{"title":"A Time Series based Study of MODIS NDVI for Vegetation Cover","authors":"H. Srivastava, T. Pant","doi":"10.1109/InGARSS48198.2020.9358952","DOIUrl":null,"url":null,"abstract":"In this paper, the vegetation cover of Prayagraj, Uttar Pradesh has been studied with the time series data. For the study, MODIS NDVI 250m time series data have been used. For the classification, a pixel based SVM classifier is applied on 20 images of the data set. The classified images are used pairwise as pre and post harvesting outputs to generate change detection map, and to calculate the percentage vegetation cover of the study area. Further, a data set containing 158 samples with ARIMA time series model has been tested. The high vegetation class for the testing samples is predicted with mean squared error of 0.00604.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"79 1","pages":"21-24"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/InGARSS48198.2020.9358952","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, the vegetation cover of Prayagraj, Uttar Pradesh has been studied with the time series data. For the study, MODIS NDVI 250m time series data have been used. For the classification, a pixel based SVM classifier is applied on 20 images of the data set. The classified images are used pairwise as pre and post harvesting outputs to generate change detection map, and to calculate the percentage vegetation cover of the study area. Further, a data set containing 158 samples with ARIMA time series model has been tested. The high vegetation class for the testing samples is predicted with mean squared error of 0.00604.
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