Improving Aboveground Forest Biomass Maps: From High-Resolution to National Scale
Improving Aboveground Forest Biomass Maps: From High-Resolution to National Scale
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DOI：
10.3390/rs11070795
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发表时间：
2019-04
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通讯作者：
Pilar Durante;Santiago Martín-Alcón;Assu Gil‐Tena;Nur Algeet;J. L. Tomé;L. Recuero;A. Palacios-Orueta;C. Oyonarte
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作者：
Pilar Durante;Santiago Martín-Alcón;Assu Gil‐Tena;Nur Algeet;J. L. Tomé;L. Recuero;A. Palacios-Orueta;C. Oyonarte
大范围和高时间分辨率的森林地上生物量（AGB）估计是至关重要的管理地中海森林生态系统，已被预测为气候变化的影响非常敏感。虽然许多模拟程序已被测试，以评估森林AGB，其中大多数覆盖小面积，并达到高精度的评估，很难更新和外推没有很大的不确定性。在这项研究中，专注于在西班牙的穆尔西亚地区（11,313平方公里），我们集成森林AGB估计，获得高精度的机载激光扫描（ALS）数据校准与绘图水平的地面测量和生物地球物理光谱变量（八个不同的指数来自MODIS计算在不同的时间分辨率），以及地形因素作为预测因子。我们使用分位数回归森林（QRF）的空间预测生物量和相关的不确定性。以归一化植被指数（NDVI）为主要植被指数，结合地形变量作为环境驱动因子，拟合模型的拟合效果令人满意（R2为0.71，RMSE为9.99 t·ha−1）。对最终预测的生物量图进行的独立验证显示了令人满意的预测稳健模型（R2 0.70和RMSE 10.25 t·ha−1），证实了其在较粗分辨率下的适用性。
Forest aboveground biomass (AGB) estimation over large extents and high temporal resolution is crucial in managing Mediterranean forest ecosystems, which have been predicted to be very sensitive to climate change effects. Although many modeling procedures have been tested to assess forest AGB, most of them cover small areas and attain high accuracy in evaluations that are difficult to update and extrapolate without large uncertainties. In this study, focusing on the Region of Murcia in Spain (11,313 km2), we integrated forest AGB estimations, obtained from high-precision airborne laser scanning (ALS) data calibrated with plot-level ground-based measures and bio-geophysical spectral variables (eight different indices derived from MODIS computed at different temporal resolutions), as well as topographic factors as predictors. We used a quantile regression forest (QRF) to spatially predict biomass and the associated uncertainty. The fitted model produced a satisfactory performance (R2 0.71 and RMSE 9.99 t·ha−1) with the normalized difference vegetation index (NDVI) as the main vegetation index, in combination with topographic variables as environmental drivers. An independent validation carried out over the final predicted biomass map showed a satisfactory statistically-robust model (R2 0.70 and RMSE 10.25 t·ha−1), confirming its applicability at coarser resolutions.