西藏高原中部草地地上生物量遥感估算方法

Aboveground Biomass Estimate Methods of Grassland in the Central Tibet

为了充分利用现有MODIS卫星遥感数据和NASA等提供的相关陆地数据产品,实现对西藏高原草地生物量定量化业务监测和草地退化研究,利用西藏高原中部2004年5—9月草地植被生长期的实测地上生物量,结合同期的MODIS 16 d合成产品MOD13Q1 NDVI和EVI数据,建立了草地生长期地上生物量遥感监测模型和方法;同时,从植被非生长季等不同时段遥感监测业务需求出发,研究了以2个月为时间尺度的草地地上生物量定量化监测和估算模型。得出的主要结论如下:1.基于MODIS NDVI的指数函数模型是监测和估算西藏高原中部草地生长期地上生物量大小的最优模型,在所有模型中有最高的相关系数(0.778)和F检验值(127.557)。2.由于绿色植被所独有的光谱响应特征,无论是MODIS NDVI还是EVI,对植被生长期鲜草生物量估算的精度要好于总地上生物量。3.以2个月为时间尺度的监测结果来看,除1—2月草地总地上生物量和5—6月的鲜草生物量与MODIS NDVI之间分别表现为乘幂函数和线性关系外,其他都呈基于NDVI的指数函数关系,且相关系数都大于0.64,其中植被生长时期的相关系数要大于非生长季节;对于草地总地上生物量,最高的相关程度出现在8—9月,为0.749,最低出现在非生长季节1—2月,为0.644;对鲜草生物量估算,基于NDVI的相关系数都>0.73,最高的8—9月达0.826。

To take full advantage of MODIS remote sensing NASA MODIS Land Products distributed from LP DAAC for the aboveground biomass （AGB） estimate methods of data obtained from the receiving station and existing grassland biomass monitoring and degradation study, grassland in Tibet are developed at growing season from May to September and bimonthly level by integrating AGB data collected from 11 sites in the central Tibet from January to December in 2004 and concurrent vegetation index （VI） derived from MODIS MOD13Q1 products. The main re- suits show that there are exponential relationships between AGB and NDVI during the vegetation growing season from May to September with 0. 778 of correlation coefficient and 127. 557 of F test value; at bimonthly level the ex- ponential relationships between AGB and NDVI exist except the power relationship between AGB and NDVI from January to February and linear relationship between fresh AGB and NDVI from May to Jun; the correlation coeffi- cients between NDVI and AGB are above 0. 64 with the highest value of 0. 749 0 for AGB estimates from August to September and the lowest value of 0. 644 0 from January to February; the correlation coefficients between NDVI and fresh AGB are above 0. 72 with the highest value of 0. 826 0 from August to September and the lowest value of 0. 727 5 from May to Jun. The results by P 〈 0. 01 test of significance indicate the highly significant positive corre- lation between aboveground biomass and MODIS NDVI. In contrast, MODIS NDVI is the optimum vegetation index for AGB and fresh AGB estimates in the central Tibet. The study suggests that satellite remote sensing is only practi- cal means of monitoring grass biomass and other vegetation parameters at large scale in Tibet due to its vast area and complexity of grassland ecosystem. However, remote sensing based estimate methods of AGB should be further vali- dated and improved using more ground based measurement data to compensate for its inadequacies. The AGB esti- mates developed in this study is suitable for the central Tibet and its accuracy will decrease if applied in whole Tibet plateau, which means that more detailed observation and investigation should be carried out in the future to further improve these biomass estimation models in order to be suitable for monitoring grasslandAGB and other vegetation parameters of the entire Tibetan Plateau.