融合MODIS和Landsat数据的青海湖流域典型区NDVI重构与年内最大值变化分析  

Integrating MODIS and Landsat data to reconstruct the Landsat NDVI of a typical region in the Qinghai Lake Basin and changes in the intra-annual NDVI maximum

作　　者：李芳 王广军[1] 杜海波 李萌[1] 梁四海[3] 彭红明 LI Fang;WANG Guang-jun;DU Hai-bo;LI Meng;LIANG Si-hai;PENG Hong-ming(School of Land Science and Technology,China University of Geosciences(Beijing),Beijing 100083,China;Inner Mongolia Survey Team of Coalfield Geology Bureau,Hohhot 010010,China;School of Water Resources and Environment,China University of Geosciences(Beijing),Beijing 100083,China;Qinghai Bureau of Environmental Geology Exploration,Xining 810007,China;Key Laboratory of Environmental Geology of Qinghai Province,Xining 810007,China)

机构地区：[1]中国地质大学(北京)土地科学技术学院,北京100083 [2]内蒙古煤田地质局勘测队,内蒙古呼和浩特010010 [3]中国地质大学(北京)水资源与环境学院,北京100083 [4]青海省环境地质勘查局,青海西宁810007 [5]青海省环境地质重点实验室,青海西宁810007

出　　处：《草业学报》2023年第8期28-39,共12页Acta Prataculturae Sinica

基　　金：中国科学院战略性先导科技专项(XDA20100103);青海省应用基础研究项目(2017-ZJ-743)资助

摘　　要：归一化植被指数(NDVI)能够较准确表达出植被覆盖和生长状况,对其进行时间序列分析已成为研究全球、国家或区域植被生长的重要方式。针对当前NDVI时序产品空间分辨率不高,难以应用于小尺度的精细研究,以及利用Landsat不同时相NDVI评估生态环境质量受植被季相和年际变化影响较大等问题,首先基于增强型时空自适应反射率融合模型(ESTARFM)融合MOD09Q1和Landsat数据,对植被年内生长季NDVI数据进行预测插补,之后利用Logistic模型重构2001-2020年植被生长季NDVI曲线,通过引入MODIS逐日NDVI数据确定NDVI年内最大值日期,逐像素求解出最优的Landsat NDVI年内最大值,并将其应用于青海湖流域布哈河附近局部典型区域植被生长状况评估。结果表明:1)融合MODIS和Landsat数据的Landsat NDVI年内最大值求解结果在3倍中误差以内的占98.5%,求解结果具有较高的精度;2)利用Landsat NDVI年内最大值进行植被生长状况评估,能弱化Landsat数据因时相差异引起的误差;3)研究区植被NDVI年内最大值呈南北高中间低的空间分布特点,年际变化整体先降低再增加,植被生长状况呈向好趋势;高寒嵩草、杂类草草甸NDVI年内最大值呈减少趋势且波动剧烈,应是青海湖流域监测的重点植被类型。The normalized difference vegetation index(NDVI)can provide accurate information about vegetation cover and growing status,and the changes in NDVI over a period of time are important indicators of vegetation growth on a global,national,or regional scale.However,there are two main problems to address:First,the spatial resolution of existing NDVI long-time-series products is coarse,so these data can only be used on a large scale,and not on a fine scale.Second,NDVI data obtained from medium-resolution images at different times,such as Landsat images,are greatly influenced by seasonal and inter-annual changes in vegetation as well as the quality of the ecological environment.To solve these two problems,we used the enhanced spatial and temporal adaptive reflectance fusion model(ESTARFM),which integrates MOD09Q1 and Landsat data,to predict and interpolate the Landsat NDVI for the annual growing season.Then,the Logistic model was used to reconstruct NDVI curves covering vegetation growth seasons from 2001 to 2020.The date corresponding to the annual maximum of Landsat NDVI was obtained from the MODIS daily NDVI data.After reconstructing the curve,we introduced the date parameter to solve the optimal intra-annual NDVI maximum pixel by pixel.Finally,we used the annual maximum of Landsat NDVI to analyze the vegetation growth status and evaluate the change in vegetation in a typical local area near the Buha River in the Qinghai Lake basin.The results show that:1)The annual maximum of Landsat NDVI was fitted after combining the MODIS and Landsat data.Most(98.5%)of the pixels in the results were within the triple root mean square error(RMSE),indicative of high precision of the model.2)When assessing vegetation growth status,the annual maximum value of Landsat NDVI was able to capture detailed changes in vegetation and reduced the errors caused by time differences in the Landsat data.3)The spatial distribution of the maximum NDVI of vegetation in the study area was high in the north and south and low in the middle.The inter-an

关 键 词：青海湖流域 LANDSAT NDVI年内最大值 时空融合算法 LOGISTIC模型 时空变化 

分 类 号：Q948[生物学—植物学]