Centre de documentation
scientifique

Above-ground biomass change estimation using national forest inventory data with Sentinel-2 and Landsat / Stefano Puliti in Remote sensing of environment, vol 265 (November 2021)  

Public [article]  inRemote sensing of environment > vol 265 (November 2021) . - n° 112644 Titre : Above-ground biomass change estimation using national forest inventory data with Sentinel-2 and Landsat Type de document : Article/Communication Auteurs : Stefano Puliti, Auteur ; Johannes Breidenbach, Auteur ; Johannes Schumacher, Auteur ; Marius Hauglin, Auteur ; T.F. Klingenberg, Auteur ; Rasmus Astrup, Auteur Année de publication : 2021 Article en page(s) : n° 112644 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] biomasse aérienne [Termes IGN] estimation statistique [Termes IGN] forêt boréale [Termes IGN] image Landsat [Termes IGN] image Sentinel-MSI [Termes IGN] inventaire forestier étranger (données) [Termes IGN] Norvège [Termes IGN] Picea abies [Termes IGN] Pinus sylvestris [Termes IGN] puits de carbone [Termes IGN] surveillance forestière Résumé : (auteur) This study aimed at estimating total forest above-ground net change (ΔAGB; Gg) over five years (2014–2019) based on model-assisted estimation utilizing freely available satellite imagery. The study was conducted for a boreal forest area (approx. 1.4 Mha) in Norway where bi-temporal national forest inventory (NFI), Sentinel-2, and Landsat data were available. Biomass change was modelled based on a direct approach. The precision of estimates using only the NFI data in a basic expansion estimator was compared to four different alternative model-assisted estimates using 1) Sentinel-2 or Landsat data, and 2) using bi- or uni-temporal remotely sensed data. We found that spaceborne optical data improved the precision of the purely field-based estimates by a factor of up to three. The most precise estimates were found for the model-assisted estimation using bi-temporal Sentinel-2 (standard error; SE = 1.7 Gg). However, the decrease in precision when using Landsat data was small (SE = 1.92 Gg). We also found that ΔAGB could be precisely estimated when remotely sensed data were available only at the end of the monitoring period. We conclude that satellite optical data can considerably improve ΔAGB estimates, when repeated and coincident field data are available. The free availability, global coverage, frequent update, and long-term time horizon make data from programs such as Sentinel-2 and Landsat a valuable data source for consistent and durable monitoring of forest carbon dynamics. Numéro de notice : A2021-938 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112644 Date de publication en ligne : 25/08/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112644 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99746 [article]

Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data / Johannes Schumacher in Forest ecosystems, vol 7 (2020)  

Public [article]  inForest ecosystems > vol 7 (2020) . - n° 60 Titre : Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data Type de document : Article/Communication Auteurs : Johannes Schumacher, Auteur ; Marius Hauglin, Auteur ; Rasmus Astrup, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 60 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte forestière [Termes IGN] dendrochronologie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] gestion forestière [Termes IGN] hauteur des arbres [Termes IGN] image Sentinel-MSI [Termes IGN] inventaire forestier étranger (données) [Termes IGN] Norvège [Termes IGN] peuplement forestier [Termes IGN] Picea abies [Termes IGN] régression linéaire [Termes IGN] semis de points Résumé : (auteur) Background: The age of forest stands is critical information for forest management and conservation, for example for growth modelling, timing of management activities and harvesting, or decisions about protection areas. However, area-wide information about forest stand age often does not exist. In this study, we developed regression models for large-scale area-wide prediction of age in Norwegian forests. For model development we used more than 4800 plots of the Norwegian National Forest Inventory (NFI) distributed over Norway between latitudes 58° and 65° N in an 18.2 Mha study area. Predictor variables were based on airborne laser scanning (ALS), Sentinel-2, and existing public map data. We performed model validation on an independent data set consisting of 63 spruce stands with known age. Results: The best modelling strategy was to fit independent linear regression models to each observed site index (SI) level and using a SI prediction map in the application of the models. The most important predictor variable was an upper percentile of the ALS heights, and root mean squared errors (RMSEs) ranged between 3 and 31 years (6% to 26%) for SI-specific models, and 21 years (25%) on average. Mean deviance (MD) ranged between − 1 and 3 years. The models improved with increasing SI and the RMSEs were largest for low SI stands older than 100 years. Using a mapped SI, which is required for practical applications, RMSE and MD on plot level ranged from 19 to 56 years (29% to 53%), and 5 to 37 years (5% to 31%), respectively. For the validation stands, the RMSE and MD were 12 (22%) and 2 years (3%), respectively. Conclusions: Tree height estimated from airborne laser scanning and predicted site index were the most important variables in the models describing age. Overall, we obtained good results, especially for stands with high SI. The models could be considered for practical applications, although we see considerable potential for improvements if better SI maps were available. Numéro de notice : A2020-811 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1186/s40663-020-00274-9 Date de publication en ligne : 10/11/2020 En ligne : https://doi.org/10.1186/s40663-020-00274-9 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96983 [article]