Preferences help
enabled [disable] Abstract
Number of results
2018 | 95 | 224-234
Article title

3D Mapping by Photogrammetry and LiDAR in Forest Studies

Title variants
Languages of publication
Aerial imagery have long been used for forest Inventories due to the high correlation between tree height and forest biophysical properties to determine the vertical canopy structure which is an important variable in forest inventories. The development in photogrammetric softwares and large availability of aerial imagery has carved the path in 3D mapping and has accelerated significantly the use of photogrammetry in forest inventory. There is tremendous capacity of 3D mapping which has been recognized in research, development and management of forest ecosystem. The aim of this article is to provide insights of 3D mapping (photogrammetry including Lidar) in forest-related disciplines. We utilizing the satellite stereo pair and LiDAR point cloud as a case study for producing the anaglyph map and Canopy Height Model (CHM) respectively. The study also revealed the area verses canopy height graph. Present study has some strength because it was demonstrated the use of advance software module of ARC/GIS and Erdas Imagine for 3D mapping using Photogrammetry and LiDAR data sets which is highly useful in forest management, planning and decision making.
Physical description
  • Vindhyan Ecology and Natural History Foundation, Mirzapur, Uttar Pradesh, India
  • Department of Mathematics, Ranchi University, Ranchi, Jharkhand, India
  • Vindhyan Ecology and Natural History Foundation, Mirzapur, Uttar Pradesh, India
  • [1] Ahmad F, Goparaju L, Qayum A (2017) Natural Resource Mapping Using Landsat and Lidar towards Identifying Digital Elevation, Digital Surface and Canopy Height Models. Int J Environ Sci Nat Res 2: 555580 DOI: 10.19080/IJESNR.2017.02.555580
  • [2] Aicardi, I., Garbarino, M., Lingua, A., Lingua, E., Marzano, R. & Piras, M. (2016) Monitoring Post-Fire Forest Recovery Using Multitemporal Digital Surface Models Generated from Different Platforms. Earsel Eproceedings 15 (1): 1–8. doi:10.12760/01-2016-1-01.
  • [3] American Society of Photogrammetry. Photogrammetric Engineering and Remote Sensing (PHOTOGRAMM ENG REM S). XLVI.10 (1980): 1249.
  • [4] Antonarakis, A. S., Saatchi, S. S., Chazdon, R. L., & Moorcroft, P. R. (2011). Using LIDAR and RADAR measurements to constrain predictions of forest ecosystem structure and function. Ecological Applications, 21, 1120–1137.
  • [5] Balenovic, I. Seletkovic, A. & Pernar, R (2017) Accuracy comparison of photogrammetricaly estimated forest stand attributes on aerial images of different spatial resolution. Sumarski list, 141 (1-2): 15-28.
  • [6] Balenovic, I., Marjanovic, H & Benk, M.(2010) Application of Aerial Photographs in Forest Management in Croatia. Sumarski list 134(11-12): 623-630
  • [7] Baltsavias, E.P. (1999). A Comparison between Photogrammetry and Laser Scanning. Journal of Photogrammetry and Remote Sensing, 54, 83-94.
  • [8] Behera, M.D. & Roy. P.S. (2002) Lidar remote sensing for forestry applications: The Indian context. Curr. Sci. 83: 1320–1328.
  • [9] Bohlin J., Wallerman J., Fransson J.E.S. (2015). Deciduous forest mapping using change detection of multi-temporal canopy height models from aerial images acquired at leaf-on and leaf-off conditions. Scandinavian Journal of Forest Research 31(5): 517–525.
  • [10] Bohlin, J., Wallerman, J. & Fransson, J.E.S (2012) Forest variable estimation using photogrammetric matching of digital aerial images in combination with a high-resolution DEM. Scandinavian Journal of Forest Research 27: 7.
  • [11] Bohlin, J., Wallermandan, J. and Fransson, J.E.S. (2012). Forest variable estimation using photogrammetric matching of digital aerial images in combination with a high-resolution DEM. Scand. J. For. Res. 2012, 27, 692–699, doi:10.1080/02827581.2012.686625.
  • [12] Coops NC,Wulder MA, Culvenor DS, St-Onge B (2004). Comparison of forest attributes extracted from fine spatial resolution multispectral and lidar data. Can J Remote Sens 30: 855–866.
  • [13] Feng, Z.K., Yang, B.G., Luo, X., Han, G.S., & Guo, X.X. (2008). Experiment of Estimating Forest Stand Volume with Lidar Technology. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B8
  • [14] Flaspohler DJ, Giardina CP, Asner GP, Hart P, Price J, Lyons CK, Castaneda X (2010). Long-term effects of fragmentation and fragment properties on bird species richness in Hawaiian forests. Biol Conserv 143: 280–288.
  • [15] Ginzler, C & Waser, L.T (2017). Progress in remote sensing for forestry applications. Schweizerische Zeitschrift fur Forstwesen 168(3): 118-126 DOI:10.3188/szf.2017.0118
  • [16] Hirshmuller, H. 2008. Stereo processing by semi-global matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 2008, 30, 328–341, doi:10.1109/TPAMI.2007.1166.
  • [17] Hirshmuller, H. (2008). Stereo processing by semi-global matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 2008, 30, 328–341,doi:10.1109/TPAMI.2007.1166.
  • [18] Hoxha, B (2012). Two-phased inventory of standing volume in mountain forests with the use of aerial photographs. Folia Forestalia Polonica 54 (2):123-133
  • [19] Hyyppa J, Inkinen M (1999). Detecting and estimating attributes for single trees using laser scanner. Photogramm J Finland 16: 27–42.
  • [20] Jarnstedt, J., Pekkarinen, A., Tuominen, S., Ginzler, C., Holopainen, M. and Viitala, R. (2012). Forest variable estimation using a high-resolution digital surface model. Journal of Photogrammetry and Remote Sensing, 74, pp. 78– 84.
  • [21] Kardos, M. (2013). Methods of digital photogrammetry in forest management in Slovakia. J. Forest Sci. 59(2): 54-63.
  • [22] Korpela, I.A.P. (2004). Appraisal of the Mean Height of Trees by Means of Image Matching of Digitized Aerial Photographs. The Photogrammetric Journal of Finland, 19.
  • [23] Lassau SA, Cassis G, Flemons PKJ, Wilkie L, Hochuli DF (2005). Using high-resolution multispectral imagery to estimate habitat complexity in open-canopy forests: can we predict ant community patterns? Ecography 28:495–504.
  • [24] Leberl, F., Irschara, A., Pock, T., Meixner, P., Gruber, M., Scholz, S. and Wiechert, A. (2010). Point clouds: LiDAR versus three-dimensional vision. Photogramm. Eng. Remote Sens. 2010, 76, 1123–1134.
  • [25] Lefsky M, Harding D, Keller M, Cohen W, Carabajal C (2005). Estimates of forest canopy height and aboveground biomass using ICESat. Geophys Res Lett 32: L22S02.
  • [26] Lefsky MA, Cohen WB, Spies TA (2001). An evaluation of alternate remote sensing products for forest inventory, monitoring, and mapping of Douglas-fir forests in western Oregon. Can J For Res 31: 78–87.
  • [27] Lehmann, J. R. K., Nieberding, F., Prinz, T. & Knoth, C. (2015). Analysis of Unmanned Aerial System Based CIR Images in Forestry - A New Perspective to Monitor Pest Infestation Levels. Forests 6 (3): 594-612. doi:10.3390/f6030594.
  • [28] Liang,Y., Monteiro, S.T.& Saber. S (2016.) Transfer learning for high resolution aerial image classification. IEEE Applied Imagery Pattern Recognition Workshop (AIPR) DOI: 10.1109/AIPR.2016.8010600.
  • [29] Liu, Q., Shiming, L. Zengyuan, L., Liyong, F. & Kailong, H. (2017). Review on the Applications of UAV-Based LiDAR and Photogrammetry in Forestry. Scientia Silvae Sinicae, 53(7): 134-148. DOI: 10.11707/j.1001-7488.20170714
  • [30] Maltamo M, Malinen J, Packalén P, Suvanto A, Kangas J (2006.) Nonparametric estimation of stem volume using airborne laser scanning, aerial photography, and stand-register data. Can J For Res 36: 426–436.
  • [31] Means JE, Acker SA, Fitt BJ, Renslow M, Emerson L, Hendrix CJ (2000). Predicting forest stand characteristics with airborne laser scanning LIDAR. Photogramm Eng Remote Sens 66: 1367–1371.
  • [32] Muller J, Stadler J, Brandl R (2010). Composition versus physiognomy of vegetation as predictors of bird assemblages: the role of lidar. Remote Sens Environ 114: 490–495.
  • [33] Naesset E (1997a) Determination of mean tree height of forest stands using airborne laser scanner data. ISPRS J Photogramm Remote Sens 52:49–56.
  • [34] Nasi, R., Honkavaara, E., Lyytikainen-Saarenmaa, P., Blomqvist, M., Litkey, P., Hakala, T., Viljanen, N., Kantola, T., Tanhuanpaa, T. & Holopainen, M. (2015). Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level. Remote Sensing 7 (11): 15467–15493. doi:10.3390/rs71115467.
  • [35] Nurminen, K., Karjalainen, M., Yu, X., Hyyppä, J. and Honkavaara, E. (2013). Preformance of dense digital surface models based on image matching in the estimation of plot-level forest variables. ISPRS Journal of Photogrammetry and Remote Sensing, Vol 83, pp. 104–115.
  • [36] Taniguchi, S.-i. (1961). Forest Inventory by Aerial Photographs. Researcg Bullettins of the College Experiment Forest Hokkaido University, 21, 1-80
  • [37] Wolf., P.R. (1983). Elements of photogrammetry with air photo interpretation and remote sensing. McGraw-Hill in New York.
  • [38] Zhang, Y., (2002). Natural colour urban 3D modeling: A stereoscopic approach with IKONOS multispectrsal and panchromatic data. International Archives of Photogrammetry and Remote Sensing, Volume 34, Part 4 (ISPRS “GeoSpatial Theory, Processing and Applications”, Ottawa, July 2002).
  • [39] Zihlavnik, S., Chudy, F& Kardos, M (2007). Utilization of digital photogrammetry in forestry mapping. Journal of Forest Science, 53 (5): 222–230.
Document Type
Publication order reference
YADDA identifier
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.