LULC change detection and future LULC modelling using RF and MLPNN-Markov algorithms in the uMngeni catchment, KwaZulu-Natal, South Africa
Water catchment areas are the key strategic water sources with a variety of ecological benefits. However, the trajectory of Land Cover and Land Use Changes (LULC-C change poses a significant threat to water catchment areas, negatively affecting water quality. Thus, the adoption of remote sensing dat...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Environmental Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenvs.2025.1543524/full |
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| author | Orlando Bhungeni Michael Gebreslasie Ashadevi Ramjatan |
| author_facet | Orlando Bhungeni Michael Gebreslasie Ashadevi Ramjatan |
| author_sort | Orlando Bhungeni |
| collection | DOAJ |
| description | Water catchment areas are the key strategic water sources with a variety of ecological benefits. However, the trajectory of Land Cover and Land Use Changes (LULC-C change poses a significant threat to water catchment areas, negatively affecting water quality. Thus, the adoption of remote sensing data and Machine Learning Algorithms (MLAs) is a novel approach that provides spatiotemporal data on the environmental changes resulting from LULC dynamics. Hence, this work harnessed Landsat imageries and the Random Forests (RF) classification as well as a hybrid model from the Multi-Layer Perceptron and Markov chain (MLPNN-Markov) to detect changes in LULC and forecast future changes. At every 5 years interval, the RF model generated more accurate maps for 2003–2023. The LULC prediction for 2019 also produced acceptable values for the kappa accuracy matrices, which were 65.50%, 58.4%, 90.90%, and 0.52 for overall accuracy, kappa location, kappa histogram, and kappa overall, respectively. The findings highlighted the decline of forest areas, with a strong negative correlation with built-up and mining areas. The secondary invasion of the abandoned cropland occupied by grassland members was observed. Thus, grassland displayed increasing trends between 2019 and 2023. Wetlands and water, however, exhibited a steady trend with minor variations. On the other hand, each of these trends persisted in the future, with the exception of grassland areas that displayed scaling-down behaviour in 2032. The outcomes of this work will offer a piece of updated information on the LULC-C and hints at the possible future direction for the trends by 2032. This is crucial to local bodies tasked to protect the integrity of the water catchment areas with the aim of improving the water quality. |
| format | Article |
| id | doaj-art-9b365db8e86f431db8a6e683810aaa5e |
| institution | Kabale University |
| issn | 2296-665X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Environmental Science |
| spelling | doaj-art-9b365db8e86f431db8a6e683810aaa5e2025-08-20T03:53:38ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2025-04-011310.3389/fenvs.2025.15435241543524LULC change detection and future LULC modelling using RF and MLPNN-Markov algorithms in the uMngeni catchment, KwaZulu-Natal, South AfricaOrlando BhungeniMichael GebreslasieAshadevi RamjatanWater catchment areas are the key strategic water sources with a variety of ecological benefits. However, the trajectory of Land Cover and Land Use Changes (LULC-C change poses a significant threat to water catchment areas, negatively affecting water quality. Thus, the adoption of remote sensing data and Machine Learning Algorithms (MLAs) is a novel approach that provides spatiotemporal data on the environmental changes resulting from LULC dynamics. Hence, this work harnessed Landsat imageries and the Random Forests (RF) classification as well as a hybrid model from the Multi-Layer Perceptron and Markov chain (MLPNN-Markov) to detect changes in LULC and forecast future changes. At every 5 years interval, the RF model generated more accurate maps for 2003–2023. The LULC prediction for 2019 also produced acceptable values for the kappa accuracy matrices, which were 65.50%, 58.4%, 90.90%, and 0.52 for overall accuracy, kappa location, kappa histogram, and kappa overall, respectively. The findings highlighted the decline of forest areas, with a strong negative correlation with built-up and mining areas. The secondary invasion of the abandoned cropland occupied by grassland members was observed. Thus, grassland displayed increasing trends between 2019 and 2023. Wetlands and water, however, exhibited a steady trend with minor variations. On the other hand, each of these trends persisted in the future, with the exception of grassland areas that displayed scaling-down behaviour in 2032. The outcomes of this work will offer a piece of updated information on the LULC-C and hints at the possible future direction for the trends by 2032. This is crucial to local bodies tasked to protect the integrity of the water catchment areas with the aim of improving the water quality.https://www.frontiersin.org/articles/10.3389/fenvs.2025.1543524/fullrandom forestLULClandsatchange detectionuMngeni river catchmentSouth Africa |
| spellingShingle | Orlando Bhungeni Michael Gebreslasie Ashadevi Ramjatan LULC change detection and future LULC modelling using RF and MLPNN-Markov algorithms in the uMngeni catchment, KwaZulu-Natal, South Africa Frontiers in Environmental Science random forest LULC landsat change detection uMngeni river catchment South Africa |
| title | LULC change detection and future LULC modelling using RF and MLPNN-Markov algorithms in the uMngeni catchment, KwaZulu-Natal, South Africa |
| title_full | LULC change detection and future LULC modelling using RF and MLPNN-Markov algorithms in the uMngeni catchment, KwaZulu-Natal, South Africa |
| title_fullStr | LULC change detection and future LULC modelling using RF and MLPNN-Markov algorithms in the uMngeni catchment, KwaZulu-Natal, South Africa |
| title_full_unstemmed | LULC change detection and future LULC modelling using RF and MLPNN-Markov algorithms in the uMngeni catchment, KwaZulu-Natal, South Africa |
| title_short | LULC change detection and future LULC modelling using RF and MLPNN-Markov algorithms in the uMngeni catchment, KwaZulu-Natal, South Africa |
| title_sort | lulc change detection and future lulc modelling using rf and mlpnn markov algorithms in the umngeni catchment kwazulu natal south africa |
| topic | random forest LULC landsat change detection uMngeni river catchment South Africa |
| url | https://www.frontiersin.org/articles/10.3389/fenvs.2025.1543524/full |
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