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

doi:10.11686/cyxb2017010

摘要/Abstract

摘要： 利用2015-2016年8月采集的黄河源区草地生物量数据和MODIS卫星遥感资料,结合农业多光谱相机(agricultural digital camera,ADC)获取的植被指数数据,比较分析3种ADC植被指数(NDVI_ADC、SAVI_ADC和GNDVI_ADC)与野外实测草地地上生物量(above-ground biomass,AGB)数据的相关性,筛选出适合构建草地AGB反演模型的ADC植被指数;结合MODIS NDVI(记作NDVI_MOD)构建草地地上生物量反演模型,采用留一法交叉验证方法评价各模型精度,确立适宜模拟研究区草地AGB的最优模型;并利用NDVI_ADC校正NDVI_MOD,获得高分辨率、高精度的草地AGB遥感监测改进模型。结果表明,1)基于ADC获取的3种植被指数中,NDVI_ADC与高寒草地地上生物量关系最为密切,其次为SAVI_ADC,拟合效果最差的是GNDVI_ADC;2)基于NDVI_ADC建立的草地AGB监测模型的精度(RMSEP介于383.55~393.18 kg DW/hm²;r范围为0.65~0.66)远高于NDVI_MOD的模型精度(RMSEP介于421.08~427.00 kg DW/hm²;r范围为0.55~0.58),NDVI_ADC反演得到的草地AGB更接近于黄河源区草地实际生物量,且相较于NDVI_ADC,NDVI_MOD的样本值整体偏高;3)在NDVI_ADC构建的4类模型中,线性和乘幂模型模拟研究区草地AGB的能力较好,但线性模型精度更高(y=3248.93×NDVI_ADC-305.59,RMSEP=383.55 kg DW/hm²,r=0.66),该模型为黄河源区草地生物量的估测提供了一个新型且易操作的方法;4)NDVI_ADC与NDVI_MOD相关性较高,利用NDVI_ADC校正NDVI_MOD可以改进草地AGB遥感反演模型,优化模型为y=2455.54×NDVI_MOD-301.69。该模型可在大尺度范围内估测黄河源区的草地生物量,且模型精度接近于地表测量法的监测精度。

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 (NDVI_ADC, SAVI_ADC, and GNDVI_ADC) 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 NDVI_MOD) 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 NDVI_ADC was used to correct the NDVI_MOD 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 NDVI_ADC was most closely related to the AGB of alpine grassland, followed by SAVI_ADC and GNDVI_ADC. 2) The NDVI_ADC-based AGB monitoring model (RMSEP: 383.55-393.18 kg DW/ha; r: 0.65-0.66) was more accurate than the NDVI_MOD model (RMSEP: 421.08-427.00 kg DW/ha; r: 0.55-0.58). Therefore, the grassland AGB inversion value from the NDVI_ADC-based model was much closer to the actual grassland AGB in YRHR, and the sampling values of NDVI_MOD were higher than those of NDVI_ADC as a whole. 3) Among the four models based on NDVI_ADC, the linear and power models showed better performance in grassland AGB simulations. The linear model (y=3248.93×NDVI_ADC-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 NDVI_ADC and NDVI_MOD; therefore, we could obtain an optimized grassland AGB model by using NDVI_ADC to correct NDVI_MOD. The optimized model was y=2455.54×NDVI_MOD-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.

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.

GE Jing, MENG Bao-Ping, YANG Shu-Xia, GAO Jin-Long, YIN Jian-Peng, ZHANG Ren-Ping, FENG Qi-Sheng, LIANG Tian-Gang. 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[J]. Acta Prataculturae Sinica, 2017, 26(7): 23-34.

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