基于多源遥感数据的草地净初级生产力质量评价

植被净初级生产力NPP(Net Primary Production)的遥感估算与分析对全球生态系统具有重要指导意义,不同尺度的遥感影像上占主导地位的地物景观信息是不同的,现代生态研究多尺度分析至关重要。以青海省海北藏族自治州为研究区,使用Landsat 8 OLI遥感影像、天宫二号宽波段成像仪影像、融合影像(Landsat8 OLI影像和天宫二号宽波段成像仪影像融合)联同MODIS影像,作为CASA模型的输入参数,探究不同尺度下的研究区域NPP的空间分布情况,并对比分析不同数据源数据在估算NPP时的精度。结果表明:(1)Landsat 8 OLI数据的NPP值位于150—200 g C m^-2 a^-1所占比例最高;天宫二号宽波段成像仪影像数据和融合后影像的NPP值位于50—100 g C m^-2 a^-1所占比例最高;MODIS数据的NPP值位于小于50 g C m^-2 a^-1比例最高。(2)天宫二号宽波段成像仪影像数据的均方根误差(RMSE)、平均绝对误...

Evaluation of grassland NPP quality based on multi-source remote sensing data

Remote sensing estimation and analysis of Net Primary Production (NPP) have important roles for global ecosystem conservation. The dominant information of ground object landscape in remote sensing images of different scales is different. The multi-scale analysis of modern ecological research is crucial. This study area is Haibei Tibetan Autonomous Prefecture in Qinghai Province. We used Landsat 8 OLI remote sensing image, Wide-band Imaging Spectrometer (WIS) of Tiangong-2, fusion image (Landsat 8 OLI images and WIS of Tiangong-2 fusion), and MODIS image as input parameters of CASA model. We aim to explore the spatial distribution of NPP at different scales in study area, and to comparatively analyze different source data in estimating the accuracy of the NPP. The results showed that:(1) the NPP value using Landsat 8 OLI data was 150-200 g C m^-2 a^-1 and the proportion was the highest. The NPP value of the WIS of Tiangong-2 data and the fusion image were 50-100 g C m^-2 a^-1, which accounted for the highest proportion. The NPP value of MODIS data''s proportion of less than 50 g C m^-2 a^-1 was the highest. (2) The root mean square error (RMSE), MAE, and MAPE of the WIS of Tiangong-2 data were the smallest. The correlation between the WIS of Tiangong-2 image''s grassland NPP value inversed by Landsat 8 OLI image was the highest. It indicated that the accuracy of the WIS of Tiangong-2 data for describing grassland NPP was higher than that of fusion image and MODIS image. The MODIS image has a large error value and the lowest accuracy in describing the grassland NPP in this region.