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Top 10+1 indicators for assessing forest ecosystem conditions: A five-decade fragmentation analysis.

Bruna Almeida¹, Pedro Cabral², Catarina Fonseca³

¹NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal; Centre for Ecology, Evolution and Environmental Changes (cE3c); Azorean Biodiversity Group (GBA), University of the Azores (UAc), Faculty of Sciences and Technology (FCT-UAc); Rua Mãe de Deus, Campus Universitário de Ponta Delgada, Edifício do Complexo Científico, 9500-321 Ponta Delgada, Portugal.

The Science of the Total Environment

|November 17, 2024

Summary

Forest aboveground biomass (AGB) is declining due to land use change. This study uses Cellular Automata (CA) to predict forest distribution and assess ecosystem health, revealing significant carbon stock loss by 2054.

Keywords:

Carbon storage and sequestration Corine land use land cover Ecological applications Environmental conservation Forestry GIS modelling

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Last Updated: Jun 7, 2025

A Method for Quantifying Foliage-Dwelling Arthropods

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Published on: October 20, 2019

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Published on: August 30, 2018

Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils

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Published on: November 25, 2016

Area of Science:

Ecology
Environmental Science
Forestry

Background:

Land use change globally leads to greater losses than gains in aboveground biomass (AGB).
Forest fragmentation is a major driver of biodiversity loss and natural capital depletion.
Understanding forest landscape patterns is crucial for economic and human well-being assessments.

Purpose of the Study:

To predict national forest distribution for 2036 and 2054 using a Cellular Automata (CA) system.
To assess ecosystem conditions using landscape metrics.
To identify key indicators for forest ecosystem health and carbon stock assessment.

Main Methods:

Calculated 130 landscape metrics and applied Variance Threshold and Principal Component Analysis (PCA).
Utilized Feature Importance techniques to select top 10 landscape indicators.
Included aboveground biomass (AGB) density as the eleventh indicator for ecosystem condition.

Main Results:

National AGB forest carbon stock decreased from 131.5 to 91.3 Megatons (Mt) between 2000 and 2018.
Predicted AGB stock to further decline to 71.8 Mt in 2036 and 55.3 Mt in 2054.
Landscape metrics quantitatively described forest dynamics and landscape evolution.

Conclusions:

Cellular Automata (CA) models effectively map forest resources and predict future scenarios.
The study provides crucial data for conservation and environmental management decisions.
Findings support Ecosystem Accounting by assessing forest extent and condition indicators.