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The convergence of new EO data flows, new methodological developments and cloud computing infrastructure calls for a paradigm shift in operational agriculture monitoring. The Copernicus Sentinel-2 mission providing a systematic 5-day revisit cycle and free data access opens a completely new avenue for near real-time crop specific monitoring at parcel level over large countries. This research investigated the feasibility to propose methods and to develop an open source system able to generate, at national scale, cloud-free composites, dynamic cropland masks, crop type maps and vegetation status indicators suitable for most cropping systems. The so-called Sen2-Agri system automatically ingests and processes Sentinel-2 and Landsat 8 time series in a seamless way to derive these four products, thanks to streamlined processes based on machine learning algorithms and quality controlled in situ data. It embeds a set of key principles proposed to address the new challenges arising from countrywide 10 m resolution agriculture monitoring. The full-scale demonstration of this system for three entire countries (Ukraine, Mali, South Africa) and five local sites distributed across the world was a major challenge met successfully despite the availability of only one Sentinel-2 satellite in orbit. In situ data were collected for calibration and validation in a timely manner allowing the production of the four Sen2-Agri products over all the demonstration sites. The independent validation of the monthly cropland masks provided for most sites overall accuracy values higher than 90%, and already higher than 80% as early as the mid-season. The crop type maps depicting the 5 main crops for the considered study sites were also successfully validated: overall accuracy values higher than 80% and F1 Scores of the different crop type classes were most often higher than 0.65. These respective results pave the way for countrywide crop specific monitoring system at parcel level bridging the gap between parcel visits and national scale assessment. These full-scale demonstration results clearly highlight the operational agriculture monitoring capacity of the Sen2-Agri system to exploit in near real-time the observation acquired by the Sentinel-2 mission over very large areas. Scaling this open source system on cloud computing infrastructure becomes instrumental to support market transparency while building national monitoring capacity as requested by the AMIS and GEOGLAM G-20 initiatives.
Pierre Defourny; Sophie Bontemps; Nicolas Bellemans; Cosmin Cara; Gérard Dedieu; Eric Guzzonato; Olivier Hagolle; Jordi Inglada; Laurentiu Nicola; Thierry Rabaute; Mickael Savinaud; Cosmin Udroiu; Silvia Valero; Agnès Bégué; Jean-François Dejoux; Abderrazak El Harti; Jamal Ezzahar; Nataliia Kussul; Kamal Labbassi; Valentine Lebourgeois; Zhang Miao; Terence Newby; Adolph Nyamugama; Norakhan Salh; Andrii Shelestov; Vincent Simonneaux; Pierre Sibiry Traore; Souleymane S. Traore; Benjamin Koetz. Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world. Remote Sensing of Environment 2018, 221, 551 -568.
AMA StylePierre Defourny, Sophie Bontemps, Nicolas Bellemans, Cosmin Cara, Gérard Dedieu, Eric Guzzonato, Olivier Hagolle, Jordi Inglada, Laurentiu Nicola, Thierry Rabaute, Mickael Savinaud, Cosmin Udroiu, Silvia Valero, Agnès Bégué, Jean-François Dejoux, Abderrazak El Harti, Jamal Ezzahar, Nataliia Kussul, Kamal Labbassi, Valentine Lebourgeois, Zhang Miao, Terence Newby, Adolph Nyamugama, Norakhan Salh, Andrii Shelestov, Vincent Simonneaux, Pierre Sibiry Traore, Souleymane S. Traore, Benjamin Koetz. Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world. Remote Sensing of Environment. 2018; 221 ():551-568.
Chicago/Turabian StylePierre Defourny; Sophie Bontemps; Nicolas Bellemans; Cosmin Cara; Gérard Dedieu; Eric Guzzonato; Olivier Hagolle; Jordi Inglada; Laurentiu Nicola; Thierry Rabaute; Mickael Savinaud; Cosmin Udroiu; Silvia Valero; Agnès Bégué; Jean-François Dejoux; Abderrazak El Harti; Jamal Ezzahar; Nataliia Kussul; Kamal Labbassi; Valentine Lebourgeois; Zhang Miao; Terence Newby; Adolph Nyamugama; Norakhan Salh; Andrii Shelestov; Vincent Simonneaux; Pierre Sibiry Traore; Souleymane S. Traore; Benjamin Koetz. 2018. "Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world." Remote Sensing of Environment 221, no. : 551-568.
Accurate maps of surface water extent are of paramount importance for water management, satellite data processing and climate modeling. Several maps of water bodies based on remote sensing data have been released during the last decade. Nonetheless, none has a truly (90∘N/90∘S) global coverage while being thoroughly validated. This paper describes a global, spatially-complete (void-free) and accurate mask of inland/ocean water for the 2000–2012 period, built in the framework of the European Space Agency (ESA) Climate Change Initiative (CCI). This map results from the synergistic combination of multiple individual SAR and optical water body and auxiliary datasets. A key aspect of this work is the original and rigorous stratified random sampling designed for the quality assessment of binary classifications where one class is marginally distributed. Input and consolidated products were assessed qualitatively and quantitatively against a reference validation database of 2110 samples spread throughout the globe. Using all samples, overall accuracy was always very high among all products, between 98% and 100%. The CCI global map of open water bodies provided the best water class representation (F-score of 89%) compared to its constitutive inputs. When focusing on the challenging areas for water bodies’ mapping, such as shorelines, lakes and river banks, all products yielded substantially lower accuracy figures with overall accuracies ranging between 74% and 89%. The inland water area of the CCI global map of open water bodies was estimated to be 3.17 million km2 ± 0.24 million km2. The dataset is freely available through the ESA CCI Land Cover viewer.
Céline Lamarche; Maurizio Santoro; Sophie Bontemps; Raphaël D’Andrimont; Julien Radoux; Laura Giustarini; Carsten Brockmann; Jan Wevers; Pierre Defourny; Olivier Arino. Compilation and Validation of SAR and Optical Data Products for a Complete and Global Map of Inland/Ocean Water Tailored to the Climate Modeling Community. Remote Sensing 2017, 9, 36 .
AMA StyleCéline Lamarche, Maurizio Santoro, Sophie Bontemps, Raphaël D’Andrimont, Julien Radoux, Laura Giustarini, Carsten Brockmann, Jan Wevers, Pierre Defourny, Olivier Arino. Compilation and Validation of SAR and Optical Data Products for a Complete and Global Map of Inland/Ocean Water Tailored to the Climate Modeling Community. Remote Sensing. 2017; 9 (1):36.
Chicago/Turabian StyleCéline Lamarche; Maurizio Santoro; Sophie Bontemps; Raphaël D’Andrimont; Julien Radoux; Laura Giustarini; Carsten Brockmann; Jan Wevers; Pierre Defourny; Olivier Arino. 2017. "Compilation and Validation of SAR and Optical Data Products for a Complete and Global Map of Inland/Ocean Water Tailored to the Climate Modeling Community." Remote Sensing 9, no. 1: 36.
Accurate and timely information on the global cropland extent is critical for food security monitoring, water management and earth system modeling. Principally, it allows for analyzing satellite image time-series to assess the crop conditions and permits isolation of the agricultural component to focus on food security and impacts of various climatic scenarios. However, despite its critical importance, accurate information on the spatial extent, cropland mapping with remote sensing imagery remains a major challenge. Following an exhaustive identification and collection of existing land cover maps, a multi-criteria analysis was designed at the country level to evaluate the fitness of a cropland map with regards to four dimensions: its timeliness, its legend, its resolution adequacy and its confidence level. As a result, a Unified Cropland Layer that combines the fittest products into a 250 m global cropland map was assembled. With an evaluated accuracy ranging from 82% to 95%, the Unified Cropland Layer successfully improved the accuracy compared to single global products.
François Waldner; Steffen Fritz; Antonio Di Gregorio; Dmitry Plotnikov; Sergey Bartalev; Nataliia Kussul; Peng Gong; Prasad Thenkabail; Gerard Hazeu; Igor Klein; Fabian Löw; Jukka Miettinen; Vinay Kumar Dadhwal; Céline Lamarche; Sophie Bontemps; Pierre Defourny. A Unified Cropland Layer at 250 m for Global Agriculture Monitoring. Data 2016, 1, 3 .
AMA StyleFrançois Waldner, Steffen Fritz, Antonio Di Gregorio, Dmitry Plotnikov, Sergey Bartalev, Nataliia Kussul, Peng Gong, Prasad Thenkabail, Gerard Hazeu, Igor Klein, Fabian Löw, Jukka Miettinen, Vinay Kumar Dadhwal, Céline Lamarche, Sophie Bontemps, Pierre Defourny. A Unified Cropland Layer at 250 m for Global Agriculture Monitoring. Data. 2016; 1 (1):3.
Chicago/Turabian StyleFrançois Waldner; Steffen Fritz; Antonio Di Gregorio; Dmitry Plotnikov; Sergey Bartalev; Nataliia Kussul; Peng Gong; Prasad Thenkabail; Gerard Hazeu; Igor Klein; Fabian Löw; Jukka Miettinen; Vinay Kumar Dadhwal; Céline Lamarche; Sophie Bontemps; Pierre Defourny. 2016. "A Unified Cropland Layer at 250 m for Global Agriculture Monitoring." Data 1, no. 1: 3.
The exploitation of new high revisit frequency satellite observations is an important opportunity for agricultural applications. The Sentinel-2 for Agriculture project S2Agri (http://www.esa-sen2agri.org/SitePages/Home.aspx) is designed to develop, demonstrate and facilitate the Sentinel-2 time series contribution to the satellite EO component of agriculture monitoring for many agricultural systems across the globe. In the framework of this project, this article studies the construction of a dynamic cropland mask. This mask consists of a binary “annual-cropland/no-annual-cropland” map produced several times during the season to serve as a mask for monitoring crop growing conditions over the growing season. The construction of the mask relies on two classical pattern recognition techniques: feature extraction and classification. One pixel- and two object-based strategies are proposed and compared. A set of 12 test sites are used to benchmark the methods and algorithms with regard to the diversity of the agro-ecological context, landscape patterns, agricultural practices and actual satellite observation conditions. The classification results yield promising accuracies of around 90% at the end of the agricultural season. Efforts will be made to transition this research into operational products once Sentinel-2 data become available.
Silvia Valero; David Morin; Jordi Inglada; Guadalupe Sepulcre; Marcela Arias; Olivier Hagolle; Gérard Dedieu; Sophie Bontemps; Pierre Defourny; Benjamin Koetz. Production of a Dynamic Cropland Mask by Processing Remote Sensing Image Series at High Temporal and Spatial Resolutions. Remote Sensing 2016, 8, 55 .
AMA StyleSilvia Valero, David Morin, Jordi Inglada, Guadalupe Sepulcre, Marcela Arias, Olivier Hagolle, Gérard Dedieu, Sophie Bontemps, Pierre Defourny, Benjamin Koetz. Production of a Dynamic Cropland Mask by Processing Remote Sensing Image Series at High Temporal and Spatial Resolutions. Remote Sensing. 2016; 8 (1):55.
Chicago/Turabian StyleSilvia Valero; David Morin; Jordi Inglada; Guadalupe Sepulcre; Marcela Arias; Olivier Hagolle; Gérard Dedieu; Sophie Bontemps; Pierre Defourny; Benjamin Koetz. 2016. "Production of a Dynamic Cropland Mask by Processing Remote Sensing Image Series at High Temporal and Spatial Resolutions." Remote Sensing 8, no. 1: 55.
Developing better agricultural monitoring capabilities based on Earth Observation data is critical for strengthening food production information and market transparency. The Sentinel-2 mission has the optimal capacity for regional to global agriculture monitoring in terms of resolution (10–20 meter), revisit frequency (five days) and coverage (global). In this context, the European Space Agency launched in 2014 the “Sentinel2 for Agriculture” project, which aims to prepare the exploitation of Sentinel-2 data for agriculture monitoring through the development of open source processing chains for relevant products. The project generated an unprecedented data set, made of “Sentinel-2 like” time series and in situ data acquired in 2013 over 12 globally distributed sites. Earth Observation time series were mostly built on the SPOT4 (Take 5) data set, which was specifically designed to simulate Sentinel-2. They also included Landsat 8 and RapidEye imagery as complementary data sources. Images were pre-processed to Level 2A and the quality of the resulting time series was assessed. In situ data about cropland, crop type and biophysical variables were shared by site managers, most of them belonging to the “Joint Experiment for Crop Assessment and Monitoring” network. This data set allowed testing and comparing across sites the methodologies that will be at the core of the future “Sentinel2 for Agriculture” system.
Sophie Bontemps; Marcela Arias; Cosmin Cara; Gérard Dedieu; Eric Guzzonato; Olivier Hagolle; Jordi Inglada; Nicolas Matton; David Morin; Ramona Popescu; Thierry Rabaute; Mickael Savinaud; Guadalupe Sepulcre; Silvia Valero; Ijaz Ahmad; Agnès Bégué; Bingfang Wu; Diego De Abelleyra; Alhousseine Diarra; Stéphane Dupuy; Andrew French; Ibrar Ul Hassan Akhtar; Nataliia Kussul; Valentine Lebourgeois; Michel Le Page; Terence Newby; Igor Savin; Santiago R. Verón; Benjamin Koetz; Pierre Defourny. Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2. Remote Sensing 2015, 7, 16062 -16090.
AMA StyleSophie Bontemps, Marcela Arias, Cosmin Cara, Gérard Dedieu, Eric Guzzonato, Olivier Hagolle, Jordi Inglada, Nicolas Matton, David Morin, Ramona Popescu, Thierry Rabaute, Mickael Savinaud, Guadalupe Sepulcre, Silvia Valero, Ijaz Ahmad, Agnès Bégué, Bingfang Wu, Diego De Abelleyra, Alhousseine Diarra, Stéphane Dupuy, Andrew French, Ibrar Ul Hassan Akhtar, Nataliia Kussul, Valentine Lebourgeois, Michel Le Page, Terence Newby, Igor Savin, Santiago R. Verón, Benjamin Koetz, Pierre Defourny. Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2. Remote Sensing. 2015; 7 (12):16062-16090.
Chicago/Turabian StyleSophie Bontemps; Marcela Arias; Cosmin Cara; Gérard Dedieu; Eric Guzzonato; Olivier Hagolle; Jordi Inglada; Nicolas Matton; David Morin; Ramona Popescu; Thierry Rabaute; Mickael Savinaud; Guadalupe Sepulcre; Silvia Valero; Ijaz Ahmad; Agnès Bégué; Bingfang Wu; Diego De Abelleyra; Alhousseine Diarra; Stéphane Dupuy; Andrew French; Ibrar Ul Hassan Akhtar; Nataliia Kussul; Valentine Lebourgeois; Michel Le Page; Terence Newby; Igor Savin; Santiago R. Verón; Benjamin Koetz; Pierre Defourny. 2015. "Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2." Remote Sensing 7, no. 12: 16062-16090.
The mapping of water bodies at global scale has been undertaken primarily using multi-spectral optical Earth Observation data. Limitations of optical data associated with non-uniform and temporally variable spectral signatures suggested investigating alternative approaches towards a more consistent and reliable detection of water bodies. Multi-year (2005–2012) observations of SAR backscattered intensities at moderate resolution from the Envisat Advanced Synthetic Aperture Radar (ASAR) instrument were used in this study to generate an indicator of open permanent water bodies (SAR-WBI) for the year 2010 time frame and for all land surfaces excluding Antarctica and the Greenland ice sheet. A first map of potential water bodies with a spatial resolution of 150 m was obtained with a global detection algorithm based on a set of thresholds applied to multi-temporal metrics of the SAR backscatter (temporal variability, TV, and minimum backscatter, MB). Local refinements were then used to reduce systematic commission and omission errors (4.6% of the total area mapped) due to the similarity of TV and MB over open water bodies and other land surface types primarily in cold and arid environments. The refinement rules are here explained by means of a detailed signature analysis of the SAR backscatter in such environments. The accuracy of the SAR-WBI was 80% when compared against 2078 manually interpreted footprints with a size of 150 × 150 m2. Omission errors were primarily observed along coast- and shorelines whereas commission errors were associated with (i) ephemeral water bodies, (ii) seasonally inundated areas, and (iii) an incorrect choice of the local refinement.
Maurizio Santoro; Urs Wegmüller; Céline Lamarche; Sophie Bontemps; Pierre Defourny; Olivier Arino. Strengths and weaknesses of multi-year Envisat ASAR backscatter measurements to map permanent open water bodies at global scale. Remote Sensing of Environment 2015, 171, 185 -201.
AMA StyleMaurizio Santoro, Urs Wegmüller, Céline Lamarche, Sophie Bontemps, Pierre Defourny, Olivier Arino. Strengths and weaknesses of multi-year Envisat ASAR backscatter measurements to map permanent open water bodies at global scale. Remote Sensing of Environment. 2015; 171 ():185-201.
Chicago/Turabian StyleMaurizio Santoro; Urs Wegmüller; Céline Lamarche; Sophie Bontemps; Pierre Defourny; Olivier Arino. 2015. "Strengths and weaknesses of multi-year Envisat ASAR backscatter measurements to map permanent open water bodies at global scale." Remote Sensing of Environment 171, no. : 185-201.
Cropland mapping relies heavily on field data for algorithm calibration, making it, in many cases, applicable only at the field campaign scale. While the recently launched Sentinel-2 satellite will be able to deliver time series over large regions, it will not really be compatible with the current mapping approach or the available in situ data. This research introduces a generic methodology for mapping annual cropland along the season at high spatial resolution with the use of globally available baseline land cover and no need for field data. The methodology is based on cropland-specific temporal features, which are able to cope with the diversity of agricultural systems, prior information from which mislabeled pixels have been removed and a cost-effective classifier. Thanks to the JECAM network, eight sites across the world were selected for global cropland mapping benchmarking. Accurate cropland maps were produced at the end of the season, showing an overall accuracy of more than 85%. Early cropland maps were also obtained at three-month intervals after the beginning of the growing season, and these showed reasonable accuracy at the three-month stage (>70% overall accuracy) and progressive improvement along the season. The trimming-based method was found to be key for using spatially coarse baseline land cover information and, thus, avoiding costly field campaigns for prior information retrieval. The accuracy and timeliness of the proposed approach shows that it has substantial potential for operational agriculture monitoring programs.
Nicolas Matton; Guadalupe Sepulcre Canto; François Waldner; Silvia Valero; David Morin; Jordi Inglada; Marcela Arias; Sophie Bontemps; Benjamin Koetz; Pierre Defourny. An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series. Remote Sensing 2015, 7, 13208 -13232.
AMA StyleNicolas Matton, Guadalupe Sepulcre Canto, François Waldner, Silvia Valero, David Morin, Jordi Inglada, Marcela Arias, Sophie Bontemps, Benjamin Koetz, Pierre Defourny. An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series. Remote Sensing. 2015; 7 (10):13208-13232.
Chicago/Turabian StyleNicolas Matton; Guadalupe Sepulcre Canto; François Waldner; Silvia Valero; David Morin; Jordi Inglada; Marcela Arias; Sophie Bontemps; Benjamin Koetz; Pierre Defourny. 2015. "An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series." Remote Sensing 7, no. 10: 13208-13232.
Crop area extent estimates and crop type maps provide crucial information for agricultural monitoring and management. Remote sensing imagery in general and, more specifically, high temporal and high spatial resolution data as the ones which will be available with upcoming systems, such as Sentinel-2, constitute a major asset for this kind of application. The goal of this paper is to assess to what extent state-of-the-art supervised classification methods can be applied to high resolution multi-temporal optical imagery to produce accurate crop type maps at the global scale. Five concurrent strategies for automatic crop type map production have been selected and benchmarked using SPOT4 (Take5) and Landsat 8 data over 12 test sites spread all over the globe (four in Europe, four in Africa, two in America and two in Asia). This variety of tests sites allows one to draw conclusions applicable to a wide variety of landscapes and crop systems. The results show that a random forest classifier operating on linearly temporally gap-filled images can achieve overall accuracies above 80% for most sites. Only two sites showed low performances: Madagascar due to the presence of fields smaller than the pixel size and Burkina Faso due to a mix of trees and crops in the fields. The approach is based on supervised machine learning techniques, which need in situ data collection for the training step, but the map production is fully automatic.
Jordi Inglada; Marcela Arias; Benjamin Tardy; Olivier Hagolle; Silvia Valero; David Morin; Gérard Dedieu; Guadalupe Sepulcre; Sophie Bontemps; Pierre Defourny; Benjamin Koetz. Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery. Remote Sensing 2015, 7, 12356 -12379.
AMA StyleJordi Inglada, Marcela Arias, Benjamin Tardy, Olivier Hagolle, Silvia Valero, David Morin, Gérard Dedieu, Guadalupe Sepulcre, Sophie Bontemps, Pierre Defourny, Benjamin Koetz. Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery. Remote Sensing. 2015; 7 (9):12356-12379.
Chicago/Turabian StyleJordi Inglada; Marcela Arias; Benjamin Tardy; Olivier Hagolle; Silvia Valero; David Morin; Gérard Dedieu; Guadalupe Sepulcre; Sophie Bontemps; Pierre Defourny; Benjamin Koetz. 2015. "Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery." Remote Sensing 7, no. 9: 12356-12379.
Land cover is one of the essential climate variables of the ESA Climate Change Initiative (CCI). In this context, the Land Cover CCI (LC CCI) project aims at building global land cover maps suitable for climate modeling based on Earth observation by satellite sensors. The challenge is to generate a set of successive maps that are both accurate and consistent over time. To do so, operational methods for the automated classification of optical images are investigated. The proposed approach consists of a locally trained classification using an automated selection of training samples from existing, but outdated land cover information. Combinations of local extraction (based on spatial criteria) and self-cleaning of training samples (based on spectral criteria) are quantitatively assessed. Two large study areas, one in Eurasia and the other in South America, are considered. The proposed morphological cleaning of the training samples leads to higher accuracies than the statistical outlier removal in the spectral domain. An optimal neighborhood has been identified for the local sample extraction. The results are coherent for the two test areas, showing an improvement of the overall accuracy compared with the original reference datasets and a significant reduction of macroscopic errors. More importantly, the proposed method partly controls the reliability of existing land cover maps as sources of training samples for supervised classification.
Julien Radoux; Céline Lamarche; Eric Van Bogaert; Sophie Bontemps; Carsten Brockmann; Pierre Defourny. Automated Training Sample Extraction for Global Land Cover Mapping. Remote Sensing 2014, 6, 3965 -3987.
AMA StyleJulien Radoux, Céline Lamarche, Eric Van Bogaert, Sophie Bontemps, Carsten Brockmann, Pierre Defourny. Automated Training Sample Extraction for Global Land Cover Mapping. Remote Sensing. 2014; 6 (5):3965-3987.
Chicago/Turabian StyleJulien Radoux; Céline Lamarche; Eric Van Bogaert; Sophie Bontemps; Carsten Brockmann; Pierre Defourny. 2014. "Automated Training Sample Extraction for Global Land Cover Mapping." Remote Sensing 6, no. 5: 3965-3987.
Time series of vegetation indices (VIs) obtained by remote sensing are widely used to study phenology on regional and global scales. The aim of the study is to design a method and to produce a reference data set describing the seasonal and inter-annual variability of the land-surface phenology on a global scale. Specific constraints are inherent in the design of such a global reference data set: (1) the high diversity of vegetation types and the heterogeneous conditions of observation, (2) a near-daily resolution is needed to follow the rapid changes in phenology, (3) the time series used to depict the baseline vegetation cycle must be long enough to be representative of the current vegetation dynamic and encompass anomalies, and (4) a spatial resolution consistent with a land-cover-specific analysis should be privileged. This study focuses on the SPOT (Satellite Pour l'Observation de la Terre)-VEGETATION sensor and its 13-year time series of reflectance values. Five steps addressing the noise and the missing data in the reflectance time series were selected to process the daily multispectral reflectance observations. The final product provides, for every pixel, three profiles for 52 × 7-day periods: a mean, a median, and a standard deviation profile. The mean and median profiles represent the reference seasonal pattern for variation of the vegetation at a specific location whereas the standard deviation profile expresses the inter-annual variability of VIs. A quality flag at the pixel level demonstrated that the reference data set can be considered as a reliable representation of the vegetation phenology in most parts of the Earth. © 2014 Taylor & Francis
Astrid Verhegghen; Sophie Bontemps; Pierre Defourny. A global NDVI and EVI reference data set for land-surface phenology using 13 years of daily SPOT-VEGETATION observations. International Journal of Remote Sensing 2014, 35, 2440 -2471.
AMA StyleAstrid Verhegghen, Sophie Bontemps, Pierre Defourny. A global NDVI and EVI reference data set for land-surface phenology using 13 years of daily SPOT-VEGETATION observations. International Journal of Remote Sensing. 2014; 35 (7):2440-2471.
Chicago/Turabian StyleAstrid Verhegghen; Sophie Bontemps; Pierre Defourny. 2014. "A global NDVI and EVI reference data set for land-surface phenology using 13 years of daily SPOT-VEGETATION observations." International Journal of Remote Sensing 35, no. 7: 2440-2471.
Improving systematic observations of land cover, as an Essential Climate Variable, should contribute to a better understanding of the global climate system and thus improve our ability to predict climatic change. The aim of this paper is to bring global land cover observations closer to meeting the needs of climate science. First, consultation mechanisms were established with the climate modeling community to identify its specific requirements in terms of satellite-based global land cover products. This assessment highlighted specific needs in terms of land cover characterization, accuracy of products, as well as stability and consistency needs that are currently not met or even addressed. The current land cover representation and mapping techniques were then called into question to specifically focus on the critical need of stable products expressed by climate users. Decoupling the stable and dynamic components of the land cover characterization and using a multi-year dataset were proposed as two key approaches to allow generating consistent suites of global land cover products over time.
S. Bontemps; M. Herold; L. Kooistra; A. van Groenestijn; A. Hartley; O. Arino; I. Moreau; P. Defourny. Revisiting land cover observation to address the needs of the climate modeling community. Biogeosciences 2012, 9, 2145 -2157.
AMA StyleS. Bontemps, M. Herold, L. Kooistra, A. van Groenestijn, A. Hartley, O. Arino, I. Moreau, P. Defourny. Revisiting land cover observation to address the needs of the climate modeling community. Biogeosciences. 2012; 9 (6):2145-2157.
Chicago/Turabian StyleS. Bontemps; M. Herold; L. Kooistra; A. van Groenestijn; A. Hartley; O. Arino; I. Moreau; P. Defourny. 2012. "Revisiting land cover observation to address the needs of the climate modeling community." Biogeosciences 9, no. 6: 2145-2157.
Monitoring land cover over large areas on a yearly basis is challenging. The spatial and temporal consistency of an object-based change detection algorithm was tested through a multi-year application on the forest of Borneo, using SPOT-VEGETATION time series from 2000 to 2008. Continuous change thresholds allowed the tuning of the algorithm according to specific requirements in terms of omission and commission errors. The accuracy of the method was assessed using the ROC (relative operating characteristics) curves, which were found useful to evaluate the performance of the method independently of the selected threshold and to support the selection of an optimal threshold. The forest area that annually changed between 2000 and 2008 was detected and a cumulative change map was produced. The resulting change rates and the distribution of the forest change patterns were in agreement with other sources of information. These results demonstrated the very promising temporal consistency of the proposed approach. Further work aims at testing it at larger scales.
Sophie Bontemps; Andreas Langner; Pierre Defourny. Monitoring forest changes in Borneo on a yearly basis by an object-based change detection algorithm using SPOT-VEGETATION time series. International Journal of Remote Sensing 2012, 33, 4673 -4699.
AMA StyleSophie Bontemps, Andreas Langner, Pierre Defourny. Monitoring forest changes in Borneo on a yearly basis by an object-based change detection algorithm using SPOT-VEGETATION time series. International Journal of Remote Sensing. 2012; 33 (15):4673-4699.
Chicago/Turabian StyleSophie Bontemps; Andreas Langner; Pierre Defourny. 2012. "Monitoring forest changes in Borneo on a yearly basis by an object-based change detection algorithm using SPOT-VEGETATION time series." International Journal of Remote Sensing 33, no. 15: 4673-4699.
Tracking land cover changes using remotely-sensed data contributes to evaluating to what extent human activities impact the environment. Recent studies have pointed out some limitations of single-date comparisons between years and have emphasized the usefulness of time series. However, less effort has hitherto been dedicated to properly account for the temporal dependences typifying the successive images of a time series. An automated change detection method based on a per-object approach and on a probabilistic procedure is proposed here to better cope with this issue. This innovative procedure is applied to a tropical forest environment using high temporal resolution SPOT-VEGETATION time series from 2001 and 2004 in the Brazilian state of Rondônia. The principle of the method is to identify the objects that most deviate from an unchanged reference defined by objective rules. A probabilistic changed-unchanged threshold provides a change map where each object is associated with a likelihood of having changed. This improvement on a binary diagnostic makes the method relevant to meet the requirements of different users, ranging from a comprehensive detection of changes to a detection of the most dramatic changes. According to the threshold value, overall accuracy indices of up to 91% were obtained, with errors involving change omissions for the most part. The isolation of changes within objects was made possible through a segmentation procedure implemented in a temporal context. In addition, the method was formulated so as to differentiate between inter- and intra-annual vegetation dynamics. These technical peculiarities will likely make this analytical framework suitable for detecting changes in environments subject to a strongly marked phenology.
Sophie Bontemps; Patrick Bogaert; Nicolas Titeux; Pierre Defourny. An object-based change detection method accounting for temporal dependences in time series with medium to coarse spatial resolution. Remote Sensing of Environment 2008, 112, 3181 -3191.
AMA StyleSophie Bontemps, Patrick Bogaert, Nicolas Titeux, Pierre Defourny. An object-based change detection method accounting for temporal dependences in time series with medium to coarse spatial resolution. Remote Sensing of Environment. 2008; 112 (6):3181-3191.
Chicago/Turabian StyleSophie Bontemps; Patrick Bogaert; Nicolas Titeux; Pierre Defourny. 2008. "An object-based change detection method accounting for temporal dependences in time series with medium to coarse spatial resolution." Remote Sensing of Environment 112, no. 6: 3181-3191.