Applications of Landsat-8 Data: a Survey

Abstract

Landsat 8 was launched in 2013 by the National Aeronautics and Space Administration (NASA). On board of the Landsat 8 is the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). Data for visible, panchromatic band, short-wave infrared spectral bands are collected by the OLI while TIRS collect images in the thermal region. As data for Landsat 8 is available to be used for public, researchers have utilized the data for numerous applications. However, to the best of our knowledge, there is yet a review paper on the various applications of Landsat 8 data. Hence, this paper presented an innovative survey on Landsat 8 data in the application of agriculture and forestry, land use and mapping, geology, hydrology, coastal resources and environmental monitoring. The potential of utilizing Landsat 8 data for power utility companies is also discussed in this paper. As Landsat 8 data is predicted to be available for more years to come, this paper provides insight for researchers to utilize the data better for their research.

State of the Art of Monitoring Technologies and Data Processing for Precision Viticulture

“…The last launched Landsat mission satellite is the Landsat 8 [34,35], which operates in the visible, near-infrared, short wave infrared and thermal infrared spectrums.…”

The fate of rangelands: Revealing past and predicting future land‐cover transitions from 1985 to 2036 in the drylands of Central Iran

“…Therefore, satellite remote sensing is an excellent source of information to study LULCC at local and regional scales because satellite images, from on‐board of LANDSAT satellites for instance, can cover a large extent and have a higher temporal coverage compared to field‐based observations and aerial surveys (Hansen & Loveland, 2012; Loveland & Dwyer, 2012). LANDSAT images from the freely accessible United States Geological Survey (USGS) archive are used widely to evaluate the quantitative parameters of land, especially LULCC, due to long historical repetitive coverage, the spatial resolution of LANDSAT images that match LULCC phenomena, and relevant radiometric resolution suited to map vegetation change, including drylands and rangelands (Eisavi et al, 2015; Ridwan et al, 2018; Roy et al, (2014)). Moreover, the change of paradigm about free accessibility of LANDSAT records made this data relevant for the scientific community to monitor LULCC (Wulder, Masek, Cohen, Loveland, & Woodcock, 2012).…”

Improving the efficiency of using deep learning model to determine shoreline position in high-resolution satellite imagery

“…Google Earth data has the advantage of ultra-high spatial resolution, but only 3 visible image channels Red, Green and Blue were provided. Remote sensing satellite systems such as Landsat 8, Sentinel 2 often provide data with more image channels, corresponding to more acquired spectral bands, which allows many objects in an image to be identified by the reflection spectrum of that object [12]. Previous studies have not taken advantage of multichannel remote sensing satellite image.…”