Mapping wood volume in seasonally dry vegetation of Caatinga in Bahia State, Brazil
Mapping wood volume in seasonally dry vegetation of Caatinga in Bahia State, Brazil
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DOI：
10.1590/1678-992x-2022-0161
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发表时间：
2023-04
影响因子：
2.6
通讯作者：
Thaine Teixeira Silva;R. B. Lima;R. L. M. D. Souza;P. Moonlight;D. Cardoso;Héveli Kalini Viana Santos;C. P. Oliveira;E. Veenendaal;L. P. Queiroz;P. M. S. Rodrigues;R. M. Santos;T. Sarkinen;A. Paula;P. Barreto-Garcia;T. Pennington;O. Phillips
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作者：
Thaine Teixeira Silva;R. B. Lima;R. L. M. D. Souza;P. Moonlight;D. Cardoso;Héveli Kalini Viana Santos;C. P. Oliveira;E. Veenendaal;L. P. Queiroz;P. M. S. Rodrigues;R. M. Santos;T. Sarkinen;A. Paula;P. Barreto-Garcia;T. Pennington;O. Phillips
巴西的Caatinga生物群落包括世界上最大和最连续的季节性干燥热带森林（SDTF）；然而，尽管它具有重要的生态和生物地理意义，但它是最受威胁和研究最少的物种之一。卡廷加森林木材蓄积量的空间分布及其与环境因子的关系尚不清楚。因此，本研究拟量化分析巴西巴伊亚州Caatinga植被木材体积的空间分布与环境变量的关系。在地块和碎片水平上获得了体积估计。采用多元线性回归技术，以该地区的环境变量作为预测因子。利用地球统计克里格方法对模型残差进行空间建模。所建立的模型具有较好的拟合效果，r2为0.54，均方根误差（RMSE）为10.9。普通残差的克里格表明，在未采样位置的误差估计较低，在模型的过低估计和过高估计中达到平衡。回归克里格方法提供了更详细的全球木材蓄积量图，产生的体积估计值范围为0.01至109立方米公顷。海拔、年平均温度和最干旱月份的降水是估算体积的有力环境预测因子。这些信息对于在巴西巴伊亚州制定可持续管理和利用Caatinga SDTF的行动计划是必要的。
: 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 m 3 ha with r 2 of 0.54 and Root Mean Square Error (RMSE) of 10.9 m 3 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 m 3 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.