Insights in forest structural diversity indicators with machine learning: what is indicated?

编号 030038105

推送时间 20230206

研究领域 森林经理 

年份 2022 

类型 期刊 

语种 英语

标题 Insights in forest structural diversity indicators with machine learning: what is indicated?

来源期刊 BIODIVERSITY AND CONSERVATION

期 第381期

发表时间 20230124

关键词 Biodiversity assessments;  Biodiversity indicator choice;  European beech forests;  Forest inventories;  Indicator-indicandum relationships;  R randomForest; 

摘要 Indicator choice is a crucial step in biodiversity assessments.?Forest?inventories have the potential to overcome data deficits for biodiversity monitoring on large spatial scales which is fundamental to reach biodiversity policy targets. Structural diversity indicators were taken from information theory to describe?forest?spatial heterogeneity. Their indicative value for?forest?stand variables is largely unknown. This case study explores these indicator-indicandum relationships in a lowland, European beech (Fagus sylvatica) dominated?forest?in Austria, Central Europe. We employed five indicators as surrogates for structural diversity which is an important part of?forest?biodiversity i.e., Clark & Evans-, Shannon, Stand Density, Diameter Differentiation Index, and Crown Competition factor. The indicators are evaluated by machine learning, to detect statistic inter-correlation in an indicator set and the relationship to twenty explanatory stand variables and five variable groups on a landscape scale. Using the R packages randomForest, VSURF, and randomForest Explainer, 1555 sample plots are considered in fifteen models. The model outcome is decisively impacted by the type and number of explanatory variables tested. Relationships to interval-scaled, common stand characteristics can be assessed most effectively. Variables of "stand age & density' are disproportionally indicated by our indicator set while other?forest?stand characteristics relevant to biodiversity are neglected. Within the indicator set, pronounced inter-correlation is detected. The Shannon Index indicates the overall high-est, the Stand Density Index the lowest number of stand characteristics. Machine learning proves to be a useful tool to overcome knowledge gaps and provides additional insights in indicator-indicandum relationships of structural diversity indicators.

服务人员 付贺龙

服务院士 唐守正

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