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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. [ABSTRACT FROM AUTHOR]

Multispectral remote sensing application in thematic urban land-use or land-cover (LULC) classification has gained popularity in the recent past. However, as a result of the complexity of urban landscapes and spectral limitations in commonly used imagery, accurate urban LULC classification has often been impeded by confusion of spectra among multiple urban LULC types. The emergence of multispectral aerial photographs, characterised by high spatial resolution and multispectral information, offers great potential for LULC classification. In this study, we hypothesised that textural information using optimum Haralick textural features inherent in multispectral aerial photographs can be used to generate reliable land-cover maps in heterogeneous urban landscapes. Haralick textural feature optimisation and object-based classification were used to discriminate diverse urban LULC types. Grey-level co-occurrence matrix (GLCM) Entropy, GLCM Mean and GLCM Angular Second Moment texture features were used to discriminate different LULC types while the Jeffreys-Matisuta separability analysis was used to identify optimum thresholds for the development of object-based classification rules. Results from object-based classification were also compared to classification output using the aerial photograph's spectral information. Results show that use of both object-based Haralick textural features and the spectral characteristics on multispectral aerial photographs can be used to generate reliable LULC classes. Classification based on object-based Haralick textural features produced higher accuracy than that based on spectral information. Multispectral aerial photographs using both object-based Haralick textural features and spectral information offer great potential in mapping urban landscapes often characterised by heterogeneous cover types. [ABSTRACT FROM AUTHOR]

Ecosystems provide a wide range of services crucial for human well-being and decision-making processes at various levels. This study analyzed the major land cover types of north-central Ethiopia and their impact on total and per-capita ecosystem service value (ESV). The ESV was estimated using the benefit-transfer method along the established global and local coefficient values for the periods 1973, 1986, 2001, 2016, and 2024. The findings show that agricultural lands continued to expand at a rate of 563.4 ha year−1, at the expense of forests and grasslands. As a result, the total ESV of the study area declined from $101.4 to $61.03 million and $60.08–$43.69 million, respectively. The ESV per capita was also diminished by $152.4 (37.7%) and $257 (40.6%), respectively. However, land-cover improvement during the period 2001–2016 enhanced the total and per capita ESV in the study area. Therefore, potential future research may be required to develop a valid approach for assessing the robustness and sensitivity of value coefficients for the valuation of the ESV at the landscape level. [ABSTRACT FROM AUTHOR]

Understanding the often-heterogeneous land cover in urban areas is critical for, among other things, environmental monitoring, spatial planning, and enforcement. Recently, several earth observation satellites were developed with an enhanced spatial resolution that provides for precise and detailed representations of image objects. Morphological image analysis techniques provide useful tools for extracting spatial features from high-resolution, remotely sensed images. This study investigated the efficacy of mathematical morphological (MM) techniques in the land cover classification of a heterogeneous urban landscape using very high-resolution pan-sharpened Pleiades imagery. Specifically, the study evaluated two morphological profiles (MP) techniques (i.e., concatenation of morphological profiles (CMPs) and multi-morphological profiles (MMPs)) in the classification of a heterogeneous urban land cover. The overall accuracies for CMP were 83.14% and 83.19% over the two study areas. Similarly, the MMP overall accuracies were 84.42% and 84.08% for the two study sites. The study concluded that CMP and MMP can greatly improve the classification of heterogeneous landscapes that typify urban areas by effectively representing the structural landscape information necessary for discriminating related land cover classes. In general, similar and visually acceptable results were produced for land cover classification using either CMP or MMP image analysis techniques [ABSTRACT FROM AUTHOR]