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Spatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series / Meng Lu in ISPRS Journal of photogrammetry and remote sensing, vol 117 (July 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 117 (July 2016) . - pp 227 – 236 Titre : Spatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series Type de document : Article/Communication Auteurs : Meng Lu, Auteur ; Edzer J. Pebesma, Auteur ; Alber Sanchez, Auteur ; Jan Verbesselt, Auteur Année de publication : 2016 Article en page(s) : pp 227 – 236 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Amazonie [Termes IGN] Brésil [Termes IGN] corrélation automatique de points homologues [Termes IGN] déboisement [Termes IGN] détection de changement [Termes IGN] image Aqua-MODIS [Termes IGN] image Terra-MODIS [Termes IGN] points de rupture [Termes IGN] série temporelle Résumé : (auteur) Growing availability of long-term satellite imagery enables change modeling with advanced spatio-temporal statistical methods. Multidimensional arrays naturally match the structure of spatio-temporal satellite data and can provide a clean modeling process for complex spatio-temporal analysis over large datasets. Our study case illustrates the detection of breakpoints in MODIS imagery time series for land cover change in the Brazilian Amazon using the BFAST (Breaks For Additive Season and Trend) change detection framework. BFAST includes an Empirical Fluctuation Process (EFP) to alarm the change and a change point time locating process. We extend the EFP to account for the spatial autocorrelation between spatial neighbors and assess the effects of spatial correlation when applying BFAST on satellite image time series. In addition, we evaluate how sensitive EFP is to the assumption that its time series residuals are temporally uncorrelated, by modeling it as an autoregressive process. We use arrays as a unified data structure for the modeling process, R to execute the analysis, and an array database management system to scale computation. Our results point to BFAST as a robust approach against mild temporal and spatial correlation, to the use of arrays to ease the modeling process of spatio-temporal change, and towards communicable and scalable analysis. Numéro de notice : A2016-586 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.03.007 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.03.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81727 [article]

Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia / Loïc Paul Dutrieux in ISPRS Journal of photogrammetry and remote sensing, vol 107 (September 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 107 (September 2015) . - pp 112 - 125 Titre : Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia Type de document : Article/Communication Auteurs : Loïc Paul Dutrieux, Auteur ; Jan Verbesselt, Auteur ; Lammert Kooistra, Auteur ; Martin Herold, Auteur Année de publication : 2015 Article en page(s) : pp 112 - 125 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Bolivie [Termes IGN] déboisement [Termes IGN] détection de changement [Termes IGN] forêt tropicale [Termes IGN] image Terra-MODIS [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] sécheresse [Termes IGN] série temporelle [Termes IGN] variabilité Résumé : (auteur) Automatically detecting forest disturbances as they occur can be extremely challenging for certain types of environments, particularly those presenting strong natural variations. Here, we use a generic structural break detection framework (BFAST) to improve the monitoring of forest cover loss by combining multiple data streams. Forest change monitoring is performed using Landsat data in combination with MODIS or rainfall data to further improve the modelling and monitoring. We tested the use of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) with varying spatial aggregation window sizes as well as a rainfall derived index as external regressors. The method was evaluated on a dry tropical forest area in lowland Bolivia where forest cover loss is known to occur, and we validated the results against a set of ground truth samples manually interpreted using the TimeSync environment. We found that the addition of an external regressor allows to take advantage of the difference in spatial extent between human induced and naturally induced variations and only detect the processes of interest. Of all configurations, we found the 13 by 13 km MODIS NDVI window to be the most successful, with an overall accuracy of 87%. Compared with a single pixel approach, the proposed method produced better time-series model fits resulting in increases of overall accuracy (from 82% to 87%), and decrease in omission and commission errors (from 33% to 24% and from 3% to 0% respectively). The presented approach seems particularly relevant for areas with high inter-annual natural variability, such as forests regularly experiencing exceptional drought events. Numéro de notice : A2015-726 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.03.015 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.03.015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78378 [article]

Monitoring herbaceaous fuel moisture content with Spot-Vegetation times-series for fire risk prediction in savanna ecosystems / Jan Verbesselt in Remote sensing of environment, vol 108 n° 4 (29 June 2007)  

Public [article]  inRemote sensing of environment > vol 108 n° 4 (29 June 2007) . - pp 357 - 368 Titre : Monitoring herbaceaous fuel moisture content with Spot-Vegetation times-series for fire risk prediction in savanna ecosystems Type de document : Article/Communication Auteurs : Jan Verbesselt, Auteur ; B. Somers, Auteur ; et al., Auteur Année de publication : 2007 Article en page(s) : pp 357 - 368 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] combustible [Termes IGN] herbe [Termes IGN] image SPOT-Végétation [Termes IGN] indice de végétation [Termes IGN] prévention des risques [Termes IGN] risque naturel [Termes IGN] savane [Termes IGN] surveillance écologique [Termes IGN] teneur en eau de la végétation Résumé : (Auteur) This paper evaluated the capacity of SPOT VEGETATION time-series to monitor herbaceous fuel moisture content (FMC) in order to improve fire risk assessment in the savanna ecosystem of Kruger National Park in South Africa. In situ herbaceous FMC data were used to assess the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Vegetation Dryness Index (VDI), Improved VDI (IVDI), and Accumulated Relative NDVI Decrement (ARND) during the dry season. The effect of increasing amounts of dead vegetation on the monitoring capacity of derived indices was studied by sampling mixed live and dead FMC. The IVDI was proposed as an improvement of the VDI to monitor herbaceous FMC during the dry season. The IVDI is derived by replacing NDVI with the integrated Relative Vegetation Index (iRVI), as an approximation of yearly herbaceous biomass, when analyzing the 2-dimensional space with NDWI. It was shown that the iRVI offered more information than the NDVI in combination with NDWI to monitor FMC. The VDI and IVDI exhibited a significant relation to FMC with R2 of 0.25 and 0.73, respectively. The NDWI, however, correlated best with FMC (R2 = 0.75), while the correlation of ARND and FMC was weaker (R2 = 0.60) than that found for NDVI, NDWI, and IVDI. The use of in situ herbaceous FMC consequently indicated that NDWI is appropriate as spatio-temporal information source of herbaceous FMC variation which can be used to optimize fire risk and behavior assessment for fire management in savanna ecosystems. Copyright Elsevier Numéro de notice : A2007-299 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.11.019 En ligne : https://doi.org/10.1016/j.rse.2006.11.019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28662 [article]