LiDAR-based Forest Warehouse concept. Martin Opferkuch October 6, 2015 Gero Becker, Thomas Smaltschinski, Dirk Jaeger

Transcription

1 LiDAR-based Forest Warehouse concept Martin Opferkuch October 6, 2015 Gero Becker, Thomas Smaltschinski, Dirk Jaeger

2 Outline Problem definition Context und solution approach Objective Concept development and result Conclusion Martin Opferkuch October 6,

3 Problem definition Wood industry Demand formulation Demand Forest enterprise Matching process Faller Log demand product specific Offer Stand selection Assortment & grade information Heki Forest Warehouse Martin Opferkuch October 6,

4 Context: data generation in forest enterprise Traditional forest inventory no early information on trees to be harvested During/after harvest good data base (e. g. harvester) after harvesting decision too late Manual pre-harvest inventory possible time-consuming incomplete (static) Local knowledge and experience possible depends on the individual scarce Martin Opferkuch October 6,

5 Solution approach: utilization of LiDAR data Airborne Laserscanning (ALS) Detection of large areas Single-tree detection & -computation coordinates, tree height & crown Toposys (changed) Terrestrial Laserscanning (TLS) Small area samples Single-tree detection & -computation Faro coordinates & stem Martin Opferkuch October 6,

6 Ojective Concept development: How to use data from Laserscanning to allow preharvest offer Differentiated early sufficiently precise Volume & assortment Log quality and log diameter class distribution? Martin Opferkuch October 6,

7 Case study Focus: Pine sawlogs 17 TLS sample-plots 77 TLS trees w/ stem sections d 1.3 d 7 x/y 78 stands w/ ALS data (520 ha) Mixed forest Selective cutting, flat terrain ALS: > 40,000 pines w/ crown data Mainly Pinus sylvestris South East Germany Tree quality assessment Martin Opferkuch October 6,

8 Concept (1) Matching & Regression TLS: stems Reference r = 15 m ALS: crowns (2) Transfer on stands and tree-wise stock computation Volume share [%] Diameter class centre [cm] (3) Utilization modelling Volume share [%] Diameter class centre [cm] (4) Assortment computation Martin Opferkuch October 6,

9 Matching & Regression Data d 1,3 [cm] d 7 [mm] Crown top height (h ALS ) [m] Crown diameter (Crd) [m] Crl [m] Crown proj. area (Cpa) [m²] Crown volume (CrVol) [m³] d 1,3 f h ALS, Crl, Crd, Cpa, CrVol d 7 f d 1,3 r² 0,91 r² 0,97 d 1,3, d 7 & h for each ALS-derived pine tree Martin Opferkuch October 6,

10 Transfer on stand level Height & crown parameters of all pines Transfer of the regressions d 1,3 = f (height & crown) d 7 = f (d 1,3 ) Martin Opferkuch October 6,

11 Tree-wise stock computation Data d 1,3 [cm] d 7 [mm] h ALS [m] Crd [m] Crl [m] Cpa [m²] CrVol [m³] Single-tree wise butt diameter taper (d 1,3 & d 7 ) BDATpro (Kublin 2003) stem volume Utilization volume Dob [cm] taper curve Height [m] volume stock min. top diam. util. limit BDATpro, 2003 Martin Opferkuch October 6,

12 Utilization modelling: stand differentiation Diameter distribution for each stand Differentiation of all stands into stand types according to three natural age classes*: minor medium major tree size stand thousand number of trees n Diameter classes [cm] * Burschel & Huss 2003 Speidel 1972 Martin Opferkuch October 6,

13 Utilization modelling: Weibull * Bailey & Dell (1973) **Smaltschinski (1981) ***Gerold (1990) Wenk (1985) Weibull-function* for stand modelling Calculation of Weibull parameters for all stands ** cutting volume Integration of thinning*** in Weibull modelling Parameter k (0,4 < k < 1,5) ~ resembles thinning from below to from above Simulation of harvesting regime type by setting of k & share of cut volume per stand type (0 < Df < 1) Martin Opferkuch October 6,

14 Utilization modelling: example k=0,8 Df=0,1 k=1,1 Df=0,1 Vol-% stock util. Diameter classes [cm] k=1,2 Df=0,4 Martin Opferkuch October 6,

15 Assortment calculation: saw logs example (I) Input: Sawmill industry demand (log lengths, MD, SED, LED) Diameter distribution and harvesting type per stand (number of trees, volume, taper) Quality distribution from standing tree assessment* per diameter-class and stand type Output * Mahler et al. (2001) computation of single log data bucking sim. (BDATpro) Differentiation into size classes and assortments Revenue calculation and allocation Martin Opferkuch October 6,

16 Assortment calculation: saw logs example (II) Assortment combination Assortment combination Vol [m³ ub] Size classes Assortment Sortiment #1 #2 #3 3m B/C 6, m C/D 34, m sonst. other 19,4 7,5 18,5 Summe 3m 60,5 22,5 18,5 LH B/C 0 8,9 0 LH C/D 0 13,9 41,7 LH sonst. other 0 12,1 0,9 Summe LH 0 34,9 42,6 Summe gesamt Total 60,5 57,4 61,1 Assortment combination Martin Opferkuch October 6,

17 Conclusion concept of Forest Warehouse based on Laserscanning (ALS & TLS) works operational pre-harvest offer flexible regarding time and type of utilization Data base: Regression accuracy improvable by specific sampling design Accuracy of ALS improvable stand structure dependant Tree species detection, quality assessment and matching ALS/TLS still manual work Future inventory based on ALS-TLS-combination Martin Opferkuch October 6,

18 References Bailey RL, Dell TR (1972): Quantifying Diameter Distributions with the Weibull Function. Forest Sci. 19 (2): Burschel P, Huss J (2003): Grundriss des Waldbaus. Ein Leitfaden für Studium und Praxis. 3. unchanged edition. Eugen Ulmer Verlag Gerold D (1990) : Modellierung des Wachstums von Waldbeständen auf der Basis der Durchmesserstruktur. Doctoral thesis at the Technical University of Dresden Kublin E (2003): Einheitliche Beschreibung der Schaftform - Methoden und Programme - BDATpro. Forstw. Cbl. 122: Mahler G, Willmann U, Wurster M (2001): Güteansprache am stehenden Stamm Bundeswaldinventur II Baden-Württemberg, FVA Abt. Arbeitswirtschaft und Forstbenutzung Opferkuch M (2015): Allokation Forstlicher Nutzungen auf Basis von Laserscanning am Beispiel der Sägeindustrie. Doctoral Thesis at the University of Freiburg Smaltschinski T (1981): Bestandesdichte und Verteilungsstruktur. Doctoral thesis at the Faculty of forest science at the University of Freiburg Speidel G (1972): Planung im Forstbetrieb. Grundlagen und Methoden der Forsteinrichtung. Verlag Paul Parey, Hamburg, Berlin Wenk G (1985): Waldertragslehre. Unpublished manuscript Martin Opferkuch October 6,

19 Acknowledgement Coordination Martin Opferkuch Telefon 0761 / martin.opferkuch@foresteng.uni-freiburg.de