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Unprecedented pluri-decennial increase in the growing stock of French forests is persistent and dominated by private broadleaved forests / Jean-Daniel Bontemps in Annals of Forest Science [en ligne], vol 77 n° 4 (December 2020)
Titre : Unprecedented pluri-decennial increase in the growing stock of French forests is persistent and dominated by private broadleaved forests Type de document : Article/Communication Auteurs : Jean-Daniel Bontemps Année de publication : 2020 Projets : ARBRE / AgroParisTech Article en page(s) : n° 98 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] bois sur pied
[Termes IGN] changement d'utilisation du sol
[Termes IGN] forêt de feuillus
[Termes IGN] forêt privée
[Termes IGN] inventaire forestier national (données France)
[Termes IGN] modèle de régression
[Termes IGN] politique forestière
[Termes IGN] puits de carbone
[Termes IGN] série temporelle
[Termes IGN] surface forestière
[Vedettes matières IGN] Economie forestièreRésumé : (auteur) Key message: French forests exhibit the fastest relative changes across Europe. Growing stock increases faster than area, and is greatest in low-stocked private broadleaved forests. Past areal increases and current GS levels show positive effects on GS expansion, with GS increases hence expected to persist.
Context: Strong increases in growing stocks (GS) of European forests for decades remain poorly understood and of unknown duration. French forests showing the greatest relative changes across Europe form the investigated case study.
Aims: The magnitudes of net area, GS, and GS density (GSD) changes were evaluated across forest categories reflecting forest policy and land-use drivers. The roles of forest areal changes, GS and GSD levels on GS changes were investigated.
Methods: National Forest Inventory data were used to produce time series of area, GS and GSD across forest categories over 1976–2014, and exploratory causal models of GS changes.
Results: GS (+ 57%) increased three times faster than area, highlighting an advanced stage in the forest transition. Low-stocked private forests exhibited strong changes in GS/GSD, greatest in private broadleaved forests, stressing the contribution of returning forests on abandoned lands. Regression models demonstrated positive effects of both past areal increases and current GS, on GS expansion.
Conclusion: Aerial C-sink in French forests is expected to persist in future decades.Numéro de notice : A2020-647 Affiliation des auteurs : LIF+Ext (2020- ) Autre URL associée : vers HAL Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-01003-6 date de publication en ligne : 12/10/2020 En ligne : https://doi.org/10.1007/s13595-020-01003-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96075
in Annals of Forest Science [en ligne] > vol 77 n° 4 (December 2020) . - n° 98[article]
Titre : Ancient forest statistics provide centennial perspective over the status and dynamics of forest area in France Type de document : Article/Communication Auteurs : Thimotée Audinot Année de publication : 2020 Projets : ARBRE / AgroParisTech Article en page(s) : 24 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse diachronique
[Termes IGN] carte forestière
[Termes IGN] changement d'utilisation du sol
[Termes IGN] forêt de haute futaie
[Termes IGN] forêt privée
[Termes IGN] forêt publique
[Termes IGN] France (administrative)
[Termes IGN] politique forestière
[Termes IGN] surface forestière
[Termes IGN] taillis
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Key message: Centenary forest statistics informing major attributes of French forests were digitized, checked for consistency, and used to infer forest dynamics. Comparison to forest inventory data highlights increases in forest area and tree diversity, and substantial maturation of forests. Dataset access at https://doi.org/10.5281/zenodo.3739458
Context: The history of European forest dynamic remains fragmental. In France, the Daubrée statistics (1908) and agricultural statistics (1892, 1929) formed fundamental material to fill this gap.
Aims: Release, test, and summarize the digitalized dataset. Analyze long-term forest changes in forest area, composition, and structure.
Methods: Primary data on forest area across NUTS-3 geographic units, split by forest management and ownership categories and dominating tree species (Daubrée), were digitized and cross-compared. Centennial changes in forest attributes were assessed from modern forest inventory data.
Results: Cross-comparison revealed: (1) strong temporal consistency in forest changes over time, (2) systematic and interpretable biases in ownership/management categories between Daubrée and agricultural statistics. Strong shift from coppices to high forests, increased prevalence of private ownership, and constant proportion of broadleaf- and conifer-dominated forests were highlighted, with increased tree species diversity at country scale.
Conclusion: Ancient statistics are shown to play a major role in retrospective land-use and forest policy analysis.Numéro de notice : A2020-593 Affiliation des auteurs : LIF+Ext (2012-2019) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-00987-5 date de publication en ligne : 05/08/2020 En ligne : https://doi.org/10.1007/s13595-020-00987-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95928
in Annals of Forest Science [en ligne] > vol 77 n° 3 (September 2020) . - 24 p.[article]
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 aéroportéRésumé : (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
in International Journal of Remote Sensing IJRS > vol 41 n°12 (20 - 30 March 2020) . - pp 4551 - 4569[article]
Titre : Generation of digital terrain model for forest areas using a new particle swarm optimization on LiDAR data Type de document : Article/Communication Auteurs : Behnaz Bigdeli, Auteur ; Masoomeh Gomroki, Auteur ; Parham Pahlavani, Auteur Année de publication : 2020 Article en page(s) : pp 115 - 125 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] erreur moyenne quadratique
[Termes IGN] filtrage de la végétation
[Termes IGN] interpolation polynomiale
[Termes IGN] Iran
[Termes IGN] modèle numérique de terrain
[Termes IGN] optimisation par essaim de particules
[Termes IGN] semis de points
[Termes IGN] surface forestièreRésumé : (auteur) Since Light Detection and Ranging (LiDAR) data are capable of distinguishing vegetation from bare earth, these data are used nowadays to produce digital terrain models (DTMs) for forest regions. In this research, raw LiDAR data were filtered using hybrid and slope-based filtering methods and the filtered data were then interpolated using the new modified particle swarm optimisation (PSO) and accordingly the results were compared with those achieved by the other intelligent and conventional interpolation methods. The new modified PSO optimized the polynomial degree for interpolation and found suitable parameters for optimisation. Two data sets from two forest regions in some northern regions of Iran located in Golestan province were selected to compare these methods. Region 1 with dense vegetation and region 2 with grass vegetation. The results indicated that the hybrid filter performed lower RMSE than the slope-based filter. Finally, the DTM with lowest RMSE was obtained using the hybrid filter and the modified PSO interpolation method with RMSE of 6 mm for region 1 (Tavar-kuh) and 61 mm for region 2 (Shastkola River Basin). Numéro de notice : A2020-078 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2018.1530331 date de publication en ligne : 10/10/2018 En ligne : https://doi.org/10.1080/00396265.2018.1530331 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94640
in Survey review > vol 52 n° 371 (March 2020) . - pp 115 - 125[article]
Titre : Partition idéalisée et régionalisée de la composition en espèces ligneuses des forêts françaises Titre original : Idealized and regionalized partitioning of the tree species composition of French Forests Type de document : Article/Communication Auteurs : Jean-Daniel Bontemps Année de publication : 2019 Projets : ARBRE / AgroParisTech Article en page(s) : pp 291 - 308 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biodiversité végétale
[Termes IGN] bois
[Termes IGN] espèce végétale
[Termes IGN] grande région écologique
[Termes IGN] inventaire forestier national (données France)
[Termes IGN] ressources forestières
[Termes IGN] surface forestière
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Forest tree species strongly influence forest dynamics and management. French forests have the greatest compositional diversity in Europe, which constrains the quantitative analysis of associated wood resources. A partition of French forests according to dominant tree species composition and stratified by biogeographical regions (GRECO) was developed in order to handle this diversity. The partition relies on forest composition as measured by the national forest inventory (2006–2015, > 65,000 plots). It builds on the J-shaped distribution of elementary composition abundance, identifies dominant compositions describing at least 50% of the GRECO's area, and groups minor compositions. An ecological assessment of this partition and its application to the analysis of the growing stock are developed. The partition describes 61.4% of the forest area (66% of the growing stock) according to 29 dominant compositions, demonstrating its efficiency. These compositions revealed the importance of broadleaved mixtures, and of neglected forest strata (pine species in Northern France). Growing stock density appeared lowest in broadleaved compositions (Mediterranean oaks), and highest in some coniferous compositions (silver fir/Norway spruce mixture in mountains). Partitioning highlights the role of ecological contexts and forest management on tree diversity. Numéro de notice : A2019-328 Affiliation des auteurs : LIF (2012-2019) Autre URL associée : vers HAL Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/11956860.2019.1588511 date de publication en ligne : 14/04/2019 En ligne : https://doi.org/10.1080/11956860.2019.1588511 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93331
in Ecoscience > vol 26 n° 4 (2019) . - pp 291 - 308[article]Changements du stock de bois sur pied des forêts françaises : description, analyse et simulation sur des horizons temporels pluri-décennal (1975 - 2015) et séculaire à partir des données de l'inventaire forestier national et de statistiques anciennes / Anaïs Denardou-Tisserand (2019)
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