Using Land Surface Temperature Data for Sustainable Practices

🌡️ Urban heat islands are hard to analyze: thermal data sits in one silo, green infrastructure in another, and spatial stats in yet another. I've added an end-to-end #urbanheatisland workflow to the greenR R package: one function that connects satellite temperature with local environmental features. 🔹 Pulls land surface temperature (Landsat/ECOSTRESS) 🔹 Fetches green spaces, trees, buildings (OSM, GHSL) 🔹 Runs Getis-Ord Gi* hotspot detection with significance tests 🔹 Outputs correlations, regressions, Moran's I 🔹 Generates interactive & static maps (publication-ready) The Zurich demo below shows a strong negative correlation between green coverage and temperature. The hotspot map pinpoints where heat clusters. Also in the package: street-level green index, accessibility analysis, 1m canopy height modeling, 3D visualizations. Open source: 📦 CRAN: https://lnkd.in/eDvehz62 💻 GitHub: https://lnkd.in/gmXsDuzK #UrbanAnalytics #OpenSource #UrbanHeat #RStats #GIS #SmartCities #ClimateResilience

🔥 Land Surface Temperature (LST) Analysis for 2013–2023 using MODIS & Google Earth Engine I recently completed a 10-year Land Surface Temperature (LST) analysis using the MODIS/061/MOD11A1 (LST_Day_1km) dataset in Google Earth Engine (GEE). This work highlights how geospatial technologies can support climate research, environmental planning, and urban resilience. 🌍 What I Did -Processed and analyzed MODIS LST time-series data (2013–2023). -Converted LST values from Kelvin to °C. -Computed annual mean LST for my Area of Interest (AOI). Generated yearly LST maps, including a custom legend and color palette. Produced: 📊 Annual LST bar chart 📈 Annual LST line chart with a linear trendline 🎞️ GIF-style animation showing LST changes from 2013 to 2023 Calculated the temperature trend (°C/year) using linear regression. 📌 Applications of This Analysis -Climate Change Monitoring: Detect long-term warming or cooling trends. -Urban Heat Island Assessment: Identify temperature hotspots for urban -planning and heat mitigation. -Environmental & Ecosystem Health: Monitor heat stress on vegetation and land degradation. -Agriculture & Drought Monitoring: Support early warning systems and agricultural planning. -Water Resources & Hydrology: Improve evapotranspiration and water balance modeling. -Disaster Risk & Heatwave Management: Map heat-prone zones for climate resilience planning. -Land Use/Land Cover Impact: Understand how land cover types influence surface temperature. ⚡ Advantages of This Approach -High Temporal Resolution: Daily MODIS data ensures reliable annual LST estimates. -Large Spatial Coverage: Suitable for regional/national-scale assessments. Reliable Dataset: MODIS LST is globally validated for environmental monitoring. -Automated & Reproducible: GEE scripting makes the workflow scalable and repeatable. -Fast Cloud Processing: No need for local downloads or heavy computation. Clear Visual Outputs: Maps, charts, and animations enhance communication. -Decision-Support Ready: Useful for planners, climate researchers, and policy makers. Source code: https://lnkd.in/eT6hYq4S If you're interested in remote sensing-based environmental monitoring, feel free to connect or reach out! #GIS #RemoteSensing #GoogleEarthEngine #MODIS #ClimateAnalysis #LST #Geospatial #DataScience

As 'Heat' becomes the hot topic globally, we have something meaningful to share. Tamil Nadu as a state is gaining international attention for its evolving heat governance model—a proactive, science-based approach to tackling rising temperatures. Along with other nodal departments, the Tamil Nadu State Planning Commission and the Tamil Nadu State Land Use Research Board TNSLURB have been supporting this effort through evidence-based research and analytics to mainstream Heat Action at the sub-national level. Released by the Hon’ble Chief Minister on 07.07.2025, this recent report is part of Tamil Nadu’s continuum of work on heat mitigation, UHIE analysis, and climate resilience. It was conceived to move beyond state-wide averages and bring attention to localized heat stress trends at the block level, where governance and adaptation efforts can be most impactful. The study covered 389 blocks, using high-resolution data on: — Land Surface Temperature (LST) – day & night — Air Temperature (ERA5) – max, min, mean — Building footprint changes — Urban growth patterns — Universal Thermal Comfort Index (UTCI) Two layers of analysis were undertaken: 🔹 Decadal Heat Stress: Blocks showing consistent warming over decades 🔹 Current Heat Stress: Blocks currently experiencing above-average exposure (2018–2023) The Key findings highlight a list of blocks showing significant heat rise over time and some facing high current exposure while some blocks fall under both categories which are in the priority zone for immediate interventions. This kind of detailed, spatially disaggregated analysis is helping Tamil Nadu to identify and prioritise heat-vulnerable geographies, support Heat Action Plans at block and city levels, to promote sustainable cooling and nature-based solutions. The full report is available at: www.spc.tn.gov.in Swipe through this carousel to explore the highlights. #HeatResilience #UrbanPlanning #ClimateGovernance #SustainableCooling #SmartCities #TamilNadu #SPC #TNSLURB #SDGs #ClimateAction #UrbanHeat

GISCARTA and Marat B. from GEOBOX, INC created a new project 🌍 Urban Green & Heat Analysis — Almaty This project visualizes the Green Deficit Index (GIGA) and the actual roof surface temperature (°C) in the central part of Almaty, Kazakhstan. 🛰 It also includes regular grid layers showing how land surface temperature and NDVI (vegetation index) have changed over the last 25 years. 📊 Using this data, the project helps identify more livable and less favorable areas in the city based on green coverage and heat conditions. 🌿🏙 A data-driven way to understand how urban greenery and temperature impact city life. 🌿 Green Deficit Index (GIGA) The Green Deficit Index (GIGA) is a metric used in urban planning and environmental analysis to evaluate how much green space a city lacks compared to recommended standards. It helps identify whether a city has enough trees, parks, and vegetation to support healthy urban living. 🌱 What the index measures The index usually considers several key factors: 🌳 Area of green spaces — parks, gardens, forests, and urban trees. 👥 Population size — how many people live in the area. 🚶 Accessibility of green spaces — whether residents can easily walk to a park or green area. 🏙 Urban density — how densely buildings and infrastructure are developed. 📊 The index shows the green space deficit per person or within a specific city district. 🌍 Why GIGA matters The Green Deficit Index is used for: 🏙 Urban planning — helping cities design healthier environments. 🌿 Assessing environmental quality of neighborhoods. 🔥 Reducing urban heat islands caused by dense infrastructure and lack of vegetation. 🌳 Identifying where new trees or parks should be planted to improve urban ecosystems. 🌡 Land Surface Temperature (LST) Map An LST map shows how hot or cool the Earth’s surface is — including asphalt, buildings, soil, water, and vegetation. Why it matters: 🏙 Detect urban heat islands 🌿 Analyze environmental conditions 🌡 Monitor climate change 🌳 Support better urban planning #GISCARTA #GEOBOX #GIS #UrbanPlanning #GreenInfrastructure #UrbanEcology #LandSurfaceTemperature #LST #NDVI #ClimateData #UrbanHeatIsland #RemoteSensing #SatelliteData #EnvironmentalAnalysis #SmartCities #UrbanClimate #Geospatial #SpatialAnalysis #CityData #Sustainability #UrbanGreenery #Almaty #Kazakhstan #ClimateChange #EarthObservation #UrbanData #CityAnalytics #GreenCities #GeoAI #SpatialData #UrbanEnvironment #HeatMapping #GeoVisualization #map #geodata #dataviz #geo

🚀 𝗨𝗿𝗯𝗮𝗻 𝗛𝗲𝗮𝘁 𝗶𝘀 𝗥𝗶𝘀𝗶𝗻𝗴 - 𝗔𝗻𝗱 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝘃𝗲𝘀 𝗜𝘁. I just completed a comparative Land Surface Temperature (LST) analysis of Dhaka for 2015 vs 2025, and the results are not just interesting — they’re alarming. 🔍 𝐖𝐡𝐚𝐭 𝐈 𝐝𝐢𝐝: Extracted LST from satellite data using Google Earth Engine (GEE) Processed, classified, and visualized the outputs in ArcGIS Pro Generated spatial distribution maps to compare thermal patterns over time ⚙️ 𝐇𝐨𝐰 𝐈 𝐝𝐢𝐝 𝐢𝐭 (𝐬𝐢𝐦𝐩𝐥𝐢𝐟𝐢𝐞𝐝 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰): Collected Landsat imagery (2015 & 2025) via GEE Applied LST algorithms (radiance → brightness temperature → emissivity correction) Exported processed raster data Classified temperature ranges and mapped them in ArcGIS Pro Compared spatial patterns to identify heat intensity changes 🔥 𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐦𝐚𝐩𝐬 𝐜𝐥𝐞𝐚𝐫𝐥𝐲 𝐢𝐧𝐝𝐢𝐜𝐚𝐭𝐞: Significant increase in high-temperature zones (red/orange areas) in 2025 Rapid urban expansion replacing vegetation Eastern and peripheral zones that were cooler in 2015 are now heating up Heat is no longer localized — it’s spreading across the city ⚠️𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: Urban Heat Island (UHI) effect is intensifying Increased risks to public health, energy demand, and livability Signals poor urban planning and uncontrolled land-use change 💡 𝐊𝐞𝐲 𝐭𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬: Dhaka is getting hotter, faster than expected Green cover loss is directly linked to rising surface temperatures Without intervention, this trend will accelerate 🧠 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 (𝐛𝐚𝐬𝐞𝐝 𝐨𝐧 𝐝𝐚𝐭𝐚, 𝐧𝐨𝐭 𝐠𝐮𝐞𝐬𝐬𝐰𝐨𝐫𝐤): Increase urban green spaces (parks, rooftop gardens, vertical greening) Implement cool roofing & reflective materials Protect remaining water bodies and vegetation zones Integrate heat risk into urban planning policies Promote sustainable land-use management 📊 This isn’t just a map comparison — it’s a warning signal backed by geospatial evidence. If you're working in urban planning, climate science, or GIS, this is exactly the kind of analysis we need more of. 💬 Curious to hear your thoughts — what do you think is driving this change the most? #GIS #RemoteSensing #GoogleEarthEngine #ArcGISPro #UrbanHeatIsland #ClimateChange #Dhaka #GeospatialAnalysis #Sustainability #DataScience #EnvironmentalScience #SmartCities #UrbanPlanning #SatelliteData #ClimateAction 🚨🌍🔥

🌍 MODIS LST & NDVI Correlation Analysis (Western Maharashtra, Jan 2024) 📡 Source Code : https://lnkd.in/dXeJV2MG 🚀 Just completed a geospatial study exploring the correlation between land surface temperature (LST) and vegetation health (NDVI) across Western Maharashtra. 🌡️🌱 🎯 THE CHALLENGE 🌡️ High surface temperatures often stress vegetation 🌱 NDVI reflects vegetation greenness/health ⚡ Understanding their correlation is key for drought, heat stress & ecosystem monitoring 🗺️ MY APPROACH ✅ MODIS MOD11A2 → Extracted LST (scaled, Jan 2024 mean) ✅ MODIS MOD13A2 → Extracted NDVI (scaled, Jan 2024 mean) ✅ Clipped both datasets to WMH district boundary (AOI) ✅ Random sampling (500 points, 1 km resolution) ✅ Generated scatter plot (NDVI vs LST) with regression trendline in GEE 🚀 KEY INSIGHTS 📍 Inverse relationship observed: higher LST usually corresponds to lower NDVI (vegetation stress) 📊 Scatter plot + regression trendline confirm heat–vegetation interaction 🌍 Spatial visualization highlights hotspots where vegetation greenness decreases with warming 💡 WHY IT MATTERS 🌾 Supports crop stress & drought vulnerability monitoring 🌱 Helps identify vegetation at risk from rising temperatures 📈 Useful for climate resilience, agricultural planning, and ecosystem studies 💻 TECH STACK 🌐 Google Earth Engine – Data processing, visualization, sampling 🛰️ MODIS (LST & NDVI) – Core datasets 📊 GEE Chart API – Scatter plot & trendline 📂 Outputs 📊 Scatter chart (LST vs NDVI with trendline) 🌡️ MODIS LST map (Jan 2024) 🌱 MODIS NDVI map (Jan 2024) 📝 Sample dataset (LST & NDVI values per point #RemoteSensing #GIS #GeospatialAnalysis #GoogleEarthEngine #MODIS #NDVI #LST #VegetationMonitoring #ClimateChange #DroughtMonitoring #HeatStress #Geoinformatics #EarthObservation #BigData #DataScience #SpatialAnalysis #GeospatialTechnology #ClimateResilience #Agriculture #PrecisionAgriculture #SustainableAgriculture #EcosystemMonitoring #LandSurfaceTemperature #VegetationIndex #ClimateSmartAgriculture #DroughtAssessment #EnvironmentalMonitoring #GeoAI #MachineLearning #SatelliteData #CropHealthMonitoring #GeospatialData #EarthAnalytics #Agroclimatology #AgriTech #ClimateAdaptation #ClimateAction #SpatialDataScience #GeospatialResearch #SustainableDevelopment #EnvironmentalScience #RemoteSensingApplications #ClimateStudies #DisasterRiskReduction #Mapping