Conserved land represents an important mechanism for protecting ecologically sensitive lands while maintaining working landscapes. Despite their significance, nationwide tools for continuous hydrological monitoring of conservation easement lands remain limited. This study conceptualizes surface-water inundation as an indicator of hydrologic connectivity and ecosystem function, reflecting how water dynamics influence the resilience and ecological performance of conservation easement landscapes. We present a scalable framework to assess inundation dynamics across more than 340,000 conservation sites between 2018 and 2024 by integrating Sentinel-2 satellite imagery, Dynamic World land-cover data, and machine-learning classifiers (Support Vector Machine, Random Forest, and CART) within the Google Earth Engine platform. Spectral water indices (NDWI, MNDWI, and NDMI) were combined with Dynamic World classifications to generate quarterly inundation maps at 10 m spatial resolution, enabling consistent detection of surface-water presence across space and time. Among the evaluated classifiers, the Support Vector Machine (SVM) model achieved the highest performance in surface-water detection. Results reveal strong regional and seasonal variability in inundation patterns across conservation land. Higher inundation frequencies were observed in the Midwest, Gulf Coast, and Pacific Northwest, where wetland-associated easements showed persistent inundation (>50%) during spring and early summer, highlighting their role in supporting biodiversity, groundwater recharge, and flood mitigation. Overlay analysis with the National Wetlands Inventory (NWI) and SSURGO hydric soils confirmed a strong spatial correspondence between inundation occurrence and wetland-prone landscapes, extending the same Sentinel-2 and machine-learning framework to conservation land and enabling the first systematic cross-program comparison of hydrological dynamics across two major U.S. conservation mechanisms. This work highlights the critical role of conservation lands including Conservation Reserve Program (CRP) areas and conservation easements in supporting inundation dynamics and hydrological connectivity. These functions are essential for sustaining wetland habitats, maintaining water quality, and enhancing flood mitigation at the national scale.

Land use and land cover (LULC) dynamics are increasingly shaped by environmental and socio-economic activities, which has modified natural habitats and altered ecosystem functions across many African cities. However, comprehensive spatiotemporal assessments of land use and land cover (LULC) change in relation to population growth remain limited. Remote sensing combined with machine learning and deep learning algorithms provides an effective means of capturing spatiotemporal environmental changes associated with land use and land cover dynamics. This study examined land use and land cover (LULC) dynamics in Gaborone, Botswana, from 1991 to 2021 using the random forest classification algorithm. The study also predicted LULC changes for the year 2031 using One-dimensional convolutional neural networks (1D-CNN). Furthermore, statistical analysis (Pearson correlation and regression) was used to examine the relationship between population dynamics and LULC change. The Random Forest model produced more accurate land cover maps at ten-year intervals between 1991 and 2021. The LULC prediction for 2031 also produced acceptable values, with overall accuracy values of 0.88, 0.85, 0.82 and 0.84, respectively. The findings revealed a notable increase in the built-up area from 15% in 1991 to 31% in 2021, bare ground increased from 2% to 7%, while vegetation declined from 57% to 41% and agriculture decreased 5% to 3%. 1D-CNN projections for 2031 indicated that built-up areas will expand to 41%, with a decrease in vegetation and agricultural land. Statistical analysis revealed a positive correlation between population growth and the expansion of built-up areas and negatively correlated with the vegetation category. This study provides insights on future land use and land cover transformation, which can be used to achieve sustainable urban development.