Pub Date : 2011-07-12DOI: 10.1109/MULTI-TEMP.2011.6005042
J. Verbesselt, M. Herold, Rob J Hyndman, A. Zeileis, D. Culvenor
The majority of phenological studies have focussed on extracting critical points, i.e. phenological metrics such as start-of-season, in the seasonal growth cycle. These metrics do not exploit the full temporal detail of time series, depend on their definition or threshold, and are influenced by disturbances. Here, we evaluated a robust phenological change detection ability of a method for detecting abrupt, gradual, and phenological changes within time series. BFAST, Breaks For Additive Seasonal and Trend method, integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting change within trend and seasonal (i.e. phenology) component. We tested BFAST by analysing 16-day MODIS NDVI composites (MOD13C1 collection 5) between 2000–2009 covering Australia. This illustrated that the method is able to detect the timing of major phenological changes within time series while accounting for abrupt disturbances and gradual trends. It was also shown that the phenological change detection is influenced by the signal-to-noise ratio of the time series. The BFAST method is a generic change detection method which can be applied to any time series data. The methods are available in the BFAST package for R [1] from CRAN (http://CRAN.R-project. org/package=bfast).
大多数物候研究都集中在提取关键点上，即季节性生长周期中的物候指标，如季节开始。这些度量不利用时间序列的全部时间细节，依赖于它们的定义或阈值，并且受到干扰的影响。在这里，我们评估了一种检测时间序列内突变、渐变和物候变化的方法的鲁棒物候变化检测能力。BFAST (breaking For Additive Seasonal and Trend method)将时间序列分解为趋势、季节和剩余分量，并结合了检测趋势和季节(即物候)分量变化的方法。我们通过分析2000-2009年覆盖澳大利亚的16天MODIS NDVI复合材料(MOD13C1收集5)来测试BFAST。这表明，该方法能够检测时间序列内主要物候变化的时间，同时考虑突变干扰和渐进趋势。物候变化检测还受到时间序列信噪比的影响。BFAST方法是一种通用的变化检测方法，适用于任何时间序列数据。这些方法可以从CRAN (http://CRAN.R-project)的r[1]的BFAST包中获得。org/package = bfast)。
{"title":"A robust approach for phenological change detection within satellite image time series","authors":"J. Verbesselt, M. Herold, Rob J Hyndman, A. Zeileis, D. Culvenor","doi":"10.1109/MULTI-TEMP.2011.6005042","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005042","url":null,"abstract":"The majority of phenological studies have focussed on extracting critical points, i.e. phenological metrics such as start-of-season, in the seasonal growth cycle. These metrics do not exploit the full temporal detail of time series, depend on their definition or threshold, and are influenced by disturbances. Here, we evaluated a robust phenological change detection ability of a method for detecting abrupt, gradual, and phenological changes within time series. BFAST, Breaks For Additive Seasonal and Trend method, integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting change within trend and seasonal (i.e. phenology) component. We tested BFAST by analysing 16-day MODIS NDVI composites (MOD13C1 collection 5) between 2000–2009 covering Australia. This illustrated that the method is able to detect the timing of major phenological changes within time series while accounting for abrupt disturbances and gradual trends. It was also shown that the phenological change detection is influenced by the signal-to-noise ratio of the time series. The BFAST method is a generic change detection method which can be applied to any time series data. The methods are available in the BFAST package for R [1] from CRAN (http://CRAN.R-project. org/package=bfast).","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124703726","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 : 2011-07-12DOI: 10.1109/MULTI-TEMP.2011.6005097
A. Verhegghen, P. Defourny
In order to study anomalies and trends of the land surface phenology for different vegetation types in the world and more specifically in tropical regions, the design of a phenological reference dataset is investigated. The main objective is to get a description of the seasonal behaviour and interannual variations in order to study anomalies and potential trends of the vegetation. The work was based on time series acquired during the last 10 years by the SPOT VEGETATION sensor with a 1km spatial resolution. The NDVI was used as an indicator of the vegetation growing cycle. Daily surface reflectance values were composited into decades to reduce clouds and haze effects, using the mean compositing algorithm. The decadal NDVI values were spatially averaged for each pixels belonging to a similar vegetation type and temporally for the 10 years of data. The result is a smooth profile representing the seasonal reference pattern as well as the interannual variability inherent to a specific vegetation type.
{"title":"Phenology of the natural vegetation: A land cover specific approach for a reference dataset in Central Africa","authors":"A. Verhegghen, P. Defourny","doi":"10.1109/MULTI-TEMP.2011.6005097","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005097","url":null,"abstract":"In order to study anomalies and trends of the land surface phenology for different vegetation types in the world and more specifically in tropical regions, the design of a phenological reference dataset is investigated. The main objective is to get a description of the seasonal behaviour and interannual variations in order to study anomalies and potential trends of the vegetation. The work was based on time series acquired during the last 10 years by the SPOT VEGETATION sensor with a 1km spatial resolution. The NDVI was used as an indicator of the vegetation growing cycle. Daily surface reflectance values were composited into decades to reduce clouds and haze effects, using the mean compositing algorithm. The decadal NDVI values were spatially averaged for each pixels belonging to a similar vegetation type and temporally for the 10 years of data. The result is a smooth profile representing the seasonal reference pattern as well as the interannual variability inherent to a specific vegetation type.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128779228","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 : 2011-07-12DOI: 10.1109/MULTI-TEMP.2011.6005089
R. Zorer, D. Rocchini, L. Delucchi, F. Zottele, F. Meggio, M. Neteler
Heat requirements for grapevine varieties have been widely used to characterize potential growing regions for viticulture. One of the most important indices is the Winkler Index (WI) defined as the total summation of daily average air temperature above 10 °C from 1st of April to 31th of October in the Northern hemisphere [1]. Mapping of the WI is commonly based on temperature data from meteorological stations. However, in complex terrain such as the European Alps, these are usually irregularly and sparsely distributed or unavailable. This renders traditional geospatial interpolation approaches difficult to become reliable. As an alternative, thermal remote sensing data, which are intrinsically spatialised, can be used. The aim of this work was to provide time series of Winkler Index maps from 2003 to 2010, by means of the MODIS Land Surface Temperature (LST) data and to validate the maps using ground truth data, collected by two weather station networks.
{"title":"Use of multi-annual MODIS Land Surface Temperature data for the characterization of the heat requirements for grapevine varieties","authors":"R. Zorer, D. Rocchini, L. Delucchi, F. Zottele, F. Meggio, M. Neteler","doi":"10.1109/MULTI-TEMP.2011.6005089","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005089","url":null,"abstract":"Heat requirements for grapevine varieties have been widely used to characterize potential growing regions for viticulture. One of the most important indices is the Winkler Index (WI) defined as the total summation of daily average air temperature above 10 °C from 1st of April to 31th of October in the Northern hemisphere [1]. Mapping of the WI is commonly based on temperature data from meteorological stations. However, in complex terrain such as the European Alps, these are usually irregularly and sparsely distributed or unavailable. This renders traditional geospatial interpolation approaches difficult to become reliable. As an alternative, thermal remote sensing data, which are intrinsically spatialised, can be used. The aim of this work was to provide time series of Winkler Index maps from 2003 to 2010, by means of the MODIS Land Surface Temperature (LST) data and to validate the maps using ground truth data, collected by two weather station networks.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128048962","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 : 2011-07-12DOI: 10.1109/MULTI-TEMP.2011.6005102
K. Johansen, K. Johansen
Tropical forests are being cleared at alarming rates. The release of the Landsat image archive represents an opportunity to assess rainforest clearing over time through time-series analysis. The objective was to map the extent of rainforest clearing and assess land cover trends at the object level within a selected study area in Sabah, Borneo using Landsat images from 1991, 2000, 2004 and 2008. The images were delineated based on image interpretation cues and validated against existing high spatial resolution images on Google Earth. Overall mapping accuracies were >94%. Time-series trends for each delineated object were classified into trend classes to quantify the land cover history per object. The results showed that approximately 31% or 5,500 km2 of land cover within the study area changed between 1991 and 2008. This research presents an effective method for time-series analysis that can be used to regularly monitor forest clearing on Borneo.
{"title":"Time-series analysis of rainforest clearing in Sabah, Borneo using Landsat imagery","authors":"K. Johansen, K. Johansen","doi":"10.1109/MULTI-TEMP.2011.6005102","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005102","url":null,"abstract":"Tropical forests are being cleared at alarming rates. The release of the Landsat image archive represents an opportunity to assess rainforest clearing over time through time-series analysis. The objective was to map the extent of rainforest clearing and assess land cover trends at the object level within a selected study area in Sabah, Borneo using Landsat images from 1991, 2000, 2004 and 2008. The images were delineated based on image interpretation cues and validated against existing high spatial resolution images on Google Earth. Overall mapping accuracies were >94%. Time-series trends for each delineated object were classified into trend classes to quantify the land cover history per object. The results showed that approximately 31% or 5,500 km2 of land cover within the study area changed between 1991 and 2008. This research presents an effective method for time-series analysis that can be used to regularly monitor forest clearing on Borneo.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126331622","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 : 2011-07-12DOI: 10.1109/MULTI-TEMP.2011.6005080
R. Abileah, S. Vignudelli
Near shore bathymetry of Lake Nasser, Egypt was derived by fusing shoreline contours from 58 Landsat images spanning the years 1998–2003 with water levels from the various satellite radar altimeters operated by US and European agencies. A least-square fit is made on paired water area (from Landsat) with water levels (from the altimeters) observations. The fitted function is then used to assign a relative depth to each Landsat image shoreline. A series of shorelines are interpolated into depth contours at 1 m intervals. The bathymetry resulting from this process has rmse ∼10 cm.
{"title":"Bathymetry from fusion of multi-temporal Landsat and radar altimetery","authors":"R. Abileah, S. Vignudelli","doi":"10.1109/MULTI-TEMP.2011.6005080","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005080","url":null,"abstract":"Near shore bathymetry of Lake Nasser, Egypt was derived by fusing shoreline contours from 58 Landsat images spanning the years 1998–2003 with water levels from the various satellite radar altimeters operated by US and European agencies. A least-square fit is made on paired water area (from Landsat) with water levels (from the altimeters) observations. The fitted function is then used to assign a relative depth to each Landsat image shoreline. A series of shorelines are interpolated into depth contours at 1 m intervals. The bathymetry resulting from this process has rmse ∼10 cm.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123025214","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 : 2011-07-12DOI: 10.1109/MULTI-TEMP.2011.6005067
N. Méger, Romain Jolivet, Cécile Lasserre, E. Trouvé, C. Rigotti, F. Lodge, M. Doin, Stephane Guillaso, Andreea Julea, P. Bolon
In this paper, an original approach for analyzing InSAR time series is presented. The interferograms forming such time series allow ground deformation occurring between acquisition dates to be measured with high precision. Nevertheless, they can be affected by variations in atmospheric conditions. The proposed approach is designed to handle these varying atmospheric conditions. The stratified atmosphere is first removed and the phase evolution is built using a Small BAseline Subsets (SBAS) strategy. Then, frequent grouped sequential patterns are extracted. These patterns allow InSAR time series to be described spatially and temporally while discarding atmospheric perturbations. Experimental results on an ENVISAT InSAR time series covering the Haiyuan fault in the northeastern boundary of the Tibetan plateau are presented.
{"title":"Spatiotemporal mining of ENVISAT SAR interferogram time series over the Haiyuan fault in China","authors":"N. Méger, Romain Jolivet, Cécile Lasserre, E. Trouvé, C. Rigotti, F. Lodge, M. Doin, Stephane Guillaso, Andreea Julea, P. Bolon","doi":"10.1109/MULTI-TEMP.2011.6005067","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005067","url":null,"abstract":"In this paper, an original approach for analyzing InSAR time series is presented. The interferograms forming such time series allow ground deformation occurring between acquisition dates to be measured with high precision. Nevertheless, they can be affected by variations in atmospheric conditions. The proposed approach is designed to handle these varying atmospheric conditions. The stratified atmosphere is first removed and the phase evolution is built using a Small BAseline Subsets (SBAS) strategy. Then, frequent grouped sequential patterns are extracted. These patterns allow InSAR time series to be described spatially and temporally while discarding atmospheric perturbations. Experimental results on an ENVISAT InSAR time series covering the Haiyuan fault in the northeastern boundary of the Tibetan plateau are presented.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123691350","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 : 2011-07-12DOI: 10.1109/MULTI-TEMP.2011.6005095
P. K. Palacharla, S. Durbha, R. King, B. Gokaraju, G. Lawrence
Rotylenchulus reniformis is a newly emerging nematode species affecting the cotton crop and quickly spreading throughout the southeastern United States. Effective use of nematicides at a variable rate is the only economic counter measure. It requires the nematode population in the field to be known, which in turn depends on the collection of soil samples from the field and analyzing them in the laboratory. This process is economically prohibitive. Hence there is a need to develop alternative methods through which the actual numbers of reniform nematode present in the field can be determined. In this paper we propose a methodology in which a canopy reflectance model (PROSAIL) is inverted using machine learning approaches to retrieve the biophysical parameters, and relate the key variables to the nematode levels, so that it is possible to quantify at all multi-temporal intervals the nematode infestation at geographically distributed fields. A Support Vector Machine (SVM) Regression method is used for the inversion and retrieval of key biophysical parameters which help to understand and quantify the nature of the nematode infested vegetation. The performance of this approach is analyzed by the accuracy measures of RMSE and N-fold cross validation average on a considerable data set. Finally, a graphical web portal is being developed to facilitate the end users to use their field collected data to determine the extent of the nematode infestation in their crop and retrieve other spatio-temporal statistics.
{"title":"A hyperspectral reflectance data based model inversion methodology to detect reniform nematodes in cotton","authors":"P. K. Palacharla, S. Durbha, R. King, B. Gokaraju, G. Lawrence","doi":"10.1109/MULTI-TEMP.2011.6005095","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005095","url":null,"abstract":"Rotylenchulus reniformis is a newly emerging nematode species affecting the cotton crop and quickly spreading throughout the southeastern United States. Effective use of nematicides at a variable rate is the only economic counter measure. It requires the nematode population in the field to be known, which in turn depends on the collection of soil samples from the field and analyzing them in the laboratory. This process is economically prohibitive. Hence there is a need to develop alternative methods through which the actual numbers of reniform nematode present in the field can be determined. In this paper we propose a methodology in which a canopy reflectance model (PROSAIL) is inverted using machine learning approaches to retrieve the biophysical parameters, and relate the key variables to the nematode levels, so that it is possible to quantify at all multi-temporal intervals the nematode infestation at geographically distributed fields. A Support Vector Machine (SVM) Regression method is used for the inversion and retrieval of key biophysical parameters which help to understand and quantify the nature of the nematode infested vegetation. The performance of this approach is analyzed by the accuracy measures of RMSE and N-fold cross validation average on a considerable data set. Finally, a graphical web portal is being developed to facilitate the end users to use their field collected data to determine the extent of the nematode infestation in their crop and retrieve other spatio-temporal statistics.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121555539","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 : 2011-07-12DOI: 10.1109/MULTI-TEMP.2011.6005040
L. A. Romani, R. R. V. Gonçalves, B. Amaral, D. Y. T. Chino, J. Zullo, C. Traina, E. P. M. Sousa, A. J. Traina
This paper discusses how to take advantage of clustering techniques to analyze and extract useful information from multi-temporal images of low spatial resolution satellites to monitor the sugarcane expansion. Additionally, we introduce the SatImagExplorer system that was developed to automatically extract time series from a huge volume of remote sensing images as well as provide algorithms of clustering analysis and geospatial visualization. According to experiments accomplished with spectral images of sugarcane fields, this proposed approach can be satisfactorily used in crop monitoring.
{"title":"Clustering analysis applied to NDVI/NOAA multitemporal images to improve the monitoring process of sugarcane crops","authors":"L. A. Romani, R. R. V. Gonçalves, B. Amaral, D. Y. T. Chino, J. Zullo, C. Traina, E. P. M. Sousa, A. J. Traina","doi":"10.1109/MULTI-TEMP.2011.6005040","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005040","url":null,"abstract":"This paper discusses how to take advantage of clustering techniques to analyze and extract useful information from multi-temporal images of low spatial resolution satellites to monitor the sugarcane expansion. Additionally, we introduce the SatImagExplorer system that was developed to automatically extract time series from a huge volume of remote sensing images as well as provide algorithms of clustering analysis and geospatial visualization. According to experiments accomplished with spectral images of sugarcane fields, this proposed approach can be satisfactorily used in crop monitoring.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126536328","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 : 2011-07-12DOI: 10.1109/MULTI-TEMP.2011.6005049
C. Small
Spatiotemporal dimensionality refers to the structure of the continuum of spatial and temporal patterns in an image time series. Time-Space characterization refers to an approach for representing this continuum as combinations of spatial and temporal components with a minimum of assumptions about the forms of the patterns. Patterns can be related to processes through modeling — both deterministic and statistical. By combining characterization and modeling, two complementary analytical tools can be used together so that each resolves a key limitation of the other. Empirical Orthogonal Function analysis, used in conjunction with Temporal Mixture Models, provide a way to 1) Represent the spatiotemporal dimensionality of an image time series, 2) Identify distinct temporal modes and their spatial distributions, and 3) Map the relative contributions of these modes to the observed image time series as spatially continuous fields. Some strengths and limitations of Time-Space characterization are illustrated using multitemporal MODIS EVI time series of vegetation dynamics on the Ganges-Brahmaputra delta.
{"title":"Spatiotemporal dimensionality and time-space characterization of vegetation phenology from multitemporal MODIS EVI","authors":"C. Small","doi":"10.1109/MULTI-TEMP.2011.6005049","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005049","url":null,"abstract":"Spatiotemporal dimensionality refers to the structure of the continuum of spatial and temporal patterns in an image time series. Time-Space characterization refers to an approach for representing this continuum as combinations of spatial and temporal components with a minimum of assumptions about the forms of the patterns. Patterns can be related to processes through modeling — both deterministic and statistical. By combining characterization and modeling, two complementary analytical tools can be used together so that each resolves a key limitation of the other. Empirical Orthogonal Function analysis, used in conjunction with Temporal Mixture Models, provide a way to 1) Represent the spatiotemporal dimensionality of an image time series, 2) Identify distinct temporal modes and their spatial distributions, and 3) Map the relative contributions of these modes to the observed image time series as spatially continuous fields. Some strengths and limitations of Time-Space characterization are illustrated using multitemporal MODIS EVI time series of vegetation dynamics on the Ganges-Brahmaputra delta.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127055687","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 : 2011-07-12DOI: 10.1109/MULTI-TEMP.2011.6005084
R. Rasi, O. Kissiyar, M. Vollmar
This paper presents the results of research on common change detection techniques. More specifically it looks into the optimization of threshold values for these investigated change detection techniques: image differencing, normalized image differencing, image ratioing, normalized variance differencing, normalized spectral Euclidean distance and Tasseled Cap parameters difference. The threshold values were optimized for the detection of land cover change/no-change based on the comparison with an existing validated classification of five broad land cover classes. For this study a sample set of 104 image pairs was selected, each of 20 × 20 km, cut from Landsat TM/ETM+ imagery series. An object based approach was applied for the land cover change detection. The results showed that the threshold of normalized variance difference had most stable values across the sample set, however applying optimized thresholds the achieved accuracy was comparable for all tested methods.
{"title":"Land cover change detection thresholds for Landsat data samples","authors":"R. Rasi, O. Kissiyar, M. Vollmar","doi":"10.1109/MULTI-TEMP.2011.6005084","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005084","url":null,"abstract":"This paper presents the results of research on common change detection techniques. More specifically it looks into the optimization of threshold values for these investigated change detection techniques: image differencing, normalized image differencing, image ratioing, normalized variance differencing, normalized spectral Euclidean distance and Tasseled Cap parameters difference. The threshold values were optimized for the detection of land cover change/no-change based on the comparison with an existing validated classification of five broad land cover classes. For this study a sample set of 104 image pairs was selected, each of 20 × 20 km, cut from Landsat TM/ETM+ imagery series. An object based approach was applied for the land cover change detection. The results showed that the threshold of normalized variance difference had most stable values across the sample set, however applying optimized thresholds the achieved accuracy was comparable for all tested methods.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133138365","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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