Urban expansion encroaches on green spaces and weakens ecosystem services, potentially leading to a trade-off between ecological conditions and socio-economic growth. Effectively coordinating the two elements is essential for achieving sustainable development goals at the urban scale. However, few studies have measured urban–ecological linkage in terms of trade-off. In this study, we propose a framework by linking the degraded ecological conditions and urban land use efficiency from a return on investment perspective. Taking a rapidly expanding city as a case study, we comprehensively quantified urban–ecological conditions in four aspects: urban heat island, flood regulating service, habitat quality, and carbon sequestration. These conditions were assessed on 1 km² grids, along with urban land use efficiency at the same spatial scale. We employed the slack-based measure model to evaluate trade-off efficiency and applied the geo-detector method to identify its driving factors. Our findings reveal that while urban–ecological conditions in Zhengzhou’s periphery degraded over the past two decades, the inner city showed improvement in urban heat island and carbon sequestration. Trade-off efficiency exhibited an overall upward trend during 2000–2020, despite initial declines in some inner city areas. Interaction detection demonstrates significant synergistic effects between pairs of drivers, such as the Normalized Difference Vegetation Index and building height, and the number of patches of green spaces and the patch cohesion index of built-up land, with q-values of 0.298 and 0.137, respectively. In light of the spatiotemporal trend of trade-off efficiency and its drivers, we propose adaptive management strategies. The framework could serve as guidance to assist decision-makers and urban planners in monitoring urban–ecological conditions in the context of urban expansion.

Nature-based Solutions (NbS) are vital for achieving sustainable urban development. While urban tree planting is one of the most important approaches to enhance NbS, current practices often concentrate on tree quantity or green coverage targets. NbS planning studies are also limited to priority identification, revealing a notable gap in both study and practice. This study presents a tree species composition optimization-based framework for maximizing regional NbS by linking local demands and the effectiveness of tree species through a multi-objective optimization algorithm. A case study was designed in Philadelphia, and the optimal tree species composition was explored on 0.25 km ² grids. The spatial heterogeneity of local demands and the varying effectiveness of tree species highlight the significance of strategic tree placement. The current tree species composition in Philadelphia is suboptimal for achieving NbS. Future greening efforts should increase the proportion of advantageous species, such as Acer saccharinum , Acer rubrum , and Liquidambar styraciflua . Compared with the baseline, the optimized species composition can provide substantially greater ecosystem services, for instance, about 20% more in stormwater management and up to 80% more in microclimate regulation. Two general planning recommendations are proposed: (1) maximize carbon sequestration capacity in areas with insignificant urban hazards; and (2) strategically place trees with specific advantages in targeted response to specific urban hazards. The developed framework provides guidance for efficient urban greening projects, enabling maximization of NbS under given greening quantity targets.

Urban land use intensity (U-LUI) is a widely used term for describing urban development processes, yet its conceptualisation and measurement remain inconsistent. Existing approaches focus on isolated dimensions, such as structural density, functional activity, and socio-economic indicators, resulting in limited comparability and weak integration across scales and data sources. This paper reviews and synthesises current approaches to U-LUI with a focus on remote sensing (RS), in situ data and emerging urban data sources. It analyses definitions, related concepts of urban intensity and existing monitoring frameworks at national, European and global levels, and compares methodological approaches for observing U-LUI. Based on this synthesis, U-LUI is defined as a continuous, multidimensional and spatio-temporally dynamic property of urban systems that reflects the intensity of anthropogenic use. To operationalise this concept, the paper develops an integrative, trait-based framework comprising six indicator families: traits, genesis, structure, taxonomy, function and socio-economics. The proposed framework is illustrated and supported through the synthesis of existing RS approaches, urban monitoring concepts and representative examples from the literature, demonstrating its potential for consistent and scalable U-LUI assessment. These dimensions link physically observable characteristics with functional and contextual aspects of urban systems and provide a basis for more consistent quantification and comparison. The results highlight key challenges for U-LUI monitoring, including limited conceptual harmonisation, incomplete integration of dimensions and the need for improved data integration. The proposed framework supports more coherent and scalable assessments of U-LUI in research, monitoring and planning contexts.

Forest Land Use Intensity (F-LUI) is a multidimensional and dynamic property of forest ecosystems that cannot be directly observed by Remote Sensing (RS). Existing approaches commonly quantify individual management activities, forest attributes or ecological responses and therefore capture only specific dimensions of forest-use intensity. This concept-driven narrative review proposes a trait-based conceptual framework in which forest traits constitute the common observational interface linking anthropogenic forest use, ecosystem responses and RS observations. Based on this principle forest traits and their spatial, temporal and functional expressions are interpreted within six complementary F-LUI indicator families: Trait, Genesis, Structure, Taxonomy, Function and Socio-economics. Rather than defining a universal F-LUI index, the framework provides a multidimensional, sensor-independent architecture for integrating RS with in situ observations, forest inventories, management information and environmental data. Emerging approaches including Artificial Intelligence (AI) and Foundation Models, multi-sensor data fusion, semantic technologies, Knowledge Graphs and Digital Twins may support its operationalisation. Key challenges remain in standardisation, uncertainty quantification, attribution of management effects and transferability across forest types, management regimes and spatial and temporal scales. The proposed framework provides an open and extensible basis for harmonised, reproducible and operational trait-based monitoring of F-LUI.

Accurate characterization of land surface temperature (LST) is essential for urban thermal environment analysis. However, commonly used LST representations have their inherent limitations. For example, single-year LST may be influenced by year-specific anomalies, whereas multiyear means may obscure recent changes. Neither representation addresses how relevance to the target year and persistent historical information can be balanced within a single spatial LST surface. This study therefore proposes a temporally weighted land surface temperature (TWLST) method, which uses temporal decay weights and a half-life parameter to achieve the above balance and produce a continuous LST surface for spatial analysis. The framework was applied at regional and city scales in the Beijing–Tianjin–Hebei region and Beijing city, respectively, to examine its applicability. Spatial overlap between TWLST high-temperature (HT) areas and reference LST HT areas ranged from 0.577 to 0.733 and from 0.666 to 0.863 in the two applications. Historical annual LST anomalies were higher in TWLST-specific HT areas than in target-year-specific HT areas, with median paired differences of 1.16 °C and 2.49 °C, respectively. These results indicated that TWLST preferentially identified persistently warm locations while preserving major high-temperature patterns. The modeling and interpretation analyses further indicated that changes in LST representation could influence model performance and the relative importance of explanatory variables.