Pub Date : 2005-05-16DOI: 10.1109/AMTRSI.2005.1469856
Q. Du, L. Wasson, R. King
The linear unmixing technique is investigated for change detection in multitemporal airborne hyperspectral imagery. Several practical implementation issues are discussed. The preliminary study using the CASI data shows its feasibility when the noise level is moderate and some prior information about endmembers is known. Keywords— linear mixture model; unsupervised linear unmixing; change detection; multitemporal airborne hyperspectral imagery.
{"title":"Unsupervised linear unmixing for change detection in multitemporal airborne hyperspectral imagery","authors":"Q. Du, L. Wasson, R. King","doi":"10.1109/AMTRSI.2005.1469856","DOIUrl":"https://doi.org/10.1109/AMTRSI.2005.1469856","url":null,"abstract":"The linear unmixing technique is investigated for change detection in multitemporal airborne hyperspectral imagery. Several practical implementation issues are discussed. The preliminary study using the CASI data shows its feasibility when the noise level is moderate and some prior information about endmembers is known. Keywords— linear mixture model; unsupervised linear unmixing; change detection; multitemporal airborne hyperspectral imagery.","PeriodicalId":302923,"journal":{"name":"International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, 2005.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125488078","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 : 2005-05-16DOI: 10.1109/AMTRSI.2005.1469846
F. Bovolo, L. Bruzzone
This paper presents a novel wavelet-based multiscale technique for unsupervised change detection in multitemporal synthetic aperture radar (SAR) images. The proposed approach is based on the analysis of a set of scale-dependent images characterized by a different trade-off between speckle reduction and preservation of geometrical details. The different scales are obtained by means of a multiresolution decomposition of the log- ratio image (obtained by a comparison of a pair of co-registered images acquired at different times on the same area). The final change-detection map is derived according to an adaptive scale- driven fusion algorithm, which properly exploits information at different resolution levels. According to an automatic local analysis of the statistic of the data, for each pixel only a sub-set of reliable scales is selected and exploited in the decision process thus producing an accurate and reliable change-detection map in both homogeneous and border areas. Experimental results confirm the effectiveness of the proposed technique. In order to address the above limitations of the standard methods, in this paper we present a novel approach to change detection in multitemporal SAR images. The proposed approach exploits a wavelet-based multiscale decomposition of the log-ratio image (obtained by a comparison of the original multitemporal data) aimed at achieving different scales (levels) of representation of the changed areas. Each scale is characterized by a different trade-off between speckle reduction and preservation of geometrical details. Then scale- dependent log-ratio images are analyzed to obtain the final change-detection result according to an adaptive scale-driven fusion algorithm. The fusion step aims at properly exploiting the different behaviors at different scales for producing an accurate and reliable change-detection map. In greater detail, a set of reliable resolution levels is defined according to an adaptive comparison between the pixel local statistics and global statistics independently performed at each scale. A scale-driven fusion strategy is applied at decision or feature level to compute the final change-detection map. The basic idea is to use high-resolution levels only in the analysis of the expected edge (or details) pixels and to consider also low- resolution levels in the processing of pixels in homogeneous areas. Thus, the proposed method exhibits both a high sensitivity to geometrical details (e.g., border of changed area are well preserved) and a high robustness to speckle noise in homogeneous areas.
{"title":"A wavelet-based change-detection technique for multitemporal SAR images","authors":"F. Bovolo, L. Bruzzone","doi":"10.1109/AMTRSI.2005.1469846","DOIUrl":"https://doi.org/10.1109/AMTRSI.2005.1469846","url":null,"abstract":"This paper presents a novel wavelet-based multiscale technique for unsupervised change detection in multitemporal synthetic aperture radar (SAR) images. The proposed approach is based on the analysis of a set of scale-dependent images characterized by a different trade-off between speckle reduction and preservation of geometrical details. The different scales are obtained by means of a multiresolution decomposition of the log- ratio image (obtained by a comparison of a pair of co-registered images acquired at different times on the same area). The final change-detection map is derived according to an adaptive scale- driven fusion algorithm, which properly exploits information at different resolution levels. According to an automatic local analysis of the statistic of the data, for each pixel only a sub-set of reliable scales is selected and exploited in the decision process thus producing an accurate and reliable change-detection map in both homogeneous and border areas. Experimental results confirm the effectiveness of the proposed technique. In order to address the above limitations of the standard methods, in this paper we present a novel approach to change detection in multitemporal SAR images. The proposed approach exploits a wavelet-based multiscale decomposition of the log-ratio image (obtained by a comparison of the original multitemporal data) aimed at achieving different scales (levels) of representation of the changed areas. Each scale is characterized by a different trade-off between speckle reduction and preservation of geometrical details. Then scale- dependent log-ratio images are analyzed to obtain the final change-detection result according to an adaptive scale-driven fusion algorithm. The fusion step aims at properly exploiting the different behaviors at different scales for producing an accurate and reliable change-detection map. In greater detail, a set of reliable resolution levels is defined according to an adaptive comparison between the pixel local statistics and global statistics independently performed at each scale. A scale-driven fusion strategy is applied at decision or feature level to compute the final change-detection map. The basic idea is to use high-resolution levels only in the analysis of the expected edge (or details) pixels and to consider also low- resolution levels in the processing of pixels in homogeneous areas. Thus, the proposed method exhibits both a high sensitivity to geometrical details (e.g., border of changed area are well preserved) and a high robustness to speckle noise in homogeneous areas.","PeriodicalId":302923,"journal":{"name":"International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, 2005.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133913475","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 : 2005-05-16DOI: 10.1109/AMTRSI.2005.1469871
R. Lecerf, T. Corpetti, L. Hubert‐Moy, V. Dubreuil
Image time series from medium resolution sensors such as NASA EOS/MODIS are frequently used to monitor vegetation phenology at regional and global scales. Facing the limitations of high resolution sensors, that is small coverage areas and low revisit frequencies, data from medium resolution sensors are now assessed to monitor subtle vegetation changes at meso or large scales, even in fragmented landscapes. However, monitoring of subtle changes is difficult to perform with such data without important pre-processing steps. Previous studies showed that time series extracted from original images are often corrupted and hence not exploitable, due to atmospheric and geometric distortions and others artifacts (angle variations, clouds, aerosols for example). In this paper we present an approach to reconstruct high accurate NASA EOS/MODIS time series. Firstly, we propose a method to correct images from atmospheric and geometric distortions. The comparison between different pre-processed NDVI MODIS images and SPOT HRVIR high resolution data points out significant differences, highlighting the necessity of properly pre-processing time serie data. Moreover, on the basis of these first results obtained in using pre-processed series of MODIS images through the smoothing technique developed here to recover the winter vegetation phenology, it is now possible to undertake the identification of subtle changes on land surfaces.
{"title":"Monitoring land use and land cover changes in oceanic and fragmented landscapes with reconstructed MODIS time series","authors":"R. Lecerf, T. Corpetti, L. Hubert‐Moy, V. Dubreuil","doi":"10.1109/AMTRSI.2005.1469871","DOIUrl":"https://doi.org/10.1109/AMTRSI.2005.1469871","url":null,"abstract":"Image time series from medium resolution sensors such as NASA EOS/MODIS are frequently used to monitor vegetation phenology at regional and global scales. Facing the limitations of high resolution sensors, that is small coverage areas and low revisit frequencies, data from medium resolution sensors are now assessed to monitor subtle vegetation changes at meso or large scales, even in fragmented landscapes. However, monitoring of subtle changes is difficult to perform with such data without important pre-processing steps. Previous studies showed that time series extracted from original images are often corrupted and hence not exploitable, due to atmospheric and geometric distortions and others artifacts (angle variations, clouds, aerosols for example). In this paper we present an approach to reconstruct high accurate NASA EOS/MODIS time series. Firstly, we propose a method to correct images from atmospheric and geometric distortions. The comparison between different pre-processed NDVI MODIS images and SPOT HRVIR high resolution data points out significant differences, highlighting the necessity of properly pre-processing time serie data. Moreover, on the basis of these first results obtained in using pre-processed series of MODIS images through the smoothing technique developed here to recover the winter vegetation phenology, it is now possible to undertake the identification of subtle changes on land surfaces.","PeriodicalId":302923,"journal":{"name":"International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, 2005.","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126343837","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 : 2005-05-16DOI: 10.1109/AMTRSI.2005.1469875
R. B. Huseby, L. Aurdal, L. Eikvil, R. Solberg, D. Vikhamar, A. Solberg
This work concerns the alignment of growth seasons based on satellite data. This work is motivated by a high mountain vegetation classification problem in Norway. Vegetation classes are characterized by their temporal evolution through a growth season. Data of high spatial resolution, like LANDSAT data, are often temporally sparse. In order to get a longer sequence of images, data from different years can be combined into one single synthetic sequence. We describe a method for determining the correspondence between the chronological time of the image acquisition and the time at which the phenological state of the vegetation cover shown in the image would typically occur. The task is considered as a minimization problem and is solved by dynamic programming. The methodology is based on the normalized difference vegetation index (NDVI) computed from data having a coarse spatial resolution such as MODIS or AVHRR data. The proposed methodology has been tested on data from several years covering a region in Norway including mountainous areas. It is evident from plots of the original data that NDVI curves from different seasons are shifted relative to one another. By applying the proposed time warping methodology to adjust the time scale within each year the shifts become less apparent. We conclude that the methodology can be used for alignment of growth seasons from satellite data.
{"title":"Alignment of growth seasons from satellite data","authors":"R. B. Huseby, L. Aurdal, L. Eikvil, R. Solberg, D. Vikhamar, A. Solberg","doi":"10.1109/AMTRSI.2005.1469875","DOIUrl":"https://doi.org/10.1109/AMTRSI.2005.1469875","url":null,"abstract":"This work concerns the alignment of growth seasons based on satellite data. This work is motivated by a high mountain vegetation classification problem in Norway. Vegetation classes are characterized by their temporal evolution through a growth season. Data of high spatial resolution, like LANDSAT data, are often temporally sparse. In order to get a longer sequence of images, data from different years can be combined into one single synthetic sequence. We describe a method for determining the correspondence between the chronological time of the image acquisition and the time at which the phenological state of the vegetation cover shown in the image would typically occur. The task is considered as a minimization problem and is solved by dynamic programming. The methodology is based on the normalized difference vegetation index (NDVI) computed from data having a coarse spatial resolution such as MODIS or AVHRR data. The proposed methodology has been tested on data from several years covering a region in Norway including mountainous areas. It is evident from plots of the original data that NDVI curves from different seasons are shifted relative to one another. By applying the proposed time warping methodology to adjust the time scale within each year the shifts become less apparent. We conclude that the methodology can be used for alignment of growth seasons from satellite data.","PeriodicalId":302923,"journal":{"name":"International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, 2005.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130178414","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 : 2005-05-16DOI: 10.1109/AMTRSI.2005.1469869
A. Drunpob, N. Chang, M. Beaman, C. Wyatt, C. Slater
This study presents multi-temporal soil moisture using RADARSAT-1 Synthetic Aperture Radar (SAR) satellite imagery in Choke Canyon Reservoir Watershed (CCRW). Soil moisture is a critical element of hydrological cycle that drastically impacts humans’ activities in semi-arid area. Point measurements of soil moisture across different geographical landscapes are impossible to comprehend the soil moisture variations temporally and spatially. RADARSAT-1 is a promising tool for measuring the surface soil moisture over seasons with its all-weather capability and the short-period return of its orbiting. Time constraint is almost negligible since the RADARSAT-1 is able to capture surface soil moisture over a large area in a matter of seconds, if the area is within its swath. The CCRW was selected as the study area contributing to the reservoir, which is mostly agricultural and range land in a semi-arid coastal environment, South Texas. RADARSAT-1 images presented at here were captured in three acquisitions in 2004, including April, September and December. Essential radiometric and geometric calibrations of the multitemporal SAR images were performed to improve the accuracy of information and location, with the aid of five corner reflectors deployed by Alaska Satellite Facility (ASF). The horizontally spatial errors were reduced from initially 560 m down to less than 5 m at the best trial-and-true. Slope data, land cover data, aspect data, and soil type data were incorporated into the regression models, derived from genetic programming algorithm, to predict soil moisture using SAR data. It is necessary to use slope data and aspect data together to represent the effect of the geological slope to the radar backscatter because the slope data only represents the magnitudes of elevation change, while the aspect represents the direction of the slope. The soil moisture estimations show that soil moisture wholly varies in space and season. Keywords-component; RADARSAT-1, SAR, soil moisture, multi-temporal remote sensing, Ecohydrology
{"title":"Seasonal soil moisture variation analysis using RADARSAT-1 satellite image in a semi-arid coastal watershed","authors":"A. Drunpob, N. Chang, M. Beaman, C. Wyatt, C. Slater","doi":"10.1109/AMTRSI.2005.1469869","DOIUrl":"https://doi.org/10.1109/AMTRSI.2005.1469869","url":null,"abstract":"This study presents multi-temporal soil moisture using RADARSAT-1 Synthetic Aperture Radar (SAR) satellite imagery in Choke Canyon Reservoir Watershed (CCRW). Soil moisture is a critical element of hydrological cycle that drastically impacts humans’ activities in semi-arid area. Point measurements of soil moisture across different geographical landscapes are impossible to comprehend the soil moisture variations temporally and spatially. RADARSAT-1 is a promising tool for measuring the surface soil moisture over seasons with its all-weather capability and the short-period return of its orbiting. Time constraint is almost negligible since the RADARSAT-1 is able to capture surface soil moisture over a large area in a matter of seconds, if the area is within its swath. The CCRW was selected as the study area contributing to the reservoir, which is mostly agricultural and range land in a semi-arid coastal environment, South Texas. RADARSAT-1 images presented at here were captured in three acquisitions in 2004, including April, September and December. Essential radiometric and geometric calibrations of the multitemporal SAR images were performed to improve the accuracy of information and location, with the aid of five corner reflectors deployed by Alaska Satellite Facility (ASF). The horizontally spatial errors were reduced from initially 560 m down to less than 5 m at the best trial-and-true. Slope data, land cover data, aspect data, and soil type data were incorporated into the regression models, derived from genetic programming algorithm, to predict soil moisture using SAR data. It is necessary to use slope data and aspect data together to represent the effect of the geological slope to the radar backscatter because the slope data only represents the magnitudes of elevation change, while the aspect represents the direction of the slope. The soil moisture estimations show that soil moisture wholly varies in space and season. Keywords-component; RADARSAT-1, SAR, soil moisture, multi-temporal remote sensing, Ecohydrology","PeriodicalId":302923,"journal":{"name":"International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, 2005.","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128682906","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 : 2005-05-16DOI: 10.1109/AMTRSI.2005.1469833
K. Kovacs, K. Ranson, V. Kharuk
As part of a NASA supported Siberian disturbance mapping project, the capabilities of multi- temporal MODIS data to detect insect damage in the boreal forest were evaluated. Multi-temporal in the context of this study includes both multi-annual and multi- seasonal data. More specifically, the aim of this study was to ascertain what combination of multi-temporal MODIS Enhanced Vegetation Index (EVI) and Middle Infrared (MIR) data is best for detecting insect disturbance with or without a priori knowledge.
{"title":"Detecting siberian silk moth damage in central siberia using multi-temporal MODIS data","authors":"K. Kovacs, K. Ranson, V. Kharuk","doi":"10.1109/AMTRSI.2005.1469833","DOIUrl":"https://doi.org/10.1109/AMTRSI.2005.1469833","url":null,"abstract":"As part of a NASA supported Siberian disturbance mapping project, the capabilities of multi- temporal MODIS data to detect insect damage in the boreal forest were evaluated. Multi-temporal in the context of this study includes both multi-annual and multi- seasonal data. More specifically, the aim of this study was to ascertain what combination of multi-temporal MODIS Enhanced Vegetation Index (EVI) and Middle Infrared (MIR) data is best for detecting insect disturbance with or without a priori knowledge.","PeriodicalId":302923,"journal":{"name":"International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, 2005.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130656390","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 : 2005-05-16DOI: 10.1109/AMTRSI.2005.1469839
R. McKellip, R. Ryan, S. Błoński, D. Prados
Effective response to crop disease outbreaks requires rapid identification and diagnosis of an event. A near-daily vegetation index product, such as a Normalized Difference Vegetation Index (NDVI), at moderate spatial resolution may serve as a good method for monitoring quick-acting diseases. NASA s Moderate Resolution Imaging Spectroradiometer (MODIS) instrument flown on the Terra and Aqua satellites has the temporal, spatial, and spectral properties to make it an excellent coarse-resolution data source for rapid, comprehensive surveillance of agricultural areas. A proof-of-concept wide area crop surveillance system using daily MODIS imagery was developed and tested on a set of San Joaquin cotton fields over a growing season. This area was chosen in part because excellent ground truth data were readily available. Preliminary results indicate that, at least in the southwestern part of the United States, near-daily NDVI products can be generated that show the natural variations in the crops as well as specific crop practices. Various filtering methods were evaluated and compared with standard MOD13 NDVI MODIS products. We observed that specific chemical applications that produce defoliation, which would have been missed using the standard 16-day product, were easily detectable with the filtered daily NDVI products.
{"title":"Crop surveillance demonstration using a near-daily MODIS derived vegetation index time series","authors":"R. McKellip, R. Ryan, S. Błoński, D. Prados","doi":"10.1109/AMTRSI.2005.1469839","DOIUrl":"https://doi.org/10.1109/AMTRSI.2005.1469839","url":null,"abstract":"Effective response to crop disease outbreaks requires rapid identification and diagnosis of an event. A near-daily vegetation index product, such as a Normalized Difference Vegetation Index (NDVI), at moderate spatial resolution may serve as a good method for monitoring quick-acting diseases. NASA s Moderate Resolution Imaging Spectroradiometer (MODIS) instrument flown on the Terra and Aqua satellites has the temporal, spatial, and spectral properties to make it an excellent coarse-resolution data source for rapid, comprehensive surveillance of agricultural areas. A proof-of-concept wide area crop surveillance system using daily MODIS imagery was developed and tested on a set of San Joaquin cotton fields over a growing season. This area was chosen in part because excellent ground truth data were readily available. Preliminary results indicate that, at least in the southwestern part of the United States, near-daily NDVI products can be generated that show the natural variations in the crops as well as specific crop practices. Various filtering methods were evaluated and compared with standard MOD13 NDVI MODIS products. We observed that specific chemical applications that produce defoliation, which would have been missed using the standard 16-day product, were easily detectable with the filtered daily NDVI products.","PeriodicalId":302923,"journal":{"name":"International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, 2005.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127422747","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 : 2005-05-16DOI: 10.1109/AMTRSI.2005.1469857
L. D. Yarbrough, G. Easson, J. Kuszmaul
The Tasseled Cap Transform (TCT) was originally created for agricultural land investigations. It is a vegetative index commonly used as an indicator of vegetation health and assessing vegetation and land cover change. The nature of the TCT requires linear combinations specific to each sensor. Additionally, the varying units of the reported digital number (DN) require supplementary eigenvectors. TCTs were derived for the at-sensor radiance and at-sensor reflectance and compared using differing change detection application in Mississippi. The Tasseled Cap Soil Brightness Index (SBI) and the Greenness Vegetative Index (GVI) were conducted and evaluated. It was found that the at-sensor radiance based TCT was most useful in a change detection analysis. The desired spectral characteristics were well contrasted while the at-sensor reflectance based TCT tended to be less effective.
{"title":"Using at-sensor radiance and reflectance tasseled cap transforms applied to change detection for the ASTER sensor","authors":"L. D. Yarbrough, G. Easson, J. Kuszmaul","doi":"10.1109/AMTRSI.2005.1469857","DOIUrl":"https://doi.org/10.1109/AMTRSI.2005.1469857","url":null,"abstract":"The Tasseled Cap Transform (TCT) was originally created for agricultural land investigations. It is a vegetative index commonly used as an indicator of vegetation health and assessing vegetation and land cover change. The nature of the TCT requires linear combinations specific to each sensor. Additionally, the varying units of the reported digital number (DN) require supplementary eigenvectors. TCTs were derived for the at-sensor radiance and at-sensor reflectance and compared using differing change detection application in Mississippi. The Tasseled Cap Soil Brightness Index (SBI) and the Greenness Vegetative Index (GVI) were conducted and evaluated. It was found that the at-sensor radiance based TCT was most useful in a change detection analysis. The desired spectral characteristics were well contrasted while the at-sensor reflectance based TCT tended to be less effective.","PeriodicalId":302923,"journal":{"name":"International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, 2005.","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124221222","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 : 2005-05-16DOI: 10.1109/AMTRSI.2005.1469830
C.A. Collins, D. W. Wilkinson, D. Evans
The use of Landsat data to aid in forest sampling stratification, area estimation, and future resource assessment through growth models is currently being investigated for the state of Mississippi with the goal of better understanding present and future wood resources. In such analyses, and as a part of this investigation, change detection techniques are being exploited to help determine these forest stand ages in approximate five year intervals. This preliminary report looks at post classification comparisons and temporal image differencing as two means to find these dates. The results find the post classification comparisons techniques, in an unrefined use, to work moderately well (overall accuracy = 0.6157, KHAT = 0.5386) and temporal image differencing with NDVI and tasseled cap transformations to disagree with each other in predicted age class sizes with no assessment data to validate accuracy at this time.
{"title":"Multi-temporal analysis of landsat data to determine forest age classes for the mississippi statewide forest inventory~preliminary results","authors":"C.A. Collins, D. W. Wilkinson, D. Evans","doi":"10.1109/AMTRSI.2005.1469830","DOIUrl":"https://doi.org/10.1109/AMTRSI.2005.1469830","url":null,"abstract":"The use of Landsat data to aid in forest sampling stratification, area estimation, and future resource assessment through growth models is currently being investigated for the state of Mississippi with the goal of better understanding present and future wood resources. In such analyses, and as a part of this investigation, change detection techniques are being exploited to help determine these forest stand ages in approximate five year intervals. This preliminary report looks at post classification comparisons and temporal image differencing as two means to find these dates. The results find the post classification comparisons techniques, in an unrefined use, to work moderately well (overall accuracy = 0.6157, KHAT = 0.5386) and temporal image differencing with NDVI and tasseled cap transformations to disagree with each other in predicted age class sizes with no assessment data to validate accuracy at this time.","PeriodicalId":302923,"journal":{"name":"International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, 2005.","volume":"235 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122951795","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 : 2005-05-16DOI: 10.1109/AMTRSI.2005.1469850
J. Beaubien, N. Walsworth, D. Leckie
The Enhancement Classification Method (ECM) has demonstrated considerable success in mapping Canada's forests and here is extended to facilitate clustering and labeling within a two-date classification. Central to the method, is an interactive cluster formulation based upon color rendition. A multi-date image enhancement is employed to facilitate an RGB rendition of change and cluster sieving is undertaken through spatial generalization. The remaining core clusters are reapplied via a minimum spectral distance. The method was tested on a 1984 - 1988 co-registered and normalized Landsat scene pair over a forest harvesting area near Petawawa, Ontario. Clusters (123) were derived and labeled. Results identified forest depletion (clear cuts and partial cuts) fairly well and captured stable forest composition moderately well and pre-change cover type moderately well. Burns, hail, forest blowdown and deforestation events were not recognized individually in single clusters, rather they were generally lumped into clearing classes. Consequently depletion requires a composite of classes to establish an intensity and localized cluster labeling.
{"title":"Testing of two date change detection using a modified enhancement classification method","authors":"J. Beaubien, N. Walsworth, D. Leckie","doi":"10.1109/AMTRSI.2005.1469850","DOIUrl":"https://doi.org/10.1109/AMTRSI.2005.1469850","url":null,"abstract":"The Enhancement Classification Method (ECM) has demonstrated considerable success in mapping Canada's forests and here is extended to facilitate clustering and labeling within a two-date classification. Central to the method, is an interactive cluster formulation based upon color rendition. A multi-date image enhancement is employed to facilitate an RGB rendition of change and cluster sieving is undertaken through spatial generalization. The remaining core clusters are reapplied via a minimum spectral distance. The method was tested on a 1984 - 1988 co-registered and normalized Landsat scene pair over a forest harvesting area near Petawawa, Ontario. Clusters (123) were derived and labeled. Results identified forest depletion (clear cuts and partial cuts) fairly well and captured stable forest composition moderately well and pre-change cover type moderately well. Burns, hail, forest blowdown and deforestation events were not recognized individually in single clusters, rather they were generally lumped into clearing classes. Consequently depletion requires a composite of classes to establish an intensity and localized cluster labeling.","PeriodicalId":302923,"journal":{"name":"International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, 2005.","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126167246","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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