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Relevant bibliographies by topics / Multisource forest inventory

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Journal articles

Academic literature on the topic 'Multisource forest inventory'

Author: Grafiati

Published: 31 August 2024

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Journal articles on the topic "Multisource forest inventory"

1

Castilla, Guillermo, Ronald J. Hall, Rob Skakun, et al. "The Multisource Vegetation Inventory (MVI): A Satellite-Based Forest Inventory for the Northwest Territories Taiga Plains." Remote Sensing 14, no. 5 (2022): 1108. http://dx.doi.org/10.3390/rs14051108.

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Sustainable forest management requires information on the spatial distribution, composition, and structure of forests. However, jurisdictions with large tracts of noncommercial forest, such as the Northwest Territories (NWT) of Canada, often lack detailed forest information across their land base. The goal of the Multisource Vegetation Inventory (MVI) project was to create a large area forest inventory (FI) map that could support strategic forest management in the NWT using optical, radar, and light detection and ranging (LiDAR) satellite remote sensing anchored on limited field plots and airb

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2

Kandel, 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 (2013): 12–22. http://dx.doi.org/10.3126/banko.v23i1.9463.

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Analyzing forest monitoring costs and accuracy of forest carbon stock estimates are important criteria in the framework of Reducing Emission from Deforestation and Forest Degradation (REDD), because Monitoring, Reporting and Verification (MRV) system has been seen as an investment that aims to generate financial benefits to forest owners. Thus, comparisons of cost efficiency and accuracy were carried out between the LiDAR (Light Detection and Ranging) Assisted Multisource Programme (LAMP) and the field-based multisource Forest Resource Assessment (FRA) applied in the 23500 km2 Terai Arc Landsc

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3

Malambo, 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 (2023): 506. http://dx.doi.org/10.3390/f14030506.

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Spatially detailed monitoring of forest resources is important for sustainable management but limited by a lack of field measurements. The increasing availability of multisource datasets offers the potential to characterize forest attributes at finer resolutions with regional coverage. This study aimed to assess the potential of mapping stem volume at a 30 m scale in eastern Texas using multisource datasets: airborne lidar, Landsat and LANDFIRE (Landscape Fire and Resource Management Planning Tools Project) datasets. Gradient-boosted trees regression models relating total volume, estimated fro

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4

Katila, 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 (2000): 1329–39. http://dx.doi.org/10.1139/x99-234.

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A multisource inventory method has been applied in the Finnish National Forest Inventory (NFI) since 1990.The method utilizes satellite images and digital map data,in addition to field measurements, and produces estimates of allfield parameters for computation units as well as thematic maps. Information from base mapsis employed in delineating forestry land from other land use classes.The map data are not necessarily up-to-date and often containsignificant errors. This paper introduces a statistical calibration method aimed atreducing the effect of map errors on multisource forest resourceesti

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5

Tuominen, 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 (2003): 624–34. http://dx.doi.org/10.1139/x02-199.

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Multisource forest inventory with two-phase sampling offers several advantages in the forest management planning when compared with the traditional visual inventory by stands. For example, by combining data from remote sensing imagery with field measurements, it is possible to estimate the forest characteristics of large areas at a more reasonable cost than by using the traditional visual inventory by stands. In this study, the k-nearest-neighbours estimation (k-nn), stand inventory data, and geostatistical interpolation were combined for estimation of five forest variables (mean diameter, mea

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6

Katila, Matti, and Erkki Tomppo. "Selecting estimation parameters for the Finnish multisource National Forest Inventory." Remote Sensing of Environment 76, no. 1 (2001): 16–32. http://dx.doi.org/10.1016/s0034-4257(00)00188-7.

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7

Irulappa-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 (2019): 991. http://dx.doi.org/10.3390/rs11080991.

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Multisource forest inventory methods were developed to improve the precision of national forest inventory estimates. These methods rely on the combination of inventory data and auxiliary information correlated with forest attributes of interest. As these methods have been predominantly tested over coniferous forests, the present study used this approach for heterogeneous and complex deciduous forests in the center of France. The auxiliary data considered included a forest type map, Landsat 8 spectral bands and derived vegetation indexes, and 3D variables derived from photogrammetric canopy hei

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8

Zhu, 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 (2020): 163. http://dx.doi.org/10.3390/f11020163.

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Forest biomass reflects the material cycle of forest ecosystems and is an important index to measure changes in forest structure and function. The accurate estimation of forest biomass is the research basis for measuring carbon storage in forest systems, and it is important to better understand the carbon cycle and improve the efficiency of forest policy and management activities. In this study, to achieve an accurate estimation of meso-scale (regional) forest biomass, we used Ninth Beijing Forest Inventory data (FID), Landsat 8 OLI Image data and ALOS-2 PALSAR-2 data to establish different fo

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9

Räty, 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 (2018): 749–57. http://dx.doi.org/10.1139/cjfr-2017-0414.

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The National Forest Inventory of Finland (NFI) produces national- and regional-level statistics for sustainability assessment and strategical-level decision making. So far, the regional-level statistics are based on a systematic sampling design with geographical stratification. Auxiliary information such as remote sensing is not used for design or estimation at the regional level, but it is used at the small-area level, i.e., for municipality-level results. To improve the cost efficiency of the NFI, possibilities for using auxiliary data in both the design and estimation are of interest. We as

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10

Zhang, 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 (2022): 3022. http://dx.doi.org/10.3390/rs14133022.

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Forests are crucial in carbon sequestration and oxygen release. An accurate assessment of forest carbon storage is meaningful for Chinese cities to achieve carbon peak and carbon neutrality. For an accurate estimation of regional-scale forest aboveground carbon density, this study applied a Sentinel-2 multispectral instrument (MSI), Advanced Land Observing Satellite 2 (ALOS-2) L-band, and Sentinel-1 C-band synthetic aperture radar (SAR) to estimate and map the forest carbon density. Considering the forest field-inventory data of eastern China from 2018 as an experimental sample, we explored th

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