Anthropogenic Disturbances Affect the Relationship Between Spectral Indices and the Biometric Variables of Brazilian Savannas

ABSTRACT According to previous studies involving biometric variables modeling using remote sensing (RS), data did not consider the effects of anthropogenic disturbance as relevant factor. The main objective of this study was to model aboveground biomass (AGB) and total wood volume (TWV) of Brazilian Savanna biome using vegetation indices (VI) from LANDSAT 5 TM. Multiple linear regression (MLR) and random forest (RF) algorithm were applied across 641 field plots of cerrado sensu stricto of the state of Minas Gerais, Brazil, comparing two models: non-stratified, and stratified according to plot’s anthropization degree. AGB and TWV obtained from non-anthropized plots presented linear relation with VIs (R2 = 0.82 and 0.74, respectively) and, on the other hand, presented nonlinear relation when plots were affected by anthropogenic disturbances or were not stratified. This finding helps improving estimates by stratifying plots into their anthropization degree, mainly in the Brazilian Savanna biome undergoing anthropogenic disturbances.

摘要：过往采用遥感（Remote Sensing，RS）开展生物参数建模的相关研究中，均未将人为干扰作为相关影响因素纳入考量范围。本研究的核心目标为，依托陆地卫星5号专题制图仪（LANDSAT 5 TM）获取的植被指数（Vegetation Indices，VI），对巴西稀树草原生物群系的地上生物量（Aboveground Biomass，AGB）与总木材体积（Total Wood Volume，TWV）开展建模。本研究针对巴西米纳斯吉拉斯州境内的641处狭义塞拉多植被（Cerrado sensu stricto）野外样地，采用多元线性回归（Multiple Linear Regression，MLR）与随机森林（Random Forest，RF）两种算法，对比两类模型的效果：非分层模型与依据样地人为干扰程度进行分层的模型。从无人类干扰的样地中获取的地上生物量与总木材体积数据，与植被指数呈现出线性相关关系（决定系数R²分别为0.82与0.74）；而当样地受到人为干扰或未进行分层处理时，二者与植被指数则呈现非线性相关关系。该研究结果表明，依据样地的人为干扰程度进行分层建模，有助于提升生物量估算精度，这一点在正遭受人为干扰的巴西稀树草原生物群系中尤为关键。