Mapping tropical dry forest succession using multiple criteria spectral mixture analysis

The study introduces the MCSMA method to improve the mapping of tropical dry forest succession stages by utilizing spectral and spatial information and an evaluation framework based on RMSE, spatial distance, and fraction consistency. Tested in Costa Rica's Santa Rosa National Park, the method outperformed the MESMA approach in reducing error across early, intermediate, and late successional stages.

• The proposed multiple criteria spectral mixture analysis (MCSMA) method demonstrated superior performance compared to multiple endmember spectral mixture analysis (MESMA) across early, intermediate, and late successional stages, showing lower root mean square error, mean absolute error, and systematic error.
• Shortwave infrared (SWIR) data is significant for distinguishing between different successional stages of tropical dry forests, which exhibit high spectral variability due to differences in biomass.
• The MCSMA method utilizes an evaluation framework consisting of three criteria—root mean square error (RMSE), spatial distance (SD), and fraction consistency (FC)—to select the best-fit endmember model.

APA

Inter-American Institute for Global Change Research (2025). Mapping tropical dry forest succession using multiple criteria spectral mixture analysis. https://iai.int/es/pub_acad/mapping-tropical-dry-forest-succession-using-multiple-criteria-spectral-mixture-analysis/

Chicago

Inter-American Institute for Global Change Research. Mapping tropical dry forest succession using multiple criteria spectral mixture analysis. 2025. https://iai.int/es/pub_acad/mapping-tropical-dry-forest-succession-using-multiple-criteria-spectral-mixture-analysis/.

Wikipedia

{{cite report |author=Inter-American Institute for Global Change Research |title=Mapping tropical dry forest succession using multiple criteria spectral mixture analysis |date=15 January 2025 |url=https://iai.int/es/pub_acad/mapping-tropical-dry-forest-succession-using-multiple-criteria-spectral-mixture-analysis/ |access-date=17 August 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{interamericaninstituteforglobalchangeresearch2025mapping, author = {{Inter-American Institute for Global Change Research}}, title = {{Mapping tropical dry forest succession using multiple criteria spectral mixture analysis}}, institution = {Inter-American Institute for Global Change Research}, year = {2025}, month = jan, url = {https://iai.int/es/pub_acad/mapping-tropical-dry-forest-succession-using-multiple-criteria-spectral-mixture-analysis/}, urldate = {2026-08-17}, note = {Indexed by Climate Insights Directory} }