Estimation of forest stand parameters using airborne LIDAR data

Year 2013, Volume: 14 Issue: 1, 31 - 39, 19.02.2013

İbrahim Özdemir

Abstract

This study was carried out to examine the potential of LIDAR (Light Detection and Ranging) data in estimating some forest stand parameters. The stand parameters including Basal Area (BA), Number of Trees (N), Reineke's Density Index (RDI), and Mean Diameter at Breast Height (MDBH) were determined by field measurement at 30 sampling plots from the stands with different characteristics. The LIDAR metrics including the height percentiles (50th, 90th, 95th, and 99th) and the ratio of non-ground points (NGP) were calculated based on the LIDAR points that correspond to the sampling plots. Correlation analysis was conducted to investigate the relationships between the LIDAR metrics and stand parameters. There were no associations between the LIDAR metrics and the stand parameters, BA and RDI. On the other hand, statistically significant (p<0.01) correlations were determined between the LIDAR metrics and the stand parameters, N and MDBH (highest correlation coefficients (r) are 0.70 and 0.72, respectively). The same analyses were also carried out for the sampling plots (19 plots) taken from the pure conifer or conifer-dominated stands. A clear increase was observed in the correlation coefficients of the relations between the LIDAR metrics and the stand parameters. The highest correlation coefficients calculated for the N and MDBH were 0.82 and 0.84, respectively. In addition, statistically significant but weak relationships were found between the NGP and the stand parameters, BA and RDI when the 19 conifer plots were used (r is 0.46 and 0.58, respectively). Therefore, it may be concluded that if stands parameters are estimated with LIDAR data in complex forest ecosystems, the forest stands should be pre-stratified by definite criteria. The regression models developed by means of stepwise procedure explained 0.82% and 0.70% of the variation in N and MDBH, respectively. As a result, the N and MDBH can be predicted at plot level in conifer-dominated forest stands using airborne laser scanning data.

Keywords

Airborne laser scanning, Forest stand parameters, Forest stand structure, Forest inventory

Havasal LIDAR verileri kullanılarak meşcere parametrelerinin tahmin edilmesi

Abstract

This study was carried out to examine the potential of LIDAR (Light Detection and Ranging) data in estimating some forest stand parameters. The stand parameters including Basal Area (BA), Number of Trees (N), Reineke's Density Index (RDI), and Mean Diameter at Breast Height (MDBH) were determined by field measurement at 30 sampling plots from the stands with different characteristics. The LIDAR metrics including the height percentiles (50th, 90th, 95th, and 99th) and the ratio of non-ground points (NGP) were calculated based on the LIDAR points that correspond to the sampling plots. Correlation analysis was conducted to investigate the relationships between the LIDAR metrics and stand parameters. There were no associations between the LIDAR metrics and the stand parameters, BA and RDI. On the other hand, statistically significant (p<0.01) correlations were determined between the LIDAR metrics and the stand parameters, N and MDBH (highest correlation coefficients (r) are 0.70 and 0.72, respectively). The same analyses were also carried out for the sampling plots (19 plots) taken from the pure conifer or conifer-dominated stands. A clear increase was observed in the correlation coefficients of the relations between the LIDAR metrics and the stand parameters. The highest correlation coefficients calculated for the N and MDBH were 0.82 and 0.84, respectively. In addition, statistically significant but weak relationships were found between the NGP and the stand parameters, BA and RDI when the 19 conifer plots were used (r is 0.46 and 0.58, respectively). Therefore, it may be concluded that if stands parameters are estimated with LIDAR data in complex forest ecosystems, the forest stands should be pre-stratified by definite criteria. The regression models developed by means of stepwise procedure explained 0.82% and 0.70% of the variation in N and MDBH, respectively. As a result, the N and MDBH can be predicted at plot level in conifer-dominated forest stands using airborne laser scanning data.
Keywords: Airborne laser scanning, Forest stand parameters, Forest stand structure, Forest inventory

Keywords

Havadan lazer tarama, Meşcere parametreleri, Meşcere yapısı, Orman envanteri

References

• Akay, A.E., Oguz, H., Karas, I.R., Aruga, K., 2009. Using LIDAR technology in forestry activities. Environmental Monitoring and Assessment, 151:1-4, 117-125.
• Akay, A.E., Wing, M.G., Sessions, J., 2012. Estimating structural properties of riparian forests with airborne lidar data, International Journal of Remote Sensing, 33:22, 7010-7023.
• Carson, W., Andersen, H.E., Reutebuch, S.E., McGaughey. R.J. 2004. LIDAR applications in forestry: An overview. Proceedings of the Annual ASPRS Conference, Denver, May 23-28, 2004. American Society of Photogrammetry and Remote Sensing, Bethesda, MD.
• García, M., Riaño, D., Chuvieco, E., Danson, F.M., 2010. Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LIDAR height and intensity data. Remote Sensing of Environment, 114:4, 816–830.
• Gobakken, T., Næsset, E., 2004. Estimation of diameter and basal area distributions in coniferous forest by means of airborne laser scanner data. Scandinavian Journal of Forest Research, 19, 529- 542.
• Gobakken, T., Næsset., E., 2005. Weibull and percentile models for LIDAR-based estimation of basal area distribution. Scandinavian Journal of Forest Research, 20:6, 490-502.
• Goncalves-Seco, L., Gonzalez-Ferreiro, E., Dieguez-Aranda, U., Fraga- Bugallo, B., Crecente, R., Miranda, D., 2011. Assessing the attributes of high-density Eucalyptus globulus stands using airborne laser scanner data. International Journal of Remote Sensing, 32:24, 9821-9841.
• González-Ferreiro, E., Diéguez-Aranda, U., Miranda, D., 2012. Estimation of stand variables in Pinus radiata D. Don plantations using different LIDAR pulse densities. Forestry, 85:2, 281-292.
• Harding, D.J., Lefsky, M.A., Parker G.G., Blair, J.B., 2001. Laser altimeter canopy height profiles: methods and validation for closed-canopy, broadleaf forest. Remote Sensing of Environment, 76, 283–297.
• Heurich M., Thoma, F., 2008. Estimation of forestry stand parameters using laser scanning data in temperate, structurally rich natural European beech (Fagus sylvatica) and Norway spruce (Picea abies) forests. Forestry, 81, 645-661.
• Hofton, M.A., Rocchio, L.E., Blair J.B., Dubayah, R., 2002. Validation of vegetation measurements for a dense tropical forest. Journal of Geodynamics, 34:3–4, 491–502. sub-canopy topography
• Jaskierniak, D., Lane, P.N.J., Robinson, A., Lucieer, A., 2011. Extracting LIDAR indices to characterise multilayered forest structure using mixture distribution functions, Remote Sensing of Environment 115, 573–585.
• Kwak, D.A., Lee, W.K., Cho, H.K., Lee, S.H., Son, Y., Kafatos, M., Kim, S.R., 2010. Estimating stem volume and biomass of Pinus koraiensis using LIDAR data. Journal of Plant Research, 123:4, 421-432.
• Lefsky, M.A., Cohen, W.B., Parker, G.G., Harding, D.J., 2002. Lidar remote sensing for ecosystem studies, BioScience, 52, 19–30.
• Mathieu, D., Thiery, C., Meriem, F., 2011. The use of terrestrial LIDAR technology in forest science: application fields, benefits and challenges, Annals of Forest Science , 68, 959-974.
• Özdemir, İ., Donoghue, D.N.M., 2013. Modelling tree size diversity from Airborn Laser Scanning using Canopy Height Models with image texture measures. Forest Ecology and Management, http://dx.doi.org/10.1016/j.foreco.2012.12.044
• Parker, G.G., Harding, D.J., Berger, M.L., 2004. A portable LIDAR system for rapid determination of forest canopy structure, Journal of Applied Ecology, 41:4, 755-767.
• Popescu, S.C., 2011. Lidar Remote Sensing, Advances in Environmental Remote Sensing, Sensors, Algorithms, and Applications, Ed: Qihao Weng, CEC Pres, Taylor-Francis series in Remote Sensing Applications, UK, 589 s.
• Thomas, V., Oliver, R.D., Lim, K., Woods. M., 2008. LIDAR and Weibull modeling of diameter and basal area, The Forestry Chronicle, 84, 866-875.
• Watt, P.J., Donoghue, D.N.M., 2005. Measuring forest structure with terrestral laser scanning, İnternational Journal of Remote Sensing, 26, 1437-1446.
• Zhao, K., Popescu, S., Nelson, R., 2009. Lidar remote sensing of forest biomass: A scale-invariant estimation approach using airborne lasers, Remote Sensing of Environment, 113, 182–196.
• Zimble, D.A., Evans, D.L., Carlson, G.C., Parker, R.C., Grado, S.C., Gerard, P.D., 2003. Characterizing vertical forest structure using small-footprint airborne LIDAR, Remote Sensing of Environment, 87, 171–182.