Netsianda Andisani, Mhangara Paidamwoyo
School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg, 2000, South Africa.
Council for Geosciences, 280 Pretoria Street, Pretoria, 0184, Silverton, South Africa.
Environ Monit Assess. 2025 Jan 6;197(2):138. doi: 10.1007/s10661-024-13610-1.
The grassland ecosystem forms a critical part of the natural ecosystem, covering up to 15-26% of the Earth's land surface. Grassland significantly impacts the carbon cycle and climate regulation by storing carbon dioxide. The organic matter found in grassland biomass, which acts as a carbon source, greatly expands the carbon stock in terrestrial ecosystems. Correct estimation of above ground biomass (AGB) and its spatial and temporal changes is vital for determining the carbon cycle of the grassland. Datasets from multiple sources were fused to accomplish the objective of the study. The Sentinel-2 sensor band, vegetation index (NDVI), and Shuttle Radar Topography Mission (SRTM) DEM products were used as predictor variables, while Global Ecosystem Dynamics Investigations (GEDI) mean above-ground biomass density (AGBD) data was used to train the model. Random forest (RF) and gradient boosting were used to estimate the AGB of the grassland biome. We also identified the correlation between Sentinel-2-derived vegetation indices and ground-based measurements of leaf area index (LAI). The processing duration, parameter requirements, and human intervention are reduced with RF and gradient boosting algorithms. Due to its fundamental concept, ensemble algorithms effectively handled multi-modal data and automatically conducted spectral selection. The findings show variations in the study area's AGB concentration throughout five years. According to the results, gradient boosting models outperformed RF models in both years. RF achieved the highest R value of 0.5755 Mg/ha, while gradient boosting achieved the highest R value of 0.7298 Mg/ha. Sentinel-2-derived VI vs LAI results show that NDVI was the best-performing model with an R value of 0.6396 m m and an RMSE of 0.159893 m m, followed by OSAVI, NDRE, and MSAVI. This result shows that sensor data and field biophysical data can map the terrestrial ecosystem's biomass.
草原生态系统是自然生态系统的重要组成部分，覆盖了地球陆地表面的15%-26%。草原通过储存二氧化碳对碳循环和气候调节产生重大影响。草原生物量中的有机物质作为碳源，极大地增加了陆地生态系统中的碳储量。准确估计地上生物量（AGB）及其时空变化对于确定草原的碳循环至关重要。为实现研究目标，融合了多个来源的数据集。使用哨兵-2传感器波段、植被指数（NDVI）和航天飞机雷达地形测绘任务（SRTM）数字高程模型（DEM）产品作为预测变量，同时使用全球生态系统动态调查（GEDI）平均地上生物量密度（AGBD）数据训练模型。采用随机森林（RF）和梯度提升算法来估计草原生物群落的AGB。我们还确定了哨兵-2衍生植被指数与叶面积指数（LAI）地面测量值之间的相关性。RF和梯度提升算法减少了处理时间、参数要求和人工干预。由于其基本概念，集成算法有效地处理了多模态数据并自动进行了光谱选择。研究结果显示了研究区域内AGB浓度在五年间的变化。结果表明，在这两年中，梯度提升模型的表现均优于RF模型。RF达到的最高R值为0.5755 Mg/ha，而梯度提升达到的最高R值为0.7298 Mg/ha。哨兵-2衍生植被指数与LAI的结果表明，NDVI是表现最佳的模型，R值为0.6396 mm，均方根误差（RMSE）为0.159893 mm，其次是土壤调整植被指数（OSAVI）、归一化差值红边指数（NDRE）和修正型土壤调整植被指数（MSAVI）。这一结果表明，传感器数据和野外生物物理数据能够绘制陆地生态系统的生物量。
