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

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.