草业学报2017，Vol.26Issue(7)：23-34,12.DOI:10.11686/cyxb2017010
基于ADC和MODIS遥感数据的高寒草地地上生物量监测研究——以黄河源区为例
Monitoring of above-ground biomass in alpine grassland based on agricultural digital camera and MODIS remote sensing data: A case study in the Yellow River Headwater Region
摘要
Abstract
We collected grassland biomass and MODIS satellite remote sensing data,and calculated vegetation indices (VIs) from data obtained by an agricultural digital camera (ADC) in the Yellow River Headwater Region (YRHR) in August of 2015-2016.We explored the correlations between each of three ADC vegetation indices (NDVIADC,SAVIADC,and GNDVIADC) and field-measured grassland above-ground biomass (AGB),and selected the optimal ADC vegetation index to construct an AGB inversion model.Grassland AGB inversion models based on ADC vegetation indices and MODIS NDVI (denoted as NDVIMOD) were constructed,and the accuracy of each model was evaluated by leave-one-out cross validation (LOOCV) to identify the optimal grassland AGB monitoring model.The NDVIADC was used to correct the NDVIMOD to obtain the optimized grassland AGB model with high resolution and accuracy.The results showed that:1) among the three VIs-based ADC indices,the NDVIADC was most closely related to the AGB of alpine grassland,followed by SAVIADC and GND-VIADC.2) The NDVIADC-based AGB monitoring model (RMSEP:383.55-393.18 kg DW/ha;r:0.65-0.66)was more accurate than the NDVIMOD model (RMSEP:421.08-427.00 kg DW/ha;r:0.55-0.58).Therefore,the grassland AGB inversion value from the NDVIADC-based model was much closer to the actual grassland AGB in YRHR,and the sampling values of NDVIMoD were higher than those of NDVIADC as a whole.3) Among the four models based on NDVIADC,the linear and power models showed better performance in grassland AGB simulations.The linear model (y=3248.93XNDVIADC-305.59,RMSEP=383.55 kg DW/ha,r=0.66) was more accurate than the power model,and the linear model provided a novel and simple method to estimate grassland biomass in the study area.4) There was a strong correlation between NDVIADC and NDVIMOD;therefore,we could obtain an optimized grassland AGB model by using NDVIADC to correct NDVIMOD.The optimized model was y=2455.54XNDVIMoD-301.69.This model could be used to estimate the grassland biomass in YRHR on a large scale,and its precision was close to that of the field measurements.关键词
黄河源区/草地地上生物量/归一化差值植被指数/农业多光谱相机/MODIS/反演模型Key words
Yellow River headwater region/grassland above-ground biomass/normalized difference vegetation index/agricultural digital camera/MODIS/inversion model引用本文复制引用
葛静,孟宝平,杨淑霞,高金龙,殷建鹏,张仁平,冯琦胜,梁天刚..基于ADC和MODIS遥感数据的高寒草地地上生物量监测研究——以黄河源区为例[J].草业学报,2017,26(7):23-34,12.基金项目
国家自然科学基金项目(31672484,31372367,41401472),中国气象局气候变化专项项目(CCSF201603)和长江学者创新团队发展计划(IRT13019)资助. （31672484,31372367,41401472）
