Details

Autor(en) / Beteiligte

• Liang, Tiangang
• Yang, Shuxia
• Feng, Qisheng
• Liu, Baokang
• Zhang, Renping
• Huang, Xiaodong
• Xie, Hongjie

Titel

Multi-factor modeling of above-ground biomass in alpine grassland: A case study in the Three-River Headwaters Region, China

Ist Teil von

• Remote sensing of environment, 2016-12, Vol.186, p.164-172

Ort / Verlag

New York: Elsevier Inc

Erscheinungsjahr

2016

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• In this study, we evaluate various methods for estimating the above-ground biomass (AGB) of alpine grassland vegetation using Moderate Resolution Imaging Spectroradiometer (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. The results show that (1) over the past 12years, there were considerable spatial variations in the grassland AGB and NDVI, with the average AGB in the peak period of grassland growth in the range of 329–3653kg DW/ha, corresponding to an average NDVI of 0.25–0.72; (2) Grassland AGB is affected by various factors, such as geographic location, topography, climate, soil, and grass types. Single-factor AGB models only account for 15–49% of the variations in the grassland AGB during the peak period of grass growth, with NDVI-based AGB model to be the best (46%) among all linear remote sensing models we tested; and (3) although the multi-factor model (based on latitude, longitude, and grass cover and height) performs the best (70%) in estimating the AGB, it is not possible for operation due to the current difficulty of grass height modeling. The alternative and operational multi-factor model f(x,y,c) (latitude, longitude, and grass cover) can achieve reasonable estimation of AGB (63%), with the grass cover modeled from the MODIS reflectance, which would be further improved in conjunction with unmanned aerial vehicle technology in the future. Using this model f(x,y,c), the root-mean-square error (RMSE) of AGB estimation is reduced by 20% (i.e., 151kg DW/ha) as compared with the best single-factor model based on the NDVI (RMSE of 887kg DW/ha). •NDVI-based AGB model is the best among all linear models we tested.•The multi-factor model can achieve reasonable estimation of AGB in study area.•The grass cover inversions can be improved with UAV technology in future.