Forest Cover Dynamics of a Lowland Rainforest in Southwestern Nigeria Using GIS and Remote Sensing Techniques

Oluwajuwon, Tomiwa V.; Alo, Akintunde; Ogana, Friday Nwabueze; Adekugbe, Oluwaseun A.

doi:10.4236/jgis.2021.132006

Cited by 7 publications

(12 citation statements)

References 22 publications

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“…The alarming rate of deforestation and its repercussions have raised concerns on a global scale (Caravaggio 2020;Hite and Seitz 2021). The illicit harvesting of natural forests, as well as the expansion of agricultural and development projects into previously forested areas, is the leading cause of forest loss (Chakravarty et al 2012;Islam and Sato 2012;Hishe et al 2021;Oluwajuwon et al 2021). According to Lambin et al (2001), the primary causes and reasons driving global and regional land cover changes are tropical deforestation, grassland alteration, agricultural intensification, and urbanization.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, effective forest management requires evaluation and monitoring of forest land cover changes. Poorly vegetated or bare ground areas express concern about the forest (Park et al 2017;Oluwajuwon et al 2021). Meanwhile, in Bangladesh, the forestry sector places less emphasis on monitoring and determining the extent of land cover than on the ecological inventory of conventional forest plots.…”

Section: Introductionmentioning

confidence: 99%

“…Detecting and monitoring changes in forested land using widely available remote sensing data and methods also contribute to implementing climate change mitigation initiatives like Reducing Emissions from Deforestation and Forest Degradation (REDD+) and Clean Development Mechanism (CDM) (Sangermano et al 2012;Potapov et al 2014). The use of vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI), has frequently been reported for mapping forest cover (Nath and Acharjee 2013;Nath 2014;Bera and Prakash 2018;Islam et al 2021;Oluwajuwon et al 2021). Studies have also demonstrated how land surface temperature (LST) affects vegetation indices, such as NDVI (Alam et al 2022;Hussain et al 2022).…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal change detection of land use land cover (LULC) in Fashiakhali wildlife sanctuary (FKWS) impact area, Bangladesh, employing multispectral images and GIS

Hossain

Khan

Oluwajuwon

et al. 2023

Model. Earth Syst. Environ.

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Land cover change has posed significant concerns to biodiversity and climate change in Bangladesh and globally. Despite the country’s designation of forest regions as protected areas to conserve their valuable resources, deforestation and forest conversion remained unabated. Fashiakhali Wildlife Sanctuary (FKWS), a protected area in the Chittagong Hill Tracts, and its surrounding forested impact area have experienced considerable changes over the years, yet are deficient in extensive assessment. This study evaluated the land use land cover (LULC) changes in the FKWS impact area over almost 3 decades (1994–2021) using multispectral remotely sensed data. The Landsat images of 1994, 2001, 2010, and 2021 were classified using a maximum likelihood algorithm and analyzed for change detection. The comparative potential of vegetation indices, including Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI), in forest cover assessment, and the relationship between Land Surface Temperature (LST) and NDVI was also assessed. A significant forest cover loss of around 1117.17 ha (16%) was recorded in the FKWS impact area between 1994 and 2021, with the hugest proportion of 867.78 ha (12.24%) deforested in the first period (1994–2001). Agricultural land also declined by 593.73 ha (8.37%) within the entire period, despite its initial increase of 392.04 ha (5.53%) between 2001 and 2010, being the primary driver of earlier deforestation. However, in the recent decade (2010–2021), settlement expansion of 963.90 ha (13.59%) due to massive human migration in the area contributed to the most remarkable overall land cover change of 1731.51 ha (24.42%). Furthermore, NDVI provided a better and more accurate forest cover assessment than SAVI and was recommended to aid in the quick evaluation and monitoring of the future impacts of agriculture, settlement, and other sorts of land use on the forest cover. In tandem with the widely acknowledged issue of increased temperature due to climate change, an absolute negative correlation was found between the NDVI and LST, confirming the negative impact of climate change on forest loss in the FKWS impact area.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal change detection of land use land cover (LULC) in Fashiakhali wildlife sanctuary (FKWS) impact area, Bangladesh, employing multispectral images and GIS

Hossain

Khan

Oluwajuwon

et al. 2023

Model. Earth Syst. Environ.

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“…Studies have examined LULC dynamics of diverse forest reserves (FRs) across the country, reporting an exasperatingly high rate of continuous forest cover reduction and changes. Examples include Shasha FR [21], Okomu and Gilli-Gilli FRs [22], Ogbese FR [23], Akure FR [24], Osho FR [25], Ago-Owu FR [26], Falgore Game Reserve [27] and Onigambari FR [28]. Overall, Nigeria's forest ecosystems are experiencing massive spatiotemporal changes over the last decades [10] [27], with a 5% net annual forest loss between 2010 and 2015, which is predominantly driven by agricultural expansion, uncontrolled logging, urban expansion, fuelwood and charcoal production [23] [29] [30].…”

Section: Introductionmentioning

confidence: 99%

Land Use Land Cover Dynamics of Oba Hills Forest Reserve, Nigeria, Employing Multispectral Imagery and GIS

Bukoye,

Oluwajuwon,

Alo

et al. 2023

ARS

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Land use Land cover (LULC) has undergone progressive changes worldwide over the years. However, there is limited information available about these changes in Oba Hills Forest Reserve, Nigeria. The existing spatial analysis of the forest excluded important land use classes like settlements. Therefore, this study aimed at assessing the dynamics of LULC in Oba Hills Forest Reserve between 1987 and 2019. Images from Landsat 5, Landsat 7, and Landsat 8 for the years 1987, 2001, 2013, and 2019 were obtained and subjected to preprocessing and classification using the maximum likelihood algorithm, change detection, and Normalized Differential Vegetation Index (NDVI). The coordinates of specific benchmark locations and other points were acquired for ground-truthing and developing Digital Elevation Model (DEM). Three distinct LULC classes were identified: forest, bare land (including open spaces, agriculture, rocks, and grasslands), and built-up areas. The forest cover in the reserve gradually decreased from 56% in 1987 to 47% in 2019, resulting in a total area loss of 455.4 hectares. Correspondingly, the other LULC classes experienced exponential expansion. Bare land increased from 44% in 1987 to 52% in 2019, while the built-up area expanded by 57.28 hectares. These changes are attributed to prevalent anthropogenic activities such as agriculture, grazing, logging, firewood collection, and population growth within the catchment area. The declining NDVI values in the forest reserve, from 0.52 to 0.44 within the years of assessment, further substantiated the substantial loss of forest cover. The DEM and topographical map highlighted notable steep slopes and elevations of up to over 550 m above sea level (asl) within the reserve, which have implications for forest growth and dynamics.

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“…It has been noted that the increase in bare land is as a result of the decline in forest area. This trend is of great concern to environmentalists and policy makers even in other countries beyond Zambia(Oluwajuwon et al 2021;Hossain et al 2023). Figure2shows the trend of LULC between 2000 and 2020.…”

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confidence: 99%

Multi-Decade Land Cover/Land Use Dynamics and Future Predictions for Zambia: 2000 - 2030

Chisanga,

Phiri,

Mubanga

2023

Preprint

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Accurate and up-to-date information on land use/land cover change (LULCC) is important in land use planning and natural resource management; however, in sub-Saharan Africa, detailed information on LULCC is still lacking. Therefore, this study assessed the dynamics of LULC change (2000–2020) and future projections (2020–2030) for Zambia. The 2000 and 2010 LULC maps were used to simulate the 2020 LULC scenario using Artificial Neural Network (Multi-layer Perception) algorithms in Modules for Land Use Change Evaluation (MOLUSCE) plugin in QGIS 2.18.14. The 2010 and 2020 maps were used to predict the 2030 LULC classes. The reference 2020 and predicted 2020 LULC maps were used to validate the model. The validation between the predicted and observed 2020 LULC map, Kappa (loc) was 0.9869. The ANN-MLP simulated the 2020 LULC patterns successfully as indicated by the high accuracy level of more than 95%. LULC classes were predicted for 2030 using the 2010–2020 calibration period. The expected LULC types for 2030 revealed that built-up area will increase by 447.20 km2 (71.44%), while 327.80 km2 (0.73%) of cropland will be lost relative to 2020 LULC map. Dense forest (0.19%), grassland (0.85%) and bare land (1.37%) will reduce from 2020–2030. However, seasonally flooded, sparse forest, shrub land, wetland and water body will increase marginally. The largest LULC change is from forest into other LULC types. The insights from this study show that ANN-MLP can be used to predict LULCC, and that the generated information can be employed in land use planning at a national scale.

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Forest Cover Dynamics of a Lowland Rainforest in Southwestern Nigeria Using GIS and Remote Sensing Techniques

Cited by 7 publications

References 22 publications

Spatiotemporal change detection of land use land cover (LULC) in Fashiakhali wildlife sanctuary (FKWS) impact area, Bangladesh, employing multispectral images and GIS

Spatiotemporal change detection of land use land cover (LULC) in Fashiakhali wildlife sanctuary (FKWS) impact area, Bangladesh, employing multispectral images and GIS

Land Use Land Cover Dynamics of Oba Hills Forest Reserve, Nigeria, Employing Multispectral Imagery and GIS

Multi-Decade Land Cover/Land Use Dynamics and Future Predictions for Zambia: 2000 - 2030

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