Multi-Decadal Spatial and Temporal Forest Cover Change Analysis of Nkandla Natural Reserve, South Africa

Gyamfi-Ampadu, Enoch; Gebreslasie, Michael; Mendoza‐Ponce, Alma

doi:10.1080/10549811.2021.1891441

Cited by 2 publications

(2 citation statements)

References 75 publications

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“…Most notably, it mitigates climate change and acts as a carbon sink, hence preventing the deterioration of water quality [6,76]. As a result, efforts to preserve the current natural plant cover should be undertaken, and as suggested by [6,77,78], actions aimed at increasing the area covered by these classes may be encouraged.…”

Section: Discussionmentioning

confidence: 99%

Evaluating Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal

Bhungeni,

Ramjatan,

Gebreslasie

2024

Remote Sensing

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Analysis of land use/land cover (LULC) in catchment areas is the first action toward safeguarding freshwater resources. LULC information in the watershed has gained popularity in the natural science field as it helps water resource managers and environmental health specialists develop natural resource conservation strategies based on available quantitative information. Thus, remote sensing is the cornerstone in addressing environmental-related issues at the catchment level. In this study, the performance of four machine learning algorithms (MLAs), namely Random Forests (RFs), Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Naïve Bayes (NB), were investigated to classify the catchment into nine relevant classes of the undulating watershed landscape using Landsat 8 Operational Land Imager (L8-OLI) imagery. The assessment of the MLAs was based on a visual inspection of the analyst and commonly used assessment metrics, such as user’s accuracy (UA), producers’ accuracy (PA), overall accuracy (OA), and the kappa coefficient. The MLAs produced good results, where RF (OA = 97.02%, Kappa = 0.96), SVM (OA = 89.74%, Kappa = 0.88), ANN (OA = 87%, Kappa = 0.86), and NB (OA = 68.64%, Kappa = 0.58). The results show the outstanding performance of the RF model over SVM and ANN with a significant margin. While NB yielded satisfactory results, its sensitivity to limited training samples could primarily influence these results. In contrast, the robust performance of RF could be due to an ability to classify high-dimensional data with limited training data.

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Section: Discussionmentioning

confidence: 99%

Evaluating Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal

Bhungeni,

Ramjatan,

Gebreslasie

2024

Remote Sensing

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“…Most previous studies focused on traditional methods using hand-crafted features, such as multi-variant change vector analysis (MVCA), normalized difference vegetation index (NVDI), and so on [1], [2], [5]- [9]. They have drawbacks that prevent their wide application, especially for non-domain experts in forestry and remote sensing technology.…”

Section: Introductionmentioning

confidence: 99%

Coastal forest cover change detection using satellite images and convolutional neural networks in Vietnam

Nguyen-Trong

Tran-Xuan²

2022

IJ-AI

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<span lang="EN-US">Monitoring forest cover changes is an important task for forest resource management and planning. In this context, remote sensing images have shown a high potential in forest cover changes detection. In Vietnam, although the existence of a large number of such images and ground-truth labels, current researches still relied on classical methods employed manual indices, such as multi-variant change vector analysis (MVCA) and normalized difference vegetation index. These methods highly require domain knowledge to determine threshold values for forest change that are applicable only for studied areas. Therefore, in this paper, we propose a method to detect coastal forest cover changes, which can exploit available dataset and ground-truth labels. Moreover, the proposed method does not require much domain knowledge. We used multi-temporal Sentinel-2 imagery to train a segmentation model, that is based on the U-Net network. It was used then to detect forest areas at the same location taken at different times. Lastly, we compared obtained results to identify forest disturbances. Experimental results demonstrated that our method provided a high accuracy of 95.4% on the testing set. Furthermore, we compared our model with the MVCA method and found that our model outperforms this popular method by 3.8%.</span>

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Multi-Decadal Spatial and Temporal Forest Cover Change Analysis of Nkandla Natural Reserve, South Africa

Cited by 2 publications

References 75 publications

Evaluating Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal

Evaluating Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal

Coastal forest cover change detection using satellite images and convolutional neural networks in Vietnam

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