Land use and land cover changes implications on biodiversity in the Owabi catchment of Atwima Nwabiagya North District, Ghana

Baidoo, Richard; Arko-Adjei, Anthony; Quaye‐Ballard, Jonathan Arthur; Boansi, Michael Poku; Somuah, Dorcas Peggy

doi:10.1016/j.heliyon.2023.e15238

Cited by 10 publications

(2 citation statements)

References 49 publications

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“…This degradation has a profound impact on the environment. For instance, land cover degradation results in the destruction of natural habitats, leading to a decline in biodiversity (Baidoo et al, 2023;Maitima et al, 2009). This, in turn, has a domino effect on the entire ecosystem, as the loss of one species can affect the survival of others.…”

Section: Effect Of Land Cover Degradationmentioning

confidence: 99%

Integrated use of the CA-Markov model and the Trends.Earth module to enhance the assessment of land cover degradation: Application in the Upper Zambezi Basin, southern Africa

Zimba,

Banda,

Mbewe

et al. 2024

Preprint

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This study aims to demonstrate the potential of assessing future land cover degradation status by combining the forecasting capabilities of the Cellular-Automata-Markov chain (CA-Markov) models in Idris Selva with the land cover degradation (LCD) model in the Trends.Earth module. The study focuses on the upper Zambezi Basin (UZB) in southern Africa, which is one of the regions with high rates of land degradation globally. Landsat satellite imagery is utilised to generate historical (1993–2023) land cover and land use (LCLU) maps for the UZB, while the European Space Agency Climate Change Initiative (ESA CCI) global LCLU maps are obtained from the Trends.Earth module. The CA-Markov chain model is employed to predict future LCLU changes between 2023 and 2043. The LCD model in the Trends.Earth module in QGIS 3.34 is then used to assess the historical and forecasted land cover degradation status. The findings reveal that land cover degradation maps produced from local LCLU classifications provide more detailed information compared to those produced from the ESA CCI global LCLU product. Between 2023 and 2043, the UZB is predicted to experience a net reduction of approximately 3.2 million hectares of forest cover, with an average annual reduction rate of -0.13%. In terms of land cover degradation, the UZB is forecasted to remain generally stable, with 87% and 96% of the total land cover area expected to be stable during the periods 2023–2033 and 2033–2043, respectively, relative to the base years 2023 and 2033. Reduction in forest cover due to the expansion of grassland, human settlements, and cropland is projected to drive land cover degradation, while improvements in forest cover are anticipated through the conversion of grassland and cropland into forested areas. By leveraging the predictive power of the CA-Markov model and the capabilities of the LCD model, as evidenced in this study, valuable information can be effectively obtained for monitoring land cover degradation. This information can then be used to implement targeted interventions that align with the objective of realising the United Nations' land degradation neutral world target by 2030.

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Section: Effect Of Land Cover Degradationmentioning

confidence: 99%

Integrated use of the CA-Markov model and the Trends.Earth module to enhance the assessment of land cover degradation: Application in the Upper Zambezi Basin, southern Africa

Zimba,

Banda,

Mbewe

et al. 2024

Preprint

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show abstract

“…As a result, these land use changes, including urbanization, deforestation, and increased agriculture, cause the fragility of habitat patches which providing survival habitat space for wildlife species 3 , 4 . This phenomenon has several side effects such as habitat loss, fragmentation, worse connectivity for wildlife species, loss of biodiversity, the extinction of some wildlife species, numerous environmental problems, as well as negative impacts on human well-being of citizens 5 .…”

Section: Introductionmentioning

confidence: 99%

Land use change and Ecological Network in rapid urban growth region in Selangor region, Malaysia

Ma,

Teh,

Kho

2024

Sci Rep

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Rapid urbanization will cause various land use changes and the vast occupation of green spaces, a critical factor in the deterioration of biodiversity in urbanized areas. Some species of wildlife are endangered due to habitat shrunk and fragmentation. However, Malaysia's current biodiversity protection range is still limited. The Ecological Network (EN) refers to a framework of ecological components, which can be obtained by geographical and technical approaches to support more ecological diversity ranges. Furthermore, little research has been found on EN in Malaysia and the impact of land use change on EN. Therefore, the Selangor region is selected as the study area. This paper quantifies land use change and measures the extent of land use change to obtain the EN’s change. The result has shown that forestland has decreased, explored by people for housing and agriculture from 2000 to 2020. The EN has a trend of fragmentation. Overall, this study's results imply that the land use change led to EN's worsened performance from 2000 to 2020 in the study area. This paper hopes that this research could help supply information on conserving biodiversity in future development and urban sustainable planning in Malaysia.

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LULC change detection using support vector machines and cellular automata-based ANN models in Guna Tana watershed of Abay basin, Ethiopia

Fetene,

Lohani,

Mohammed

2023

Environ Monit Assess

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Land use and land cover changes implications on biodiversity in the Owabi catchment of Atwima Nwabiagya North District, Ghana

Cited by 10 publications

References 49 publications

Integrated use of the CA-Markov model and the Trends.Earth module to enhance the assessment of land cover degradation: Application in the Upper Zambezi Basin, southern Africa

Integrated use of the CA-Markov model and the Trends.Earth module to enhance the assessment of land cover degradation: Application in the Upper Zambezi Basin, southern Africa

Land use change and Ecological Network in rapid urban growth region in Selangor region, Malaysia

LULC change detection using support vector machines and cellular automata-based ANN models in Guna Tana watershed of Abay basin, Ethiopia

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