Monitoring basin-scale land cover changes in Kagera Basin of Lake Victoria using ancillary data and remote sensing

Wasige, John Ejiet; Groen, T.A.; Smaling, E.M.A.; Jetten, Victor

doi:10.1016/j.jag.2012.08.005

Cited by 87 publications

(51 citation statements)

References 58 publications

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“…The period between 2003 and 2015 also experienced an increasing growth of forestland which could be attributed to afforestation and conservation efforts to restore the environment. A similar observation was noted by Wasige et al (2013). Also, as people tend to grow trees around their residential and workplaces, it contributes to increasing the vegetative cover of the area.…”

Section: Discussionsupporting

confidence: 78%

Assessing Land Use and Land Cover Changes in the Murchison Bay Catchment of Lake Victoria Basin in Uganda

Kiggundu

Anaba

Banadda

et al. 2018

JSD

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The Murchison Bay catchment in the northern shoreline of Lake Victoria basin is a high valued ecosystem because of the numerous human-related activities it supports in Uganda. The catchment has undergone tremendous human-induced land use/cover changes, which have not been quantified. This study aimed at quantifying the land use/cover changes as well as the rate at which these changes occurred over the last three decades in the catchment. This was achieved using remote sensing techniques and Geographic Information System (GIS) to analyse and contextualize the changes. To that effect, images of Landsat satellites MSS, TM, ETM+ and OLI were interpreted using supervised image classification technique to determine the land use/land cover changes from 1984 to 2015. The obtained results indicated that the catchment has undergone huge land use and land cover transformations over the last three decades attributable to rapid population growth and urbanization. The prevailing changes in footprint between 1984 and 2015 were expansions of built-up land (20.58% to 49.59%) and open water bodies (not detected in 1984 to 1.74%), and decreases in the following sectors: agricultural lands (from 43.88% to 26.10%), forestland (from 23.78% to 17.49%), and wetlands (from 11.76% to 5.08%). The changes pose a threat to the environment and water quality of the Murchison Bay and consequently increases National Water and Sewerage Corporation water treatment costs. Therefore, there is the need to take critical and practical measures to regulate and police land use, water use rights and conserve the environment especially wetlands.

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Section: Discussionsupporting

confidence: 78%

Assessing Land Use and Land Cover Changes in the Murchison Bay Catchment of Lake Victoria Basin in Uganda

Kiggundu

Anaba

Banadda

et al. 2018

JSD

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“…To predict and manage the impacts of land cover/land use changes on hydrology, it is imperative to understand the land cover/land use change processes, the rates and possible causes of the changes. Satellite sensors, with their wide spatial and synoptic coverage over large areas, can provide spatially continuous and consistent data, which can be analysed to derive land cover maps and change statistics According to Coppin et al (2004) Wasige et al, 2013). It is advantageous to use post-classification comparison methods because individual images are classified separately, which minimizes the requirement to conduct absolute radiometric corrections and the techniques also produce change matrices (Coppin et al, 2004), which are needed to identify change trajectories.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Land Cover Changes in the Bonsa Catchment, Ankobra Basin, Ghana

Aduah¹

2015

Appl Ecol Env Res

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PBAG x01, 3209, Scottsville, Pietermaritzburg,Abstract. Multi-temporal land cover changes in the Bonsa catchment of the Ankobra Basin in Ghana were determined using four satellite images from 1986, 1991, 2002 and 2011. The results indicate that evergreen forest is the largest class occupying an area of 68% in 1986, 62% in 1991, 50% in 2002 and 51% in 2011 and during the past 26 years, the largest land cover change has been the conversion of evergreen and secondary forests to shrubs/farms, mining areas and settlements. During this period, mining areas increased over two-fold, while settlements and shrubs/farms increased more than three and four-fold, respectively. This resulted in increasing annual deforestation rate of 0.33% between 1986 and 1991, 0.70% between 1991 and 2002 and 2% between 2002 and 2011. The results suggests that the drivers of the land cover changes in the Bonsa catchment, are both local and global and include, international trade, local population growth, agriculture extensification and urbanization. The identified land cover changes have the potential to impact negatively on the hydrological regimes of the Bonsa River and the local communities by leading to flooding, soil erosion and the siltation and pollution of the river during peak seasons and the scarcity of water during dry seasons. Therefore, the maps and statistics generated can be applied to assess the impacts of the land use changes on the local hydrology and provide a better basis for future land use planning. Through these findings the importance of multi-temporal analysis of satellite imagery for planning in data poor regions is highlighted.

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“…It is likely that these changes will affect the biodiversity, soil erosion and soil and water quality of the area; however, the magnitude of impact will depend mainly upon the direction of land conversion. Conversion of forest to cropland can result in soil erosion, if good farming techniques are not adopted (Wasige et al, 2013). Decrease in forest may lead to loss of indigenous plant species and reduction of biodiversity (Aduah, 2014).…”

Section: Discussionmentioning

confidence: 99%

An Analysis of Land Use and Land Cover Dynamics and Causative Drivers in a Thickly Populated Yamuna River Basin of India

Bansal¹,

Srivastav²,

Roy³

et al. 2016

Appl Ecol Env Res

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Abstract. Understanding the land use and land cover (LULC) change processes is of great importance for formulating the land use policy. In the present study, Remote Sensing, Geographic Information System (GIS) and statistical techniques are used to understand the LULC dynamics and possible driving forces in a thickly populated Yamuna river basin of India. The National Capital Territory of Delhi, one of the world's largest urban agglomerations, occurs in this river basin. The LULC maps on 1:250,000 scales prepared at decadal interval (1985, 1995 and 2005) using satellite images are used for studying the LULC dynamics. The major LULC changes observed during the study period are increase in cropland (65.7% to 67.9%) and built-up (0.9% to 1.9%) and decrease in forest (17.1% to 16.4%) and water bodies (2.3% to 1.9%). The dominant processes of the LULC changes include transitions from forest, grassland, shrub land, water bodies and wasteland to cropland; transitions from cropland, shrub land, wasteland, grassland and forest to built-up; conversion of forest to cropland, shrub land, plantation and built-up; and transitions from water bodies to cropland, shrub land and wasteland. Most of the transitions are reversible in nature. The statistical analysis (logistic regression) between decadal LULC maps and drivers indicates that LULC changes are driven by a multitude of factors in different combinations and with different importance. Socioeconomic, demographic and proximity drivers mainly influence the LULC change processes in the study area in decreasing order of importance. The insights gained are important for simulating future LULC scenarios as input for land management policy making.

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Monitoring basin-scale land cover changes in Kagera Basin of Lake Victoria using ancillary data and remote sensing

Cited by 87 publications

References 58 publications

Assessing Land Use and Land Cover Changes in the Murchison Bay Catchment of Lake Victoria Basin in Uganda

Assessing Land Use and Land Cover Changes in the Murchison Bay Catchment of Lake Victoria Basin in Uganda

Analysis of Land Cover Changes in the Bonsa Catchment, Ankobra Basin, Ghana

An Analysis of Land Use and Land Cover Dynamics and Causative Drivers in a Thickly Populated Yamuna River Basin of India

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