class: center, middle, inverse, title-slide .title[ # Principles of Remote sensing ] .subtitle[ ## ⚔️ xaringan +<br/>😎 xaringanthemer ] .author[ ### Idris Baba ] .date[ ### 2025-04-04 ] --- class: center, middle ## Remote sensing Remote sensing is the acquisition of information about an object or phenomenon without making physical contact with the object, in contrast to in situ or on-site observation. The term is applied especially to acquiring information about Earth and other planets. Remote sensing is used in numerous fields, including geophysics, geography, land surveying and most Earth science disciplines (e.g. exploration geophysics, hydrology, ecology, meteorology, oceanography, glaciology, geology). It also has military, intelligence, commercial, economic, planning, and humanitarian applications, among others. - **Source**: [https://en.wikipedia.org/wiki/Remote_sensing](https://en.wikipedia.org/wiki/Remote_sensing) <iframe width="560" height="315" src="https://www.youtube.com/embed/YGOxPPSfduY" frameborder="0" allowfullscreen></iframe> --- ## Benefits of Landsat Imagery The Landsat program has evolved over its 50-year history, offering significant advantages: - **Continuous Earth Monitoring:** Since 1972, Landsat has provided the longest global environmental satellite record, enabling the detection and analysis of long-term changes. - **Free and Open Data Policy:** Implemented in 2008, this policy drastically increased data usage and expanded scientific applications, fostering innovation and collaboration. - **Enabling Global Collaboration:** Landsat data's open availability supports nations without their own satellite programs, promoting confidence and cooperation in international monitoring. - **Mapping the Human Footprint:** Landsat enables global urban monitoring, advanced urban remote sensing, and land-use mapping (e.g., croplands, deforestation), aiding sustainability assessments. - **Wide Range of Applications:** Contributions include agricultural crop mapping, water use, climate change impacts, ecosystem monitoring, and tracking humanity’s changing footprint. - **Support for Time Series Analysis:** Free data and Landsat processing enable large-scale time series analysis using science-grade datasets. -**source:M.A. Wulder et al 2022** --- ## Sentinel-2 (Optical Imagery) - **High Spatial Resolution** - Spatial resolution: Up to 10 meters - Application: Detailed land feature analysis - **Frequent Revisit Times** - Satellites: Sentinel-2A and Sentinel-2B - Purpose: Monitoring vegetation, land cover, and water changes - **Wide Swath Width** - Swath width: 290 kilometers - Application: Broad area coverage - **Dual Polarization** - Polarization modes: Horizontal transmit/vertical receive and vice versa - Benefit: Enhanced surface data acquisition --- ##Sentinel-2 and Landsat 8 comparism Sentinel-2 and Landsat 8 satellite imagery was compared for predicting forest characteristics like tree volume, height, and diameter in a boreal region of Finland. The study found that Sentinel-2 generally outperformed Landsat 8 in these predictions, attributing this to its additional spectral bands, particularly the red-edge, and its higher spatial resolution. The research suggests that Sentinel-2 data can be a primary resource for assessing forest resources, offering improved accuracy for forest management. Ultimately, the findings support the increased utility of Sentinel-2 for operational forestry applications compared to Landsat. **DOI**: [https://doi.org/10.1016/j.rse.2019.01.019](https://doi.org/10.1016/j.rse.2019.01.019)  - **Source**: (Heikki Astola et al., 2019) --- ## Landsat Versus Sentinel ## Spectral Bands - Sentinel-2 MSI | **Band** | **Description** | **Wavelengths (nm)** | **Resolution (m)** | **Feature Set** | |----------|------------------------------|----------------------|--------------------|----------------------| | 1 | Coastal aerosol | 433–453 | 60 | – – – – – | | 2 | Blue | 458–523 | 10 | A B C D | | 3 | Green | 543–578 | 10 | A B C D | | 4 | Red | 650–680 | 10 | A B C D | | 5 | Vegetation Red Edge (RE1) | 698–713 | 20 | A – – – E | | 6 | Vegetation Red Edge (RE2) | 733–748 | 20 | A – – – E | | 7 | Vegetation Red Edge (RE3) | 773–793 | 20 | A – – – E | | 8 | Near-Infrared (NIR) | 785–900 | 10 | A – C D | | 8a | Narrow NIR (nNIR) | 855–875 | 20 | A B C – E | | 9 | Water vapor | 935–955 | 60 | – – – – – | | 10 | Shortwave infrared - Cirrus | 1360–1390 | 60 | – – – – – | | 11 | Shortwave infrared (SWIR1) | 1565–1655 | 20 | A B C – E | | 12 | Shortwave infrared (SWIR2) | 2100–2280 | 20 | A B C – E | --- ## Spectral Bands - Landsat 8 OLI | **Band** | **Description** | **Wavelengths (nm)** | **Resolution (m)** | **Feature Set** | |----------|-------------------------------|----------------------|--------------------|-----------------| | 1 | Violet-deep Blue (V-D Blue) | 433–453 | 30 | A – | | 2 | Blue | 450–515 | 30 | A B | | 3 | Green | 525–600 | 30 | A B | | 4 | Red | 630–680 | 30 | A B | | 5 | Near-Infrared (NIR) | 845–885 | 30 | A B | | 8 | Pan-Chromatic | 500–680 | 30 | – – | | 9 | SWIR - Cirrus | 1360–1390 | 30 | – – | | 6 | Shortwave infrared (SWIR1) | 1560–1660 | 30 | A B | | 7 | Shortwave infrared (SWIR2) | 2100–2300 | 30 | A B | --- #Passive and Active Sensors `#` | **Sensor Type** | **Definition** | **Operation** | |--------------------|-------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------| | **Active Sensor** | Emits energy or signals into the environment and measures the response or reflection of that energy. | Generates and emits signals (e.g., radio waves, sound waves, lasers) and analyzes responses from the target object. | | **Passive Sensor** | Relies on naturally available energy, such as sunlight, to measure reflected or emitted signals from objects. | Detects and measures energy naturally emitted or reflected without actively generating signals. | --- ## What is Active and Passive Remote Sensing? class: center, middle <iframe width="560" height="315" src="https://www.youtube.com/embed/vzfGMMEEz5w" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> --- ## Multispectral And Hyperspectral Multispectral imaging is a remote sensing technique that involves capturing and analyzing electromagnetic radiation from multiple discrete bands or spectral regions of the electromagnetic spectrum [23]. Unlike true-color images that are composed of red, green, and blue bands, multispectral images consist of data captured in several additional bands beyond the visible range, such as near-infrared (NIR) and shortwave infrared (SWIR) regions while **Hyper spectral imaging** is an advanced remote sensing technique that involves capturing and processing data in hundreds or even thousands of narrow and contiguous spectral bands across the electromagnetic spectrum.  --- ## Electromagnetic Spectrum The electromagnetic (EM) spectrum is the range of all types of EM radiation. Radiation is energy that travels and spreads out as it goes – the visible light that comes from a lamp in your house and the radio waves that come from a radio station are two types of electromagnetic radiation. The other types of EM radiation that make up the electromagnetic spectrum are microwaves, infrared light, ultraviolet light, X-rays and gamma-rays...  --- class: center, middle # Thanks! Slides created via the R packages: [**xaringan**](https://github.com/yihui/xaringan)<br> [gadenbuie/xaringanthemer](https://github.com/gadenbuie/xaringanthemer) The chakra comes from [remark.js](https://remarkjs.com), [**knitr**](http://yihui.name/knitr), and [R Markdown](https://rmarkdown.rstudio.com).