TY - JOUR
T1 - Mapping wood volume in seasonally dry vegetation of Caatinga in Bahia State, Brazil
AU - Silva, Thaine Teixeira
AU - de Lima, Robson Borges
AU - de Souza, Rafael Lucas Figueiredo
AU - Moonlight, Peter W.
AU - Cardoso, Domingos
AU - Santos, Héveli Kalini Viana
AU - de Oliveira, Cinthia Pereira
AU - Veenendaal, Elmar
AU - de Queiroz, Luciano Paganucci
AU - Rodrigues, Priscyla Maria Silva
AU - Dos Santos, Rubens Manoel
AU - Sarkinen, Tiina
AU - de Paula, Alessandro
AU - Barreto-Garcia, Patrícia Anjos Bittencourt
AU - Pennington, Toby
AU - Phillips, Oliver Lawrence
PY - 2023/4/7
Y1 - 2023/4/7
N2 - 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.
AB - 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.
KW - geostatistical modeling
KW - regression kriging
KW - seasonally dry tropical forests
UR - http://doi.org/10.6084/m9.figshare.22578294
U2 - 10.1590/1678-992X-2022-0161
DO - 10.1590/1678-992X-2022-0161
M3 - Article
AN - SCOPUS:85159645846
SN - 0103-9016
VL - 80
JO - Scientia agricola
JF - Scientia agricola
M1 - e20220161
ER -