class: center, title-slide, middle background-image: url("img/nasa.jpg") background-size: cover background-position: center # Week 2 Learning Diary ## Introducing Landsat 8 ### Summary, Application, Reflection ### 23/03/2023 <a href="https://github.com/asdfgukyu/rm-site" class="github-corner" aria-label="View source on GitHub"><svg width="80" height="80" viewBox="0 0 250 250" style="fill:#fff; color:#151513; position: absolute; top: 0; border: 0; left: 0; transform: scale(-1, 1);" aria-hidden="true"><path d="M0,0 L115,115 L130,115 L142,142 L250,250 L250,0 Z"></path><path d="M128.3,109.0 C113.8,99.7 119.0,89.6 119.0,89.6 C122.0,82.7 120.5,78.6 120.5,78.6 C119.2,72.0 123.4,76.3 123.4,76.3 C127.3,80.9 125.5,87.3 125.5,87.3 C122.9,97.6 130.6,101.9 134.4,103.2" fill="currentColor" style="transform-origin: 130px 106px;" class="octo-arm"></path><path d="M115.0,115.0 C114.9,115.1 118.7,116.5 119.8,115.4 L133.7,101.6 C136.9,99.2 139.9,98.4 142.2,98.6 C133.8,88.0 127.5,74.4 143.8,58.0 C148.5,53.4 154.0,51.2 159.7,51.0 C160.3,49.4 163.2,43.6 171.4,40.1 C171.4,40.1 176.1,42.5 178.8,56.2 C183.1,58.6 187.2,61.8 190.9,65.4 C194.5,69.0 197.7,73.2 200.1,77.6 C213.8,80.2 216.3,84.9 216.3,84.9 C212.7,93.1 206.9,96.0 205.4,96.6 C205.1,102.4 203.0,107.8 198.3,112.5 C181.9,128.9 168.3,122.5 157.7,114.1 C157.9,116.9 156.7,120.9 152.7,124.9 L141.0,136.5 C139.8,137.7 141.6,141.9 141.8,141.8 Z" fill="currentColor" class="octo-body"></path></svg></a><style>.github-corner:hover .octo-arm{animation:octocat-wave 560ms ease-in-out}@keyframes octocat-wave{0%,100%{transform:rotate(0)}20%,60%{transform:rotate(-25deg)}40%,80%{transform:rotate(10deg)}}@media (max-width:500px){.github-corner:hover .octo-arm{animation:none}.github-corner .octo-arm{animation:octocat-wave 560ms ease-in-out}}</style> --- ### Overview: Landsat 8 <!-- #### Launched in 2013, Landsat 8 marked the beginning of modern satellite imagery. --> Utilised new push-broom sensor that created instantaneous scan lines with a line of 7,000 detectors, instead of just a few detectors reflected with mirrors with the whisk broom sensor. This caused problems resulting in a zig-zaged image in Landsat 7. One of the most important contributions of Landsat 8 is the **continuous and consistent data record** it provides, allowing scientists to monitor changes in land use and land cover over time. **Spatial Resolution** : moderate to high resolution (15m - 100m) **Temporal Frequency** : ~725 scenes day, huge increase from Landsat 7 (350) **Spectral Resolution** : 5 visible and near infrared (NVIR) bands, 3 short waved infrared (SWIR) bands, 1 panchromatic image (PAN), 2 thermal infrared (TIR) bands. .pull-left[ <img src="img/whisk_broom.jpeg" width="40%" style="display: block; margin: auto 0 auto auto;" /> ] .pull-right[ <img src="img/push_broom.jpeg" width="35%" style="display: block; margin: auto auto auto 0;" /> ] Image: [L3Harris](https://www.l3harrisgeospatial.com/Support/Self-Help-Tools/Help-Articles/Help-Articles-Detail/ArtMID/10220/ArticleID/16262/Push-Broom-and-Whisk-Broom-Sensors) --- ### Scientific Instruments .pull-left[ #### **Operational Land Imager OLI** - Responsible for the new push broom tech, designed to provide data that is consistently accurate and highly calibrated. - Measures in visible, near infrared, and short wave infrared - Higher signal-to-noise ratio compared to previous Landsat instruments, meaning higher-quality data ] .pull-right[ #### **Thermal InfraRed Sensor TIRS** - Measures invisible thermal infrared by detecting 2 thermal infrared wavelength bands (1 for Earth's surface, 1 for atmosphere at 100m. This was not available in Landsat 7. - Used to study urbanization and deforestation, as well as to monitor the extent and health of vegetation, and to detect and monitor fires. ] <img src="img/BandpassesL7vL8_Jul20131-1024x611-1.jpeg" width="30%" style="display: block; margin: auto;" /> Image: [NASA](file:///Users/asdfgukyu/Zotero/storage/WGRQXWQG/landsat-data-continuity-mission.html) --- class:, center, title-slide, middle background-image: url("img/imaginearth-la-terre-en-images-opZ--yE1-hg-unsplash.jpg") background-size: cover background-position: center # Application --- class: inverse ## Application ### Land Surface Temperature .pull-left[ #### **Urban Heat Island**: - <a name=cite-kaplanUrbanHeatIsland2018></a>[Kaplan, Avdan, and Avdan (2018)](#bib-kaplanUrbanHeatIsland2018) used Landsat 8 to collect LST data across varying land covers and vegetation abundance in Macedonia. - Normalised Difference Vegetation Index (NDVI) was used for vegetation extraction and Normalised Difference Built-up Index (NDBI) was used for urban extractions. ] .pull-right[ #### **Evapotranspiration**: - Using TIR data to measure the evaporation from soil surface evaporation of water intercepted by the canopy, and transpiration from vegetation. - <a name=cite-senayEvaluatingLandsatEvapotranspiration2016></a>[Senay, Friedrichs, Singh, and Velpuri (2016)](#bib-senayEvaluatingLandsatEvapotranspiration2016) used 528 Landsat 8 images to estimate evapotranspiration of the Colorado River Basin, assessing impact of surrounding 5 irrigation districts. ] --- class: inverse ## Application ### Water monitoring - Using OLI Band 1 which is particularly good for imaging shallow water to measure water depths and surface composition. - <a name=cite-pachecoRetrievalNearshoreBathymetry2015></a>[Pacheco, Horta, Loureiro, and Ferreira (2015)](#bib-pachecoRetrievalNearshoreBathymetry2015) developed a linear algorithm to retrieve bathymetry from multispectral Landsat 8 imagery, using LiDAR data for tuning. - In a similar vein, <a name=cite-trinhApplicationLandsatMonitoring2017></a>[Trinh, Fichot, Gierach, Holt, Malakar, Hulley, and Smith (2017)](#bib-trinhApplicationLandsatMonitoring2017) used Landsat 8 OLI and Aqua MODIS to extract areas of chlorophyll-a concentration as a result of improper wastewater management in Santa Monica, California. ### Foliage observation Since vegetation tracking often replies on SWIR, this field has been robust since Landsat 7. However Landsat 8’s higher spatial temporal and narrower spectral resolution allows for more detailed observations - <a name=cite-karlsonMappingTreeCanopy2015></a>[Karlson, Ostwald, Reese, Sanou, Tankoano, and Mattsson (2015)](#bib-karlsonMappingTreeCanopy2015) Used Lansat 8 and random forest machine learning technique to map tree canopy cover and aboveground biomass in Sudano-Sahelian Woodlands. --- class:, center, title-slide, middle background-image: url("img/nasa-6-jTZysYY_U-unsplash.jpg") background-size: cover background-position: center # Reflection --- ## Reflection - Landsat 8 marked the **beginning of modern satellite imagery**. It almost doubled Landsat 7’s temporal resolution, added 3 additional bands, increased ability to detect noise and removed line scan errors entirely. - It’s interesting to understand how a satellite series evolve and important to recognised the **research fields that are made possible** because of advancements of these instruments. - Landsat 8 in particular enabled Earth Data to be used widely because of it's **consistent and accurate data** for just under a decade. - In the age of Anthropocene, Landsat 8 enabled us to prove and measure how human activities are impacting the natural world, be it deforestation, water depletion, track temperature change, analysing the water cycle and evaluating vegetation health. this allows for more **ecological centered urban strategy** across the world. - Intrigued about what’s to come: Landsat Next is said to have **26 bands** and collects **20 times more** imagery than Landsat 8 & 9. - Satellites can measure **a lot** of things. Colours, water content, depths, chemical concentration. As long as we know **how the electromagnetic spectrum interacts** with the thing we want to measure, we can use remote sensing tools to configure it. --- ## References <a name=bib-kaplanUrbanHeatIsland2018></a>[Kaplan, G., U. Avdan, and Z. Y. Avdan](#cite-kaplanUrbanHeatIsland2018) (2018). "Urban Heat Island Analysis Using the Landsat 8 Satellite Data: A Case Study in Skopje, Macedonia". In: _Proceedings_ 2.7, p. 358. <a name=bib-karlsonMappingTreeCanopy2015></a>[Karlson, M., M. Ostwald, H. Reese, et al.](#cite-karlsonMappingTreeCanopy2015) (2015). "Mapping Tree Canopy Cover and Aboveground Biomass in Sudano-Sahelian Woodlands Using Landsat 8 and Random Forest". In: _Remote Sensing_ 7.8, pp. 10017-10041. <a name=bib-pachecoRetrievalNearshoreBathymetry2015></a>[Pacheco, A., J. Horta, C. Loureiro, et al.](#cite-pachecoRetrievalNearshoreBathymetry2015) (2015). "Retrieval of Nearshore Bathymetry from Landsat 8 Images: A Tool for Coastal Monitoring in Shallow Waters". In: _Remote Sensing of Environment_ 159, pp. 102-116. <a name=bib-senayEvaluatingLandsatEvapotranspiration2016></a>[Senay, G. B., M. Friedrichs, R. K. Singh, et al.](#cite-senayEvaluatingLandsatEvapotranspiration2016) (2016). "Evaluating Landsat 8 Evapotranspiration for Water Use Mapping in the Colorado River Basin". In: _Remote Sensing of Environment_. Landsat 8 Science Results 185, pp. 171-185. <a name=bib-trinhApplicationLandsatMonitoring2017></a>[Trinh, R. C., C. G. Fichot, M. M. Gierach, et al.](#cite-trinhApplicationLandsatMonitoring2017) (2017). "Application of Landsat 8 for Monitoring Impacts of Wastewater Discharge on Coastal Water Quality". In: _Frontiers in Marine Science_ 4.