Assessment of land cover changes in the hinterland of Barranquilla (Colombia) using Landsat imagery and logistic regression
Metadatos
Afficher la notice complèteAuteur
Schubert, Henry; Caballero Calvo, Andrés; Rauchecker, Markus; Rojas Zamora, Óscar; Brokamp, Grischa; Schütt, BrigittaEditorial
MDPI
Materia
Colombian Caribbean Urbanisation Tropical dry forest Random forest classifier Woody vegetation changes
Date
2018Referencia bibliográfica
SCHUBERT, H., CABALLERO CALVO, A., RAUCHECKER, M., ROJAS ZAMORA, O., BROKAMP, G. y SCHÜTT, B. (2018). Assessment of land cover changes in the hinterland of Barranquilla (Colombia) using Landsat imagery and logistic regression. Land, 7(4), 152, p. 24. ISSN: 2073-445X. https://doi.org/10.3390/land7040152
Patrocinador
Federal Ministry of Education and Research of Germany (BMBF, ColBioDiv, project number: 01DN17006); German Research Foundation (DFG, project number: SCHU 949/14–1); German Research Foundation and the Open Access Publication Fund of the Freie Universität BerlinRésumé
Barranquilla is known as a dynamically growing city in the Colombian Caribbean.
Urbanisation induces land use and land cover (LULC) changes in the city and its hinterland affecting
the region’s climate and biodiversity. This paper aims to identify the trends of land use and land
cover changes in the hinterland of Barranquilla corresponding to 13 municipalities in the north
of the Department Atlántico. Landsat TM/ETM/OLI imagery from 1985 to 2017 was used to
map and analyse the spatio-temporal development of land use and land cover changes. During the
investigation period, the settlement areas grew by approximately 50% (from 103.3 to 153.6 km2), while
areas with woody vegetation cover experienced dynamic changes and increased in size since 2001.
Peri-urban and rural areas were characterized by highly dynamic changes, particularly regarding
clearing and recovery of vegetated areas. Regression analyses were performed to identify the impact
factors of detected vegetation cover changes. Computed logistic regression models included 20
independent variables, such as relief, climate, soil, proximity characteristics and socio-economic
data. The results of this study may act as a basis to enable researchers and decision-makers to focus
on the most important signals of systematic landscape transformations and on the conservation of
ecosystems and the services they provide.