Spatially explicit reconstruction of historical land cover change is a prerequisite for a more comprehensive understanding of environmental changes. Anthropogenic activities have dramatically altered the land cover of the conterminous United States (CONUS), encroaching heavily on the primary vegetation. However, few datasets exist that depict the historical trajectory of forest and grassland cover changes in CONUS over the last millennium, and previous efforts have only focused on reconstructions for the last four centuries. By integrating remote sensing-derived land use/cover change (LUCC) data and potential vegetation data, we determined the potential extent of natural forest (PENF) and grassland (PENG) in CONUS. Based on a qualitative analysis of the trends and driving forces of forest and grassland changes, we devised a method of subtracting reconstructed historical cropland (1000–2000 AD) and built-up land (1850–2000 AD) from PENG and PENF to reconstruct a 5 min × 5 min grid dataset of forest and grassland cover at 13 time-points over the past millennium. The results showed that forest and grassland cover in CONUS underwent a slow decline (1000–1600 AD), an accelerated decline (1600–1800 AD), a dramatic decline (1800–1950 AD), and finally, a recovery (1950–2000 AD) over the study period. The modelled forest fraction decreased from 49% in 1000 AD to 33% in 2000 AD, representing a 32% area reduction, whereas the modelled grassland fraction decreased from 37% to 22%, representing a 42% area reduction. The reduction occurred primarily in the last 200 years, with forest and grassland reductions accounting for 86% and 97% of the total reduction over the millennium, respectively. Spatially, more than 80% of the land was originally covered by forests and grasslands, and the loss occurred mainly in the eastern CONUS and Great Plains over the past millennium. After the 1930s, farmland abandonment began in central and eastern CONUS, simultaneously with environmental protection laws. Federal government regeneration programs for forest and grassland resources and the Shelterbelt Project all contributed to a slowdown in forest and grassland decline and recovery in cover.

Investigating the ecological and environmental impacts stemming from historical land use and land cover change (LUCC) holds paramount importance in systematically comprehending the fundamental human-land relationship, a pivotal focus within geographical research. The Yellow River Basin (YRB), often referred to as the cradle of Chinese civilization, ranks as the fifth-largest river basin globally. Early inhabitants made significant alterations to the landscape, resulting in substantial damage to natural vegetation, giving rise to prominent regional ecological challenges. By now, the examination of historical LUCC in the YRB over the past millennium remains in the qualitative research stage, primarily due to the limited availability of high-confidence gridded historical LUCC data. This study aims to advance the current historical LUCC research in the YRB from primarily qualitative analysis to an exploration incorporating timing, positioning, and quantification. Based on reconstructed historical cropland, forest, and grassland grid data of 10 km × 10 km from 1000 AD to 2000 AD, the degree of cropland development and the depletion of forests and grasslands were calculated, respectively. Then, the kernel density method was employed for spatiotemporal analysis and interpretation of dynamic changes in land cover. Subsequently, a cartographic visualization depicting the migration trajectories of the land cover gravity centers was generated, allowing for an assessment of the distance and direction of the centroids’ movement of cropland, forest, and grassland. The results indicate that the cropland coverage in the YRB escalated from the initial 11.65% to 29.97%, while the forest and grassland coverage dropped from 63.36% to 44.49%. The distribution of cultivated land continually expanded outward from the southeast of the Loess Plateau and the southwest of the North China Plain. All three types of land cover experienced a westward shift in their gravity centers between 1000 and 2000 AD. Besides the population growth and technological advancements, the regime shifts induced by wars, along with land use policies in distinct periods, always served as the predominant factors influencing the conversion between different land covers. This research will present a paradigmatic regional case study contributing to the investigation of historical changes in land use and land cover. Additionally, it will offer historical perspectives beneficial for the advancement of China’s objectives in “Ecological Conservation and High-Quality Development of the Yellow River Basin”.

China’s urban development is shifting from extensive expansion to intensive improvement, making the identification of inefficient stock land essential for sustainable urban renewal. Yet, existing approaches are often limited by incomplete data sources and low spatial precision. To address these issues, this study proposes a scalable framework that integrates multi-source big data, including land-use surveys, socioeconomic statistics, spatiotemporal trajectories, and ecological metrics. Using Shenzhen as a case study, we developed a multidimensional evaluation system across social, economic, and ecological dimensions, comprising eight specific indicators. Indicator weights were objectively determined using the entropy weight method, and GIS-based spatial analysis (mean-standardization and Moran’s I) was applied to characterize spatial patterns. Results identify 65.37 km² of inefficient land—about 7% of Shenzhen’s construction land in 2019—exhibiting an “edge aggregation and corridor extension” pattern, mainly distributed along urban–rural fringes and administrative boundaries. The spatial configuration is shaped by historical development, ecological constraints, and the city’s spatial structure. This study provides an objective and replicable framework for the precise identification of inefficient land, supporting data-driven urban renewal and spatial governance in high-density cities.

China’s urban development is shifting from extensive expansion to stock-oriented renewal, making the precise identification of inefficient land stock essential for sustainable spatial governance. To overcome the limitations of single-source data and coarse delineation, we propose a multi-source geospatial framework integrating land-use surveys, socioeconomic statistics, spatiotemporal mobility trajectories, and ecological indicators. Using Shenzhen as a case study, we construct an eight-indicator system across social, economic, and ecological dimensions and apply entropy-based objective weighting to support GIS-based weighted overlay evaluation. The results identify 65.37 km² of inefficient land in 2019, accounting for approximately 7% of Shenzhen’s construction land, with a distinctive “edge aggregation and corridor extension” pattern concentrated along urban–rural fringes and administrative boundaries. Inefficient land is highly uneven across districts, with Longgang (21.11 km²) and Bao’an (12.57 km²) contributing 51.5% of the total and exhibiting statistically spatial clustering (p < 0.01). The observed configuration reflects path-dependent historical development and policy–ecology constraints, including the interaction between ecological control boundaries and peripheral expansion. Overall, by integrating multi-source spatiotemporal big data within a multi-dimensional evaluation framework, the framework offers an objective and transferable approach for diagnosing inefficient land stock and informing targeted urban renewal strategies in high-density cities worldwide.

China's urban development is transitioning from an extensive expansion model to an intensive improvement approach. Promoting urban renewal through stock land resources has become a key focus in contemporary urban spatial governance. As cities enter the stock development era, accurately identifying the quantity and spatial distribution of inefficient stock land is crucial for improving land use efficiency. However, existing identification methods for inefficient existing space face challenges including limited data sources, over-reliance on expert experience and insufficient identification accuracy. These factors impede the realization of large-scale, high-precision, and universally applicable identification frameworks. This study proposes an identification method integrating multi-source big data, including land surveys, socioeconomic data, spatiotemporal trajectories, and air quality metrics. Using Shenzhen as a case study, we developed an index system with three dimensions (social, economic, and ecological) and eight indicators. The entropy weight method determined indicator weights, while mean-standardization and Moran’s I index characterized spatial distribution. The study identified a total of 65.37 km² of inefficient existing land in Shenzhen, accounting for approximately 7% of the city's construction land in 2019. Clusters mainly appear in the northwestern part of the city, including administrative boundary zones and urban fringe areas. Longgang District (21.11 km²) and Baoan District (12.57 km²) collectively contribute 51.5% of the inefficient land. Shenzhen's inefficient existing land exhibits an "edge aggregation and corridor extension" spatial pattern, and its formation is subject to the compound influence of historical development patterns, ecological control policies, and urban spatial structure. This study provides a scientific basis for urban renewal and inefficient land redevelopment through the integration of multidimensional big data and spatial statistical analysis. The proposed framework offers replicable technical support for sustainable urban governance and precision regeneration strategies in high-density Chinese cities.