Soil depth prediction by digital soil mapping and its impact in pine forestry productivity in South Brazil

• Published in: Forest ecology and management Vol. 488; p. 118983
• Main Authors: Horst-Heinen, Taciara Zborowski, Dalmolin, Ricardo Simão Diniz, ten Caten, Alexandre, Moura-Bueno, Jean Michel, Grunwald, Sabine, Pedron, Fabrício de Araújo, Rodrigues, Miriam Fernanda, Rosin, Nícolas Augusto, da Silva-Sangoi, Daniely Vaz
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15-05-2021
• Subjects: Pedometrics; Precision forestry; Random forest; Spatial prediction; Spatial prediction; Random forest; Pedometrics; Precision forestry
• ISSN: 0378-1127; 1872-7042
• DOI: 10.1016/j.foreco.2021.118983

•Soil depth was predicted at regional scale in a landscape with complex topography.•The total height of the 30-year-old Pinus taeda L. forest was predicted.•Soil depth played a critical role to predict the height of Pinus taeda L.•Global depth to bedrock predictions were not useful to predict tree height.•Soil depth and tree height predictions can be used to assess forestry production.
Based on the premise that the modeling and mining of soil and environmental data are capable of generating useful spatial information for use and planning in the forestry supply chain, we have established the following objectives: i) predict soil depth (SoD) in a topographically complex landscape via Digital Soil Mapping (DSM), ii) evaluate the potential of incorporating spatial data on SoD and topographic attributes in the prediction of the height of 30-year old Pinus taeda L., and iii) assess whether the global predictions of depth to bedrock (DTB) from Soil Grids is as effective as the local predictions for use in silviculture. The study was conducted in a 1.08-km2Pinus taeda L. forest, in first rotation, 30 years old, and in the mountain region of Santa Catarina, Brazil. The dendometric (tree height) and pedologic (SoD) data were measured at 102 points and used to train random forest (RF) models by leave-one-out cross-validation (LOOCV). Nine topographic covariates derived from a digital elevation model were used to spatially predict SoD. For spatial prediction of tree height, the models were trained using three set of covariates: 1) four topographic covariates (model 1), 2) SoD map predicted by RF plus four topographic covariates (model 2), and 3) DBT plus four topographic covariates (model 3). The RF model could adequately describe SoD and the general characteristics of the distribution of data measured in a landscape with complex topography using terrain attributes as covariates. The model obtained R2 = 0.91 and RMSE = 0.17 m. The tree height was predicted with R2 up to 0.93 and RMSE = 0.82 m. SoD and elevation were the most important covariates for it. The SoD covariate stood out compared to the others, improving the fit of model 2, while DBT was not considered important in model 3. Our results showed that SoD played a critical role to predict the tree height. However, local predictions of SoD are needed to obtain accurate predictions of tree height. These products, generated by DSM, showed to be useful for establishing methodologies to guide the long-term soil and forest management practices.