Species-specific forest variable estimation using non-parametric modeling of multi-spectral photogrammetric point cloud data

The recent development in software for automatic photogrammetric processing of multispectral aerial imagery, and the growing nation-wide availability of Digital Elevation Model (DEM) data, are about to revolutionize data capture for forest management planning in Scandinavia. Using only already available aerial imagery and ALS-assessed DEM data, raster estimates of the forest variables mean tree height, basal area, total stem volume, and species-specific stem volumes were produced and evaluated. The study was conducted at a coniferous hemi-boreal test site in southern Sweden (lat. 58° N, long. 13° E). Digital aerial images from the Zeiss/Intergraph Digital Mapping Camera system were used to produce 3D point-cloud data with spectral information. Metrics were calculated for 696 field plots (10 m radius) from point-cloud data and used in k-MSN to estimate forest variables. For these stands, the tree height ranged from 1.4 to 33.0 m (18.1 m mean), stem volume from 0 to 829 m3 ha-1 (249 m3 ha-1 mean) and basal area from 0 to 62.2 m2 ha-1 (26.1 m2 ha-1 mean), with mean stand size of 2.8 ha. Estimates made using digital aerial images corresponding to the standard acquisition of the Swedish National Land Survey (Lantmäteriet) showed RMSEs (in percent of the surveyed stand mean) of 7.5% for tree height, 11.4% for basal area, 13.2% for total stem volume, 90.6% for pine stem volume, 26.4 for spruce stem volume, and 72.6% for deciduous stem volume. The results imply that photogrammetric matching of digital aerial images has significant potential for operational use in forestry

Estimating stem diameter distributions from airborne laser scanning data and their effects on long term forest management planning

Data obtained from airborne laser scanning (ALS) are frequently used for acquiring forest data. Using a relatively low number of laser pulses per unit area (≤5 pulses per m2), this technique is typically used to estimate stand mean values. In this study stand diameter distributions were also estimated, with the aim of improving the information available for effective forest management and planning. Plot level forest data, such as stem number and mean height, together with diameter distributions in the form of Weibull distributions, were estimated using ALS data. Stand-wise tree lists were then estimated. These estimations were compared to data obtained from a field survey of 124 stands in northern Sweden. In each stand an average of seven sample plots (radius 5–10 m) were systematically sampled. The ALS approach was then compared to a mean value approach where only mean values are estimated and tree lists are simulated using a forest decision support system (DSS). The ALS approach provided a better match to observed diameter distributions: ca. 35% lower error indices used as a measure of accuracy and these results are in line with the previous studies. Moreover – which is unique compared to earlier studies – suboptimal losses were assessed. Using the Heureka DSS the suboptimal losses in terms of net present value due to erroneous decisions were compared. Although no large difference was found, the ALS approach showed smaller suboptimal loss than the mean value approach

Soil moisture controls the partitioning of carbon stocks across a managed boreal forest landscape

Boreal forests sequester and store vast carbon (C) pools that may be subject to significant feedback effects induced by climatic warming. The boreal landscape consists of a mosaic of forests and peatlands with wide variation in total C stocks, making it important to understand the factors controlling C pool sizes in different ecosystems. We therefore quantified the total C stocks in the organic layer, mineral soil, and tree biomass in 430 plots across a 68 km2 boreal catchment. The organic layer held the largest C pool, accounting for 39% of the total C storage; tree and mineral C pools accounted for 38% and 23%, respectively. The size of the soil C pool was positively related to modelled soil moisture conditions, especially in the organic soil layer (R2 = 0.50). Conversely, the tree C pool exhibited a unimodal relationship: storage was highest under intermediate wetness conditions. The magnitude and variation in the total soil C stocks observed in this work were comparable to those found at the national level in Sweden, suggesting that C accumulation in boreal landscapes is more sensitive to local variation resulting primarily from differences in soil moisture conditions than to regional differences in climate, nitrogen deposition, and parent material

Prediction of Site Index and Age Using Time Series of TanDEM-X Phase Heights

Site index and stand age are important variables in forestry. Site index describes the growing potential at a given location, expressed as the height that trees can attain at a given age under favorable growing conditions. It is traditionally used to classify forests in terms of future timber yield potential. Stand age is used for the planning of management activities such as thinning and harvest. SI has previously been predicted using remote sensing, but usually relying on either very short time series or repeated ALS acquisitions. In this study, site index and forest stand age were predicted from time series of interferometric TanDEM-X data spanning seven growth seasons in a hemi-boreal forest in Remningstorp, a test site located in southern Sweden. The goal of the study was to see how satellite-based radar time series could be used to estimate site index and stand age. Compared to previous studies, we used a longer time series and applied a penetration depth correction to the phase heights, thereby avoiding the need for calibration using ancillary field or ALS data. The time series consisted of 30 TanDEM-X strip map scenes acquired between 2011 and 2018. Established height development curves were fitted to the time series of TanDEM-X-based top heights. This enabled simultaneous estimation of both age and site index on 91 field plots with a 10 m radius. The RMSE of predicted SI and age were 6.9 m and 38 years for untreated plots when both SI and age were predicted. When predicting SI and the age was known, the RMSE of the predicted SI was 4.0 m. No significant prediction bias was observed for untreated plots, while underestimation of SI and overestimation of age increased with the intensity of treatment

Improving dynamic treatment unit forest planning with cellular automata heuristics

We present a model for conducting dynamic treatment unit (DTU) forest planning using a heuristic cellular automata (CA) approach. The clustering of DTUs is driven by entry costs associated with treatments, thus we directly model the economic incentive to cluster. The model is based on the work presented in the literature but enhanced by adding a third phase to the CA algorithm where DTUs are mapped in high detail. The model allows separate but nearby forest areas to be included in the same DTU and shares the entry cost if they are within a defined distance. The model is applied to a typical long-term forest planning problem for a 1 182 ha landscape in northern Sweden, represented by 4 218 microsegments with an average size of 0.28 ha. The added phase increased the utility by 1.5-32.2%. The model produced consistent solutions-more than half of all microsegments were managed with the same treatment program in 95% of all solutions when multiple solutions were found

Operational prediction of forest attributes using standardised harvester data and airborne laser scanning data in Sweden

With cut-to-length harvesters, tree stems are measured and cut into different timber assortments at the time of felling. These measurement data collected from harvested trees can be used for decision-support at different levels of the forest industry chain and also for forest planning when combined with remote sensing data. The aim of this study was to examine the operational application for predicting merchantable stem volume, basal area, basal area-weighted mean tree height, basal area-weighted mean stem diameter and diameter distribution at stand level with airborne laser scanning data and harvester data from final felling operations. The area-based approach using k-MSN estimation was evaluated for six different variants of spatial partitioning. The results were stand level predictions with relative root mean square errors of 11-14%, 10-15%, 3-4% and 6-7% for merchantable stem volume, basal area, basal area-weighted mean tree height and basal area-weighted mean stem diameter, respectively. Predictions of stem diameter distributions resulted in error indices of 0.13-0.14. The results demonstrate that harvester data from cut forests may serve as ground truth to airborne laser scanning data and provide accurate forest estimates at stand level. The predicted diameter distributions could be useful for improving yield estimates and bucking simulations

Mapping site index in coniferous forests using bi-temporal airborne laser scanning data and field data from the Swedish national forest inventory

Recent advancements in remote sensing of forests have demonstrated the capabilities of three-dimensional data acquired by airborne laser scanning (ALS) and, consequently, have become an integral part of enhanced forest inventories in Northern Europe. In Sweden, the first national laser scanning revolutionised forest management planning through low-cost production of large-scale and spatially explicit maps of forest attributes such as basal area, volume, and biomass, compared to the earlier practice based on field survey data. A second scanning at the national level was launched in 2019, and it provides conditions for the estimation of height growth and site index. Accurate and up-to-date information about site productivity is relevant for planning silvicultural treatments and for the prognosis of forest status and development over time. In this study, we explored the potential of bi-temporal ALS data and other auxiliary information to predict and map site productivity by site index according to site properties (SIS) of Norway spruce (Picea abies (L.) Karst) and Scots pine (Pinus sylvestris L.) in even aged stands in Sweden. We linked ground survey data of SIS from more than 11,500 plots of the Swedish National Forest Inventory (NFI) to bi-temporal ALS data to predict and map site index using an area-based method and two regression modelling strategies: (1) a multiple linear regression (MLR) model with an ordinary least-squares parameter estimation method, and (2) a non-parametric random forests (RF) model optimised for hyper parameter tuning. For model development, permanent plots were used, whereas the validation was done on the temporary plots of the Swedish NFI and an independent stand-level dataset. Species-specific models were developed, and the root mean square error (RMSE) metric was used to quantify the residual variability around model predictions. For both species, the MLR model gave precise and accurate estimates of SIS. The RMSE for SIS predictions was in the range of 1.96 - 2.11 m, and the relative RMSE was less than 10 % (7.68 - 9.49 %) of the reference mean value. Final predictors of site index include metrics of 90th percentile height and annual increment in the 95th percentile height, altitude, distance to coast, and soil moisture. Country-wide maps of SIS and the corresponding pixel-level prediction errors at a spatial resolution of 12.5 m grid cells were produced for the two species. Independent validations show the site index maps are suitable for use in operational forest management planning in Sweden

Predictions of Biomass Change in a Hemi-Boreal Forest Based on Multi-Polarization L- and P-Band SAR Backscatter

Above-ground biomass change accumulated during four growth seasons in a hemi-boreal forest was predicted using airborne L- and P-band synthetic aperture radar (SAR) backscatter. The radar data were collected in the BioSAR 2007 and BioSAR 2010 campaigns over the Remningstorp test site in southern Sweden. Regression models for biomass change were developed from biomass maps created using airborne LiDAR data and field measurements. To facilitate training and prediction on image pairs acquired at different dates, a backscatter offset correction method for L-band data was developed and evaluated. The correction, based on the HV/VV backscatter ratio, facilitated predictions across image pairs almost identical to those obtained using data from the same image pair for both training and prediction. For P-band, previous positive results using an offset correction based on the HH/VV ratio were validated. The best L-band model achieved a root mean square error (RMSE) of 21 t/ha, and the best P-band model achieved an RMSE of 19 t/ha. Those accuracies are similar to that of the LiDAR-based biomass change of 18 t/ha. The limitation of using LiDAR-based data for training was considered. The findings demonstrate potential for improved biomass change predictions from L-band backscatter despite varying environmental conditions and calibration uncertainties

Potential of mapping forest damage from remotely sensed data

Remote sensing is an efficient tool for mapping, monitoring, and assessing forest damage and the risk of damage. This report presents ongoing research on those topics with preliminary results as well as research planned by the Department of Forest Resource Management, SLU in Umeå, in the near future. The damage types include spruce bark beetle attacks, storm damage, and forest fire. The report also outlines proposed continued research in the area and possible collaborations within and outside SLU