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Land Cover and Landscape Structural Changes Using Extreme Gradient Boosting Random Forest and Fragmentation Analysis

Matyukira, Charles ; Mhangara, Paidamwoyo

Remote sensing (Basel, Switzerland), 2023-12, Vol.15 (23), p.5520 [Periódico revisado por pares]

Basel: MDPI AG

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Título:
Land Cover and Landscape Structural Changes Using Extreme Gradient Boosting Random Forest and Fragmentation Analysis
Autor: Matyukira, Charles ; Mhangara, Paidamwoyo
Assuntos: Accuracy ; Algorithms ; Analysis ; Bayes, Thomas ; Biodiversity ; Change detection ; Classification ; Classifiers ; Climate change ; Data mining ; Degradation ; environmental degradation ; Environmental impact ; Fertilizer industry ; Fertilizers ; Food sources ; Fragmentation ; Grasslands ; Habitat fragmentation ; Habitat loss ; Historic sites ; Impact analysis ; International economic relations ; Land cover ; Land degradation ; Land use ; Landscape ; landscape structure ; Local economy ; Machine learning ; Natural areas ; Nature reserves ; Naïve Bayers Classifier ; Outcrops ; Remote sensing ; Soil erosion ; Support vector machines ; Sustainable tourism ; Topography ; Tourism ; Vegetation ; XGBRFClassifier
É parte de: Remote sensing (Basel, Switzerland), 2023-12, Vol.15 (23), p.5520
Descrição: Land use and land cover change constitute a significant driver of land degradation worldwide, and machine-learning algorithms are providing new opportunities for effectively classifying land use and land cover changes over time. The aims of this study are threefold: Firstly, we aim to compare the accuracies of the parametric classifier Naïve Bayes with the non-parametric classifier Extreme Gradient Boosting Random Forest algorithm on the 2020 LULC dataset. Secondly, we quantify land use and land cover changes in the Cradle of Humankind from 1990 to 2020 using the Extreme Gradient Boosting Random Forest algorithm and post-classification change detection. Thirdly, the study uses landscape metrics to examine landscape structural changes occurring in the same area due to fragmentation. The classification results show that while Naïve Bayers and XGB Random Forest produce classification results of high accuracy, the XGB Random Forest Classifier produced superior results compared to the Naïve Bayers Classifier. From 1990 to 2020, bare ground/rock outcrop significantly increased by 39%, and open bush by 32%. Indigenous forests and natural grasslands lost area (26% and 12%, respectively). The results from this study indicate increasing land cover fragmentation and attest to land degradation, as shown by increases in bare ground and a reduction in indigenous forest and natural grassland. The decline in indigenous forests and natural grassland indicates the degradation of native vegetation, considered as prehistoric plant food sources. The high classification results also attest to the efficacy of the XGBRFClassifier executed in GEE. Land degradation evident in the nature reserve has long-term ecological consequences, such as loss of habitat, biodiversity decline, soil erosion, and alteration of local ecosystems, which together diminish the aesthetic value of the heritage site and negatively impact its tourism value. Consequently, it destroys crucial local economies and threatens sustainable tourism.
Editor: Basel: MDPI AG
Idioma: Inglês

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Land Cover and Landscape Structural Changes Using Extreme Gradient Boosting Random Forest and Fragmentation Analysis

Matyukira, Charles ; Mhangara, Paidamwoyo

Basel: MDPI AG

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