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Détection à haute résolution spatiale de la desserte forestière en milieu montagneux par lidar aéroporté / Clément Mallet in Forêt entreprise, n° 226 (janvier/février 2016)
[article]
Titre : Détection à haute résolution spatiale de la desserte forestière en milieu montagneux par lidar aéroporté Type de document : Article/Communication Auteurs : Clément Mallet , Auteur ; António Ferraz , Auteur Année de publication : 2016 Article en page(s) : pp 38 - 41 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] chemin rural
[Termes IGN] couvert forestier
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] modèle numérique de terrain
[Termes IGN] montagne
[Termes IGN] pente
[Termes IGN] semis de points
[Termes IGN] Vosges, massif desRésumé : (Auteur) La télédétection par lidar aéroporté est une technologie mature fournissant des informations quantitatives à la fois sur les forêts et sur la topographie du terrain sous-jacent. Cet article présente une approche traitant des données lidar afin d’extraire de manière automatique, fiable et sur de grandes étendues, la desserte forestière en zones de pente. Les avantages et inconvénients de cette technique, tant qualitativement que quantitativement, sont précisés. Numéro de notice : A2016--106 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84696
in Forêt entreprise > n° 226 (janvier/février 2016) . - pp 38 - 41[article]Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité IFN-001-P001790 PER Revue Nogent-sur-Vernisson Salle périodiques Exclu du prêt Accelerated deforestation driven by large-scale land acquisitions in Cambodia / Kyle Frankel Davis in Nature geoscience, vol 8 n° 10 (October 2015)
[article]
Titre : Accelerated deforestation driven by large-scale land acquisitions in Cambodia Type de document : Article/Communication Auteurs : Kyle Frankel Davis, Auteur ; Kailiang Yu, Auteur ; Maria Cristina Rulli, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 772 - 775 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Cambodge
[Termes IGN] couvert forestier
[Termes IGN] couvert végétal
[Termes IGN] déboisementRésumé : (auteur) Investment in agricultural land in the developing world has rapidly increased in the past two decades. In Cambodia, there has been a surge in economic land concessions, in which long-term leases are provided to foreign and domestic investors for economic development. More than two million hectares have been leased so far, sparking debate over the consequences for local communities and the environment. Here we combined official records of concession locations with a high-resolution data set of changes in forest cover to quantify the contribution of land concessions to deforestation between 2000 and 2012. We used covariate matching to control for variables other than classification as a concession that may influence forest loss. Nearly half of the area where concessions were granted between 2000 and 2012 was forested in 2000; this area then represented 12.4% of forest land cover in Cambodia. Within concessions, the annual rate of forest loss was between 29% and 105% higher than in comparable land areas outside concessions. Most of the deforestation within concessions occurred after the contract date, and whether an investor was domestic or foreign had no effect on deforestation rates. We conclude that land acquisitions can act as powerful drivers of deforestation. Numéro de notice : A2015-500 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1038/ngeo2540 En ligne : https://doi.org/10.1038/ngeo2540 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77330
in Nature geoscience > vol 8 n° 10 (October 2015) . - pp 772 - 775[article]A semiautomated probabilistic framework for tree-cover delineation from 1-m NAIP imagery using a high-performance computing architecture / S. Basu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)
[article]
Titre : A semiautomated probabilistic framework for tree-cover delineation from 1-m NAIP imagery using a high-performance computing architecture Type de document : Article/Communication Auteurs : S. Basu, Auteur ; Sangram Ganguly, Auteur ; Ramakrishna R. Nemani, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 5690 - 5708 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] architecture des systèmes d'information
[Termes IGN] classification non dirigée
[Termes IGN] couvert forestier
[Termes IGN] Etats-Unis
[Termes IGN] réseau neuronal artificiel
[Termes IGN] segmentation d'imageRésumé : (Auteur) Accurate tree-cover estimates are useful in deriving above-ground biomass density estimates from very high resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform tree-cover delineation in high-to-coarse-resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR data sets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree-cover estimates for the whole of Continental United States, using a high-performance computing architecture. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on conditional random field, which helps in capturing the higher order contextual dependence relations between neighboring pixels. Once the final probability maps are generated, the framework is updated and retrained by incorporating expert knowledge through the relabeling of misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates (FPRs). The tree-cover maps were generated for the state of California, which covers a total of 11 095 NAIP tiles and spans a total geographical area of 163 696 sq. miles. Our framework produced correct detection rates of around 88% for fragmented forests and 74% for urban tree-cover areas, with FPRs lower than 2% for both regions. Comparative studies with the National Land-Cover Data algorithm and the LiDAR high-resolution canopy height model showed the effectiveness of our algorithm for generating accurate high-resolution tree-cover maps. Numéro de notice : A2015-753 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2428197 Date de publication en ligne : 26/05/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2428197 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78743
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 10 (October 2015) . - pp 5690 - 5708[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015101 SL Revue Centre de documentation Revues en salle Disponible Tropical forest canopy cover estimation using satellite imagery and airborne lidar reference data / Lauri Korhonen in Silva fennica, vol 49 n° 5 ([01/10/2015])
[article]
Titre : Tropical forest canopy cover estimation using satellite imagery and airborne lidar reference data Type de document : Article/Communication Auteurs : Lauri Korhonen, Auteur ; Daniela Ali-Sisto, Auteur ; Timo Tokola, Auteur Année de publication : 2015 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] canopée
[Termes IGN] couvert forestier
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt tropicale
[Termes IGN] image ALOS-AVNIR2
[Termes IGN] image optique
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] Laos
[Termes IGN] placette d'échantillonnage
[Termes IGN] régression logistique
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) The fusion of optical satellite imagery, strips of lidar data and field plots is a promising approach for the inventory of tropical forests. Airborne lidars also enable an accurate direct estimation of the forest canopy cover (CC), and thus a sample of lidar strips can be used as reference data for creating CC maps which are based on satellite images. In this study, our objective was to validate CC maps obtained from an ALOS AVNIR-2 satellite image wall-to-wall, against a lidar-based CC map of a tropical forest area located in Laos. The reference CC values which were needed for model training were obtained from a sample of four lidar strips. Zero-and-one inflated beta regression (ZOINBR) models were applied to link the spectral vegetation indices derived from the ALOS image with the lidar-based CC estimates. In addition, we compared ZOINBR and logistic regression models in the forest area estimation by using >20% CC as a forest definition. Using a total of 409 217 30 × 30 m population units as validation, our model showed a strong correlation between lidar-based CC and spectral satellite features (root mean square error = 12.8%, R2 = 0.82). In the forest area estimation, a direct classification using logistic regression provided better accuracy than the estimation of CC values as an intermediate step (kappa = 0.61 vs. 0.53). It is important to obtain sufficient training data from both ends of the CC range. The forest area estimation should be done before the CC estimation, rather than vice versa. Numéro de notice : A2015-673 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.14214/sf.1405 En ligne : http://www.silvafennica.fi/article/1405 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78293
in Silva fennica > vol 49 n° 5 [01/10/2015][article]Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm / Oumer S. Ahmed in ISPRS Journal of photogrammetry and remote sensing, vol 101 (March 2015)
[article]
Titre : Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm Type de document : Article/Communication Auteurs : Oumer S. Ahmed, Auteur ; Steven E. Franklin, Auteur ; Michael A. Wulder, Auteur ; Joanne C. White, Auteur Année de publication : 2015 Article en page(s) : pp 89 - 101 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] canopée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] composition d'un peuplement forestier
[Termes IGN] couvert forestier
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] hauteur des arbres
[Termes IGN] image Landsat
[Termes IGN] régression multiple
[Termes IGN] série temporelle
[Termes IGN] Vancouver (Colombie britannique)Résumé : (auteur) Many forest management activities, including the development of forest inventories, require spatially detailed forest canopy cover and height data. Among the various remote sensing technologies, LiDAR (Light Detection and Ranging) offers the most accurate and consistent means for obtaining reliable canopy structure measurements. A potential solution to reduce the cost of LiDAR data, is to integrate transects (samples) of LiDAR data with frequently acquired and spatially comprehensive optical remotely sensed data. Although multiple regression is commonly used for such modeling, often it does not fully capture the complex relationships between forest structure variables. This study investigates the potential of Random Forest (RF), a machine learning technique, to estimate LiDAR measured canopy structure using a time series of Landsat imagery. The study is implemented over a 2600 ha area of industrially managed coastal temperate forests on Vancouver Island, British Columbia, Canada. We implemented a trajectory-based approach to time series analysis that generates time since disturbance (TSD) and disturbance intensity information for each pixel and we used this information to stratify the forest land base into two strata: mature forests and young forests. Canopy cover and height for three forest classes (i.e. mature, young and mature and young (combined)) were modeled separately using multiple regression and Random Forest (RF) techniques. For all forest classes, the RF models provided improved estimates relative to the multiple regression models. The lowest validation error was obtained for the mature forest strata in a RF model (R2 = 0.88, RMSE = 2.39 m and bias = −0.16 for canopy height; R2 = 0.72, RMSE = 0.068% and bias = −0.0049 for canopy cover). This study demonstrates the value of using disturbance and successional history to inform estimates of canopy structure and obtain improved estimates of forest canopy cover and height using the RF algorithm. Numéro de notice : A2015-470 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.11.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.11.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77172
in ISPRS Journal of photogrammetry and remote sensing > vol 101 (March 2015) . - pp 89 - 101[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2015031 RAB Revue Centre de documentation En réserve L003 Disponible Evaluation de dégâts de tempête à l'échelle infra-parcellaire à partir d'une image Pléiades à très haute résolution sur un massif forestier feuillu en France / Anne Jolly in Revue Française de Photogrammétrie et de Télédétection, n° 209 (Janvier 2015)PermalinkMulti-UAV surveillance over forested regions / Vengatesan Govindaraju in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 12 (December 2014)PermalinkIntegration of Lidar and Landsat to estimate forest canopy cover in coastal British Columbia / Oumer S. Ahmed in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 10 (October 2014)PermalinkApproche de détermination de signature de texture : application à la classification de couverts forestiers d’image satellitaire à haute résolution / Wala Zaaboub in Revue Française de Photogrammétrie et de Télédétection, n° 207 (Juillet 2014)PermalinkUn vaste champ d'applications / Françoise de Blomac in DécryptaGéo le mag, n° 155 (01/03/2014)PermalinkAdaptive algorithm for large scale DTM interpolation from lidar data for forestry applications in steep forested terrain / Almasi S. Maguya in ISPRS Journal of photogrammetry and remote sensing, vol 85 (November 2013)PermalinkCartographie des continuités écologiques : quelles données pour quelles échelles territoriales ? Application à la sous-trame forestière / Laurence Hubert-Moy in Revue internationale de géomatique, vol 22 n° 4 (décembre 2012 – février 2013)PermalinkThe electronically steerable flash Lidar : A full waveform scanning system for topographic and ecosystem structure applications / H. Duong in IEEE Transactions on geoscience and remote sensing, vol 50 n° 11 Tome 2 (November 2012)PermalinkQuantifying deforestation in the Brazilian Amazon using advanced land observing satellite phased array L-band synthetic aperture radar (ALOS PALSAR) and shuttle imaging radar (SIR)-C data / M. Rahman in Geocarto international, vol 27 n° 6 (October 2012)PermalinkA longitudinal study of malaria associated with deforestation in Sonitpur district of Assam, India / M. Nath in Geocarto international, vol 27 n° 1 (February 2012)Permalink