{"title":"安大略省南部多时相MODIS和TM影像季节变化检测的比较","authors":"Dongmei Chen, Jamie Fitzgibbon","doi":"10.1109/EORSA.2008.4620292","DOIUrl":null,"url":null,"abstract":"In this paper a change detection study was conducted using multi-temporal images from two commonly used sensors, MODIS and TM, between June and October, 2003 over Southern Ontario, Canada to evaluate the sensitivity of MODIS images for seasonal land cover changes. Post-classification change detection was used to determine the type of change that had occurred and allow for from-to types of changes to be evaluated. NDVI image differencing was also performed on the MODIS and TM images to compare the vegetation index changes at different spatial resolutions. It was found that MODIS classifications approximated those produced with TM data only when incorporating the thermal band in the classification procedure which takes advantage of the urban heat island effect. Results demonstrate that MODIS post-classification change detection can approximate the levels of change/no-change compared to TM post-classification however the type of change was not accurate due to the spectral mixing that occurs at the coarser 250 meter spatial resolution of MODIS data. The more change at TM level for a MODIS pixel, the higher the likelihood of this corresponding to change at the MODIS level. This study demonstrates that MODIS data would be best suited for detecting changes in large agricultural areas with large field size of homogeneous crop type and growth stage or large areas of forest stands with similar characteristics.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Comparison of seasonal change detection from multi-temporal MODIS and TM images in Southern Ontario\",\"authors\":\"Dongmei Chen, Jamie Fitzgibbon\",\"doi\":\"10.1109/EORSA.2008.4620292\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper a change detection study was conducted using multi-temporal images from two commonly used sensors, MODIS and TM, between June and October, 2003 over Southern Ontario, Canada to evaluate the sensitivity of MODIS images for seasonal land cover changes. Post-classification change detection was used to determine the type of change that had occurred and allow for from-to types of changes to be evaluated. NDVI image differencing was also performed on the MODIS and TM images to compare the vegetation index changes at different spatial resolutions. It was found that MODIS classifications approximated those produced with TM data only when incorporating the thermal band in the classification procedure which takes advantage of the urban heat island effect. Results demonstrate that MODIS post-classification change detection can approximate the levels of change/no-change compared to TM post-classification however the type of change was not accurate due to the spectral mixing that occurs at the coarser 250 meter spatial resolution of MODIS data. The more change at TM level for a MODIS pixel, the higher the likelihood of this corresponding to change at the MODIS level. This study demonstrates that MODIS data would be best suited for detecting changes in large agricultural areas with large field size of homogeneous crop type and growth stage or large areas of forest stands with similar characteristics.\",\"PeriodicalId\":142612,\"journal\":{\"name\":\"2008 International Workshop on Earth Observation and Remote Sensing Applications\",\"volume\":\"56 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 International Workshop on Earth Observation and Remote Sensing Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EORSA.2008.4620292\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Workshop on Earth Observation and Remote Sensing Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EORSA.2008.4620292","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparison of seasonal change detection from multi-temporal MODIS and TM images in Southern Ontario
In this paper a change detection study was conducted using multi-temporal images from two commonly used sensors, MODIS and TM, between June and October, 2003 over Southern Ontario, Canada to evaluate the sensitivity of MODIS images for seasonal land cover changes. Post-classification change detection was used to determine the type of change that had occurred and allow for from-to types of changes to be evaluated. NDVI image differencing was also performed on the MODIS and TM images to compare the vegetation index changes at different spatial resolutions. It was found that MODIS classifications approximated those produced with TM data only when incorporating the thermal band in the classification procedure which takes advantage of the urban heat island effect. Results demonstrate that MODIS post-classification change detection can approximate the levels of change/no-change compared to TM post-classification however the type of change was not accurate due to the spectral mixing that occurs at the coarser 250 meter spatial resolution of MODIS data. The more change at TM level for a MODIS pixel, the higher the likelihood of this corresponding to change at the MODIS level. This study demonstrates that MODIS data would be best suited for detecting changes in large agricultural areas with large field size of homogeneous crop type and growth stage or large areas of forest stands with similar characteristics.