Analysis on Forest Vegetational Cover Changes in Yuxi and its Driving Force

doi:10.13466/j.cnki.lyzygl.2021.01.016

Abstract

Abstract:

Forest covers can reflect the health of the forest ecosystem and space-time change is the basis for estimating the direction of the forest ecosystem development.This study attempts to analyze the change of time and space pattern of forest vegetational cover in Yuxi,Southwest China's Yunnan Province,from 2010 to 2019,and establish the regression model by single-factor and multi-regression analysis methods,respectively,to explore the drivers for the improvement and degradation of vegetational covers.The results show that the forest vegetational cover show a good trend and the factors and slope are not simple linear relationship.The study finds that the improvement rate of road and residential areas and forest vegetational cover are negatively related and positively related to the rate of degradation.Moreover,DEM,slope,large water body,public welfare forest management and forest cover improvement rate are positively correlated with the degradation rate and the slope direction is positively correlated with the improvement rate of stand vegetational covers,and the topographic fluctuation degree is negatively correlated with the degradation rate of stand vegetation cover.The driving forces for the improvement and degradation of forest vegetational covers are not the same and the influence mechanism is also different,which provides a scientific basis for better formulaing ecological protection policies.

Key words: MODIS EVI, vegetational cover, driving force, single factor analysis, multiple regression analysis

CLC Number:

X173

ZHU Hongqin, WANG Jingwen, XV Yanhong, QI juncheng, LUO Hang, PENG Zexi. Analysis on Forest Vegetational Cover Changes in Yuxi and its Driving Force[J]. FOREST RESOURCES WANAGEMENT, 2021, (1): 121-131.

Figures/Tables 16

Fig.1

Tab.1

Tab.2

Variation of Forest Subplant Coverage in Yuxi

变化趋势	变化率	面积/hm²	百分比/%
严重退化	-0.06032≤ $b^$ <-0.01142	93317.31	8.68%
轻微退化	-0.01142≤ $b^$ <-0.00001	327236.40	30.45%
基本不变	-0.00001≤ $b^$ <0.00001	797.04	0.07%
轻微改善	0.00001≤ $b^$ <0.01498	541238.22	50.36%
明显改善	0.01498≤ $b^$ <0.05004	112177.53	10.44%

Tab.2

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Tab.3

Tab.4

Tab.5

Tab.6

References 26

[1]	李玉婷, 曹银贵, 王舒菲, 等. 黄土露天矿区排土场重构土壤典型物理性质空间差异分析[J]. 生态环境学报, 2020,29(3):615-623.
[2]	D. Stow, S. Daeschner, A. Hope. et al. Variability of the seasonally integrated normalized difference vegetation index across the North slope of Alaska in the 1990s[J]. International Journal of Remote Sensing, 2003,24(5):1111-1117.
[3]	李登科, 范建忠, 权文婷. 陕西省植被退化及其驱动因素分析[J]. 生态学杂志, 2015,34(10):2907-2913.
[4]	龚文峰, 曹凯华, 周莹. 基于RS和GIS的甘南县2006—2016年植被覆盖度时空演变分析[J]. 湖北农业科学, 2019,58(12):52-57.
[5]	吴见, 彭建, 谷双喜. 黄山市近30a植被盖度时空变化遥感分析[J]. 黑龙江工程学院学报, 2020,34(5):6-10.
[6]	龚文峰, 吴娟, 王笑峰, 等. 基于RS与GIS密云区植被盖度动态演变及其预测研究[J]. 黑龙江大学工程学报, 2016,7(3):25-31.
[7]	信忠保, 肖玉玲, 王冬梅, 等. 广西桂林漓江河岸带植被配置类型与退化机制研究[J]. 生态科学, 2014,33(4):631-641.
[8]	宋春桥, 游松财, 刘高焕, 等. 那曲地区草地植被时空格局与变化及其人文因素影响研究[J]. 草业学报, 2012,21(3):1-10.
[9]	李杰, 张军, 刘陈立, 等. 基于MODIS-NDVI的中老缅交界区近16年植被覆盖时空变化特征[J]. 林业科学, 2019,55(8):9-18.
[10]	谷雷, 岳彩荣, 赵勋, 等. 基于Google Earth Engine的云南省1999—2018年植被覆盖变化分析[J]. 西部林业科学, 2020,49(5):74-80.
[11]	吕国旭, 陈艳梅, 邹长新, 等. 京津冀植被覆盖退化的空间格局及人为驱动因素分析[J]. 生态与农村环境学报, 2017,33(5):417-425.
[12]	李广宇, 陈爽, 余成, 等. 长三角地区植被覆盖退化的空间格局及影响因素分析[J]. 长江流域资源与环境, 2015,24(4):572-577.
[13]	周婷, 张勇, 严力蛟. 生态公益林补偿政策对植被覆盖时空格局的影响—以杭州市临安区为例[J]. 生态学报, 2018,38(13):4800-4808.
[14]	刘虎, 姜岳, 夏明宇. 等. 基于30 a遥感监测的矿区生态环境变化—以南四湖周边矿区为例[J/OL].金属矿山:1-15(2020-10-28)[2020-12-11].http://kns.cnki.net/kcms/detail/34.1055.TD.20201027.2006.002.html.
[15]	马伟波, 赵立君, 田佳榕, 等. 基于地形位置指数的赤水河流域植被时空变化研究[J/OL]. 环境科学研究:1-11.(2020-10-26)[2020-12-11].https://doi.org/10.13198/j.issn.1001-6929.2020.10.19.
[16]	刘思怡, 丁建丽, 张钧泳, 等. 艾比湖流域草地生态系统环境健康遥感诊断[J]. 草业学报, 2020,29(10):1-13.
[17]	韦惠兰, 祁应军. 基于遥感监测的青藏高原草地退化及其人文驱动力分析[J]. 草业科学, 2016,33(12):2576-2586.
[18]	李文梅, 覃志豪, 李文娟, 等. MODIS NDVI与MODIS EVI的比较分析[J]. 遥感信息, 2010(6):73-78.
[19]	王正兴, 刘闯, 陈文波, 等. MODIS增强型植被指数EVI与NDVI初步比较[J]. 武汉大学学报:信息科学版, 2006(5):407-410.
[20]	Planque C, Carrer D, Roujean J-L. Analysis of MODIS albedo changes over steady woody covers in France during the period of 2001-2013[J]. Remote Sensing of Environment, 2017,191:13-29.
[21]	张军, 李晓东, 陈春艳, 等. 新疆地势起伏度的分析研究[J]. 兰州大学学报:自然科学版, 2008,000(S1):13-16.
[22]	涂汉明, 刘振东. 中国地势起伏度研究[J]. 测绘学报, 1991(4):311-319.
[23]	Song Wei, Deng Xiangzheng. Land-use/land-cover change and ecosystem service provision in China[J]. Science of the Total Environment, 2017,576:705-719.
[24]	曹俊忠. 一元线性回归显著性检验方法分析[J]. 西安工程科技学院学报, 1988(Z1):78-82.
[25]	Paul J Ferraro, Agnes Kiss. Direct payments to conserve biodiversity[J]. Science, 2002,298(5599):1718-1719. doi: 10.1126/science.1078104 pmid: 12459569
[26]	Yu Li, Andrés Viña, Wu Yang, et al. Effects of conservation policies on forest cover change in giant panda habitat regions,China[J]. Land Use Policy, 2013,33:42-53.

EVI等级	1	2	3	4	5	6	7	8	9	10	合计
1	0.65	0.72	0.59	0.44	0.25	0.14	0.07	0.03	0.02	0.01	2.92
2	0.42	1.29	2.15	2.26	1.63	0.94	0.53	0.32	0.16	0.08	9.78
3	0.24	0.99	2.26	3.19	3.20	2.31	1.31	0.78	0.38	0.20	14.85
4	0.15	0.67	1.69	2.94	3.78	3.38	2.25	1.29	0.68	0.31	17.14
5	0.09	0.46	1.21	2.34	3.33	3.66	2.90	1.75	0.96	0.44	17.15
6	0.05	0.26	0.80	1.60	2.66	3.11	2.78	1.89	1.05	0.50	14.71
7	0.02	0.15	0.45	1.00	1.69	2.10	2.06	1.64	0.92	0.40	10.43
8	0.01	0.08	0.25	0.58	1.04	1.36	1.47	1.17	0.72	0.32	7.00
9	0.01	0.04	0.14	0.31	0.59	0.81	0.83	0.74	0.47	0.22	4.17
10	0.00	0.02	0.06	0.14	0.26	0.35	0.39	0.32	0.21	0.10	1.84
合计	1.64	4.68	9.59	14.79	18.43	18.16	14.60	9.93	5.59	2.59	100.00

模型	R	R²	调整后R²	标准估算的错误
1	0.551	0.304	0.304	0.003653634
2	0.589	0.346	0.346	0.003540470
3	0.598	0.358	0.358	0.003508635
4	0.602	0.363	0.363	0.003495542
5	0.603	0.363	0.363	0.003494882
6	0.603	0.363	0.363	0.003494327
7	0.603	0.364	0.363	0.003493891

模型	变量	未标准化系数B	t	显著性	VIF
7	(常量)	1.95×10^-4	7.717	0.000
	DEM	1.05×10^-9	74.971	0.000	1.541
	坡度	2.48×10^-6	38.350	0.000	1.358
	坡向	1.67×10^-13	22.331	0.000	1.262
	道路	-6.38×10^-10	-8.371	0.000	2.594
	大型水体	9.53×10^-11	3.605	0.000	1.191
	公益林管护区	6.48×10^-10	3.268	0.001	1.002
	居民区	-3.50×10^-10	-2.925	0.003	2.476

模型	R	R²	调整后R²	标准估算的错误
1	0.564	0.318	0.318	0.003426681
2	0.577	0.333	0.333	0.003387655
3	0.578	0.334	0.334	0.003384938
4	0.579	0.335	0.335	0.003383251
5	0.579	0.335	0.335	0.003381761
6	0.579	0.336	0.336	0.003381011
7	0.580	0.336	0.336	0.003380763

模型	变量	未标准化系数B	t	显著性	VIF
7	(常量)	1.43×10^-5	0.479	0.632
	DEM	-9.83×10^-10	-58.599	0.000	2.773
	居民区	4.73×10^-4	23.294	0.000	3.267
	地形起伏度	-4.82×10^-5	-4.580	0.000	2.143
	道路	4.11×10^-10	7.202	0.000	1.103
	大型水体	-1.16×10^-10	-5.833	0.000	1.077
	坡度	-2.25×10^-7	-4.372	0.000	1.602
	公益林管护区	-6.10×10^-10	-2.576	0.010	1.012