Mapping tropical dry forest succession using multiple criteria spectral mixture analysis

This research paper introduces a multiple criteria spectral mixture analysis (MCSMA) method using shortwave infrared (SWIR) data from HyMap imagery to map the successional stages of tropical dry forests (TDFs) in Costa Rica, demonstrating improved accuracy over existing methods.

• The study proposes a new multiple criteria spectral mixture analysis (MCSMA) method that utilizes representative endmembers with both spectral and spatial information. To select the best-fit model, MCSMA employs an evaluation framework based on three criteria: root mean square error (RMSE), spatial distance (SD), and fraction consistency (FC).
• The MCSMA approach outperformed the multiple endmember spectral mixture analysis (MESMA) across early, intermediate, and late successional stages in terms of root mean square error, mean absolute error, and systematic error.
• The research indicates that tropical dry forests exhibit high spectral variability due to biomass variability and emphasizes that shortwave infrared (SWIR) is important for differentiating between successional stages in these forests.

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} }