基于RSEI的延河流域生态环境质量监测与预估

doi:10.13866/j.azr.2022.03.28

Abstract

Abstract:

Based on Landsat remote sensing images of the Yanhe River Basin in 1998, 2004, 2010, and 2016, the remote sensing ecological index (RSEI) was calculated to analyze and evaluate temporal and spatial changes of environmental quality in the Yanhe River Basin. The CA-Markov model was also used to simulate and predict the ecological environment in 2022 and 2028. Results show that (1) the RSEI has good applicability in the Yanhe River Basin and can be used to monitor and evaluate temporal and spatial characteristics of the ecological environment in the basin. (2) For the time scale, the mean RSEI value changed from 0.392 in 1998 to 0.530 in 2016 and showed an increasing trend year by year. On a spatial scale, the RSEI gradually increased from southeast to northwest. RSEI grades were divided by 0.2 intervals and the total area of RSEI grades was mainly poor and moderate. These grades accounted for a total of 62.4%, which was distributed mainly in the valley of the basin, while the high grade area accounted for the least, at approximately 8.1%, which was distributed mainly in the mountainous region of the southern part of the basin. (3) There is a large spatial difference between the effects of natural and human factors on changes in environmental quality in the watershed. In the area with an altitude of 1200-1800 m, the change of eco-environmental quality was affected mainly by natural factors. However, areas below 1200 m are affected mainly by human activities. (4) It is predicted that by 2028, the ecological environment quality of the basin will continue to improve, and the area of good and high grade will account for 66%, but there are still big problems in the northwest of the basin because of the fragile ecological environment background.

Key words: Yanhe River Basin, remote sensing ecological index, ecological environment quality, principal component analysis, CA-Markov model

WANG Lixia,ZHAO Rui,LIU Zhao,ZHANG Shuangcheng,KONG Jinling,YANG Yun. Monitoring and prediction of ecological environmental quality in the Yanhe River Basin based on the remote sensing ecological index[J].Arid Zone Research, 2022, 39(3): 943-954.

Figures/Tables 14

Fig. 1

Tab. 1

Tab. 2

Calculation formula of each index"

指标	公式	参数说明
NDVI	$N D V I = N I R - R e d / N I R + R e d$	Red为红波段反射率;NIR为近红外波段反射率。分别可对应Landsat5 TM的3、4波段反射率和Landsat8 OLI的4、5波段反射率
Wet	$W e t T M = 0.0315 ρ 1 + 0.2021 ρ 2 + 0.3102 ρ 3 + 0.1594 ρ 4 - 0.6806 ρ 5 - 0.6109 ρ 6$ $W e t O L I = 0.1511 ρ 1 + 0.1973 ρ 2 + 0.3283 ρ 3 + 0.3407 ρ 4 - 0.7117 ρ 5 - 0.4559 ρ 6$	$ρ i$ (1、2、3、4、5、6)分别代表Landsat5 TM的1、2、3、4、5、7波段反射率和Landsat8 OLI的2、3、4、5、6、7波段反射率
NDBSI	$N D B S I = S I + I B I 2$ $S I = ρ 5 + ρ 3 - ρ 4 + ρ 1 / ρ 5 + ρ 3 + ρ 4 + ρ 1$ $I B I = 2 ρ 5 ρ 4 + ρ 5 - ρ 4 ρ 3 + ρ 4 + ρ 2 ρ 2 + ρ 5 / 2 ρ 5 ρ 4 + ρ 5 + ρ 4 ρ 3 + ρ 4 + ρ 2 ρ 2 + ρ 5$	$ρ i$ (1、2、3、4、5)分别代表Landsat5 TM波段1、2、3、4、5的反射率和Landsat8 OLI波段2、3、4、5、6的反射率
LST	$L S T = / T b / 1 + λ T b / ρ ε - 237.15$ $T b = K 2 / l n K 1 L 6 + 1$ $L 6 = g a i n × D N + b i a s$	DN为Landsat数据像元的灰度值;gain和bias分别为波段增益值和偏置值;L₆代表TM/TIRS热红外波段的辐射值;K₁和K₂为定标参数;T_b为亮度温度;λ为TM影像第6波段的中心波长11.5 μm和OLI数据的第10波段中心波长10.9 μm;ρ等于1.438×10^-2 mK;ε为比辐射率

Tab. 2

Tab. 3

Tab. 4

Fig. 2

Tab. 5

Fig. 3

Tab. 6

Tab. 7

Fig. 4

Fig. 5

Fig. 6

Fig. 7

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数据		数据来源	时间	空间分辨率/m	处理方法
影像数据	Landsat TM	地理空间数据云 http://www.gscloud.cn/	1998年6月16日 2004年6月14日 2010年6月30日	30	辐射定标、大气校正、影像拼接和裁剪等预处理
影像数据	Landsat OLI	地理空间数据云 http://www.gscloud.cn/	2016年6月17日	30	辐射定标、大气校正、影像拼接和裁剪等预处理
辅助数据	DEM	地理空间数据云 http://www.gscloud.cn/	2009年	30	投影变换、拼接和裁剪
	逐日降雨和温度数据	中国科学院资源环境科学与数据中心 https://www.resdc.cn/	1996年、1998年、2000年、2002年、2004年、2006年、2008年、2010年、2012年、2014年、2016年、2018年	30	选取研究区内、外共27个站点数据,计算各站点年均温度和年降水量,采用反距离插值法将计算得到的数据插值为空间分辨率与遥感影像一致的栅格数据,求取均值
	人口和GDP	陕西省统计年鉴	1998—2019年	-	对延河流域各县人口数据和GDP数据进行统计

年份	参数	第一主成分	第二主成分	第三主成分	第四主成分
1998	NDVI	0.521	0.420	-0.383	0.636
	Wet	0.525	-0.102	0.834	0.139
	NDBSI	-0.530	-0.340	0.166	0.759
	LST	-0.414	0.835	0.362	0.006
	特征值	0.137	0.022	0.008	0.000
	特征值贡献率/%	81.640	13.350	4.850	0.160
2004	NDVI	0.493	0.418	-0.478	-0.595
	Wet	0.514	0.119	0.834	-0.161
	NDBSI	-0.534	-0.242	0.213	-0.782
	LST	-0.456	0.868	0.175	0.091
	特征值	0.126	0.021	0.009	0.001
	特征值贡献率/%	79.950	13.150	5.680	0.870
2010	NDVI	0.548	0.328	-0.388	0.665
	Wet	0.547	0.003	0.837	0.036
	NDBSI	-0.541	-0.223	0.322	0.744
	LST	-0.329	0.918	0.214	-0.057
	特征值	0.138	0.019	0.006	0.001
	特征值贡献率/%	84.050	11.610	3.570	0.770
2016	NDVI	0.565	0.298	-0.548	0.541
	Wet	0.502	0.061	0.819	0.273
	NDBSI	-0.558	-0.216	0.093	0.796
	LST	-0.344	0.928	0.144	-0.006
	特征值	0.167	0.021	0.004	0.001
	特征值贡献率/%	86.720	10.710	2.280	0.290

年份	参数	NDVI	Wet	NDBSI	LST	RSEI
1998	均值	0.322	0.478	0.685	0.507	0.392
1998	对PC1荷载值	0.521	0.525	-0.530	-0.414	-
2004	均值	0.323	0.507	0.675	0.513	0.410
2004	对PC1荷载值	0.493	0.514	-0.534	-0.456	-
2010	均值	0.471	0.503	0.619	0.539	0.441
2010	对PC1荷载值	0.548	0.547	-0.541	-0.329	-
2016	均值	0.581	0.565	0.504	0.420	0.530
2016	对PC1荷载值	0.565	0.502	-0.558	-0.344	-

RSEI 等级	1998年		2004年		2010年		2016年		均值
RSEI 等级	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%
很差	1240.430	16.2	1344.149	17.6	948.261	12.4	687.401	9.0	1055.060	13.8
较差	3123.612	40.9	2731.561	35.7	2563.76	33.5	1650.651	21.6	2517.396	32.9
中等	2177.107	28.5	2167.987	28.3	2428.126	31.8	2229.879	29.2	2250.775	29.5
良好	706.533	9.3	878.767	11.5	1252.57	16.4	1969.7	25.8	1201.892	15.7
优良	390.561	5.1	528.387	6.9	452.606	5.9	1103.360	14.4	618.729	8.1
合计	7638.244	100.00	7650.851	100.00	7645.323	100.00	7640.991	100.00	7643.852	100.00

类别	差值	1998—2004年		2004—2010年		2010—2016年		1998—2016年
类别	差值	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%
变差	-4	0.118	0.000	0.572	0.010	2.343	0.030	1.577	0.020
	-3	2.410	0.030	6.343	0.080	12.629	0.170	13.319	0.170
	-2	68.338	0.890	67.848	0.890	64.073	0.840	116.252	1.520
	-1	1198.591	15.690	1224.513	16.020	506.432	6.630	891.650	11.690
不变	0	4499.537	58.910	4238.542	55.460	3571.693	46.770	2596.093	34.020
变好	1	1764.221	23.100	1737.903	22.740	2894.729	37.910	2328.290	30.510
	2	99.059	1.300	286.970	3.750	518.229	6.790	1085.440	14.230
	3	5.306	0.070	69.857	0.910	63.083	0.830	449.429	5.890
	4	0.428	0.010	10.327	0.140	3.549	0.050	148.193	1.940

Monitoring and prediction of ecological environmental quality in the Yanhe River Basin based on the remote sensing ecological index

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区域	样本点数	多元线性回归方程	R	R²	P
a	158	RSEI_i=1.688+0.147NDVI_i+0.347Wet_i-0.363NDBSI_i-0.009LST_i	0.789	0.622	<0.01
b	300	RSEI_i=2.332+0.086NDVI_i+0.199Wet_i-0.238NDBSI_i-0.220LST_i	0.585	0.342	<0.01
c	250	RSEI_i=2.520+0.012NDVI_i+0.195Wet_i-0.384NDBSI_i-0.032LST_i	0.643	0.413	<0.01
d	534	RSEI_i=1.335+0.150NDVI_i+0.368Wet_i-0.188NDBSI_i-0.090LST_i	0.641	0.411	<0.01
e	100	RSEI_i=1.201+0.385NDVI_i+0.263Wet_i-0.131NDBSI_i-0.112LST_i	0.772	0.596	<0.01

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