Pub Date : 2011-07-12DOI: 10.1109/MULTI-TEMP.2011.6005059
R. Colditz, R. Llamas
Vegetation productivity models and many others for hydrology and biogeochemistry studies require biophysical variables such as the leaf area index (LAI). LAI is part of the 13 essential terrestrial variables to monitor climate change. The index can be retrieved by various methods from optical satellite data and is a standard product in the MODIS processing chain at 1km spatial resolution. This study explores the temporal relations between LAI and vegetation indices and applies regression functions to obtain a 250m LAI product.
{"title":"Generation of 250m MODIS LAI time series by temporal regression","authors":"R. Colditz, R. Llamas","doi":"10.1109/MULTI-TEMP.2011.6005059","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005059","url":null,"abstract":"Vegetation productivity models and many others for hydrology and biogeochemistry studies require biophysical variables such as the leaf area index (LAI). LAI is part of the 13 essential terrestrial variables to monitor climate change. The index can be retrieved by various methods from optical satellite data and is a standard product in the MODIS processing chain at 1km spatial resolution. This study explores the temporal relations between LAI and vegetation indices and applies regression functions to obtain a 250m LAI product.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"5 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":"124396349","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.2011.6005052
J. Inglada, O. Hagolle, G. Dedieu
In the coming years, several optical space-borne systems with high resolution, high temporal frequency revisit and constant viewing angles will be launched. The availability of these data opens the opportunity for the development of new applications which require to closely monitor the temporal trajectory of the characteristics of land surfaces. However, due to cloud cover and even to some rapid changes, a higher temporal resolution may be needed for some applications. One of the ways to improve the temporal resolution for these satellites is to merge their data with higher temporal resolution systems. For now, these other systems will fatally have a lower spatial resolution or a limited field of view. The goal of our work is to assess the usefulness of image fusion techniques for the joint use of Proba-V/Sentinel-3 data and Venus/Sentinel−2 images for land-cover monitoring. We are interested in the generation of land-cover maps and time profiles of surface reflectances with a spatial resolution of 10 to 30 m. with an update frequency of about 10 days.
{"title":"Low and high spatial resolution time series fusion for improved land cover map production","authors":"J. Inglada, O. Hagolle, G. Dedieu","doi":"10.1109/MULTI-TEMP.2011.6005052","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005052","url":null,"abstract":"In the coming years, several optical space-borne systems with high resolution, high temporal frequency revisit and constant viewing angles will be launched. The availability of these data opens the opportunity for the development of new applications which require to closely monitor the temporal trajectory of the characteristics of land surfaces. However, due to cloud cover and even to some rapid changes, a higher temporal resolution may be needed for some applications. One of the ways to improve the temporal resolution for these satellites is to merge their data with higher temporal resolution systems. For now, these other systems will fatally have a lower spatial resolution or a limited field of view. The goal of our work is to assess the usefulness of image fusion techniques for the joint use of Proba-V/Sentinel-3 data and Venus/Sentinel−2 images for land-cover monitoring. We are interested in the generation of land-cover maps and time profiles of surface reflectances with a spatial resolution of 10 to 30 m. with an update frequency of about 10 days.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"2 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":"115482410","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.2011.6005072
L. Gueguen, M. Pesaresi, D. Ehrlich, Linlin Lu
A method for analyzing the urbanization process from multitemporal SPOT 5 panchromatic images is presented. A region-based local Mutual Information change indicator is proposed to perform the change analysis of large scenes. Experiments are conducted for mapping the urbanization of Tangshang, China, in between 2003 and 2008. The results show the efficiency of the change detection method.
{"title":"Urbanization analysis by mutual information based change detection between SPOT 5 panchromatic images","authors":"L. Gueguen, M. Pesaresi, D. Ehrlich, Linlin Lu","doi":"10.1109/MULTI-TEMP.2011.6005072","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005072","url":null,"abstract":"A method for analyzing the urbanization process from multitemporal SPOT 5 panchromatic images is presented. A region-based local Mutual Information change indicator is proposed to perform the change analysis of large scenes. Experiments are conducted for mapping the urbanization of Tangshang, China, in between 2003 and 2008. The results show the efficiency of the change detection method.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"29 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":"130357558","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.2011.6005071
W. Bijker, N. Hamm, Julian Ijumulana, Misganaw Kebede Wole
This study shows two approaches to including uncertainty of the mapped feature in multi-temporal analysis. This is demonstrated on a series of Landsat ETM+ images of Lake Naivasha, Kenya, with fuzzy boundaries resulting from marshes and floating vegetation. The first approach creates image segments, merges these to image objects through object-based classification and calculates the uncertainty for the lake image object in each image. The second approach uses a soft classifier to calculate memberships for lake and land. The lake area is calculated for 6 different thresholds on membership for each “lake” membership image, reflecting thresholds on the uncertainty in the estimate. The method based on image objects and attached uncertainty provided a quick overview and highlights uncertainty related to image quality and time of observation. The method based on thresholding of membership gave more spatial detail, highlighting the effect of fuzzy boundaries.
{"title":"Monitoring a fuzzy object: The case of Lake Naivasha","authors":"W. Bijker, N. Hamm, Julian Ijumulana, Misganaw Kebede Wole","doi":"10.1109/MULTI-TEMP.2011.6005071","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005071","url":null,"abstract":"This study shows two approaches to including uncertainty of the mapped feature in multi-temporal analysis. This is demonstrated on a series of Landsat ETM+ images of Lake Naivasha, Kenya, with fuzzy boundaries resulting from marshes and floating vegetation. The first approach creates image segments, merges these to image objects through object-based classification and calculates the uncertainty for the lake image object in each image. The second approach uses a soft classifier to calculate memberships for lake and land. The lake area is calculated for 6 different thresholds on membership for each “lake” membership image, reflecting thresholds on the uncertainty in the estimate. The method based on image objects and attached uncertainty provided a quick overview and highlights uncertainty related to image quality and time of observation. The method based on thresholding of membership gave more spatial detail, highlighting the effect of fuzzy boundaries.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"5 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":"114194659","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.2011.6005094
S. M. Issa, A. Al Shuwaihi
Remote sensing data integrated with GIS techniques were used together with socio-economic data in a post-classification analysis to map the spatial dynamics of land use/cover changes and identify the urbanization process in Al Ain resort city, United Arab Emirates. Land use/cover statistics, extracted from Landsat Multi-spectral Scanner (MSS). Thematic Mapper (TM) and Enhanced Thematic Mapper plus (ETM +) images for 1972. 1990 and 2000 respectively, revealed that the built-up area has expanded by about 170.53km2. The city was found to have a tendency for major expansion in four different directions: North, North-east, southeast, and south-west. GIS overlay analysis of multi-temporal satellite data helped us tracking the different classes' trajectories by adopting a GIS coding system unique to each class.
{"title":"Analysis of LULC changes and urban expansion of the resort city of Al Ain using remote sensing and GIS","authors":"S. M. Issa, A. Al Shuwaihi","doi":"10.1109/MULTI-TEMP.2011.6005094","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005094","url":null,"abstract":"Remote sensing data integrated with GIS techniques were used together with socio-economic data in a post-classification analysis to map the spatial dynamics of land use/cover changes and identify the urbanization process in Al Ain resort city, United Arab Emirates. Land use/cover statistics, extracted from Landsat Multi-spectral Scanner (MSS). Thematic Mapper (TM) and Enhanced Thematic Mapper plus (ETM +) images for 1972. 1990 and 2000 respectively, revealed that the built-up area has expanded by about 170.53km2. The city was found to have a tendency for major expansion in four different directions: North, North-east, southeast, and south-west. GIS overlay analysis of multi-temporal satellite data helped us tracking the different classes' trajectories by adopting a GIS coding system unique to each class.","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":"114710227","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.2011.6005075
Adriano de Oliveira Vasconcelos, L. Landau, Fernando Pellon de Miranda
Construction of the Petrochemical Complex of Rio de Janeiro (COMPERJ) will introduce a new scenario to the Guapi-Mirim Environmental Protection Area (EPA) in the coming years, since it will require constant environmental monitoring so as to portray its ecological evolution. Therefore, the objective of this paper is to perform a multi-temporal analysis of the Guapi-Mirim EPA, using object-based segmentation and classification techniques applied to IKONOS II images, in order to characterize changes in land use and cover types in the investigated site. Two scenes of the IKONOS II sensor acquired on 2006 and 2008 were chosen for the study. Overall results reveal a regeneration stage for the mangrove ecosystem and a stagnation of the urban area growth within the limits of the Guapi-Mirim EPA.
{"title":"Multi-temporal analysis of a mangrove ecosystem in Southeastern Brazil using object-based classification applied to IKONOS II data","authors":"Adriano de Oliveira Vasconcelos, L. Landau, Fernando Pellon de Miranda","doi":"10.1109/MULTI-TEMP.2011.6005075","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005075","url":null,"abstract":"Construction of the Petrochemical Complex of Rio de Janeiro (COMPERJ) will introduce a new scenario to the Guapi-Mirim Environmental Protection Area (EPA) in the coming years, since it will require constant environmental monitoring so as to portray its ecological evolution. Therefore, the objective of this paper is to perform a multi-temporal analysis of the Guapi-Mirim EPA, using object-based segmentation and classification techniques applied to IKONOS II images, in order to characterize changes in land use and cover types in the investigated site. Two scenes of the IKONOS II sensor acquired on 2006 and 2008 were chosen for the study. Overall results reveal a regeneration stage for the mangrove ecosystem and a stagnation of the urban area growth within the limits of the Guapi-Mirim EPA.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"7 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":"124317343","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.2011.6005053
J. Amorós-López, L. Gómez-Chova, L. Guanter, L. Alonso, J. Moreno, Gustau Camps-Valls
Monitoring Earth dynamics from current and future observation satellites is one of the most important objectives for the remote sensing community. In this regard, the exploitation of image time series from sensors with different characteristics provides an opportunity to increase the knowledge about environmental changes, which are needed in many operational applications, such as monitoring vegetation dynamics and land cover/use changes. Many studies in the literature have proven that high spatial resolution sensors like Landsat are very useful for monitoring land cover changes. However, the cloud cover probability of many areas and the 15-days temporal resolution restrict its use to monitor rapid variation phenomena. On the contrary, sensors with coarser spatial resolution like MERIS acquire images every 1-3 days. In this paper, Landsat/TM and ENVISAT/MERIS sensors are combined in a synergistic manner to enhance image time series at high spatial resolution using the temporal information provided by the MERIS sensor. The capabilities of the proposed methodology are illustrated using a temporal image series of both sensors acquired over Albacete (Spain) in 2004. Additionally, the temporal profile of the NDVI is selected as demonstrative application of agricultural monitoring.
{"title":"Multitemporal fusion of Landsat and MERIS images","authors":"J. Amorós-López, L. Gómez-Chova, L. Guanter, L. Alonso, J. Moreno, Gustau Camps-Valls","doi":"10.1109/MULTI-TEMP.2011.6005053","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005053","url":null,"abstract":"Monitoring Earth dynamics from current and future observation satellites is one of the most important objectives for the remote sensing community. In this regard, the exploitation of image time series from sensors with different characteristics provides an opportunity to increase the knowledge about environmental changes, which are needed in many operational applications, such as monitoring vegetation dynamics and land cover/use changes. Many studies in the literature have proven that high spatial resolution sensors like Landsat are very useful for monitoring land cover changes. However, the cloud cover probability of many areas and the 15-days temporal resolution restrict its use to monitor rapid variation phenomena. On the contrary, sensors with coarser spatial resolution like MERIS acquire images every 1-3 days. In this paper, Landsat/TM and ENVISAT/MERIS sensors are combined in a synergistic manner to enhance image time series at high spatial resolution using the temporal information provided by the MERIS sensor. The capabilities of the proposed methodology are illustrated using a temporal image series of both sensors acquired over Albacete (Spain) in 2004. Additionally, the temporal profile of the NDVI is selected as demonstrative application of agricultural monitoring.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"23 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":"125733008","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.2011.6005078
A. Cord, D. Klein, S. Dech
Predictions of species occurrence as indicators of ecosystem integrity are of high relevance for decision-makers in conservation biology, invasive species' management, and climate change research. Remote sensing data can serve as valuable input for Species Distribution Models (SDMs) since they provide information on current habitat conditions and disturbance factors besides bioclimatic suitability which is commonly derived from climatic data. However, little is known about the usefulness of multi-temporal remote sensing data in general for modeling species distributions and the related effects of inter-annual variability on the extent and accuracy of modeled distribution ranges. This study investigates the above-mentioned questions for two tropical tree species, Brosimum alicastrum and Liquidambar macrophylla, in Mexico. From the MODIS 16-day vegetation index product (MOD13A2), 18 annual phenological metrics (time-related, NPP-related and seasonality-related) were computed for the period from 2001 to 2009 and combined to a set of multi-year average values (covering 3, 5, 7, and 9 years). The results show that inter-annual variability has a significant impact on model predictions and that models based on longer composite periods show less deviance from observed species presence-absence field data.
{"title":"The impact of inter-annual variability in remote sensing time series on modeling tree species distributions","authors":"A. Cord, D. Klein, S. Dech","doi":"10.1109/MULTI-TEMP.2011.6005078","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005078","url":null,"abstract":"Predictions of species occurrence as indicators of ecosystem integrity are of high relevance for decision-makers in conservation biology, invasive species' management, and climate change research. Remote sensing data can serve as valuable input for Species Distribution Models (SDMs) since they provide information on current habitat conditions and disturbance factors besides bioclimatic suitability which is commonly derived from climatic data. However, little is known about the usefulness of multi-temporal remote sensing data in general for modeling species distributions and the related effects of inter-annual variability on the extent and accuracy of modeled distribution ranges. This study investigates the above-mentioned questions for two tropical tree species, Brosimum alicastrum and Liquidambar macrophylla, in Mexico. From the MODIS 16-day vegetation index product (MOD13A2), 18 annual phenological metrics (time-related, NPP-related and seasonality-related) were computed for the period from 2001 to 2009 and combined to a set of multi-year average values (covering 3, 5, 7, and 9 years). The results show that inter-annual variability has a significant impact on model predictions and that models based on longer composite periods show less deviance from observed species presence-absence field data.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"27 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":"122279167","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.2011.6005055
M. P. Mello, F. Martins, L. Sato, R. Cantinho, D. A. Aguiar, B. Rudorff, Rafael Santos
Spectral-Temporal Analysis by Response Surface (STARS), which exploits both multispectral and multitemporal information using fitted response surfaces, was used to describe deforestation patterns in the Brazilian Amazon. The STARS was conducted upon a MODIS dataset formed by 21 selected cloud free images (eight days composition) acquired from August 2003 to August 2004. The Multi-Coefficient Image (MCI) resulted from the STARS was used as input attributes for the three tested classifiers: Instance Based K-nearest neighbor (IBK), Decision Tree (DT) and Neural Network (NN). The IBK classifier presented the highest accuracy (K=0.93) in detecting deforestation also indicating the deforestation period (early or late in the year). The results showed that the STARS is promising to describe spectral change patterns over time, allowing detection of the deforestation process which occurs in the Brazilian Amazon.
{"title":"Spectral-Temporal Analysis by Response Surface applied to detect deforestation in the Brazilian Amazon","authors":"M. P. Mello, F. Martins, L. Sato, R. Cantinho, D. A. Aguiar, B. Rudorff, Rafael Santos","doi":"10.1109/MULTI-TEMP.2011.6005055","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005055","url":null,"abstract":"Spectral-Temporal Analysis by Response Surface (STARS), which exploits both multispectral and multitemporal information using fitted response surfaces, was used to describe deforestation patterns in the Brazilian Amazon. The STARS was conducted upon a MODIS dataset formed by 21 selected cloud free images (eight days composition) acquired from August 2003 to August 2004. The Multi-Coefficient Image (MCI) resulted from the STARS was used as input attributes for the three tested classifiers: Instance Based K-nearest neighbor (IBK), Decision Tree (DT) and Neural Network (NN). The IBK classifier presented the highest accuracy (K=0.93) in detecting deforestation also indicating the deforestation period (early or late in the year). The results showed that the STARS is promising to describe spectral change patterns over time, allowing detection of the deforestation process which occurs in the Brazilian Amazon.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"22 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":"130199192","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.2011.6005065
B. Ventura, T. Schellenberger, C. Notarnicola, M. Zebisch, T. Nagler, H. Rott, V. Maddalena, R. Ratti, L. Tampellini
The multi-temporal technique for the detection of wet snow, based on the different response of snow and no-snow area was applied to COSMO-SkyMed (CSK) images acquired over South Tyrol (Northern Italy) during the first year of activity in the project “SNOX — snow cover and glacier monitoring in alpine areas with COSMO-SkyMed X-band data” funded by the Italian Space Agency. A standard threshold value of −3 dB and a less restrictive threshold of −2.3 dB are compared for an improved distinction of the snow and no-snow distributions. The application of these two different thresholds determines a variation of snow cover area (SCA) between 2% and 8%. Furthermore, it has been proved that the choice of the reference image for the no-snow areas is critical and can determine a variability in the resulting SCA up to 10%. The depolarization factor, σ0VH/σ0VV, is also exploited to evaluate its contribution to the identification of snow covered areas. Preliminary results indicate that the contribution of the depolarization factor to the SCA detection is very limited.
{"title":"Snow cover monitoring in alpine regions with COSMO-SkyMed images by using a multitemporal approach and depolarization ratio","authors":"B. Ventura, T. Schellenberger, C. Notarnicola, M. Zebisch, T. Nagler, H. Rott, V. Maddalena, R. Ratti, L. Tampellini","doi":"10.1109/MULTI-TEMP.2011.6005065","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005065","url":null,"abstract":"The multi-temporal technique for the detection of wet snow, based on the different response of snow and no-snow area was applied to COSMO-SkyMed (CSK) images acquired over South Tyrol (Northern Italy) during the first year of activity in the project “SNOX — snow cover and glacier monitoring in alpine areas with COSMO-SkyMed X-band data” funded by the Italian Space Agency. A standard threshold value of −3 dB and a less restrictive threshold of −2.3 dB are compared for an improved distinction of the snow and no-snow distributions. The application of these two different thresholds determines a variation of snow cover area (SCA) between 2% and 8%. Furthermore, it has been proved that the choice of the reference image for the no-snow areas is critical and can determine a variability in the resulting SCA up to 10%. The depolarization factor, σ0VH/σ0VV, is also exploited to evaluate its contribution to the identification of snow covered areas. Preliminary results indicate that the contribution of the depolarization factor to the SCA detection is very limited.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"35 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":"134014428","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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