Abstract
Animal husbandry is the main agricultural type over the Tibetan Plateau, above ground biomass (AGB) is very important to monitor the productivity for administration of grassland resources and grazing balance. The MODIS vegetation indices have been successfully used in numerous studies on grassland AGB estimation in the Tibetan Plateau area. However, there are considerable differences of AGB estimation models both in the form of the models and the accuracy of estimation. In this study, field measurements of AGB data at Sangke Town, Gansu Province, China in four years (2013-2016) and MODIS indices (NDVI and EVI) are combined to construct AGB estimation models of alpine meadow grassland. The field measured AGB are also used to evaluate feasibility of models developed for large scale in applying to small area. The results show that (1) the differences in biomass were relatively large among the 5 sample areas of alpine meadow grassland in the study area during 2013-2016, with the maximum and minimum biomass values of 3,963 kg DW/ha and 745.5 kg DW/ha, respectively, and mean value of 1,907.7 kg DW/ha; the mean of EVI value range (0.42-0.60) are slightly smaller than the NDVI’s (0.59-0.75); (2) the optimum estimation model of grassland AGB in the study area is the exponential model based on MODIS EVI, with root mean square error of 656.6 kg DW/ha and relative estimation errors (REE) of 36.3%; (3) the estimation errors of grassland AGB models previously constructed at different spatial scales (the Tibetan Plateau, the Gannan Prefecture, and Xiahe County) are higher than those directly constructed based on the small area of this study by 9.5%–31.7%, with the increase of the modeling study area scales, the REE increasing as well. This study presents an improved monitoring algorithm of alpine natural grassland AGB estimation and provides a clear direction for future improvement of the grassland AGB estimation and grassland productivity from remote sensing technology.
Highlights
Grassland biome is supported by the basic principles of primary productivity, especially for various animals of the grassland [1]
In this study, considering the factors discussed above, a region of alpine meadow grassland in the Tibetan Plateau (TP) area is used to conduct the following investigations: (1) to construct and analyze the above-ground biomass (AGB) estimation models and their accuracies based on the MODIS NDVI and EVI in study area; (2) the data from the sample plots observed inside the study area are used to validate the applicability of previous grassland models based on MODIS data and to investigate the reasons for errors from different models, and (3) based on the above research results, we propose a method to improve the accuracy of grassland AGB estimation
In order to study the relative estimation error of the alpine meadow grassland AGB monitoring model on the regional scale, the relative estimation errors (REE) are used to assess the performance of each previous model
Summary
Grassland biome is supported by the basic principles of primary productivity, especially for various animals of the grassland [1]. Grassland AGB can be estimated by using both traditional (or ground based) and remote sensing methods. The most suitable type of remote sensing estimation model is currently unknown and the applicability of these models still needs to be proved. Conduct the following investigations: (1) to construct and analyze the AGB estimation models and their accuracies based on the MODIS NDVI and EVI in study area; (2) the data from the sample plots observed inside the study area are used to validate the applicability of previous grassland models based on MODIS data and to investigate the reasons for errors from different models, and (3) based on the above research results, we propose a method to improve the accuracy of grassland AGB estimation. Approximately once a month and total of 9 field investigations were conducted over the four years
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