ABSTRACT

We investigated the potential of hyperspectral remote sensing to estimate aboveground biomass (AGB) over the Brazilian savannas (Cerrado), the second-largest source of carbon emissions in Brazil. For this purpose, a Hyperion/Earth Observing-1 (EO-1) image was collected in the dry season at the Ecological Station of Águas Emendadas (ESAE). In order to estimate the AGB, we evaluated the performance of five machine learning models (Classification and Regression Trees – CART; Cubist – CB, Partial Least Squares Regression – PLS; Random Forest – RF; and Support Vector Machine – SVM) and four sets of metrics (reflectance, narrowband vegetation indices – VIs; absorption band parameters; and the combination of these attributes). The lowest root mean square error (RMSE) was obtained for RF using VIs (29%) and a combination of metrics (28%). For VIs, RF differed from CUB, PLS and SVM at 5% significance level. From cross-validation results, the RMSE was 26.36% for grasslands, 35.04% for open savannas, and 24.85% for dense savannas. The RF model with VIs had the most stable predictive performance across the models, as indicated by small variations in RMSE from CART to SVM. The five most important ranked VIs in the RF model were the Normalized Difference Vegetation Index (NDVI), Pigment Specific Simple Ratio (PSSR), Enhanced Vegetation Index (EVI), Red Edge Normalized Difference Vegetation Index (RENDVI) and Structure Insensitive Pigment Index (SIPI). Most of their relationships with AGB were non-linear. The resultant AGB estimates showed consistent results with a vegetation cover map of the ESAE. Areas of the ESAE with AGB lower than 10 Mg.ha⁻¹ were coincident with the occurrence of grassland physiognomies (savanna grasslands and shrub savannas), while areas with AGB higher than 25 Mg.ha⁻¹ matched the occurrence of dense savanna physiognomies (woodland savanna and dense woodland savanna). Grassland areas showed larger values of coefficient of variation (CV) than areas of dense savannas. These first-hand results set a baseline of models and metrics for AGB modeling of savannas during the future transition from current sampling-type hyperspectral missions (< 10 km of swath) to large-coverage hyperspectral satellites (> 100 km of swath).

摘要

我们研究了高光谱遥感在估计巴西热带稀树草原 ( Cerrado )地上生物量 (AGB) 的潜力，巴西热带稀树草原是巴西的第二大碳排放源。为此，在Águas Emendadas生态站的旱季收集了 Hyperion/Earth Observing-1 (EO-1) 图像(ESAE)。为了估计 AGB，我们评估了五个机器学习模型（分类和回归树 – CART；Cubist – CB，偏最小二乘回归 – PLS；随机森林 – RF；和支持向量机 – SVM）和四个集合的性能指标（反射率、窄带植被指数 – VI；吸收带参数；以及这些属性的组合）。使用 VI (29%) 和指标组合 (28%) 获得了 RF 的最低均方根误差 (RMSE)。对于 VI，RF 在 5% 的显着性水平上与 CUB、PLS 和 SVM 不同。从交叉验证结果来看，草原的 RMSE 为 26.36%，开阔的稀树草原为 35.04%，密集的稀树草原为 24.85%。从 CART 到 SVM 的 RMSE 微小变化表明，带有 VI 的 RF 模型在所有模型中具有最稳定的预测性能。RF 模型中五个最重要的排序 VI 是归一化差异植被指数 (NDVI)、颜料比简单比 (PSSR)、增强型植被指数 (EVI)、红边归一化差异植被指数 (RENDVI) 和结构不敏感颜料指数 ( SIPI）。他们与 AGB 的大部分关系都是非线性的。由此产生的 AGB 估计结果与 ESAE 的植被覆盖图一致。AGB 低于 10 Mg.ha 的 ESAE 区域 由此产生的 AGB 估计结果与 ESAE 的植被覆盖图一致。AGB 低于 10 Mg.ha 的 ESAE 区域 由此产生的 AGB 估计结果与 ESAE 的植被覆盖图一致。AGB 低于 10 Mg.ha 的 ESAE 区域^-1与草原地貌（稀树草原草原和灌木稀树草原）的发生同时发生，而 AGB 高于 25 Mg.ha ^{-1 的}区域与稠密稀树草原地貌（林地稀树草原和稠密林地稀树草原）的发生相匹配。草原地区的变异系数 (CV) 值比茂密的稀树草原地区大。这些第一手结果为未来从当前的采样型高光谱任务（< 10 公里的条带）过渡到大覆盖高光谱卫星（> 100 公里的条带）期间为热带草原的 AGB 建模设定了模型和指标的基线。