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Academic literature on the topic 'Multisource forest inventory'
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Journal articles on the topic "Multisource forest inventory"
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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 airborne LiDAR data. A new landcover map based on Landsat imagery was the first step to stratify forestland into broad forest types. A modelling chain linking FI plots to airborne and spaceborne LiDAR was then developed to circumvent the scarcity of field data in the region. The developed models allowed the estimation of forest attributes in thousands of surrogate FI plots corresponding to spaceborne LiDAR footprints distributed across the project area. The surrogate plots were used as a reference dataset for estimating each forest attribute in each 30 m forest cell within the project area. The estimation was based on the k-nearest neighbour (k-NN) algorithm, where the selection of the four most similar surrogate FI plots to each cell was based on satellite, topographic, and climatic data. Wall-to-wall 30 m raster maps of broad forest type, stand height, crown closure, stand volume, total volume, aboveground biomass, and stand age were created for a ~400,000 km2 area, validated with independent data, and generalized into a polygon GIS layer resembling a traditional FI map. The MVI project showed that a reasonably accurate FI map for large, remote, predominantly non-inventoried boreal regions can be obtained at a low cost by combining limited field data with remote sensing data from multiple sources.
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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 Landscape (TAL) of Nepal in 2011 to estimate Above Ground Biomass (AGB). The model-based LAMP was applied by integrating 5% LiDAR sampling, wall to wall RapidEye satellite image and field sample plot inventory. The design-based FRA was carried out to generate comprehensive forest resource information. Administrative and initial variable costs of both approaches were calculated separately, and converted to unit costs for comparison. To compare the subsequent forest monitoring costs, cumulative costs were derived on the basis of the calculated present variable items and expenditures. The accuracies were calculated by using mean error of mean biomass estimates (tons/ha) at different spatial scales ranging from 1 to 350,000 ha forests. Design-based FRA was found to be cost-efficient (USD 0.22/ha) as compared to the LAMP approach (USD 0.28/ha) for baseline data collection, whereas administrative cost of multisource FRA (USD 0.26/ha) was significantly higher. Although a huge amount of data were generated through multisource FRA in each cycle, the LAMP approach appears to be cost-efficient to estimate AGB in subsequent forest inventory. The mean errors in the LAMP-derived mean biomass estimate were significantly smaller at all spatial resolutions than the FRA-plot-derived mean biomass estimate. The study concludes that spatial accuracy of LAMP is good enough to estimate biomass stock of Community Forests (CFs) where average size of CF is 150 ha in the study area. Banko Janakari, Vol. 23, No. 1, Page 12-22 DOI: http://dx.doi.org/10.3126/banko.v23i1.9463
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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 from airborne lidar measurements and allometric equations, and multitemporal Landsat and LANDFIRE predictors were developed and evaluated. The fitted models showed moderate to high correlation (R2 = 0.52–0.81) with reference stem volume estimates, with higher correlation in pine forests (R2 = 0.70–0.81) than mixed forests (R2 = 0.52–0.67). The models were also precise, with relative percent mean absolute errors (pMAE) of 13.8–21.2%. The estimated volumes also consistently agreed with volumes estimated in independent sites (R2 = 0.51, pMAE = 34.7%) and with US Forest Service Forest Inventory Analysis county-level volume estimates (R2 = 0.93, pBias = −10.3%, pMAE = 11.7%). This study shows the potential of developing regional stem volume products using airborne lidar and multisource datasets, supporting forest productivity and carbon modeling at spatially detailed scales.
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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 resourceestimates. The correction is based on theconfusion matrix between land use classes of the field sample plots and corresponding map information.The proposed method is illustrated in a realistic setting using data from the ninth NFI.
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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, mean height, mean age, basal area, and volume) per sample plot and stand. Digitized aerial photograph features, visually interpreted aerial photograph features, and updated stand inventory data were used as the auxiliary data sources in the estimation of forest variables. The results show that, at the sample plot level, the k-nn estimates based on the auxiliary data sources were more accurate than the updated stand inventory data transferred to the plot level. At the stand level, the updated stand inventory data were more accurate than the k-nn estimates. When the k-nn estimates were combined with the updated stand inventory data, the accuracy of the estimates was significantly improved at both the sample plot and stand level. The geostatistical interpolation, which was tested on the stand level estimation, did not result in any further improvement in the accuracy of the estimates.
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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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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 height models. On a subset area, changes in canopy height estimated from two successive photogrammetric models were also used. A model-assisted inference framework, using a k nearest-neighbors approach, was used to predict 11 field inventory variables simultaneously. The results showed that among the auxiliary variables tested, 3D metrics improved the precision of dendrometric estimates more than other auxiliary variables. Relative efficiencies (RE) varying from 2.15 for volume to 1.04 for stand density were obtained using all auxiliary variables. Canopy height changes also increased RE from 3% to 26%. Our results confirmed the importance of 3D metrics as auxiliary variables and demonstrated the value of canopy change variables for increasing the precision of estimates of forest structural attributes such as density and quadratic mean diameter.
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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 forest types (coniferous forest, mixed forest, and broadleaf forest) of biomass models in Beijing. We assessed the potential of forest inventory, optical (Landsat 8 OLI) and radar (ALOS-2 PALSAR-2) data in estimating and mapping forest biomass. From these data, a wide range of parameters related to forest structure were obtained. Random forest (RF) models were established using these parameters and compared with traditional multiple linear regression (MLR) models. Forest biomass in Beijing was then estimated. The results showed the following: (1) forest inventory data combined with multisource remote sensing data can better fit forest biomass than forest inventory data alone. Among the three forest types, mixed forest has the best fitting model. Forest inventory variables and multisource remote sensing variables can match each other in time and space, capturing almost all spatial variability. (2) The 2016 forest biomass density in Beijing was estimated to be 52.26 Mg ha−1 and ranged from 19.1381–195.66 Mg ha−1. The areas with high biomass were mainly distributed in the north and southwest of Beijing, while the areas with low biomass were mainly distributed in the southeast and central areas of Beijing. (3) The estimates from the RF model are better than those from the MLR model, showing a high R 2 and a low root mean square error (RMSE). The R 2 values of the MLR models of three forest types were greater than 0.5, and RMSEs were less than 15.5 Mg ha−1, The R 2 values of the RF models were higher than 0.6, and the RMSEs were lower than 13.5 Mg ha−1. We conclude that the methods in this paper can help improve the accurate estimation of regional biomass and provide a basis for the planning of relevant forestry decision-making departments.
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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 assessed the improvements obtainable by using an interpreted satellite image — the multisource NFI result from a previous NFI — as auxiliary information in the design phase. The results show that even though the multisource NFI map is not very accurate, significant improvements in efficiency can be obtained by using either the local pivotal method (LPM) or stratification. LPM improves efficiency by matching the sample distribution to population distribution. These results encourage us to further investigate (i) what would be the improvement with more accurate auxiliary information, for example, laser scanning data, and (ii) how LPM fits in a real-life situation where part of the plots are permanent and it would be used to select only the temporary plots.
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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 the potential of the deep-learning algorithms convolutional neural network (CNN) and Keras. The results showed that vegetation indices from Sentinel-2, backscatter and texture characters from ALOS-2, and coherence from Sentinel-1 were principal contributors to the forest carbon-density estimation. Furthermore, the CNN model was found to perform better than traditional models. Results of forest carbon-density estimation validated the improvements effectively by combining the optical and radar data. Compared with traditional regression methods, deep learning has a higher potential for accurately estimating forest carbon density using multisource remote-sensing data.