Predicting Forest Inventory Attributes Using Airborne Laser Scanning, Aerial Imagery, and Harvester Data

The aim of the study was to develop a new method to use tree stem information recorded by harvesters along operative logging in remote sensing-based prediction of forest inventory attributes in mature stands. The reference sample plots were formed from harvester data, using two different tree positions: harvester positions (XYH) in global satellite navigation system and computationally improved harvester head positions (XYHH). Study materials consisted of 158 mature Norway-spruce-dominated stands located in Southern Finland that were clear-cut during 2015–16. Tree attributes were derived from the stem dimensions recorded by the harvester. The forest inventory attributes were compiled for both stands and sample plots generated for stands for four different sample plot sizes (254, 509, 761, and 1018 m2). Prediction models between the harvester-based forest inventory attributes and remote sensing features of sample plots were developed. The stand-level predictions were obtained, and basal-area weighted mean diameter (Dg) and basal-area weighted mean height (Hg) were nearly constant for all model alternatives with relative root-mean-square errors (RMSE) roughly 10–11% and 6–8%, respectively, and minor biases. For basal area (G) and volume (V), using either of the position methods, resulted in roughly similar predictions at best, with approximately 25% relative RMSE and 15% bias. With XYHH positions, the predictions of G and V were nearly independent of the sample plot size within 254–761 m2. Therefore, the harvester-based data can be used as ground truth for remote sensing forest inventory methods. In predicting the forest inventory attributes, it is advisable to utilize harvester head positions (XYHH) and a smallest plot size of 254 m2. Instead, if only harvester positions (XYH) are available, expanding the sample plot size to 761 m2 reaches a similar accuracy to that obtained using XYHH positions, as the larger sample plot moderates the uncertainties when determining the individual tree position

Predicting Tree Diameter Distributions from Airborne Laser Scanning, SPOT 5 Satellite, and Field Sample Data in the Perm Region, Russia

A tree list is a list of trees in the area of interest containing, for example, the species, diameter, height, and stem volume of each tree. Tree lists can be used to derive various characteristics of the growing stock, and are therefore versatile and informative sources of data for several forest management purposes. Especially in heterogonous and unmanaged forest structures with multiple species, tree list estimates imputed from local reference field data can provide an alternative to mean value estimates of growing stock (e.g., basal area, total stem volume, mean tree diameter, mean tree height, and number of trees). In this study, reference field plots, airborne laser scanning (ALS) data, and SPOT 5 satellite (Satellite Pour l’Observation de la Terre) imagery were used for tree list imputation applying the k most similar neighbors (k-MSN) estimation method in the West Ural taiga region of the Russian Federation for diameter distribution estimation. In k-MSN, weighted average of k field reference plots with highest similarity between field reference plot and target (forest grid cell, or field plot) based on ALS and SPOT 5 features were used to predict the mean values of growing stock and tree lists for the target object simultaneously. Diameter distributions were then constructed from the predicted tree lists. The prediction of mean values and diameter distributions was tested in 18 independent validation plots of 0.25–0.5 ha in size, whose species specific diameter distributions were measured in the field and grouped into three functional groups (Pines, Spruce/Fir, Broadleaf Group), each containing several species. In terms of root mean squared error relative to mean of validation plots, the accuracy of estimation was 0.14 and 0.17 for basal area and total stem volume, respectively. Reynolds error index values and visual inspection showed encouraging results in evaluating the goodness-of-fit statistics of the estimated diameter distributions. Although estimation accuracy was worse for functional group mean values and diameter distributions, the results indicate that it is possible to predict diameter distributions in forests of the test area with the tested methodology and materials

Seasonal dynamics of albedo across European boreal forests : Analysis of MODIS albedo and structural metrics from airborne LiDAR

Uncertainties in estimation of albedo-related radiative forcing cause ambiguity in evaluation of net climate effects of forests and forest management. Numerous studies have reported local relations between forest structure and albedo in the boreal zone. However, more research is needed to establish these relations for geographically extensive areas, and to examine seasonal courses of albedo to understand the effects of forest structure on mean annual shortwave energy balance. Remote sensing is a viable option for accomplishing these goals, but there are many challenges related to e.g. long periods of cloud cover and low solar elevations in high latitudes. We used the new MODIS Collection 6 (MCD43A3) daily albedo product, and analyzed MODIS albedo dependence on airborne LiDAR-based forest structure in 22 study sites in Estonia, Finland, Sweden, and Russia (57°–69° N, 12°–57° E). Wall-to-wall LiDAR data allowed us to take into account the effective spatial resolution of MODIS, which notably improved correlations between albedo and forest structure. Use of the best quality backup algorithm (magnitude inversion) together with main algorithm results in the MODIS albedo product did not reduce the correlations compared to using main algorithm only. We quantified the effects of landscape-level forest structure (forest height, canopy cover, fraction of young forest) and fraction of broadleaved deciduous forest on mean annual albedo. We showed that because the forest structure-albedo relations are the strongest in snow-covered periods, and because the snow-covered period is longest in the north, the effect of forest structure on mean annual albedo increases towards the north. On the other hand, the effect of broadleaved fraction did not show such latitudinal trend. Our results indicate that even within a single climatic zone the optimal forest management solution to mitigate climate change depends on geographic location.Peer reviewe

Combining Camera Relascope-Measured Field Plots and Multi-Seasonal Landsat 8 Imagery for Enhancing the Forest Inventory of Boreal Forests in Central Russia

The study considers a forest inventory for the mean volume, basal area, and coniferous/deciduous mapping of a large territory in central Siberia (Russia), employing a camera relascope at arbitrary sized sample plots and medium resolution satellite imagery Landsat 8 from the leaf-on and leaf-off seasons. The research bases are on field plots and satellite data that are acquired for the real operational forest inventory, performed for industrial purposes during summer–fall 2015. Sparse Bayesian regression was used to estimate linear regression models between field-measured variables and features derived from satellite data. Coniferous/deciduous mapping was done, applying maximum likelihood classification. The study reported the root mean square error for the mean volume and basal area under 25% for both the plot level and compartment level. The overall accuracy of the forest-type classification in coniferous, mixed coniferous/deciduous, and deciduous classes was 71.6%. The features of Landsat 8 images from both seasons were selected in almost every model, indicating that the use of satellite imagery from different seasons improved the estimation accuracy. It has been shown that the combination of camera relascope-based field data and medium-resolution satellite imagery gives accurate enough results that compare well with previous studies in that field, and provide fast and solid data about forests of large areas for efficient investment decision making

Integrating detailed timber assortments into airborne laser scanning (Als)-based assessments of logging recoveries

The methodology presented here can assist in making timber markets more efficient when assessing the value of harvestable timber stands and the amounts of timber assortments during the planning of harvesting operations. Information on wood quality and timber assortments is essential for wood valuation and procurement planning as varying wood dimensions and qualities may be utilized and refined in different places, including sawmills, plywood mills, pulp mills, heating plants or combined heat and power plants. We investigate here alternative approaches for generating detailed timber assortments for Norway spruce (Picea abies (L.) H.Karst.), Scots pine (Pinus sylvestris L.) and birch (Betula spp.) from airborne laser scanning (ALS) data, aerial images, harvester data and field data. For this purpose, we used 665 circular plots, and logging recovery information recorded from 249 clear-cut stands using cut-to-length harvesters. We estimated timber assortment volumes, economic values and wood paying capabilities (WPC) for each stand in different bucking scenarios, and used the resulting timber assortment estimates to assess logging recoveries. The bucking scenarios were (1) bucking-to-value using maximum sawlog and pulpwood volumes excluding quality (theoretical maximum), and (2) bucking-to-value using sawlog lengths at 30 cm intervals for Norway spruce and Scots pine and veneer logs of lengths 4.7 m, 5.0 m, 6.0 m and 6.7 m for birch, either excluding quality (the usual business practice) or including quality (a novel business practice). The results showed that our procedure can assist in locating stands that are likely to be more valuable and have the desired timber assortment distributions. We conclude that the method can estimate WPC with root mean square errors of 28.7%, 66.0% and 45.7% in Norway spruce, Scots pine and birch, respectively, for sawlogs and 19.3%, 63.7% and 29.5% for pulpwood

Airborne Laser Scanning Based Forest Inventory: Comparison of Experimental Results for the Perm Region, Russia and Prior Results from Finland

Airborne laser scanning (ALS) based stand level forest inventory has been used in Finland and other Nordic countries for several years. In the Russian Federation, ALS is not extensively used for forest inventory purposes, despite a long history of research into the use of lasers for forest measurement that dates back to the 1970s. Furthermore, there is also no generally accepted ALS-based methodology that meets the official inventory requirements of the Russian Federation. In this paper, a method developed for Finnish forest conditions is applied to ALS-based forest inventory in the Perm region of Russia. Sparse Bayesian regression is used with ALS data, SPOT satellite images and field reference data to estimate five forest parameters for three species groups (pine, spruce, deciduous): total mean volume, basal area, mean tree diameter, mean tree height, and number of stems per hectare. Parameter estimates are validated at both the plot level and stand level, and the validation results are compared to results published for three Finnish test areas. Overall, relative root mean square errors (RMSE) were higher for forest parameters in the Perm region than for the Finnish sites at both the plot and stand level. At the stand level, relative RMSE generally decreased with increasing stand size and was lower when considered overall than for individual species groups

Airborne Laser Scanning Based Forest Inventory: Comparison of Experimental Results for the Perm Region, Russia and Prior Results from Finland

Airborne laser scanning (ALS) based stand level forest inventory has been used in Finland and other Nordic countries for several years. In the Russian Federation, ALS is not extensively used for forest inventory purposes, despite a long history of research into the use of lasers for forest measurement that dates back to the 1970s. Furthermore, there is also no generally accepted ALS-based methodology that meets the official inventory requirements of the Russian Federation. In this paper, a method developed for Finnish forest conditions is applied to ALS-based forest inventory in the Perm region of Russia. Sparse Bayesian regression is used with ALS data, SPOT satellite images and field reference data to estimate five forest parameters for three species groups (pine, spruce, deciduous): total mean volume, basal area, mean tree diameter, mean tree height, and number of stems per hectare. Parameter estimates are validated at both the plot level and stand level, and the validation results are compared to results published for three Finnish test areas. Overall, relative root mean square errors (RMSE) were higher for forest parameters in the Perm region than for the Finnish sites at both the plot and stand level. At the stand level, relative RMSE generally decreased with increasing stand size and was lower when considered overall than for individual species groups