基于MODIS数据与机器学习的青藏高原草地地上生物量研究

A study of grassland aboveground biomass on the Tibetan Plateau using MODIS data and machine learning

青藏高原位于我国西部,又被称为“世界第三极”,对我国和世界的生态以及气候变化影响显著。为了评估2000-2020年青藏高原草地地上生物量(aboveground biomass,AGB)的变化情况,本研究采用多种机器学习方法结合MCD43A4产品数据模拟了草地地上生物量,并对该区域草地地上生物量的时空特征进行分析。结果表明:1)构建的机器学习模型中,Rborist模型精度最高,基于筛选后变量的R2达到0.6484。“prec_05”、“prec_06”、“tp_12”、“NDPI”、“prec_04”、“tmax_01”、“prec_08”、“prec_12”这8个变量与生物量相关;2)青藏高原东南部的生物量要高于西北部,呈现由东南向西北递减趋势;3)2000-2020年间青藏高原草地生物量稳步增长,整体向好发展。青藏高原61.38%的草地变化趋势不具有可持续性,4.67%的草地持续性轻微恶化,持续性明显恶化的区域占比1.19%,呈稳定或恢复趋势的区域占比32.76%。

The Tibetan Plateau,often referred to as“the third pole of the world”,is located in the Southwest of China,and makes significant contribution to ecology and climate change in China and around the word. Our study evaluated the change in aboveground biomass(AGB)on the Tibetan Plateau from 2000 to 2020. We used multiple machine learning methods combined with MCD43A4 product data to simulate the aboveground biomass and analyzed the temporal and spatial characteristics of AGB in this region. The main results were as follows:1)Among the constructed machine learning models,Rborist model demonstrated the highest accuracy,with an R2of 0. 6484 based on screened variables,and eight variables were found to be highly correlated with biomass:precipitation in May,precipitation in June, average temperature in December, normalized difference phenology index(NDPI),precipitation in April,maximum temperature in January,precipitation in August and precipitation in December;2)AGB in the southeast of the Tibetan Plateau was higher than that in the northwest,with a decreasing trend from the southeast to northwest;3) AGB on the Tibetan Plateau increased steadily from 2000 to 2020,with an overall positive movement. However,61. 38% of Tibetan Plateau grasslands showed a trend of unsustainability,4. 67%showed a slight deterioration trend,1. 19% showed a significant deterioration trend,and 32. 76% were stable or showed a recovery trend.