RT info:eu-repo/semantics/article
T1 Mapping and Monitoring of the Invasive Species Dichrostachys cinerea (Marabú) in Central Cuba Using Landsat Imagery and Machine Learning (1994–2022)
A1 Valero Jorge, Alexey
A1 González De Zayas, Roberto
A1 Matos Pupo, Felipe
A1 Becerra González, Ángel Luis
A1 Álvarez Taboada, María Flor
A2 Ingenieria Agroforestal
K1 Ingeniería forestal
K1 Dichrostachys cinerea
K1 Landsat
K1 Classification
K1 Maximum likelihood
K1 Support vector machine
K1 Random forest
AB [EN] Invasive plants are a serious problem in island ecosystems and are the main cause of the extinction of endemic species. Cuba is located within one of the hotspots of global biodiversity, which, coupled with high endemism and the impacts caused by various disturbances, makes it a region particularly sensitive to potential damage by invasive plants like Dichrostachys cinerea (L.) Wight & Arn. (marabú). However, there is a lack of timely information for monitoring this species, as well as about the land use and land cover (LULC) classes most significantly impacted by this invasion in the last few decades and their spatial distribution. The main objective of this study, carried out in Central Cuba, was to detect and monitor the spread of marabú over a 28-year period. The land covers for the years 1994 and 2022 were classified using Landsat 5 TM and 8 OLI images with three different classification algorithms: maximum likelihood (ML), support vector machine (SVM), and random forest (RF). The results obtained showed that RF outperformed the other classifiers, achieving AUC values of 0.92 for 1994 and 0.97 for 2022. It was confirmed that the area covered by marabú increased by 29,555 ha, from 61,977.59 ha in 1994 to 91,533.47 ha in 2022 (by around 48%), affecting key land covers like woodlands, mangroves, and rainfed croplands. These changes in the area covered by marabú were associated, principally, with changes in land uses and tenure and not with other factors, such as rainfall or relief in the province. The use of other free multispectral imagery, such as Sentinel 2 data, with higher temporal and spatial resolution, could further refine the model’s accuracy.
PB MDPI
LK https://hdl.handle.net/10612/19015
UL https://hdl.handle.net/10612/19015
NO Valero-Jorge A, González-De Zayas R, Matos-Pupo F, Becerra-González AL, Álvarez-Taboada F. Mapping and Monitoring of the Invasive Species Dichrostachys cinerea (Marabú) in Central Cuba Using Landsat Imagery and Machine Learning (1994–2022). Remote Sensing. 2024; 16(5):798. https://doi.org/10.3390/rs16050798
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 15-jun-2024