10/23/2024 URBAN CLIMATOLOGY 5. Urban Remote Sensing Paper to read Author manuscript, published in "Urban Remote Sensing Event 2007, Paris : France (2007)" 2007 Urban Remote Sensing Joint Event Application of satellite Remote Sensing for Urban Risk Analysis: a case study of the 2003 extreme heat wave in Paris Ben£dicle Dousset Hawaii Institute of Geophysics and Planetology University of Hawaii. Honolulu, USA bdou s se t@ hawaii. edu Francoise Gourmelon Laboratoiie Geomer CNRS UMR-6554. Institut Universitaire Europeen de la Mer Plouzane*. France Francoise.G c unne 1 c n <£' univ-brest.fr Elena Mauri Istituto Nazionale di Oceanografica e di Geofisica Sperimentale Trieste, Italy emauri@Qgs.mesie.it https://is.muni.cz/auth/el/sci/podzim2022/ZX601/um/67875456/05_Dousset-URS-07.pdf 1 5.1 Remote Sensing Principle 0.4 0.5 0.6 0.7 Blue Green Red UNITS 1 micrometer (Urn) = 1 x 1Q-6 meters 1 millimeter (mm) =1x10"3 meters 1 centimeter (cm) = 1 x 10-J meters '"N Incoming from Sun INFRARED Emitted by Earth MICROWAVE (RADAR) I IS 111 71 B7 74 111 66 52 77 95 ■:! et 14 S7 ť. 89 ...i 102 125 ;„) 70 ■"" Ml ■It 1 ' i r 0.1 urn 1 urn ID ^Im 1D0 1 nnm 1 cm 10 cm 1m Wavelength (logarithmic scale) http://www.remote-sensing.net/concepts.html 5.1 Remote Sensing Principle Stefan-BoItzmann law: The thermal energy radiated by a black body* is proportional to the fourth power of the absolute temperature Black body M M - thermal energy T - absolute temperature a - the Stefan-Boltzmann constant * black body is ideal absober / emitter of EM energy Real surfaces M = eoT1 emissivity Emissivity is the measure of an object's ability to emit infrared energy. Emitted energy indicates the temperature of the object. Emissivity can have a value from 0 (shiny mirror) to 1.0 (blackbody). Most organic, painted, or oxidized surfaces have emissivity values close to 0.95. There are at least two problems in urban remote sensing: 1) How to determine emissivity of real surfaces in highly heterogeneous urban environment 2) How to recalculate LST - Land Surface Temperature to air temperature 5.2 LST derivation from thermal imagery ETM+ band R 1 - '■' atmospheric correction mm.a im i'' i 1,2,3,4 Emissivity Land surface Temperature (Snyder et al. 1998) LST derivation from several thermal images Land Surface Temperatures Emissivity at bands 10-14 Appearance and interpretation of thermal imagery 10/23/2024 LST applications in Urban Climatology LST spatial and temporal variability LST_Brno_2013/04/15_ LST_Brno_2013/05/01_ LST_Brno_2013/08/05 Intensity of SUHI estimate 6 Heat waves mapping rmingam City Extents villages/farms suburban light suburban dense suburban urban/transport urban light urban/open water woodland/open land m : jSa ■ ■■■ « ■■ mmi ■■■ ■■ llr ■ ■■ ■■ ■ ■■■■■ ' - 7 ! Jk.rv A Spatial distribution of land classification (left) and SUHI magnitude (right) within Birmingham city extents for heatwave event at 18 July 2006 (Tomlinson et al., 2010) Urban Risk Analysis Remote Sensing for Urban Risk Analysis: a case study of the 2003 extreme heat wave in Paris Average Land Surface Temperature infrared images over the heat wave event of August 4 to 13, 2003, for each of the diurnal time intervals. The color scales are in degrees Celsius. Analysis was based on 50 images sensed by NOAA satellites 12,16 and 17, over the Paris basin, from 4 to 13 August 2003. The areas of the Paris region most vulnerable to heat stress were identified. Modification of LST field due to anthropic activities in big CZ. cities Data: eight-day composites of mean surface temperatures from the AAOĎIS scanner with 1 x 1 km spatial resolution. PRŮMĚRNÉ LST V PRAZE A JEJÍM PŘILEHLÉM OKOLÍ OD 1.1. 2008 DO 1.1. 2018 noóni LST (X) 20 km 3-4 a. -.é- 1 1 Vysvětlivky ' záslavba Autor: Malin BUREŠ Scur. sy5t ■ WGS 19a4ÍUTM 33N 2droje datj CÚ2K. EARTHDATA Datum a místo: Brno 2018. Spatial differentiation of surface temperatures (LST) in daytime (left), nighttime (middle) hours and their average (right) in Prague and its _surroundings in the period 2008-2018_ Modification of LST field due to anthropic activities in big CZ cities ) ^ ' v ' - .. " i: C V .. ' ■ ■ • -As- V - ' " ' •^ NIGHT 6 — • g r • * ľ £ • • 5,5 • • • • • 5 • • • • 2 14 0 5 • - • • • # • • » 12 • • t/ * * ,3? ,<-° S* ŕ ŕ n* řr ŕ • Denní or úmerne tcolot; v icst-víné oblasti De" "í průměrná teplota v zázemí urban rural • Noční yiunitít 'ib teplota v ŕasLa'.'tí'ití oblasLi • Ncíni p - úriärnď teplota u íúuímú urban rural Average LST of the ten largest cities of the Czech Republic in built-up and rural areas in the period 2008-2018. 10/23/2024 Another useful Remotely Sensed variables for UC 3D building model (active laser scanning) Various parameters derived from 3D model of buildings and from Digital Elevation Model _explain spatial variability of land surface temperatures._ Vegetation mapping NDvT - Normalized Difference Vegetati on Index natural colors NDVT I reflectance [%} 40 A 20 A 0.5 NIR - RED RED NIR Vegetation amount and vigor strongly correlates with LSTs f 1 9 Mapping of land use / land cover changes, UHI growth V30 LETECH 19.STOLETÍ , 2 , , , ,„„ V70. LETECH 19. STOLETÍ Urban atlas CORINE Land Cover Úroveň 1 pro měřítka menší než 1:1000 000 - obsahuje 5 tříd Třídy CORIME 1. úrovně Úroveň 2 {1:500 000 až 1:1 000 000) -obsahuje 15, tříd, v ČR 13 dy CORIHE 2. úrovně [vyskytujíc! se v ČR) Úroveň 3 (1:100 000) -obsahuje 44 tříd, ČR 28. Třídy CORINF 71. n-u jur.\ > li i ící se V CR) 11, Urbanizovaná území 2. Zemědělské plochy 13. Lesy a pclopřTrodní í 4. Humidní území 15. Vodní plochy . obytné plechy . Průmyslové a obchodní zóny, komumkačn . Doly, skládky a staveniště ■ Plochy umělé, nezemědělské zeleně D rr a půda . Stálé kultury . Pastviny ■. Různorodé zemědělské plochy . Lesy . Plochy s křovinnou a travnatou vegetací Otevřené plochy s malým zastoupením ' bez vegetace . Vnitrozemské humidní území =,e\riní--:e ,~z-: www.cenia.cz Silniem' a železniční sľť s okolím Přístavy Letiště Haldy a skládky 11.3.3. Staveniště 1.4.1. Městské zelene plochy 1.4.2. Sportovní a rekreační plochy 2.1.1. Nezahazovaná orná purta 12.2.1. Viníce 12-2-2- Sady, chmelnice a zahradní plantáže 2.3.1. Louky a pastviny 2.4.2. směsicí polí, luk a trvalých plodin 2.4.3. Zemědělské oblasti s přirozenou vegetací 3.1,1. Listnatě lesy 13.1.2. Jehličnatě lesy ' 3.1.3, Smíšené lesy 3.2.1. Přírodmí louky 3.2.2, Stepi a kroviny 3.2.4. Nízký porost v lese Aircraft remote sensing • Detailed LST mapping • Urban Vegetation and Ecology Monitoring I ^Czeel Globe CZECHGLOBE Global Change Research Institute, CAS Brno in summer - surface temperature Weather RADAR active system ground based r'\,'V\A Returning [\j\J\f\ Outgoing wavelength Weather radar image Binetti et al. 2022 precipitation monitoring real-time („now-casting") lower atmosphere 3D structure severe weather phenomena (thunderstorms, hails, tornadoes,...) https://climavision.com/resources/what-is-weather-radar-guide/ on orbit r Oklahoma City N EX RAD base reflectivity, 3 May 1999, 1912 CDT Precipitation and weather RADAR Spatial distribution of radar reflectivity (maximum values in vertical direction) measured at meteorological radars Skalky and Brdy at 15 July 2009, 19:25 hours of central European summer time Spatial distribution of daily precipitation totals (mm) computed as a combination of radar-based precipitation estimate and rain-gauge measurements from 15 July 2009 (measured at 16 July 2009, 08 h CBSTj. Spatial distribution of precipitation totals is given in 1 x 1 km grid LST satellite data sources spatial resolution spectral resolution time resolution Meteorological satellites Satellite systems for environmental applications LST of Krakow (Poland) derived form Meteosat, NOAA, and Landsat satellite data ^% .-; «■ ,«c %i ■*3"*" ■ ■ 4 i el' i ■ j; Walawenderand Hajto, 2008 LST data sources • LST calculated from raw thermal imagery and metadata • LST offered as a standard product https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature rusGS Landsat Collection 2 Surface Temperature :'':cu',iEn Lancsat surface temperature measure;" the Earth's surface tenipetature in Kelvin and is an important geophvsical parameier in global er.ergv balance studies anc. iivdrologic modeling. Surface temperature cata are also useful lot monitoring crop and vegetation health, and extreme heat events such as natural criasters (e.g., volcanic eruptions, wildfires), and urban neat .slaud eitectj. llit Luiiibul Surface Itmperalure Ott Luridjul Stiel«* PrudmJs Overview Landsat8 11 Sep 2018 Yellowstone 10/23/2024 Cities from Space https://sedac.ciesin.columbia.edu/data/set/ulandsat-cities-from-space Socioeconomic Data and Applications Center (sedac) AD^Ce^mX&UEmhObitn^&rmOaaaMlqfarmMionSyirm/EQlJiJSl—Hoir*ify!2ESI2i at Columbia MAPS • THEMES • RESOURCES • SOCIAL MEDIA ■ft DATA Urban Landsat: Cities from Space, vl 11999—2003J » Maps Theme Location Follow Us Data S*u - Rezicn NonhAmnica* PDFIPXC Hi-RfiohiMen PDF 75G Hi-Kesoiuuon PDF FXC LaarliaE Imif t Guadalajara Lao dial ImsBf Guatemala Landiailnisge Nlaiiasua. Land^ai Image Mexico City. Mexico 'II'- Hi-Rewiuiion PDF PNG Hi-Rewlntion PDI Satellite climatology http://earthobservatory.nasa.gov/ Earth Observatory Home linage of the Day Feature Article* New; Natural Hazard; Global Map; Elogs Global Maps Aerosol Optical Depth Aerosol Size Carbon Monoxide Chlorophyll Cloud Fraction Fire Lard Surface Temperature Land Surface Temperature Anomaly Net Primary Productivity Net Radiation Sea Surface Temperature Sea Surface Temperature Anomaly Snow Cower Total Rainfall Vegetation Water Vapor Global Maps NASA, satellites give us a global view of what's happening on our planet. To explore hotkey parts of Earth's climate system change from month to month, click on one of the maps below or select from the complete list on the left. Land Surface Temperature Net Radiation Sea Surface Temperatui Total Rainfall 14 10/23/2024 5.5 Final remarks and questions l. What are limitations of URS in terms of spectral, spatial and temporal resolution? 2. What are the main benefits of URS for heat wave studies compared to air temperature analysis? 3. How can be URS used for practical urban planning, regional development and for better adaptation to climate change? 15