Forest Management Inventories Research Articles

The effects of data aggregation on long-term projections of forest stands development

Forest management planning often relies on Airborne Laser Scanning (ALS)-based Forest Management Inventories (FMIs) for sustainable and efficient decision-making. Employing the area-based (ABA) approach, these inventories estimate forest characteristics for grid cell areas (pixels), which are then usually summarized at the stand level. Using the ALS-based high-resolution Norwegian Forest Resource Maps (16 ​m ​× ​16 ​m pixel resolution) alongside with stand-level growth and yield models, this study explores the impact of three levels of pixel aggregation (stand-level, stand-level with species strata, and pixel-level) on projected stand development. The results indicate significant differences in the projected outputs based on the aggregation level. Notably, the most substantial difference in estimated volume occurred between stand-level and pixel-level aggregation, ranging from −301 to +253 ​m3⋅ha−1 for single stands. The differences were, on average, higher for broadleaves than for spruce and pine dominated stands, and for mixed stands and stands with higher variability than for pure and homogenous stands. In conclusion, this research underscores the critical role of input data resolution in forest planning and management, emphasizing the need for improved data collection practices to ensure sustainable forest management.

Airborne laser scanning to optimize the sampling efficiency of a forest management inventory in South-Eastern Germany

Effective forest stewardship relies on comprehensive field-inventories describing forest resources. Increasing demands for data and indicators that improve understanding of climate change impacts, timber production, and ecosystem processes make access to robust field inventories crucial. A trade-off between cost and statistical efficacy exists however, necessitating that practitioners be familiar with the spatial and structural composition and variability of their management areas. Remotely sensed data, like airborne laser scanning (ALS), can improve data availability and sampling efficiency. In this study, we simulate sampling approaches and provide an indication of the benefits of incorporating ALS-derived auxiliary data. We evaluate the ability of sub-samples from an existing field-inventory to accurately estimate ALS structural metrics. Additionally, we explore data-driven approaches to allocate new field plots, reducing bias and improving accuracy. The Monte Carlo simulation compared the local pivotal method (LPM), Latin hypercube sampling (LHS), and simple random sampling (SRS) at a variety of sample sizes. Precision and variability measures were used to comparatively assess the efficacy of sampling method and sample size. Results demonstrate the value of ALS as an auxiliary dataset, with LPM and LHS achieving sampling efficiencies over SRS of up to 88.6% and 94.3%, respectively. By applying the adapted Latin hypercube evaluation of a legacy sample (AHELS) algorithm, we reduced the mean average percent deviation (MAPD) by over 20% between sample measures and wall-to-wall ALS metrics. These methods can aid practitioners in planning cost-effective and statistically rigorous forest inventory campaigns, particularly in determining where to re-sample within an existing plot network.

Evaluation of a forest radiative transfer model using an extensive boreal forest inventory database

The forest reflectance and transmittance model (FRT) is applicable over a wide swath of boreal forest landscapes mainly because its stand-specific inputs can be generated from standard forest inventory variables. We quantified the accuracy of this model over an extensive region for the first time. This was done by carrying out a simulation study over a large number (12,369) of georeferenced forest plots from operational forest management inventories conducted in Southern Finland. We compared the FRT simulated bidirectional reflectance factors (BRF) with those measured by Landsat 8 satellite Operational Land Imager (OLI). We also quantified the relative importance of several explanatory factors that affected the magnitude of the discrepancy between the measured and simulated BRFs using a linear mixed effects modelling framework. A general trend of FRT overestimating BRFs is seen across all tree species and spectral bands examined: up to ∼0.05 for the red band, and ∼0.10 for the near infrared band. The important explanatory factors associated with the overestimations included the dominant tree species, understory type of the forest plot, timber volume (acts as a proxy for stand maturity), vegetation heterogeneity and time of the year. Our analysis suggests that approximately 20% of the error is caused by the non-representative spectra of canopy foliage and understory. Our results demonstrate the importance of collecting representative spectra from a diverse set of forest stands, and over the full range of seasons.

An Approach Integrating Multi-Source Data with LandTrendr Algorithm for Refining Forest Recovery Detection

Disturbances to forests are getting worse with climate change and urbanization. Assessing the functionality of forest ecosystems is challenging because it requires not only a large amount of input data but also comprehensive estimation indicator methods. The object of the evaluation index of forest ecosystem restoration relies on the ecosystem function instead of the area. To develop the appropriate index with ecological implications, we built the hybrid assessment approach including ecosystem structure-function-habitat representatives. It was based on the Normalized Burn Ratio (NBR) spectral indicator and combined with the local forest management inventory (LFMI), Landsat, Light Detection and Ranging (LiDAR) data. The results of the visual interpretation of Google Earth’s historical imagery showed that the total accuracy of the hybrid approach was 0.94. The output of the hybrid model increased as the biodiversity index value increased. Furthermore, to solve the multi-source data availability problem, the random forest model (R2 = 0.78, RMSE = 0.14) with 0.77 total accuracy was built to generate an annual recovery index. A random forest model based on tree age is provided to simplify the hybrid approach while extending the results on time series. The recovery index obtained by the random forest model could facilitate monitoring the forest recovery rate of cold spots. The regional ecological recovery time could be predicted. These two results could provide a scientific basis for forest managers to make more effective forest restoration plans. From the perspective of space, it could ensure that the areas with slow recovery would be allocated enough restoration resources. From the perspective of time, the implementation period of the closed forest policy could also be estimated.

Estimation of Bamboo Forest Aboveground Carbon Using the RGLM Model Based on Object-Based Multiscale Segmentation of SPOT-6 Imagery

Remote sensing is an important tool for the quantitative estimation of forest carbon stock. This study presents a multiscale, object-based method for the estimation of aboveground carbon stock in Moso bamboo forests. The method differs from conventional pixel-based approaches and is more suitable for Chinese forest management inventory. This research indicates that the construction of a SPOT-6 multiscale hierarchy with the 30 scale as the optimal segmentation scale achieves accurate information extraction for Moso bamboo forests. The producer’s and user’s accuracy are 88.89% and 86.96%, respectively. A random generalized linear model (RGLM), constructed using the multiscale hierarchy, can accurately estimate carbon storage of the bamboo forest in the study area, with a fitting and test accuracy (R2) of 0.74 and 0.64, respectively. In contrast, pixel-based methods using the RGLM model have a fitting and prediction accuracy of 0.24 and 0.01, respectively; thus, the object-based RGLM is a major improvement. The multiscale object hierarchy correctly analyzed the multiscale correlation and responses of bamboo forest elements to carbon storage. Objects at the 30 scale responded to the microstructure of the bamboo forest and had the strongest correlation between estimated carbon storage and measured values. Objects at the 60 scale did not directly inherit the forest information, so the response to the measured carbon storage of the bamboo forest was the smallest. Objects at the 90 scale serve as super-objects containing the forest feature information and have a significant correlation with the measured carbon storage. Therefore, in this study, a carbon storage estimation model was constructed based on the multiscale characteristics of the bamboo forest so as to analyze correlations and greatly improve the fitting and prediction accuracy of carbon storage.

The impact of climate and management on recent mortality in Pinus pinaster resin-tapped forests of inland Spain

Abstract Recent forest decline and amplified mortality have been documented around the world, mainly triggered by the rising water stress associated with more frequent extreme weather events. However, other abiotic and biotic factors may predispose and contribute to these processes. Mediterranean forests are among the biomes considered highly vulnerable to drought-induced decline and mortality. Pinus pinaster ssp. mesogeensis, is a typical western Mediterranean Forest species widely distributed in Spain, where traditional management has focused on a mixed timber-resin production. In the last decades, the species has experienced a severe and generalized process of decline and mortality, especially on inland areas in the Spanish Northern Plateau. The main objectives of this study were to (1) provide an accurate assessment of P. pinaster mortality in resin-tapped forests within the region, (2) identify the main predisposing and inciting abiotic factors controlling the process of dieback and mortality of the species and (3) develop a model for forecasting the annual rate of mortality at landscape scale. We used operational data collected by the Forest Service during 2012–2019, including annual censuses of tree mortality carried out in lots under resin-tapping and periodical forest management inventories. Analysis using spatiotemporal generalized linear mixed models indicated widespread mortality of the species in the territory, which in some areas reached 20 per cent of the trees over an 8-year period. Tree mortality is triggered in warm and dry years and was accelerated following the extreme droughts of 2017 and 2019. High stand stocking and tree aging have been identified as predisposing factors increasing susceptibility to forest decline and mortality. In addition, stands where the species grows mixed with Pinus pinea are more vulnerable, pointing to a possible displacement of P. pinaster in the territory. The developed spatiotemporal generalized linear mixed model allows unbiased estimates of the annual rate of mortality to be calculated through the territory. The model may be used by forest managers in order to identify the most vulnerable areas where the application of adaption strategies should be prioritized, in order to preserve these forests and their associated provision of ecosystem services.

Detecting and excluding disturbed forest areas improves site index determination using bitemporal airborne laser scanner data

Abstract Bitemporal airborne laser scanning (ALS) data are increasingly being used in forest management inventories for the determination of site index (SI). SI determination using bitemporal ALS data requires undisturbed height growth of dominant trees. Therefore, areas with disturbed top height development are unsuitable for SI determination, and should be identified and omitted before modelling, predicting and estimating SI using bitemporal ALS data. The aim of this study was to explore methods for classifying the suitability of forest areas for SI determination based on bitemporal ALS data. The modelling approaches k-nearest neighbour, logistic regression and random forest were compared for classifying disturbed (at least one dominant tree has disappeared) and undisturbed plots. A forest inventory with plot re-measurements and corresponding bitemporal ALS data from the Petawawa Research Forest in Ontario, Canada, was used as a case study. Based on the field data, two definitions of a disturbed plot were developed: (1) at least one dominant tree had died, was harvested or had fallen during the observation period, or (2) at least one dominant tree was harvested or had fallen during the observation period. The first definition included standing dead trees, which we hypothesized would be more difficult to accurately classify from bitemporal ALS data. Models of disturbance definition 1 and 2 yielded Matthews correlation coefficients of 0.46–0.59 and 0.62–0.80, respectively. Fit statistics of SI prediction models fitted to undisturbed plots were significantly better (P < 0.05) than fit statistics of SI prediction models fitted to all plots. Our results show that bitemporal ALS data can be used to separate disturbed from undisturbed forest areas with moderate to high accuracy in complex temperate mixedwood forests and that excluding disturbed forest areas significantly improves fit statistics of SI prediction models.

WHU-Helmet: A Helmet-Based Multisensor SLAM Dataset for the Evaluation of Real-Time 3-D Mapping in Large-Scale GNSS-Denied Environments

Real-time 3D mapping of large-scale Global Navigation Satellite System (GNSS)-denied environments plays an important role in forest inventory management, disaster emergency response, and underground facility maintenance. Compact helmet laser scanning (HLS) systems keep the same direction as the user’s line of sight and have the advantage of “what you see is what you get”, providing a promising and efficient solution for 3D geospatial information acquisition. However, the violent motion of the helmet, the limited field of view of the laser scanner, and the repeated symmetrical geometric structures in GNSS-denied environments pose enormous challenges for the existing simultaneous localization and mapping (SLAM) algorithms. To promote the development of HLS and explore its application in large-scale GNSS-denied environments, the first large-scale HLS dataset covering multiple difficult GNSS-denied areas (e.g., forests, mountains, underground spaces) was built in this study. Besides using an additional very high accuracy fiber-optic inertial measurement unit (IMU), a novel post-processing multi-source fusion method—progressive trajectory correction (PTC)—is proposed to generate a reliable ground-truth trajectory for the benchmark, which overcomes the problems of scan matching degradation and non-rigid distortion. The accuracies of the ground truth are controlled and checked by manually surveyed feature points along the trajectory. Finally, the existing state-of-the-art SLAM methods were evaluated on the WHU-Helmet dataset, summarizing the future HLS SLAM research trends. The full dataset is available for download at: https: //github.com/kafeiyin00/WHU-HelmetDataset.

Improving aboveground biomass estimation of natural forests on the Tibetan Plateau using spaceborne LiDAR and machine learning algorithms

• ICESat-2 has the potential to improve the estimation accuracy and efficiency of forest aboveground biomass on a large scale. • The Google Earth Engine cloud platform was used for Sentinel-2 images acquisition and composition. • The optimized extreme learning machine achieved the highest estimation accuracy and reasonable forest aboveground biomass spatial distribution. Natural forests have the most complex structure and richest biodiversity among terrestrial ecosystems and are essential for maintaining the carbon balance and stability of the biosphere. Aboveground biomass (AGB) is a primary indicator used to evaluate forests and can directly measure forest growth and the quality of natural forests. Accurate and rapid AGB estimations can significantly improve the efficiency of forest management and deepen the understanding of the forest carbon cycle. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), one of the most recently launched spaceborne light detection and ranging (LiDAR) instruments, can penetrate forest canopies and obtain accurate, large-scale forest vertical parameter measurements. However, the discrete footprints offered by ICESat-2 cannot provide comprehensive spatial AGB distribution data. In this study, an optimized extreme learning machine (ELM) method was proposed to estimate the AGB of natural forests on the eastern Qinghai-Xizang Plateau, China. The synthetic Sentinel-2 images acquired from the Google Earth Engine (GEE) were synergized with ICESat-2 to realize continuous AGB mapping. To verify the effectiveness of the optimized method, support vector machine (SVM), k-nearest neighbor (kNN), and backpropagation (BP) neural network models were also established for comparison. The measured AGB data extracted from the forest management inventory (FMI) were used for model accuracy evaluation. The results show that the optimized ELM achieved the best estimation effect among all the analyzed models, with an R 2 value of 0.68 and a root mean square error (RMSE) value of 25.14 mg/ha. The optimized ELM obtained the minimum RMSE and greatly improved the AGB prediction efficiency. These findings prove the ability of ICESat-2 in estimating the AGB of natural forests, which can provide a new way for large-scale forest resources investigation in high-altitude areas with a harsh environment.

Circular or square plots in ALS-based forest inventories—does it matter?

Abstract In airborne laser scanning (ALS)-based forest inventories, there is commonly a discrepancy between the plot shape used for model fitting (typically circular) and the shape of population elements (typically square) where predictions are needed. Circular plots are easy to establish, locate and have the smallest number of edge trees on average. Therefore, a circle is the most common plot shape in both traditional and remote sensing-based forest inventories. In contrast, the shape of population elements used for remote sensing-based predictions is nearly always a square because it enables division of the target population into a grid of non-overlapping plots. In this study, we investigate shape effects for ALS-based forest inventories using circular and square plot shapes. This has not been examined earlier. Aboveground biomass was used as the response variable. The sampling design was created in a way that the probability of selection for any location inside a stem-mapped 30 m × 30 m plot was the same for the circular (radius 7.95 m) and square (side length 14.09 m) plot. This configuration enabled us to compare circular and square plots with the same areas and identical sampling probabilities for every tree in the population. Our primary finding is that for equal area square and circular plots, there is no evidence of systematic prediction error when a model fitted to one shape is used to predict for the other shape. Our secondary finding is that root mean square error (RMSE) value is slightly underestimated (1.2 per cent) when a model fitted to circular plots is used to predict for square plots. A small underestimation of RMSE due to plot shape effect has hardly practical significance in stand-level forest management inventories, but the plot shape effect may be problematic in large area forest surveys.

Estimating Species-Specific Stem Size Distributions of Uneven-Aged Mixed Deciduous Forests Using ALS Data and Neural Networks

Sustainable forest management requires accurate fine-scale description of wood resources. Stem size distribution (SSD) by species is used by foresters worldwide as a representative overview of forest structure and species composition suitable for informing management decisions at shorter and longer terms. In mixed uneven-aged deciduous forests, tree data required for SSD estimation are most often collected in the field through traditional forest management inventories (FMIs), but these are time-consuming and costly with respect to the sampled area. Combining FMIs with remote sensing methods such as airborne laser scanning (ALS), which has high potential for predicting forest structure and composition, and is becoming increasingly accessible and affordable, could provide cheaper and faster SSD data across large areas. In this study, we developed a method for estimating species-specific SSDs by combining FMIs and dual-wavelength ALS data using neural networks (NNs). The proposed method was tested and validated using 178 FMI plots within 22,000 ha of a mixed uneven-aged deciduous forest in Belgium. The forest canopy was segmented, and metrics were derived from the ALS point cloud. A NN with a custom architecture was set up to simultaneously predict the three components required to compute species-specific SSDs (species, circumference, and number of stems) at segment level. Species-specific SSDs were thereafter estimated at stand level by aggregating the estimates for the segments. A robustness test was set up using fully independent plots to thoroughly assess the method precision at stand-level on a larger area. The global Reynolds index for the species-specific SSDs was 21.2 for the training dataset and 54.0 for the independent dataset. The proposed method does not require allometric models, prior knowledge of the structure, or the predefinition of variables; it is versatile and thus potentially adaptable to other forest types having different structures and compositions.

Estimating forest attributes in airborne laser scanning based inventory using calibrated predictions from external models

Forest management inventories assisted by airborne laser scanner data rely on predictive models traditionally constructed and applied based on data from the same area of interest. However, forest attributes can also be predicted using models constructed with data external to where the model is applied, both temporal and geographically. When external models are used, many factors influence the predictions’ accuracy and may cause systematic errors. In this study, volume, stem number, and dominant height were estimated using external model predictions calibrated using a reduced number of up-to-date local field plots or using predictions from reparametrized models. We assessed and compared the performance of three different calibration approaches for both temporally and spatially external models. Each of the three approaches was applied with different numbers of calibration plots in a simulation, and the accuracy was assessed using independent validation data. The primary findings were that local calibration reduced the relative mean difference in 89% of the cases, and the relative root mean squared error in 56% of the cases. Differences between application of temporally or spatially external models were minor, and when the number of local plots was small, calibration approaches based on the observed prediction errors on the up-to-date local field plots were better than using the reparametrized models. The results showed that the estimates resulting from calibrating external models with 20 plots were at the same level of accuracy as those resulting from a new inventory.

Mapping Large-Scale Forest Disturbance Types with Multi-Temporal CNN Framework

Forests play a vital role in combating gradual developmental deficiencies and balancing regional ecosystems, yet they are constantly disturbed by man-made or natural events. Therefore, developing a timely and accurate forest disturbance detection strategy is urgently needed. The accuracy of traditional detection algorithms depends on the selection of thresholds or the formulation of complete rules, which inevitably reduces the accuracy and automation level of detection. In this paper, we propose a new multitemporal convolutional network framework (MT-CNN). It is an integrated method that can realize long-term, large-scale forest interference detection and distinguish the types (forest fire and harvest/deforestation) of disturbances without human intervention. Firstly, it uses the sliding window technique to calculate an adaptive threshold to identify potential interference points, and then a multitemporal CNN network is designed to render the disturbance types with various disturbance duration periods. To illustrate the detection accuracy of MT-CNN, we conducted experiments in a large-scale forest area (about 990 km2) on the west coast of the United States (including northwest California and west Oregon) with long time-series Landsat data from 1986 to 2020. Based on the manually annotated labels, the evaluation results show that the overall accuracies of disturbance point detection and disturbance type recognition reach 90%. Also, this method is able to detect multiple disturbances that continuously occurred in the same pixel. Moreover, we found that forest disturbances that caused forest fire repeatedly appear without a significant coupling effect with annual temporal and precipitation variations. Potentially, our method is able to provide large-scale forest disturbance mapping with detailed disturbance information to support forest inventory management and sustainable development.

Russian Approaches to the Forest Type Classification

The results of researches characterizing the geographical distribution of forest-ecological, phytocoenotic, and genetic classifications of forest types in the Russian Federation nowadays are presented in the thesis. A comparative analysis was carried out for the following items: the inclusive concept of a classification unit (a type of habitat conditions; a type of forest); features of distinguishing the border of the classification units; classification features used to determine the type of habitat conditions; features of the classification of phytocoenoses used to determine the forest type; the degree to which the successional dynamics of forest stands are taken into consideration; the degree to which the influence of anthropogenic factors are taken into consideration; the level of implementation in forest management and forestry practice; regions of implementation. In the process of development of forest typologies, the concept of a forest type changed from understanding it as a forest area homogeneous by composition, structure, and appearance (homogeneity in space) in natural classifications to the concepts of a forest type, in which priority is given to homogeneity in origin (genesis), as well as developmental processes and dynamics (homogeneity in time) in genetic and dynamic typologies. Currently, there is the following forest type classification in the Russian Federation: forest-ecological, phytocoenotic, genetic, and dynamic. When classifying forest areas within the forest-ecological direction provided by E.V. Alekseev – P.S. Pogrebnyak, the priority is given to the characteristics of the habitat conditions. Within the phytocoenotic direction provided by V.N. Sukachev, the priority is given to the phytocoenosis characteristics. Within the genetic approach provided by B.A. Ivashkevich – B.P. Kolesnikov, a forest type is considered as a series of alternating phases – types of phytocoenosis within the same type of habitat conditions. In this case, phytocoenotic classifications can be a part of the genetic classifications for the climax forest phytocoenosis. And the dynamic approach provided by I.S. Melekhov is very close to the genetic one and is a superstructure over the classical phytocoenotic forest typology provided by V.N. Sukachev. The current use of forest typological classifications by forest inventory management enterprises in the Russian Federation was studied. A map of the geographical distribution of forest typologies of the above-listed directions of forest typology researches was created. Forest-ecological classifications are used mainly in the southern regions of the European part of Russia and the North Caucasus. Forest typologies created based on a genetic approach to the forest type classification are used in Western Siberia, in the south of the Far East and Eastern Siberia, and in some regions of the Urals. Phytocoenotic classifications of forest types are used in other regions of the Russian Federation.

Height Allometry of Pinus nigra Arn. in Troodos National Forest Park, Cyprus

Total height is one of the basic morphometric tree variables included in all forest management inventories, because it is connected with several forest processes and functions related to the estimation of the woody tree volume and sustainable forest yield. The current research, based on a total sample of 1059 measured Black pine (Pinus nigra Arn.) trees from Cyprus, is an attempt to explore the biological processes related to the tree height allometry of this species and develop a generalized mixed-effects model for tree height prediction. The proposed model, with three additional basic covariates and two random parameters, explained almost 96% of the height variance. The model results showed that although competition and site-connected variables affected the total height, it was the crown base height that explained, to a large degree, the height expression. Through the mixed-effects modeling approach it was possible to explore the complex biological processes related to the tree allometry of Black pine and depict those within a mathematical formulation. The proposed generalized model decreased the error significantly, and therefore it can be used for operational forest management purposes. However, for marginal predictions, use of only the fixed part of the basic model could suffice, since this also provided unbiased parameter estimates.

Timber volume estimation based on airborne laser scanning \u2014 comparing the use of national forest inventory and forest management inventory data

Key messageLarge-scale forest resource maps based on national forest inventory (NFI) data and airborne laser scanning may facilitate synergies between NFIs and forest management inventories (FMIs). A comparison of models used in such a NFI-based map and a FMI indicate that NFI-based maps can directly be used in FMIs to estimate timber volume of mature spruce forests.ContextTraditionally, FMIs and NFIs have been separate activities. The increasing availability of detailed NFI-based forest resource maps provides the possibility to eliminate or reduce the need of field sample plot measurements in FMIs if their accuracy is similar.AimsWe aim to (1) compare a timber volume model used in a NFI-based map and models used in a FMI, and (2) evaluate utilizing additional local sample plots in the model of the NFI-based map.MethodsAccuracies of timber volume estimates using models from an existing NFI-based map and a FMI were compared at plot and stand level.ResultsEstimates from the NFI-based map were similar to or more accurate than the FMI. The addition of local plots to the modeling data did not clearly improve the model of the NFI-based map.ConclusionThe comparison indicates that NFI-based maps can directly be used in FMIs for timber volume estimation in mature spruce stands, leading to potentially large cost savings.

Large-area inventory of species composition using airborne laser scanning and hyperspectral data

Tree species composition is an essential attribute in stand-level forest management inventories and remotely sensed data might be useful for its estimation. Previous studies on this topic have had several operational drawbacks, e.g., performance studied at a small scale and at a single tree-level with large fieldwork costs. The current study presents the results from a large-area inventory providing species composition following an operational area-based approach. The study utilizes a combination of airborne laser scanning and hyperspectral data and 97 field sample plots of 250 m collected over 350 km of productive forest in Norway. The results show that, with the availability of hyperspectral data, species-specific volume proportions can be provided in operational forest management inventories with acceptable results in 90% of the cases at the plot level. Dominant species were classified with an overall accuracy of 91% and a kappa-value of 0.73. Species-specific volumes were estimated with relative root mean square differences of 34%, 87%, and 102% for Norway spruce ( (L.) Karst.), Scots pine ( L.), and deciduous species, respectively. A novel tree-based approach for selecting pixels improved the results compared to a traditional approach based on the normalized difference vegetation index.22Picea abiesPinus sylvestris

Large scale mapping of forest attributes using heterogeneous sets of airborne laser scanning and National Forest Inventory data

BackgroundThe Norwegian forest resource map (SR16) maps forest attributes by combining national forest inventory (NFI), airborne laser scanning (ALS) and other remotely sensed data. While the ALS data were acquired over a time interval of 10 years using various sensors and settings, the NFI data are continuously collected. Aims of this study were to analyze the effects of stratification on models linking remotely sensed and field data, and assess the accuracy overall and at the ALS project level.Materials and methodsThe model dataset consisted of 9203 NFI field plots and data from 367 ALS projects, covering 17 Mha and 2/3 of the productive forest in Norway. Mixed-effects regression models were used to account for differences among ALS projects. Two types of stratification were used to fit models: 1) stratification by the three main tree species groups spruce, pine and deciduous resulted in species-specific models that can utilize a satellite-based species map for improving predictions, and 2) stratification by species and maturity class resulted in stratum-specific models that can be used in forest management inventories where each stand regularly is visually stratified accordingly. Stratified models were compared to general models that were fit without stratifying the data.ResultsThe species-specific models had relative root-mean-squared errors (RMSEs) of 35%, 34%, 31%, and 12% for volume, aboveground biomass, basal area, and Lorey’s height, respectively. These RMSEs were 2–7 percentage points (pp) smaller than those of general models. When validating using predicted species, RMSEs were 0–4 pp. smaller than those of general models. Models stratified by main species and maturity class further improved RMSEs compared to species-specific models by up to 1.8 pp. Using mixed-effects models over ordinary least squares models resulted in a decrease of RMSE for timber volume of 1.0–3.9 pp., depending on the main tree species. RMSEs for timber volume ranged between 19%–59% among individual ALS projects.ConclusionsThe stratification by tree species considerably improved models of forest structural variables. A further stratification by maturity class improved these models only moderately. The accuracy of the models utilized in SR16 were within the range reported from other ALS-based forest inventories, but local variations are apparent.

Volumes by tree species can be predicted using photogrammetric UAS data, Sentinel-2 images and prior field measurements

Photogrammetric point clouds obtained with unmanned aircraft systems (UAS) have emerged as an alternative source of remotely sensed data for small area forest management inventories (FMI). Nonetheless, it is often overlooked that small area FMI require considerable field data in addition to UAS data, to support the modelling of forest attributes. In this study, we propose a method whereby tree volumes by species are predicted with photogrammetric UAS data and Sentinel-2 images, using models fitted with airborne laser scanning data. The study area is in a managed boreal forest area in Eastern Finland. First, we predicted total volume with UAS point cloud metrics using a prior regression model fitted in another area with ALS data. Tree species proportions were then predicted by nearest neighbor (-NN) imputation based on bi-seasonal Sentinel-2 images without measuring new field plot data. Species-specific volumes were then obtained by multiplying the total volume by species proportions. The relative root mean square error (RMSE) values for total and species-specific volume predictions at the validation plot level (30 m × 30 m) were 9.0%, and 33.4–62.6%, respectively. Our approach appears promising for species-specific small area FMI in Finland and in comparable forest conditions in which suitable field plots are available.kk

From comprehensive field inventories to remotely sensed wall-to-wall stand attribute data — a brief history of management inventories in the Nordic countries

Forest management inventories (FMIs) provide critical information, usually at the stand level, for forest management planning. A typical FMI includes (i) the delineation of the inventory area to stands by applying auxiliary information; (ii) the classification of the stands according to categorical attributes such as age, site fertility, main tree species, and stand development; and (iii) measurement, modelling, and prediction of stand attributes of interest. The emergence of wall-to-wall remote-sensing data has enabled a paradigm change in FMIs from highly subjective, visual assessments to objective, model-based inferences. Previously, optical remote-sensing data were used to complement visual assessments, especially in stand delineation and height measurements. The evolution of airborne laser scanning (ALS) has made objective estimation of forest characteristics with known accuracy possible. New optical and Lidar-based sensors and platforms will allow further improvements of accuracy. However, there are still bottlenecks related to species-specific stand attribute information in mixed stands and assessments of tree quality. Here, we concentrate on approaches and methods that have been applied in the Nordic countries in particular.