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• W2117767148 abstract "The information about the location and area of vegetation fields has great impact on emerging Remote sensing applications. Remote sensing techniques have been developed to allow researchers to accurately clas- sify large vegetation area at reduced cost. In this paper the ultimate intention is to develop an operational method for assessing vegetation area that would facilitate developing remote sensing based algorithms for assessing vegetation fields and land surfaces in major areas. The Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite offers a good potential for as- sessing vegetation area. Here MOD13A3 of the MODIS/Terra Vegetation Indices is used. By applying segmentation techniques vegeta- tion area is differentiated from non crop area. It provides opportunity to retrieve parameters that can be used to assess fields whether the acquired field is vegetation area (crop) or land surface. This significant improvement can help researchers to create superior crop type classification maps and therefore have the opportunity to make better informed decisions. General Terms Segmentation, Classification, Training, Testing. This paper deals with four themes: The Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra satel- lite offers a good potential for assessing vegetation area as well as provide opportunity to retrieve parameters that can be used to assess vegetation yields. 1. It describes the use of MODIS Terra data at 500 m resolution for classification of vegetation area in cuddalore district of Ta- mil Nadu. 2. Imfilter is used for pixel correction. Since the majority of the area in the provinces were natural vegetation (trees, shrubs, rangeland) it was important to develop a vegetation classifica- tion to monitor the vegetation condition during the growing season. 3. The normalized difference vegetation index was tracked through the growing season to assess the changes in vegetation area that is an indication of potential yields for the current sea- son in comparison with previous years. 4. K-means clustering is applied to segment vegetation and land surface area as it is a partitioning method. The function k-means partitions data into k mutually exclusive clusters, and returns the index of the cluster to which it has assigned each observa- tion. The iterative algorithm minimizes the sum of distances from each object to its cluster centroid, over all clusters. This algo- rithm moves objects between clusters until the sum cannot be decreased further. The result is a set of clusters." @default.
• W2117767148 created "2016-06-24" @default.
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• W2117767148 date "2012-06-01" @default.
• W2117767148 modified "2023-10-16" @default.
• W2117767148 title "Vegetation Area Classification Using Modis Imagery" @default.
• W2117767148 cites W1996379121 @default.
• W2117767148 doi "https://doi.org/10.15373/22778179/apr2014/60" @default.
• W2117767148 hasPublicationYear "2012" @default.
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