Urbanization in China has been rapid over the past three decades causing substantial replacement of the natural landscape by built-up land. In this paper, we present a comparison of Sentinel-2A MSI (S2A) and Landsat-8 OLI (L8) data in the retrieval of five built-up indices, namely Urban Index (UI), Normalized Difference Built-up Index (NDBI), Index-based Built-up Index (IBI) and two visible based indices, i.e. VgNIR-BI and VrNIR-BI. All the built-up indices maps water-masked were classified into built-up and non-built-up land using Otsu’s method. Simultaneously, the support vector machine (SVM) algorithm was employed to classify the two imageries into three respective classes. The accuracy assessment results show that all built-up indices had higher Overall Accuracy for S2A (up to 98.14% for VrNIR-BI) and L8 (up to 98.42% for VrNIR-BI) imageries compared to SVM. The percentage differences demonstrate that L8 estimates higher built-up area compared to S2A between 1.48% and 8.45% via the built-up indices and 13.40% compared to the SVM. Cross-checking with the Statistical Yearbook, S2A is superior to L8 in built-up land mapping capability, especially utilizing built-up indices. The difference caused by spatial resolution and spectral response functions should be taken into consideration in synergistic scientific application.

Remote sensing data provide significant information about surface geological features, but they have not been fully investigated as a tool for delineating mineral prospective targets using the latest advancements in machine learning predictive modeling. In this study, besides available geological data (lithology, structure, lineaments), Landsat-8, Sentinel-2, and ASTER multispectral remote sensing data were processed to produce various predictor maps, which then formed four distinct datasets (namely Landsat-8, Sentinel-2, ASTER, and Data-integration). Remote sensing enhancement techniques, including band ratio (BR), principal component analysis (PCA), and minimum noise fraction (MNF), were applied to produce predictor maps related to hydrothermal alteration zones in Hamissana area, while geological-based predictor maps were derived from applying spatial analysis methods. These four datasets were used independently to train a random forest algorithm (RF), which was then employed to conduct data-driven gold mineral prospectivity modeling (MPM) of the study area and compare the capability of different datasets. The modeling results revealed that ASTER and Sentinel-2 datasets achieved very similar accuracy and outperformed Landsat-8 dataset. Based on the area under the ROC curve (AUC), both datasets had the same prediction accuracy of 0.875. However, ASTER dataset yielded the highest overall classification accuracy of 73%, which is 6% higher than Sentinel-2 and 13% higher than Landsat-8. By using the data-integration concept, the prediction accuracy increased by about 6% (AUC: 0.938) compared with the ASTER dataset. Hence, these results suggest that the framework of exploiting remote sensing data is promising and should be used as an alternative technique for MPM in case of data availability issues.

Carbon storage is closely connected to the productivities and climate regulation capacities of ecosystems. Assessing the effects of urban expansion on carbon storage has become increasingly important for achieving urban sustainability. This study analyzed the effects of urban expansion on terrestrial carbon storage in Xuzhou City, China during 2000–2025. The cellular automata (CA) model was developed to simulate future urban expansion under three scenarios, namely, the business as usual (BAU), ecological protection (ECO), and planning strengthened (PLS) scenarios. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was further applied to explore the consequences of urban expansion on carbon storage. The results show that urban expansion resulted in 6.099 Tg of carbon storage loss from 2000–2015. Moreover, significant differences in the effects of the urban expansion scenarios on carbon storage were identified in terms of both magnitude and spatial pattern from 2015–2025. Compared with the other scenarios, the PLS scenario could be considered as a good option that would allow future development to achieve the objectives of the lowest carbon storage losses. The findings improve the understanding of the effects of urban expansion on carbon storage and may be used to support urban planning and management.

This study compares the capability of Landsat-8, Sentinel-2, and ASTER multispectral data in mapping lithological units in the Bukadaban Peak, China, by evaluating the performance of the Random Forest (RF) classifier. Moreover, the study assesses the importance of remote sensing original bands and the bands derived from enhancement techniques such as Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) to the classification process. Results revealed that RF selected PC8, PC9, PC12, and MNF1 as the most important features in the Sentinel-2 dataset. Several MNF bands of ASTER were more important than the original and PC bands. The original bands were the strongest predictors in Landsat-8 dataset, while PC2, PC5, and MNF5 were relatively significant. Sentinal-2 and ASTER datasets achieved very similar classification accuracy but outperformed Landsat-8 dataset. ASTER dataset yielded the highest overall accuracy 81.8%, which is 0.18% higher than Sentinel-2 and 3.86% higher than Landsat-8.

Ecosystem services are highly affected by human activities, especially in the fast-urbanizing regions. It is important that the regional development or urbanization strategy be implemented by maintaining or protecting the long-term provision ability of multiple ecosystem services. The spatial pattern of ecosystem services and zoning for spatial conservation and restoration are the preconditions of sustainable development. With the Beijing-Tianjin-Hebei (BTH) region as the research area, an approach for spatial zoning was proposed on the basis of the modeling results of key ecosystem services (water retention, soil retention, heat mitigation, and carbon storage). Our results show that the hot spots of ecosystem services are mainly in the north and the west at high altitudes and with large vegetation coverage, while the cold spots are mainly in the plain area of the southeast in the BTH region. In addition, the whole region is divided into five ecological zones: the ecological restoration zone, ecological transition zone, coastal ecological protection zone, soil and water retention zone, and ecological security shelter. Each zone has applied different strategies for ecological restoration and conservation. The results represent the spatial heterogeneity and major functions in different zones, and they can provide planning guidance for supporting the coordinated development of the BTH region.