Predicting the provisioning potential of forest ecosystem services using airborne laser scanning data and forest resource maps

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Use of Aerial Laser Scanning to Assess the Effect on C Sequestration of Oak (Quercus ilex L. subsp. ballota [Desf.]Samp-Q. suber L.) Afforestation on Agricultural Land

Conversion of agricultural lands to forest plantations to mitigate rising atmospheric carbon dioxide (CO2) has been proposed, but it depends on accurate estimation of the on-site carbon (C) stocks distribution. The use of aerial laser scanning (ALS) data is a rapidly evolving technology for the quantification of C stocks. We evaluated the use of allometric models together with high-density ALS data for the quantification of biomass and soil C stocks in a 14-year-old Quercus ilex and Q. suber plantation in Southwestern Spain. In 2010, a field survey was performed and tree dasometric and biomass variables were measured. Forty-five soil profiles (N = 180 soil samples) were taken systematically and the soil organic C content (SOC) was determined. Biomass and soil organic C values were regressed against individual dasometric variables and total tree height was used as a predictor variable. Aerial laser scanning data were acquired with a point density of 12 points m−2. Relationships among ALS metrics and tree height were determined using stepwise regression models and used in the allometric models to estimate biomass and SOC C stocks. Finally, a C stock map of the holm-cork oak cover in the study area was generated. We found a tree total biomass of 27.9 kg tree−1 for holm oak and 41.1 kg tree−1 for cork oak. In the holm oak plantation, the SOC content was 36.90 Mg ha−1 for the layer 0–40 cm (SOC40) under the tree crown and 29.26 Mg ha−1 for the inter-planted area, with significant differences from the reference agricultural land (33.35 Mg ha−1). Linear regression models were developed to predict the biomass and SOC at the tree scale, based on tree height (R2 >0.72 for biomass, and R2 >0.62 for SOC). The overall on-site C stock in the holm-cork oak plantation was 35.11 Mg ha−1, representing a net C stock rise of 0.47 Mg ha−1 yr−1. The ALS data allows a reliable estimation of C stocks in holm and cork oak plantations and high-resolution maps of on-site C stocks are useful for silvicultural planning. The cost of ALS data acquisition has decreased and this method can be generalised to plantations of other Mediterranean species established on agricultural lands at regional scales. However, an increase of filed data and the availability of local biomass and, in particular, SOC will improve accurate quantification of the C stocks from allometric equations, and extrapolation to large planted areas

Predicting growing stock volume of Eucalyptus plantations using 3-D point clouds derived from UAV imagery and ALS data

Estimating forest inventory variables is important in monitoring forest resources and mitigating climate change. In this respect, forest managers require flexible, non-destructive methods for estimating volume and biomass. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes. The main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations. Object-based image analysis (OBIA) techniques were applied for individual tree crown (ITC) delineation. The ITC algorithm applied correctly detected and delineated 199 trees from ALS-derived data, while 192 trees were correctly identified using DAP-based point clouds acquired fromUnmannedAerialVehicles(UAV), representing accuracy levels of respectively 62% and 60%. Addressing volume modelling, non-linear regression fit based on individual tree height and individual crown area derived from the ITC provided the following results: Model E ciency (Mef) = 0.43 and 0.46, Root Mean Square Error (RMSE) = 0.030 m3 and 0.026 m3, rRMSE = 20.31% and 19.97%, and an approximately unbiased results (0.025 m3 and 0.0004 m3) using DAP and ALS-based estimations, respectively. No significant di erence was found between the observed value (field data) and volume estimation from ALS and DAP (p-value from t-test statistic = 0.99 and 0.98, respectively). The proposed approaches could also be used to estimate basal area or biomass stocks in Eucalyptus spp. plantationsinfo:eu-repo/semantics/publishedVersio

Data Assimilation of Growing Stock Volume Using a Sequence of Remote Sensing Data from Different Sensors

Airborne Laser Scanning (ALS) has implied a disruptive transformation of how data are gathered for forest management planning in Nordic countries. We show in this study that the accuracy of ALS predictions of growing stock volume can be maintained and even improved over time if they are forecasted and assimilated with more frequent but less accurate remote sensing data sources like satellite images, digital photogrammetry, and InSAR. We obtained these results by introducing important methodological adaptations to data assimilation compared to previous forestry studies in Sweden. On a test site in the southwest of Sweden (58 degrees 27 ' N, 13 degrees 39 ' E), we evaluated the performance of the extended Kalman filter and a proposed modified filter that accounts for error correlations. We also applied classical calibration to the remote sensing predictions. We evaluated the developed methods using a dataset with nine different acquisitions of remotely sensed data from a mix of sensors over four years, starting and ending with ALS-based predictions of growing stock volume. The results showed that the modified filter and the calibrated predictions performed better than the standard extended Kalman filter and that at the endpoint the prediction based on data assimilation implied an improved accuracy (25.0% RMSE), compared to a new ALS-based prediction (27.5% RMSE)

Potential of Bayesian formalism for the fusion and assimilation of sequential forestry data in time and space

Forest resource assessments based on multi-source and multi-temporal data have become more common. Therefore, enhancing the prediction capabilities of forestry dynamics by efficiently pooling and analyzing time-series and spatial sequential data is now more pivotal. Bayesian filtering and smoothing provide a well-defined formalism for the fusion or assimilation of various data. We ascertained how often the generic, standardized Bayesian framework is used in the scientific literature and whether such an approach is beneficial for forestry applications. A review of the literature showed that the use of Bayesian methods appears to be less common in forestry than in other disciplines, particularly remote sensing. Specifically, time-series analyses were found to favor ad hoc methods. Our review did not reveal strong numeric evidence for better performance by the various Bayesian approaches, but this result may be partly due to the challenge in comparing a variety of methods for different prediction tasks. We identified methodological challenges related to assimilating predictions of forest development; in particular, combining modelled growth with disturbances due to both forest operations and natural phenomena. Nevertheless, the Bayesian frameworks provide possibilities to efficiently combine and update prior and posterior predictive distributions and derive related uncertainty measures that appear under-utilized in forestry.Peer reviewe

A disposition of interpolation techniques

A large collection of interpolation techniques is available for application in environmental research. To help environmental scientists in choosing an appropriate technique a disposition is made, based on 1) applicability in space, time and space-time, 2) quantification of accuracy of interpolated values, 3) incorporation of ancillary information, and 4) incorporation of process knowledge. The described methods include inverse distance weighting, nearest neighbour methods, geostatistical interpolation methods, Kalman filter methods, Bayesian Maximum Entropy methods, etc. The applicability of methods in aggregation (upscaling) and disaggregation (downscaling) is discussed. Software for interpolation is described. The application of interpolation techniques is illustrated in two case studies: temporal interpolation of indicators for ecological water quality, and spatio-temporal interpolation and aggregation of pesticide concentrations in Dutch surface waters. A valuable next step will be to construct a decision tree or decision support system, that guides the environmental scientist to easy-to-use software implementations that are appropriate to solve their interpolation problem. Validation studies are needed to assess the quality of interpolated values, and the quality of information on uncertainty provided by the interpolation method