2021
DOI: 10.3390/f12010077
|View full text |Cite
|
Sign up to set email alerts
|

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 enviro… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2

Citation Types

0
3
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
10

Relationship

2
8

Authors

Journals

citations
Cited by 12 publications
(5 citation statements)
references
References 90 publications
0
3
0
Order By: Relevance
“…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.…”
Section: Discussionmentioning
confidence: 61%
“…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.…”
Section: Discussionmentioning
confidence: 61%
“…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.…”
Section: Discussionmentioning
confidence: 99%
“…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.…”
Section: Introductionmentioning
confidence: 99%