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Evaluation of mapped-plot variance estimators across a range of partial nonresponse in a post-stratified national forest inventory / James A. Westfall in Canadian Journal of Forest Research, Vol 52 n° 2 (February 2022)  

Public [article]  inCanadian Journal of Forest Research > Vol 52 n° 2 (February 2022) . - pp 280 - 285 Titre : Evaluation of mapped-plot variance estimators across a range of partial nonresponse in a post-stratified national forest inventory Type de document : Article/Communication Auteurs : James A. Westfall, Auteur ; Andrew J. Lister, Auteur ; Charles T. Scott, Auteur Année de publication : 2022 Article en page(s) : pp 280 - 285 Note générale : bibliographie NB Note technique et non pas article de recherche Langues : Français (fre) Anglais (eng) Descripteur : [Termes IGN] erreur systématique [Termes IGN] estimation statistique [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] placette d'échantillonnage [Termes IGN] variance [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) When conducting a forest inventory, sometimes portions of plots cannot be measured due to inaccessibility. Two primary methods have been presented to account for partial nonresponse in the estimation phase: (i) use a ratio-to-size estimator or (ii) apply an adjustment factor to all plot observations in proportion to the missing area. Both approaches provide identical estimates of the population mean, but the estimates of variance differ when partial nonresponse is present. The performance of variance estimators was examined for a range of population forest area and partial nonresponse proportions in the sample. The ratio-to-size variance estimator performed unbiasedly with respect to simulation results, but the adjustment factor variance estimates were biased, with magnitude and direction dependent upon the forest area proportion and amount of partial nonresponse. The bias is relatively small when the partial nonresponse is small, which is often the case; however, the ratio-to-size method is preferred to ensure accurate variance estimation for a wide range of circumstances. Numéro de notice : A2022-312 Affiliation des auteurs : non IGN Autre URL associée : Draft Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.1139/cjfr-2021-0159 Date de publication en ligne : 10/08/2021 En ligne : https://doi.org/10.1139/cjfr-2021-0159 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100409 [article]

Use of remote sensing data to improve the efficiency of National Forest Inventories: A case study from the United States National Forest Inventory / Andrew J. Lister in Forests, vol 11 n° 12 (December 2020)  

Public [article]  inForests > vol 11 n° 12 (December 2020) . - n° 1364 Titre : Use of remote sensing data to improve the efficiency of National Forest Inventories: A case study from the United States National Forest Inventory Type de document : Article/Communication Auteurs : Andrew J. Lister, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 1364 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] échantillonnage [Termes IGN] Etats-Unis [Termes IGN] image aérienne [Termes IGN] inventaire forestier étranger (données) [Termes IGN] surveillance forestière [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Globally, forests are a crucial natural resource, and their sound management is critical for human and ecosystem health and well-being. Efforts to manage forests depend upon reliable data on the status of and trends in forest resources. When these data come from well-designed natural resource monitoring (NRM) systems, decision makers can make science-informed decisions. National forest inventories (NFIs) are a cornerstone of NRM systems, but require capacity and skills to implement. Efficiencies can be gained by incorporating auxiliary information derived from remote sensing (RS) into ground-based forest inventories. However, it can be difficult for countries embarking on NFI development to choose among the various RS integration options, and to develop a harmonized vision of how NFI and RS data can work together to meet monitoring needs. The NFI of the United States, which has been conducted by the USDA Forest Service’s (USFS) Forest Inventory and Analysis (FIA) program for nearly a century, uses RS technology extensively. Here we review the history of the use of RS in FIA, beginning with general background on NFI, FIA, and sampling statistics, followed by a description of the evolution of RS technology usage, beginning with paper aerial photography and ending with present day applications and future directions. The goal of this review is to offer FIA’s experience with NFI-RS integration as a case study for other countries wishing to improve the efficiency of their NFI programs. Numéro de notice : A2020-844 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f11121364 Date de publication en ligne : 19/12/2020 En ligne : https://doi.org/10.3390/f11121364 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98632 [article]