Remote sensing (RS) and geographic information systems (GIS) increasingly support ecosystem and biodiversity monitoring, yet evidence remains fragmented across sensors, indicators, analytical methods, and policy applications. This review synthesises 163 peer-reviewed studies published from 2015 to 2025 to determine how RS and GIS indicators can be selected, validated, and translated into decision-relevant evidence. The synthesis compares satellite and unmanned aerial vehicle platforms, vegetation indices, change-detection techniques, landscape metrics, machine-learning models, cloud-computing workflows, and multi-scale validation strategies. No sensor, index, or classifier is universally optimal. Landsat and Sentinel-2 provide the strongest basis for long-term and large-area monitoring, whereas commercial imagery and unmanned aerial vehicles better resolve fine habitat features and localised disturbance. Vegetation indices remain useful ecosystem proxies but cannot independently represent species composition, ecological integrity, or causal degradation processes. Reliable applications therefore require context-specific indicator selection, transparent preprocessing, representative ground reference data, uncertainty reporting, and integration of spectral, structural, ecological, and socio-economic evidence. Data fusion and machine learning improve monitoring performance when validation is rigorous, but computational sophistication cannot compensate for weak reference data. The review concludes that policy value depends on converting remotely sensed change into explicit, validated decision rules for zoning, restoration, protected-area management, and biodiversity reporting.
T. Mkilima, A. Zhidebayeva, S. Syrlybekkyzy et al.· Environmental Engineering Re...· 0 citations
Urban soils are sensitive indicators of environmental quality and anthropogenic pressure, particularly in arid regions where pollutant accumulation is enhanced by limited precipitation and intense evaporation. This study aimed to assess physicochemical properties, heavy metal contamination, spatial heterogeneity, and potential pollution sources in urban soils of Turkestan, Kazakhstan. Surface soil samples were collected from 15 representative sites covering industrial, transport, residential, peripheral, and recreational land-use zones. The samples were analyzed for pH(H2O), total dissolved solids, petroleum hydrocarbons, organic matter, and total concentrations of Cu, Zn, Pb, and Cd. Spatial distribution mapping, Pearson correlation analysis, principal component analysis (PCA), and heatmap visualization were applied to identify contamination patterns and likely pollution sources. The investigated soils exhibited stable alkaline conditions, with pH values ranging from 8.52 to 8.74, typical of arid urban environments. Zinc showed the highest variability, with concentrations ranging from 68.0 to 203 mg kg-1 and a coefficient of variation exceeding 50%, indicating localized enrichment. Cadmium displayed pronounced spatial heterogeneity (0.14-2.80 mg kg-1) and enrichment near industrial and transport-related areas. Copper concentrations ranged from below the detection limit to 61.3 mg kg-1, whereas lead exhibited comparatively lower spatial variability. Spatial distribution maps revealed contamination hotspots associated with transport corridors, industrial facilities, and fuel stations. Multivariate statistical analysis demonstrated relationships among metals, petroleum hydrocarbons, and urban infrastructure, while PCA distinguished metal-enriched sites from mixed urban and low-impact areas. Urban soils of Turkestan are influenced by localized anthropogenic activities and can serve as useful indicators of technogenic pressure in arid urban environments. The integrated application of spatial mapping and multivariate statistical approaches provides a practical framework for environmental monitoring, pollution management, and sustainable urban development in data-limited Central Asian cities.
N. Abdimutalip, G. Toychibekova, S. Koibakova et al.· Ecologica Montenegrina· 0 citations
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