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 DOI Creative Commons

Henry Zimba,

Kawawa Banda,

Stephen Mbewe

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: May 9, 2024

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

Language: Английский

Towards Improving Sustainable Water Management in Geothermal Fields: SVM and RF Land Use Monitoring DOI Creative Commons
Widya Utama, Rista Fitri Indriani, Maman Hermana

et al.

Journal of Human Earth and Future, Journal Year: 2024, Volume and Issue: 5(2), P. 216 - 242

Published: June 1, 2024

The management and monitoring of land use in geothermal fields are crucial for the sustainable utilization water resources, as well striking a balance between production renewable energy preservation environment. This study primarily compared Support Vector Machine (SVM) Random Forest (RF) machine learning methods, using satellite imagery from Landsat 8 Sentinel 2 2021 2023, to monitor Patuha area. objective is improve practices by accurately categorizing different cover types. comparative analysis assessed efficacy these techniques upholding sustainability regions. examined application SVM RF techniques, with particular emphasis on parameter refinement model assessment, enhance classification accuracy. By employing Kernlab e1071 algorithm comparison, research sought produce precise Land Use Model Map, which underscores significance advanced analytical environmental management. approach was utmost importance improving reinforcing practices. evaluation methods demonstrates superiority terms accuracy, stability, precision, particularly intricate urban settings, hence establishing it preferred tasks demanding high reliability. areas alignment Sustainable Development Goals (SDGs) 6 15, fosters conservation ecosystems. Doi: 10.28991/HEF-2024-05-02-06 Full Text: PDF

Language: Английский

Citations

16
Unveiling hydrological responses in Madagascar’s major river basins: addressing data scarcity and land-use change impacts DOI
Rakotoarimanana Zy Harifidy, Hiroshi Ishidaira,

Jun Magome

et al.

Hydrological Sciences Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 20, 2025

Language: Английский

Citations

0
A Ramsar site catchment undergoing major land use/land cover dynamics: Scenarios from Elephant Marsh, Malawi DOI Creative Commons
Rodgers Makwinja, Solomon G. Tesfamichael, C. J. Curtis

et al.

Remote Sensing Applications Society and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 101508 - 101508

Published: Feb. 1, 2025

Language: Английский

Citations

0
Evaluation of machine learning algorithm capability for Bosten Lake Wetland classification based on multi-temporal Sentinel-2 data DOI Creative Commons

Feiying Xia,

Guanghui Lv

Ecological Informatics, Journal Year: 2024, Volume and Issue: unknown, P. 102839 - 102839

Published: Sept. 1, 2024

Language: Английский

Citations

2
Remote Sensing Image-based Assessment of Land Dynamics Transformation into Tea Plantations using Support Vector Machine DOI Creative Commons
Md. Sahadat Hossan, Masud Ibn Afjal, Md. Faruq Hasan

et al.

Trees Forests and People, Journal Year: 2024, Volume and Issue: unknown, P. 100703 - 100703

Published: Oct. 1, 2024

Language: Английский

Citations

1
Integrated water quality assessment of open water bodies using empirical equations and remote sensing techniques in Bangweulu Wetland lakes, Zambia DOI
Misheck Lesa Chundu, Kawawa Banda,

Henry M. Sichingabula

et al.

Journal of Great Lakes Research, Journal Year: 2024, Volume and Issue: unknown, P. 102451 - 102451

Published: Oct. 1, 2024

Language: Английский

Citations

1
Integrated use of the CA–Markov model and the Trends.Earth module to enhance the assessment of land cover degradation DOI Creative Commons

Henry Zimba,

Kawawa Banda,

Stephen Mbewe

et al.

ENVIRONMENTAL SYSTEMS RESEARCH, Journal Year: 2024, Volume and Issue: 13(1)

Published: July 14, 2024

Abstract This study aims to demonstrate the potential of assessing future land cover degradation status by combining forecasting capabilities Cellular-Automata and Markov chain (CA-Markov) models in Idris Selva with (LCD) model Trends.Earth module. The focuses on upper Zambezi Basin (UZB) southern Africa, which is one regions high rates globally. Landsat satellite imagery utilised generate historical (1993–2023) use (LCLU) maps for UZB, while global European Space Agency Climate Change Initiative (ESA CCI) LCLU are obtained from CA-Markov employed predict changes between 2023 2043. LCD module QGIS 3.32.3 then used assess forecasted status. findings reveal that produced local classifications provide more detailed information compared those ESA CCI product. Between 2043, UZB predicted experience a net reduction approximately 3.2 million hectares forest cover, an average annual rate − 0.13%. In terms degradation, remain generally stable, 87% 96% total area expected be stable during periods 2023–2033 2033–2043, respectively, relative base years 2033. Reduction due expansion grassland, human settlements, cropland projected drive improvements anticipated through conversion grassland into forested areas. It appears using locally high-resolution images provides better assessments than products. By leveraging opportunities offered capacity such as CA–Markov model, evidenced this study, valuable can effectively monitoring degradation. implement targeted interventions align objective realising United Nations' neutral world target 2030.

Language: Английский

Citations

1
Forecasting Wetland Transformation to Dust Source by Employing CA-Markov Model and Remote Sensing: A Case Study of Shadgan International Wetland DOI

Vaad Khanfari,

Hossein Mohammad Asgari,

Ali Dadollahi-Sohrab

et al.

Wetlands, Journal Year: 2024, Volume and Issue: 44(7)

Published: Sept. 11, 2024

Language: Английский

Citations

1
A Comparative Study of High-level Classification Algorithms for Land Use and Land Cover Classification and Periodic Change Analysis Over Transboundary Ruvu River Basin, Tanzania DOI
Deus Michael, Ray Singh Meena, Brijesh Kumar

et al.

Remote Sensing in Earth Systems Sciences, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 19, 2024

Language: Английский

Citations

1
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 DOI Creative Commons

Henry Zimba,

Kawawa Banda,

Stephen Mbewe

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: May 9, 2024

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

Language: Английский

Citations

0