Добірка наукової літератури з теми "Multisource forest inventory"
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Статті в журналах з теми "Multisource forest inventory"
Castilla, Guillermo, Ronald J. Hall, Rob Skakun, Michelle Filiatrault, André Beaudoin, Michael Gartrell, Lisa Smith, Kathleen Groenewegen, Chris Hopkinson, and Jurjen van der Sluijs. "The Multisource Vegetation Inventory (MVI): A Satellite-Based Forest Inventory for the Northwest Territories Taiga Plains." Remote Sensing 14, no. 5 (February 24, 2022): 1108. http://dx.doi.org/10.3390/rs14051108.
Повний текст джерелаKandel, PN. "Monitoring above-ground forest biomass: A comparison of cost and accuracy between LiDAR assisted multisource programme and field-based forest resource assessment in Nepal." Banko Janakari 23, no. 1 (December 26, 2013): 12–22. http://dx.doi.org/10.3126/banko.v23i1.9463.
Повний текст джерелаMalambo, Lonesome, Sorin C. Popescu, Jim Rakestraw, Nian-Wei Ku, and Tunde A. Owoola. "Regional Stem Volume Mapping: A Feasibility Assessment of Scaling Tree-Level Estimates." Forests 14, no. 3 (March 3, 2023): 506. http://dx.doi.org/10.3390/f14030506.
Повний текст джерелаKatila, M., J. Heikkinen, and E. Tomppo. "Calibration of small-area estimates for map errors in multisource forest inventory." Canadian Journal of Forest Research 30, no. 8 (August 1, 2000): 1329–39. http://dx.doi.org/10.1139/x99-234.
Повний текст джерелаTuominen, Sakari, Stuart Fish, and Simo Poso. "Combining remote sensing, data from earlier inventories, and geostatistical interpolation in multisource forest inventory." Canadian Journal of Forest Research 33, no. 4 (April 1, 2003): 624–34. http://dx.doi.org/10.1139/x02-199.
Повний текст джерелаKatila, Matti, and Erkki Tomppo. "Selecting estimation parameters for the Finnish multisource National Forest Inventory." Remote Sensing of Environment 76, no. 1 (April 2001): 16–32. http://dx.doi.org/10.1016/s0034-4257(00)00188-7.
Повний текст джерелаIrulappa-Pillai-Vijayakumar, Dinesh Babu, Jean-Pierre Renaud, François Morneau, Ronald E. McRoberts, and Cédric Vega. "Increasing Precision for French Forest Inventory Estimates using the k-NN Technique with Optical and Photogrammetric Data and Model-Assisted Estimators." Remote Sensing 11, no. 8 (April 25, 2019): 991. http://dx.doi.org/10.3390/rs11080991.
Повний текст джерелаZhu, Yan, Zhongke Feng, Jing Lu, and Jincheng Liu. "Estimation of Forest Biomass in Beijing (China) Using Multisource Remote Sensing and Forest Inventory Data." Forests 11, no. 2 (January 31, 2020): 163. http://dx.doi.org/10.3390/f11020163.
Повний текст джерелаRäty, Minna, Juha Heikkinen, and Annika Kangas. "Assessment of sampling strategies utilizing auxiliary information in large-scale forest inventory." Canadian Journal of Forest Research 48, no. 7 (July 2018): 749–57. http://dx.doi.org/10.1139/cjfr-2017-0414.
Повний текст джерелаZhang, Fanyi, Xin Tian, Haibo Zhang, and Mi Jiang. "Estimation of Aboveground Carbon Density of Forests Using Deep Learning and Multisource Remote Sensing." Remote Sensing 14, no. 13 (June 23, 2022): 3022. http://dx.doi.org/10.3390/rs14133022.
Повний текст джерелаДисертації з теми "Multisource forest inventory"
Schleich, Anouk. "Apport du lidar spatial pour le développement de méthodes d'inventaire forestier multisource adaptées à la gestion durable des forêts dans un contexte de changement global." Electronic Thesis or Diss., Paris, AgroParisTech, 2024. http://www.theses.fr/2024AGPT0002.
Повний текст джерелаThe thesis focuses on the contribution of spaceborne lidar to the development of Multisource Forest Inventory (MFI) methods. In France, the National Forest Inventory (NFI) method addresses the requirements of public policies at regional and national levels. However, on smaller territories, precision is often insufficient to meet the needs of management activities. MFI methods better address these needs by combining inventory data with remote sensing data. This thesis aims to improve NFI accuracy at sub-regional to local scales by integrating data from the spaceborne lidar GEDI into multisource approaches.Unfortunately, this integration is complicated due to the lack of spatial correspondence between field samples (inventory plots) and GEDI footprints. Additionally, GEDI data are poorly georeferenced, making them difficult to integrate into certain MFI approaches. This thesis focuses on these issues and is divided into three main parts.As a first step, a method for improving GEDI georeferencing, based on a high-resolution reference digital elevation model (DEM) was developed. This method compares, for a series of positions around the location indicated in the GEDI products, the ground elevations of the GEDI footprints with those of the reference DEM, generating an error map according to X and Y offsets. Using a flow accumulation algorithm on this error map, an improved position minimizing the distance from the DEM is proposed for each GEDI footprint.Next, two approaches for using GEDI data with NFI data were developed. The study sites are located in the Vosges and use ∼ 500 IFN plots and over 100,000 GEDI footprints.The first approach is a double sampling for stratification (2SS) approach, based on common variables between GEDI and NFI, without requiring spatial correspondence of the two data sources. 2SS approaches are generally based on probabilistic data samples, which is not a priori the case for GEDI's sampling pattern. Thus, a preliminary analysis was required to understand the characteristics of the spatial distribution of the GEDI sample. The relevance of the chosen common variable, i.e. the maximum tree height, was also verified. Compared with estimates based only on NFI data, the 2SS approach improved the variance of growing stock volume estimates by up to 56%.The second approach is based on a link between GEDI data and NFI data, established indirectly by using spatially exhaustive data sources, the Sentinel-2 and Sentinel-1 images. To establish the model linking the different data sources, we chose to use the k-nearest neighbor (kNN) method combined with bagging (bootstrap aggregation). The aim is to propagate information from field plots to GEDI footprints in order to "densify" NFI plots by taking advantage of GEDI forest structure measurements, which are well correlated with the forest attributes of interest (e.g. growing stock volume). First, for each NFI plot, we looked for the GEDI footprints with the characteristics of the Sentinel link variables, supplemented or not with a height link variable, that are closest to those of the NFI point. Using a kNN-bagging approach, the set of GEDI variables is therefore estimated for each NFI plot. Next, a regression model is established by kNN-bagging to estimate the volume using the best predicted GEDI variables from the previous step and the Sentinel variables. The volume is estimated at the level of all GEDI footprints. The strategy supplemented by a height link variable performed best and reached a coefficient of determination of 58%. Subsequently, using the resulting dense sample of volume plots, standard methods for small area estimation (scale of the municipality or district) or high-resolution volume mapping can be implemented
Частини книг з теми "Multisource forest inventory"
Pekkarinen, A., and S. Tuominen. "Stratification of a Forest Area for Multisource Forest Inventory by Means of Aerial Photographs and Image Segmentation." In Advances in Forest Inventory for Sustainable Forest Management and Biodiversity Monitoring, 111–23. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-017-0649-0_9.
Повний текст джерела