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Using handheld mobile LiDAR technology in forest inventories: Artvin-Şavşat case

Year 2022, , 81 - 96, 15.06.2022
https://doi.org/10.17568/ogmoad.1016879

Abstract

This study aims to; (i) demonstrate how to use handheld laser scanning (LiDAR) technology in forest inventories, and (ii) compare stand parameters calculated with LiDAR and traditional measurements. To this end, sample plots were scanned by a LiDAR device in Şavşat, NE Turkey. Then, the sensitivity of LiDAR data was examined by comparing it with ground truth. No significant difference was found between tree DBHs measured by LiDAR and caliper (p>0.05). Taking ground measurements as reference; DBH, the number of trees, stand top height, and stand volume parameters were captured by LiDAR with mean errors of 0.68 cm (2.2%), 14 trees/ha (2.0%), 0.8 m (3.4%), and 155.7 m3/ha (24.6%), respectively. Since the mean error was high for stand volume, six standing trees were scanned by LiDAR, and then, they were felled and volumized using the section method. Ground measurements showed that LiDAR calculated stem volumes with a mean error of 0.061 m3 (5.1%). Thus, the high error rate in stand volumes was attributed to the reference data derived by existing volume tables. On the other hand, tree species and stand types could not be identified with LiDAR. It was concluded that mobile LiDAR technology could calculate many stand parameters with acceptable accuracy levels efficiently.

References

  • Beucher, S., Lantuejoul, C. (1979). Use of Watersheds in Contour Detection.In: International Workshop on Image Processing: Real-Time Edge and Motion Detection/Estimation, Rennes.
  • Cadge, S. (2016). Welcome to the ZEB REVOlution. GEOmedia, 20(3), 22-25.
  • de Conto, T., Olofsson, K., Gorgens, E. B., Rodriguez, L. C. E., Almeida, G. (2017). Performance of stem denoising and stem modelling algorithms on single tree point clouds from terrestrial laser scanning. Computers and Electronics in Agriculture, 143, 165-176. doi:10.1016/j.compag.2017.10.019
  • Del Perugia, B., Giannetti, F., Chirici, G., Travaglini, D. (2019). Influence of Scan Density on the Estimation of Single-Tree Attributes by Hand-Held Mobile Laser Scanning. Forests, 10(3).
  • Hijmans, R. J. (2021). raster: Geographic Data Analysis and Modeling. https://CRAN.R-project.org/package=raster
  • Hyyppä, E., Kukko, A., Kaijaluoto, R., White, J. C., Wulder, M. A., Pyörälä, J., Liang, X., Yu, X., Wang, Y., Kaartinen, H., Virtanen, J.-P., Hyyppä, J. (2020). Accurate derivation of stem curve and volume using backpack mobile laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing, 161, 246-262. doi:10.1016/j.isprsjprs.2020.01.018
  • Illingworth, J., Kittler, J. (1987). The adaptive hough transform. IEEE Transactions Pattern Analysis and Machine Intelligence, 9(5), 690-698. doi:10.1109/tpami.1987.4767964
  • Jurjević, L., Liang, X., Gašparović, M., Balenović, I. (2020). Is field-measured tree height as reliable as believed – Part II, A comparison study of tree height estimates from conventional field measurement and low-cost close-range remote sensing in a deciduous forest. ISPRS Journal of Photogrammetry and Remote Sensing, 169, 227-241. doi:10.1016/j.isprsjprs.2020.09.014
  • Kayacan B, Zengin H, Kadiogullari AI (2016). Chapter 44: Turkey. In: Vidal C, Alberdi I, Hernandez L, Redmond J (editors). National Forest Inventories: Assessment of Wood Availability and Use. Cham, Switzerland: Springer, pp. 807-827.
  • Liu, L., Zhang, A., Xiao, S., Hu, S., He, N., Pang, H., Zhang, X., Yang, S. (2021). Single Tree Segmentation and Diameter at Breast Height Estimation With Mobile LiDAR. Ieee Access, 9, 24314-24325. doi:10.1109/access.2021.3056877
  • Lukács, G., Marshall, A. D., Martin, R. R. (1997). Geometric least-squares fitting of spheres, cylinders, cones and tori. RECCAD, 2, 671-675.
  • MGM, 2012. Artvin ili Şavşat ilçesi İklim İstasyonuna ait rasat verileri. Meteoroloji Genel Müdürlüğü, http://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx#sfU (Ziyaret tarihi: 13.10.2021).
  • Mikhail, E. M., Ackermann, F. (1976). Observations and Least Squares: Univ Pr of Amer.
  • OGM (2013). Veliköy Orman İşletme Şefliği Ekosistem Tabanlı Fonksiyonel Amenajman Planı (2013-2032). Orman Genel Müdürlüğü, Ankara.
  • OGM (2017). Ekosistem Tabanlı Fonksiyonel Orman Amenajman Planlarının Düzenlenmesine Ait Usul ve Esaslar (299 sayılı tebliğ−düzeltmeli son baskı), Orman İdaresi ve Planlama Dairesi Başkanlığı, Ankara.
  • Qiu, Z., Feng, Z., Jiang, J., Lin, Y., Xue, S. (2018). Application of a Continuous Terrestrial Photogrammetric Measurement System for Plot Monitoring in the Beijing Songshan National Nature Reserve. Remote Sensing, 10(7). doi:10.3390/rs10071080
  • Rousseeuw, P. j. (1987). Robust Regression and Outlier Detection: John Wiley & Sons.
  • Schnabel, R., Wahl, R., Klein, R. (2007). Efficient RANSAC for point-cloud shape detection. Computer Graphics Forum, 26(2), 214-226. doi:10.1111/j.1467-8659.2007.01016.x
  • Team, R. C. (2021). R: A Language and Environment for Statistical Computing. https://cran.r-project.org/. (Ziyaret tarihi: 13/10/2021)
  • Trochta, J., Krucek, M., Vrska, T., Kral, K. (2017). 3D Forest: An application for descriptions of three-dimensional forest structures using terrestrial LiDAR. Plos One, 12(5), e0176871. doi:10.1371/journal.pone.0176871
  • URL-1. http://forsys.cfr.washington.edu/fusion/fusion_overview.html, 11.02.2014. Erişim tarihi: 14 Ekim 2021.
  • URL-2. http://www.rslab.se/2017/10/17/terrestrial-laser-scanner/ Erişim tarihi: 14 Ekim 2021.
  • URL-3. https://geoslam.com/ 14 Ekim 2021.
  • Vatandaşlar, C., Zeybek, M. (2020). Application of handheld laser scanning technology for forest inventory purposes in the NE Turkey. Turkish Journal of Agriculture and Forestry, 44(3), 229-242. doi:10.3906/tar-1903-40
  • Vatandaşlar, C., Zeybek, M. (2021). Extraction of forest inventory parameters using handheld mobile laser scanning: A case study from Trabzon, Turkey. Measurement, 177. doi:10.1016/j.measurement.2021.109328
  • Venables, W. N., Ripley, B. D. (2002). Modern Applied Statistics with S: Springer-Verlag New York.
  • Wang, Y., Pyörälä, J., Liang, X., Lehtomäki, M., Kukko, A., Yu, X., Kaartinen, H., Hyyppä, J. (2019). In situ biomass estimation at tree and plot levels: What did data record and what did algorithms derive from terrestrial and aerial point clouds in boreal forest. Remote Sensing of Environment, 232. doi:10.1016/j.rse.2019.111309
  • Yurtseven, H., Coban, S., Akgul, M., Akay, A. O. (2019). Individual tree measurements in a planted woodland with terrestrial laser scanner. Turkish Journal of Agriculture and Forestry, 43(2), 192-208. doi:10.3906/tar-1805-5
  • Zeybek, M., Vatandaşlar, C. (2021). An Automated Approach for Extracting Forest Inventory Data from Individual Trees Using a Handheld Mobile Laser Scanner. Croatian Journal of Forest Engineering, 42(3), 515-528. doi:10.5552/crojfe.2021.1096

El tipi mobil LiDAR teknolojisinin orman envanterlerinde kullanımı: Artvin-Şavşat örneği

Year 2022, , 81 - 96, 15.06.2022
https://doi.org/10.17568/ogmoad.1016879

Abstract

Bu çalışmanın amacı; (i) orman envanterlerinde mobil lazer tarama (LiDAR) teknolojisinden yararlanma olanaklarını araştırmak ve (ii) meşcere parametrelerine ilişkin LiDAR verilerini, uygulamada tespit edilen değerlerle karşılaştırmaktır. Bu doğrultuda, Şavşat’ta arazi ölçümleri gerçekleştirilen örnek alanlar el tipi LiDAR cihazı ile taranmıştır. Daha sonra örnek alanlardan elde edilen veri setleri birbiriyle karşılaştırılarak LiDAR’ın hassasiyeti sınanmıştır. Yapılan istatistik testler sonucunda, LiDAR ve çapölçer ile ölçülen ağaçların çapları arasında anlamlı bir fark bulunmamıştır (p>0,05). Yersel ölçümler referans kabul edilirse; göğüs çapı, ağaç sayısı, meşcere üst boyu ve meşcere hacmi parametreleri LiDAR cihazıyla sırasıyla; ort. 0,68 cm (%2,2), 14 ad/ha (%2,0), 0,8 m (%3,4) ve 155,7 m3/ha (%24,6) hata ile tahmin edilebilmiştir. Hacimde gözlenen yüksek hata üzerine, arazideki altı adet ağaç önce LiDAR ile dikili halde taranmış ve sonra kesilerek, bölümleme yöntemiyle hacimlendirilmiştir. Yerde ölçülen gövde hacimlerinin LiDAR ile ort. 0,061 m3 (%5,1) hata ile tespit edilebildiği görülmüştür. Dolayısıyla, meşcere hacimlerindeki yüksek hata oranlarının LiDAR yönteminden değil, envanterde kullanılan tek girişli hacim tablolarından kaynaklandığı anlaşılmıştır. Buna karşılık, LiDAR nokta bulutları üzerinden ağaç türü ve meşcere tipleri belirlenememiştir. Çalışmanın sonunda, amenajman planlarındaki birçok meşcere parametresine ait değerlerin mobil LiDAR teknolojisiyle arazide daha az vakit harcanarak kabul edilebilir doğruluk düzeylerinde hesaplanabildiği sonucuna ulaşılmıştır. 

References

  • Beucher, S., Lantuejoul, C. (1979). Use of Watersheds in Contour Detection.In: International Workshop on Image Processing: Real-Time Edge and Motion Detection/Estimation, Rennes.
  • Cadge, S. (2016). Welcome to the ZEB REVOlution. GEOmedia, 20(3), 22-25.
  • de Conto, T., Olofsson, K., Gorgens, E. B., Rodriguez, L. C. E., Almeida, G. (2017). Performance of stem denoising and stem modelling algorithms on single tree point clouds from terrestrial laser scanning. Computers and Electronics in Agriculture, 143, 165-176. doi:10.1016/j.compag.2017.10.019
  • Del Perugia, B., Giannetti, F., Chirici, G., Travaglini, D. (2019). Influence of Scan Density on the Estimation of Single-Tree Attributes by Hand-Held Mobile Laser Scanning. Forests, 10(3).
  • Hijmans, R. J. (2021). raster: Geographic Data Analysis and Modeling. https://CRAN.R-project.org/package=raster
  • Hyyppä, E., Kukko, A., Kaijaluoto, R., White, J. C., Wulder, M. A., Pyörälä, J., Liang, X., Yu, X., Wang, Y., Kaartinen, H., Virtanen, J.-P., Hyyppä, J. (2020). Accurate derivation of stem curve and volume using backpack mobile laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing, 161, 246-262. doi:10.1016/j.isprsjprs.2020.01.018
  • Illingworth, J., Kittler, J. (1987). The adaptive hough transform. IEEE Transactions Pattern Analysis and Machine Intelligence, 9(5), 690-698. doi:10.1109/tpami.1987.4767964
  • Jurjević, L., Liang, X., Gašparović, M., Balenović, I. (2020). Is field-measured tree height as reliable as believed – Part II, A comparison study of tree height estimates from conventional field measurement and low-cost close-range remote sensing in a deciduous forest. ISPRS Journal of Photogrammetry and Remote Sensing, 169, 227-241. doi:10.1016/j.isprsjprs.2020.09.014
  • Kayacan B, Zengin H, Kadiogullari AI (2016). Chapter 44: Turkey. In: Vidal C, Alberdi I, Hernandez L, Redmond J (editors). National Forest Inventories: Assessment of Wood Availability and Use. Cham, Switzerland: Springer, pp. 807-827.
  • Liu, L., Zhang, A., Xiao, S., Hu, S., He, N., Pang, H., Zhang, X., Yang, S. (2021). Single Tree Segmentation and Diameter at Breast Height Estimation With Mobile LiDAR. Ieee Access, 9, 24314-24325. doi:10.1109/access.2021.3056877
  • Lukács, G., Marshall, A. D., Martin, R. R. (1997). Geometric least-squares fitting of spheres, cylinders, cones and tori. RECCAD, 2, 671-675.
  • MGM, 2012. Artvin ili Şavşat ilçesi İklim İstasyonuna ait rasat verileri. Meteoroloji Genel Müdürlüğü, http://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx#sfU (Ziyaret tarihi: 13.10.2021).
  • Mikhail, E. M., Ackermann, F. (1976). Observations and Least Squares: Univ Pr of Amer.
  • OGM (2013). Veliköy Orman İşletme Şefliği Ekosistem Tabanlı Fonksiyonel Amenajman Planı (2013-2032). Orman Genel Müdürlüğü, Ankara.
  • OGM (2017). Ekosistem Tabanlı Fonksiyonel Orman Amenajman Planlarının Düzenlenmesine Ait Usul ve Esaslar (299 sayılı tebliğ−düzeltmeli son baskı), Orman İdaresi ve Planlama Dairesi Başkanlığı, Ankara.
  • Qiu, Z., Feng, Z., Jiang, J., Lin, Y., Xue, S. (2018). Application of a Continuous Terrestrial Photogrammetric Measurement System for Plot Monitoring in the Beijing Songshan National Nature Reserve. Remote Sensing, 10(7). doi:10.3390/rs10071080
  • Rousseeuw, P. j. (1987). Robust Regression and Outlier Detection: John Wiley & Sons.
  • Schnabel, R., Wahl, R., Klein, R. (2007). Efficient RANSAC for point-cloud shape detection. Computer Graphics Forum, 26(2), 214-226. doi:10.1111/j.1467-8659.2007.01016.x
  • Team, R. C. (2021). R: A Language and Environment for Statistical Computing. https://cran.r-project.org/. (Ziyaret tarihi: 13/10/2021)
  • Trochta, J., Krucek, M., Vrska, T., Kral, K. (2017). 3D Forest: An application for descriptions of three-dimensional forest structures using terrestrial LiDAR. Plos One, 12(5), e0176871. doi:10.1371/journal.pone.0176871
  • URL-1. http://forsys.cfr.washington.edu/fusion/fusion_overview.html, 11.02.2014. Erişim tarihi: 14 Ekim 2021.
  • URL-2. http://www.rslab.se/2017/10/17/terrestrial-laser-scanner/ Erişim tarihi: 14 Ekim 2021.
  • URL-3. https://geoslam.com/ 14 Ekim 2021.
  • Vatandaşlar, C., Zeybek, M. (2020). Application of handheld laser scanning technology for forest inventory purposes in the NE Turkey. Turkish Journal of Agriculture and Forestry, 44(3), 229-242. doi:10.3906/tar-1903-40
  • Vatandaşlar, C., Zeybek, M. (2021). Extraction of forest inventory parameters using handheld mobile laser scanning: A case study from Trabzon, Turkey. Measurement, 177. doi:10.1016/j.measurement.2021.109328
  • Venables, W. N., Ripley, B. D. (2002). Modern Applied Statistics with S: Springer-Verlag New York.
  • Wang, Y., Pyörälä, J., Liang, X., Lehtomäki, M., Kukko, A., Yu, X., Kaartinen, H., Hyyppä, J. (2019). In situ biomass estimation at tree and plot levels: What did data record and what did algorithms derive from terrestrial and aerial point clouds in boreal forest. Remote Sensing of Environment, 232. doi:10.1016/j.rse.2019.111309
  • Yurtseven, H., Coban, S., Akgul, M., Akay, A. O. (2019). Individual tree measurements in a planted woodland with terrestrial laser scanner. Turkish Journal of Agriculture and Forestry, 43(2), 192-208. doi:10.3906/tar-1805-5
  • Zeybek, M., Vatandaşlar, C. (2021). An Automated Approach for Extracting Forest Inventory Data from Individual Trees Using a Handheld Mobile Laser Scanner. Croatian Journal of Forest Engineering, 42(3), 515-528. doi:10.5552/crojfe.2021.1096
There are 29 citations in total.

Details

Primary Language Turkish
Subjects Forest Industry Engineering
Journal Section Forest Management
Authors

Can Vatandaşlar 0000-0001-5552-5670

Mustafa Zeybek 0000-0001-8640-1443

Ergin Çağatay Çankaya This is me 0000-0003-2553-8707

Tugay Demiraslan This is me 0000-0002-3808-2908

Cahit Şahin This is me 0000-0002-3973-1084

Yasin Gündüz This is me 0000-0001-6256-2322

Ümit Korkmaz This is me 0000-0001-6839-1587

Mehmet Latif Avcı This is me 0000-0002-4047-9613

Publication Date June 15, 2022
Submission Date November 1, 2021
Published in Issue Year 2022

Cite

APA Vatandaşlar, C., Zeybek, M., Çankaya, E. Ç., Demiraslan, T., et al. (2022). El tipi mobil LiDAR teknolojisinin orman envanterlerinde kullanımı: Artvin-Şavşat örneği. Ormancılık Araştırma Dergisi, 9(1), 81-96. https://doi.org/10.17568/ogmoad.1016879