Centre dedocumentation
scientifique

Coastal water remote sensing from sentinel-2 satellite data using physical, statistical, and neural network retrieval approach / Frank S. Marzano in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 915 - 928 Titre : Coastal water remote sensing from sentinel-2 satellite data using physical, statistical, and neural network retrieval approach Type de document : Article/Communication Auteurs : Frank S. Marzano, Auteur ; Michele Iacobelli, Auteur ; Massimo Orlandi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 915 - 928 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Adriatique, mer [Termes descripteurs IGN] bathymétrie [Termes descripteurs IGN] chlorophylle [Termes descripteurs IGN] correction atmosphérique [Termes descripteurs IGN] couleur de l'océan [Termes descripteurs IGN] eaux côtières [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] incertitude spectrale [Termes descripteurs IGN] matière organique [Termes descripteurs IGN] Méditerranée, mer [Termes descripteurs IGN] réseau neuronal artificiel Résumé : (auteur) Recent optical remote sensing satellite missions, such as Sentinel-2 with the MultiSpectral Imager (MSI) onboard, allow the estimation of coastal water key parameters with very high spatial resolutions (down to 10 m). In this article, multiple approaches are proposed for retrieving chlorophyll-a (Chl-a) and total suspended matter (TSM) along the Adriatic and Tyrrhenian coasts in Italy, using both empirical and model-based frameworks to design regressive and neural network (NN) estimation methods. The latter proves to be more accurate on a regional scale, where standard ocean color physical models exhibit high uncertainty in their local parameterization due to the complex spectral characteristics of the observed scene. Retrieval results are encouraging for Chl-a with a coefficient of determination R2 up to 0.72 with a root-mean-square error (RMSE) of 0.33 mg m−3 , using an empirical NN. The TSM algorithms exhibit higher uncertainty, mainly due to scarcity of in situ measurements and model parameterizations, with R2=0.52 and RMSE = 1.95 g/m 3 using NNs. The bio-optical model, used for the development of model-based algorithms, shows some inadequacies in representing the inherent and apparent optical properties for the case study areas, especially considering the different spectral features between the oligotrophic Tyrrhenian Sea and the eutrophic Adriatic Sea. This study confirms the potential of Sentinel-2 MSI products for coastal water monitoring, but it also highlights key issues to be further tackled such as the atmospheric correction impact, the need of reliable in situ measurements, and possible bathymetry effects near the shores. Numéro de notice : A2021-110 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2980941 date de publication en ligne : 09/12/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2980941 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96912 [article]

A GIS- and AHP-based approach to map fire risk: a case study of Kuan Kreng peat swamp forest, Thailand / Narissara Nuthammachot in Geocarto international, vol 36 n° 2 ([01/02/2021])  

Public [article]  inGeocarto international > vol 36 n° 2 [01/02/2021] . - pp 212 - 225 Titre : A GIS- and AHP-based approach to map fire risk: a case study of Kuan Kreng peat swamp forest, Thailand Type de document : Article/Communication Auteurs : Narissara Nuthammachot, Auteur ; Dimitris Stratoulias, Auteur Année de publication : 2021 Article en page(s) : pp 212 - 225 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] climat [Termes descripteurs IGN] forêt marécageuse [Termes descripteurs IGN] historique des données [Termes descripteurs IGN] incendie de forêt [Termes descripteurs IGN] outil d'aide à la décision [Termes descripteurs IGN] prévention des risques [Termes descripteurs IGN] processus d'analyse hiérarchisée [Termes descripteurs IGN] Thaïlande [Termes descripteurs IGN] tourbière Résumé : (auteur) Forest fires are abrupt transformations of the natural ecosystem and management authorities are required to take preventive measures to tackle fire events. Geographic information system (GIS) is a powerful tool for providing information with a spatial context and analytical hierarchy process (AHP) is a well-established technique for multiple criteria decision making. In this study, GIS and AHP are combined to analyse seven fire-related factors related to climate, topography and human influence. Fire risk for a peat swamp forested area in Kuan Kreng, Nakorn Sri Thammarat province, Thailand is estimated in five categories. 705 historic fire events from 2006 to 2017 are used to validate our approach. 82% of the historic fire incidents occurred within the highest fire risk class categories while only a few omission errors were recorded. The combined approach of GIS and AHP techniques can yield useful fire risk maps, which can consequently be used for future planning and management of fire prone areas. Numéro de notice : A2021-083 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1611946 date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1611946 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96832 [article]

Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning / Maryam Pourshamsi in ISPRS Journal of photogrammetry and remote sensing, Vol 172 (February 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > Vol 172 (February 2021) . - pp 79 - 94 Titre : Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning Type de document : Article/Communication Auteurs : Maryam Pourshamsi, Auteur ; Junshi Xia, Auteur ; Naoto Yokoya, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 79 - 94 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] bande L [Termes descripteurs IGN] canopée [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données polarimétriques [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] Gabon [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] image captée par drone [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] Rotation Forest classification [Termes descripteurs IGN] semis de points Résumé : (auteur) Forest height is an important forest biophysical parameter which is used to derive important information about forest ecosystems, such as forest above ground biomass. In this paper, the potential of combining Polarimetric Synthetic Aperture Radar (PolSAR) variables with LiDAR measurements for forest height estimation is investigated. This will be conducted using different machine learning algorithms including Random Forest (RFs), Rotation Forest (RoFs), Canonical Correlation Forest (CCFs) and Support Vector Machine (SVMs). Various PolSAR parameters are required as input variables to ensure a successful height retrieval across different forest heights ranges. The algorithms are trained with 5000 LiDAR samples (less than 1% of the full scene) and different polarimetric variables. To examine the dependency of the algorithm on input training samples, three different subsets are identified which each includes different features: subset 1 is quiet diverse and includes non-vegetated region, short/sparse vegetation (0–20 m), vegetation with mid-range height (20–40 m) to tall/dense ones (40–60 m); subset 2 covers mostly the dense vegetated area with height ranges 40–60 m; and subset 3 mostly covers the non-vegetated to short/sparse vegetation (0–20 m) .The trained algorithms were used to estimate the height for the areas outside the identified subset. The results were validated with independent samples of LiDAR-derived height showing high accuracy (with the average R2 = 0.70 and RMSE = 10 m between all the algorithms and different training samples). The results confirm that it is possible to estimate forest canopy height using PolSAR parameters together with a small coverage of LiDAR height as training data. Numéro de notice : A2021-086 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.11.008 date de publication en ligne : 19/12/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.11.008 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96846 [article]

From local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2 / Yousra Hamrouni in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 76 - 100 Titre : From local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2 Type de document : Article/Communication Auteurs : Yousra Hamrouni, Auteur ; Eric Paillassa, Auteur ; Véronique Chéret, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 76 - 100 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] base de données forestières [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] couvert forestier [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] France (administrative) [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] mise à jour de base de données [Termes descripteurs IGN] populus (genre) [Termes descripteurs IGN] série temporelle Résumé : (auteur) Reliable estimates of poplar plantations area are not available at the French national scale due to the unsuitability and low update rate of existing forest databases for this short-rotation species. While supervised classification methods have been shown to be highly accurate in mapping forest cover from remotely sensed images, their performance depends to a great extent on the labelled samples used to build the models. In addition to their high acquisition cost, such samples are often scarce and not fully representative of the variability in class distributions. Consequently, when classification models are applied to large areas with high intra-class variance, they generally yield poor accuracies because of data shift issues. In this paper, we propose the use of active learning to efficiently adapt a classifier trained on a source image to spatially distinct target images with minimal labelling effort and without sacrificing the classification performance. The adaptation consists in actively adding to the initial local model new relevant training samples from other areas in a cascade that iteratively improves the generalisation capabilities of the classifier leading to a global model tailored to these different areas. This active selection relies on uncertainty sampling to directly focus on the most informative pixels for which the algorithm is the least certain of their class labels. Experiments conducted on Sentinel-2 time series revealed their high capacity to identify poplar plantations at a local scale with an average F-score ranging from 89.5% to 99.3%. For large area adaptation, the results showed that when the same number of training samples was used, active learning outperformed random sampling by up to 5% of the overall accuracy and up to 12% of the class F-score. Additionally, and depending on the class considered, the random sampling model required up to 50% more samples to achieve the same performance of an active learning-based model. Moreover, the results demonstrate the suitability of the derived global model to accurately map poplar plantations among other tree species with overall accuracy values up to 14% higher than those obtained with local models. The proposed approach paves the way for a national scale mapping in an operational context. Numéro de notice : A2021-013 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.018 date de publication en ligne : 20/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.018 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96417 [article]

Monitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations / Shengbiao Wu in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 36 - 48 Titre : Monitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations Type de document : Article/Communication Auteurs : Shengbiao Wu, Auteur ; Jing Wang, Auteur ; Zhengbing Yan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 36 - 48 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Chine [Termes descripteurs IGN] forêt tempérée [Termes descripteurs IGN] houppier [Termes descripteurs IGN] image captée par drone [Termes descripteurs IGN] image MODIS [Termes descripteurs IGN] image PlanetScope [Termes descripteurs IGN] phénologie [Termes descripteurs IGN] photosynthèse [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] surveillance forestière Résumé : (auteur) In temperate forests, autumn leaf phenology signals the end of leaf growing season and shows large variability across tree-crowns, which importantly mediates photosynthetic seasonality, hydrological regulation, and nutrient cycling of forest ecosystems. However, critical challenges remain with the monitoring of autumn leaf phenology at the tree-crown scale due to the lack of spatially explicit information for individual tree-crowns and high (spatial and temporal) resolution observations with nadir view. Recent availability of the PlanetScope constellation with a 3 m spatial resolution and near-daily nadir view coverage might help address these observational challenges, but remains underexplored. Here we developed an integration of PlanetScope with drone observations for improved monitoring of crown-scale autumn leaf phenology in a temperate forest in Northeast China. This integration includes: 1) visual identification of individual tree-crowns (and species) from drone observations; 2) extraction of time series of PlanetScope vegetation indices (VIs) for each identified tree-crown; 3) derivation of three metrics of autumn leaf phenology from the extracted VI time series, including the start of fall (SOF), middle of fall (MOF), and end of fall (EOF); and 4) accuracy assessments of the PlanetScope-derived phenology metrics with reference from local phenocams. Our results show that (1) the PlanetScope-drone integration captures large inter-crown phenological variations, with a range of 28 days, 25 days, and 30 days for SOF, MOF, and EOF, respectively, (2) the extracted crown-level phenology metrics strongly agree with those derived from local phenocams, with a root-mean-square-error (RMSE) of 4.1 days, 3.0 days and 5.4 days for SOF, MOF, and EOF, respectively, and (3) PlanetScope maps large variations in autumn leaf phenology over the entire forest landscape with spatially explicit information. These results demonstrate the ability of our proposed method in monitoring the large spatial heterogeneity of crown-scale autumn leaf phenology in the temperate forest, suggesting the potential of using high-resolution satellites to advance crown-scale phenology studies over large geographical areas. Numéro de notice : A2021-011 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.017 date de publication en ligne : 13/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.017 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96305 [article]

Retrieving surface soil water content using a soil texture adjusted vegetation index and unmanned aerial system images / Haibin Gu in Remote sensing, vol 13 n° 1 (January 2021)  

Permalink

Steps-based tree crown delineation by analyzing local minima for counting the trees in very high resolution satellite imagery / Debasish Chakraborty in Geocarto international, vol 36 n° 1 ([01/01/2021])  

Permalink

Topographic, edaphic and climate influences on aspen (Populus tremuloides) drought stress on an intermountain bunchgrass prairie / Andrew Neary in Forest ecology and management, vol 479 ([01/01/2021])  

Permalink

Variations in temperate forest biomass ratio along three environmental gradients are dominated by interspecific differences in wood density / Baptiste Kerfriden in Plant ecology, vol inconnu (January 2021)  

Permalink

Competition overrides climate as trigger of growth decline in a mixed Fagaceae Mediterranean rear-edge forest / Alvaro Rubio-Cuadrado in Annals of Forest Science [en ligne], vol 77 n° 4 (December 2020)  

Permalink

Does recent fire activity impact fire-related traits of Pinus halepensis Mill. and Pinus sylvestris L. in the French Mediterranean area? / Bastien Romero in Annals of Forest Science [en ligne], vol 77 n° 4 (December 2020)  

Permalink

Exploring the inclusion of Sentinel-2 MSI texture metrics in above-ground biomass estimation in the community forest of Nepal / Santa Pandit in Geocarto international, vol 35 n° 16 ([01/12/2020])  

Permalink

Improving aboveground biomass estimates by taking into account density variations between tree components / Antoine Billard in Annals of Forest Science [en ligne], vol 77 n° 4 (December 2020)  

Permalink

Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks / Felix Schiefer in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)  

Permalink

A meta-analysis of changes in soil organic carbon stocks after afforestation with deciduous broadleaved, sempervirent broadleaved, and conifer tree species / Guolong Hou in Annals of Forest Science [en ligne], vol 77 n° 4 (December 2020)  

Permalink