class: center, middle, inverse, title-slide .title[ # Landsat 8 ] .subtitle[ ## remote sensor ] .author[ ### Yanbo Liu ] .institute[ ### University College London, UCL ] .date[ ### 2023-01-18 ] --- # What is Landsat 8? .pull-left[ * a US satellite launched in February 11, 2013 * has two senors: * Operational Land Imager (OLI) * Thermal Infrared Sensor (TIRS) * orbit: polar, sun-synchronous * altitude: 705 km * managed by NASA and the US Geological Survey (USGS) * to collect imagery used for observing and managing the Earth’s resources ] .pull-right[ <img src="image/lansat.jpg" width="100%" /> .small[Landsat 8 Spacecraft. Source: [Landsat Science](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/landsat-8-spacecraft/) ] ] --- ## Spatial resolution .pull-left[ * OLI are 15 meters and 30 meters * TIRS are 100 meters ] .pull-right[ <img src="image/spatial.png" width="90%" style="display: block; margin: auto;" /> .small[ Source: [(Byoungchul et al., 2015)](https://www.researchgate.net/publication/279283935_Classification_of_Potential_Water_Bodies_Using_Landsat_8_OLI_and_a_Combination_of_Two_Boosted_Random_Forest_Classifiers) ] ] ## Radiometric resolution * 12-bit scaled to 16-bit integers * 4096 possible values to 32,767 possible values --- ## Spectral resolution * OLI with 9 bands, and TIRS with 2 bands. <img src="image/Spectral_resolution.jpg" width="70%" /> .small[Source: [Landsat Science](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/landsat-8-spacecraft/) ] ## Temporal resolution * 16 days --- # Case study: active fire detection .pull-left[ * Applied algorithms on OLI data of Landsat 8 * Improved small fire detection and large wildfire delineation * Reduced error rate in daytime and night ] .pull-right[ <img src="image/fire1.png" width="120%" style="display: block; margin: auto;" /> ] <img src="image/fire2.jpg" width="75%" style="display: block; margin: auto;" /> .small[Source: [(Wilfrid et al., 2016)](https://www.sciencedirect.com/science/article/pii/S0034425715301206)] --- # Case study: water use mapping in the Colorado River Basin * Mapping evapotranspiration by using Landsat data in spatial resolution with 100 meters * Found croplands is the largest consumer of managed surface and ground water and shrublands is the largest consumer of natural rainfall and soil moisture * Further studies can be identified and recorded water footprints of different fields using historical Landsat data <img src="image/water.jpg" width="55%" style="display: block; margin: auto;" /> .small[Source: [(Gabriel et al., 2016)](https://www.sciencedirect.com/science/article/pii/S0034425715302650)] --- # Case study: spruce beetles epidemic in southern Colorado .pull-left[ * Evaluated the severity (0 - 100% dead) of mortality for forests * Displayed the distribution of spruce beetles outbreaks * Potential efficient monitoring of spruce beetles outbreaks by using Landsat ] .pull-right[ <img src="image/spruce.jpg" width="65%" /> .small[Source: [(Brian et al., 2018)](https://www.mdpi.com/1999-4907/9/6/336)] ] --- # Reflection on Landsat 8 data * Landsat 8 has OLI sensor and TIRS sensor, including 11 bands, and the applications of them are useful: * band 1 (OLI) for coastal studies * band 2 (OLI) for distinguishing deciduous from coniferous vegetation * band 4 (OLI) for identifying vegetation slopes * band 9 (OLI) for detection of cirrus * band 10 and 11 (TIRS) for thermal mapping * Landsat data can be used through temporal aspect. * analyzing the dynamics of canopy cover between 1990 and 2020. * Landsat data can be utilized in machine learning techniques such as deep learning for image classification, object detection and etc. --- # Reflection on how to use it in my work * The thermal data will be a good source for me to explore the temperature variables in my study, like exploring the thermal inequality in different race groups. * The visualization of data are informative for me to find some potential trends, like potential direction of development for urban area. * The data also can be a applied in some further studies that are related to forest management, climate change, agriculture and natural resources management, and etc. --- # Reference * Ko, B., Kim, H. and Nam, J. (2015). ‘Classification of Potential Water Bodies Using Landsat 8 OLI and a Combination of Two Boosted Random Forest Classifiers’. Sensors, 15, pp. 13763–13777. doi: 10.3390/s150613763. * Schroeder, W., Oliva, P., Giglio, L., Quayle, B., Lorenz, E. and Morelli, F. (2016). ‘Active fire detection using Landsat-8/OLI data’. Remote Sensing of Environment. (Landsat 8 Science Results), 185, pp. 210–220. doi: 10.1016/j.rse.2015.08.032. * Senay, G. B., Friedrichs, M., Singh, R. K. and Velpuri, N. M. (2016). ‘Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin’. Remote Sensing of Environment. (Landsat 8 Science Results), 185, pp. 171–185. doi: 10.1016/j.rse.2015.12.043. * Woodward, B. D., Evangelista, P. H. and Vorster, A. G. (2018). ‘Mapping Progression and Severity of a Southern Colorado Spruce Beetle Outbreak Using Calibrated Image Composites’. Forests. Multidisciplinary Digital Publishing Institute, 9 (6), p. 336. doi: 10.3390/f9060336.