by Megan Kanaga Creutzburg

None Known

One common application of remotely-sensed images to rangeland management is the creation of maps of land cover, vegetation type, or other discrete classes by remote sensing software. In supervised classification, the image processing software is guided by the user to specify the land cover classes of interest. The user defines “training sites” – areas in the map that are known to be representative of a particular land cover type – for each land cover type of interest. The software determines the spectral signature of the pixels within each training area, and uses this information to define the mean and variance of the classes in relation to all of the input bands or layers. Each pixel in the image is then assigned, based on its spectral signature, to the class it most closely matches. It is important to choose training areas that cover the full range of variability within each land cover type to allow the software to accurately classify the rest of the image. Some of the more common classification algorithms used for supervised classification include the Minimum-Distance to the Mean Classifier, Parallelepiped Classifier, and Gaussian Maximum Likelihood Classifier.

Supervised classification can be very effective and accurate in classifying satellite images and can be applied at the individual pixel level or to image objects (groups of adjacent, similar pixels). However, for the process to work effectively, the person processing the image needs to have a priori knowledge (field data, aerial photographs, or other knowledge) of where the classes of interest (e.g., land cover types) are located, or be able to identify them directly from the imagery. This method is often used with unsupervised classification in a process called hybrid classification. Unsupervised classification can be used first to determine the spectral class composition of the image and to see how well the intended land cover classes can be defined from the image. After this initial step, supervised classification can be used to classify the image into the land cover types of interest.

• Unsupervised Classification
• Hybrid or combined classification (combination of both supervised and unsupervised classification methods)
• Object-based Classification

Supervised classification methods are used to generate a map with each pixel assigned to a class based on its multispectral composition. The classes are determined based on the spectral composition of training areas defined by the user.

Satellite images can be classified based on many distinguishable cover types that are specified by the user, including:

• Land cover classes
• Major vegetation types
• Distinguishing native vs invasive species cover
• Vegetation condition
• Disturbed areas (eg. fire)
• Land use change

• Alrababah, M.A., and M.N. Alhamad. 2006. Land use/cover classification of arid and semi-arid Mediterranean landscapes using Landsat ETM. International Journal of Remote Sensing 27: 2703–2718 - used unsupervised and supervised classification methods to map land use, and showed that supervised classification improved map accuracy
• Eve, M.D., W.G. Whitford, and K.M. Havstad. 1999. Applying satellite imagery to triage assessment of ecosystem health. Environmental Monitoring and Assessment 54: 205–227 – used supervised classification to map irreversibly degraded rangelands
• Hudak, A.T., and B.H.Brockett. 2004. Mapping fire scars in a southern African savannah using Landsat imagery. International Journal of Remote Sensing 25: 3231–3243 – used supervised classification to map fire burn severity
• Lauver, C.L. 1997. Mapping species diversity patterns in the Kansas shortgrass region by integrating remote sensing and vegetation analysis. Journal of Vegetation Science 8: 387-394 – used supervised classification to differentiate high and low quality grasslands
• Yüksel, A., A.E. Akay, and R. Gundogan. 2008. Using ASTER Imagery in Land Use/cover Classification of Eastern Mediterranean Landscapes According to CORINE Land Cover Project. Sensors 8: 1237-1251 – used supervised classification to map major land use types

• Cingolani, A.M., D. Renison, M.R. Zak, and M.R. Cabido. 2004. Mapping vegetation in a heterogeneous mountain rangeland using landsat data: an alternative method to define and classify land-cover units. Remote Sensing of Environment 92: 84-97.
• Congalton, R.G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment 37:35-46.
• Geerken, R., B. Zaitchik, and J.P. Evans. 2005. Classifying rangeland vegetation type and coverage from NDVI time series using Fourier Filtered Cycle Similarity. International Journal of Remote Sensing 26: 5535-5554.
• Ghorbani, A., D. Bruce, and F. Tiver. 2006. Specification: A problem in rangeland monitoring. In: Proceedings of the 1st International Conference on Object-based Image Analysis (OBIA), 4th-5th July 2006, Salzburg, Austria.
• Karl, J. W., and B. A. Maurer. 2009. Multivariate correlations between imagery and field measurements across scales: comparing pixel aggregation and image segmentation. Landscape Ecology. DOI: 10.1007/s10980-009-9439-4

Supervised classification can be much more accurate than unsupervised classification, but depends heavily on the prior knowledge,skill of the individual processing the image, and distinctness of the classes. If the designated training sites are not representative of the range of variability found within a particular land cover type, the classification may be much less accurate. Likewise, if two or more classes are very similar to each other in terms of their spectral reflectance (e.g., annual-dominated grasslands vs. perennial grasslands), misclassifications will be high. A combination of supervised and unsupervised classification (hybrid classification) is often employed; this allows the remote sensing program to classify the image based on the user-specified land cover classes, but will also classify other less common or lesser known cover types into separate groups. Supervised and unsupervised classification are both pixel-based classification methods, and may be less accurate than object-based classification (Ghorbani et al. 2006, Karl and Maurer 2009).

Unsupervised classification can be performed with any number of different remote-sensing or GIS-derived inputs. Commonly, spectral bands from satellite or airborne sensors, band ratios or vegetation indices (e.g., NDVI), and topographic data (e.g., elevation, slope, aspect) are used as inputs for unsupervised classification.

Unsupervised classification is relatively easy to perform in any remote sensing software (e.g., Erdas Imaging, ENVI, Idrisi), and even in many GIS programs (e.g., ArcGIS with Spatial Analyst or Image Analysis extensions, GRASS).

This image shows the use of training sites, shown as colored polygons, to inform the remote sensing software of major land cover and vegetation classes in the image for a supervised classification (image source: Short, N. 2009. The Remote Sensing Tutorial, Section 1).

• Emporia State University page on explaining and comparing unsupervised and supervised classification. Accessible athttp://www.ida.liu.se/~746A27/Literature/Supervised%20and%20Unsupervised%20Land%20Use%20Classification.pdf for a pdf.
• Chulalongkorn University page on supervised and unsupervised classification. http://www.sc.chula.ac.th/courseware/2309507/Lecture/remote18.htm

Many of the current land cover maps that are routinely used in rangeland management were developed using supervised classification techniques. Some examples include:

• The National Land Cover Dataset - available at http://www.mrlc.gov/ or http://seamless.usgs.gov
• The ShrubMap land cover product developed as part of the SageMap project - available at http://sagemap.wr.usgs.gov/Shrubmap.aspx
• Many of the land cover maps developed for the Gap Analysis project - http://gapanalysis.usgs.gov/

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