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Deforestation is a major factor reducing natural habitats, leading to tropical ecosystems and biodiversity loss worldwide. The Lacandona region in southern Mexico holds one of the largest fragments of tropical rainforest in North America. We evaluated the deforestation of the Lacandona region harmonizing concepts and methodologies. An international (FAO definition), governmental (national definition), and regional definition of deforestation with applications at different scales were analyzed and harmonized with two classification methods (likelihood and spectral angle mapper (SAM)). We used 2015 and 2018 Landsat 8 images, and likelihood and SAM classifications were applied for FAO and regional definitions of deforestation. Overall, the best evaluated classifier in quantity was likelihood for 2015 and 2018 (kappa: 0.87 and 0.70, overall accuracy: 91.8 and 80.4%, and quantity disagreement: 4.1 and 10 %, respectively). The allocation disagreement only showed exchange between classes. Nevertheless, they did not show differences between classifiers, although 2015 had less disagreement than 2018: exchange, 4.1% for likelihood and SAM; shift: 0% for likelihood and SAM. Maps based on the regional definition of deforestation showed that the likelihood classification detected 11,441 ha less deforestation than SAM (40,538 and 51,979 ha, respectively). The FAO definition of deforestation showed that likelihood classification detected 11,914 ha less deforestation than SAM classification (37,152 and 49,066 ha, respectively). Further, the likelihood classification showed 3387 ha more of deforestation according to the regional definition than the FAO definition of deforestation (40,538 and 37,152 ha, respectively). SAM classification showed that the regional definition showed 2913 ha more deforestation than the FAO definition (51,979 and 49,066, respectively). We concluded that implementation of governmental programs in the Lacandona region requires estimations based on a careful selection of deforestation definitions and methods.

The U-net is nowadays among the most popular deep learning algorithms for land use/land cover (LULC) mapping; nevertheless, it has rarely been used with synthetic aperture radar (SAR) and multispectral (MS) imagery. On the other hand, the discrimination between plantations and forests in LULC maps has been emphasized, especially for tropical areas, due to their differences in biodiversity and ecosystem services provision. In this study, we trained a U-net using different imagery inputs from Sentinel-1 and Sentinel-2 satellites, MS, SAR and a combination of both (MS + SAR); while a random forests algorithm (RF) with the MS + SAR input was also trained to evaluate the difference in algorithm selection. The classification system included ten classes, including old-growth and secondary forests, as well as old-growth and young plantations. The most accurate results were obtained with the MS + SAR U-net, where the highest overall accuracy (0.76) and average F1-score (0.58) were achieved. Although MS + SAR and MS U-nets gave similar results for almost all of the classes, for old-growth plantations and secondary forest, the addition of the SAR band caused an F1-score increment of 0.08–0.11 (0.62 vs. 0.54 and 0.45 vs. 0.34, respectively). Consecutively, in comparison with the MS + SAR RF, the MS + SAR U-net obtained higher F1-scores for almost all the classes. Our results show that using the U-net with a combined input of SAR and MS images enabled a higher F1-score and accuracy for a detailed LULC map, in comparison with other evaluated methods.

The provision of valuable ecosystem services by tropical swamp forests (mainly carbon sequestration and storage in biomass and soil) explains their ecological importance. Current efforts toward the conservation of these ecosystems, however, face strong limitations as their spatial variation is largely unknown, particularly in regions where they occur over large areas. Here, we analyze the α-diversity (i.e., effective number of species or true diversity) and community structure variability of three tropical swamp forest communities distributed along an approximately 300 km long portion of the Usumacinta River Basin, southern Mexico. We sampled eighty-three 625-m2 plots to characterize the diversity and structural attributes of the woody plant communities. We recorded 2302 individuals belonging to 60 species and 25 families. Both α-diversity and structural attributes differed significantly among the three communities. The most inland community stood out for having the highest α-diversity for three true diversity values. Interestingly, the mangrove Rhizophora mangle L. was one of the dominant species, both in the swamp forest located closest to the coast and in the community farthest away from the sea. Basal area and density of individuals also had their maxima in the most inland swamp forest. The diversity and structural differences observed among the examined swamp forests seem to be related to contrasting environmental conditions, such as water salinity, distance to the coastline, and the hydrological dynamics of the Usumacinta River. We emphasize the urgency to conserve R. mangle populations in the swamp forest located farthest away from the coast due to its unusual habitat association, which appears to be a relictual condition.