Harmonizing Definitions and Methods to Estimate Deforestation at the Lacandona Tropical Region in Southern Mexico

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2022-05, Vol.14 (10), p.2319
• Main Authors: Fernández-Montes de Oca, Ana, Ghilardi, Adrián, Kauffer, Edith, Gallardo-Cruz, José Alberto, Núñez, Juan Manuel, Sánchez-Cordero, Víctor
• Format: Article
• Language: English
• Subjects: Algorithms; Biodiversity; biodiversity hotspot; Biodiversity loss; Classification; Classifiers; concepts and methodologies harmonized; Deforestation; Ecosystems; Evaluation; Field study; Government programs; Land use; land use change; Landsat; Rainforests; Remote sensing; Satellite imagery; Systematic review; Tropical environment; Tropical environments; tropical rainforest
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14102319

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.