Trestima® is a computer vision-based smartphone application that utilises relascope theory to obtain estimates of forest attributes from smartphone photographs. The aim of this study was to investigate the accuracy of Trestima estimation and evaluate whether it is sufficiently accurate for operational use in forestry. Our data consisted of 37 forest stands, encompassing 73.5 ha in southeastern Finland, where Trestima estimates were obtained by forestry professionals during their work. The results were compared with harvester data obtained from clear-cut stands. The number of photographs taken per stand ranged between 1-29 (average: 7.3; standard deviation: 5.0). The total amount of industrial roundwood harvested from the stands was 21.531 m3 and the average harvest removal per hectare was 282 m3. The accuracy of Trestima estimation was relatively good when ≥ 10 photographs per stand were taken. In this case, the root mean square error percent (RMSE%) value associated with roundwood volume was 17.7%. When the number of photographs per stand was < 10, the accuracy of Trestima was much weaker (RMSE% 22.7-55.3%). On average, Trestima underestimated harvested volumes in Scots pine (Pinus sylvestris L.) stands (Bias% 11.4-89.2), although the bias was smaller (Bias% -12.7-12.4) with Norway spruce (Picea abies [L.] Karst.) stands. The Trestima smartphone application is a possible option for traditional field measurements in operational forestry, provided that its usage instructions are strictly followed, which is not always the case in practice.
Keywords
, , , , , ,
Citation
Vähä-Konka V, Korhonen L, Kärhä K, Maltamo M (2024). Estimating the accuracy of smartphone app-based removal estimates against actual wood-harvesting data from clear cuttings. iForest 17: 140-147. - doi: 10.3832/ifor4377-017
Academic Editor
Enrico Marchi
Paper history
Received: May 11, 2023
Accepted: Apr 22, 2024
First online: May 14, 2024
Publication Date: Jun 30, 2024
Publication Time: 0.73 months
© SISEF - The Italian Society of Silviculture and Forest Ecology 2024
Open Access
This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Breakdown by View Type
(Waiting for server response...)
Article Usage
Total Article Views: 7616
(from publication date up to now)
Breakdown by View Type
HTML Page Views: 5409
Abstract Page Views: 835
PDF Downloads: 1306
Citation/Reference Downloads: 7
XML Downloads: 59
Web Metrics
Days since publication: 209
Overall contacts: 7616
Avg. contacts per week: 255.08
Article Citations
Article citations are based on data periodically collected from the Clarivate Web of Science web site
(last update: Feb 2023)
(No citations were found up to date. Please come back later)
Publication Metrics
by Dimensions ©
Articles citing this article
List of the papers citing this article based on CrossRef Cited-by.
(1)
Abegg M, Bösch R, Kükenbrink D, Morsdorf F (2023)Tree volume estimation with terrestrial laser scanning - Testing for bias in a 3D virtual environment. Agricultural and Forest Meteorology 331: 109348.
CrossRef |
Gscholar
(2)
Aguilera M, Villasante A, Fernandez C (2021)Accuracy in estimating basal areas for forest inventories: comparison of Android-based virtual relascope and Spiegel Relaskop. Canadian Journal of Forest Research 51 (1): 132-137.
CrossRef |
Gscholar
(3)
Alexander E, Guo Q, Koppal S, Gortler SJ, Zickler T (2017)Focal flow: Velocity and depth from differential defocus through motion. International Journal of Computer Vision 126 (10): 1062-1083.
CrossRef |
Gscholar
(4)
Bitterlich W (1984)The relascope idea. Relative measurements in forestry. Commonwealth Agricultural Bureaux, London, UK, pp. 242.
Gscholar
(5)
Fan Y, Feng Z, Mannan A, Khan TU, Shen C, Saeed S (2018)Estimating tree position, diameter at breast height, and tree height in real-time using a mobile phone with RGB-D SLAM. Remote Sensing 10 (11): 1845.
CrossRef |
Gscholar
(6)
Fan G, Dong Y, Chen D, Chen F (2020a)New method for forest resource data collection based on smartphone fusion with multiple sensors. Mobile Information Systems 2020 (4): 1-11.
CrossRef |
Gscholar
(7)
Fan Y, Feng Z, Shen C, Khan TU, Mannan A, Gao X, Chen P, Saeed S (2020b)A trunk-based SLAM backend for smartphones with online SLAM in largescale forest inventories. ISPRS Journal of Photogrammetry and Remote Sensing 162: 41-49.
CrossRef |
Gscholar
(8)
FINLEX (2013a)Laki puutavaran mittauksesta [Act on timber measurement]. Web site. [in Finnish]
Online |
Gscholar
(9)
FINLEX (2013b)Maa- ja metsätalousministeriön asetus puutavaran mittauksen mittausmenet elmäryhmien tarkemmasta sisällöstä sekä mittauslaitteiden käytöstä [Decree of the Ministry of Agriculture and Forestry on the more detailed content of measurement method groups for timber measurement and the use of measuring devices]. Web site. [in Finnish]
Online |
Gscholar
(10)
Gollob C, Ritter T, Kranitzer R, Tockner A, Nothdurft A (2021)Measurement of forest inventory parameters with Apple iPad Pro and integrated LiDAR technology. Remote Sensing 13 (16): 3129.
CrossRef |
Gscholar
(11)
Haara A, Kangas A, Tuominen S (2019)Economic losses caused by tree species proportions and site type errors in forest management planning. Silva Fennica 53 (2): 10089.
CrossRef |
Gscholar
(12)
Haara A, Korhonen KT (2004)Kuvioittaisen arvioinnin luotettavuus [Reliability of standwise evaluation]. Metsätieteen Aikakauskirja 2004 (4): 5667. [in Finnish]
CrossRef |
Gscholar
(13)
Holopainen M, Hyyppä J, Vastaranta M (2013)Laserkeilaus metsävarojen hallinnassa [Laser scanning in the management of forest resources]. Helsingin yliopiston metsätieteiden laitoksen julkaisuja, vol. 5, pp. 75. [in Finnish]
Gscholar
(14)
Huang XD, Feng Z (2015)Obtainment of sample tree’s DBH based on digital camera. Transactions of the Chinese Society for Agricultural Machinery 46 (9): 266-272.
Gscholar
(15)
Jihua W, Quanying Z, Huicui M (2018)Depth information estimation of image texture direction and spatial distribution features. In: “2018 International Conference on Smart Grid and Electrical Automation (ICSGEA)”. Changsha (China) 9-10 June 2018, pp. 391-394.
CrossRef |
Gscholar
(16)
Kim DH, Kim SJ, Sung EJ, Kim DG (2021)Development of a smartphone application for the measurement of tree height and diameter at breast height. Journal of Korean Society Forest Science 110 (1): 72-81.
CrossRef |
Gscholar
(17)
Kärhä K, Anäkkälä J, Hakonen O, Palander T, Sorsa JA, Räsänen T, Moilanen T (2017)Analyzing the antecedents and consequences of manual log bucking in mechanized wood harvesting. Mechanics, Materials Science and Engineering Journal 12: 1-15.
Gscholar
(18)
Kärhä K, Räsänen M, Palander T (2019)The profitability of cross-cutting practices in butt-rotten
Picea abies final-felling stands. Forests 10 (10): 874.
CrossRef |
Gscholar
(19)
Liu C, Xing Y, Duanmu J, Tian X (2018)Evaluating different methods for estimating diameter at breast height from terrestrial laser scanning. Remote Sensing 10 (4): 513.
Gscholar
(20)
Liu W, Zhong T, Song Y (2017)Prediction of trees diameter at breast height based on unmanned aerial vehicle image analysis. Transactions of the Chinese Society of Agricultural Engineering 33 (21): 99-104.
Gscholar
(21)
Maltamo M, Packalén P (2014)Species-specific management inventory in Finland. In: “Forestry Applications of Airborne Laser Scanning: Concepts and Case Studies” (Maltamo M, Naesset E, Vauhkonen J eds). Managing Forest Ecosystems 27: 241-252.
CrossRef |
Gscholar
(22)
Maltamo M, Hauglin KM, Naesset E, Gobakken T (2019)Estimating stand level stem diameter distribution utilizing accurately positioned tree-level harvester data and airborne laser scanning. Silva Fennica 53 (3): 10075.
CrossRef |
Gscholar
(23)
Marzulli MI, Raumonen P, Greco R, Persia M, Tartarino P (2020)Estimating tree stem diameters and volume from smartphone photogrammetric point clouds. Forestry 93 (3): 411-429.
CrossRef |
Gscholar
(24)
Mei J, Zhang D, Ding Y (2017)Monocular vision for pose estimation in space based on cone projection. Optical Engineering 56 (10): 103108.
CrossRef |
Gscholar
(25)
Melkas T, Riekki K, Sorsa JA (2020)Automated method for delineating harvested stands based on harvester location data. Remote Sensing 12 (17): 2754.
CrossRef |
Gscholar
(26)
Melkas T (2022)Wood measuring methods used in Finland 2021. Metsäteho, Result Series 6-EN/2022, Metsäteho Oy, Vantaa, Finland, pp. 14.
Gscholar
(27)
Ming A, Wu T, Ma J, Sun F, Zhou Y (2016)Monocular depth-ordering reasoning with occlusion edge detection and couple layers inference. IEEE Intelligent Systems 31 (2): 54-65.
CrossRef |
Gscholar
(28)
Mokroš M, Mikita T, Singh A, Tomaštík J, Chudá J, Wezyk P, Kuelka K, Surovy P, Klimánek M, Zieba-Kulawik K, Bobrowski R, Liang X (2021)Novel low-cost mobile mapping systems for forest inventories as terrestrial laser scanning alternatives. International Journal of Applied Earth Observation and Geoinformation 104: 102512.
CrossRef |
Gscholar
(29)
Naesset E (2002)Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sensing of Environment 80 (1): 88-99.
CrossRef |
Gscholar
(30)
Packalén P, Maltamo M (2008a)Estimation of species-specific diameter distributions using airborne laser scanning and aerial photographs. Canadian Journal of Forest Research 38 (7): 1750-1760.
CrossRef |
Gscholar
(31)
Packalén P, Maltamo M (2008b)The k-MSN method for the prediction of species-specific stand attributes using airborne laser scanning and aerial photographs. Remote Sensing of Environment 109 (3): 328-341.
CrossRef |
Gscholar
(32)
Pitkänen TP, Räty M, Hyvönen P, Korhonen KT, Vauhkonen J (2021)Using auxiliary data to rationalize smartphone-based pre-harvest forest mensuration. Forestry 95 (2): 247-260.
CrossRef |
Gscholar
(33)
Reynolds MR, Burk TE, Huang WC (1988)Goodness-of-fit tests and model selection procedures for diameter distribution models. Forest Science 34 (2): 373-399.
CrossRef |
Gscholar
(34)
Rouvinen T (2014)Kuvia metsästä [Photos from forests]. Metsätieteen Aikakauskirja 2014 (2): 119-122. [in Finnish]
Gscholar
(35)
Royden CS, Parsons D, Travatello J (2016)The effect of monocular depth cues on the detection of moving objects by moving observers. Vision Research 124: 7-14.
CrossRef |
Gscholar
(36)
Shang C, Treitz P, Caspersen J, Jones T (2017)Estimating stem diameter distributions in a management context for a tolerant hardwood forest using ALS height and intensity data. Canadian Journal of Remote Sensing 43 (1): 79-94.
CrossRef |
Gscholar
(37)
Shi J, Li Y, Qi G, Sheng A (2017)Machine vision based passive tracking algorithm with intermittent observations. Journal of Huazhong University of Science and Technology 45 (6): 33-37.
Gscholar
(38)
Siipilehto J (1999)Improving the accuracy of predicted basal-area diameter distributionin advanced stands by determining stem number. Silva Fennica 33 (4): 281-301.
CrossRef |
Gscholar
(39)
Siipilehto J, Lindeman H, Vastaranta M, Yu X, Uusitalo J (2016)Reliability of the predicted stand structure for clearcut stands using optional methods: airborne laser scanning-based methods, smartphone-based forest inventory application Trestima and pre-harvest measurement tool EMO. Silva Fennica 50 (3): 1568.
CrossRef |
Gscholar
(40)
Skogforsk (2021)StanForD 2010 - modern kommunikation med skogsmaskiner [StanForD 2010 - modern communication with forest machines]. Skogforsk, Uppsala, Sweden, pp. 16.
Online |
Gscholar
(41)
Sun W, Chen L, Hu B, Ren L, Wu X (2012)Binocular vision-based position determination algorithm and system. In: “2012 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring”. Zhagjiajie (China) 5-6 March 2012, pp. 170-173.
CrossRef |
Gscholar
(42)
Tatsumi S, Yamaguchi K, Furuya N (2023)ForestScanner: a mobile application for measuring and mapping trees with LiDAR-equipped iPhone and iPad. Methods in Ecology and Evolution 14: 1603-1609.
CrossRef |
Gscholar
(44)
Täll K (2020)Accuracy of mobile forest inventory application Katam™ Forest - Evaluation of accuracy in different forest types and comparison to conventional inventory methods. Master Thesis 333, Swedish University of Agricultural Sciences, Faculty of Forest Sciences, Southern Swedish Forest Research Centre, pp. 59.
Gscholar
(45)
Vastaranta M, Latorre EG, Luoma V, Saarinen N, Holopainen M, Hyyppä J (2015)Evaluation of a smartphone app for forest sample plot measurements. Forests 6 (4): 1179-1194.
CrossRef |
Gscholar
(46)
Vergara FP, Palma CD, Nelson JD (2015)Impact of timber volume and grade estimation error on the British Columbia Coastal supply chain. Journal of Science and Technology for Forest Products and Processes 5 (5): 16-25.
Gscholar
(47)
Vähä-Konka V, Maltamo M, Pukkala T, Kärhä K (2020)Evaluating the accuracy of ALS-based removal estimates against actual logging data. Annals of Forest Science 77: 84.
CrossRef |
Gscholar
(48)
Woo H, Kim I, Choi B (2021)Computer vision techniques in forest inventory assessment: improving accuracy of tree diameter measurement using smartphone camera and photogrammetry. Sensors and Materials 33 (11): 3835-3845.
CrossRef |
Gscholar
(49)
Wu X, Zhou S, Xu A, Chen B (2019)Passive measurement method of tree diameter at breast height using a smartphone. Computers and Electronics in Agriculture 163: 104875.
CrossRef |
Gscholar