USGS stands for United States Geological Survey, Originally, the US Geological Survey was committed to discovering the geological and mineral resources of Western lands, but it has grown over its 139-year history to greatly extend our understanding of natural science.
Most mapping in the United States was performed by military expeditions and several independent government surveys before USGS was established. Upon its formation, the USGS adopted a more systematic survey method and sought to identify public lands—examining their geographical composition, natural resources and goods. This scientific evaluation of land capacity and mineral resources altered the method of government surveying and promoted growth, industrial expansion and more productive nationwide production.
By supplying accurate scientific evidence to explain and understand the World, USGS serves the government. The scientific work of the USGS aims to mitigate the loss of life and property from natural disasters, and leads to water, ecological, oil and mineral resource assessment and analysis. The USGS focuses its work on its main mission areas, such as wildlife, energy and minerals, public protection, natural disasters and climate.
Earth Explorer is an online tool that offers access to earth science data from the USGS archives as an interface. Earth Explorer provides internet scanning, browsing, uploading metadata, and accessing files from the USGS archives for earth science data. The Earth explorer is essentially your window into the world; the largest group of civilians on the surface of the Earth. This includes Earth photos from LANDSAT 8, high-resolution orthoimagery, national topographic and bathymetric databases, cover archive of Landsat-derived National Land and natural color images from the mild imaging spectroradiometer on the terra satellite of NASA.
We gather collections of data for various wavelengths as we investigate ground cover using the instruments onboard Landsat satellites. Some are red, green and blue visible lights corresponding to those in infrared. To create numerous representations of the earth’s surface, we merge either of the three bands.
|1.560-1.660||Shortwave Infrared 1|
|2.100-2.300||Shortwave Infrared 2|
Steps to Download LANDSAT 8 Satellite Image from USGS Earth Explorer
- Open your browser and go to the URL: https://earthexplorer.usgs.gov/
- That will bring you to an interactive web explorer type interface where you can pan and zoom to the different part of the world.
- Go to Login Option and Create a new account it is free of cost.
- After setting up your account you will be navigated to the same homepage again.
- On the left side, click on the option “Select a Geocoding Method” and select the “Address/Places” option.
- In the search bar “Address/Place” search for your area of interest.
- Click show.
- Click on the option of your area of interest.
- You are now navigated to your area of interest and you can zoom in and out on the map accordingly.
- You can specify the date you are searching the data for.
- Now click on Data Sets button.
- An option bar of various types of data sets appears.
- Expand the LANDSAT option.
- In the LANDSAT menu expand Landsat Collection 2 Level-2, and further check the box against LANDSAT 8 OLI/TIRS C2 L2.
- Click on Results button.
- Various search results appear for your search criteria, select the best suited option.
- Choose for the dataset which has least distortions caused due to the cloud cover.
- Click on the download button to download the desired dataset.
- A product download window option appears.
- Click on the Landsat Collection 2 Level-2 Product Bundle option.
- It will download a compressed .tar.gz file further you have to uncompress and extract all the files using any extraction tool.
Different Spectral Indices calculated using the bands of Landsat Imagery
Spectral indices are spectral reflectance combinations of two or more wavelengths that signify the relative abundance of interesting characteristics. Vegetation indices are the most common type, but for burnt areas, man-made (built-up) characteristics, water, and geological features, other indices are available.
- Simple Ratio: The simple ratio provides useful vegetation biomass or LAI results. It is highly vulnerable to differences in biomass and/or LAI in high-biomass plants, such as forests.
- Normalized Difference Vegetation Index (NDVI): Functionally, the NDVI is similar to the simple ratio; that is, in an SR vs. ND VI plot there is no scatter, and each SR value has a fixed ND VI value. We find that the NDVI approximates a nonlinear transformation of the simple ratio when we plot the mean NDVI and SR values for different biomes.
Seasonal and inter-annual changes in vegetation growth and activity can be monitored.
- Normalized Difference Moisture Index (NDMI): Also known as Normalized Difference Water Index (NDWI). In agriculture, forestry, and hydrology, researchers found that the NDMI or NDWI) dependent on Landsat TM near- and middle-infrared bands are strongly associated with the quality of canopy water and more closely monitored changes in plant biomass and water stress than the NDVI.
- Perpendicular Vegetation Index (PVI): The development of the perpendicular distance to the “soil line” as an indicator of plant development. The “soil line,” which is a two-dimensional analog of the Kauth-Thomas soil brightness index, was estimated by linear regression. The Perpendicular Vegetation Index (PVI) based on MSS band 4 data was:
Where a & b are slope and intercepts of soil line respectively.
- Soil Adjusted Vegetation Index (SAVI): Emphasis has been given to the development of improved vegetation indices that take advantage of calibrated sensor systems such as the MODIS. Although the NDVI has been shown to be useful in estimating vegetation properties, many important external and internal influences restrict its global utility. The improved indices typically incorporate a soil background and/or atmospheric adjustment factor. Hence SAVI is used for more specific operations.
Where L is the canopy background adjustment factor that accounts for differential red and near infrared extinction through the canopy. The value of L depends on the proportional vegetation cover as well as the vegetation density For very sparse vegetation or bare soils, L approximates one, whereas L converges to zero in densely vegetated areas (in this case the SAVI and NDVI become equal.
- Atmospherically Resistant Vegetation Index (ARVI): SAVI was made much less sensitive to atmospheric effects by normalizing the radiance in the blue, red, and near-infrared bands. This became the Atmospherically Resistant Vegetation Index (ARVI).
ARVI uses the difference in the radiance between the blue channel and the red channel to correct the radiance in the red channel and thus reduce atmospheric effects. Unless the aerosol model is known a priori, gamma (r) is normally equal to 1.0 to minimize atmospheric effects.
- Soil and Atmospherically Resistant Vegetation Index (SARVI): Huete and Liu (1994) integrated the L function from SAVI and the blue-band normalization in ARVI to derive a Soil and Atmospherically Resistant Vegetation Index (SARVI) that corrects for both soil and atmospheric noise, as would a modified SARVI (MSARVI):
If there were a total atmospheric correction, then there would mainly be “Soil noise,” and the SAVI and MSARVI would be the best equations to use, and the ND VI and ARVI would be the worst.
- Triangular Vegetation Index (TVI): The TVI index is calculated as the area of the triangle defined by the green peak, the chlorophyll absorption minimum, and the near-infrared shoulder in spectral space. It is based on the fact that both chlorophyll absorption causing a decrease of red reflectance and leaf tissue abundance causing increased near-infrared reflectance will increase the total area of the triangle. The TVI index encompasses the area spanned by the triangle ABC with the coordinates given in spectral space:
- Reduced Simple Ratio (RSR): Chen et al. (2002) modified the simple ratio algorithm to include information from the short-wavelength infrared (SW IR) band found in the SPOT VEG ETATION sensor. They used the sensor to map the spatial distribution of leaf-area-index in Canada. The Reduced Simple Ratio (RSR) is:
Image Credit: storage.googleapis.com