Shuxia Yang

TL;DR: In this paper, a back-propagation artificial neural network (BP ANN) was used to select the variables that contribute the most to the model's estimation of AGB, and then they built the model.

TL;DR: In this article, four types of remote sensing retrieval models (i.e., pixel dichotomy models, univariate vegetation index (VI) regression models, multivariate regression models and a support vector machine (SVM) model) are built to derive grassland cover based on moderate resolution imaging spectroradiometer (MODIS) data and the measured grassland coverage data collected by unmanned aerial vehicle during the grassland peak growing season from 2014 to 2016.

TL;DR: In this article, the authors evaluate various methods for estimating the above-ground biomass (AGB) of alpine grassland vegetation using MODIS vegetation indices, in combination with long-term climate and grassland monitoring data collected at 15 site-specific stations, in the pastoral area of southern Qinghai Province (i.e., the Three-River Headwaters Region) of China.

TL;DR: Results showed that filtering the MODIS NDVI using the Savitzky–Golay (SG), logistic and Gaussian approaches can reduce the AGB estimation error; in particular, the SG method performs the best, with the smallest errors at both the sample plot scale and the entire study area.

TL;DR: In this article, a model based on multiple variables (biogeographical, meteorological, and moderate resolution imaging spectroradiometer (MODIS) product) using a random forest (RF) algorithm to predict the spatial distribution of grassland height in the Tibetan Plateau (TP) from 2003 to 2017 was constructed.