Pub Date : 2011-07-12DOI: 10.1109/MULTI-TEMP.2011.6005041
M. Moller, C. Glaser, J. Birger
The German joint project DeCover 2 is developing a methodological framework to cope with the increasing demand for up-to-date land cover information using remote sensing techniques. New satellite systems like RapidEye provide both data of high geometric resolution and high repetition rates. Because of the Germany-wide diversity of natural conditions, same acquisition dates don't correspond to same phenological phases. Thus, a phenological structuring of the available imagery over the year is needed for the assessment of Rapid-Eye imagery regarding their suitability for the classification and distinction of vegetation classes. On the example of the phenological phase ‘Yellow Ripeness’ of Winter Wheat in 2010, the presented algorithm demonstrates for the total area of Germany how daily phenological phases can be automatically interpolated on demand, in real-time and considering interpolation accuracies. As input, daily provided point data on temperature and phenological phases from the extensive network of the German Weather Service as well as a SRTM digital elevation model are used. The modeling results enable the identification of temporal phenological windows for specific test sites.
{"title":"Automatic interpolation of phenological phases in Germany","authors":"M. Moller, C. Glaser, J. Birger","doi":"10.1109/MULTI-TEMP.2011.6005041","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005041","url":null,"abstract":"The German joint project DeCover 2 is developing a methodological framework to cope with the increasing demand for up-to-date land cover information using remote sensing techniques. New satellite systems like RapidEye provide both data of high geometric resolution and high repetition rates. Because of the Germany-wide diversity of natural conditions, same acquisition dates don't correspond to same phenological phases. Thus, a phenological structuring of the available imagery over the year is needed for the assessment of Rapid-Eye imagery regarding their suitability for the classification and distinction of vegetation classes. On the example of the phenological phase ‘Yellow Ripeness’ of Winter Wheat in 2010, the presented algorithm demonstrates for the total area of Germany how daily phenological phases can be automatically interpolated on demand, in real-time and considering interpolation accuracies. As input, daily provided point data on temperature and phenological phases from the extensive network of the German Weather Service as well as a SRTM digital elevation model are used. The modeling results enable the identification of temporal phenological windows for specific test sites.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"227 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":"116044750","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.2011.6005101
M. Hall-Beyer
The interannual variability of NDVI (STD(t)) was calculated for each semi-monthly interval over the period 1982–2006, using GIMMS NDVI images of Alberta, Canada. Forested areas usually show maximum interannual variability in spring and fall (temperature dependence), while grasslands have maximum variability in summer (moisture dependence). In moister areas., grasslands show less summer variability and approach the forest pattern. Croplands mimic the temporal pattern of grasslands located in the same ecoregion. In the ecotone between naturally forest and naturally grassland ecoregions, crops show greater summer variability than their nearby grasslands, indicating a greater sensitivity by crops than by grasslands to moisture stress. This pattern divergence may be used to show crop particularly sensitive to drought; this would be particularly useful where detailed local meteorological and crop data are not compiled. Changes in patterns over time can also help plan agricultural adaptation to climate change in a spatially complete form.
{"title":"Year-to-year variability of NDVI in croplands and grasslands across a regional grasslands-forest ecotone in Central Alberta, Canada","authors":"M. Hall-Beyer","doi":"10.1109/MULTI-TEMP.2011.6005101","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005101","url":null,"abstract":"The interannual variability of NDVI (STD(t)) was calculated for each semi-monthly interval over the period 1982–2006, using GIMMS NDVI images of Alberta, Canada. Forested areas usually show maximum interannual variability in spring and fall (temperature dependence), while grasslands have maximum variability in summer (moisture dependence). In moister areas., grasslands show less summer variability and approach the forest pattern. Croplands mimic the temporal pattern of grasslands located in the same ecoregion. In the ecotone between naturally forest and naturally grassland ecoregions, crops show greater summer variability than their nearby grasslands, indicating a greater sensitivity by crops than by grasslands to moisture stress. This pattern divergence may be used to show crop particularly sensitive to drought; this would be particularly useful where detailed local meteorological and crop data are not compiled. Changes in patterns over time can also help plan agricultural adaptation to climate change in a spatially complete form.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"18 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":"122344184","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.2011.6005091
W. L. da Silva, R. R. V. Gonçalves, A. S. Siqueira, J. Zullo, F. A. M. G. Neto
One of the biggest problems of agribusiness in Brazil is related to estimation and forecasting of agricultural crops. In this problem, time series classification enters as a way to help production estimation. In this paper, we are concerned with the development of an automatic classifier that identifies the areas covered with the sugarcane culture by using Normalized Difference Vegetation Index (NDVI) time series, from the AVHRR/NOAA data warehouse of Center of Meteorological and Climatic Research Applied to Agriculture (CEPAGRI). We assumed that a multidimensional space generated by information obtained in the harmonics is a appropriate space to study the similarity between time series. Here we used the word features of a series to refer the coefficients extracted by time series in Fourier decomposition. The proposed methodology has shown to be efficient with a high success rate for the classification of the culture of sugarcane in images from Jaboticabal city, in Brazil, 2004/2005.
{"title":"Feature extraction for NDVI AVHRR/NOAA time series classification","authors":"W. L. da Silva, R. R. V. Gonçalves, A. S. Siqueira, J. Zullo, F. A. M. G. Neto","doi":"10.1109/MULTI-TEMP.2011.6005091","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005091","url":null,"abstract":"One of the biggest problems of agribusiness in Brazil is related to estimation and forecasting of agricultural crops. In this problem, time series classification enters as a way to help production estimation. In this paper, we are concerned with the development of an automatic classifier that identifies the areas covered with the sugarcane culture by using Normalized Difference Vegetation Index (NDVI) time series, from the AVHRR/NOAA data warehouse of Center of Meteorological and Climatic Research Applied to Agriculture (CEPAGRI). We assumed that a multidimensional space generated by information obtained in the harmonics is a appropriate space to study the similarity between time series. Here we used the word features of a series to refer the coefficients extracted by time series in Fourier decomposition. The proposed methodology has shown to be efficient with a high success rate for the classification of the culture of sugarcane in images from Jaboticabal city, in Brazil, 2004/2005.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"58 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":"126118511","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.2011.6005063
Y. Julien, J. Sobrino
Global vegetation has been traditionally monitored mainly through the use of the Normalized Difference Vegetation Index (NDVI). Land surface temperature (LST) provides additional information, and is generally less affected by atmospheric conditions when water vapor is taken into account. The Yearly Land Cover Dynamics (YLCD) method can then be used to retrieve 3 parameters which allow for a good differentiation between biomes at the global and local levels. Using NASA's Long Term Data Record (LTDR), the YLCD method has been applied to IDR (iterative Interpolation for Data Reconstruction) reconstructed LTDR data, in order to account for atmospheric contamination of part of the dataset for a few selected pixels. The evolution of the retrieved YLCD parameters is monitored throughout the 20-year span of the LTDR dataset.
{"title":"Monitoring global vegetation with the Yearly Land Cover Dynamics (YLCD) method","authors":"Y. Julien, J. Sobrino","doi":"10.1109/MULTI-TEMP.2011.6005063","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005063","url":null,"abstract":"Global vegetation has been traditionally monitored mainly through the use of the Normalized Difference Vegetation Index (NDVI). Land surface temperature (LST) provides additional information, and is generally less affected by atmospheric conditions when water vapor is taken into account. The Yearly Land Cover Dynamics (YLCD) method can then be used to retrieve 3 parameters which allow for a good differentiation between biomes at the global and local levels. Using NASA's Long Term Data Record (LTDR), the YLCD method has been applied to IDR (iterative Interpolation for Data Reconstruction) reconstructed LTDR data, in order to account for atmospheric contamination of part of the dataset for a few selected pixels. The evolution of the retrieved YLCD parameters is monitored throughout the 20-year span of the LTDR dataset.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"64 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":"125068074","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.2011.6005093
R. d’Andrimont, Jean-François Pekel, P. Defourny
This paper proposes to use a water detection methodology based on a colorimetric approach to develop a near real time system allowing to monitor and to detect anomalies at a fine time resolution and in a systematic way The algorithm was calibrated over Africa using daily reflectance MODIS data from 2003 to 2011. The proposed approach has 3 major outputs updatable in near real time: (1) a permanent water mask (2) a every 10-days surface water map consolidated with time series and (3) an anomalies detection using 10 years of detection reanalysis. Three validation approaches are developed to deal with the large coverage and the high temporal resolution. The methodology is generic and could be applied to other extent and sensors.
{"title":"Monitoring African surface water dynamic using medium resolution daily data allows anomalies detection in nearly real time","authors":"R. d’Andrimont, Jean-François Pekel, P. Defourny","doi":"10.1109/MULTI-TEMP.2011.6005093","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005093","url":null,"abstract":"This paper proposes to use a water detection methodology based on a colorimetric approach to develop a near real time system allowing to monitor and to detect anomalies at a fine time resolution and in a systematic way The algorithm was calibrated over Africa using daily reflectance MODIS data from 2003 to 2011. The proposed approach has 3 major outputs updatable in near real time: (1) a permanent water mask (2) a every 10-days surface water map consolidated with time series and (3) an anomalies detection using 10 years of detection reanalysis. Three validation approaches are developed to deal with the large coverage and the high temporal resolution. The methodology is generic and could be applied to other extent and sensors.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"137 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":"121633712","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.2011.6005044
A. Rodrigues, A. Marçal, M. Cunha
The availability of temporal satellite image data has increased considerably in recent years. A number of satellite sensors currently observe the Earth with high temporal frequency thus providing a tool for monitoring/understanding the Earth-surface variability more precisely, for several applications such as the analysis of vegetation dynamics. However, the extraction of vegetation phenology information from Earth Observation Satellite (EOS) data is not easy, requiring efficient processing algorithms to properly handle the large amounts of data gathered. The purpose of this work is to present a new, easy-to-use software tool that produces phenology information from EOS vegetation temporal data — PhenoSat. This paper describes PhenoSat, focusing on two new features: the determination of the beginning and maximum of a double growth season, and the selection of a temporal sub-region of interest in order to reduce and control the data evaluated.
{"title":"PhenoSat — A tool for vegetation temporal analysis from satellite image data","authors":"A. Rodrigues, A. Marçal, M. Cunha","doi":"10.1109/MULTI-TEMP.2011.6005044","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005044","url":null,"abstract":"The availability of temporal satellite image data has increased considerably in recent years. A number of satellite sensors currently observe the Earth with high temporal frequency thus providing a tool for monitoring/understanding the Earth-surface variability more precisely, for several applications such as the analysis of vegetation dynamics. However, the extraction of vegetation phenology information from Earth Observation Satellite (EOS) data is not easy, requiring efficient processing algorithms to properly handle the large amounts of data gathered. The purpose of this work is to present a new, easy-to-use software tool that produces phenology information from EOS vegetation temporal data — PhenoSat. This paper describes PhenoSat, focusing on two new features: the determination of the beginning and maximum of a double growth season, and the selection of a temporal sub-region of interest in order to reduce and control the data evaluated.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"151 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":"121991495","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.2011.6005054
S. Cui, M. Datcu
In this paper, a patch based method for multi-temporal analysis of high resolution image is proposed. Conventionally, multi-temporal analysis performed at pixel level suffer from several restrictions, e.g., registration, bi-temporal analysis. To overcome these restrictions, two methods for multi-temporal analysis are proposed at patch level. One is for change detection in time series data by classifying all pairs of patches along time axis in the whole sequence into two classes. Features used for classification are similarity measures based on local statistical models and histogram of local patterns. The other aims at evolution analysis in long image time series. To characterize the evolution patterns, spatio-temporal local pattern features are extracted from time series data. ν-support vector machine (ν-SVM) is applied to classify different kinds of evolution at patch level. Performance is evaluated based on our database produced by iterative classification.
{"title":"Coarse to fine patches-based multitemporal analysis of very high resolution satellite images","authors":"S. Cui, M. Datcu","doi":"10.1109/MULTI-TEMP.2011.6005054","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005054","url":null,"abstract":"In this paper, a patch based method for multi-temporal analysis of high resolution image is proposed. Conventionally, multi-temporal analysis performed at pixel level suffer from several restrictions, e.g., registration, bi-temporal analysis. To overcome these restrictions, two methods for multi-temporal analysis are proposed at patch level. One is for change detection in time series data by classifying all pairs of patches along time axis in the whole sequence into two classes. Features used for classification are similarity measures based on local statistical models and histogram of local patterns. The other aims at evolution analysis in long image time series. To characterize the evolution patterns, spatio-temporal local pattern features are extracted from time series data. ν-support vector machine (ν-SVM) is applied to classify different kinds of evolution at patch level. Performance is evaluated based on our database produced by iterative classification.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"82 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":"129334069","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.2011.6005066
S. Hachicha, C. Deledalle, F. Chaabane, F. Tupin
Multitemporal SAR images are a very useful source of information for geophysicists, especially for change monitoring. In this paper, a new SAR change detection and monitoring approach is proposed through the analysis of a time series of SAR images covering the same region. The first contribution of this work is the SAR filtering preprocessing step using an extension of the spatial NL-means filter to the temporal domain. Then, the Rayleigh Kullback Leibler measure is used to detect the changes between a reference image and each SAR image. This leads to the second contribution which consists on a temporal classification based on changes images and describing the temporal behaviour of the changing regions.
{"title":"Multi-temporal SAR classification according to change detection operators","authors":"S. Hachicha, C. Deledalle, F. Chaabane, F. Tupin","doi":"10.1109/MULTI-TEMP.2011.6005066","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005066","url":null,"abstract":"Multitemporal SAR images are a very useful source of information for geophysicists, especially for change monitoring. In this paper, a new SAR change detection and monitoring approach is proposed through the analysis of a time series of SAR images covering the same region. The first contribution of this work is the SAR filtering preprocessing step using an extension of the spatial NL-means filter to the temporal domain. Then, the Rayleigh Kullback Leibler measure is used to detect the changes between a reference image and each SAR image. This leads to the second contribution which consists on a temporal classification based on changes images and describing the temporal behaviour of the changing regions.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"33 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":"132440262","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.2011.6005039
Shuang Li, Yichun Xie, L. Meng
The grassland in China occupies more than 40% of its rural land area. However, grassland degradation has been a serious problem in recent years. Thus, a policy of returning cultivated land into grassland is enacted. An object-oriented image classification using different feature objects was adopted to classify grassland and a hierarchy of layers in different years for change detection was deployed in this paper to monitor land cover changes. An experiment was conducted in Hulun Buir Meadow in Inner Mongolia, China. The experiment shows that the accuracy of classification obtained by the object-oriented method is much higher than that of the traditional unsupervised ISODATA classification. Grassland protection action is taking effect maintaining a sustainable use of grassland ecosystem.
{"title":"Monitoring land cover changes in Hulun Buir by using object-oriented method","authors":"Shuang Li, Yichun Xie, L. Meng","doi":"10.1109/MULTI-TEMP.2011.6005039","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005039","url":null,"abstract":"The grassland in China occupies more than 40% of its rural land area. However, grassland degradation has been a serious problem in recent years. Thus, a policy of returning cultivated land into grassland is enacted. An object-oriented image classification using different feature objects was adopted to classify grassland and a hierarchy of layers in different years for change detection was deployed in this paper to monitor land cover changes. An experiment was conducted in Hulun Buir Meadow in Inner Mongolia, China. The experiment shows that the accuracy of classification obtained by the object-oriented method is much higher than that of the traditional unsupervised ISODATA classification. Grassland protection action is taking effect maintaining a sustainable use of grassland ecosystem.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"463 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":"116234340","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.2011.6005061
A. Verger, F. Baret, M. Weiss, S. Kandasamy, E. Vermote
Scaling variations and shifts in the timing of seasonal phenology are central features of global change research. In this study, we propose a novel climatology fitting approach to quantify inter-annual anomalies in LAI seasonality. A consistent archive of daily LAI estimates was first derived from historical AVHRR satellite data for the 1981–2000 period over a globally representative sample of sites. The climatology values were then computed by averaging multi-year LAI profiles, gap filling and smoothing to eliminate possible high temporal frequency residual artifacts. The inter-annual variations in LAI were finally quantified by scaling and shifting the seasonal climatological patterns to the actual observations. In addition to capturing LAI dynamics and quantifying anomalies, this climatology fitting approach allows improving the continuity and consistency of time series by filling gaps and smoothing LAI dynamics.
{"title":"Quantification of LAI interannual anomalies by adjusting climatological patterns","authors":"A. Verger, F. Baret, M. Weiss, S. Kandasamy, E. Vermote","doi":"10.1109/MULTI-TEMP.2011.6005061","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005061","url":null,"abstract":"Scaling variations and shifts in the timing of seasonal phenology are central features of global change research. In this study, we propose a novel climatology fitting approach to quantify inter-annual anomalies in LAI seasonality. A consistent archive of daily LAI estimates was first derived from historical AVHRR satellite data for the 1981–2000 period over a globally representative sample of sites. The climatology values were then computed by averaging multi-year LAI profiles, gap filling and smoothing to eliminate possible high temporal frequency residual artifacts. The inter-annual variations in LAI were finally quantified by scaling and shifting the seasonal climatological patterns to the actual observations. In addition to capturing LAI dynamics and quantifying anomalies, this climatology fitting approach allows improving the continuity and consistency of time series by filling gaps and smoothing LAI dynamics.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"101 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":"114602193","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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