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Large-area inventory of species composition using airborne laser scanning and hyperspectral data / Hans Ole Ørka in Silva fennica, vol 55 n° 4 (September 2021)  

Public [article]  inSilva fennica > vol 55 n° 4 (September 2021) . - n° 10244 Titre : Large-area inventory of species composition using airborne laser scanning and hyperspectral data Type de document : Article/Communication Auteurs : Hans Ole Ørka, Auteur ; Endre H. Hansen, Auteur ; Michele Dalponte, Auteur ; Terje Gobakken, Auteur ; Erik Naesset, Auteur Année de publication : 2021 Article en page(s) : n° 10244 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] composition d'un peuplement forestier [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image hyperspectrale [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] Norvège [Termes IGN] Picea abies [Termes IGN] Pinus sylvestris [Termes IGN] régression [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Tree species composition is an essential attribute in stand-level forest management inventories and remotely sensed data might be useful for its estimation. Previous studies on this topic have had several operational drawbacks, e.g., performance studied at a small scale and at a single tree-level with large fieldwork costs. The current study presents the results from a large-area inventory providing species composition following an operational area-based approach. The study utilizes a combination of airborne laser scanning and hyperspectral data and 97 field sample plots of 250 m2 collected over 350 km2 of productive forest in Norway. The results show that, with the availability of hyperspectral data, species-specific volume proportions can be provided in operational forest management inventories with acceptable results in 90% of the cases at the plot level. Dominant species were classified with an overall accuracy of 91% and a kappa-value of 0.73. Species-specific volumes were estimated with relative root mean square differences of 34%, 87%, and 102% for Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and deciduous species, respectively. A novel tree-based approach for selecting pixels improved the results compared to a traditional approach based on the normalized difference vegetation index. Numéro de notice : A2021-736 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.10244 En ligne : https://doi.org/10.14214/sf.10244 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98695 [article]

Effects of terrain slope and aspect on the error of ALS-based predictions of forest attributes / Hans Ole Ørka in Forestry, an international journal of forest research, vol 91 n° 2 (April 2018)  

Public [article]  inForestry, an international journal of forest research > vol 91 n° 2 (April 2018) . - pp 225 - 237 Titre : Effects of terrain slope and aspect on the error of ALS-based predictions of forest attributes Type de document : Article/Communication Auteurs : Hans Ole Ørka, Auteur ; Ole Martin Bollandsås, Auteur ; Endre H. Hansen, Auteur ; Erik Naesset, Auteur ; Terje Gobakken, Auteur Année de publication : 2018 Article en page(s) : pp 225 - 237 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de variance [Termes IGN] données dendrométriques [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] inventaire forestier étranger (données) [Termes IGN] modèle de simulation [Termes IGN] Norvège [Termes IGN] pente [Termes IGN] régression non linéaire [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) Wall-to-wall forest management inventories with the area-based method using airborne laser scanner (ALS) data are operational in many countries. With this method, empirical relationships are established between ALS metrics and ground reference observations of forest attributes, and wall-to-wall predictions can be made over large areas. However, the prediction errors may be influenced by terrain slope and aspect because the properties of the ALS point cloud are dependent on these factors. Two datasets covering wide ranges of terrain slope and aspect, collected in the western part of Norway, were analysed. The first dataset represented sample plots from an ordinary operational forest management inventory and the second dataset were collected as an experimental dataset where clusters of sample plots were distributed on slopes with different inclinations. Six forest attributes were predicted using non-linear regression and the prediction errors were analysed using univariate- and multivariate analysis of variance. The results showed that slope and aspect affected the prediction errors, but that the effects were small in magnitude. Thus, the current study concludes that terrain effects seem to be negligible in operational forest inventories. Numéro de notice : A2018-652 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/forestry/cpx058 Date de publication en ligne : 30/01/2018 En ligne : https://doi.org/10.1093/forestry/cpx058 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93238 [article]

Value of airborne laser scanning and digital aerial photogrammetry data in forest decision making / Annika S. Kangas in Silva fennica, vol 52 n° 1 ([01/02/2018])  

Public [article]  inSilva fennica > vol 52 n° 1 [01/02/2018] Titre : Value of airborne laser scanning and digital aerial photogrammetry data in forest decision making Type de document : Article/Communication Auteurs : Annika S. Kangas, Auteur ; Terje Gobakken, Auteur ; Stefano Puliti, Auteur ; Marius Hauglin, Auteur ; Erik Naesset, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] aide à la décision [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Finlande [Termes IGN] image aérienne [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] inventaire forestier étranger (données) [Termes IGN] Norvège [Termes IGN] qualité des données [Termes IGN] volume en bois [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) Airborne laser scanning (ALS) has been the main method for acquiring data for forest management planning in Finland and Norway in the last decade. Recently, digital aerial photogrammetry (DAP) has provided an interesting alternative, as the accuracy of stand-based estimates has been quite close to that of ALS while the costs are markedly smaller. Thus, it is important to know if the better accuracy of ALS is worth the higher costs for forest owners. In many recent studies, the value of forest inventory information in the harvest scheduling has been examined, for instance through cost-plus-loss analysis. Cost-plus-loss means that the quality of the data is accounted for in monetary terms through calculating the losses due to errors in the data in the forest management planning context. These costs are added to the inventory costs. In the current study, we compared the losses of ALS and DAP at plot level. According to the results, the data produced using DAP are as good as data produced using ALS from a decision making point of view, even though ALS is slightly more accurate. ALS is better than DAP only if the data will be used for more than 15 years before acquiring new data, and even then the difference is quite small. Thus, the increased errors in DAP do not significantly affect the results from a decision making point of view, and ALS and DAP data can be equally well recommended to the forest owners for management planning. The decision of which data to acquire, can thus be made based on the availability of the data on first hand and the costs of acquiring it on the second hand. Numéro de notice : A2018-498 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14214/sf.9923 Date de publication en ligne : 24/01/2018 En ligne : https://doi.org/10.14214/sf.9923 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91187 [article]

Digital aerial photogrammetry can efficiently support large-area forest inventories in Norway / Lars Johannes in Forestry, an international journal of forest research, vol 90 n° 5 (December 2017)  

Public [article]  inForestry, an international journal of forest research > vol 90 n° 5 (December 2017) . - pp 710 - 718 Titre : Digital aerial photogrammetry can efficiently support large-area forest inventories in Norway Type de document : Article/Communication Auteurs : Lars Johannes, Auteur ; Johannes Breidenbach, Auteur ; Svein Solberg, Auteur ; Erik Naesset, Auteur ; Rasmus Astrup, Auteur Année de publication : 2017 Article en page(s) : pp 710 - 718 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] biomasse forestière [Termes IGN] hauteur des arbres [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] inventaire forestier étranger (données) [Termes IGN] Norvège [Termes IGN] photogrammétrie aérienne [Termes IGN] semis de points [Termes IGN] surface terrière [Termes IGN] volume en bois Résumé : (Auteur) The use of digital aerial photogrammetry (DAP) for forest inventory purposes has been widely studied and can produce comparable accuracy compared with airborne laser scanning (ALS) in small, homogeneous areas. However, the accuracy of DAP for large scale applications with heterogeneous terrain and forest vegetation has not yet been reported. In this study we examined the accuracy of timber volume, biomass and basal area prediction models based on DAP and national forest inventory (NFI) data on a large area in central Norway. Two separate point clouds were derived from aerial image acquisitions of 2010 and 2013. Vegetation heights were extracted by subtracting terrain elevation derived from ALS. A large number of NFI sample plots (483) measured between 2010 and 2014 were used as reference data to fit linear models for timber volume, biomass and basal area with height metrics derived from the DAP data as explanatory variables. Variables describing the heterogeneous environmental and image acquisition conditions were calculated and their influence on the model accuracy was tested. The results showed that forest parameter prediction using DAP works well when applied to a large area. The model fits of the timber volume, biomass and basal area models were good with R2 of 0.80, 0.81, 0.81 and RMSEs of 41.43 m3 ha−1 (55% of the mean observed value), 32.49 t ha−1 (47%), 5.19 m2 ha−1 (41%), respectively. Only a small proportion of the variation could be attributed to the heterogeneous conditions. The inclusion of the relative sun inclination led to an improvement of the model RMSEs by 2% of the mean observed values. The relatively low cost and stability across large areas make DAP an attractive source of auxiliary information for large scale forest inventories. Numéro de notice : A2017-905 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/forestry/cpx027 En ligne : https://doi.org/10.1093/forestry/cpx027 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93207 [article]

The effects of temporal differences between map and ground data on map-assisted estimates of forest area and biomass / Ronald E. McRoberts in Annals of Forest Science, vol 73 n° 4 (December 2016)  

Public [article]  inAnnals of Forest Science > vol 73 n° 4 (December 2016) . - pp 839 - 847 Titre : The effects of temporal differences between map and ground data on map-assisted estimates of forest area and biomass Type de document : Article/Communication Auteurs : Ronald E. McRoberts, Auteur ; Erik Naesset, Auteur ; Terje Gobakken, Auteur Année de publication : 2016 Article en page(s) : pp 839 - 847 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse diachronique [Termes IGN] biomasse [Termes IGN] données de terrain [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] estimation statistique [Termes IGN] forêt [Termes IGN] image Landsat [Termes IGN] inventaire forestier étranger (données) [Termes IGN] Minnesota (Etats-Unis) [Termes IGN] Norvège [Termes IGN] régression [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Key message : When areas of interest experience little change, remote sensing-based maps whose dates deviate from ground data can still substantially enhance precision. However, when change is substantial, deviations in dates reduce the utility of such maps for this purpose. Context : Remote sensing-based maps are well-established as means of increasing the precision of estimates of forest inventory parameters. The general practice is to use maps whose dates correspond closely to the dates of ground data. However, as national forest inventories move to continuous inventories, deviations between map and ground data dates increase. Aims : The aim was to assess the degree to which remote sensing-based maps can be used to increase the precision of estimates despite differences between map and ground data dates. Methods : For study areas in the USA and Norway, maps were constructed for each of two dates, and model-assisted regression estimators were used to estimate inventory parameters using ground data whose dates differed by as much as 11 years from the map dates. Results : For the Minnesota study area that had little change, 7-year differences in dates had little effect on the precision of estimates of proportion forest area. For the Norwegian study area that experienced considerable change, 11-year differences in dates had a detrimental effect on the precision of estimates of mean biomass per unit area. Conclusions : The effects of differences in map and ground data dates were less important than temporal change in the study area. Numéro de notice : A2016--168 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s13595-015-0485-6 Date de publication en ligne : 12/05/2015 En ligne : https://doi.org/10.1007/s13595-015-0485-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87210 [article]

A functional regression model for inventories supported by aerial laser scanner data or photogrammetric point clouds / Magnussen, Steen in Remote sensing of environment, vol 184 (October 2016)  

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Statistical rigor in LiDAR-assisted estimation of aboveground forest biomass / Timothy G. Gregoire in Remote sensing of environment, vol 173 (February 2016)  

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Assessing forest inventory information obtained from different inventory approaches and remote sensing data sources / Even Bergseng in Annals of Forest Science, vol 72 n° 1 (January 2015)  

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Deriving airborne laser scanning based computational canopy volume for forest biomass and allometry studies / Jari Vauhkonen in ISPRS Journal of photogrammetry and remote sensing, vol 96 (October 2014)  

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Performance of GPS precise point positioning under conifer forest canopies / Erik Naesset in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 5 (May 2008)  

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Efficient multiresolution spatial predictions for large data arrays / Magnussen, Steen in Remote sensing of environment, vol 109 n° 4 (30 August 2007)  

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