C. Toté, Katia Beringhs, E. Swinnen, Gerard Govers
{"title":"Monitoring environmental change in the Andes based on SPOT-VGT and NOAA-AVHRR time series analysis","authors":"C. Toté, Katia Beringhs, E. Swinnen, Gerard Govers","doi":"10.1109/MULTI-TEMP.2011.6005100","DOIUrl":null,"url":null,"abstract":"Environmental change is an important issue in the Andes region. The objectives of this research are to study NDVI dynamics in the Andes region based on time series analysis of SPOT-Vegetation and NOAA-AVHRR, and to recognize to which extent this variability can be attributed to either climatic variability or human induced impacts. Correlation analysis between NDVI and SPI were performed in order to identify the best lag per pixel. Trends in SDVI and SPI were investigated using linear least square regression. Significant vegetation trends are found in 46% of the area. Both NDVI time series lead to different results, but the coupling of vegetation and precipitation is more pronounced for the SPOT-Vegetation data.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MULTI-TEMP.2011.6005100","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Environmental change is an important issue in the Andes region. The objectives of this research are to study NDVI dynamics in the Andes region based on time series analysis of SPOT-Vegetation and NOAA-AVHRR, and to recognize to which extent this variability can be attributed to either climatic variability or human induced impacts. Correlation analysis between NDVI and SPI were performed in order to identify the best lag per pixel. Trends in SDVI and SPI were investigated using linear least square regression. Significant vegetation trends are found in 46% of the area. Both NDVI time series lead to different results, but the coupling of vegetation and precipitation is more pronounced for the SPOT-Vegetation data.