Assessing the Spatial Drivers of Land Use and Land Cover Change in the Protected and Communal Areas of the Zambezi Region, Namibia (original) (raw)
Related papers
European Journal of Environment and Earth Sciences
Understanding the drivers of land cover changes (LULCC) is very crucial for the development of management strategies as well as policy improvement and the sustenance of ecosystem services. This is crucial in preventing further degradation and proper planning of sustainable natural resources management. In this study, an attempt has been made to identify the drivers of LULCC in the Kavango East and West Regions of Namibia, from 1990 to 2018. Remotely Sensed Images were used to compute indices. Socio-economic surveys were conducted using structured interviews to share the past experiences of the local people, some key informants, and other stakeholders in the region. A combination of this information together with the Remote Sensing data was then used to derive the drivers of LULCC in the study area. Results of the study showed that changes were triggered by the interplay of more than five drivers identified and related to the environment, socio-economic, and other technical factors. ...
Analysis of land cover land use change in the greater Gaborone area of South Eastern Botswana
Acta Ecologica Sinica, 2023
Changes in land cover land use (LCLU) have long been considered to be among the many factors responsible for global environmental challenges. This study focused on assessing LCLU changes in the Greater Gaborone area of South Eastern Botswana between 1988 and 2022. The study employed remote sensing (RS) and geographic information systems (GIS) tools for analyzing LCLU changes in the study area during the study period. Landsat images of 1988 and 2002 and Sentinel-2A images of 2022 were used to detect LCLU changes. Image classification was done using a Supervised classification approach based on a Maximum Likelihood Classifier. Six LCLU types such as water body, trees dominated, cropland, shrubland, bare land, and built-up, were identified in the area. Post Classification Comparison (PCC) approach was used to detect LCLU change during the study period. Shrubland class was found to be the dominant LCLU type in the study area. A significant gain was observed in the built-up class (75.12 km 2), while significant losses were observed in shrubland (24.16 km 2) and trees dominated (33.32 km 2) classes in the entire study period. Given the rapid increase in built-up areas, this recommends that land managers and policymakers should invest in implementing sustainable land management interventions to prevent undesirable LCLU changes.
2017
Analyzing the land use land cover (LULC) change over time and space is important for effective city space planning, monitoring and management especially in fast growing cities like Lusaka. Nowadays, satellite based earth observation and monitoring has been used to detect, manage and monitor land use land cover changes over large areas of land. In the present study, we analyze the spatialtemporal change of LULC for Lusaka City using classified Landsat imageries for 1995, 2005 and 2015 in ERDAS Imagine 2014 and ArcGIS 10.2 environments. The classification is based on a predefined LULC classification scheme consisting of five classes, namely Industrial / Commercial / Residential, Water, Open/Unutilized Land, Miombo Light Forest and Mixed Cultivation/Plantation, developed from field knowledge and the Lusaka Integrated Development Plan. Supervised classification was employed using Maximum Likelihood Classifier parametric decision rule. The results show that between 1995 and 2015 Industri...
Journal of Geographic Information System
Identifying spatiotemporal patterns of land use and land cover changes (LULCC) and their impacts on the natural environment is essential in policy decisions for effective, sustainable natural resource management solutions. This study employed supervised image classification in Google Earth Engine (GEE) cloud-based platform to assess the land cover land use changes for the past 30 years (1989-2020), as well as predict the land cover states and the risk of future forest loss in the next ten years, using TerrSet 20 software in Hurungwe district, Zimbabwe. The study findings revealed a net forest area and shrub loss of 32% and 10%, while croplands, water bodies, and bare lands have increased by about 171%, 7%, and 119% between 1989 and 2020, respectively. Croplands are the major contributor to the net change in forests, particularly tobacco farming. The predictive model estimated that by 2030 the district would lose approximately 7% of the current forest cover area, most likely converted into croplands, shrubs, and settlements. The results reinforce the importance of bridging the gap between socioeconomic activities and institutional policies to ensure proper natural resource management. Integrating institutional policy and socioeconomic goals is indispensable to ensure sustainable development.
Land Use Land Cover (LULC) Change Analysis of the Akuapem-North Municipality, Eastern Region; Ghana
International Journal of Research and Innovation in Social Science, 2021
Land-use changes are a significant determinant of land cover changes; this is on the grounds that it is human specialists; people, families, and private firms that make explicit moves that drive land-use change. An increment in family size, traveler populace, and abatement in the monetary prosperity of the indigenous area compels agricultural expansion. This paper aimed at analysing the Land-use Land-cover change pattern in the Akuapem-North Municipality and provide experimental record of land-cover changes in the municipality thereby broadening the insight of local authorities and land managers to better comprehend and address the complicated land-use system of the area and develop an improved land-use management strategies that could better balance urban expansion and environmental protection. Land cover change was observed through advanced processing and classification dependent on five multi-temporal medium resolution satellite symbolism (Landsat: 1986, 1990, 2002, 2017) into five classes. From this, precisely arranged pixel data were assigned to decide each land cover class size and the quantity of changed pixels into different classes through spatial change detection. It was discovered that land cover from 1986 to 2017 shows rapid changes in the landscape as there is high growth in built-up area. However, farmland and forest cover areas has reduced. Urban built-up area has extended outwards from the central-eastern part to the rest of the areas and has covered most of the northern, western, and southern parts. If the present growth trend continues, most of the vegetated areas will be converted into built-up areas in the near future, which may create ecological imbalance and affect the climate of the municipality.
The South African land reform policy under the democratic government (after 1994) was designed to redress the inequality of land ownership aiming to reverse the land ownership injustice that had occurred in the past era as a deliberate act of policy by the colonial and the apartheid government. This study aimed at investigating the effects of the South African land reform policy on land use and land cover change on a land restitution project in Makotopong, Limpopo province, South Africa. To determine land cover dynamics, 1994 and 2007 Landsat images were used. A maximum likelihood classification for each image was computed for identification of land cover variables. Trends in land cover change depict a decline in post-transfer activities with agricultural cropland decreasing from 78.04 ha in 1994 to 20.43 ha in 2007. Inadvertently, land under fallow and shrubs increased by 6% and 30%, respectively. This accelerated decline of agricultural activity is mainly attributable to change of land ownership and management skills from the commercial farmers to the inexperienced land claimants. Results suggest that quantification of the changes in land use and land cover types can be very useful in assessing environmental and social condition of the land reform project. The study recommends that spatial data analysis through remote sensing procedures should form the information base in monitoring and evaluating the land reform projects to ensure productivity and management.
International Journal of Research Publication and Reviews, 2024
The rapid urbanization and agricultural expansion in Kumbotso Local Government Area, Kano State, Nigeria, have prompted significant land use and land cover (LULC) changes over recent decades. To understand the interactions of human activities within the physical environment, it is pertinent to study the land use and land cover of the area under investigation. This study employs Remote Sensing and Geographic Information System (GIS) techniques to assess these changes from 2000 to 2023. Satellite imagery from Landsat 8–9 OLI/TIRS C2 L2, coupled with supervised classification and change detection methods using ArcGIS 10.8 software, reveals substantial shifts in land use patterns. The results obtained show some changes in land use and land cover classes within the period (2003–2023); bare land experienced a rapid increase of 52.3 km2, the built-up area increased by 49.8 km2, and vegetation decreased by 99.5 km2. Decreases were also observed in water bodies, with a reduction of 5.6 km2. The study underscores significant urbanization impacts and the importance of sustainable land management practices to mitigate adverse environmental impacts. It is, therefore, recommended that implementing land use zoning, promoting green infrastructure, and fostering community engagement address evolving land use dynamics and enhance environmental resilience. The findings inform policy formulation and guide future research towards sustainable development in the study area.
Simple algebraic change detection techniques viz. image difference and image ratio were applied to the South African national land use / cover (NLC) datasets of years 2000 and 2014, prepared in grid format covering the Klerksdorp-Orkney-Stilfontein-Hartebeestfontein (KOSH) region in order to assess land use/land cover changes. Both the 2000 and 2014 NLC datasets were generated from Landsat images using different classification schemes and the code values & attributes of the land cover classes of the two datasets were different/not comparable. In order to make these datasets comparable for change detection, the NLC2000 dataset was examined in ArcView GIS by superimposing it onto the NLC2014 dataset and similarities and differences were identified. For each cover type of the NLC2000 dataset, comparable cover type of the 2014 dataset was identified by making a query to the NLC2000 dataset and after viewing the spatial distributions of selected units in respect of the NLC2014 dataset. Suitable code values of NLC2014 dataset were identified for the NLC2000 dataset and it was later reclassified. The land use / cover change detection study reveals that increase in areas were observed for the cover types: Cultivated common fields (low), Cultivated common fields (med), Mines 2 semi-bare, Wetlands, Urban commercial and Plantations/woodlots mature. The Grassland, Thicket/dense bush, Urban residential (dense trees/bush), Mines 1 bare, and Cultivated common pivots (high) showed a decrease in places. During the 14 years, Grassland had decreased from 2,132.47 km2 (77.35% of the total area) to 1,629.78 km2 (59.11% of the total area) owing to landscape transformation to other land covers (e.g. Cultivated common fields and Urban residential) due to human activities. The percentage increase in areas observed for the Cultivated common fields (low and medium) were 8.21% and 2.96% while the Mines 2 semi-bare, Wetlands, Urban commercial, Plantations/woodlots mature showed increases of 0.67%, 0.32%, 0.28% and 0.23% respectively. The area of Thicket/dense bush decreased from 108.15 km2 to 56.71 km2 (change of 1.87%). Maps of land use/land cover changes and statistics obtained for the changed areas are very useful for identifying various changes occurring in different classes and for monitoring land use dynamics.
Remote Sensing Study of Land Use/Cover Change in West Africa
Increasing population and other anthropogenic activities have profound effect on large areas of forested land and other land use/cover forms throughout the world. There is a certain cause and effect relationship between changing practice for development and land use change, thus necessitating an assessment of land use dynamics and the projection trend. A combination of geospatial and remote techniques were utilized to evaluate the present and future landuse/ landcover scenario of southern part of the Western Region of Ghana. Multi-temporal satellite imageries of the Landsat series and DMC were used to map the changes in land use from 1990 to 2010. Four major land use classes (Forest, Agriculture, Built-up and water) were considered as the most dynamic land cover/use (LULC) practice. Markov modelling was applied for prediction of probable land use/ land cover change scenario for the years 2020, 2030 and 2040. The study showed that in years 2020 to 2040 in the predictable future, there will be a gradual increase in built up areas, while a stability in agricultural land use is envisaged. Agricultural land use would still remain the dominant land use type. Forests would be drastically reduced from close to 87% in 1990 to just fewer than 20% in 2040. This precarious situation would demand that prudent land use decisions to be made to keep Ghana's REDD+ program on track and to mitigate the effects of the climate change phenomenon.
Sensors
Forest loss, unbridled urbanisation, and the loss of arable lands have become contentious issues for the sustainable management of land. Landsat satellite images for 1986, 2003, 2013, and 2022, covering the Kumasi Metropolitan Assembly and its adjoining municipalities, were used to analyse the Land Use Land Cover (LULC) changes. The machine learning algorithm, Support Vector Machine (SVM), was used for the satellite image classification that led to the generation of the LULC maps. The Normalised Difference Vegetation Index (NDVI) and Normalised Difference Built-up Index (NDBI) were analysed to assess the correlations between the indices. The image overlays of the forest and urban extents and the calculation of the annual deforestation rates were evaluated. The study revealed decreasing trends in forestlands, increased urban/built-up areas (similar to the image overlays), and a decline in agricultural lands. However, there was a negative relationship between the NDVI and NDBI. The re...