Abstract
Natural and human-induced drivers within the constraints of multiple socioeconomic and political conditions have intensified the extent of land use and land cover (LULC) change at different scales. Hence, up-to-date spatial and statistical information about LULC change is of utmost importance for the development and implementation of appropriate Earth resource management strategies and monitoring environmental changes. With the increased availability of remotely sensed data from various Earth observation satellite and continuous advances in computing technology, these pieces of information are commonly retrieved based on automated digital image classification algorithms. However, in-depth quantitative evaluation of different classification algorithms is critical to select an efficient classifier or multiple ways to obtain more accurate land use maps. Taking selected hydrological catchments of the Lake Haramaya Watershed in the East Hararghe Ethiopian highland as an example, we statistically compared the land use classification performance of three nonlinear machine-learning algorithms (MLAs), namely: support vector machine (SVM), random forest (RF), and artificial neural network (ANN) and object-based image analysis (OBIA) using open-source Landsat-8 satellite images. At the same time, the classification accuracy was assessed using sample points from high spatial resolution Google-Earth Pro image. The confusion matrix analysis has indicated that the generated land use maps had a divergent classification accuracy. However, the SVM achieved the highest overall classification accuracy (94%) compared to the RF (92%) and the ANN (89%). On the contrary, the OBIA classifier achieved the least overall accuracy (75%) compared to the former. Thus, the results presented here reveal that though all supervised MLAs can be considered robust classifiers, the SVM algorithm was found to be the best classifier to improve the classification accuracy at an individual land use class level.
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Conceptualization: GWW; Methodology: GWW, DT, TEM; Formal analysis and investigation: GWW, TEM and TBG; Writing-original draft preparation: GWW; Writing-review and editing: GWW, DT. All authors read and approved the final manuscript.
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Woldemariam, G.W., Tibebe, D., Mengesha, T.E. et al. Machine-learning algorithms for land use dynamics in Lake Haramaya Watershed, Ethiopia. Model. Earth Syst. Environ. 8, 3719–3736 (2022). https://doi.org/10.1007/s40808-021-01296-0
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DOI: https://doi.org/10.1007/s40808-021-01296-0