Anamika Shreevastava

This dataset contains the derived geospatial data used in the study “Linking urban growth trajectories to decadal surface urban heat island changes in Indian cities.” The study examines how different urban growth trajectories, particularly horizontal urban expansion and urban densification, are associated with daytime and nighttime surface urban heat island (SUHI) changes from 2013 to 2024 in four Indian cities: Delhi, Lucknow, Pune, and Kolkata. The dataset includes Local Climate Zone (LCZ) maps for 2013 and 2024, LCZ-based urban growth trajectory maps, annual daytime and nighttime SUHI intensity maps, and pixel-level SUHI trend estimates. LCZ maps were generated using a supervised Random Forest classification framework based on manually delineated LCZ reference samples and multi-source remote-sensing and ancillary predictors. Urban growth trajectories were derived from pixel-wise LCZ transitions between 2013 and 2024. Annual SUHI intensity was calculated from summer-season land surface temperature anomalies relative to unchanged natural LCZ reference areas. Pixel-level SUHI trends were estimated for 2013–2024 separately for daytime and nighttime conditions.

This dataset contains the derived geospatial data used in the study “Linking urban growth trajectories to decadal surface urban heat island changes in Indian cities.” The study examines how different urban growth trajectories, particularly horizontal urban expansion and urban densification, are associated with daytime and nighttime surface urban heat island (SUHI) changes from 2013 to 2024 in four Indian cities: Delhi, Lucknow, Pune, and Kolkata. The dataset includes Local Climate Zone (LCZ) maps for 2013 and 2024, LCZ-based urban growth trajectory maps, annual daytime and nighttime SUHI intensity maps, and pixel-level SUHI trend estimates. LCZ maps were generated using a supervised Random Forest classification framework based on manually delineated LCZ reference samples and multi-source remote-sensing and ancillary predictors. Urban growth trajectories were derived from pixel-wise LCZ transitions between 2013 and 2024. Annual SUHI intensity was calculated from summer-season land surface temperature anomalies relative to unchanged natural LCZ reference areas. Pixel-level SUHI trends were estimated for 2013–2024 separately for daytime and nighttime conditions.

As cities continue to warm, where should cooling investments be deployed to maximise public health benefits? Here we address this question for cities like Los Angeles, where intensifying heat, entrenched socio-economic inequality, and uneven adaptive capacity intersect. To identify priority “hotspots” where cooling interventions would most effectively reduce temperatures and save lives, we map the intersection of three key variables: (a) high heat exposure, (b) socio-economic vulnerability, and (c) feasibility of intervention strategies. We take an inventory of the urban built form and map the current albedo to evaluate where reflective coating can be deployed. We then formulate a city-specific temperature–mortality relationship to optimize city-wide public health benefits of the potential reduction in temperature. Applying this framework to Los Angeles shows that reflective surface treatments can produce substantial local air temperature reductions in select high-risk areas and yield large mortality benefits relative to the treated area. The resulting benefit distribution is sharply skewed as treating only 9% the city generates half of the total potential reduction in heat-related deaths, highlighting a strong opportunity for targeted, high-return investment. We have also developed an interactive web-based tool that would allow practitioners to explore the cooling potential in every neighborhood, visualize the life years that could be saved, and identify priority neighborhoods for heat adaptation. The utility of this work extends beyond Los Angeles by offering a scalable framework for other cities seeking to deploy equitable and life-saving heat adaptation strategies.

WUDAPT, an International community generated urban canopy information and modeling infrastructure (Portal) to facilitate urban focused climate, weather, air quality, and energy use modeling application studies.

The roots of intra-urban heat disparity in the U.S. often trace back to historical discriminatory practices, such as redlining, which categorized neighborhoods by race or ethnicity. In this study, we compare the relative impacts of historic redlining and current income inequality on thermal disparities in Los Angeles. A key innovation of our work is the use of land surface temperature data from the ECOSTRESS instrument aboard the International Space Station, enabling us to capture diurnal trends in urban thermal disparities. Our findings reveal that present-day income inequality is a stronger predictor of heat burden than the legacy of redlining. Additionally, land surface temperature disparities exhibit a seasonal hysteresis effect, intensifying during extreme heat events by 5-7 °C. Sociodemographic analysis highlights that African-American and Hispanic populations in historically and economically disadvantaged areas are often the most vulnerable. Our findings suggest that while the legacy of redlining may persist, the present-day heat disparities are not necessarily an immutable inheritance, where targeted investments and interventions can pave the way for a more thermally just future for these communities.

Rapid urbanization in India, with its urban population projected to reach 590 million by 2030, has intensified urban overheating and the Urban Heat Island (UHI) effect, further exacerbated by climate change. This study investigates decadal changes (2014–2024) in urban landforms and land surface temperature (LST) dynamics across major Indian cities, each representing distinct climate zones. By mapping Local Climate Zones (LCZs) and analyzing spatio-temporal variations in VIIRS and ECOSTRESS satellite-derived LST data, we quantify surface UHI (SUHI) intensity across diverse LCZ types for summer daytime and nighttime observations. Urban growth trajectories—classified as ‘expansion,’ ‘densification,’ and ‘stable urbanized’—were integrated with LCZ schemes to assess their thermal impacts. Our findings reveal distinct SUHI patterns among cities, shaped by both LCZ types and urban growth trajectories. For instance, Delhi exhibits a less pronounced SUHI effect during the day but a significant nighttime increase, while Kolkata shows strong daytime SUHI alongside moderately elevated nighttime temperatures. Furthermore, we quantify that the contributions of urban growth trajectories to rising temperatures vary by city: while densification plays a primary role in temperature increases in some cities, such as Delhi and Kolkata, expansion tends to have a stronger influence in others. By integrating LCZ classifications with urban growth trajectories, this study provides a robust framework for understanding the thermal impacts of urbanization and offers actionable insights for sustainable urban planning strategies to mitigate overheating risks.

Urban areas worldwide confront escalating threats from extreme heat, a climate impact poised to become the most lethal and economically devastating of our time. Rising global temperatures are leading to urban heatwaves becoming longer, more frequent, and more severe, resulting in an increase in extreme heat exposure, especially among vulnerable population groups. A valuable and popular tool for studying fine-scale urban temperatures is with thermal infrared (TIR) remote sensing data. With TIR data we can quantify the magnitude of the surface urban heat island (SUHI) effect across all permutations of urban temperature gradients and complexity. The availability of TIR data at spatial resolutions of 100 m or less is required for distinguishing temperatures of different urban materials that can be made useful for urban planning and heat mitigation efforts. Multispectral thermal infrared data (TIR: 8-12 micron) from the ECOSTRESS mission launched to the ISS in mid-2018, and upcoming TIR missions including LSTM, TRISHNA, and the NASA Surface Biology and Geology (SBG) to launch in 2026-2029 will provide an unprecedented availability of high spatial resolution (< 100m), multispectral, twice-daily global TIR data. In this study we explore the use of a combination of ECOSTRESS (70m) and airborne HyTES TIR data (5m) to better understand the urban heat environment and societal applications. We will demonstrate the use of these data for investigating diurnal patterns in urban heat exposure; pinpointing hotspots and quantifying the effects of heat mitigation interventions; and better understanding links between geopolitical segregation and urban heat disparities in the Los Angeles area.

Local climate zones (LCZs) established a standard classification system to categorize the landscape universe for improved urban climate studies. Existing LCZ mapping is guided by human interaction with geographic information systems (GIS) or modelled from remote sensing (RS) data. GIS-based methods do not scale to large areas. However, RS-based methods leverage machine learning techniques to automatize LCZ classification from RS. Yet, RS-based methods require huge amounts of manual labels for training. We propose a novel LCZ mapping framework, termed AutoLCZ, to extract the LCZ classification parameters from high-resolution RS modalities. We study the definition of numerical rules designed to mimic the LCZ definitions. Those rules model geometric and surface cover parameters from LiDAR data. Correspondingly, we enable LCZ classification from RS data in a GIS-based scheme. The proposed AutoLCZ method has potential to reduce the human labor to acquire accurate metadata. At the same time, AutoLCZ sheds light on the physical interpretability of RS-based methods. In a proof-of-concept for New York City (NYC) we leverage airborne LiDAR surveys to model four LCZ parameters to distinguish eight LCZ types. The results indicate the potential of AutoLCZ as a promising avenue for large-scale LCZ mapping from RS data.

Local climate zones (LCZs) established a standard classification system to categorize the landscape universe for improved urban climate studies. Existing LCZ mapping is guided by human interaction with geographic information systems (GIS) or modelled from remote sensing (RS) data. GIS-based methods do not scale to large areas. However, RS-based methods leverage machine learning techniques to automatize LCZ classification from RS. Yet, RS-based methods require huge amounts of manual labels for training. We propose a novel LCZ mapping framework, termed AutoLCZ, to extract the LCZ classification features from high-resolution RS modalities. We study the definition of numerical rules designed to mimic the LCZ definitions. Those rules model geometric and surface cover properties from LiDAR data. Correspondingly, we enable LCZ classification from RS data in a GIS-based scheme. The proposed AutoLCZ method has potential to reduce the human labor to acquire accurate metadata. At the same time, AutoLCZ sheds light on the physical interpretability of RS-based methods. In a proof-of-concept for New York City (NYC) we leverage airborne LiDAR surveys to model 4 LCZ features to distinguish 10 LCZ types. The results indicate the potential of AutoLCZ as promising avenue for large-scale LCZ mapping from RS data.

Abstract. The global increase in atmospheric water vapor due to climate change tends to heighten the dangers associated with both humid heat and heavy precipitation. Process-linked connections between these two extremes, particularly those which cause them to occur close together in space or time, are of special concern for impacts. Here we investigate how atmospheric rivers relate to the risk of summertime humid heat in the United States. We find that the hazards of atmospheric rivers and humid heat often occur in close proximity, most notably across the northern third of the United States. In this region, high levels of water vapor – resulting from the spatially organized horizontal moisture plumes that characterize atmospheric rivers – act to amplify humid heat, generally during the transition from dry high-pressure ridge conditions to wet low-pressure trough conditions. In contrast, the US Southeast, Southwest, and Northwest tend to experience atmospheric rivers and humid heat separately, representing an important negative correlation of joint risk.

<strong class="journal-contentHeaderColor">Abstract.</strong> The global increase in atmospheric water vapour due to climate change tends to heighten the dangers associated with both humid heat and heavy precipitation. Process-linked correlations between these two extremes, particularly those which cause them to occur close together in space or time, are of special concern for efforts to understand and mitigate their impacts. Here we investigate how atmospheric rivers relate to the risk of summertime humid heat in the US. We find that the hazards of atmospheric rivers and humid heat often occur in close proximity, most notably across the northern third of the US. In this region, high levels of water vapour &mdash; resulting from the spatially organised horizontal moisture plumes that characterise atmospheric rivers &mdash; act to amplify humid heat, generally during the transition from dry high-pressure ridge conditions to wet low-pressure trough conditions. In contrast, the Southeast, Southwest, and Northwest US tend to experience atmospheric rivers and humid heat separately, representing an important negative correlation of joint risk.

Abstract. The global increase in atmospheric water vapour due to climate change tends to heighten the dangers associated with both humid heat and heavy precipitation. Process-linked correlations between these two extremes, particularly those which cause them to occur close together in space or time, are of special concern for efforts to understand and mitigate their impacts. Here we investigate how atmospheric rivers relate to the risk of summertime humid heat in the US. We find that the hazards of atmospheric rivers and humid heat often occur in close proximity, most notably across the northern third of the US. In this region, high levels of water vapour — resulting from the spatially organised horizontal moisture plumes that characterise atmospheric rivers — act to amplify humid heat, generally during the transition from dry high-pressure ridge conditions to wet low-pressure trough conditions. In contrast, the Southeast, Southwest, and Northwest US tend to experience atmospheric rivers and humid heat separately, representing an important negative correlation of joint risk.

Abstract One of the top priorities of the Surface Biology and Geology (SBG) Earth Observing System is the detection and retrieval of elevated temperature features (ETF) usually found in the vicinity of active fires and volcanic activity. We test the ability of currently proposed midwave (MIR: 3–5 μm) and thermal infrared (TIR: 8–12 μm) bands to detect ETF within the 400–1200 K range. Specifically, our investigation aims to compare and contrast the use of the 4 and 4.8 μm MIR bands. We use land surface temperature data obtained by the airborne Hyperspectral Thermal Emission Spectrometer (HyTES) instrument over active fire and lava flows to model at‐sensor SBG radiances in the 3–12 μm range. This is achieved using the Temperature Emissivity Uncertainty Simulator (TEUSim) with the designated/proposed SBG MIR and TIR band characteristics. For ETF detection, we applied the Normalized Thermal Index (NTI) and Enhanced Thermal Index (ETI) to determine a suitable threshold for a wide range of ETF sizes and temperatures. We find that combining an NTI threshold of −0.7 followed by an ETI threshold of 0.02 accurately identifies ETFs at a 97% rate. Sensor noise up to 0.5 K has negligible effects on ETF detection in the 400–1200 K range. The currently proposed SBG MIR and TIR bands are sufficient to detect unsaturated ETFs caused by wildfire and volcanic activities at a ∼3 day revisit and subpixel ETF area of ∼9 m 2 (at 500 K) that is unattainable by current satellite TIR instruments.

Abstract Heatwaves in California manifest as both dry and humid events. While both forms have become more prevalent, recent studies have identified a shift toward more humid events. Understanding the complex interactions of each heatwave type with the urban heat island is crucial for impacts but remains understudied. Here, we address this gap by contrasting how dry versus humid heatwaves shape the intraurban heat of the greater Los Angeles area. We used a consecutive contrasting set of heatwaves from 2020 as a case study: a prolonged humid heatwave in August and an extremely dry heatwave in September. We used MERRA-2 reanalysis data to compare mesoscale dynamics, followed by high-resolution Weather Research and Forecasting modeling over urbanized Southern California. We employ moist thermodynamic variables to quantify heat stress and perform spatial clustering analysis to characterize the spatiotemporal intraurban variability. We find that, despite temperatures being 10° ± 3°C hotter in the September heatwave, the wet-bulb temperature, closely related to the risk of human heat stroke, was higher in August. While dry and humid heat display different spatial patterns, three distinct spatial clusters emerge based on nonheatwave local climates. Both types of heatwaves diminish the intraurban heat stress variability. Valley areas such as San Bernardino and Riverside experience the worst impacts, with up to 6° ± 0.5°C of additional heat stress during heatwave nights. Our results highlight the need to account for the disparity in small-scale heatwave patterns across urban neighborhoods in designing policies for equitable climate action. Significance Statement Heatwaves are the leading cause of morbidity and mortality among all environmental hazards. Moreover, their frequency and intensity are on the rise due to climate change. Southern California is no stranger to extreme heat, but persistently humid heatwaves still test the adaptability limits of its residents. We find that the set of two contrasting heatwaves that afflicted Los Angeles in the summer of 2020 forms the perfect testbed for characterizing the impacts of humid versus dry heatwaves on urban environment. Because climate model forecasts and long-term observational trends point to more humid heatwaves in the future for Southern California, our results underscore the importance of including moist heat in extreme heat warning frameworks.