Pre-harvest Assessment based on LiDAR Data

Sourcing is the first line of competitiveness of timber supply networks, identifying and locating stands to be harvested that best fit to market demands. The sourcing process is difficult because information is either only available on an aggregated level or is even unavailable, e.g. on non-industrial forest owner’s land. Our study aimed to explore a LiDAR-data-based approach to improve the sourcing of stands to be harvested. We developed a spatially explicit approach, consisting of three steps: (1) harvest screening at the management unit scale or even larger, (2) location and delineation of cutting units, and (3) characterization of tree attributes that are required for stand (cutting unit)-level bucking optimization. The study resulted in the following major findings. First, a tree map represented with Voronoi cells is a useful tool to support the harvest screening process, because it is easily readable and understandable, even by operations personnel. Second, cutting unit location and delineation can easily be done on the tree map, too. Third, the estimation of stem distribution over DBH of a cutting unit may easily be extracted from the spatial tree map database, assuming that there is a deterministic relationship between tree height and DBH. However, there are still issues to be improved, such as comparison of LiDAR-based results with ground-truth, the improvement of LiDAR-based tree delineation methods, the improvement of the estimation of stems over both DBH and tree height, or a mathematical formulation and solution of cutting unit layout

A Topography-Informed Morphology Approach for Automatic Identification of Forest Gaps Critical to the Release of Avalanches

Human assets in Alpine regions are prone to gravitational natural hazards such as rock fall, shallow landslides and avalanches. Forests make up a substantial share in that landscape and can mitigate those hazards. Management of avalanche protection forests must cope with avalanches potentially released in forest gaps, which can damage downslope forests. The Swiss guidelines “Sustainability and success monitoring in protection forests” prescribe forest-gap extents in slope-line direction critical to the release of avalanches in forested areas. This article proposes a topography-informed morphology approach (TIMA) to automate the detection of critical gaps based on a digital terrain model and a canopy height model (CHM) derived from airborne LiDAR-data. TIMA uses complementary information about topography to probe forest gaps computed from the CHM with templates meeting critical-gap extents adjusted to local topography. The method was applied to a test site in Klosters-Serneus (Switzerland). The comparison of a critical-gap map with the results of a field assessment at 19 sample locations resulted in 84% overall accuracy. Moreover, plausibility of gap detection could be improved by including linear features forest roads and torrent channels in TIMA to account for decoupled snow layer resulting from abrupt breaks on the hillslope. If the TIMA concept can be successfully applied to the case of avalanches, this would encourage its use in assessing other gravitational natural hazard processes.ISSN:2072-429

Vista detall de l'edifici de l'antiga FIB per a l'exposició "La FIB ahir i avui"

Maps of standing timber volume provide valuable decision support for forest managers and have therefore been the subject of recent studies. For map production, field observations are commonly combined with area-wide remote sensing data in order to formulate prediction models, which are then applied over the entire inventory area. The accuracy of such maps has frequently been described by parameters such as the root mean square error of the prediction model. The aim of this study was to additionally address the accuracy of timber volume classes, which are used to better represent the map predictions. However, the use of constant class intervals neglects the possibility that the precision of the underlying prediction model may not be constant across the entire volume range, resulting in pronounced gradients between class accuracies. This study proposes an optimization technique that automatically identifies a classification scheme which accounts for the properties of the underlying model and the implied properties of the remote sensing support information. We demonstrate the approach in a mountainous study site in Eastern Switzerland covering a forest area of 2000 hectares using a multiple linear regression model approach. A LiDAR-based canopy height model (CHM) provided the auxiliary information; timber volume observations from the latest forest inventory were used for model calibration and map validation. The coefficient of determination (R2 = 0.64) and the cross-validated root mean square error (RMSECV = 123.79 m3 ha−1) were only slightly smaller than those of studies in less steep and heterogeneous landscapes. For a large set of pre-defined number of classes, the optimization model successfully identified those classification schemes that achieved the highest possible accuracies for each class.ISSN:1999-490

An improved workflow to identify an optimal cable road layout for a large management unit

Cable-based technologies are crucial for the design of harvesting systems in steep terrain. The identification of a cable road layout for a management unit aims at concurrently minimizing harvesting costs and environmental impacts. Snow processes are particularly considered in an Alpine environment when minimizing for the cable roads running in the same direction as the slope line. The problem of selecting the optimal cable road layout can be formulated and solved to optimality as a multi-criteria set cover location problem as proposed by Bont (2012). The approach represents the forest subject to harvest as a raster which must be entirely covered by cable roads. Moreover, helicopter logging is assigned at sites where it outperforms cable roads. Despite the use of the high-performing solver Gurobi in the current implementation, the long time spent to calculate cable road layout alternatives for various objective weights hampers the practical application on a larger scale. Here, we present an improved workflow for the approach which aims at reducing (1) the number of computed alternatives to characterize a Pareto set and (2) computational time via parallelization of the optimization task for subunits constituting a large management unit. We have expanded the workflow with the non-inferior set estimation method (NISE) to plausibly reduce the number of alternatives required to create an approximate Pareto set. That workflow was then modified to parallelize the computation of sub-units on a computing cluster. The resulting workflow was tested for a 300ha management unit in a Subalpine forest located in Klosters (Switzerland). The management unit was partitioned into 5 subunits and the forest subject to harvest was represented as a 15x15m raster. This resulted into subunits consisting of 1’515 to 3’066 forest parcels and 11’185 to 20’935 cable road alternatives, respectively. Four to five alternatives were computed for each subunit to approximate the Pareto sets. The computational times required to solve for the subunits in parallel ranged from 10 to 230 minutes whereas the highest value sets threshold for the overall computational time. -- Bont, Leo Gallus. Spatially explicit optimization of forest harvest and transportation system layout under steep slope conditions. ETH Zurich (2012). http://dx.doi.org/10.3929/ethz-a-00755802

Kombination Forstinventur und Fernerkundungsdaten

Heute stehen neben den terrestrischen Kontrollstichproben (KSP) immer mehr flächendeckende Fernerkundungsdaten zur Verfügung, selbst die Swisstopo plant in naher Zukunft eine schweizweite LiDAR-Befliegung. Am Montagskolloquium an der ETH vom 9. Januar 2017 wird die Frage "Ist die Kombination der KSP mit Fernerkundungsdaten das perfekte Duo für die Bereitstellung von Waldinformationen im 21. Jahrhundert?" behandelt. Das Methodenseminar dient der praktischen Vertiefung. Die Teilnehmenden werden anhand eines Workflows von der Aufbereitung der Geodaten bis hin zur statistischen Auswertung an die Thematik herangeführt. In einem ersten Schritt wird eine Vorratskarte berechnet, im zweiten Schritt werden Vorratsschätzungen auf ausgewählten Managementeinheiten vorgenommen

Automated cable road layout and harvesting planning for multiple objectives in steep terrain

Cable yarding is the most commonly used technique for harvesting timber from steep terrain in central Europe. During the planning process, one important task is to define the cable road layout. This means that the harvesting technology and cable road location must be specified for a given timber parcel. Although managers must minimize harvesting costs, it is even more important that such work on forests reduces the potential for damage to the residual stand and ensures that environmental conditions remain suitable for regeneration. However, current methods are geared only toward minimizing harvesting costs and are computationally demanding and difficult to handle for the end user. These limitations hinder broad application of such methods. Further, the underlying productivity models used for cost estimation do not cover all conditions of an area and they cannot be applied over a whole harvesting area. To overcome these shortcomings, we present: (1) a multiobjective optimization approach that leads to realistic, practicable results that consider multiple conflicting design objectives, and (2) a concept for an easy-to-use application. We compare the practical applicability and performance of the results achieved with multiobjective optimization with those achieved with single-objective (cost-minimal) optimization. Based on these points, we then present and discuss a concept for a user-friendly implementation. The model was tested on two sites in Switzerland. The study produced the following major findings: (1) Single-objective alternatives have no practical relevance, whereas multiobjective alternatives are preferable in real-world applications and lead to realistic solutions; (2) the solution process for a planning unit should include analysis of the Pareto frontier; and (3) results can only be made available within a useful period of time by parallelizing computing operations.ISSN:1999-490

Perioperative intravenous fluid therapy in children: guidelines from the Association of the Scientific Medical Societies in Germany

This consensus- based S1 Guideline for perioperative infusion therapy in children is focused on safety and efficacy. The objective is to maintain or re-establish the child's normal physiological state (normovolemia, normal tissue perfusion, normal metabolic function, normal acid- base- electrolyte status). Therefore, the perioperative fasting times should be as short as possible to prevent patient discomfort, dehydration, and ketoacidosis. A physiologically composed balanced isotonic electrolyte solution (BS) with 1–2.5% glucose is recommended for the intraoperative background infusion to maintain normal glucose concentrations and to avoid hyponatremia, hyperchloremia, and lipolysis. Additional BS without glucose can be used in patients with circulatory instability until the desired effect is achieved. The additional use of colloids (albumin, gelatin, hydroxyethyl starch) is recommended to recover normovolemia and to avoid fluid overload when crystalloids alone are not sufficient and blood products are not indicated. Monitoring should be extended in cases with major surgery, and autotransfusion maneuvers should be performed to assess fluid responsiveness

Accuracy Assessment of Timber Volume Maps Using Forest Inventory Data and LiDAR Canopy Height Models

Maps of standing timber volume provide valuable decision support for forest managers and have therefore been the subject of recent studies. For map production, field observations are commonly combined with area-wide remote sensing data in order to formulate prediction models, which are then applied over the entire inventory area. The accuracy of such maps has frequently been described by parameters such as the root mean square error of the prediction model. The aim of this study was to additionally address the accuracy of timber volume classes, which are used to better represent the map predictions. However, the use of constant class intervals neglects the possibility that the precision of the underlying prediction model may not be constant across the entire volume range, resulting in pronounced gradients between class accuracies. This study proposes an optimization technique that automatically identifies a classification scheme which accounts for the properties of the underlying model and the implied properties of the remote sensing support information. We demonstrate the approach in a mountainous study site in Eastern Switzerland covering a forest area of 2000 hectares using a multiple linear regression model approach. A LiDAR-based canopy height model (CHM) provided the auxiliary information; timber volume observations from the latest forest inventory were used for model calibration and map validation. The coefficient of determination (R2 = 0.64) and the cross-validated root mean square error (RMSECV = 123.79 m3 ha−1) were only slightly smaller than those of studies in less steep and heterogeneous landscapes. For a large set of pre-defined number of classes, the optimization model successfully identified those classification schemes that achieved the highest possible accuracies for each class