Data from: Mapping tropical dry forest succession using multiple criteria spectral mixture analysis

Tropical dry forests (TDFs) in the Americas are considered the first
frontier of economic development with less than 1% of their total original
coverage under protection. Accordingly, accurate estimates of their
spatial extent, fragmentation, and degree of regeneration are critical in
evaluating the success of current conservation policies. This study
focused on a well-protected secondary TDF in Santa Rosa National Park
(SRNP) Environmental Monitoring Super Site, Guanacaste, Costa Rica. We
used spectral signature analysis of TDF ecosystem succession (early,
intermediate, and late successional stages), and its intrinsic
variability, to propose a new multiple criteria spectral mixture analysis
(MCSMA) method on the shortwave infrared (SWIR) of HyMap image. Unlike
most existing iterative mixture analysis (IMA) techniques, MCSMA tries to
extract and make use of representative endmembers with spectral and
spatial information. MCSMA then considers three criteria that influence
the comparative importance of different endmember combinations (endmember
models): root mean square error (RMSE); spatial distance (SD); and
fraction consistency (FC), to create an evaluation framework to select a
best-fit model. The spectral analysis demonstrated that TDFs have a high
spectral variability as a result of biomass variability. By adopting two
search strategies, the unmixing results showed that our new MCSMA approach
had a better performance in root mean square error (early: 0.160/0.159;
intermediate: 0.322/0.321; and late: 0.239/0.235); mean absolute error
(early: 0.132/0.128; intermediate: 0.254/0.251; and late: 0.191/0.188);
and systematic error (early: 0.045/0.055; intermediate: −0.211/−0.214; and
late: 0.161/0.160), compared to the multiple endmember spectral mixture
analysis (MESMA). This study highlights the importance of SWIR in
differentiating successional stages in TDFs. The proposed MCSMA provides a
more flexible and generalized means for the best-fit model determination
than common IMA methods.