Evaluating the impact of land use and land cover change on unprotected wetland ecosystems in the arid-tropical areas of South Africa using the Landsat dataset and support vector machine

Thamaga, Kgabo Humphrey; Dube, Timothy; Shoko, Cletah

doi:10.1080/10106049.2022.2034986

Cited by 9 publications

(6 citation statements)

References 96 publications

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“…Train datasets are used to select the best hyperplane, while test datasets are used to validate its generalization capabilities [59]. SVM has been successfully used in many LC classification studies [60][61][62]. Kernel function (Type) is the crucial parameter in the SVM algorithm.…”

Section: Image Classificationmentioning

confidence: 99%

Integration of Sentinel-1 and Sentinel-2 Data for Ground Truth Sample Migration for Multi-Temporal Land Cover Mapping

Moharrami,

Attarchi,

Gloaguen

et al. 2024

Remote Sensing

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Reliable and up-to-date training reference samples are imperative for land cover (LC) classification. However, such training datasets are not always available in practice. The sample migration method has shown remarkable success in addressing this challenge in recent years. This work investigated the application of Sentinel-1 (S1) and Sentinel-2 (S2) data in training sample migration. In addition, the impact of various spectral bands and polarizations on the accuracy of the migrated training samples was also assessed. Subsequently, combined S1 and S2 images were classified using the Support Vector Machines (SVM) and Random Forest (RF) classifiers to produce annual LC maps from 2017 to 2021. The results showed a higher accuracy (98.25%) in training sample migrations using both images in comparison to using S1 (87.68%) and S2 (96.82%) data independently. Among the LC classes, the highest accuracy in migrated training samples was found for water, built-up, bare land, grassland, cropland, and wetland. Inquiries on the efficiency of different spectral bands and polarization used in training sample migration showed that bands 4 and 8 and VV polarization in the water class were more important, while for the wetland class, bands 5, 6, 7, 8, and 8A together with VV polarization showed superior performance. The results showed that the RF classifier provided better performance than the SVM (higher overall, producer, and user accuracy). Overall, our findings suggested that shared use of S1 and S2 data can be used as a suitable means for producing up-to-date and high-quality training samples.

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Section: Image Classificationmentioning

confidence: 99%

Integration of Sentinel-1 and Sentinel-2 Data for Ground Truth Sample Migration for Multi-Temporal Land Cover Mapping

Moharrami,

Attarchi,

Gloaguen

et al. 2024

Remote Sensing

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“…Land cover change was also used to monitor long-term desertification changes in the Ternata oasis in the country of Morocco [58]. Thamaga et al used the Landsat dataset to assess the impact of land cover change on unprotected wetland ecosystems in the arid tropics of South Africa [59]. In particular, the La Plata region had major losses in grassland area from 2000 to 2014, mostly as a result of the expansion of agricultural boundaries [60].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Conflict Analysis and Prediction of Long Time Series Land Cover Changes in the Black Soil Region of Northeast China Using Remote Sensing and GIS

Ding

Jiang

Tan

et al. 2023

IJGI

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Using remote sensing and GIS techniques to monitor long time series land cover changes is of great significance to understanding the impact of human activities on spatiotemporal conflicts and changes in cropland and forest ecosystems in the black soil region of Northeast China. Spatial analysis and dynamic degree were used to analyze the evolutionary process and spatiotemporal association of land cover from 1990 to 2020; the transfer matrix was used to analyze and reveal dynamic conversions of land cover from 1990 to 2000, 2000 to 2010, and 2010 to 2020; and the GM (1,1) model was used to forecast the changes in land cover by 2025 based on historical data. The results indicated that the dominance of forest and cropland did not change from 1990 to 2020, and the average area of forest and cropland was 512,713 km2 and 486,322 km2, respectively. The mutual conversion between cropland, forest, grassland, and bare areas was the most frequent. The area of cropland converted into forest and grassland was 14,167 km2 and 25,217 km2, respectively, and the area of forest and grassland converted into cropland was 27,682 km2 and 23,764 km2, respectively, from 1990 to 2000. A similar law of land cover change was also presented from 2000 to 2020. In addition, the predicted values of cropland, forest, grassland, shrubland, wetland, water bodies, impervious surfaces, and bare areas were 466,942 km2, 499,950 km2, 231,524 km2, 1329 km2, 11,775 km2, 18,453 km2, 30,549 km2, and 189,973 km2, respectively, by 2025. The maximum and minimum residuals between the predicted and actual values were 6241 km2 and −156 km2 from 1990 to 2020. The evaluation results of the GM (1,1) model showed that all of the evaluation indices were within an acceptable range, and that the posteriori error ratio and class ratio dispersion were both less than 0.25. Through comparison with other studies, this study is not only able to provide some experience for further analyzing the spatial and temporal changes in land cover and its future prediction but also provide a basis for comprehensive management in Northeast China.

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“…LULC change is exacerbated by human activities such as population growth, urbanization, and deforestation [8]. Several studies have identifed these changes as a current challenge to the ecohydrological system of the environment [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Change of Land Use/Land Cover and Vegetation Using Multi-MODIS Satellite Data, Western Ethiopia

Seifu,

Woldesenbet,

Alemayehu

et al. 2023

The Scientific World Journal

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Land use and land cover (LULC) change and variability are some of the challenges to present-day water resource management. The purpose of this study was to determine LULC and Normalized Difference Vegetation Index (NDVI) fluctuations in western Ethiopia during the last 20 years. The first part of the study used MODIS LULC data for the change analysis, change detection, and spatial and temporal coverage in the study region. In the second part, the study analyzes the NDVI change and its spatial and temporal coverage. In this study, The Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data were applied to determine LULC and NDVI changes over four different periods. Evergreen broadleaf forests, deciduous broadleaf forests, mixed forests, woody savannas, savannas, grasslands, permanent wetlands, croplands, urban and built-up lands, and water bodies are the LULC in the period of analysis. The overall classification accuracy for the classified image from 2001 to 2020 was 85.4% and the overall kappa statistic was 81.2%. The results indicate a substantial increase in woody savannas, deciduous broadleaf, grasslands, permanent wetlands, and mixed forest areas by 119.6%, 57.7% 45.2%, 37%, and 21.3%, respectively, followed by reductions in croplands, water bodies, savannas, and evergreen broadleaf forest by 90.1%, 19.8%, 13.2%, and 4.8%, respectively, for the catchment between 2001 and 2020. The result also showed that the area’s vegetation cover increased by 64% from 2001 to 2022. This study could provide valuable information for water resource and environmental management as well as policy and decision-making.

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Evaluating the impact of land use and land cover change on unprotected wetland ecosystems in the arid-tropical areas of South Africa using the Landsat dataset and support vector machine

Cited by 9 publications

References 96 publications

Integration of Sentinel-1 and Sentinel-2 Data for Ground Truth Sample Migration for Multi-Temporal Land Cover Mapping

Integration of Sentinel-1 and Sentinel-2 Data for Ground Truth Sample Migration for Multi-Temporal Land Cover Mapping

Spatiotemporal Conflict Analysis and Prediction of Long Time Series Land Cover Changes in the Black Soil Region of Northeast China Using Remote Sensing and GIS

Spatio-Temporal Change of Land Use/Land Cover and Vegetation Using Multi-MODIS Satellite Data, Western Ethiopia

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