Mapping wood volume in seasonally dry vegetation of Caatinga in Bahia State, Brazil

ABSTRACT The Caatinga biome in Brazil comprises the largest and most continuous expanse of the seasonally dry tropical forest (SDTF) worldwide; nevertheless, it is among the most threatened and least studied, despite its ecological and biogeographical importance. The spatial distribution of volumetric wood stocks in the Caatinga and the relationship with environmental factors remain unknown. Therefore, this study intends to quantify and analyze the spatial distribution of wood volume as a function of environmental variables in Caatinga vegetation in Bahia State, Brazil. Volumetric estimates were obtained at the plot and fragment level. The multiple linear regression techniques were adopted, using environmental variables in the area as predictors. Spatial modeling was performed using the geostatistical kriging approach with the model residuals. The model developed presented a reasonable fit for the volume m3 ha with r2 of 0.54 and Root Mean Square Error (RMSE) of 10.9 m3 ha–1. The kriging of ordinary residuals suggested low error estimates in unsampled locations and balance in the under and overestimates of the model. The regression kriging approach provided greater detailing of the global wood volume stock map, yielding volume estimates that ranged from 0.01 to 109 m3 ha–1. Elevation, mean annual temperature, and precipitation of the driest month are strong environmental predictors for volume estimation. This information is necessary to development action plans for sustainable management and use of the Caatinga SDTF in Bahia State, Brazil.

摘要 巴西卡廷加生物群系（Caatinga biome）是全球面积最大且连片性最强的季节性干旱热带森林（seasonally dry tropical forest, SDTF）；尽管其具备重要的生态学与生物地理学价值，但该生物群系仍属于受威胁程度最高、研究程度最低的生态系统之一。目前，卡廷加地区木材蓄积量的空间分布特征及其与环境因子的关联尚不明确。因此，本研究旨在量化并分析巴西巴伊亚州卡廷加植被内木材蓄积量的空间分布及其与环境变量的响应关系。研究在样地与片段尺度上获取了木材蓄积量的估算数据，采用多元线性回归技术，以区域内环境变量作为预测自变量。同时，基于模型残差运用地统计克里金法开展空间建模。所构建的模型对单位面积木材蓄积量（单位：m³·ha⁻¹）具备良好的拟合效果，决定系数（R²）为0.54，均方根误差（Root Mean Square Error, RMSE）为10.9 m³·ha⁻¹。对普通残差的克里金分析结果显示，未采样区域的误差估算值较低，且模型的高估与低估情况分布均衡。回归克里金法提升了全球木材蓄积量分布图的细节表现力，得到的蓄积量估算值区间为0.01至109 m³·ha⁻¹。海拔、年平均气温以及最干月降水量是影响木材蓄积量估算的核心环境预测因子。上述研究结果可为巴西巴伊亚州卡廷加季节性干旱热带森林的可持续管理与利用规划提供必要的科学支撑。