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Remote Sensing of Mangrove Forests in an Environment of Global Change
Published in Yeqiao Wang, Coastal and Marine Environments, 2020
A great deal of remote sensing has been done, and continue being done, using aircraft as the main platform. Aircraft platforms include: reconnaissance planes, helicopters, balloons, and recently, unmanned aerial vehicles (UAVs) and drones. With the advent of artificial satellites, aircraft platforms were put somewhat on the background. The spatial, spectral, temporal, and radiometric resolutions of satellite remote sensors have continued improving since the launch of the first Earth observing satellite in 1972 by a joint effort between the US Geological Survey (USGS) and the National Aeronautics Space Administration (NASA). This first Earth observing system was renamed Landsat after its launch. Today, Landsat and other sensors’ archive data is freely available to researchers (https://earthexplorer.usgs.gov/). Table 34.2 shows some of the Earth observing systems that can be used in mangrove studies.
Geophysical investigation techniques: heat
Published in Ian Acworth, Investigating Groundwater, 2019
The Landsat 8 satellite provides imagery with a resolution of 30 m. The remarkable level of detail is shown by the two images taken 30 years apart shown in Figure 11.5. The top figure (Figure 11.5a) shows a location in the north of Saudi Arabia taken in February 1986. There are a few circles of red – indicating actively growing vegetation associated with pivot style irrigation of wheat. These early developments were deemed successful, and the decision was taken to exploit fossil groundwater resources in the area. The second image (Figure 11.5b) is from February 2016 (30 years later) showing massive development of agriculture. Groundwater resource analysis has indicated that the resource has a lifetime of 30 years.
Cloud and Cloud Shadow Detection for Landsat Images: The Fundamental Basis for Analyzing Landsat Time Series
Published in Qihao Weng, Remote Sensing Time Series Image Processing, 2018
Zhe Zhu, Shi Qiu, Binbin He, Chengbin Deng
Since 1972, Landsat satellites have provided a continuous Earth observation data record. Landsats 1–5 carried the Multispectral Scanner System (MSS) sensor with 60-meter spatial resolution. The MSS only collected images with four spectral bands, including green, red, and two Near InfraRed (NIR) bands (Table 1.1). Note that the Landsat 3 MSS also included a Thermal Infrared (TIR) band, but failed shortly after launch. The fewer bands result in known difficulties in detecting clouds and cloud shadows (Braaten et al., 2015). However, the MSS images are still crucial for LTS related analyses (Pflugmacher et al., 2012). Since the launch of Landsat 4 in 1982, the Thematic Mapper (TM) has provided more spectral information at 30-meter spatial resolution (Table 1.1). The TM sensor was also carried on Landsat 5, which was launched on March 1, 1984, and functioned for over 28 years until 2012. Landsat 7, carrying the Enhanced Thematic Mapper Plus (ETM+), was launched on April 15, 1999 (Table 1.1). This instrument also has a 30-meter spatial resolution and improved radiometric and geometric calibration accuracies, but the Scan Line Corrector (SLC) has failed since May 31, 2003. Both TM and ETM+ have a TIR band at a spatial resolution of 120-meter and 60-meter, respectively. Landsat 8 was launched on February 11, 2013. It has two sensors: Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) (Table 1.1). The OLI instrument provides 30-meter resolution optical data, while TIRS provides 100-meter resolution TIR data. Note that the TIRS has a shorter design life compared to the OLI. Additionally, the new OLI added the new blue band (Band 1: 0.435–0.451 μm) and the cirrus band (Band 9: 1.363–1.384 μm) with 30-meter spatial resolution.
Integration of Landsat time-series vegetation indices improves consistency of change detection
Published in International Journal of Digital Earth, 2023
Mingxing Zhou, Dengqiu Li, Kuo Liao, Dengsheng Lu
All available Landsat 5, 7, and 8 images (Worldwide Reference System [WRS] Path 120 and Row 41) with more than 20% clear observations (i.e. pixels with no clouds, cloud shadows, or snow) between 1986 and 2020 were acquired from the United States Geological Survey (USGS) through bulk ordering (https://espa.cr.usgs.gov/) (Figure 3). A total of 583 images (TM: 276, ETM + SLC-on: 41, ETM + SLC-off: 178, OLI: 88) were collected from Landsat Collection 1 Surface Reflectance Level-2 products, which had been preprocessed with geo-referencing, automatic atmospheric correction, and clouds and shadows detection by USGS. Only terrain corrected data (L1T) were used. The Landsat 5/7 surface reflectance data were atmospherically corrected using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) algorithm (Masek et al. 2006), and the Landsat 8 surface reflectance were generated using the Land Surface Reflectance Code (LaSRC) (Vermote et al. 2016) algorithm. Clouds, cloud shadows, and stripes were masked in each image, and all clear observations were retained based on the pixel quality assurance (QA) data created from a CFMask algorithm (Z. Zhu and Woodcock 2012).
Big Earth data: disruptive changes in Earth observation data management and analysis?
Published in International Journal of Digital Earth, 2020
Martin Sudmanns, Dirk Tiede, Stefan Lang, Helena Bergstedt, Georg Trost, Hannah Augustin, Andrea Baraldi, Thomas Blaschke
Many space agencies and research facilities provide their collected data sets directly to end-users, providing only search and download functionality. Examples include the Copernicus Open Access Hub of the ESA (https://scihub.copernicus.eu) and the EUMETSAT Data Centre of the European Organisation for the Exploitation of Meteorological (https://eoportal.eumetsat.int). The EarthExplorer, GloVis, and the USGS’s LandsatLook Viewer all provide access and/or visualisation of Landsat products (U.S. Geological Survey). The NASA sensor Web suite consists of software tools for accessing, processing and analysing data. The system allows the combination of satellite, in situ, and UAV data sets (Delin et al. 2005). In 2017, the European Commission started developing the Copernicus Data and Information Access Services (DIAS), publicly launched in June 2018. These systems not only allows access to data sets but also provides processing resources and tools for data analytics and will – once established – most likely turn into a PPP organisational structure.
A new method for generating a clear-sky Landsat composite for cropland from cloud-contaminated Landsat-7 and Landsat-8 images
Published in International Journal of Digital Earth, 2018
Landsat imagery is widely used for mapping and monitoring large-scale environmental changes due to its over 40-years data collection, providing global, multi-temporal, and multi-spectral imagery with medium resolutions (10–100 m). Since Landsat-5 was officially decommissioned on 5 June 2013, Landsat-7 (launched in 1999) and Landsat-8 (launched in 2013) have continued to supply images for users. The ETM+ and OLI sensors on board the Landsat-7 and Landsat-8 satellites provide a spatial coverage of 185 × 185 km2 per scene with a spatial resolution of 30 m. The relatively long revisit interval of 16 days for these sensors and frequent contamination by cloud largely constrain the data usability for environmental monitoring in a timely manner (Gao et al. 2006). Moreover, a mechanical fault in the Scan-Line Corrector (SLC-Off) of the Landsat-7 satellite has caused missing data strips (22–25% data loss) throughout the images since 2003, further reducing the data usability.