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Analysis of High-Resolution Aerial Images
Published in Rangachar Kasturi, Mohan M. Trivedi, Image Analysis Applications, 2020
Sensors mounted on an airborne platform record reflected or emitted radiant flux incident on the sensor aperture. In order to develop a better understanding of how the recorded intensity levels are related to the intrinsic properties of various objects appearing in the scene, it is instructive to follow the path of the illuminating beam striking the scene and the reflected beam incident on the sensor aperture. Figure 8.2 displays the four components of a remote sensing system: source, scene, sensor, and intervening medium. We shall use this figure to discuss the radiation transfer process underlying passive remote sensing in a manner similar to that described by Hugh and Frei (1983). In our presentation we consider that the images are acquired with a multispectral scanner type of sensor (Goetz et al., 1985). In such images the incident radiation associated with the same patch of scene (i.e., the “footprint”) is split into several distinct wavelength bands and imagery is recorded simultaneously in these distinct bands. Thus, analysis of these images amounts to a case of multiple sensor information integration, with each sensor providing a measure of the incident flux in a particular wavelength band. The important feature of such imagery is that the images of different bands are in perfect spatial registration with one another.
Introduction to Remote Sensing
Published in Caiyun Zhang, Multi-sensor System Applications in the Everglades Ecosystem, 2020
Currently, USGS produces three major Landsat products: Landsat Collection Level-1 and Level-2 products and Landsat Analysis Ready Data (ARD) products, which are available to users at no cost. Level-1 products are processed to standard parameters and distributed as scaled and calibrated digital numbers that can be further processed to calibrated radiance or reflectance values using the provided metadata. In 2016, USGS reorganized the Landsat archive into a tiered collection structure to ensure the consistence of Level-1 products for time-series analyses and data “stacking.” The collection consists of three categories: Tier 1, Tier 2, and Real-Time. Tier 1 meets formal geometric and radiometric quality criteria. Tier 2 does not meet the Tier 1 criteria. The Real-Time Tier contains data immediately after acquisition that use estimated parameters. Real-Time data are reprocessed and assessed for inclusion into Tier 1 or Tier 2 as soon as final parameters are available. Level-1 data products are generated from Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 4–5 Thematic Mapper ™, and Landsat 1–5 Multispectral Scanner System (MSS) instruments.
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.
Land use/cover spatiotemporal dynamics, and implications on environmental and bioclimatic factors in Chingola district, Zambia
Published in Geomatics, Natural Hazards and Risk, 2022
Jean Moussa Kourouma, Darius Phiri, Andrew T. Hudak, Stephen Syampungani
Landsat 1 Multispectral Scanner (MSS) of 1972, Landsat 5 Thematic Mapper (TM) of 1992, Landsat 7 Enhanced Thematic Mapper Plus (ETM+) of 2001, and Landsat 8 Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) (OLI/TRIS) datasets of 2013 and 2020 with their respective paths and rows [(185, 069); (172, 068); (172, 069)] (Table 1) were used in the study. This dataset was acquired from the United States Geological Survey (USGS) website (https://earthexplorer.usgs.gov) and used to evaluate LULC changes in Chingola. Images from August and September, during the dry season when clouds are minimal, were selected for this study (Table 1).
Assessment of channel shifting of Karnali Megafan in Nepal using remote sensing and GIS
Published in Annals of GIS, 2021
Biplob Rakhal, Tirtha Raj Adhikari, Sanjib Sharma, Ganesh R. Ghimire
In this study, we used space-based moderate-resolution land remote sensing data such as optical Landsat imageries from the United States Geological Survey (USGS) (Landsat 2020). We acquired a series of decadal historic Landsat imageries on the Karnali River course for the past 36 years (1977–2013). We used the 60 m resolution Multispectral Scanner (MSS) for the year 1977 and the 30 m resolution Landsat Operational Land Imager (OLI) and the Thermal InfraRed Sensor (TRIS) satellite image, and Landsat Enhanced Thematic Mapper Plus (ETM+) for years 1990, 2000, 2010 and 2013 (see Figure A1).
Effect of disturbed river sediment supply on shoreline configuration: A case study
Published in ISH Journal of Hydraulic Engineering, 2021
Arunkumar Yadav, Basavanand M Dodamani, G S Dwarakish
Both conventional and satellite data are used in the present study. Geometrically corrected and orthorectified Landsat MSS (Multispectral Scanner System), Landsat 5, Landsat-7 ETM+ (The Enhanced Thematic Mapper Plus), The Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) Landsat-8 satellite data set have been used in the present study. The details of the same are listed in Table 1.