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Automatic detection of forest trees from digital surface models derived by aerial images

Yıl 2022, Cilt: 7 Sayı: 3, 208 - 213, 15.10.2022
https://doi.org/10.26833/ijeg.908004

Öz

For the sustainable management of forests, obtaining the spatial information of the tree existence (location, number, height, and crown diameter of trees, etc.) with high accuracy and quickly is very important. In this context, the study aims to detect forest trees automatically through flow analysis applied to a 5 m resolution digital surface model by geospatial analysis. The study was carried out in five sample areas with different physical and topographic characteristics in the Antalya province of Turkey. The method consists of two steps which are identifying tree populations and determining tree peaks by applying flow analysis on the surface model. First, the canopy height model was extracted by applying a morphological filter to the image-based digital surface model. Then, the tree peak points are considered sink points, and these sink points were determined on the inverted surface model by the flow analysis approach which is frequently used in hydrological studies. The results showed that the applied method gives approximately 70% accuracy depending on the terrain conditions. Tree crown diameter, distance between trees, slope of the land, and digital surface model resolution significantly affect the accuracy of the results. It is predicted that this study will be an important guide for decision-makers in the preparation of forest plans.

Destekleyen Kurum

Ministry of National Defense, General Directorate of Mapping, Turkey.

Proje Numarası

-

Teşekkür

This work has been supported and funded by Ministry of National Defense, General Directorate of Mapping, Turkey.

Kaynakça

  • Barnes C, Balzter H, Barrett K, Eddy J, Milner S & Suárez J C (2017). Individual tree crown delineation from airborne laser scanning for diseased larch forest stands. Remote Sensing, 9, 231.
  • Bienert A, Scheller S, Keane E, Mohan F & Nugent C (2007). Tree detection and diameter estimations by analysis of forest terrestrial laser scanner point clouds. ISPRS Workshop on Laser Scanning 2007 and SilviLaser, 36, 50–55.
  • Bouvier M, Durrieu S, Fournier R A & Renaud J P (2015). Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data. In Remote Sensing of Environment, 156, 322–334.
  • Cabo C, Ordóñez C, López-Sánchez C A & Armesto J (2018). Automatic dendrometry: Tree detection, tree height and diameter estimation using terrestrial laser scanning. International Journal of Applied Earth Observation and Geoinformation, 69, 164–174.
  • Dalla Corte AP, Souza DV, Rex FE, Sanquetta CR, Mohan M, Silva CA, ... & Broadbent EN (2020). Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes. Computers and Electronics in Agriculture, 179, 105815.
  • Demir N (2018). Using UAVs For Detection of Trees from Digital Surface Models. Journal of Forestry Research, 29, 813-821.
  • Ferraz A, Saatchi S, Mallet C & Meyer V (2016). Lidar detection of individual tree size in tropical forests. Remote Sensing of Environment, 183, 318–333.
  • Hao Y, Widagdo FRA, Liu X, Quan Y, Dong L & Li F (2021). Individual tree diameter estimation in small-scale forest inventory using UAV laser scanning. Remote Sensing, 13(1), 24.
  • Hopkinson C, Chasmer L, Young-Pow C & Treitz P (2004). Assessing forest metrics with a ground-based scanning lidar. Canadian Journal of Forest Research, 34(3), 573–583.
  • Magnard C, Morsdorf F, Small D, Stilla U, Schaepman M E & Meier E (2016). Single tree identification using airborne multibaseline SAR interferometry data. Remote Sensing of Environment, 186, 567–580.
  • Mohan M, Silva C A, Klauberg C, Jat P, Catts G, Cardil A, Hudak A T & Dia M (2017). Individual tree detection from unmanned aerial vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest. Forests, 8(9), 340.
  • Paris C, Kelbe D, Van Aardt J & Bruzzone L (2017). A Novel Automatic Method for the Fusion of ALS and TLS LiDAR Data for Robust Assessment of Tree Crown Structure. IEEE Transactions on Geoscience and Remote Sensing, 55(7), 3679–3693.
  • Pitkänen J & Maltamo M (2004). Adaptive Methods for Individual Tree Detection on Airborne Laser Based Canopy Height Model. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(8), 187–191.
  • Selim S, Sonmez NK, Coslu M, & Onur I (2019). Semi-automatic tree detection from images of unmanned aerial vehicle using object-based image analysis method. Journal of the Indian Society of Remote Sensing, 47(2), 193-200.
  • Silva C A, Hudak A T, Vierling L A, Loudermilk E L, O’Brien J J, Hiers J K, Jack S B, Gonzalez-Benecke C, Lee H, Falkowski M J & Khosravipour A. (2016). Imputation of Individual Longleaf Pine (Pinus palustris Mill.) Tree Attributes from Field and LiDAR Data. Canadian Journal of Remote Sensing, 42(5), 554–573.
  • Simonse M, Aschoff T, Spiecker H & Thies M. (2003). Automatic Determination of Forest Inventory Parameters Using Terrestrial Laserscanning. In Institute for Forest Growth, 2003, 252-258.
  • Su Y, Guo Q, Xue B, Hu T, Alvarez O, Tao S & Fang J (2016). Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data. Remote Sensing of Environment, 173, 187–199.
  • Toklu E (2017). Biomass energy potential and utilization in Turkey. Renewable Energy, 107, 235–244.
  • Yang B, Dai W, Dong Z & Liu, Y (2016). Automatic forest mapping at individual tree levels from terrestrial laser scanning point clouds with a hierarchical minimum cut method. Remote Sensing, 8(5), 372.
  • Zhang KQ, Chen SC, Whitman D, Shyu ML, Yan JH, Zhang CC (2003). A progressive morphological filter for removing nonground measurements from airborne LIDAR data. IEEE Trans Geosci Remote Sens, 41, 872–882
  • Zhen Z, Quackenbush L J & Zhang L (2016). Trends in automatic individual tree crown detection and delineation-evolution of LiDAR data. Remote Sensing, 8(4), 333.
Yıl 2022, Cilt: 7 Sayı: 3, 208 - 213, 15.10.2022
https://doi.org/10.26833/ijeg.908004

Öz

Proje Numarası

-

Kaynakça

  • Barnes C, Balzter H, Barrett K, Eddy J, Milner S & Suárez J C (2017). Individual tree crown delineation from airborne laser scanning for diseased larch forest stands. Remote Sensing, 9, 231.
  • Bienert A, Scheller S, Keane E, Mohan F & Nugent C (2007). Tree detection and diameter estimations by analysis of forest terrestrial laser scanner point clouds. ISPRS Workshop on Laser Scanning 2007 and SilviLaser, 36, 50–55.
  • Bouvier M, Durrieu S, Fournier R A & Renaud J P (2015). Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data. In Remote Sensing of Environment, 156, 322–334.
  • Cabo C, Ordóñez C, López-Sánchez C A & Armesto J (2018). Automatic dendrometry: Tree detection, tree height and diameter estimation using terrestrial laser scanning. International Journal of Applied Earth Observation and Geoinformation, 69, 164–174.
  • Dalla Corte AP, Souza DV, Rex FE, Sanquetta CR, Mohan M, Silva CA, ... & Broadbent EN (2020). Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes. Computers and Electronics in Agriculture, 179, 105815.
  • Demir N (2018). Using UAVs For Detection of Trees from Digital Surface Models. Journal of Forestry Research, 29, 813-821.
  • Ferraz A, Saatchi S, Mallet C & Meyer V (2016). Lidar detection of individual tree size in tropical forests. Remote Sensing of Environment, 183, 318–333.
  • Hao Y, Widagdo FRA, Liu X, Quan Y, Dong L & Li F (2021). Individual tree diameter estimation in small-scale forest inventory using UAV laser scanning. Remote Sensing, 13(1), 24.
  • Hopkinson C, Chasmer L, Young-Pow C & Treitz P (2004). Assessing forest metrics with a ground-based scanning lidar. Canadian Journal of Forest Research, 34(3), 573–583.
  • Magnard C, Morsdorf F, Small D, Stilla U, Schaepman M E & Meier E (2016). Single tree identification using airborne multibaseline SAR interferometry data. Remote Sensing of Environment, 186, 567–580.
  • Mohan M, Silva C A, Klauberg C, Jat P, Catts G, Cardil A, Hudak A T & Dia M (2017). Individual tree detection from unmanned aerial vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest. Forests, 8(9), 340.
  • Paris C, Kelbe D, Van Aardt J & Bruzzone L (2017). A Novel Automatic Method for the Fusion of ALS and TLS LiDAR Data for Robust Assessment of Tree Crown Structure. IEEE Transactions on Geoscience and Remote Sensing, 55(7), 3679–3693.
  • Pitkänen J & Maltamo M (2004). Adaptive Methods for Individual Tree Detection on Airborne Laser Based Canopy Height Model. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(8), 187–191.
  • Selim S, Sonmez NK, Coslu M, & Onur I (2019). Semi-automatic tree detection from images of unmanned aerial vehicle using object-based image analysis method. Journal of the Indian Society of Remote Sensing, 47(2), 193-200.
  • Silva C A, Hudak A T, Vierling L A, Loudermilk E L, O’Brien J J, Hiers J K, Jack S B, Gonzalez-Benecke C, Lee H, Falkowski M J & Khosravipour A. (2016). Imputation of Individual Longleaf Pine (Pinus palustris Mill.) Tree Attributes from Field and LiDAR Data. Canadian Journal of Remote Sensing, 42(5), 554–573.
  • Simonse M, Aschoff T, Spiecker H & Thies M. (2003). Automatic Determination of Forest Inventory Parameters Using Terrestrial Laserscanning. In Institute for Forest Growth, 2003, 252-258.
  • Su Y, Guo Q, Xue B, Hu T, Alvarez O, Tao S & Fang J (2016). Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data. Remote Sensing of Environment, 173, 187–199.
  • Toklu E (2017). Biomass energy potential and utilization in Turkey. Renewable Energy, 107, 235–244.
  • Yang B, Dai W, Dong Z & Liu, Y (2016). Automatic forest mapping at individual tree levels from terrestrial laser scanning point clouds with a hierarchical minimum cut method. Remote Sensing, 8(5), 372.
  • Zhang KQ, Chen SC, Whitman D, Shyu ML, Yan JH, Zhang CC (2003). A progressive morphological filter for removing nonground measurements from airborne LIDAR data. IEEE Trans Geosci Remote Sens, 41, 872–882
  • Zhen Z, Quackenbush L J & Zhang L (2016). Trends in automatic individual tree crown detection and delineation-evolution of LiDAR data. Remote Sensing, 8(4), 333.
Toplam 21 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Articles
Yazarlar

Serdar Selim 0000-0002-5631-6253

Nusret Demir 0000-0002-8756-7127

Selen Oy Şahin 0000-0002-0741-1684

Proje Numarası -
Yayımlanma Tarihi 15 Ekim 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 7 Sayı: 3

Kaynak Göster

APA Selim, S., Demir, N., & Oy Şahin, S. (2022). Automatic detection of forest trees from digital surface models derived by aerial images. International Journal of Engineering and Geosciences, 7(3), 208-213. https://doi.org/10.26833/ijeg.908004
AMA Selim S, Demir N, Oy Şahin S. Automatic detection of forest trees from digital surface models derived by aerial images. IJEG. Ekim 2022;7(3):208-213. doi:10.26833/ijeg.908004
Chicago Selim, Serdar, Nusret Demir, ve Selen Oy Şahin. “Automatic Detection of Forest Trees from Digital Surface Models Derived by Aerial Images”. International Journal of Engineering and Geosciences 7, sy. 3 (Ekim 2022): 208-13. https://doi.org/10.26833/ijeg.908004.
EndNote Selim S, Demir N, Oy Şahin S (01 Ekim 2022) Automatic detection of forest trees from digital surface models derived by aerial images. International Journal of Engineering and Geosciences 7 3 208–213.
IEEE S. Selim, N. Demir, ve S. Oy Şahin, “Automatic detection of forest trees from digital surface models derived by aerial images”, IJEG, c. 7, sy. 3, ss. 208–213, 2022, doi: 10.26833/ijeg.908004.
ISNAD Selim, Serdar vd. “Automatic Detection of Forest Trees from Digital Surface Models Derived by Aerial Images”. International Journal of Engineering and Geosciences 7/3 (Ekim 2022), 208-213. https://doi.org/10.26833/ijeg.908004.
JAMA Selim S, Demir N, Oy Şahin S. Automatic detection of forest trees from digital surface models derived by aerial images. IJEG. 2022;7:208–213.
MLA Selim, Serdar vd. “Automatic Detection of Forest Trees from Digital Surface Models Derived by Aerial Images”. International Journal of Engineering and Geosciences, c. 7, sy. 3, 2022, ss. 208-13, doi:10.26833/ijeg.908004.
Vancouver Selim S, Demir N, Oy Şahin S. Automatic detection of forest trees from digital surface models derived by aerial images. IJEG. 2022;7(3):208-13.