Reducing the Uncertainty of Radiata Pine Site Index Maps Using an Spatial Ensemble of Machine Learning Models

Abstract

Site Index has been widely used as an age normalised metric in order to account for variation in forest height at a range of spatial scales. Although previous research has used a range of modelling methods to describe the regional variation in Site Index, little research has examined gains that can be achieved through the use of regression kriging or spatial ensemble methods. In this study, an extensive set of environmental surfaces were used as covariates to predict Site Index measurements covering the environmental range of Pinus radiata D. Don plantations in Chile. Using this dataset, the objectives of this research were to (i) compare predictive precision of a range of geostatistical, parametric, and non-parametric models, (ii) determine whether significant gains in precision can be attained through use of regression kriging, (iii) evaluate the precision of a spatial ensemble model that utilises predictions from the five most precise models, through using the model prediction with lowest error for a given pixel, and (iv) produce a map of Site Index across the study area. The five most precise models were all geostatistical and they included ordinary kriging and four regression kriging models that were based on partial least squares or random forests. A spatial ensemble model that was constructed from these five models was the most precise of those developed (RMSE = 1.851 m, RMSE% = 6.38%) and it had relatively little bias. Climatic and edaphic variables were the strongest determinants of Site Index and, in particular, variables that are related to soil water balance were well represented within the most precise predictive models. These results highlight the utility of predicting Site Index using a range of approaches, as these can be used to construct a spatial ensemble that may be more precise than predictions from the constituent models.

A Multiscale Cost–Benefit Analysis of Digital Soil Mapping Methods for Sustainable Land Management

“…Additionally, Baltensweiler et al [38] successfully integrated multiple data sources of a single environmental component in the case of unknown adequacy for soil mapping. Gavilán-Acuña et al [67] reached similar conclusions regarding EML's superiority to individual geostatistical and machine learning methods in forestry, with a larger relative accuracy of OK and RK methods. Despite convincingly achieving the highest cost-benefit score, the very high computational demand of EML currently prevents its automation within soil mapping frameworks and widespread application on a larger scale.…”

Early Prediction of Regional Red Needle Cast Outbreaks Using Climatic Data Trends and Satellite-Derived Observations

“…All the underpinning datasets that were used are widely available and are usually low-cost or free. Sentinel-2 has global coverage [54], while many countries have regional RGB imagery collected at a very high resolution [55] and weather data that can describe past conditions for a range of important variables [30,31,56]. This method is likely to be most useful for pests and diseases with clearly discernible symptoms visible from satellite data that are expressed within the upper canopy.…”

Assessing Soil Prediction Distributions for Forest Management Using Digital Soil Mapping

“…Additionally, soil depth (SoD), described as the depth of the soil profile from the top surface to the bedrock or root barriers [11], is also correlated to nutrient capacity and the plant's available water content, and it controls biological activity [12]. This soil property has been linked to site index prediction in Pinus plantations [13] as well as overall productivity in forest plantations [14]. Changes in the SoD have also been linked to variations in the basal area within hardwood plantations.…”