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Applications of remote sensing data in mapping of forest growing stock and biomass / Jose Aranha (2021)  

Public Titre : Applications of remote sensing data in mapping of forest growing stock and biomass Type de document : Monographie Auteurs : Jose Aranha, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 276 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-0365-0569-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] biomasse aérienne [Termes IGN] capital sur pied [Termes IGN] carte forestière [Termes IGN] Chine [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] foresterie [Termes IGN] forêt boréale [Termes IGN] image captée par drone [Termes IGN] image Landsat-OLI [Termes IGN] image radar moirée [Termes IGN] image Sentinel-MSI [Termes IGN] image SPOT 6 [Termes IGN] inventaire forestier étranger (données) [Termes IGN] Pinus massoniana [Termes IGN] puits de carbone [Termes IGN] service écosystémique [Termes IGN] système d'information géographique [Termes IGN] Thaïlande Résumé : (éditeur) This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques. Note de contenu : 1- Finer resolution estimation and mapping of mangrove biomass using UAV LiDAR and WorldView-2 data 2- Nondestructive estimation of the above-ground biomass of multiple tree species in boreal forests of China using Terrestrial Laser Scanning 3- Estimating forest aboveground carbon storage in Hang-Jia-Hu using Landsat TM/OLI data and random morest Model 4- Influence of variable selection and forest type on forest aboveground biomass estimation using machine learning algorithms 5- Comparative analysis of seasonal Landsat 8 images for forest aboveground biomass estimation in a subtropical forest 6- Estimating urban vegetation biomass from Sentinel-2A image data 7- Estimation of forest biomass in Beijing (China) using multisource remote sensing and forest inventory data 8- Spatially explicit analysis of trade-offs and synergies among multiple ecosystem services in Shaanxi Valley basin 9- Influence of site-specific conditions on estimation of forest above ground biomass from airborne laser scanning 10- Multi-sensor prediction of stand volume by a hybrid model of support vector machine for regression kriging 11- Applying LiDAR to quantify the plant area index along a successional gradient in a tropical forest of Thailand 12- Shrub biomass estimates in former burnt areas using Sentinel 2 images processing and classification 13- Evaluation of different algorithms for estimating the growing stock volume of pinus massoniana plantations using spectral and spatial information from a SPOT6 image Numéro de notice : 15305 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-0365-0569-5 En ligne : https://doi.org/10.3390/books978-3-0365-0569-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99903

Shrub biomass estimates in former burnt areas using Sentinel 2 images processing and classification / Jose Aranha in Forests, vol 11 n° 5 (May 2020)  

Public [article]  inForests > vol 11 n° 5 (May 2020) . - 19 p. Titre : Shrub biomass estimates in former burnt areas using Sentinel 2 images processing and classification Type de document : Article/Communication Auteurs : Jose Aranha, Auteur ; Teresa Enes, Auteur ; Ana Calvão, Auteur ; Hélder Viana, Auteur Année de publication : 2020 Article en page(s) : 19 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] arbuste [Termes IGN] biomasse [Termes IGN] classification dirigée [Termes IGN] gestion forestière [Termes IGN] image proche infrarouge [Termes IGN] image RVB [Termes IGN] image Sentinel-MSI [Termes IGN] incendie de forêt [Termes IGN] modèle de croissance végétale [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] Portugal [Termes IGN] signature spectrale [Termes IGN] sous-bois [Termes IGN] système d'information géographique [Termes IGN] zone sinistrée Résumé : (auteur) Shrubs growing in former burnt areas play two diametrically opposed roles. On the one hand, they protect the soil against erosion, promote rainwater infiltration, carbon sequestration and support animal life. On the other hand, after the shrubs’ density reaches a particular size for the canopy to touch and the shrubs’ biomass accumulates more than 10 Mg ha−1, they create the necessary conditions for severe wild fires to occur and spread. The creation of a methodology suitable to identify former burnt areas and to track shrubs’ regrowth within these areas in a regular and a multi temporal basis would be beneficial. The combined use of geographical information systems (GIS) and remote sensing (RS) supported by dedicated land survey and field work for data collection has been identified as a suitable method to manage these tasks. The free access to Sentinel images constitutes a valuable tool for updating the GIS project and for the monitoring of regular shrubs’ accumulated biomass. Sentinel 2 VIS-NIR images are suitable to classify rural areas (overall accuracy = 79.6% and Cohen’s K = 0.754) and to create normalized difference vegetation index (NDVI) images to be used in association to allometric equations for the shrubs’ biomass estimation (R2 = 0.8984, p-value Numéro de notice : A2020-654 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f11050555 Date de publication en ligne : 14/05/2020 En ligne : https://doi.org/10.3390/f11050555 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96116 [article]