神木市植被NDVI动态变化及时空格局分析  

Spatial-temporal dynamic characteristics of NDVI in Shenmu city

作　　者：邓目丽 蒋馥根 龙依 王天宏 孙华[1,2,3] DENG Muli;JIANG Fugen;LONG Yi;WANG Tianhong;SUN Hua(Research Center of Forestry Remote Sensing&Information Engineering,Central South University of Forestry&Technology,Changsha 410004,Hunan,China;Key Laboratory of Forestry Remote Sensing Based Big Data&Ecological Security for Hunan Province,Central South University of Forestry&Technology,Changsha 410004,Hunan,China;Key Laboratory of National Forestry and Grassland Administration on Forest Resources Management and Monitoring in Southern China,Central South University of Forestry&Technology,Changsha 410004,Hunan,China)

机构地区：[1]中南林业科技大学林业遥感信息工程研究中心,湖南长沙410004 [2]中南林业科技大学林业遥感大数据与生态安全湖南省重点实验室,湖南长沙410004 [3]中南林业科技大学南方森林资源经营与监测国家林业和草原局重点实验室,湖南长沙410004

出　　处：《中南林业科技大学学报》2023年第7期109-119,140,共12页Journal of Central South University of Forestry & Technology

基　　金：国家重点研发计划课题“三北防护林体系建设重大生态工程生态效益监测评估”(2017YFC0506502);国家林业和草原局荒漠化监测专项(101-9899);湖南省教育厅科学研究重点项目(17A225)。

摘　　要：【目的】归一化植被指数(normalized difference vegetation index,NDVI)可以指示植被生长和覆盖状况,探究其长时间序列时空动态变化特征规律,对明确区域植被生态变化情况具有重要意义。【方法】利用谷歌地球引擎(google earth engine,GEE)云平台获取植被生长季(7—9月)长时间序列Landsat影像并构建NDVI指数。通过变异系数、Sen+Mann-Kendall趋势分析、未来趋势变化分析和空间自相关性计算,分析2000—2020年神木市NDVI时空变化特征及空间格局,并提取NDVI不同聚集区面积结合植被生长季累计降水量和平均气温进行分析。【结果】2000—2020年神木市NDVI显著增加,增速为1.25%·a^(-1);NDVI增加区域、稳定不变区域和退化区域分别占总面积的97.8%、0.4%和1.8%,退化区域主要分布在矿区和城市聚集区;NDVI变异系数主要集中在0.3~0.5之间,整体波动比较剧烈,变异程度呈现“东南高、西北较低”的空间格局;全区NDVI平均Hurst指数0.69,持续显著增加面积占比93.7%,未来植被变化趋势良好。全局Moran’I指数波动下降,NDVI全局空间自相关性逐渐减弱;NDVI低-低型聚集区的面积波动减少,与植被生长季累计降水量表现为显著负相关(P<0.05),低-高型聚集区面积与植被生长季平均气温表现为显著负相关,高-高型聚集区的面积波动增加,与植被生长季累计降水量表现为显著正相关(P<0.05)。【结论】近21年神木市NDVI整体变化较为剧烈,呈现增加趋势,其空间分布趋于破碎化,降水和升温对植被恢复有一定促进作用,且降水对于植被恢复的影响更大,未来植被覆盖状况变化表现为持续性显著增加。【Objective】Normalized difference vegetation index(NDVI)can indicate the growth and coverage of vegetation,and explore the spatio-temporal dynamic change characteristics of long-time series,which is of great significance for clarifying the ecological change of regional vegetation.【Method】Google Earth Engine(GEE)cloud platform was used to obtain the long-term sequence Landsat images of vegetation growing seasons(July-September)and construct NDVI indexes.Through the variation coefficient,Sen+Mann-Kendall trend analysis,future trend change analysis and spatial autocorrelation calculation,the temporal and spatial variation characteristics and spatial pattern of NDVI in Shenmu city from 2000 to 2020 were analyzed,and the driving force analysis was carried out by extracting different aggregation areas combined with the data of cumulative precipitation and mean temperature in vegetation growing seasons.【Result】The NDVI in Shenmu increased significantly from 2000 to 2020,with a growth rate of 1.25%·a^(-1).The areas with increased,stable and degraded NDVI accounted for 97.8%,0.4%and 1.8%of the total area,respectively.The degraded areas were mainly distributed in the urban agglomeration areas.The NDVI variation coefficient was mainly concentrated in the range of 0.3-0.5,and the overall fluctuation was intense.The variation degree showed a spatial pattern of“high in the southeast and low in the northwest”.The average Hurst index of NDVI in the whole region was 0.69,and 93.7%of the area continuously increased significantly,indicating that the vegetation change in the future was good.The global Moran’I index decreased in volatility,and the global spatial autocorrelation of NDVI decreased gradually.The fluctuation of the aggregation area with low-low NDVI decreased,which was significantly negatively correlated with the cumulative precipitation in vegetation growing seasons(P<0.05).The area of low-high type aggregation area was significantly negatively correlated with the average temperature in vegetation grow

关 键 词：NDVI google earth engine 变异系数 Sen+Mann-Kendall 空间格局 神木市 

分 类 号：S758.51[农业科学—森林经理学]