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Mapping aboveground biomass and its prediction uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors / Svetlana Saarela in Forest ecosystems, vol 7 (2020)  

Public [article]  inForest ecosystems > vol 7 (2020) . - n° 43 Titre : Mapping aboveground biomass and its prediction uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors Type de document : Article/Communication Auteurs : Svetlana Saarela, Auteur ; André Wästlund, Auteur ; Emma Hölmstrom, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 43 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] biomasse aérienne [Termes IGN] carte thématique [Termes IGN] données allométriques [Termes IGN] données de terrain [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] erreur de modèle [Termes IGN] inférence statistique [Termes IGN] modèle d'incertitude [Termes IGN] modèle de croissance végétale [Termes IGN] modèle non linéaire [Termes IGN] semis de points [Termes IGN] Suède Résumé : (auteur) Background: The increasing availability of remotely sensed data has recently challenged the traditional way of performing forest inventories, and induced an interest in model-based inference. Like traditional design-based inference, model-based inference allows for regional estimates of totals and means, but in addition for wall-to-wall mapping of forest characteristics. Recently Light Detection and Ranging (LiDAR)-based maps of forest attributes have been developed in many countries and been well received by users due to their accurate spatial representation of forest resources. However, the correspondence between such mapping and model-based inference is seldom appreciated. In this study, we applied hierarchical model-based inference to produce aboveground biomass maps as well as maps of the corresponding prediction uncertainties with the same spatial resolution. Further, an estimator of mean biomass at regional level, and its uncertainty, was developed to demonstrate how mapping and regional level assessment can be combined within the framework of model-based inference. Results: Through a new version of hierarchical model-based estimation, allowing models to be nonlinear, we accounted for uncertainties in both the individual tree-level biomass models and the models linking plot level biomass predictions with LiDAR metrics. In a 5005 km2 large study area in south-central Sweden the predicted aboveground biomass at the level of 18 m ×18 m map units was found to range between 9 and 447 Mg ·ha−1. The corresponding root mean square errors ranged between 10 and 162 Mg ·ha−1. For the entire study region, the mean aboveground biomass was 55 Mg ·ha−1 and the corresponding relative root mean square error 8%. At this level 75% of the mean square error was due to the uncertainty associated with tree-level models. Conclusions: Through the proposed method it is possible to link mapping and estimation within the framework of model-based inference. Uncertainties in both tree-level biomass models and models linking plot level biomass with LiDAR data are accounted for, both for the uncertainty maps and the overall estimates. The development of hierarchical model-based inference to handle nonlinear models was an important prerequisite for the study. Numéro de notice : A2020-814 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1186/s40663-020-00245-0 Date de publication en ligne : 03/07/2020 En ligne : https://doi.org/10.1186/s40663-020-00245-0 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96987 [article]

Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation / Göran Stahl in Forest ecosystems, vol 3 (2016)  

Public [article]  inForest ecosystems > vol 3 (2016) Titre : Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation Type de document : Article/Communication Auteurs : Göran Stahl, Auteur ; Svetlana Saarela, Auteur ; Sebastian Schnell, Auteur ; Sören Holm, Auteur ; et al., Auteur Année de publication : 2016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] échantillonnage [Termes IGN] estimation statistique [Termes IGN] inférence statistique [Termes IGN] inventaire forestier (techniques et méthodes) [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation frameworks where models play a core role: model-assisted, model-based, and hybrid estimation. The first two are well known, whereas the third has only recently been introduced in forest surveys. Hybrid inference mixes design-based and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the target variable from the auxiliary data..We review studies on large-area forest surveys based on model-assisted, model-based, and hybrid estimation, and discuss advantages and disadvantages of the approaches. We conclude that no general recommendations can be made about whether model-assisted, model-based, or hybrid estimation should be preferred. The choice depends on the objective of the survey and the possibilities to acquire appropriate field and remotely sensed data. We also conclude that modelling approaches can only be successfully applied for estimating target variables such as growing stock volume or biomass, which are adequately related to commonly available remotely sensed data, and thus purely field based surveys remain important for several important forest parameters. Numéro de notice : A2016--161 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1186/s40663-016-006 En ligne : https://doi.org/10.1186/s40663-016-0064-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87015 [article]

Use of remotely sensed auxiliary data for improving sample-based forest inventories / Svetlana Saarela (2015)  

Public Titre : Use of remotely sensed auxiliary data for improving sample-based forest inventories Type de document : Thèse/HDR Auteurs : Svetlana Saarela, Auteur Editeur : Vantaa [Finlande] : Finnish Society of Forest Science Année de publication : 2015 Collection : Dissertationes forestales, ISSN 1795-7389 num. 201 Format : 21 x 30 cm ISBN/ISSN/EAN : 978-951-6514911-8 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse multivariée [Termes IGN] données auxiliaires [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] échantillonnage de données [Termes IGN] Finlande [Termes IGN] forêt boréale [Termes IGN] image Landsat [Termes IGN] inférence statistique [Termes IGN] inventaire forestier étranger (données) [Termes IGN] placette d'échantillonnage [Termes IGN] tronc [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Over the past decades it has been shown that remotely sensed auxiliary data have a potential to increase the precision of key estimators in sample-based forest surveys. This thesis was motivated by the increasing availability of remotely sensed data, and the objectives were to investigate how this type of auxiliary data can be used for improving both the design and the estimators in sample-based surveys. Two different modes of inference were studied: model-based inference and design-based inference. Empirical data for the studies were acquired from a boreal forest area in the Kuortane region of western Finland. The data comprised a combination of auxiliary information derived from airborne LiDAR and Landsat data, and field sample plot data collected using a modification of the 10th Finnish National Forest Inventory. The studied forest attribute was growing stock volume. The results of this thesis are important for the development of forest inventories to meet the requirements which stem from an increasing number of international commitments and agreements related to forests. Numéro de notice : 14978 Affiliation des auteurs : non IGN Thématique : FORET Nature : Thèse étrangère Note de thèse : PhD : Forest sciences : University of Helsinki : 2015 En ligne : Over the past decades it has been shown that remotely sensed auxiliary data have [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78366