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Multi-Model Approaches to the Spatialization of Tree Vitality Surveys: Constructing a National Tree Vitality Map

Authors
Choi, YuyoungChung, Hye InLim, Chul-HeeLee, Jun-HeeChoi, Won IlJeon, Seong Woo
Issue Date
8월-2021
Publisher
MDPI
Keywords
forest health; forest management; multi-model ensemble; spatialization; tree vitality
Citation
FORESTS, v.12, no.8
Indexed
SCIE
SCOPUS
Journal Title
FORESTS
Volume
12
Number
8
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/137027
DOI
10.3390/f12081009
ISSN
1999-4907
Abstract
It is essential to maintain the health of forests so that they are protected against a diverse range of stressors and show improved resilience. An area-based forest health map is required for efficient forest management on a national scale however, most national forest inventories are based on in-situ observations. This study examined methodologies to establish an area-based map on tree vitality grade using field survey data, particularly that containing information on several trees at one point. The forest health monitoring dataset of the Republic of Korea was used in combination with 37 satellite-based environmental predictors. Four methods were considered: Multinomial logistic regression (MLR), random forest classification (RF), indicator kriging (IK), and multi-model ensemble (MME) approaches using species distribution models. The MLR and RF produced biased results, whereby almost all regions were classified as first grade; the spatialization results of these methods were considered inappropriate for forest management. The maps produced using the IK and MME methods improved the distinctions between the distributions of five grades compared to the previous two methodologies however, the MME method produced better results, reliably reflecting topographical and climatic characteristics. Comparisons with the vegetation condition index and bioclimate vulnerability index also emphasized the usefulness of the MME. This study is particularly relevant to the national forest managers who struggle to find the most effective forest monitoring and management strategies. Suggestions to improve spatialization of field survey data are further discussed.
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