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• Developed a volume-driven framework to estimate material stocks using multi-source geographical data. • Generated spatially explicit material stocks maps of buildings and infrastructures at a 10 m resolution. • Estimated total material stocks within BTH-UA at 195,000 Mt in 2020, amounting to 182 t/capita and 2.43 Mt/km². • Identified significant correlations between material stocks, population size, and economic activity at the administrative level. Spatial patterns of material stocks in buildings and infrastructures are crucial for understanding resource use, spatial planning and environmental management. So far, spatially explicit stock-driven approaches face substantial data limitations, requiring costly and time-intensive efforts to map stocks at urban micro-scales. Herein, we developed a volume-based framework for material stocks mapping in buildings and infrastructures at a 10 m resolution, applied across the entire Beijing–Tianjin–Hebei urban agglomeration (BTH-UA) in China, a key growth pole for regional economic development. We integrated multi-source remote sensing data, GIS products, crowdsourced data, and material intensity databases, covering ten bulk materials. The findings reveal that the total mass of buildings and infrastructures in BTH-UA amounts to 195,000 Mt, equivalent to 182 t/cap, 2.43 Mt/km2. Material stocks are spatially clustered and correlate with the population size and economic activity of the secondary industry. This research contributes to the quantitative description and detailed mapping of material stocks in built environment. [ABSTRACT FROM AUTHOR]

In Shanxi Province, China, groundwater is a major problem and exploration of groundwater potential zones (GWPZs) is a great necessity. This paper contributed to integrate RS-GIS to delineate GWPZ and applied Fuzzy-AHP method in a single platform. The main objective includes delineation GWPZs with RS and geo-environmental factors using Fuzzy-AHP method. Fuzzy-AHP method was employed to calculate weight of factors. RS-GIS was used to create maps and discover groundwater availability. GWPZs were classified in five separate classes. Results indicated that 13.26%, 27.02%, 26.35%, 23.64%, and 9.71% area classified as very good, good, moderate, poor, and very poor GWPZs. The validated analytical results revealed 82.5%, 12.3%, 3.5%, and 1.7% existing water wells exhibited in very good, good, moderate, and poor/very poor GWPZs. This indicates Fuzzy-AHP model generated findings were in very good agreement with ground-truth data. This RS-GIS based Fuzzy-AHP method is proficient and efficient in identification and delineation of GWPZs. Image 1 • Groundwater potential zones (GWPZs) in semi-arid area delineated with RS-GIS. • Seven geo-environmental factors are used for GWPZ. • RS-GIS for GWPZ and Fuzzy-AHP model are used in a single platform. • Fuzzy-AHP method was employed to calculate the weight of the factors. • GWPZs are classified as very good, good, moderate, poor, and very poor GWPZ. [ABSTRACT FROM AUTHOR]