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Mapping and Monitoring Forest Cover / Russell G. Congalton (2021)    

Public Titre : Mapping and Monitoring Forest Cover Type de document : Monographie Auteurs : Russell G. Congalton, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 144 p. ISBN/ISSN/EAN : 978-3-0365-2043-8 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte forestière [Termes IGN] couvert forestier [Termes IGN] forêt [Termes IGN] image aérienne [Termes IGN] image satellite [Termes IGN] inventaire forestier local [Termes IGN] mangrove Note de contenu : - Mapping and Monitoring Forest Cover - Spatial and Temporal Changes in Vegetation in the Ruoergai Region, China - Monitoring Mangrove Forest Degradation and Regeneration: Landsat Time Series Analysis of Moisture and Vegetation Indices at Rabigh Lagoon, Red Sea - Monitoring Carbon Stock and Land-Use Change in 5000-Year-Old Juniper Forest Stand of Ziarat, Balochistan, through a Synergistic Approach - Forest Cover Mapping Based on a Combination of Aerial Images and Sentinel-2 Satellite Data Compared to National Forest Inventory Data - Use of Remote Sensing Data to Improve the Efficiency of National Forest Inventories: A Case Study from the United States National Forest Inventory - A Comparison of Forest Tree Crown Delineation from Unmanned Aerial Imagery Using Canopy Height Models vs. Spectral Lightness Numéro de notice : 17141 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE/IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-0365-2044-5 En ligne : https://doi.org/10.3390/books978-3-0365-2044-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98631

Volumes by tree species can be predicted using photogrammetric UAS data, Sentinel-2 images and prior field measurements / Mikko Kukkonen in Silva fennica, vol 55 n° 1 (January 2021)  

Public [article]  inSilva fennica > vol 55 n° 1 (January 2021) . - n° 10360 Titre : Volumes by tree species can be predicted using photogrammetric UAS data, Sentinel-2 images and prior field measurements Type de document : Article/Communication Auteurs : Mikko Kukkonen, Auteur ; Eetu Kotivuori, Auteur ; Matti Maltamo, Auteur ; Lauri Korhonen, Auteur ; Petteri Packalen, Auteur Année de publication : 2021 Article en page(s) : n° 10360 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] classification barycentrique [Termes IGN] données de terrain [Termes IGN] données localisées 3D [Termes IGN] Finlande [Termes IGN] forêt boréale [Termes IGN] image captée par drone [Termes IGN] image Sentinel-MSI [Termes IGN] inventaire forestier local [Termes IGN] modèle de simulation [Termes IGN] régression [Termes IGN] semis de points [Termes IGN] volume en bois Résumé : (auteur) Photogrammetric point clouds obtained with unmanned aircraft systems (UAS) have emerged as an alternative source of remotely sensed data for small area forest management inventories (FMI). Nonetheless, it is often overlooked that small area FMI require considerable field data in addition to UAS data, to support the modelling of forest attributes. In this study, we propose a method whereby tree volumes by species are predicted with photogrammetric UAS data and Sentinel-2 images, using models fitted with airborne laser scanning data. The study area is in a managed boreal forest area in Eastern Finland. First, we predicted total volume with UAS point cloud metrics using a prior regression model fitted in another area with ALS data. Tree species proportions were then predicted by k nearest neighbor (k-NN) imputation based on bi-seasonal Sentinel-2 images without measuring new field plot data. Species-specific volumes were then obtained by multiplying the total volume by species proportions. The relative root mean square error (RMSE) values for total and species-specific volume predictions at the validation plot level (30 m × 30 m) were 9.0%, and 33.4–62.6%, respectively. Our approach appears promising for species-specific small area FMI in Finland and in comparable forest conditions in which suitable field plots are available. Numéro de notice : A2021-738 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.10360 En ligne : https://doi.org/10.14214/sf.10360 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98703 [article]

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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 170 (December 2020) . - pp 205-215 Titre : Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks Type de document : Article/Communication Auteurs : Felix Schiefer, Auteur ; Teja Kattenborn, Auteur ; Annett Frick, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 205-215 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond [Termes IGN] arbre (flore) [Termes IGN] carte forestière [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] espèce végétale [Termes IGN] Forêt-Noire, massif de la [Termes IGN] image à haute résolution [Termes IGN] image captée par drone [Termes IGN] image RVB [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] inventaire forestier local [Termes IGN] segmentation sémantique [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) The use of unmanned aerial vehicles (UAVs) in vegetation remote sensing allows a time-flexible and cost-effective acquisition of very high-resolution imagery. Still, current methods for the mapping of forest tree species do not exploit the respective, rich spatial information. Here, we assessed the potential of convolutional neural networks (CNNs) and very high-resolution RGB imagery from UAVs for the mapping of tree species in temperate forests. We used multicopter UAVs to obtain very high-resolution ( Numéro de notice : A2020-706 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.015 date de publication en ligne : 03/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.015 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96236 [article]

Under-canopy UAV laser scanning for accurate forest field measurements / Eric Hyyppä in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 164 (June 2020) . - pp 41 - 60 Titre : Under-canopy UAV laser scanning for accurate forest field measurements Type de document : Article/Communication Auteurs : Eric Hyyppä, Auteur ; Juha Hyyppä, Auteur ; Teemu Hakala, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 41 - 60 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] balayage laser [Termes IGN] canopée [Termes IGN] Cartographie et localisation simultanées [Termes IGN] densité du bois [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] données lidar [Termes IGN] erreur moyenne quadratique [Termes IGN] Finlande [Termes IGN] forêt boréale [Termes IGN] hauteur à la base du houppier [Termes IGN] hauteur des arbres [Termes IGN] image captée par drone [Termes IGN] inventaire forestier local [Termes IGN] modèle de croissance végétale [Termes IGN] semis de points [Termes IGN] télédétection aérienne [Termes IGN] télémètre laser terrestre [Termes IGN] télémétrie laser aéroporté [Termes IGN] tronc Résumé : (auteur) Surveying and robotic technologies are converging, offering great potential for robotic-assisted data collection and support for labour intensive surveying activities. From a forest monitoring perspective, there are several technological and operational aspects to address concerning under-canopy flying unmanned airborne vehicles (UAV). To demonstrate this emerging technology, we investigated tree detection and stem curve estimation using laser scanning data obtained with an under-canopy flying UAV. To this end, we mounted a Kaarta Stencil-1 laser scanner with an integrated simultaneous localization and mapping (SLAM) system on board an UAV that was manually piloted with the help of video goggles receiving a live video feed from the onboard camera of the UAV. Using the under-canopy flying UAV, we collected SLAM-corrected point cloud data in a boreal forest on two 32 m 32 m test sites that were characterized as sparse ( = 42 trees) and obstructed ( = 43 trees), respectively. Novel data processing algorithms were applied for the point clouds in order to detect the stems of individual trees and to extract their stem curves and diameters at breast height (DBH). The estimated tree attributes were compared against highly accurate field reference data that was acquired semi-manually with a multi-scan terrestrial laser scanner (TLS). The proposed method succeeded in detecting 93% of the stems in the sparse plot and 84% of the stems in the obstructed plot. In the sparse plot, the DBH and stem curve estimates had a root-mean-squared error (RMSE) of 0.60 cm (2.2%) and 1.2 cm (5.0%), respectively, whereas the corresponding values for the obstructed plot were 0.92 cm (3.1%) and 1.4 cm (5.2%). By combining the stem curves extracted from the under-canopy UAV laser scanning data with tree heights derived from above-canopy UAV laser scanning data, we computed stem volumes for the detected trees with a relative RMSE of 10.1% in both plots. Thus, the combination of under-canopy and above-canopy UAV laser scanning allowed us to extract the stem volumes with an accuracy comparable to the past best studies based on TLS in boreal forest conditions. Since the stems of several spruces located on the test sites suffered from severe occlusion and could not be detected with the stem-based method, we developed a separate work flow capable of detecting trees with occluded stems. The proposed work flow enabled us to detect 98% of trees in the sparse plot and 93% of the trees in the obstructed plot with a 100% correction level in both plots. A key benefit provided by the under-canopy UAV laser scanner is the short period of time required for data collection, currently demonstrated to be much faster than the time required for field measurements and TLS. The quality of the measurements acquired with the under-canopy flying UAV combined with the demonstrated efficiency indicates operational potential for supporting fast and accurate forest resource inventories. Numéro de notice : A2020-240 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.03.021 date de publication en ligne : 11/04/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.03.021 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94994 [article]

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How far can we trust forestry estimates from low-density LiDAR acquisitions? The Cutfoot Sioux experimental forest (MN, USA) case study / Enrico Borgogno Mondino in International Journal of Remote Sensing IJRS, vol 41 n°12 (20 - 30 March 2020)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 41 n°12 (20 - 30 March 2020) . - pp 4551 - 4569 Titre : How far can we trust forestry estimates from low-density LiDAR acquisitions? The Cutfoot Sioux experimental forest (MN, USA) case study Type de document : Article/Communication Auteurs : Enrico Borgogno Mondino, Auteur ; Vanina Fissore, Auteur ; Michael J. Falkowski, Auteur ; Brian Palik, Auteur Année de publication : 2020 Article en page(s) : pp 4551 - 4569 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] auscultation topographique [Termes IGN] diamètre des arbres [Termes IGN] données dendrométriques [Termes IGN] données lidar [Termes IGN] feuillu [Termes IGN] hauteur des arbres [Termes IGN] image Landsat-OLI [Termes IGN] inventaire forestier local [Termes IGN] Minnesota (Etats-Unis) [Termes IGN] modèle d'erreur [Termes IGN] Pinophyta [Termes IGN] semis de points [Termes IGN] structure d'un peuplement forestier [Termes IGN] surface forestière [Termes IGN] télémètre laser aéroporté Résumé : (auteur) Aerial discrete return LiDAR (Light Detection And Ranging) technology (ALS – Aerial Laser Scanner) is now widely used for forest characterization due to its high accuracy in measuring vertical and horizontal forest structure. Random and systematic errors can still occur and these affect the native point cloud, ultimately degrading ALS data accuracy, especially when adopting datasets that were not natively designed for forest applications. A detailed understanding of how uncertainty of ALS data could affect the accuracy of derivable forest metrics (e.g. tree height, stem diameter, basal area) is required, looking for eventual error biases that can be possibly modelled to improve final accuracy. In this work a low-density ALS dataset, originally acquired by the State of Minnesota (USA) for non-forestry related purposes (i.e. topographic mapping), was processed attempting to characterize forest inventory parameters for the Cutfoot Sioux Experimental Forest (north-central Minnesota, USA). Since accuracy of estimates strictly depends on the applied species-specific dendrometric models a first required step was to map tree species over the forest. A rough classification, aiming at separating conifers from broadleaf, was achieved by processing a Landsat 8 OLI (Operational Land Imager) scene. ALS-derived forest metrics initially greatly overestimated those measured at the ground in 230 plots. Conversely, ALS-derived tree density was greatly underestimated. To reduce ALS uncertainty, trees belonging to the dominated plane were removed from the ground dataset, assuming that they could not properly be detected by low-density ALS measures. Consequently, MAE (Mean Absolute Error) values significantly decreased to 4.0 m for tree height and to 0.19 cm for diameter estimates. Remaining discrepancies were related to a bias affecting the native ALS point cloud, which was modelled and removed. Final MAE values were 1.32 m for tree height, 0.08 m for diameter, 8.5 m2 ha−1 for basal area, and 0.06 m for quadratic mean diameter. Specifically focusing on tree height and diameter estimates, the significance of differences between ground and ALS estimates was tested relative to the expected ‘best accuracy’. Results showed that after correction: 94.35% of tree height differences were lower than the corresponding reference value (2.86 m); 70% of tree diameter differences were lower than the corresponding reference value (4.5 cm for conifers and 6.8 cm for broadleaf). Finally, forest parameters were computed for the whole Cutfoot Sioux Experimental Forest. Main findings include: 1) all forest estimates based on a low-density ALS point cloud can be derived at plot level and not at a tree level; 2) tree height estimates obtained by low-density ALS point clouds at the plot level are highly reasonably accurate only after testing and modelling eventual error bias; 3) diameter, basal area, and quadratic mean diameter estimates have large uncertainties, suggesting the need for a higher point density and, probably, a better mapping of tree species (if possible) than achieved with a remote sensing-based approach. Numéro de notice : A2020-450 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2020.1723173 date de publication en ligne : 20/02/2020 En ligne : https://doi.org/10.1080/01431161.2020.1723173 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95535 [article]

Fusion of 3D point clouds and hyperspectral data for the extraction of geometric and radiometric features of trees / Eduardo Alejandro Tusa Jumbo (2020)  

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Patrimoine arboré : pousser de nouvelles pratiques / Xavier Fodor in SIGmag, n° 22 (octobre 2019)

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Estimating forest stand density and structure using Bayesian individual tree detection, stochastic geometry, and distribution matching / Kasper Kansanen in ISPRS Journal of photogrammetry and remote sensing, vol 152 (June 2019)  

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Estimation of aboveground biomass and carbon in a tropical rain forest in Gabon using remote sensing and GPS data / Kalifa Goïta in Geocarto international, vol 34 n° 3 ([01/03/2019])  

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Integrating urban and national forest inventory data in support of rural–urban assessments / James A. Westfall in Forestry, an international journal of forest research, vol 91 n° 5 (December 2018)  

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Adaptive stopping criterion for top-down segmentation of ALS point clouds in temperate coniferous forests / Nina Amiri in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)  

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Semi-supervised SVM for individual tree crown species classification / Michele Dalponte in ISPRS Journal of photogrammetry and remote sensing, vol 110 (December 2015)  

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Detection of fallen trees in ALS point clouds using a Normalized Cut approach trained by simulation / Przemyslaw Polewski in ISPRS Journal of photogrammetry and remote sensing, vol 105 (July 2015)  

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Flore méditerranéenne et changement climatique : la course-poursuite est engagée / Michel Vennetier ; Christian Ripert in Forêt méditerranéenne, vol 31 n° 1 (mars 2010)

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Proceedings of the 8th Annual Forest Inventory and Analysis Symposium, 2006 October 16–19, Monterey, CA. Washington (DC) / Ronald E. McRoberts (2009)  

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