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Auteur Rasmus Fensholt |
Documents disponibles écrits par cet auteur (3)
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High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach / Martin Schwartz (2022)
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Titre : High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach Type de document : Article/Communication Auteurs : Martin Schwartz, Auteur ; Philippe Ciais, Auteur ; Catherine Ottle, Auteur ; Aurélien de Truchis, Auteur ; Cédric Vega , Auteur ; Ibrahim Fayad, Auteur ; Martin Brandt, Auteur ; Rasmus Fensholt, Auteur ; Nicolas Baghdadi, Auteur ; François Morneau
, Auteur ; David Morin, Auteur ; Dominique Guyon, Auteur ; Sylvia Dayau, Auteur ; Jean-Pierre Wigneron, Auteur
Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] forêt
[Termes IGN] Global Ecosystem Dynamics Investigation lidar
[Termes IGN] hauteur des arbres
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Landes de Gascogne
[Termes IGN] PinophytaRésumé : (auteur) In intensively managed forests in Europe, where forests are divided into stands of small size and may show heterogeneity within stands, a high spatial resolution (10 - 20 meters) is arguably needed to capture the differences in canopy height. In this work, we developed a deep learning model based on multi-stream remote sensing measurements to create a high-resolution canopy height map over the "Landes de Gascogne" forest in France, a large maritime pine plantation of 13,000 km2 with flat terrain and intensive management. This area is characterized by even-aged and mono-specific stands, of a typical length of a few hundred meters, harvested every 35 to 50 years. Our deep learning U-Net model uses multi-band images from Sentinel-1 and Sentinel-2 with composite time averages as input to predict tree height derived from GEDI waveforms. The evaluation is performed with external validation data from forest inventory plots and a stereo 3D reconstruction model based on Skysat imagery available at specific locations. We trained seven different U-net models based on a combination of Sentinel-1 and Sentinel-2 bands to evaluate the importance of each instrument in the dominant height retrieval. The model outputs allow us to generate a 10 m resolution canopy height map of the whole "Landes de Gascogne" forest area for 2020 with a mean absolute error of 2.02 m on the Test dataset. The best predictions were obtained using all available satellite layers from Sentinel-1 and Sentinel-2 but using only one satellite source also provided good predictions. For all validation datasets in coniferous forests, our model showed better metrics than previous canopy height models available in the same region. Numéro de notice : P2022-002 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Preprint nature-HAL : Préprint DOI : 10.48550/arXiv.2212.10265 Date de publication en ligne : 20/12/2022 En ligne : https://doi.org/10.48550/arXiv.2212.10265 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102850 Assessment of textural differentiations in forest resources in Romania using fractal analysis / Ion Andronache in Forests, vol 8 n° 3 (March 2017)
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Titre : Assessment of textural differentiations in forest resources in Romania using fractal analysis Type de document : Article/Communication Auteurs : Ion Andronache, Auteur ; Rasmus Fensholt, Auteur ; et al., Auteur Année de publication : 2017 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse fractale
[Termes IGN] déboisement
[Termes IGN] image Landsat-ETM+
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] ressources forestières
[Termes IGN] Roumanie
[Termes IGN] texture d'image
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Deforestation and forest degradation have several negative effects on the environment including a loss of species habitats, disturbance of the water cycle and reduced ability to retain CO2, with consequences for global warming. We investigated the evolution of forest resources from development regions in Romania affected by both deforestation and reforestation using a non-Euclidean method based on fractal analysis. We calculated four fractal dimensions of forest areas: the fractal box-counting dimension of the forest areas, the fractal box-counting dimension of the dilated forest areas, the fractal dilation dimension and the box-counting dimension of the border of the dilated forest areas. Fractal analysis revealed morpho-structural and textural differentiations of forested, deforested and reforested areas in development regions with dominant mountain relief and high hills (more forested and compact organization) in comparison to the development regions dominated by plains or low hills (less forested, more fragmented with small and isolated clusters). Our analysis used the fractal analysis that has the advantage of analyzing the entire image, rather than studying local information, thereby enabling quantification of the uniformity, fragmentation, heterogeneity and homogeneity of forests. Numéro de notice : A2017-644 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f8030054 En ligne : http://doi.org/10.3390/f8030054 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86996
in Forests > vol 8 n° 3 (March 2017)[article]Operationalizing measurement of forest degradation: Identification and quantification of charcoal production in tropical dry forests using very high resolution satellite imagery / K. Dons in International journal of applied Earth observation and geoinformation, vol 39 (July 2015)
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[article]
Titre : Operationalizing measurement of forest degradation: Identification and quantification of charcoal production in tropical dry forests using very high resolution satellite imagery Type de document : Article/Communication Auteurs : K. Dons, Auteur ; C. Smith-Hall, Auteur ; H. Meilby, Auteur ; Rasmus Fensholt, Auteur Année de publication : 2015 Article en page(s) : pp 18 - 27 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse (combustible)
[Termes IGN] charbon de bois
[Termes IGN] classification dirigée
[Termes IGN] déboisement
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image Quickbird
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] sous-bois
[Termes IGN] TanzanieRésumé : (auteur) Quantification of forest degradation in monitoring and reporting as well as in historic baselines is among the most challenging tasks in national REDD+ strategies. However, a recently introduced option is to base monitoring systems on subnational conditions such as prevalent degradation activities. In Tanzania, charcoal production is considered a major cause of forest degradation, but is challenging to quantify due to sub-canopy biomass loss, remote production sites and illegal trade. We studied two charcoal production sites in dry Miombo woodland representing open woodland conditions near human settlements and remote forest with nearly closed canopies. Supervised classification and adaptive thresholding were applied on a pansharpened QuickBird (QB) image to detect kiln burn marks (KBMs). Supervised classification showed reasonable detection accuracy in the remote forest site only, while adaptive thresholding was found acceptable at both locations. We used supervised classification and manual digitizing for KBM delineation and found acceptable delineation accuracy at both sites with RMSEs of 25–32% compared to ground measurements. Regression of charcoal production on KBM area delineated from QB resulted in R2s of 0.86–0.88 with cross-validation RMSE ranging from 2.22 to 2.29 Mg charcoal per kiln. This study demonstrates, how locally calibrated remote sensing techniques may be used to identify and delineate charcoal production sites for estimation of charcoal production and associated extraction of woody biomass. Numéro de notice : A2015-299 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2015.02.001 En ligne : http://www.sciencedirect.com/science/article/pii/S0303243415000331 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76475
in International journal of applied Earth observation and geoinformation > vol 39 (July 2015) . - pp 18 - 27[article]