Integration of high-quality harvester data and new log scaling technology for efficient control of wood flow in German wood supply chains

Transcription

1 Integration of high-quality harvester data and new log scaling technology for efficient control of wood flow in German wood supply chains Dirk Jaeger 1, Martin Opferkuch 1, Siegmar Schönherr 1, Thilo Wagner² 1 Chair of Forest Operations University of Freiburg ²Director of Centre of Forestry Education (Forest work and Forest operations) Precision Forestry Symposium 5 th of March 2014 Albert-Ludwigs-Universität Freiburg What is the problem? Mechanized timber harvesting is increasing in Germany Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 2 1

2 share in total harvested volume [%] Number of harvesters Introduction Forest area in Germany: 11.1 Mio. hectares Annual Timber harvest volume about 55 Mio. m³ Mio. m³ Year Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 3 Introduction Development of mechanized harvesting in Germany About half of the annual cut is done by mechanized harvesting fully mechanized [%] motor manual/semi-mechanized [%] number of harvesters year Source: KWF (estimation) Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 4 2

3 What is the problem? Mechanized timber harvesting is increasing in Germany The wood supply chain is composed of many different entities Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 5 Introduction CTL wood supply chain with different stakeholders involved Source: Forest Energy Portal Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 6 3

4 Introduction Gap of production data Harvesting Processing Bucking Extraction Forwarding Piling at roadside Transport Mill intake Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 7 What is the problem? Mechanized timber harvesting is increasing in Germany The wood supply chain is composed of many different entities Data of harvested timber at roadside is lacking Quality control of extraction process Base for pay of harvester and forwarding contractors Planning of hauling process Quality control of delivery process to mill Documentation for forest owner Missing data is manually generated, very time consuming and costly Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 8 4

5 What is the problem? Manual measurement of pile volume Section method for logs 2.5 to 6.0 m in length; output is volume in stacked cubic metre (rm); section lengths vary from 1 to 10 m depending on length of pile Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 9 What is the problem? Manual measurement of pile volume End face method for logs 2.5 to 6.0 m in length; output is volume in cubic metre; distribution of log sizes, average log volume spacing of assessment lines 0.5 to 2.0 m measurement of end face diameters (u.b.) along assessment lines on front and rear side of pile Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 10 5

6 Goal of this study To evaluate a method for easy and accurate assessment of log volumes at roadside Solution strategy Using high quality of harvester data operating to newly established quality standards (output average log volume per product) combined with Counting exact number of logs per product piled at roadside using the photo-optical method PolterLuchs Deriving total volume of each product at roadside Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 11 Introduction Questions of interest: 1. Harvester 1.1 How accurate is the average log volume derived by a harvester calibrated to standard compared to mill measurements? 1.2 What are potential factors influencing accuracy of diameter and/or length measurements of the harvester 2. PolterLuchs 2.1 How accurate is the log number of log piles assessed by this method 2.2 What are potential factors influencing accuracy of derived log numbers 3. How accurate are derived harvest volumes by harvester/polterluchs at roadside compared to manual and mill assessments of volume Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 12 6

7 Introduction Introduction - Problem definition - Solution strategy Material and Methods - Quality standard for harvester - PolterLuchs - Trial set up Results - Average log volume of harvester compared to mill measurements - Potential factors influencing accuracy of harvester data - Quality of log count by PolterLuchs - Potential factors influencing log count accuracy - Accuracy of derived log volumes at roadside compared to mill data Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 13 Material and Methods Quality standard for harvesters Standard for calibration of measurement features of harvester head was developed by KWF Standard is mandatory for harvesting contractors in Nordrhein-Westfalen and Rheinland-Pfalz Basic calibration requirements for accurate harvester measurement standard for harvesters by KWF Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 14 7

8 Material and Methods Lastenheft Harvestervermessung Diameter Error limits Target range Optimisation range Exclusion range Mean [mm] < to 2.5 >2.5 Standard deviation [mm] < to 8.0 > 8.0 Difference of means [%] ± 1.0 Extreme values ( ± 20 mm) < 3,0 % 3.0 % to 5.0 % > 5.0 % Length Mean [mm] < to 3.0 > 3.0 Standard deviation [mm] < to 5.0 > 5.0 Difference of means [%] ± 1.0 Extreme values ( ± 20 mm) < 2.0 % 2.0 % to 5.0 % > 5.0 % Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 15 Material and Methods Necessary action Immediate adjustment Frequent monitoring Differences of diameter (from at least 10 measurements) Mean [mm] Standard deviation [mm] Differences of length (from at least 3 measurements) Mean [mm] Standard deviation [cm] > 2.5 > > > 3.0 Source: Dietz & Seeling, Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 16 Source: Dietz & Seeling,

9 Material and Methods PolterLuchs Photo-optical method to automatically measure the number of logs in a round wood pile Vehicle with camera is driving along the log piles and takes series of pictures which iarecompiled by the program (from 3 m distance a 4 m pile can be recorded, at a camera height of 2 m) No spatial reference needed Output: number of logs for each pile Data is compatible with logistic software (e.g. GeoMail) Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 17 Material and Methods Trial set up Harvested stand Norway Spruce Owner: State Forest Nordrhein-Westfalen years Max. height 27 m 9 ha 18 % terrain slope Single tree harvest (thinning to favor crop trees) Operating trails at 20 m spacing Harvested volume 50 m³/ha Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 18 9

10 Material and Methods Trial set up Harvester: John Deere 1470D Eco x 3 m (LxW) - Engine: 552 c.u.-in. 9.0 L, 241 SAE gross hp (180 kw) - Balanced front gear bogie, rigid rear axle - Standard operation weight: 19,700 kg - Reach with harvester head: 10 m Harvester head: Waratah H480C - TimberMatic Software - Weight (without rotator & link) 1240 kg - Cutting Capacity: 650 / 710 mm Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 19 Material and Methods What was done? Harvester was calibrated to standard Spruce stand was thinned, 1345 saw logs were processed Logs were manually measured Logs were extracted Log piles were assessed by conventional manual methods Number of logs in log piles was derived by using PolterLuchs PolterLuchs was used at varying conditions Logs were measured in mill Harvester data was assessed against log identical mill data (diameter, length) and manual measurements Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 20 10

11 Material and Methods Manual measurements of logs in stand Log assessment at roadside Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 21 Material and Methods Certified laser assessment Diameter, length Taper, ovality Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 22 11

12 Average log volume [m³ ub] Total volume [m³ ub] Results - Harvester 1. Harvester 1.1 Average log volume of harvester compared to mill measurements Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 23 Results - Harvester 1. Harvester 1.1 Average log volume of harvester compared to mill measurements 0,25 Volume comparison [n=1031] , , , ,05 0,226 0, ,00 Harvester Mill 0 3.8% difference Average log volume [m³ ub] Total volume [m³ ub] Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 24 12

13 Number of logs [n] Average log volume [m³ ub] Results - Harvester 1.2 Potential factors influencing accuracy of harvester data Number of logs and average log volume per log position ,35 0, ,25 0,20 0,15 0,10 0,05 0 Total bottom log mid log 1 mid log 2 mid log top log 3 0,00 Log position Number of logs [n] Average log volume mill [m³ ub] Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 25 Results - Harvester 1.2 Potential factors influencing accuracy of harvester data Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 26 13

14 Results - Harvester 1.2 Potential factors influencing accuracy of harvester data Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 27 Results - Harvester 1.2 Potential factors influencing accuracy of harvester data Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 28 14

15 Results - Harvester 1.2 Potential factors influencing accuracy of harvester data Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 29 Results - Harvester 1.2 Potential factors influencing accuracy of harvester data Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 30 15

16 bark deduction [cm] Results - Harvester 1.2 Potential factors influencing accuracy of harvester data Bark deduction 2,5 2 1,5 1 regional bark deduction table measured deduction 0, a 1b 2a 2b 3a 3b 4 5 diameter class Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 31 Results - Harvester Findings Average log volume given by harvester is 3.8% smaller than from mill Harvester derived diameter and length data are lower compared to mill assessment except for upper medium and top logs - Log quality (ovality, taper) had no obvious effect on diameter and length accuracy - Bark deduction is dominating effect on diameter for logs with diameter > 29 cm Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 32 16

17 Polterluchs test scenarios Tested species: Spruce, Douglas Fir, Larch Methods: Section method, End-face method, PolterLuchs PolterLuchs Variation: - brightness setting - discoloration - species - weather (snow) - processing Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 33 Results - Polterluchs 2. PolterLuchs 2.1 Quality of log count by PolterLuchs Amount of correctly recognized logs (Norway Spruce) and time consumption for manual post-processing (clean pile, default setting, no mask) Pile Logs [n] Correctly recognized logs [n] [%] Post-processing time per 100 logs [min] % % % % % 1.21 Sum % Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 34 17

18 Results - Polterluchs 2.2 Potential factors influencing log count accuracy Brightness setting on camera (no mask, clean pile) Pile Logs [n] Correctly recognized logs Post-processing time per 100 logs [min] brightness [79%] 1066 [79%] Total 1085 [81%] 1090 [81%] 1080 [80%] Total time for all logs & piles Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 35 Results - Polterluchs Brightness setting on camera - Pile 1, brightness 70: 219 out of min - Pile 1, brightness 30: 189 out of min Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 36 18

19 Results - Polterluchs 2.2 Potential factors influencing log count accuracy Discoloration of logs Brightness 70, clean: 504 out of 554 (490 correct), 4.91 min post-processing Brightness 70, discolored: 371 out of 554 (347 correct), 4.96 min post-processing Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 37 Results - Polterluchs 2.2 Potential factors influencing log count accuracy Tree species (no mask, clean pile) Pile Logs [n] Correctly recognized logs Brightness adjustment at camera Av. Recognized logs Post-processing time per 100 logs [min] [n] [%] [n] [%] [n] [%] [%] [min] [min] [min] Douglas fir (3 m) Douglas fir (4 m) Spruce (4 m) Larch (3 m) Larch (4 m) Total Total time for all logs & piles , Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 38 19

20 Results - Polterluchs Douglas fir (3 m) from 350 (64 %) recognized logs min processing time Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 39 Results - Polterluchs 2.2 Potential factors influencing log count accuracy Weather (without and with snow (50, without mask)) from 243 recognized logs (of which 231 (95%) were correct) min post-processing time from 243 recognized logs (of which 120 (49%) are correct) min post-processing time Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 40 20

21 Results - Polterluchs 2.2 Potential factors influencing log count accuracy Processing with and without mask Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 41 Results - Polterluchs Processing without mask with mask Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 42 21

22 Results - Polterluchs 2.2 Potential factors influencing log count accuracy Processing with and without mask Pile Logs [n] Correctly recognized logs Brightness adjustment at camera Post-processing time per 100 logs [min] 50 (no mask) 50 (mask) 50 (no mask) 50 (mask) [n] [%] [n] [%] [min] [min] Douglas fir (3 m) Douglas fir (4 m) Spruce (4 m) Larch (3 m) Larch (4 m) Total Total time for all logs & piles Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 43 Results - Harvester Findings In average 81% of the Spruce logs were recognized Brightness setting is of particular importance under light conditions - Discoloration of logs did not lead to increased processing effort - Snow cover of pile reduced recognition rate and increased processing time - Douglas Fir and Larch showed lower recognition rates and increased processing time - Processing with mask did not shorten overall processing time Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 44 22

23 Diameter Length Overall Results Achieved accuracies compared to standard Calibration settings KWF Error limits Target area Optimization area Exclusion area Mill vs. Harvester (n=1304) Mean [mm] < 1,5 1,5 x 2,5 > 2,5 3,45 Standard deviation [mm] < 6,0 6,0 x 8,0 > 8,0 12,38 Difference of mean [%] ± 1,0 1,4 Extreme values ( ± 20mm) [%] < 3,0 3,0 x 5,0 > 5,0 12,4 Mean [cm] < 2,0 2,0 x 3,0 > 3,0-0,1 Standard deviation [cm] < 3,0 3,0 x 5,0 > 5,0 4,74 Difference of mean [%] ± 1,0 1 Extreme values ( ± 10 cm) [%] < 2,0 2,0 x 5,0 > 5,0 1,4 Δ Volume [%] 3, Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 45 Overall Results Accuracy and time consumption of pile assessment methods Scenario Scaling method Volume [m³sub] Volume in relation to mill volume [%] Time consumption [min] Time consumption [min/ m³sub] Time consumption in relation to end-face method [%] Status quo 1 sections % % Status quo 2 end-face % % Status quo 3 manual, logwise Mill automized, certified Harvester harvester head Combination Harvester + of avg. Log polterluchs volume x number of logs % % % % % Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 46 23

24 Results Questions of interest: 1. Harvester 1.1 How accurate is the average log volume derived by a harvester calibrated to standard compared to mill measurements? 1.2 What are potential factors influencing accuracy of diameter and/or length measurements of the harvester 2. PolterLuchs 2.1 How accurate is the log number of log piles assessed by this method 2.2 What are potential factors influencing accuracy of derived log numbers 3. How accurate are derived harvest volumes by harvester/polterluchs compared to manual and mill assessments of volume Dirk Jaeger Integration of high quality data and new allocation technology for efficient control of wood flow 47 Thank you for your attention! Special thanks and appreciation goes to Mr. Thilo Wagner, Director of the forest education center in Arnsberg, for his continuous support. Prof. Dr. Dirk Jaeger University of Freiburg Chair of Forest Operations phone dirk.jaeger@fobawi.uni-freiburg.de 24