Pub Date : 2008-09-05DOI: 10.1109/EORSA.2008.4620304
W. Gong, Jixian Zhang, Yonghong Zhang
Permanent scatterer interferometry is one of the latest developments in radar interferometric processing. It is achieved by the analysis of the interferometric phase of the individual point targets that are discrete and temporarily stable natural reflectors or permanent scatterers in long temporal series of interferometric SAR images with one master image. The wrapped phase of a point in differential interferogram can be decomposed to uncompensated topography, target motion in the time between the acquisitions, object scattering phase related to the path length traveled in the resolution cell, the atmospheric phase accounting for signal delays, the phase caused by imprecise orbit data and additive noise term. Based on this principle, it could bypass the problem of geometrical and temporal decorrelation. Furthermore, by using a large amount of data, atmospheric signal is estimated and corrected for. This paper addresses the how we use the PS-InSAR technology and Differential Interferogram procedure to estimate the velocity of deformation of Suzhou region in the time span 1992-2002. The main processing is done with the GAMMA software.
{"title":"Detecting ground deformation with Permanent scatterer of Suzhou region","authors":"W. Gong, Jixian Zhang, Yonghong Zhang","doi":"10.1109/EORSA.2008.4620304","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620304","url":null,"abstract":"Permanent scatterer interferometry is one of the latest developments in radar interferometric processing. It is achieved by the analysis of the interferometric phase of the individual point targets that are discrete and temporarily stable natural reflectors or permanent scatterers in long temporal series of interferometric SAR images with one master image. The wrapped phase of a point in differential interferogram can be decomposed to uncompensated topography, target motion in the time between the acquisitions, object scattering phase related to the path length traveled in the resolution cell, the atmospheric phase accounting for signal delays, the phase caused by imprecise orbit data and additive noise term. Based on this principle, it could bypass the problem of geometrical and temporal decorrelation. Furthermore, by using a large amount of data, atmospheric signal is estimated and corrected for. This paper addresses the how we use the PS-InSAR technology and Differential Interferogram procedure to estimate the velocity of deformation of Suzhou region in the time span 1992-2002. The main processing is done with the GAMMA software.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130497274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-09-05DOI: 10.1109/EORSA.2008.4620324
Y. Shi, R. Shibasaki, Z. Shi
Nowadays, an integration of real-time sensing and map reference from vehicles would be very effective to achieve a complete Cruise-Assist system such as what can make a driver avoid a traffic accident. However, existing digital road maps such as network data or simple 3D data for vehicle navigation cannot support Cruise-Assist well, in particular, in an urban area. In fact, various road features such as zebra, road lane mark, and boundary are required for accurate map reference, and more, those features should have higher precision and more detail information. But, so far it is labor-intensive and costly for acquiring this kind of road spatial data, therefore, an efficient method, which can acquire that kind of spatial data automatically and robustly, is required. This research focuses on efficient acquisition of road lane mark and carries out a method for extracting them by fusing vehicle-based stereo image and laser range data. A lot of experiments were performed to certify and check the efficiency of our fusion-based automatic road lane mark extraction method. From achieved results of these experiments, our fusion-based automatic extraction of road lane mark can get high success ratio (more than 90%).
{"title":"An efficient method for extracting road lane mark by fusing vehicle-based stereo image and laser range data","authors":"Y. Shi, R. Shibasaki, Z. Shi","doi":"10.1109/EORSA.2008.4620324","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620324","url":null,"abstract":"Nowadays, an integration of real-time sensing and map reference from vehicles would be very effective to achieve a complete Cruise-Assist system such as what can make a driver avoid a traffic accident. However, existing digital road maps such as network data or simple 3D data for vehicle navigation cannot support Cruise-Assist well, in particular, in an urban area. In fact, various road features such as zebra, road lane mark, and boundary are required for accurate map reference, and more, those features should have higher precision and more detail information. But, so far it is labor-intensive and costly for acquiring this kind of road spatial data, therefore, an efficient method, which can acquire that kind of spatial data automatically and robustly, is required. This research focuses on efficient acquisition of road lane mark and carries out a method for extracting them by fusing vehicle-based stereo image and laser range data. A lot of experiments were performed to certify and check the efficiency of our fusion-based automatic road lane mark extraction method. From achieved results of these experiments, our fusion-based automatic extraction of road lane mark can get high success ratio (more than 90%).","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133691040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-09-05DOI: 10.1109/EORSA.2008.4620294
Cui Lin-li, Shi Jun, Tang Ping, Huaqiang Du
In recent years the land-use/cover change is one of the important points of the global climate change. The remote sensing techniques provide strong support to the study on the wide land-use/cover change. Because of the common existing of same object having different spectral character and different object having same spectral character, the classification accuracy of traditional pixel-based has not yet satisfied the need of the monitor of the land-use/cover change. The newly object-oriented method opens a new path for the remote sensing classification. The biggest contribution is that the new method makes the theory of the abstraction of the remote sensing characteristics be perfect. Originally, it is difficult to extract the relationship of shape, location and space, now the object-oriented method makes it be possible in the condition with remote sensing data of higher spatial resolution. In this paper, these two methods ware carried out for TM data based on spectral, texture, and shape features, and the classification accuracy was compared and analyzed with that of man-expert visual interpretation. The results show that (1) TM data are also fit to the method of object-oriented classification. (2) The accuracy of object-oriented method is higher than that of pixel-based method, and the classification result has less pepper-and-salt noise, omitting trivial classification past-processing. (3) The optimal texture features group by the two methods is very similar in the smaller calculation window. (4) The classification effects with shape feature in TM data source are not outstanding.
{"title":"Comparison study on the pixel-based and object-oriented methods of land-use/cover classification with TM data","authors":"Cui Lin-li, Shi Jun, Tang Ping, Huaqiang Du","doi":"10.1109/EORSA.2008.4620294","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620294","url":null,"abstract":"In recent years the land-use/cover change is one of the important points of the global climate change. The remote sensing techniques provide strong support to the study on the wide land-use/cover change. Because of the common existing of same object having different spectral character and different object having same spectral character, the classification accuracy of traditional pixel-based has not yet satisfied the need of the monitor of the land-use/cover change. The newly object-oriented method opens a new path for the remote sensing classification. The biggest contribution is that the new method makes the theory of the abstraction of the remote sensing characteristics be perfect. Originally, it is difficult to extract the relationship of shape, location and space, now the object-oriented method makes it be possible in the condition with remote sensing data of higher spatial resolution. In this paper, these two methods ware carried out for TM data based on spectral, texture, and shape features, and the classification accuracy was compared and analyzed with that of man-expert visual interpretation. The results show that (1) TM data are also fit to the method of object-oriented classification. (2) The accuracy of object-oriented method is higher than that of pixel-based method, and the classification result has less pepper-and-salt noise, omitting trivial classification past-processing. (3) The optimal texture features group by the two methods is very similar in the smaller calculation window. (4) The classification effects with shape feature in TM data source are not outstanding.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133828005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-09-05DOI: 10.1109/EORSA.2008.4620307
R. S. Hooda, M. Yadav, Mithun Sharma, R. Prawasi
Multi-date LISS-III data from Resourcesat for the period April to June, 2006 and single date LISS-IV sensor data of mid May, wherever available, was used for area estimation of the crop for the year 2006 for four major districts of Haryana state where majority of summer paddy is grown. For area estimation for the year 2000, only multi-date LISS-III data was used. Hybrid approach of supervised and unsupervised classification was used for discrimination of minor crops in LISS-IV data. Multi-date LISS-III data based crop profile helped in precise location of training sites for LISS-IV data. To improve accuracy the non- agricultural areas like settlements, wastelands, forest etc. were masked out by using non-agricultural mask. Study indicated that an area of 39,800 ha was under summer paddy in the year 2000 which has been reduced to 1250 ha in the year 2006 in all the four districts. The district wise area for Fatehabad, Kaithal, Kurukshetra and Karnal was 4981, 3228, 9118 and 22475 ha for the year 2000 and 383, 197, 176 and 493 ha for the year 2006, respectively. District-wise crop maps of summer paddy, showing extent & distribution of the crop, were also prepared in ARC/GIS.
{"title":"Estimation of summer paddy in Haryana (India) using high resolution satellite data","authors":"R. S. Hooda, M. Yadav, Mithun Sharma, R. Prawasi","doi":"10.1109/EORSA.2008.4620307","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620307","url":null,"abstract":"Multi-date LISS-III data from Resourcesat for the period April to June, 2006 and single date LISS-IV sensor data of mid May, wherever available, was used for area estimation of the crop for the year 2006 for four major districts of Haryana state where majority of summer paddy is grown. For area estimation for the year 2000, only multi-date LISS-III data was used. Hybrid approach of supervised and unsupervised classification was used for discrimination of minor crops in LISS-IV data. Multi-date LISS-III data based crop profile helped in precise location of training sites for LISS-IV data. To improve accuracy the non- agricultural areas like settlements, wastelands, forest etc. were masked out by using non-agricultural mask. Study indicated that an area of 39,800 ha was under summer paddy in the year 2000 which has been reduced to 1250 ha in the year 2006 in all the four districts. The district wise area for Fatehabad, Kaithal, Kurukshetra and Karnal was 4981, 3228, 9118 and 22475 ha for the year 2000 and 383, 197, 176 and 493 ha for the year 2006, respectively. District-wise crop maps of summer paddy, showing extent & distribution of the crop, were also prepared in ARC/GIS.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121837088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-09-05DOI: 10.1109/EORSA.2008.4620341
Jinguo Yuan, Z. Niu
This paper evaluated the capability of FLAASH in ENVI software to make atmospheric correction for Hyperion hyperspectral image and ALI image. Hyperion and ALI sensors are two of the three instruments onboard NASA EO-1 satellite, New Millennium Program (NMP) launched on November 21, 2000, and Hyperion is the first spaceborne hyperspectral imaging spectrometer. The study area is Zhangye city (37deg28'N-39deg57'N, 97deg20'E-102deg12'E) in Heihe River valley of Gansu province, China. Using TM data with UTM projection, Hyperion hyperspectral data acquired on September 10, 2007 and ALI data on September 20, 2007 were geometrically and radiometrically corrected, and then atmospherically corrected using FLAASH. Surface reflectance spectra of corn, water body, desert and buildings were extracted from these two images and made comparison with apparent reflectance. Canopy reflectance spectra of corn were recorded using ASD Fieldspec spectroradiometer in near-real time to coincide with EO-1 satellite sensor overpass. According to filter function of ALI and central wavelength and Gaussian filter function based on full width at half maximum (FWHM) of Hyperion, the ASD reflectance spectra were resampled to corresponding Hyperion and ALI bands. Results showed that resampled ASD spectra of corn were consistent with spectra on Hyperion and ALI images after FLAASH. This demonstrated the effectiveness of atmospheric correction using FLAASH.
{"title":"Evaluation of atmospheric correction using FLAASH","authors":"Jinguo Yuan, Z. Niu","doi":"10.1109/EORSA.2008.4620341","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620341","url":null,"abstract":"This paper evaluated the capability of FLAASH in ENVI software to make atmospheric correction for Hyperion hyperspectral image and ALI image. Hyperion and ALI sensors are two of the three instruments onboard NASA EO-1 satellite, New Millennium Program (NMP) launched on November 21, 2000, and Hyperion is the first spaceborne hyperspectral imaging spectrometer. The study area is Zhangye city (37deg28'N-39deg57'N, 97deg20'E-102deg12'E) in Heihe River valley of Gansu province, China. Using TM data with UTM projection, Hyperion hyperspectral data acquired on September 10, 2007 and ALI data on September 20, 2007 were geometrically and radiometrically corrected, and then atmospherically corrected using FLAASH. Surface reflectance spectra of corn, water body, desert and buildings were extracted from these two images and made comparison with apparent reflectance. Canopy reflectance spectra of corn were recorded using ASD Fieldspec spectroradiometer in near-real time to coincide with EO-1 satellite sensor overpass. According to filter function of ALI and central wavelength and Gaussian filter function based on full width at half maximum (FWHM) of Hyperion, the ASD reflectance spectra were resampled to corresponding Hyperion and ALI bands. Results showed that resampled ASD spectra of corn were consistent with spectra on Hyperion and ALI images after FLAASH. This demonstrated the effectiveness of atmospheric correction using FLAASH.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122141770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-09-05DOI: 10.1109/EORSA.2008.4620295
S. Cui, Q. Yan, Zhenjun Liu
This paper presents a novel graph search schema for right-angle building extraction from high resolution aerial and satellite image subsets which contain a building. As the schema is a edge-driven and bottom-up approach, emphasis is put on elimination of spurious and insignificant low-level image features. In real-world, most buildings are comprised of several sequential corners. We classify the corners into four types according to the orientations of constructing edges. Each type of corners is labeled with a tag to identify the corner, such as ABCD, etc. Based on analysis of right-angle buildings, a set of geometric constraint rules could be derived. Such geometric constraints could then be used in graph construction. Different from tranditional approaches, this proposed approach is implemented by a graph search algorithm to reconstruct building shape.
{"title":"Right-angle building extraction based on graph-search algorithm","authors":"S. Cui, Q. Yan, Zhenjun Liu","doi":"10.1109/EORSA.2008.4620295","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620295","url":null,"abstract":"This paper presents a novel graph search schema for right-angle building extraction from high resolution aerial and satellite image subsets which contain a building. As the schema is a edge-driven and bottom-up approach, emphasis is put on elimination of spurious and insignificant low-level image features. In real-world, most buildings are comprised of several sequential corners. We classify the corners into four types according to the orientations of constructing edges. Each type of corners is labeled with a tag to identify the corner, such as ABCD, etc. Based on analysis of right-angle buildings, a set of geometric constraint rules could be derived. Such geometric constraints could then be used in graph construction. Different from tranditional approaches, this proposed approach is implemented by a graph search algorithm to reconstruct building shape.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120835086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-09-05DOI: 10.1109/EORSA.2008.4620311
N. Jiang, J. Zhang, H. T. Li, Xiangguo Lin
Recently, more and more high resolution remote sensing images appear, and they provide new data source for building extraction. Some building extraction methods are proposed to adapt to this trend. This paper addresses a semi-automatic method that combines segmentation and region selection. First, mean shift segmentation is applied to the image, and then the region extraction is implemented through the interactively selection of building parts. Edge detection is also involved to get the boundary of buildings. At last, the paper gives the contrast of the unsupervised classification ISODATA result and this extraction method.
{"title":"Semi-automatic building extraction from high resolution imagery based on segmentation","authors":"N. Jiang, J. Zhang, H. T. Li, Xiangguo Lin","doi":"10.1109/EORSA.2008.4620311","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620311","url":null,"abstract":"Recently, more and more high resolution remote sensing images appear, and they provide new data source for building extraction. Some building extraction methods are proposed to adapt to this trend. This paper addresses a semi-automatic method that combines segmentation and region selection. First, mean shift segmentation is applied to the image, and then the region extraction is implemented through the interactively selection of building parts. Edge detection is also involved to get the boundary of buildings. At last, the paper gives the contrast of the unsupervised classification ISODATA result and this extraction method.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124282112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-09-05DOI: 10.1109/EORSA.2008.4620327
Changyao Wang, Zitao Du, Zhengjun Liu, Y. Liu
There are two popular decision tree calculations in the international world - CART and C4.5, and boosting and bagging technology, which are new classification technology in mechanical study field. To study the decision tree and new technologypsilas use in remote sensing classification, we use 250 m resolution data of northeast China to do land cover and classification study. The result shows that a decision tree can improve classification accuracy to better than MLC when there is a large enough training sample, but when there is not enough sample, its performance is worse than MLC. It is also found that, in production of a decision tree, CART is better than C4.5 in classification accuracy and tree structure, while improvement of classification accuracy is up to the construction of tree structure and trimming. When boosting is introduced to CART, the classification accuracy is improved to 25.6% from 18.5%.
{"title":"Study on decision tree land cover classification based on MODIS data","authors":"Changyao Wang, Zitao Du, Zhengjun Liu, Y. Liu","doi":"10.1109/EORSA.2008.4620327","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620327","url":null,"abstract":"There are two popular decision tree calculations in the international world - CART and C4.5, and boosting and bagging technology, which are new classification technology in mechanical study field. To study the decision tree and new technologypsilas use in remote sensing classification, we use 250 m resolution data of northeast China to do land cover and classification study. The result shows that a decision tree can improve classification accuracy to better than MLC when there is a large enough training sample, but when there is not enough sample, its performance is worse than MLC. It is also found that, in production of a decision tree, CART is better than C4.5 in classification accuracy and tree structure, while improvement of classification accuracy is up to the construction of tree structure and trimming. When boosting is introduced to CART, the classification accuracy is improved to 25.6% from 18.5%.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116878197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-09-05DOI: 10.1109/EORSA.2008.4620292
Dongmei Chen, Jamie Fitzgibbon
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.
{"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":"https://doi.org/10.1109/EORSA.2008.4620292","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":0.0,"publicationDate":"2008-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128508532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-09-05DOI: 10.1109/EORSA.2008.4620333
Q. Wen, Shuo Liu, Zengxiang Zhang, Wei Qiao
As one of the five biggest pasturing areas of China, natural grassland in Tibet Autonomous Region, accounts for about 21% of the total area of Chinese natural grassland. Classification of rangeland types is a basic and significant study in stockbreeding. Utilizing the advantages of high temporal resolution of MODIS to construct time series EVI during the grass growth period, dividing large study area to individual regions via altitude and latitude, the paper classifies grassland in Tibet Autonomous Region to 6 types, meadow steppe, typical steppe, desert steppe, high-cold meadow steppe, high-cold typical steppe and shrub herbosa, successfully. This work is one part of the project-land cover mapping of China based on remote sensing images We provide land managers with map of the grassland types and area value of each grassland type in Tibet Autonomous Region in 2005. Average EVI of each grassland types during growth period is induced to reflect relative grass biomass among each type. Shrub herbosa has the biggest average EVI, followed with meadow steppe, high-cold meadow steppe, typical steppe, high-cold typical steppe. Desert steppe has the lowest average EVI.
{"title":"Classification of grassland types in ibet by MODIS time-series images","authors":"Q. Wen, Shuo Liu, Zengxiang Zhang, Wei Qiao","doi":"10.1109/EORSA.2008.4620333","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620333","url":null,"abstract":"As one of the five biggest pasturing areas of China, natural grassland in Tibet Autonomous Region, accounts for about 21% of the total area of Chinese natural grassland. Classification of rangeland types is a basic and significant study in stockbreeding. Utilizing the advantages of high temporal resolution of MODIS to construct time series EVI during the grass growth period, dividing large study area to individual regions via altitude and latitude, the paper classifies grassland in Tibet Autonomous Region to 6 types, meadow steppe, typical steppe, desert steppe, high-cold meadow steppe, high-cold typical steppe and shrub herbosa, successfully. This work is one part of the project-land cover mapping of China based on remote sensing images We provide land managers with map of the grassland types and area value of each grassland type in Tibet Autonomous Region in 2005. Average EVI of each grassland types during growth period is induced to reflect relative grass biomass among each type. Shrub herbosa has the biggest average EVI, followed with meadow steppe, high-cold meadow steppe, typical steppe, high-cold typical steppe. Desert steppe has the lowest average EVI.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115049536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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