Evaluation of four classification algorithms of Landsat-8 and Sentinel-2 satellite images to identify forest cover in highly fragmented regions in Costa Rica

[EN] Mapping of land use and forest cover and assessing their changes is essential in the design of strategies to manage and preserve the natural resources of a country, and remote sensing have been extensively used with this purpose. By comparing four classification algorithms and two types of satellite images, the objective of the research was to identify the type of algorithm and satellite image that allows higher global accuracy in the identification of forest cover in highly fragmented landscapes. The study included a treatment arrangement with three factors and six randomly selected blocks within the Huetar Norte Zone in Costa Rica. More accurate results were obtained for classifications based on Sentinel-2 images compared to Landsat-8 images. The best classification algorithms were Maximum Likelihood, Support Vector Machine or Neural Networks, and they yield better results than Minimum Distance Classification. There was no interaction among image type and classification methods, therefore, Sentinel-2 images can be used with any of the three best algorithms, but the best result was the combination of Sentinel-2 and Support Vector Machine. An additional factor included in the study was the image acquisition date. There were significant differences among months during which the image was acquired and an interaction between the classification algorithm and this factor was detected. The best results correspond to images obtained in April, and the lower to September, month that corresponds with the period of higher rainfall in the region studied. The higher global accuracy identifying forest cover is obtained with Sentinel-2 images from the dry season in combination with Maximum Likelihood, Support Vector Machine, and Neural Network image classification methods.[ES] Conocer y cartografiar los cambios del uso y cobertura de la tierra es esencial para la formulación de estrategias de manejo y conservación de los recursos naturales. Las herramientas que conforman la disciplina de la teledetección han sido extensamente usadas con este objetivo. Al comparar cuatro algoritmos de clasificación y dos tipos de imágenes satelitales, el objetivo de la investigación fue determinar el tipo de algoritmo e imagen satelital que permite obtener una mayor fiabilidad global en la identificación de la cobertura boscosa en paisajes de uso de la tierra con alta fragmentación. El estudio se desarrolló en la Zona Huetar Norte de Costa Rica, utilizando un diseño experimental de seis bloques con un arreglo de tratamientos con tres factores. El uso de imágenes Sentinel-2 fue superior al obtenido con Landsat-8. No existen diferencias significativas en la fiabilidad lograda con los algoritmos de clasificación de Máxima Verosimilitud, Máquinas de Vectores Soporte y Redes Neuronales, pero sí de estos con respecto a la clasificación por Mínima Distancia. No se detectó interacción entre tipo de imagen y algoritmo de clasificación, por lo que las imágenes de Sentinel-2 podrían usarse con cualquiera de los tres mejores algoritmos estudiados. Se analizó además el efecto que tuvo el mes en cada imagen adquirida, y se encontraron diferencias significativas debido a este factor, además se produce una interacción de este con el método de clasificación. Los mejores resultados se obtuvieron con imágenes de abril, y los más bajos con imágenes de septiembre, mes que coincide con la época lluviosa en la zona estudiada. Se concluye que la mayor fiabilidad en la identificación de la cobertura boscosa se logra mediante el uso de los algoritmos de Máxima Verosimilitud, Máquinas de Vectores Soporte y Redes Neuronales empleando imágenes Sentinel-2 tomadas en la temporada seca.Los autores agradecen a la Vice-Rectoría de Investigación y Extensión del ITCR por el apoyo financiero y administrativo para la realización del proyecto: Derivación indirecta de la distribución espacial y estado de desarrollo de los bosques secundarios en Costa Rica usando imágenes satelitales de mediana resolución espacial. Igualmente se agradece al programa de becas CeNAT-CONARE y al laboratorio PRIAS del Centro Nacional de Alta Tecnología (CeNAT) de Costa Rica por la facilitación de los equipos de cómputo de avanzada y el uso de las licencias de los softwares requeridos para llevar a cabo esta investigación.Ávila-Pérez, I.; Ortiz-Malavassi, E.; Soto-Montoya, C.; Vargas-Solano, Y.; Aguilar-Arias, H.; Miller-Granados, C. (2020). Evaluación de cuatro algoritmos de clasificación de imágenes satelitales Landsat-8 y Sentinel-2 para la identificación de cobertura boscosa en paisajes altamente fragmentados en Costa Rica. Revista de Teledetección. 0(57):37-49. https://doi.org/10.4995/raet.2020.13340OJS374905

Strong floristic distinctiveness across Neotropical successional forests

Forests that regrow naturally on abandoned fields are important for restoring biodiversity and ecosystem services, but can they also preserve the distinct regional tree floras? Using the floristic composition of 1215 early successional forests (≤20 years) in 75 human-modified landscapes across the Neotropic realm, we identified 14 distinct floristic groups, with a between-group dissimilarity of 0.97. Floristic groups were associated with location, bioregions, soil pH, temperature seasonality, and water availability. Hence, there is large continental-scale variation in the species composition of early successional forests, which is mainly associated with biogeographic and environmental factors but not with human disturbance indicators. This floristic distinctiveness is partially driven by regionally restricted species belonging to widespread genera. Early secondary forests contribute therefore to restoring and conserving the distinctiveness of bioregions across the Neotropical realm, and forest restoration initiatives should use local species to assure that these distinct floras are maintained

Strong floristic distinctiveness across Neotropical successional forests.

Forests that regrow naturally on abandoned fields are important for restoring biodiversity and ecosystem services, but can they also preserve the distinct regional tree floras? Using the floristic composition of 1215 early successional forests (<20 years) in 75 human-modified landscapes across the Neotropic realm, we identified 14 distinct floristic groups, with a between-group dissimilarity of 0.97. Floristic groups were associated with location, bioregions, soil pH, temperature seasonality, and water availability. Hence, there is large continental-scale variation in the species composition of early successional forests, which is mainly associated with biogeographic and environmental factors but not with human disturbance indicators. This floristic distinctiveness is partially driven by regionally restricted species belonging to widespread genera. Early secondary forests contribute therefore to restoring and conserving the distinctiveness of bioregions across the Neotropical realm, and forest restoration initiatives should use local species to assure that these distinct floras are maintained

Biodiversity recovery of Neotropical secondary forests

Unidad de excelencia María de Maeztu MdM-2015-0552Old-growth tropical forests harbor an immense diversity of tree species but are rapidly being cleared, while secondary forests that regrow on abandoned agricultural lands increase in extent. We assess how tree species richness and composition recover during secondary succession across gradients in environmental conditions and anthropogenic disturbance in an unprecedented multisite analysis for the Neotropics. Secondary forests recover remarkably fast in species richness but slowly in species composition. Secondary forests take a median time of five decades to recover the species richness of old-growth forest (80% recovery after 20 years) based on rarefaction analysis. Full recovery of species composition takes centuries (only 34% recovery after 20 years). A dual strategy that maintains both old-growth forests and species-rich secondary forests is therefore crucial for biodiversity conservation in human-modified tropical landscapes

Strong floristic distinctiveness across Neotropical successional forests

Forests that regrow naturally on abandoned fields are important for restoring biodiversity and ecosystem services, but can they also preserve the distinct regional tree floras? Using the floristic composition of 1215 early successional forests (≤20 years) in 75 human-modified landscapes across the Neotropic realm, we identified 14 distinct floristic groups, with a between-group dissimilarity of 0.97. Floristic groups were associated with location, bioregions, soil pH, temperature seasonality, and water availability. Hence, there is large continental-scale variation in the species composition of early successional forests, which is mainly associated with biogeographic and environmental factors but not with human disturbance indicators. This floristic distinctiveness is partially driven by regionally restricted species belonging to widespread genera. Early secondary forests contribute therefore to restoring and conserving the distinctiveness of bioregions across the Neotropical realm, and forest restoration initiatives should use local species to assure that these distinct floras are maintained

Multidimensional tropical forest recovery

Tropical forests disappear rapidly because of deforestation, yet they have the potential to regrow naturally on abandoned lands. We analyze how 12 forest attributes recover during secondary succession and how their recovery is interrelated using 77 sites across the tropics. Tropical forests are highly resilient to low-intensity land use; after 20 years, forest attributes attain 78% (33 to 100%) of their old-growth values. Recovery to 90% of old-growth values is fastest for soil (12 decades). Network analysis shows three independent clusters of attribute recovery, related to structure, species diversity, and species composition. Secondary forests should be embraced as a low-cost, natural solution for ecosystem restoration, climate change mitigation, and biodiversity conservation

Multidimensional tropical forest recovery

Tropical forests disappear rapidly because of deforestation, yet they have the potential to regrow naturally on abandoned lands. We analyze how 12 forest attributes recover during secondary succession and how their recovery is interrelated using 77 sites across the tropics. Tropical forests are highly resilient to low-intensity land use; after 20 years, forest attributes attain 78% (33 to 100%) of their old-growth values. Recovery to 90% of old-growth values is fastest for soil (12 decades). Network analysis shows three independent clusters of attribute recovery, related to structure, species diversity, and species composition. Secondary forests should be embraced as a low-cost, natural solution for ecosystem restoration, climate change mitigation, and biodiversity conservation