Modeling spatiotemporal patterns of land use/land cover change in Central Malawi using a neural network model

We examine Land Use Land Cover Change (LULCC) in the Dedza and Ntcheu districts of Central Malawi and model anthropogenic and environmental drivers. We present an integrative approach to understanding heterogenous landscape interactions and short- to long-term shocks and how they inform future land...

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Detalles Bibliográficos
Autores: Mungai, L.M., Messina, J.P., Zulu, L.C., Jiaguo Qi, Snapp, S.S.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:México
Institución:Centro Internacional de Mejoramiento de Maíz y Trigo
Repositorio:Repositorio Institucional de Publicaciones Multimedia del CIMMYT
OAI Identifier:oai:repository.cimmyt.org:10883/22169
Acceso en línea:https://hdl.handle.net/10883/22169
Access Level:acceso abierto
Palabra clave:AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Multilayer Perceptrons
AGRICULTURE
LAND USE
POPULATION
SATELLITE IMAGERY
TEXTURE
LAND COVER
NEURAL NETWORKS
REMOTE SENSING
Descripción
Sumario:We examine Land Use Land Cover Change (LULCC) in the Dedza and Ntcheu districts of Central Malawi and model anthropogenic and environmental drivers. We present an integrative approach to understanding heterogenous landscape interactions and short- to long-term shocks and how they inform future land management and policy in Malawi. Landsat 30-m satellite imagery for 2001, 2009, and 2019 was used to identify and quantify LULCC outcomes based on eight input classes: agriculture, built-up areas, barren, water, wetlands, forest-mixed vegetation, shrub-woodland, and other. A Multilayer Perceptron (MLP) neural network was developed to examine land-cover transitions based on the drivers; elevation, slope, soil texture, population density and distance from roads and rivers. Agriculture is projected to dominate the landscape by 2050. Dedza has a higher probability of future land conversion to agriculture (0.45 to 0.70) than Ntcheu (0.30 to 0.45). These findings suggest that future land management initiatives should focus on spatiotemporal patterns in land cover and develop multidimensional policies that promote land conservation in the local context.