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Special Issue "Remote sensing based Forest Inventories from Landscape to Global Scale"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (25 January 2019).

Special Issue Editors

Guest Editor
Assoc. Prof. Dr. Marco Heurich

Department of Conservation and Research, Bavarian
Forest National Park, Germany/ University of Freiburg, Germany
Website 1 | Website 2 | E-Mail
Interests: lidar applications in forest ecology and management; remote sensing in wildlife ecology; essential biodiversity variables
Guest Editor
PD Dr. Hooman Latifi

1. Dept of Photogrammerty and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, No. 1346 Valiasr Str., Mirdamad Crossing, Postal Code 19967-15433 Tehran, Iran
2. Dept. of Remote Sensing, Institute of Geography and Geology, University of Wuerzburg, Oswald-Kuelpe-Weg 86, 97074 Wuerzburg, Germany
Website 1 | Website 2 | E-Mail
Interests: remote sensing-assisted monitoring of forest structure, phenology and health; small-scale forest inventory; spatial statistics; modelling and optimization; LiDAR; UAV and their point cloud processing for forest inventory

Special Issue Information

Dear colleagues,

Forest ecosystems are vital on various scales for humanity. Forests provide not only merchantable timber, but also essential ecosystem functions, such as drinking water supply, regulation of climate, conservation of biodiversity, and recreation. Yet forest ecosystems are under increasing pressure due to expanding human populations, illegal harvesting, and overexploitation, which together lead to an unprecedented loss of forests worldwide.

In the past, forests were inventoried manually in the field. Nowadays, forest inventories on the landscape scale can be obtained with three-dimensional and mostly high-resolution remote-sensing techniques, such as LiDAR and other aerial surveys. These techniques are superior over traditional forest inventories in terms of quality, costs, and level of spatial and temporal details. Indeed, the enormous costs and logistics of traditional field-based inventories prohibit inventories on spatial scales larger than the landscape level. Such inventories are feasible only with the more cost-effective remote-sensing techniques, automated processing workflows, and high performance computing.

The freely available remote-sensing data from the Copernicus, the European Union’s Earth Observation Programme (Sentinel satellites 1–5), along with increasing accessibility to data from other missions, e.g., Landsat and Spot data, the availability of upcoming multiple airborne devices in combination with new sensors, such as the EnMAP hyperspectral satellite, and the laser-based instrument GEDI have led to a radical change in the direction in which inventories of forest ecosystems will be obtained in the near future.

In light of the issues pointed out above, the purpose of this Special Issue of Remote Sensing is to present a number of state-of-the-art studies on the use of remote-sensing data and methods for monitoring forest ecosystems on spatial scales of the landscape and beyond. We, the guest editors of this issue, would like to invite colleagues to submit articles about their recent research on any of the following topics:

  • Application of remote-sensing techniques for monitoring forest attributes at the forest enterprise level
  • Application of remote-sensing techniques for monitoring forest attributes for national forest inventories
  • Approaches for monitoring forests using remote-sensing techniques on larger spatial levels, such as sub-continental, continental, and global scales
  • Methods for assessment of the 3-D structure of forests on the above-mentioned spatial levels
  • Monitoring structural and functional forest biodiversity indicators
  • Monitoring changes in forest ecosystems using multi-temporal time series of remote-sensing data
  • Comparison and evaluation of different remote-sensing sensors and methods for forest inventories on the above-mentioned scales
  • Large scale in-situ data requirement and sampling design for monitoring forest ecosystems with remote-sensing techniques
  • Review articles covering one or more of these topics

Assoc. Prof. Dr. Marco Heurich
Assoc. Prof. Dr. Hooman Latifi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (10 papers)

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Editorial

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Open AccessEditorial
Multi-Scale Remote Sensing-Assisted Forest Inventory: A Glimpse of the State-of-the-Art and Future Prospects
Remote Sens. 2019, 11(11), 1260; https://doi.org/10.3390/rs11111260
Received: 7 May 2019 / Accepted: 24 May 2019 / Published: 28 May 2019
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Abstract
Advances in remote inventory and analysis of forest resources during the last decade have reached a level to be now considered as a crucial complement, if not a surrogate, to the long-existing field-based methods. This is mostly reflected in not only the use [...] Read more.
Advances in remote inventory and analysis of forest resources during the last decade have reached a level to be now considered as a crucial complement, if not a surrogate, to the long-existing field-based methods. This is mostly reflected in not only the use of multiple-band new active and passive remote sensing data for forest inventory, but also in the methodic and algorithmic developments and/or adoptions that aim at maximizing the predictive or calibration performances, thereby minimizing both random and systematic errors, in particular for multi-scale spatial domains. With this in mind, this editorial note wraps up the recently-published Remote Sensing special issue “Remote Sensing-Based Forest Inventories from Landscape to Global Scale”, which hosted a set of state-of-the-art experiments on remotely sensed inventory of forest resources conducted by a number of prominent researchers worldwide. Full article

Research

Jump to: Editorial

Open AccessArticle
Mapping Forest Type and Tree Species on a Regional Scale Using Multi-Temporal Sentinel-2 Data
Remote Sens. 2019, 11(8), 929; https://doi.org/10.3390/rs11080929
Received: 3 April 2019 / Revised: 9 April 2019 / Accepted: 15 April 2019 / Published: 17 April 2019
Cited by 1 | PDF Full-text (23932 KB) | HTML Full-text | XML Full-text
Abstract
There are a limited number of studies addressing the forest status, its extent, location, type and composition over a larger area at the regional or national levels. The dense time series and a wide swath of Sentinel-2 data are a good basis for [...] Read more.
There are a limited number of studies addressing the forest status, its extent, location, type and composition over a larger area at the regional or national levels. The dense time series and a wide swath of Sentinel-2 data are a good basis for forest mapping and tree species identification over a large area. This study presents the results of the classification of the forest/non-forest cover, forest type (broadleaf and coniferous) and the identification of eight tree species (beech, oak, alder, birch, spruce, pine, fir, and larch) using the multi-temporal Sentinel-2 data in combination with topographic information. The study was conducted over the large mountain area located in southern Poland. The Random Forest classifier was used to first derive a forest/non-forest map. Second, the forest was classified into broadleaf and coniferous. Finally, the tree species classification was carried out following two approaches: (i) Non-stratified, where all species were classified together within the forest mask and (ii) stratified, where the broadleaf and coniferous tree species were classified separately within the forest type masks. The overall accuracy for the forest/non-forest cover reached 98.3% and declined slightly to 94.8% for the classification of the forest type. The use of the topographic information did not increase the accuracy of either result. The role of the topographic variables increased significantly in the process of tree species delineation. By combining the topographic information (in particular, digital elevation model) with the multi-temporal Sentinel-2 data, the classification of eight tree species improved from 75.6% to 81.7% (approach 1). A further increase in accuracy to 89.5% for broadleaf and 82% for coniferous species was observed following the stratified approach number 2. The highest overall accuracy (above 85%) was obtained for beech, oak, birch, alder, and larch. The study confirmed the potential of the multi-temporal Sentinel-2 data for accurate delineation of the forest cover, forest type, and tree species at the regional scale. Full article
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Open AccessArticle
Improving Aboveground Forest Biomass Maps: From High-Resolution to National Scale
Remote Sens. 2019, 11(7), 795; https://doi.org/10.3390/rs11070795
Received: 26 January 2019 / Revised: 20 March 2019 / Accepted: 31 March 2019 / Published: 3 April 2019
Cited by 1 | PDF Full-text (2421 KB) | HTML Full-text | XML Full-text
Abstract
Forest aboveground biomass (AGB) estimation over large extents and high temporal resolution is crucial in managing Mediterranean forest ecosystems, which have been predicted to be very sensitive to climate change effects. Although many modeling procedures have been tested to assess forest AGB, most [...] Read more.
Forest aboveground biomass (AGB) estimation over large extents and high temporal resolution is crucial in managing Mediterranean forest ecosystems, which have been predicted to be very sensitive to climate change effects. Although many modeling procedures have been tested to assess forest AGB, most of them cover small areas and attain high accuracy in evaluations that are difficult to update and extrapolate without large uncertainties. In this study, focusing on the Region of Murcia in Spain (11,313 km2), we integrated forest AGB estimations, obtained from high-precision airborne laser scanning (ALS) data calibrated with plot-level ground-based measures and bio-geophysical spectral variables (eight different indices derived from MODIS computed at different temporal resolutions), as well as topographic factors as predictors. We used a quantile regression forest (QRF) to spatially predict biomass and the associated uncertainty. The fitted model produced a satisfactory performance (R2 0.71 and RMSE 9.99 t·ha−1) with the normalized difference vegetation index (NDVI) as the main vegetation index, in combination with topographic variables as environmental drivers. An independent validation carried out over the final predicted biomass map showed a satisfactory statistically-robust model (R2 0.70 and RMSE 10.25 t·ha−1), confirming its applicability at coarser resolutions. Full article
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Open AccessArticle
Digital Aerial Photogrammetry for Uneven-Aged Forest Management: Assessing the Potential to Reconstruct Canopy Structure and Estimate Living Biomass
Remote Sens. 2019, 11(3), 338; https://doi.org/10.3390/rs11030338
Received: 20 December 2018 / Revised: 1 February 2019 / Accepted: 6 February 2019 / Published: 8 February 2019
Cited by 2 | PDF Full-text (3179 KB) | HTML Full-text | XML Full-text
Abstract
Scientifically robust yet economical and efficient methods are required to gather information about larger areas of uneven-aged forest resources, particularly at the landscape level, to reduce deforestation and forest degradation and to support the sustainable management of forest resources. In this study, we [...] Read more.
Scientifically robust yet economical and efficient methods are required to gather information about larger areas of uneven-aged forest resources, particularly at the landscape level, to reduce deforestation and forest degradation and to support the sustainable management of forest resources. In this study, we examined the potential of digital aerial photogrammetry (DAP) for assessing uneven-aged forest resources. Specifically, we tested the performance of biomass estimation by varying the conditions of several factors, e.g., image downscaling, vegetation metric extraction (point cloud- and canopy height model (CHM)-derived), modeling method ((simple linear regression (SLR), multiple linear regression (MLR), and random forest (RF)), and season (leaf-on and leaf-off). We built dense point clouds and CHMs using high-resolution aerial imagery collected in leaf-on and leaf-off conditions of an uneven-aged mixed conifer–broadleaf forest. DAP-derived vegetation metrics were then used to predict the dominant height and living biomass (total, conifer, and broadleaf) at the plot level. Our results demonstrated that image downscaling had a negative impact on the accuracy of the dominant height and biomass estimation in leaf-on conditions. In comparison to CHM-derived vegetation metrics, point cloud-derived metrics performed better in dominant height and biomass (total and conifer) estimations. Although the SLR (%RMSE = 21.1) and MLR (%RMSE = 18.1) modeling methods produced acceptable results for total biomass estimations, RF modeling significantly improved the plot-level total biomass estimation accuracy (%RMSE of 12.0 for leaf-on data). Overall, leaf-on DAP performed better in total biomass estimation compared to leaf-off DAP (%RMSE of 15.0 using RF modeling). Nevertheless, conifer biomass estimation accuracy improved when leaf-off data were used (from a %RMSE of 32.1 leaf-on to 23.8 leaf-off using RF modeling). Leaf-off DAP had a negative impact on the broadleaf biomass estimation (%RMSE > 35% for SLR, MLR, and RF modeling). Our results demonstrated that the performance of forest biomass estimation for uneven-aged forests varied with statistical representations as well as data sources. Thus, it would be appropriate to explore different statistical approaches (e.g., parametric and nonparametric) and data sources (e.g., different image resolutions, vegetation metrics, and leaf-on and leaf-off data) to inform the interpretation of remotely sensed data for biomass estimation for uneven-aged forest resources. Full article
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Open AccessArticle
Forest Spectral Recovery and Regeneration Dynamics in Stand-Replacing Wildfires of Central Apennines Derived from Landsat Time Series
Remote Sens. 2019, 11(3), 308; https://doi.org/10.3390/rs11030308
Received: 23 December 2018 / Revised: 29 January 2019 / Accepted: 30 January 2019 / Published: 4 February 2019
Cited by 1 | PDF Full-text (17771 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Understanding post-fire regeneration dynamics is an important task for assessing the resilience of forests and to adequately guide post-disturbance management. The main goal of this research was to compare the ability of different Landsat-derived spectral vegetation indices (SVIs) to track post-fire recovery occurring [...] Read more.
Understanding post-fire regeneration dynamics is an important task for assessing the resilience of forests and to adequately guide post-disturbance management. The main goal of this research was to compare the ability of different Landsat-derived spectral vegetation indices (SVIs) to track post-fire recovery occurring in burned forests of the central Apennines (Italy) at different development stages. Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2) and a novel index called Forest Recovery Index 2 (FRI2) were used to compute post-fire recovery metrics throughout 11 years (2008–2018). FRI2 achieved the highest significant correlation (Pearson’s r = 0.72) with tree canopy cover estimated by field sampling (year 2017). The Theil–Sen slope estimator of linear regression was employed to assess the rate of change and the direction of SVIs recovery metrics over time (2010–2018) and the Mann–Kendall test was used to evaluate the significance of the spectral trends. NDVI displayed the highest amount of recovered pixels (38%) after 11 years since fire occurrence, whereas the mean value of NDMI, NBR, NBR2, and FRI2 was about 27%. NDVI was more suitable for tracking early stages of the secondary succession, suggesting greater sensitivity toward non-arboreal vegetation development. Predicted spectral recovery timespans based on pixels with a statistically significant monotonic trend did not highlight noticeable differences among normalized SVIs, suggesting similar suitability for monitoring early to mid-stages of post-fire forest succession. FRI2 achieved reliable results in mid- to long-term forest recovery as it produced up to 50% longer periods of spectral recovery compared to normalized SVIs. Further research is needed to understand this modeling approach at advanced stages of post-fire forest recovery. Full article
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Open AccessArticle
Estimating the Height and Basal Area at Individual Tree and Plot Levels in Canadian Subarctic Lichen Woodlands Using Stereo WorldView-3 Images
Remote Sens. 2019, 11(3), 248; https://doi.org/10.3390/rs11030248
Received: 12 December 2018 / Revised: 21 January 2019 / Accepted: 22 January 2019 / Published: 26 January 2019
Cited by 2 | PDF Full-text (3904 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Lichen woodlands (LW) are sparse forests that cover extensive areas in remote subarctic regions where warming due to climate change is fastest. They are difficult to study in situ or with airborne remote sensing due to their remoteness. We have tested a method [...] Read more.
Lichen woodlands (LW) are sparse forests that cover extensive areas in remote subarctic regions where warming due to climate change is fastest. They are difficult to study in situ or with airborne remote sensing due to their remoteness. We have tested a method for measuring individual tree heights and predicting basal area at tree and plot levels using WorldView-3 stereo images. Manual stereo measurements of tree heights were performed on short trees (2–12 m) of a LW region of Canada with a residual standard error of ≈0.9 m compared to accurate field or UAV height data. The number of detected trees significantly underestimated field counts, especially in peatlands in which the visual contrast between trees and ground cover was low. The heights measured from the WorldView-3 images were used to predict the basal area at individual tree level and summed up at plot level. In the best conditions (high contrast between trees and ground cover), the relationship to field basal area had a R2 of 0.79. Accurate estimates of above ground biomass should therefore also be possible. This method could be used to calibrate an extensive remote sensing approach without in-situ measurements, e.g., by linking precise structural data to ICESAT-2 footprints. Full article
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Open AccessArticle
Mitigating the Impact of Field and Image Registration Errors through Spatial Aggregation
Remote Sens. 2019, 11(3), 222; https://doi.org/10.3390/rs11030222
Received: 18 December 2018 / Revised: 16 January 2019 / Accepted: 20 January 2019 / Published: 22 January 2019
Cited by 1 | PDF Full-text (10180 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Remotely sensed data are commonly used as predictor variables in spatially explicit models depicting landscape characteristics of interest (response) across broad extents, at relatively fine resolution. To create these models, variables are spatially registered to a known coordinate system and used to link [...] Read more.
Remotely sensed data are commonly used as predictor variables in spatially explicit models depicting landscape characteristics of interest (response) across broad extents, at relatively fine resolution. To create these models, variables are spatially registered to a known coordinate system and used to link responses with predictor variable values. Inherently, this linking process introduces measurement error into the response and predictors, which in the latter case causes attenuation bias. Through simulations, our findings indicate that the spatial correlation of response and predictor variables and their corresponding spatial registration (co-registration) errors can have a substantial impact on the bias and accuracy of linear models. Additionally, in this study we evaluate spatial aggregation as a mechanism to minimize the impact of co-registration errors, assess the impact of subsampling within the extent of sample units, and provide a technique that can be used to both determine the extent of an observational unit needed to minimize the impact of co-registration and quantify the amount of error potentially introduced into predictive models. Full article
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Open AccessArticle
Airborne Laser Scanning Cartography of On-Site Carbon Stocks as a Basis for the Silviculture of Pinus Halepensis Plantations
Remote Sens. 2018, 10(10), 1660; https://doi.org/10.3390/rs10101660
Received: 6 September 2018 / Revised: 2 October 2018 / Accepted: 16 October 2018 / Published: 19 October 2018
Cited by 2 | PDF Full-text (3256 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Forest managers are interested in forest-monitoring strategies using low density Airborne Laser Scanning (ALS). However, little research has used ALS to estimate soil organic carbon (SOC) as a criterion for operational thinning. Our objective was to compare three different thinning intensities in terms [...] Read more.
Forest managers are interested in forest-monitoring strategies using low density Airborne Laser Scanning (ALS). However, little research has used ALS to estimate soil organic carbon (SOC) as a criterion for operational thinning. Our objective was to compare three different thinning intensities in terms of the on-site C stock after 13 years (2004–2017) and to develop models of biomass (Wt, Mg ha−1) and SOC (Mg ha−1) in Pinus halepensis forest, based on low density ALS in southern Spain. ALS was performed for the area and stand metrics were measured within 83 plots. Non-parametric kNN models were developed to estimate Wt and SOC. The overall C stock was significantly higher in plots subjected to heavy or moderate thinning (101.17 Mg ha−1 and 100.94 Mg ha−1, respectively) than in the control plots (91.83 Mg ha−1). The best Wt and SOC models provided R2 values of 0.82 (Wt, MSNPP) and 0.82 (SOC-S10, RAW). The study area will be able to stock 134,850 Mg of C under a non-intervention scenario and 157,958 Mg of C under the heavy thinning scenario. High-resolution cartography of the predicted C stock is useful for silvicultural planning and may be used for proper management to increase C sequestration in dry P. halepensis forests. Full article
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Open AccessArticle
Augmentation of Traditional Forest Inventory and Airborne Laser Scanning with Unmanned Aerial Systems and Photogrammetry for Forest Monitoring
Remote Sens. 2018, 10(10), 1562; https://doi.org/10.3390/rs10101562
Received: 7 September 2018 / Revised: 24 September 2018 / Accepted: 26 September 2018 / Published: 29 September 2018
Cited by 6 | PDF Full-text (2802 KB) | HTML Full-text | XML Full-text
Abstract
Forest inventories are constrained by resource-intensive fieldwork, while unmanned aerial systems (UASs) offer rapid, reliable, and replicable data collection and processing. This research leverages advancements in photogrammetry and market sensors and platforms to incorporate a UAS-based approach into existing forestry monitoring schemes. Digital [...] Read more.
Forest inventories are constrained by resource-intensive fieldwork, while unmanned aerial systems (UASs) offer rapid, reliable, and replicable data collection and processing. This research leverages advancements in photogrammetry and market sensors and platforms to incorporate a UAS-based approach into existing forestry monitoring schemes. Digital imagery from a UAS was collected, photogrammetrically processed, and compared to in situ and aerial laser scanning (ALS)-derived plot tree counts and heights on a subsample of national forest plots in Oregon. UAS- and ALS-estimated tree counts agreed with each other (r2 = 0.96) and with field data (ALS r2 = 0.93, UAS r2 = 0.84). UAS photogrammetry also reasonably approximated mean plot tree height achieved by the field inventory (r2 = 0.82, RMSE = 2.92 m) and by ALS (r2 = 0.97, RMSE = 1.04 m). The use of both nadir-oriented and oblique UAS imagery as well as the availability of ALS-derived terrain descriptions likely sustain a robust performance of our approach across classes of canopy cover and tree height. It is possible to draw similar conclusions from any of the methods, suggesting that the efficient and responsive UAS method can enhance field measurement and ALS in longitudinal inventories. Additionally, advancing UAS technology and photogrammetry allows diverse users access to forest data and integrates updated methodologies with traditional forest monitoring. Full article
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Open AccessArticle
A Double-Sampling Extension of the German National Forest Inventory for Design-Based Small Area Estimation on Forest District Levels
Remote Sens. 2018, 10(7), 1052; https://doi.org/10.3390/rs10071052
Received: 17 May 2018 / Revised: 25 June 2018 / Accepted: 26 June 2018 / Published: 3 July 2018
Cited by 1 | PDF Full-text (14953 KB) | HTML Full-text | XML Full-text
Abstract
The German National Forest Inventory consists of a systematic grid of permanent sample plots and provides a reliable evidence-based assessment of the state and the development of Germany’s forests on national and federal state level in a 10 year interval. However, the data [...] Read more.
The German National Forest Inventory consists of a systematic grid of permanent sample plots and provides a reliable evidence-based assessment of the state and the development of Germany’s forests on national and federal state level in a 10 year interval. However, the data have yet been scarcely used for estimation on smaller management levels such as forest districts due to insufficient sample sizes within the area of interests and the implied large estimation errors. In this study, we present a double-sampling extension to the existing German National Forest Inventory (NFI) that allows for the application of recently developed design-based small area regression estimators. We illustrate the implementation of the estimation procedure and evaluate its potential for future large-scale operational application by the example of timber volume estimation on two small-scale management levels (45 and 405 forest district units respectively) over the entire area of the federal German state of Rhineland-Palatinate. An airborne laserscanning (ALS) derived canopy height model and a tree species classification map based on satellite data were used as auxiliary data in an ordinary least square regression model to produce the timber volume predictions. The results support that the suggested double-sampling procedure can substantially increase estimation precision on both management levels: the two-phase estimators were able to reduce the variance of the one-phase simple random sampling estimator by 43% and 25% on average for the two management levels respectively. Full article
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