温度和热岛效应演变研究——以宁夏沿黄城市带为例[J]. 干旱区地理, 2020, 43(3): 694-705.
	[Sun Hao, Ma Liru, Cai Chuangchuang, et al. Evolution of surface temperature and heat island effect in arid areas: A case of city belt along the Yellow River in Ningxia[J]. Arid Land Geography, 2020, 43(3): 694-705.]
[11]	徐涵秋. 城市遥感生态指数的创建及其应用[J]. 生态学报, 2013, 33(24): 7853-7862.
	[Xu Hanqiu. A remote sensing urban ecological index and its application[J]. Acta Ecologica Sinica, 2013, 33(24): 7853-7862.]
[12]	刘智才, 徐涵秋, 李乐, 等. 基于遥感生态指数的杭州市城市生态变化[J]. 应用基础与工程科学学报, 2015, 23(4): 728-739.
	[Liu Zhicai, Xu Hanqiu, Li Le, et al. Ecological change in the Hangzhou area using the remote sensing based ecological index[J]. Journal of Basic Science and Engineering, 2015, 23(4): 728-739.]
[13]	排日海·合力力, 昝梅, 阿里木江·卡斯木. 乌鲁木齐市生态环境遥感评价及驱动因子分析[J]. 干旱区研究, 2021, 38(5): 1484-1496.
	[Helili Pariha, Zan Mei, Kasim Alimjan. Remote sensing evaluation of ecological environment in Urumqi City and analysis of driving factors[J]. Arid Zone Research, 2021, 38(5): 1484-1496.]
[14]	张亚球, 姜放, 纪梦达, 等. 基于遥感指数的区县级生态环境评价[J]. 干旱区研究, 2020, 37(6): 1598-1605.
	[Zhang Yaqiu, Jiang Fang, Ji Mengda, et al. Assessment of the ecological environment at district and county level based on remote sensing index[J]. Arid Zone Research, 2020, 37(6): 1598-1605.]
[15]	胡思汉, 姚玉增, 付建飞, 等. 基于RSEI指数的东北矿区生态质量变化评价: 以辽宁弓长岭区为例[J]. 生态学杂志, 2021, 40(12): 4053-4060.
	[Hu Sihan, Yao Yuzeng, Fu Jianfei, et al. Evaluation of ecological quality variation in mining area, northeast China based on RSEI index: A case study of Gongchangling District, Liaoning Province[J]. Chinese Journal of Ecology, 2021, 40(12): 4053-4060.]
[16]	岳辉, 刘英, 朱蓉. 基于遥感生态指数的神东矿区生态环境变化监测[J]. 水土保持通报, 2019, 39(2): 101-107, 114.
	[Yue Hui, Liu Ying, Zhu Rong. Monitoring ecological environment change based on remote sensing ecological index in Shendong Mining Area[J]. Bulletin of Soil and Water Conservation, 2019, 39(2): 101-107, 114.]
[17]	An M, Xie P, He W J, et al. Spatiotemporal change of ecologic environment quality and human interaction factors in Three Gorges ecologic economic corridor, based on RSEI[J]. Ecological Indicators, 2022, 141: 109090, doi: 10.1016/J.ECOLIND.2022.109090. doi: 10.1016/J.ECOLIND.2022.109090
[18]	Yang X Y, Meng F, Fu P J, et al. Spatiotemporal change and driving factors of the eco-environment quality in the Yangtze River Basin from 2001 to 2019[J]. Ecological Indicators, 2021, 131: 108214, doi: 10.1016/J.ECOLIND.2021.108214. doi: 10.1016/J.ECOLIND.2021.108214
[19]	张秦瑞, 赵良军, 林国军, 等. 改进遥感生态指数的宜宾市三江汇合区生态环境评价[J]. 自然资源遥感, 2022, 34(1): 230-237.
	[Zhang Qinrui, Zhao Liangjun, Lin Guojun, et al. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.]
[20]	Wu S P, Gao X, Lei J Q, et al. Ecological environment quality evaluation of the Sahel region in Africa based on remote sensing ecological index[J]. Journal of Arid Land, 2022, 14(1): 14-33. doi: 10.1007/s40333-022-0057-1
[21]	农兰萍, 王金亮, 玉院和. 基于改进型遥感生态指数(MRSEI)模型的滇中地区生态环境质量研究[J]. 生态与农村环境学报, 2021, 37(8): 972-982.
	[Nong Lanping, Wang Jinliang, Yu Yuanhe. Research on ecological environment quality in central Yunnan based on MRSEI model[J]. Journal of Ecology and Rural Environment, 2021, 37(8): 972-982.]
[22]	杨福山. 古浪县北部沙区治沙造林方案研究[J]. 农村实用技术, 2020(2): 131-132.
	[Yang Fushan. Study on the sand control and afforestation program in the northern sand area of Gulang County[J]. Nongcun Shiyong Jishu, 2020(2): 131-132.]
[23]	Huete A R. A soil-adjusted vegetation index (SAVI)[J]. Remote Sensing of Environment, 1988, 25(3): 295-309. doi: 10.1016/0034-4257(88)90106-X
[24]	李晶, 徐涵秋, 李霞, 等. 水土流失区马尾松林植被提取的土壤调节指数分析[J]. 地球信息科学学报, 2015, 17(9): 1128-1134. doi: 10.3724/SP.J.1047.2015.01128
	[Li Jing, Xu Hanqiu, Li Xia, et al. Vegetation information extraction of Pinus massoniana forest in soil erosion areas using soil-adjusted vegetation index[J]. Journal of Geo-information Science, 2015, 17(9): 1128-1134.] doi: 10.3724/SP.J.1047.2015.01128
[25]	Chen C, Fu J Q, Zhang S, et al. Coastline information extraction based on the tasseled cap transformation of Landsat-8 OLI images[J]. Estuarine, Coastal and Shelf Science, 2019, 217: 281-291. doi: 10.1016/j.ecss.2018.10.021
[26]	Huang C, Wylie B, Yang L, et al. Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance[J]. International Journal of Remote Sensing, 2002, 23(8): 1741-1748. doi: 10.1080/01431160110106113
[27]	李春兰, 朝鲁门, 包玉海, 等. 21世纪初期气候波动下浑善达克沙地荒漠化动态变化分析[J]. 干旱区地理, 2015, 38(3): 556-564.
	[Li Chunlan, Chao Lumen, Bao Yuhai, et al. Dynamic changes of desertification in the Hunshandake Desert under the climate fluctuation in early 21^st century[J]. Arid Land Geography, 2015, 38(3): 556-564.]
[28]	徐涵秋, 李春强, 林梦婧. RSEI应使用主成分分析(PCA)或核主成分分析(kPCA)?[J/OL]. 武汉大学学报(信息科学版): 1-11[2023-03-09]. https://doi.org/10.13203/j.whugis20220319.
	[Xu Hanqiu, Li Chunqiang, Lin Mengjing. Should RSEI use PCA or kPCA?[J]. Geomatics and Information Science of Wuhan University, 1-11[2023-03-09]. https://doi.org/10.13203/j.whugis20220319.]
[29]	徐涵秋, 邓文慧. MRSEI指数的合理性分析及其与RSEI指数的区别[J]. 遥感技术与应用, 2022, 37(1): 1-7.
	[Xu Hanqiu, Deng Wenhui. Rationality analysis of MRSEI and its difference with RSEI[J]. Remote Sensing Technology and Application, 2022, 37(1): 1-7.]
[30]	Kesteven G L. The coefficient of variation[J]. Nature, 1946, 158: 520-521.
[31]	伏润之, 崔世雄. 筑绿色屏障绘生态画卷——古浪县防沙治沙用沙纪实[N]. 甘肃日报, 2018-07-26(07).
	[Fu Runzhi, Cui Shixiong. Build a green barrier, draw an ecological picture scroll: Gulang County sand control and sand control record[N]. Gansu Daily, 2018-07-26(07).]
[32]	祁效杰. 古浪县生态移民迁出区退耕还林现状及建议[J]. 甘肃林业科技, 2021, 46(1): 37-39.
	[Qi Xiaojie. The current situation and suggestions of land retirement in ecological migrant out areas in Gulang County, Gansu[J]. Journal of Gansu Forestry Science and Technolgy, 2021, 46(1): 37-39.]
[33]	张伟, 周亮, 孙东琪, 等. 干旱区生态移民空间迁移特征与生态影响——以甘肃省古浪县为例[J]. 干旱区地理, 2022, 45(2): 618-627.
	[Zhang Wei, Zhou Liang, Sun Dongqi, et al. Spatial migration characteristics and ecological impacts of ecological migrants in arid regions: A case of Gulang County, Gansu Province[J]. Arid Land Geography, 2022, 45(2): 618-627.]

年份	SAVI	Wet	NDBSI	DI	LST	特征值	贡献率/%
1994	0.4511	0.4591	-0.4022	-0.3481	-0.5503	0.1884	78
2001	0.5053	0.3692	-0.4483	-0.3829	-0.5107	0.1934	83
2008	0.5045	0.3778	-0.4537	-0.3929	-0.4925	0.1834	85
2013	0.4798	0.4606	-0.4381	-0.4381	-0.4168	0.2868	92
2016	0.4802	0.4569	-0.4203	-0.4520	-0.4238	0.2529	84
2020	0.4562	0.4495	-0.4198	-0.4774	-0.4309	0.3021	91

年份	DRSEI	SAVI	Wet	NDBSI	DI	LST
1994	0.257	0.214	0.261	0.785	0.868	0.615
2001	0.266	0.210	0.214	0.768	0.879	0.517
2008	0.234	0.214	0.223	0.795	0.868	0.628
2013	0.269	0.250	0.333	0.791	0.790	0.654
2016	0.326	0.298	0.356	0.750	0.738	0.530
2020	0.282	0.279	0.323	0.807	0.735	0.631
极差	0.092	0.088	0.142	0.057	0.143	0.137

年份	SAVI	Wet	NDBSI	DI	LST	平均相关度
1994	0.8721	0.9316	-0.8183	-0.8613	-0.8991	0.8765
2001	0.9551	0.9216	-0.9388	-0.9441	-0.8421	0.9203
2008	0.9622	0.9153	-0.9502	-0.9593	-0.8480	0.9270
2013	0.9769	0.9655	-0.9768	-0.9771	-0.8903	0.9573
2016	0.9687	0.9410	-0.9630	-0.9689	-0.7763	0.9236
2020	0.9778	0.9599	-0.9601	-0.9782	-0.8825	0.9517

生态等级	1994年		2001年		2008年
生态等级	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%
差	2519.58	49.93	2222.09	44.04	2730.25	54.10
较差	1450.48	28.75	1819.76	36.06	1564.05	31.00
中	696.30	13.80	616.00	12.21	423.75	8.40
良	287.09	5.69	241.27	4.78	179.62	3.56
优	92.54	1.83	146.88	2.91	148.34	2.94
生态等级	2013年		2016年		2020年
生态等级	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%
差	2647.95	52.48	1885.73	37.37	2548.07	50.50
较差	1403.87	27.82	1717.92	34.04	1345.47	26.66
中	403.81	8.00	800.29	15.86	521.38	10.33
良	245.54	4.87	343.47	6.81	291.11	5.77
优	344.83	6.83	298.59	5.92	339.97	6.74

Ecological quality evaluation of Gulang County in Gansu Province based on improved remote sensing ecological index

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 33

Related Articles 15

Metrics

Comments

Recommended 0

[1]	ZHANG Junxia, HUANG Wubin, LI Antai, YANG Xiumei, LI Qian, BIAN Hongwei. Fine meteorological risk early warning forecast of main geological disasters in Gansu Province [J]. Arid Land Geography, 2023, 46(9): 1443-1452.
[2]	YANG Sicun,HUO Lin,WANG Chengbao,WEN Meijuan. Characteristics of agricultural carbon emissions in Gansu Province based on STIRPAT model [J]. Arid Land Geography, 2023, 46(9): 1493-1502.
[3]	MA Zhenbang,WANG Sijian,LI Wei,LYU Peng,GUO Xiaodong. Poverty evolution and its implications in the Liupan Mountain Area of Gansu Province from 1986 to 2020 [J]. Arid Land Geography, 2023, 46(9): 1545-1555.
[4]	CHEN Long, ZHANG Zhibin, ZHAO Xuewei, GUO Qianqian, GUO yan. Formation mechanism and development strategy of regional spatial pattern of Gansu Province [J]. Arid Land Geography, 2023, 46(9): 1556-1566.
[5]	WU Xi, CHEN Qiangqiang. Influencing factors and decoupling efforts of industry-related carbon emissions in Gansu Province [J]. Arid Land Geography, 2023, 46(2): 274-283.
[6]	LU Chenyu,HUANG Ping,ZHANG Tong,LIU Xiaowan,CHENG Wei. Spatiotemporal evolution and driving factors of the green development efficiency in Gansu Province [J]. Arid Land Geography, 2023, 46(2): 305-315.
[7]	ZHANG Juan, YAO Xiaojun, LI Jing, WANG Xiaoyan. Agricultural drought research based on multi-source remote sensing data in Gansu Province [J]. Arid Land Geography, 2023, 46(1): 11-22.
[8]	CAO Yanchao,JIAO Meiling,QIN Tuo,GUO Tong. Variation characteristics and influencing factors of summer half-year precipitation in Hedong region of Gansu Province from 1973 to 2020 [J]. Arid Land Geography, 2022, 45(6): 1695-1706.
[9]	GUO Xiaoqin,LI Guangming,SUN Zhanfeng,WANG Xingtao. Cluster analysis with statistical test of precipitation distribution in Qilian Mountains and its surrounding area and evaluation of artificial precipitation enhancement in typical watershed [J]. Arid Land Geography, 2022, 45(3): 706-714.
[10]	CAO Gang,BI Shuhai,ZHAO Mingxin,CAO Sufang,WANG Wei,NIU Jijun,LI Hongxu. Comprehensive evaluation of soil environmental factors under different mulching conditions in pear orchard in arid region [J]. Arid Land Geography, 2022, 45(3): 890-900.
[11]	LI Jinyan,GUO Jiao,CUI Lanbo,DOU Miao. Evaluation on the effect of water resources allocation in the water receiving area of the central and southern Ningxia water transfer project [J]. Arid Land Geography, 2021, 44(6): 1601-1611.
[12]	YUAN Panli,WANG Chuanjian,ZHAO Qingzhan,WANG Xuewen,REN Yuanyuan,YANG Qiyuan. Dynamic monitoring of land-use/land-cover change in cold and arid region based on deep learning: A case study of Mosuowan reclamation area in Xinjiang [J]. Arid Land Geography, 2021, 44(6): 1717-1728.
[13]	YANG Xiaoying,Yu Shan,Du Wala,Hong Mei. Risk assessment of grassland fire on the Mongolian Plateau [J]. Arid Land Geography, 2021, 44(4): 1032-1044.
[14]	HU Guigui,YANG Fenli,YANG Lian’an,ZHENG Yurong,WANG Hui,CHEN Weijun,LI Yali. Spatial prediction modeling of soil organic matter content based on principal components and machine learning [J]. Arid Land Geography, 2021, 44(4): 1114-1124.
[15]	CAI Jia,BAI Yongping,CHEN Zhijie,CHE Lei,WANG Zhiguo,XIE Lixia. Optimization of tourism spatial structure from the perspective of all-for-one tourism in Gansu Province [J]. Arid Land Geography, 2021, 44(2): 544-551.