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Multi-sensor aboveground biomass estimation in the broadleaved hyrcanian forest of Iran / Ghasem Ronoud in Canadian journal of remote sensing, vol 47 n° 6 ([01/11/2021])
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Titre : Multi-sensor aboveground biomass estimation in the broadleaved hyrcanian forest of Iran Titre original : Estimation multi-capteurs de la biomasse aérienne de la forêt de feuillus hyrcanienne d’Iran Type de document : Article/Communication Auteurs : Ghasem Ronoud, Auteur ; Parviz Fatehi, Auteur ; Ali Asghar Darvishsefat, Auteur Année de publication : 2021 Article en page(s) : pp 818 - 834 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse aérienne
[Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] estimation statistique
[Termes IGN] Fagus orientalis
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Iran
[Termes IGN] régression multiple
[Vedettes matières IGN] Inventaire forestierMots-clés libres : Support Vector Regression Résumé : (auteur) In this study, the capability of Landsat-8 (L8), Sentinel-2 (S2), Sentinel-1 (S1), and their combination was investigated for estimating aboveground biomass (AGB). A pure stand of Fagus Orientalis located in the Hyrcanian forest of Iran was selected as the study area. The performance of a parametric approach, i.e., Multiple Linear Regression (MLR) model and non-parametric approaches, i.e., k-Nearest Neighbor (k-NN), Random Forest (RF), and Support Vector Regression (SVR), were also evaluated for AGB estimations. Our results indicated that among S2 metrics, the FAPAR canopy biophysical index and NDVI index based on the red-edge band (NIR-b8a) have the highest correlation coefficient (r) of 0.420 and 0.417, respectively. The results of AGB estimation showed that a combination of S2 and S1 datasets using the k-NN algorithm had the best accuracy (R2 of 0.57 and rRMSE of 14.68%). The best rRMSE using L8, S2, and S1 datasets was 18.95, 16.99, and 19.17% using k-NN, k-NN, and MLR algorithms, respectively. The combination of L8 with S1 dataset also improved the rRMSE relative to L8 and S1 separately by 0.96 and 1.18%, respectively. We concluded that the combination of optical data (L8 or S2) with SAR data (S1) improves the broadleaved Hyrcanian AGB estimation. Numéro de notice : A2021-956 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Article DOI : 10.1080/07038992.2021.1968811 Date de publication en ligne : 07/09/2021 En ligne : https://doi.org/10.1080/07038992.2021.1968811 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99982
in Canadian journal of remote sensing > vol 47 n° 6 [01/11/2021] . - pp 818 - 834[article]A novel cotton mapping index combining Sentinel-1 SAR and Sentinel-2 multispectral imagery / Lan Xun in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)
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Titre : A novel cotton mapping index combining Sentinel-1 SAR and Sentinel-2 multispectral imagery Type de document : Article/Communication Auteurs : Lan Xun, Auteur ; Jiahua Zhang, Auteur ; Dan Cao, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 148 - 166 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] cartographie automatique
[Termes IGN] Chine
[Termes IGN] coefficient de rétrodiffusion
[Termes IGN] distribution spatiale
[Termes IGN] Etats-Unis
[Termes IGN] Gossypium (genre)
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] indice de végétation
[Termes IGN] polarisation
[Termes IGN] réflectance spectrale
[Termes IGN] série temporelleRésumé : (auteur) Cotton is an important cash crop in the world, as the main source of natural and renewable fiber for textiles. Accurate and timely monitoring of the cotton distribution is crucial for cotton cultivation management and international trade. However, most of the previous researches on cotton identification using remotely sensed images are highly dependent on training samples, and the collection of samples is time-consuming and expensive. To overcome this limitation, a new index, termed as Cotton Mapping Index (CMI), was developed in this study for automatic cotton mapping using time series of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 Multispectral Instrument (MSI) satellite data. Four sites in the United States (U.S.) and four sites in China were selected to develop and assess the performance of the CMI. The spectral characteristics derived from Sentinel-2 and backscattering coefficients derived from Sentinel-1 for cotton and non-cotton crops during the cotton growth period were analyzed. Considering the phenology differences of crops in different regions, the features at an adaptive window were adopted to construct the CMI. The results showed that at the peak greenness period, the multiplication of red-edge 1 and red-edge 2 band for cotton samples were much larger than those for non-cotton samples, whereas the spectral angle at the red band as well as the absolute values of backscattering coefficients in vertical transmit and vertical receive (VV) polarization for cotton samples were much smaller than those for non-cotton samples. Based on these findings, the CMI was developed to identify cotton cultivated area within the cropland area. The overall accuracy of classification results for the sites in the U.S. was higher than 81.20%, and the mean relative error for the sites in Xinjiang of China was 26.69%. The CMI, which incorporated optical and radar features, had a better performance than the indices using optical features solely. The advantage of the CMI over supervised classifiers (i.e., k-nearest neighbors, support vector machine and random forest) is that no training samples are required. Moreover, the cotton distribution map can be obtained before the harvest using the CMI. These results indicated the potential of the CMI for cotton mapping. The applicability of CMI in other regions with different cropping systems and crop types needs to be further assessed in the future study. Numéro de notice : A2021-775 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.08.021 Date de publication en ligne : 21/09/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.08.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98836
in ISPRS Journal of photogrammetry and remote sensing > Vol 181 (November 2021) . - pp 148 - 166[article]Quels besoins de connaissances pour le futur des forêts en France ? Au-delà du plan de relance / Maya Leroy in Revue forestière française, vol 73 n° 1 (2021)
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Titre : Quels besoins de connaissances pour le futur des forêts en France ? Au-delà du plan de relance Type de document : Article/Communication Auteurs : Maya Leroy, Auteur ; Jean-Daniel Bontemps , Auteur ; Elodie Brahic, Auteur ; Jean-Luc Dupouey, Auteur ; Pierre-Michel Forget, Auteur ; Serge Garcia, Auteur ; Valéry Gond, Auteur ; Andreas Nikolaus Kleinschmit von Lengefeld, Auteur ; Guy Landmann, Auteur ; Xavier Morin, Auteur ; Raphaël Pélissier, Auteur ; Nicolas Picard, Auteur ; Pascal Marty, Auteur
Année de publication : 2021 Projets : 1-Pas de projet / Article en page(s) : pp 7 - 19 Note générale : bibliographie Langues : Français (fre) Descripteur : [Termes IGN] acquisition de connaissances
[Termes IGN] acteurs de la filière bois-forêt
[Termes IGN] changement climatique
[Termes IGN] conservation des ressources forestières
[Termes IGN] dynamique de la végétation
[Termes IGN] enjeu
[Termes IGN] forêt
[Termes IGN] France (administrative)
[Termes IGN] protection de la biodiversité
[Termes IGN] service écosystémique
[Termes IGN] territoire
[Vedettes matières IGN] ForesterieRésumé : (auteur) Le plan France Relance lancé en septembre 2020 prévoit des mesures forestières sur 2 ans, avec un accent sur la reconstitution des peuplements forestiers sinistrés, affaiblis par les sécheresses ou attaqués par les scolytes. Cependant la crise forestière liée au changement climatique est partie pour durer et les efforts sur les connaissances à acquérir pour aider la forêt à s’adapter au changement climatique devront être poursuivis sur le long terme. Nous identifions quatre enjeux principaux, fortement liés à la préservation de la biodiversité : 1) S’assurer des conditions de succès d’établissement des forêts plantées. 2) Tirer parti des dynamiques naturelles et de la biodiversité pour limiter les risques. 3) Raisonner territorialement, impliquer davantage les acteurs. 4) Connecter les enjeux nationaux aux enjeux économiques mondiaux. Numéro de notice : A2021-794 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : BIODIVERSITE/FORET Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.20870/revforfr.2021.4992 En ligne : https://doi.org/10.20870/revforfr.2021.4992 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99092
in Revue forestière française > vol 73 n° 1 (2021) . - pp 7 - 19[article]Documents numériques
en open access
Quels besoins de connaissances pour le futur des forêts en France - pdf éditeurAdobe Acrobat PDFThinning effect of C sequestration along an elevation gradient of mediterranean pinus spp. plantations / Antonio M. Cachinero-Vivar in Forests, vol 12 n° 11 (November 2021)
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Titre : Thinning effect of C sequestration along an elevation gradient of mediterranean pinus spp. plantations Type de document : Article/Communication Auteurs : Antonio M. Cachinero-Vivar, Auteur ; Guillermo Palacios-Rodriguez, Auteur ; Miguel A. Lara-Gómez, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 1583 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] changement climatique
[Termes IGN] dendroécologie
[Termes IGN] éclaircie (sylviculture)
[Termes IGN] forêt méditerranéenne
[Termes IGN] gestion forestière
[Termes IGN] gradient d'altitude
[Termes IGN] Pinus nigra
[Termes IGN] Pinus pinaster
[Termes IGN] Pinus sylvestris
[Termes IGN] puits de carbone
[Termes IGN] service écosystémique
[Vedettes matières IGN] ForesterieRésumé : (auteur) Forests are key elements in mitigating the effects of climate change due to the fact of their carbon sequestration capacity. Forest management can be oriented to optimise the carbon sequestration capacity of forest stands, in line with other productive objectives and the generation of ecosystem services. This research aimed to determine whether thinning treatments have a positive influence on the growth patterns of some of the main Mediterranean pine species and, therefore, on their Carbon (C) fixation capacity, both in terms of living biomass and soil organic carbon. The results obtained show that C sequestration capacity (biomass and SOC) increased at higher thinning intensities due to the induced alterations in tree growth patterns. We observed almost a 1.5-fold increase in P. nigra and P. sylvestris, respectively, and over a two-fold increase in P. pinaster under heavy thinning treatments; SOC stocks were affected by the intensity of the thinning treatments. These results can contribute to improving silvicultural practices aimed at C sequestration in forest plantations located in dry areas of the Mediterranean. Numéro de notice : A2021-880 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f12111583 Date de publication en ligne : 17/11/2021 En ligne : https://doi.org/10.3390/f12111583 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99170
in Forests > vol 12 n° 11 (November 2021) . - n° 1583[article]Using LiDAR and Random Forest to improve deer habitat models in a managed forest landscape / Colin S. Shanley in Forest ecology and management, vol 499 (November-1 2021)
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Titre : Using LiDAR and Random Forest to improve deer habitat models in a managed forest landscape Type de document : Article/Communication Auteurs : Colin S. Shanley, Auteur ; Daniel R. Eacker, Auteur ; Connor P. Reynolds, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 119580 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Alaska (Etats-Unis)
[Termes IGN] Cervidae
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] coefficient de corrélation
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt
[Termes IGN] géomorphométrie
[Termes IGN] habitat animal
[Termes IGN] habitat forestier
[Termes IGN] semis de pointsRésumé : (auteur) Conservation strategies are hindered by a lack of accurate maps of important habitat for many wildlife species, but especially for species inhabiting managed forest landscapes. Prioritizing restoration efforts on Alaska’s Tongass National Forest from past extensive clearcut logging is extremely challenging given the difficulty in accurately mapping its remote, rugged temperate rainforest landscapes. We tested the application of airborne light detection and ranging (LiDAR) technology to build a winter habitat model for Sitka black-tailed deer (Odocoileus hemionus sitkensis), the primary herbivore in the coastal temperate rainforest. We analyzed the importance of geomorphometric and forest structure characteristics as predictors of deer winter habitat selection using Random Forest applied to a 3-year GPS relocation dataset collected from 40 adult female deer. The LiDAR-based habitat model had a predictive performance of 94% (Out-of-bag error = 6%), a 10% lower model error compared to air-photo interpreted polygons and modeled plot data. Random Forest also outperformed analogous resource selection function models based on a comprehensive k-fold cross-validation. Deer habitat selection patterns in the LiDAR-based model were nonlinear across geomorphometric and forest structure predictive variables, and generally supported existing studies of deer habitat selection. Besides improving deer conservation and management on the Tongass National Forest, our approach could greatly enhance the accuracy and resolution of habitat maps used for conservation and restoration planning across large managed forest landscapes. Numéro de notice : A2021-696 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.foreco.2021.119580 Date de publication en ligne : 26/08/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119580 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98529
in Forest ecology and management > vol 499 (November-1 2021) . - n° 119580[article]Variation in plant–soil interactions among temperate forest herbs / Jared J. Beck in Plant ecology, vol 222 n° 11 (November 2021)
PermalinkAge-dependence of stand biomass in managed boreal forests based on the Finnish National Forest Inventory data / Anna Repo in Forest ecology and management, vol 498 (October-15 2021)
PermalinkAutomatic detection of planted trees and their heights using photogrammetric rpa point clouds / Kênia Samara Mourão Santos in Boletim de Ciências Geodésicas, vol 27 n° 3 ([01/10/2021])
PermalinkEarly detection of pine wilt disease using deep learning algorithms and UAV-based multispectral imagery / Run Yu in Forest ecology and management, vol 497 (October-1 2021)
PermalinkImpact of beam diameter and scanning approach on point cloud quality of terrestrial laser scanning in forests / Meinrad Abegg in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)
PermalinkImproving the accuracy of spring phenology detection by optimally smoothing satellite vegetation index time series based on local cloud frequency / Jiaqi Tian in ISPRS Journal of photogrammetry and remote sensing, vol 180 (October 2021)
PermalinkPhenology-based delineation of irrigated and rain-fed paddy fields with Sentinel-2 imagery in Google Earth Engine / Daniel Marc G. dela Torre in Geo-spatial Information Science, vol 24 n° 4 (October 2021)
PermalinkProduction potential, biodiversity and soil properties of forest reclamations: Opportunities or risk of introduced coniferous tree species under climate change? / Zdeněk Vacek in European Journal of Forest Research, vol 140 n° 5 (October 2021)
PermalinkSpatial biodiversity modeling using high-performance computing cluster: A case study to access biological richness in Indian landscape / Hariom Singh in Geocarto international, vol 36 n° 18 ([01/10/2021])
PermalinkSpectral reflectance estimation of UAS multispectral imagery using satellite cross-calibration method / Saket Gowravaram in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (October 2021)
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