Bokyung Son

Enhancing carbon (C) sequestration in urban green spaces is essential for urban carbon neutrality; yet the spatial heterogeneity and structural complexity of urban landscapes hinder the development of targeted, actionable management strategies. This study introduces a decision-support framework that integrates a novel Urban Green Carbon Index (UGCI) with a multi-dimensional analytical approach for assessing and guiding urban C management. The UGCI combines 30 m-resolution spatial data on vegetation C storage, soil C storage, net C uptake, and soil C storage potential, and identifies the three most influential components based on the weights derived from their spatial variability. The multi-dimensional approach depicts the relative importance among the three core UGCI components, thereby enabling site-specific and component-focused management directions. A case study in Suwon, South Korea, showed that the UGCI captured pronounced within-patch heterogeneity, recording 21–45% lower values at edges compared with interiors, thereby identifying specific locations within green patches where localized management to improve C conditions is most warranted. Based on the limited components revealed by the ternary analysis, tailored management practices, such as surface litter retention, soil decompaction, and multi-layered planting, were proposed to improve the weaker C components. This framework provides a scientifically grounded and policy-ready basis for spatially explicit diagnosis and management of urban C, supporting a transition from generalized greening efforts toward quantifiable, site-specific C enhancement strategies.

Urban morphology and background climate are evolving drivers of the urban heat island (UHI) effect, yet the thermal influence of surrounding urban structure and its coupling with climate remain elusive. Here, we show that climate and morphology jointly shape global urban heat. Using a six-class urban typology, long-term climatology, and machine learning, we quantify the thermal influence of surrounding urban structure and project future urban heat across 2,213 cities. We define a city-level thermal impact of the surrounding built environment (T_BE) as the area-weighting UHI change induced by specific built-up types. Climatically, cold regions most frequently exhibit high daytime T_BE, while arid regions dominate high nighttime T_BE. Structurally, a universal pattern persists; high T_BE corresponds to denser and taller forms, whereas sparser and lower types dominate low T_BE during day and night. Future projections indicate that climate change dominates T_BE change in 69% of cities, whereas the Global South exhibits stronger tendencies toward morphology-driven and synergistic intensification than the Global North. Our results highlight the need for locally tailored adaptation strategies that target the dominant drivers—climatic, morphological, or both.

In wildfire burn severity assessment, change detection approaches based on satellite-derived burn severity indices (BSIs) have emerged as effective alternatives to traditional field-based methods such as the Composite Burn Index (CBI). However, previous studies have primarily focused on improving model performance using carefully curated datasets, while paying relatively limited attention to a fundamental limitation—the phenological consistency between pre- and post-fire imagery. This study proposes an automated burn severity analysis framework that incorporates phenology detrending to address this limitation. The proposed framework integrates hotspot-based automatic extraction of regions of interest, acquisition of valid pre- and post-fire imagery free from cloud contamination, and a vegetation phenology adjustment procedure to generate analysis-ready BSI datasets. By introducing the adjusted differenced BSI (adBSI) as a core component, the framework substantially increases the number of usable image pairs and enhances the stability and reliability of burn severity estimates. Validation against CBI plots and burn area data from the Monitoring Trends in Burn Severity (MTBS) program across the contiguous United States demonstrates that adBSI consistently achieves performance comparable to or better than conventional differenced BSI (dBSI). The improvement is particularly pronounced under phenologically mismatched pre- and post-fire conditions, especially when phenology-sensitive indices such as the normalized difference vegetation index (NDVI) are applied to vegetation types with strong seasonal variability, including deciduous forests. Time-series analyses further confirm that adBSI effectively suppresses seasonal fluctuations, yielding more stable and robust results than conventional dBSI. The developed framework was successfully applied to the 2025 wildfire events in the Los Angeles region, demonstrating its practical applicability. Overall, this study presents a simple yet powerful solution to a long-standing challenge in change detection–based burn severity analysis. Future work will focus on incorporating additional environmental variables and nonlinear modeling approaches to further enhance performance and extend the applicability of the proposed framework beyond wildfire burn severity analysis to a broader range of change detection applications.

Urban structures are essential for human habitation, yet they profoundly alter surface energy balances, with significant environmental and health implications. Although urban heat island phenomenon is well recognized, the thermal influence of surrounding urban structures and its interaction with climate remain insufficiently understood. This study presents a global analytical framework that quantifies the thermal impact of surrounding structures, analyzes the combined effects of climate and urban morphology, and projects future thermal trajectories across 967 cities worldwide. By integrating climate variables and local climate zones with machine learning, we assess the thermal impacts of surrounding urban structures. Through a comprehensive weighting of the constituent thermal influences, we introduce the city-scale thermal impact of surrounding urban structures (TBE), revealing that climate and morphology jointly contribute to global urban heat. Across climate zones, cities exhibiting high daytime TBE are characterized by low- and mid-rise built form, whereas sparsely built types prevail in cities with low TBE. These patterns persist at night. Future projections show that the spatial patterns of TBE will be distinct from current conditions, with combined climate-structure effects will dominate urban thermal environment in almost half of global cities. These projections also reveal significant regional disparities between the Global South and the Global North. Our findings highlight the role of combined effects in shaping present and future urban heat, informing the need for adaptation strategies tailored to individual cities.

Leaf area index (LAI) is essential for understanding vegetation dynamics, ecosystem processes, and land–atmosphere interactions. Various measurement methods exist, but gap-fraction-based indirect methods are preferred due to their reduced labor and field survey time in comparison to direct methods. However, gap-fraction-based field observations, often referred to as plant area index (PAI), frequently overestimate LAI because they include woody components. To mitigate this issue, the woody-to-total-area ratio (α) can be utilized to exclude these woody components from PAI, yielding more accurate LAI estimates (hereafter referred to as LAI_adjusted). In this study, we demonstrate a novel method to estimate α using Sentinel-2-based normalized difference vegetation index (NDVI) and time-series PAI measurements. The α estimates effectively reduce the influence of woody components in PAI within deciduous broadleaf forests (DBF). Moreover, LAI_adjusted shows good agreement with the Sentinel-2 LAI, which represents effective LAI derived from the PROSAIL model. Notably, the spatial distribution of α effectively captured the expected seasonal dynamics across various forest types. In DBF, α values increased during winter due to leaf fall when compared to the growing season, while seasonal variations were relatively small in evergreen needleleaf forest (ENF). We further confirmed that our method demonstrates greater robustness with NDVI than with other vegetation indices that are more susceptible to topographic variation. Ultimately, this framework presents a promising pathway to mitigate biases in most gap-fraction-based PAI measurements, thereby enhancing the accuracy of vegetation structural assessments and supporting broader ecological and climate-related applications.

Yeonsu Lee, Sun Jegal, Siwoo Lee, Bokyung Son, Jungho Im. Korean J. Remote Sens. 2025;41:843-57. https://doi.org/10.7780/kjrs.2025.41.5.11

Rapid urbanization and climate change are intensifying urban heat islands (UHIs), necessitating precise analysis and effective urban planning. While previous studies have examined UHI intensity based on built morphology using Local Climate Zones (LCZs) at individual city scales or across Köppen-Geiger climate zones, research on the spatial configuration of LCZs within and between cities remains relatively limited. This study proposes a novel classification framework to identify climate zones at intra- and inter-city levels, which are not adequately captured by existing Köppen-Geiger climate classifications or the element level of LCZs themselves. To achieve this, we computed UHI intensity across global cities using ERA5-Land air temperature data. We extracted latent space representations from the UHI time series using an autoencoder and applied K-means++ clustering to categorize these features into five distinct classes. The proposed classification effectively captured variations in UHI intensity both within and between cities. Notably, the moderate UHI class, characterized by an annual mean UHI close to zero, exhibited the lowest median proportion of built-type LCZs (65%) and the highest proportion of land cover types (34%). The clustering results revealed spatial patterns distinct from the Köppen-Geiger climate classification, which overlooks variations in UHI effects across cities within the same climate zone. By providing a refined categorization of UHI intensity, this study enhances our understanding of intra-city climate variations and offers a valuable framework for UHI mitigation strategies, climate-sensitive urban planning, and sustainable city development.