I am trying to show change in land cover using landsat mss tm etm+ and high resolution images. I know that it is a must to perform atmospheric correction in multi temporal data to classify and detect change. Is it possible to perform atmospheric correction of landsat tm etm+ and high resolution images. If it is possible, then how?
Perhaps these will point you in the right direction.
http://www.gisagmaps.com/landsat-8-atco-guide/
http://www.youtube.com/watch?v=EyWjHli8OBs
Article Atmospheric correction of Landsat ETM+ land surface imagery....
Hello Emranul, Please go through these points may be helpful to you i think so,
Atmospheric correction is of great importance in quantitative remote sensing studies. However, many of the atmospheric correction algorithms proposed in the literature are not easily applicable in real cases. In order to develop a practical atmospheric correction algorithm, Moderate Resolution Imaging Spectroradiometer (MODIS) imagery is employed to obtain aerosol optical depth and the total atmospheric water vapour content, which are used to compute the transmittances in a dark object subtraction (DOS) model.
Aerosol Optical Thickness (AOT) data has many important applications including atmospheric correction of satellite imagery and monitoring of particulate matter air pollution. Current data products are generally available at a kilometre-scale resolution, but many applications require far higher resolutions. For example, particulate matter concentrations vary on a metre-scale, and thus data products at a similar scale are required to provide accurate assessments of particle densities and allow effective monitoring of air quality and analysis of local air quality effects on health.
Similarly, atmospheric correction of satellite images requires a knowledge of the AOT over the image. Many atmospheric correction techniques assume a constant AOT across the whole image, but this is rarely (if ever) the case. Use of my new method should allow the AOT of high-resolution images (such as Landsat) to be retrieved for every pixel in the image, and thus used in a spatially-variable correction.
Methods will retrieve per-pixel AOT values from high-resolution (~30m) satellite data. The method will be designed to work over a wide range of land covers – including both bright and dark surfaces - and requires only standard visible and near-infrared data, making it applicable to a range of data from sensors such as Landsat, DMC, SPOT and Pleiades. The method will be based upon an extension of the Haze Optimized Transform (HOT). The HOT was originally designed for assessing areas of thick haze in satellite imagery by calculating a ‘haziness’ value for each pixel in an image as the distance from a ‘Clear Line’ in feature space, defined by the high correlation between visible bands. Here, one will adapt the HOT method and use it to provide AOT data instead. Significant extensions include Monte Carlo estimation of the ‘Clear Line’, object-based correction for land cover, and estimation of the AOT values through Radiative Transfer Modelling.
This method will enable many new applications of AOT data that were impossible with previously available low-resolution data, and has the potential to contribute significantly to our understanding of the air quality on health, the accuracy of satellite image atmospheric correction and the role of aerosols in the climate system.
Yes, you must perform the atmospheric correction when comparing results of different satellite or sensors. ENVI has MODTRAN implemented (FLAASH) and GRASS has the 6S.
Recently, a simple and good plugin was released for QGIS that performs the DOS atmospheric correction. Here you can find more information about this plugin:
http://fromgistors.blogspot.com/p/semi-automatic-classification-plugin.html
- Badges
- Science topic
- Similar topics
- Engineering
- Electrical Engineering
- Signal Processing
- Image Processing