Forest Attributes Estimation Using Aerial Laser Scanner and TM Data

S. Shataee Joibary

Abstract


Aim of study: The aim of this study was performance of four non-parametric algorithms including the k-NN, SVR, RF and ANN to estimate forest volume and basal area attributes using combination of Aerial Laser Scanner and Landsat-TM data.

Area of study: Data in small part of a mixed managed forest in the Waldkirch region, Germany.

Material and methods: The volume/ha and basal area/ha in the 411 circular plots were estimated based on DBH and height of trees using volume functions of study area. The low density ALS raw data as first and last pulses were prepared and automatically classified into vegetation and ground returns to generate two fine resolution digital terrain and surface models after noise removing. Plot-based height and density metrics were extracted from ALS data and used both separated and combined with orthorectified and processed TM bands. The algorithms implemented with different options including k-NN with different distance measures, SVR with the best regularized parameters for four kernel types, RF with regularized decision tree parameters and ANN with different types of networks. The algorithm performances were validated using computing absolute and percentage RMSe and bias on unused test samples.

Main results: Results showed that among four methods, SVR using the RBF kernel could better estimate volume/ha with lower RMSe and bias (156.02 m3 ha–1 and 0.48, respectively) compared to others. In basal area/ha, k-NN could generate results with similar RMSe (11.79 m3 ha–1) but unbiased (0.03) compared to SVR with RMSe of 11.55 m3 ha–1 but slightly biased (–1.04).

Research highlights: Results exposed that combining Lidar with TM data could improve estimations compared to using only Lidar or TM data.

Key words: forest attributes estimation; ALS; TM; non-parametric algorithms.


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References


Andersen H, 2003. Estimation of critical forest structure metrics through the spatial analysis of airborne laser scanner data. PhD dissertation. University of Washington, Seattle, WA, USA.

Andersen HE, Foster JR, Reutebuch SE, 2003. Estimating forest structure parameters within Fort Lewis Military Reservation using airborne laser scanner (LIDAR) data. In: Proceedings, 2nd Inter-national Precision Forestry Symposium. Seattle, Washington, University of Washington, College of Forest Resources. pp: 45-53.

Atzberger C, 2004. Object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models. Remote Sensing of Environment 93: 53-67. http://dx.doi.org/10.1016/j.rse.2004.06.016

Breidenbach J, Nothdurft A, Kandler G, 2010. Comparison of nearest neighbor approaches for small area estimation of tree species-specific forest inventory attributes in central Europe using airborne laser scanner data. European Journal of Forest Resources.

Breiman L, 2001. Random forests. Machine Learning 45(1): 5-32. http://dx.doi.org/10.1023/A:1010933404324

Chen G, Hay GJ, Zhou Y, 2010. Estimation of forest height, biomass and volume using support vector regression and segmentation from Lidar transects and Quickbird imagery. 18th International Conference on Geoinformatics, 18-20 June 2010, Beijing, China.

Chen G, Hay GJ, 2011. A support vector regression approach to estimate forest biophysical parameters at the object level using airborne LiDAR transects and Quick Bird data. Photogrammetric Engineering & Remote Sensing 77(7): 733-741. http://dx.doi.org/10.14358/PERS.77.7.733

Chen G, Wulder MA, White JC, Hilker T, Coops NC, 2012. LiDAR calibration and validation for geometric-optical modelling with Landsat imagery. Remote Sensing of Environment 124: 384-393. http://dx.doi.org/10.1016/j.rse.2012.05.026

Dalponte M, Coops NC, Bruzzone L, Gianelle D, 2009. Analysis on the use of multiple returns Lidar data for the estimation of tree stems volume. IEEE journal of selected topics in applied earth observations and remote sensing 2(4): 310-318.

Durbha SS, King RL, Younan NH, 2007. Support vector machines regression for retrieval of leaf area index from multi angle imaging spectroradiometer. Remote Sensing of Environment 107: 348-361. http://dx.doi.org/10.1016/j.rse.2006.09.031

Eskelson BNI, Hailemariam T, Barrett TM, 2009. Estimating current forest attributes from panelled inventory data using plot-level imputation: a study from the Pacific Northwest. Forest Science 55: 64-71.

Finley AO, McRoberts RE, Ek AR, 2006. Applying an efficient k-nearest neighbour search to forest attribute imputation. Forest Science 52: 130-135.

Franco-López H, Ek AR, Bauer ME, 2001. Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors' method. Remote Sensing of Environment 77(3): 251-274. http://dx.doi.org/10.1016/S0034-4257(01)00209-7

Jin Y, Liu C, 1997. Biomass retrieval from high-dimensional active/passive remote sensing data by using artificial neural networks. International Journal of Remote Sensing 18(4): 971-979. http://dx.doi.org/10.1080/014311697218863

He Q, Xu H, Zhang Y, 2011. Estimation of forest biophysical parameters using small-footprint Lidar with low density in a coniferous forest, International Symposium on LiDAR and Radar Mapping 2011: technologies and applications, Nanjing, China, May 26, 2011.

Holmgren J, 2004. Prediction of tree height, basal area, and stem volume using airborne laser scanning, Scandinavian Journal Forest Resources 19: 543-553. http://dx.doi.org/10.1080/02827580410019472

Hsu CW, Chang CC, Lin CJ, 2010. A practical guide to support vector classification. Department of computer science, National Taiwan University, Taipei, Taiwan. http://www.csie.ntu.edu.tw/~cjlin.

Hyvonen P, 2007. The updating of forest resource data for management planning for privately owned forests in Finland. Academic dissertation, Faculty of Forestry, University of Joensuu. 40 pp.

Kajisa T, Murakami T, Mizoue N, Kitahara F, Yoshida S, 2008. Estimation of stand volumes using the k -nearest neighbor method in Kyushu, Japan. Journal of Forest Research 13(4): 249-254. http://dx.doi.org/10.1007/s10310-008-0077-5

Latifi H, Nothdurfet A, Koch B, 2010. Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: optimization of variable selection on optical/LiDAR-derived predictors. Forestry Journal 83: 395-407. http://dx.doi.org/10.1093/forestry/cpq022

Lefsky MA, Cohen WB, Acker SA, Parker GG, Spies TA, Harding D, 1999b. LiDAR remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests. Remote Sensing of Environment 70: 339-61. http://dx.doi.org/10.1016/S0034-4257(99)00052-8

Lefsky MA, Cohen WB, Harding DJ, Parker GG, Acker SA, Gower ST, 2001b. Lidar remote sensing of aboveground biomass in three biomes. In the international archives of the Photogrammetry, remote sensing and spatial information science, Vol XXXIV, Part 3/W4, Commission III, Annapolis MD, 22-24 October. pp: 155-60.

Lim K, Treitz P, Wulder M, St-Onge B, Flood M, 2003. LiDAR remote sensing of forest structure, Progress in Physical Geography 27(1): 88-106. http://dx.doi.org/10.1191/0309133303pp360ra

Lu D, Chen Q, Wang G, Moran E, Batistella M, Zhang M, Laurin GV, Saah D, 2012. Aboveground forest biomass estimation with Landsat and Lidar data and uncertainty analysis of the estimates. International Journal of Forestry Research. http://dx.doi.org/10.1155/2012/436537

Makela H, Pekkarinen A, 2004. Estimation of forest stands volumes by Landsat TM imagery and stand-level fieldinventory data. Forest Ecology and Management 196: 245-255. http://dx.doi.org/10.1016/j.foreco.2004.02.049

Maltamo M, Eerikainen K, Packalen P, Hyyppa J, 2006a. Estimation of stem volume using laser scanning based canopy height metrics. Forestry 79: 217-229. http://dx.doi.org/10.1093/forestry/cpl007

Maltamo M, Malinen J, Packalen P, Suvanto A, Kangas J, 2006b. Non-parametric estimation of stem volume using laser scanning, aerial photography, and stand register data. Canadian Journal of Forest Resources 36: 426-436. http://dx.doi.org/10.1139/x05-246

Mattera D, Haykin S, 1999. Support vector machines for dynamic reconstruction of a chaotic system. In: Advances in Kernel methods: support vector learning (Schölkopf B, Burges CJC, Smola AJ, eds). Cambridge, MA, MIT Press. pp: 211-242.

McGaughey RJ, 2010. Manual of FUSION/LDV: software for LIDAR data analysis and visualization. United State, Department of Agriculture, Forest services, research station. 153 pp.

McInerney D, Nieuwenhuis M, 2009. A comparative analysis of k-NN and decision tree methods for the Irish National Forest Inventory, International Journal of Remote Sensing 30: 4937-4955. http://dx.doi.org/10.1080/01431160903022936

McInerney DO, Suárez-Mínguez J, Valbuena R, Nieuwenhuis M, 2010. Forest canopy height retrieval using lidar data, medium resolution satellite imagery and Knn estimation in Aberfoyle, Scotland. Forestry 83(2): 195-206. http://dx.doi.org/10.1093/forestry/cpq001

McRoberts R, Tomppo E, Finley A, Heikkinen J, 2007. Estimating aerial means and variances of forest attributes using the k-Nearest Neighbors technique and satellite imagery. Remote Sensing of Environment 111: 466-480. http://dx.doi.org/10.1016/j.rse.2007.04.002

McRoberts R, 2009. Diagnostic tools for nearest neighbours techniques when used with satellite imagery. Remote Sensing of Environment 113: 489-499. http://dx.doi.org/10.1016/j.rse.2008.06.015

Mohammadi J, Shataee Sh, Yaghmaee F, Mahiny A, 2010. Modeling forest stand volume and tree density using Landsat ETM+ data. International Journal of Remote Sensing 31(11): 2959-2975. http://dx.doi.org/10.1080/01431160903140811

Naesset E, 1997. Determination of mean tree height of forest stands using airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing 52: 49-56. http://dx.doi.org/10.1016/S0924-2716(97)83000-6

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: 88-99. http://dx.doi.org/10.1016/S0034-4257(01)00290-5

Naesset E, 2004. Practical large-scale forest stand inventory using a small airborne scanning laser. Scandinavian Journal Forest Resources 19: 164-179. http://dx.doi.org/10.1080/02827580410019544

Nelson R, Oderwald R, Gregoire TG, 1997. Separating the ground and airborne laser sampling phases to estimate tropical forest basal area, volume, and biomass. Remote Sensing of Environment 60: 311-26. http://dx.doi.org/10.1016/S0034-4257(96)00213-1

Nilsson M, 1996. Estimation of tree heights and stand volume using an airborne LiDAR system. Remote Sensing of Environment 56: 1-7. http://dx.doi.org/10.1016/0034-4257(95)00224-3

Nilsson M, 1997. Estimation of forest variables using satellite image data and airborne Lidar. PhD thesis. Swedish University of Agricultural Sciences. Department of Forest Resource Management and Geomatics, Acta Universitatis Agriculturae Sueciae. Silvestria 17.

Niska H, Skon JP, Packalen P, Tokola T, Maltamo M, Kolehmainen M, 2010. Neural networks for the prediction of species-specific plot volumes using airborne laser scanning and aerial photographs, IEEE Trans. Geosciences of Remote Sensing 48(3): 1076-1085 http://dx.doi.org/10.1109/TGRS.2009.2029864

Packalén P, 2009. Using airborne laser scanning data and digital aerial photographs to estimate growing stock by tree species. Academic dissertation, Faculty of Forest Sciences, University of Joensuu, Finland. 41 pp.

Packalén P, Maltamo M, 2007. The k-MSN method for the prediction of species-specific stand attributes using airborne laser scanning and aerial photographs. Remote Sensing Environ 109: 328-341. http://dx.doi.org/10.1016/j.rse.2007.01.005

Popescu SC, Wynne RH, 2004. Seeing the trees in the forest: using LiDAR and multispectral data fusion with local filtering and variable window size for estimating tree height. Photogrammetric Engineering & Remote Sensing 70(5): 589-604. http://dx.doi.org/10.14358/PERS.70.5.589

Schölkopf B, Smola A, Müller KR, 1998. Nonlinear component analysis as a kernel Eigen value problem. Neural Computation 10: 1299-1319. http://dx.doi.org/10.1162/089976698300017467

Shafri HZM, Ramle FSH, 2009. A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island. Information Technology Journal 8: 64-70. http://dx.doi.org/10.3923/itj.2009.64.70

Shataee Sh, Kalbi S, Fallah A, Pelz D, 2012. Forest attributes imputation using machine-learning methods and ASTER data: comparison of k-NN, SVR and random forest regression algorithms. International Journal of Remote Sensing 33: 6254-6280. http://dx.doi.org/10.1080/01431161.2012.682661

Sironen S, Kangas A, Maltamo M, 2010. Comparison of different non-parametric growth imputation methods in the presence of correlated observations. Forestry 83(1): 39-51. http://dx.doi.org/10.1093/forestry/cpp030

Stojanova D, Panov P, Gjorgjioski V, Kobler A, Dzeroski S, 2010. Estimating vegetation height and canopy cover from remotely sensed data with machine learning. Ecological Informatics 5: 256-266. http://dx.doi.org/10.1016/j.ecoinf.2010.03.004

Straub C, Weinacker H, Koch B, 2010. A comparison of different methods for forest resource estimation using information from airborne laser scanning and CIR orthophotos. European Journal of Remote Sensing 129: 1069- 1080.

Vapnik V, 1995. The nature of statistical learning theory. New York, Springer-Verlag. http://dx.doi.org/10.1007/978-1-4757-2440-0 PMid:8555380

Weinacker H, Koch B, Heyder U, Weinacker R, 2004. Development of filtering, segmentation and modelling modules for lidar and multispectral data as a fundament of an automatic forest inventory system. In: ISPRS Working Group VIII/2 "Laser scanners for forest and landscape assessment". University of Freiburg, Freiburg, Germany.

Walton T, 2008. Sub pixel urban land cover estimation: comparing cubist, random forests, and support vector regression. Photogrammetric Engineering & Remote Sensing 74(10): 1213-1222. http://dx.doi.org/10.14358/PERS.74.10.1213

Wang Z, Brenner A, 2009. An integrated method for forest canopy cover mapping using Landsat ETM+ imagery. ASPRS/MAPPS conference, Texas, USA.

Wulder M, 1998. Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters. Progress in Physical Geography 22: 449-76.

Wulder M, Magnussen S, Harding D, Boudewyn P, Seemann D, 2000. Stability of surface LiDAR height estimates on a point and polygon basis. Proceedings of the 22nd Annual Canadian Remote Sensing Symposium. Victoria, British Columbia, 21-25 August, 433-38.




DOI: 10.5424/fs/2013223-03874

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