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Interpreting effects of multiple, large-scale disturbances using national forest inventory data: A case study of standing dead trees in east Texas, USA / Christopher B. Edgar in Forest ecology and management, vol 437 (1 April 2019)  

Public [article]  inForest ecology and management > vol 437 (1 April 2019) . - pp 27-40 Titre : Interpreting effects of multiple, large-scale disturbances using national forest inventory data: A case study of standing dead trees in east Texas, USA Type de document : Article/Communication Auteurs : Christopher B. Edgar, Auteur ; James A. Westfall, Auteur ; Paul A. Klockow, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 27-40 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] agrégation temporelle [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] arbre mort [Termes descripteurs IGN] catastrophe naturelle [Termes descripteurs IGN] diamètre à hauteur de poitrine [Termes descripteurs IGN] données dendrométriques [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] gestion forestière [Termes descripteurs IGN] insecte nuisible [Termes descripteurs IGN] inventaire forestier étranger (données) [Termes descripteurs IGN] jeu de données [Termes descripteurs IGN] maladie phytosanitaire [Termes descripteurs IGN] Pinus (genre) [Termes descripteurs IGN] politique forestière [Termes descripteurs IGN] Quercus (genre) [Termes descripteurs IGN] sécheresse [Termes descripteurs IGN] tempête [Termes descripteurs IGN] Texas (Etats-Unis) [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Understanding the impacts of large-scale disturbances on forest conditions is necessary to support forest management, planning, and policy decision making. National forest inventories (NFIs) are an important information source that provide consistent data encompassing large areas, covering all ownerships, and extending through time. Here we compare how temporal aggregation approaches with NFI data affects estimates of standing dead trees as these respond to extreme disturbance events. East Texas was selected for this study owing to the occurrence of three significant disturbance events in a short span: Hurricane Rita in 2005, Hurricane Ike in 2008, and a historic drought in 2011. Wide-spread tree damage and mortality were reported after each disturbance and estimates of standing dead trees were used as the inventory variable for assessment. In the NFI of the US, the plot network is systematically divided into panels and one panel is measured each year. A measurement cycle is completed when all panels have been measured, which varies between 5 and 10 years depending on the region. Using the standard estimation approach of the US NFI, we computed population estimates using data from the full set of panels (FSP), multiple sets of panels (MSP), and single set of panels (SSP). For estimation, a single plot observation is computed from the most recent measurement of the plot that does not exceed the estimate year. Because one panel is measured per year, FSP and MSP estimates will necessarily consist of plot observations whose measurements were collected over a number of years. The SSP estimate is computed from one panel and thus all the plot observations are based on measurements collected over one year. We found that interpretations of disturbance event impacts varied depending on which sets of estimates were considered. All sets of estimates suggested a large and significant drought impact. However, differences existed among the estimates in the timing and magnitude of the impacts. The FSP estimates showed clear lag bias and smoothing of trends relative to the SSP estimates. MSP estimates were intermediate between FSP and SSP estimates. Differences in Hurricane Rita impacts were also observed between sets of estimates. Evidence of a net impact on standing dead trees following Hurricane Ike was weak among all sets of estimates. Given the potential for lag bias and smoothing, we recommend that SSP and MSP estimates be considered along with FSP estimates in assessments of large-scale disturbance impacts on forest conditions. Numéro de notice : A2019-483 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.foreco.2019.01.027 date de publication en ligne : 28/01/2019 En ligne : https://doi.org/10.1016/j.foreco.2019.01.027 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93659 [article]

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)  

Public [article]  inForestry, an international journal of forest research > vol 91 n° 5 (December 2018) . - pp 641 - 649 Titre : Integrating urban and national forest inventory data in support of rural–urban assessments Type de document : Article/Communication Auteurs : James A. Westfall, Auteur ; Paul L. Patterson, Auteur ; Christopher B. Edgar, Auteur Année de publication : 2018 Article en page(s) : pp 641 - 649 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] agrégation [Termes descripteurs IGN] Austin (Texas) [Termes descripteurs IGN] intégration de données [Termes descripteurs IGN] inventaire forestier étranger (données) [Termes descripteurs IGN] inventaire forestier local [Termes descripteurs IGN] Texas (Etats-Unis) [Termes descripteurs IGN] variance [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) Due to the interest in status and trends in forest resources, many countries conduct a national forest inventory (NFI). To better understand the characteristics of woody vegetation in areas that are typically not forested, there is an increasing emphasis on urban inventory efforts where all trees both within and outside forest areas are measured. Often, these two inventories are entirely independent endeavours from data collection through analytical reporting. To holistically explore landscape-scale phenomena across the rural–urban gradient, there is a need to combine information from both sources. In this paper, methods for combining these two data sources are examined using data from an urban inventory conducted in Austin, Texas, USA, and NFI data collected in the same and surrounding areas. Approaches to aggregating areas based on sampling intensity and plot design combinations are of considerable importance for the validity of the estimation. An additional complexity can also arise due to temporal discrepancies between the two data sources. Thus, it is imperative to accurately identify all the existing sampling intensity/plot design combinations within the population of interest. Once this difficulty is surmounted, there still exist aggregation methods that will produce erroneous results. Statistically valid variance estimation arises from maintaining independence of the two samples. This approach satisfies both the proportional allocation among strata requirement as well as the necessary partitioning of the two plot designs. Difficulty in interpretation of results can also be encountered due to differences in measurement protocols across aggregated areas. Thus, analysts should have an in-depth understanding of data sources and the differences between them to avoid unintended errors. The need for rural–urban assessments are expected to increase dramatically as urban areas expand and issues such as land conversion, wildland fire and invasive species spread become of further importance. Numéro de notice : A2018-638 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/forestry/cpy023 date de publication en ligne : 20/07/2018 En ligne : https://doi.org/10.1093/forestry/cpy023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93247 [article]

Propagating uncertainty through individual tree volume model predictions to large-area volume estimates / Ronald E. McRoberts in Annals of Forest Science [en ligne], vol 73 n° 3 (September 2016)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 73 n° 3 (September 2016) . - pp 625 – 633 Titre : Propagating uncertainty through individual tree volume model predictions to large-area volume estimates Type de document : Article/Communication Auteurs : Ronald E. McRoberts, Auteur ; James A. Westfall, Auteur Année de publication : 2016 Article en page(s) : pp 625 – 633 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] diamètre des arbres [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] incertitude des données [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] prédiction [Termes descripteurs IGN] propagation d'erreur [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Key message : The effects on large-area volume estimates of uncertainty in individual tree volume model predictions were negligible when using simple random sampling estimators for large-area estimation, but non-negligible when using stratified estimators which reduced the effects of sampling variability. Context : Forest inventory estimates of tree volume for large areas are typically calculated by adding model predictions of volumes for individual trees at the plot level and calculating the per unit area mean over plots. The uncertainty in the model predictions is generally ignored with the result that the precision of the large-area volume estimate is optimistic. Aims : The primary objective was to estimate the effects on large-area volume estimates of volume model prediction uncertainty due to diameter and height measurement error, parameter uncertainty, and model residual variance. Methods : Monte Carlo simulation approaches were used because of the complexities associated with multiple sources of uncertainty, the non-linear nature of the models, and heteroskedasticity. Results : The effects of model prediction uncertainty on large-area volume estimates of growing stock volume were negligible when using simple random sampling estimators. However, with stratified estimators that reduce the effects of sampling variability, the effects of model prediction uncertainty were not necessarily negligible. The adverse effects of parameter uncertainty and residual variance were greater than the effects of diameter and height measurement errors. Conclusion : The uncertainty of large-area volume estimates that do not account for model prediction uncertainty should be regarded with caution. Numéro de notice : A2016-711 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s13595-015-0473-x date de publication en ligne : 22/04/2015 En ligne : https://doi.org/10.1007/s13595-015-0473-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82089 [article]