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Detecting wetland encroachment and urban agriculture land classification in Uganda using hyper-temporal remote sensing

Domaine:

geospatialenvironment and energyagriculture
Créateur:
SteMolJudBea
Éditeur:
F10
Hôte:
Background: Urbanization is an important indicator of economic growth and social change but is associated with environmental degradation, which threatens the sustainable growth of African cities. One of the most vulnerable ecosystems in urban areas are wetlands. In Uganda, wetlands cover an area of 11% of the country’s land area. Half of the wetland areas in Ugandan cities have been converted to industrial and residential areas, and urban agriculture. There is limited information on the extent of wetland conversion or utilization for urban agriculture. The objective of this study was to investigate the extent of wetlands lost in two Ugandan cities, Wakiso and Kampala, in the last 30 years. Secondly, we extracted crop agriculture in the wetlands of Kampala and Wakiso from hyper-temporal satellite image analysis in an attempt to produce a spatial detail of wetland encroachment maps of urban agriculture using a reproducible mapmaking method. Methods: Using a field survey and free remote sensing data from Landsat TM 1986 and Landsat ETM 2016 we classified the rate of wetland loss and encroachment between the years 1986 and 2016. We used MODIS NDVI 16-day composites at a 500-meter spatial resolution to broaden the analysis to distinguish distinctive crops and crop mixtures in the encroached wetlands for urban agriculture using the ISODATA clustering algorithm. Results: Over 30 years, 72,828 ha (73%) of the Wakiso-Kampala wetlands have been lost meanwhile agriculture areas have doubled. Of this 16,488 ha (23%) were converted from wetlands. All cultivated agriculture in Kampala was in the wetlands while in Wakiso, 73% of crop agriculture was in the wetlands. The major crops grown in these urban wetlands were banana (20%), sugarcane (22%), maize (17%), Eucalyptus trees (12%), sweet potatoes (10%), while ornamental nurseries, pine trees, vegetables, and passion fruits were each at 5%.

Visit

doi.org

Tasks

computer visionimage classification

Licenses

http://creativecommons.org/licenses/by/4.0/

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