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Université de Lorraine
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A new small area estimation algorithm to balance between statistical precision and scale / Cédric Vega in International journal of applied Earth observation and geoinformation, vol 97 (May 2021)
Titre : A new small area estimation algorithm to balance between statistical precision and scale Type de document : Article/Communication Auteurs : Cédric Vega , Auteur ; Jean-Pierre Renaud, Auteur ; Ankit Sagar , Auteur ; Olivier Bouriaud , Auteur Année de publication : 2021 Projets : LUE / Université de Lorraine, DIABOLO / Packalen, Tuula, ARBRE/CHM-era / Jolly, Anne Article en page(s) : n° 102303 Note générale : bibliographie
This research was funded by The French Environmental Management Agency (ADEME), grant number 16-60-C0007. The methods and algorithms for processing photogrammetric data were supported by DIABOLO project from the European Union’s Horizon 2020 research and innovation program under grant agreement No 633464, as well as CHM-ERA project from the French National Research Agency (ANR) as part of the “Investissements d’Avenir” program (ANR-11-LABX-0002-01, Lab of Excellence ARBRE). Ankit Sagar received the financial support of the French PIA project “Lorraine Université d’Excellence”, reference ANR-15-IDEX-04-LUE, through the project Impact DeepSurf.
Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] arbre BSP
[Termes descripteurs IGN] capital sur pied
[Termes descripteurs IGN] données auxiliaires
[Termes descripteurs IGN] données de terrain
[Termes descripteurs IGN] estimation bayesienne
[Termes descripteurs IGN] inventaire forestier national (données France)
[Termes descripteurs IGN] réduction d'échelle
[Termes descripteurs IGN] seuillage
[Termes descripteurs IGN] surface terrière
[Vedettes matières IGN] Sylviculture
Résumé : (auteur) Combining national forest inventory (NFI) data with auxiliary information allows downscaling and improving the precision of NFI estimates for small domains, where normally too few field plots are available to produce reliable estimates. In most situations, small domains represent administrative units that could greatly vary in size and forested area. In small and poorly sampled domains, the precision of estimates often drop below expected standards.
To tackle this issue, we introduce a downscaling algorithm generating the smallest possible groups of domains satisfying prescribed sampling density and estimation error. The binary space partitioning algorithm recursively divides the population of domains in two groups while the prescribed precision conditions are fulfilled.
The algorithm was tested on two major forest attributes (i.e. growing stock and basal area) in an area of 7,500 km2 dominated by hardwood forests in the centre of France. The estimation domains consisted in 157 municipalities. The field data included 819 NFI plots surveyed during a 5 years period. The auxiliary data consisted in 48 metrics derived from a forest map, photogrammetric models and Landsat images. A model-assisted framework was used for estimation. For each forest attribute, the best model was selected using a best-subset approach using a Bayesian Information Criteria. The retained models explained 58% and 41% of the observed variance for the growing stocks and basal areas respectively. The performance of the algorithm was evaluated using a minimum of 3 NFI points per domain and estimation errors varying from 10 to 50%.
For a target estimation error set to 10%, the algorithm led to a limited number of estimation domains ( The algorithm provides a flexible estimation framework for small area estimation. The key advantages of the approach are relying on its capacity to produce estimations based on a preselected precision threshold and to produce results over the whole area of interest, avoiding areas without any estimates. The algorithm could also be used on any kind of polygon layers (not only administrative ones), provided that the field sampling design enable estimation. This makes the proposed algorithm a convenient tool notably for decision makers and forest managers.
Numéro de notice : A2021-067 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2021.102303 date de publication en ligne : 25/01/2021 En ligne : https://doi.org/10.1016/j.jag.2021.102303 Format de la ressource électronique : URL article Permalink :
in International journal of applied Earth observation and geoinformation > vol 97 (May 2021) . - n° 102303[article]Effects of thinning practice, high pruning and slash management on crop tree and stand growth in young even-aged stands of planted silver birch (Betula pendula Roth) / Jens Peter Skovsgaard in Forests, vol 12 n° 2 (February 2021)
Titre : Effects of thinning practice, high pruning and slash management on crop tree and stand growth in young even-aged stands of planted silver birch (Betula pendula Roth) Type de document : Article/Communication Auteurs : Jens Peter Skovsgaard, Auteur ; Ulf Johansson, Auteur ; Emma Hölmstrom, Auteur ; Rebecka McCarthy Tune, Auteur ; Clémentine Ols , Auteur ; Giulia Attocchi, Auteur Année de publication : 2021 Projets : ARBRE / AgroParisTech, LUE / Université de Lorraine Article en page(s) : n° 225 Note générale : bibliographie
This work was supported by the Swedish forest-owner association Södra and the Swedish national research program Future Forests. C. Ols was funded by the French National Research Agency (ANR-11-LABX-0002-01 and ANR-15-IDEX-04-LUE) during her review and editing of the paper.
Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] Betula pendula
[Termes descripteurs IGN] Canada
[Termes descripteurs IGN] croissance végétale
[Termes descripteurs IGN] éclaircie (sylviculture)
[Termes descripteurs IGN] élagage (sylviculture)
[Termes descripteurs IGN] étude d'impact
[Termes descripteurs IGN] Suède
[Termes descripteurs IGN] volume en bois
[Vedettes matières IGN] Sylviculture
Résumé : (auteur) The objective was to quantify the influence of thinning, high pruning and slash management on crop tree and stand growth in young even-aged stands of planted silver birch (Betula pendula Roth). This study was based on two field experiments, aged six and eleven years at initiation and re-measured after six and eight years, respectively. Treatments included the unthinned control, moderate thinning mainly from below (removing 28–33% of standing volume), point thinning to favor 300 trees per ha and with no thinning elsewhere in the plot (removing 16–25%), and heavy thinning leaving 600 evenly distributed potential future crop trees per ha (removing 64–75%). Slash management (extraction or retention) was applied to heavily thinned plots. High pruning removing 30–70% of the green crown was carried out in some plots with point or heavy thinning on 300 or 600 trees per ha, respectively. Stand volume growth increased with increasing pre-treatment mean annual volume increment and decreased with increasing thinning intensity as compared to the unthinned control. LS-means estimates indicated a reduction for moderate thinning by 14%, for point thinning by 12% and for heavy thinning (combined with pruning) by 62%. However, in the youngest experiment, heavy thinning (without pruning) reduced growth by 54%. Combining these results with results from a similar experiment in Canada, the reduction in stand volume growth (RedIv%) depending on thinning removal (RemV%), both expressed as a percentage of the unthinned control, was quantified as RedIv% = −23.67 + 1.16·RemV% (calibration range: 30–83%). For heavy thinning (large quantities of slash), slash extraction resulted in no reduction in stand volume growth as compared to slash retention. The instantaneous numeric reduction in the average stem diameter of the 300 thickest trees per ha (D300) due to thinning was 3.5, 15–21% and 955–11% with moderate, point and heavy thinning, respectively. The subsequent average annual increase in D300 during the observation period was 8.5%, 25 and 18%, respectively. In the youngest experiment, pruning in unthinned plots led to a reduction in the annual increase of D300 by 14%, and heavy thinning in unpruned plots led to an increase by 30%. The growth of pre-selected potential future crop trees increased with increasing thinning intensity. In heavily thinned plots, pruning reduced growth increasingly with increasing pruning severity; LS-means estimates indicated 21% larger growth on stem diameter for unpruned trees and 3% for pruned trees. As an adverse side effect, heavily thinned plots with only 600 trees per ha were at increased risk of windthrow for some years after the thinning intervention. In the oldest experiment, 95–21% of the trees in these plots were damaged by wind. Numéro de notice : A2021-171 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f12020225 date de publication en ligne : 16/02/2021 En ligne : https://doi.org/10.3390/f12020225 Format de la ressource électronique : URL article Permalink :
in Forests > vol 12 n° 2 (February 2021) . - n° 225[article]