Comparison of high-density LiDAR and satellite photogrammetry for forest inventory

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 142, Issue , 2018, Pages 257-267

  Pearse, Grant D ^a   Dash, Jonathan P ^a   Persson, Henrik J ^b   Watt, Michael S ^a  

^a SCION   (New Zealand)

^b SWEDISH UNIVERSITY OF AGRICULTURAL SCIENCES   (Sweden)

Author keywords

Airborne laser scanning; Forest inventory; LiDAR; Photogrammetry; Pl iades; Radiata pine; Remote sensing; Satellite; Stereo

Indexed keywords

FORESTRY; LASER APPLICATIONS; OPTICAL RADAR; REMOTE SENSING; SATELLITE IMAGERY; STEREO IMAGE PROCESSING; SURFACE ANALYSIS;

AIRBORNE LASER SCANNING; BASAL AREA; CANOPY HEIGHT MODELS; FOREST INVENTORY; LIDAR; PLÉIADE; POINT CLOUD DATA; RADIATA PINE; REMOTE-SENSING; STEREO;

PHOTOGRAMMETRY;

BASAL AREA; COMPARATIVE STUDY; CONIFEROUS TREE; FOREST INVENTORY; IMAGE RESOLUTION; LASER METHOD; LIDAR; PHOTOGRAMMETRY; PLANTATION FORESTRY; REMOTE SENSING; SATELLITE DATA; SATELLITE IMAGERY; STEREO IMAGE;

NEW ZEALAND;

PINUS RADIATA; RADIATA;

EID: 85048805808     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2018.06.006     Document Type: Article

References (77)

1. Alonso-Benito, A., Arroyo, L.A., Arbelo, M., Hernández-Leal, P., Fusion of WorldView-2 and LiDAR data to map fuel types in the Canary Islands. Remote Sens., 8, 2016, 669, 10.3390/rs8080669 .
2. Breidenbach, J., Astrup, R., Small area estimation of forest attributes in the Norwegian National Forest Inventory. Eur. J. Forest Res. 131 (2012), 1255–1267, 10.1007/s10342-012-0596-7.
3. Chen, G., Hay, G.J., St-Onge, B., A GEOBIA framework to estimate forest parameters from lidar transects, Quickbird imagery and machine learning: a case study in Quebec, Canada. Int. J. Appl. Earth Obs. Geoinf. 15 (2012), 28–37.
4. Chen, Q., Baldocchi, D., Gong, P., Kelly, M., Isolating individual trees in a savanna woodland using small footprint lidar data. Photogramm. Eng. Remote Sens. 72 (2006), 923–932.
5. Coops, N.C., Johnson, M., Wulder, M.A., White, J.C., Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation. Remote Sens. Environ. 103 (2006), 67–80.
6. Dandois, J.P., Ellis, E.C., High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision. Remote Sens. Environ. 136 (2013), 259–276.
7. Dandois, J.P., Olano, M., Ellis, E.C., Optimal altitude, overlap, and weather conditions for computer vision UAV estimates of forest structure. Remote Sens. 7 (2015), 13895–13920.
8. Dash, J.P., Marshall, H.M., Rawley, B., Methods for estimating multivariate stand yields and errors using k-NN and aerial laser scanning. Forestry: Int. J. Forest Res. 88 (2015), 237–247, 10.1093/forestry/cpu054.
9. Dash, J.P., Watt, M., Bhandari, S., Watt, P., Characterising forest structure using combinations of airborne laser scanning, RapidEye satellite imagery and environmental variables. Forestry, 2015, 88.
10. van Ewijk, K.Y., Treitz, P.M., Scott, N.A., Characterizing forest succession in Central Ontario using Lidar-derived Indices. Photogramm. Eng. Remote Sens. 77 (2011), 261–269, 10.14358/PERS.77.3.261.
11. Fuchs, H., Magdon, P., Kleinn, C., Flessa, H., Estimating aboveground carbon in a catchment of the Siberian forest tundra: Combining satellite imagery and field inventory. Remote Sens. Environ. 113 (2009), 518–531, 10.1016/j.rse.2008.07.017 .
12. Gobakken, T., Bollandsås, O.M., Næsset, E., Comparing biophysical forest characteristics estimated from photogrammetric matching of aerial images and airborne laser scanning data. Scand. J. For. Res. 30 (2015), 73–86.
13. Goulding, C.J., Murray, J.C., Polynomial taper equations that are compatible with tree volume equations. NZ J. Forest. Sci. 5 (1976), 313–322.
14. Hall, S.A., Burke, I.C., Box, D.O., Kaufmann, M.R., Stoker, J.M., Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests. For. Ecol. Manage. 208 (2005), 189–209.
15. Haralick, R.M., Shanmugam, K., Textural features for image classification. IEEE Trans. Syst. Man Cybernet 3 (1973), 610–621.
16. Hill, A., Breschan, J., Mandallaz, D., Accuracy assessment of timber volume maps using forest inventory data and LiDAR canopy height models. Forests 5 (2014), 2253–2275, 10.3390/f5092253 .
17. Hirata, Y., Estimation of stand attributes in Cryptomeria japonica and Chamaecyparis obtusa stands using QuickBird panchromatic data. J. Forest Res. 13 (2008), 147–154.
18. Hirschmuller, H., Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 30 (2008), 328–341, 10.1109/TPAMI.2007.1166.
19. Holmgren, J., Nilsson, M., Olsson, H., Estimation of tree height and stem volume on plots using airborne laser scanning. Forest Sci. 49 (2003), 419–428.
20. Holmgren, J., Persson, A., Identifying species of individual trees using airborne laser scanner. Remote Sens. Environ. 90 (2004), 415–423.
21. Hudak, A.T., Crookston, N.L., Evans, J.S., Falkowski, M.J., Smith, A.M.S., Gessler, P.E., Morgan, P., Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return lidar and multispectral satellite data. Can. J. Remote Sens. 32 (2006), 126–138, 10.5589/m06-007.
22. Hudak, A.T., Lefsky, M.A., Cohen, W.B., Berterretche, M., Integration of lidar and Landsat ETM plus data for estimating and mapping forest canopy height. Remote Sens. Environ. 82 (2002), 397–416.
23. Hyyppä, J., Kelle, O., Lehikoinen, M., Inkinen, M., A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners. IEEE Trans. Geosci. Remote Sens. 39 (2001), 969–975.
24. Immitzer, M., Stepper, C., Böck, S., Straub, C., Atzberger, C., Use of WorldView-2 stereo imagery and National Forest Inventory data for wall-to-wall mapping of growing stock. For. Ecol. Manage. 359 (2016), 232–246, 10.1016/j.foreco.2015.10.018 .
25. Khosravipour, A., Skidmore, A.K., Isenburg, M., Wang, T., Hussin, Y.A., Generating Pit-free Canopy Height Models from Airborne Lidar. Photogramm. Eng. Remote Sens. 80 (2014), 863–872, 10.14358/PERS.80.9.863.
26. Khosravipour, A., Skidmore, A.K., Wang, T., Isenburg, M., Khoshelham, K., Effect of slope on treetop detection using a LiDAR Canopy Height Model. ISPRS J. Photogramm. Remote Sens. 104 (2015), 44–52, 10.1016/j.isprsjprs.2015.02.013 .
27. Leboeuf, A., Beaudoin, A., Fournier, R.A., Guindon, L., Luther, J.E., Lambert, M.-C., A shadow fraction method for mapping biomass of northern boreal black spruce forests using QuickBird imagery. Remote Sens. Environ. 110 (2007), 488–500.
28. Lim, K., Treitz, P., Wulder, M., St-Onge, B., Flood, M., LiDAR remote sensing of forest structure. Prog. Phys. Geogr.: Earth Environ. 27 (2003), 88–106, 10.1191/0309133303pp360ra.
29. Lim, K.S., Treitz, P.M., Estimation of aboveground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators. Scand. J. For. Res. 19 (2004), 558–570.
30. Lisein, J., Pierrot-Deseilligny, M., Bonnet, S., Lejeune, P., A photogrammetric workflow for the creation of a forest canopy height model from small unmanned aerial system imagery. Forests 4 (2013), 922–944, 10.3390/f4040922 .
31. Liu, J., Skidmore, A.K., Heurich, M., Wang, T., Significant effect of topographic normalization of airborne LiDAR data on the retrieval of plant area index profile in mountainous forests. ISPRS J. Photogramm. Remote Sens. 132 (2017), 77–87, 10.1016/j.isprsjprs.2017.08.005 .
32. Liu, J., Skidmore, A.K., Jones, S., Wang, T., Heurich, M., Zhu, X., Shi, Y., Large off-nadir scan angle of airborne LiDAR can severely affect the estimates of forest structure metrics. ISPRS J. Photogramm. Remote Sens. 136 (2018), 13–25, 10.1016/j.isprsjprs.2017.12.004 .
33. Maltamo, M., Packalén, P., Kallio, E., Kangas, J., Uuttera, J., Heikkilä, J., 2011. Airborne laser scanning based stand level management inventory in Finland. In: Conference Secretariat, pp. 1–10.
34. McGaughey, R.J., 2016. FUSION/LDV Software for LiDAR Data Analysis and Visualization. .
35. Mora, B., Wulder, M.A., White, J.C., Segment-constrained regression tree estimation of forest stand height from very high spatial resolution panchromatic imagery over a boreal environment. Remote Sens. Environ. 114 (2010), 2474–2484.
36. Mora, B., Wulder, M.A., White, J.C., Hobart, G., Modeling stand height, volume, and biomass from very high spatial resolution satellite imagery and samples of Airborne LiDAR. Remote Sens. 5 (2013), 2308–2326, 10.3390/Rs5052308.
37. Naesset, E., Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sens. Environ. 80 (2002), 88–99, 10.1016/S0034-4257(01)00290-5 .
38. Naesset, E., Airborne laser scanning as a method in operational forest inventory: status of accuracy assessments accomplished in Scandinavia. Scand. J. For. Res. 22 (2007), 433–442.
39. Nilsson, M., Nordkvist, K., Jonzén, J., Lindgren, N., Axensten, P., Wallerman, J., Egberth, M., Larsson, S., Nilsson, L., Eriksson, J., Olsson, H., A nationwide forest attribute map of Sweden predicted using airborne laser scanning data and field data from the National Forest Inventory. Remote Sens. Environ. 194 (2017), 447–454, 10.1016/j.rse.2016.10.022 .
40. Nurminen, K., Karjalainen, M., Yu, X., Hyyppä, J., Honkavaara, E., Performance of dense digital surface models based on image matching in the estimation of plot-level forest variables. ISPRS J. Photogramm. Remote Sens. 83 (2013), 104–115.
41. Ozdemir, I., Karnieli, A., Predicting forest structural parameters using the image texture derived from WorldView-2 multispectral imagery in a dryland forest, Israel. Int. J. Appl. Earth Obs. Geoinf. 13 (2011), 701–710, 10.1016/j.jag.2011.05.006 .
42. Pearse, G.D., Watt, M.S., Morgenroth, J., Comparison of optical LAI measurements under diffuse and clear skies after correcting for scattered radiation. Agric. For. Meteorol. 221 (2016), 61–70, 10.1016/j.agrformet.2016.02.001 .
43. Perko, R., Raggam, H., Gutjahr, K., Schardt, M., Assessment of the mapping potential of Pléiades stereo and triplet data. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., 2, 2014, 103.
44. Persson, A., Holmgren, J., Soderman, U., Detecting and measuring individual trees using an airborne laser scanner. Photogramm. Eng. Remote Sens. 68 (2002), 925–932.
45. Persson, H., Wallerman, J., Olsson, H., Fransson, J.E.S., Estimating forest biomass and height using optical stereo satellite data and a DTM from laser scanning data. Can. J. Remote Sens. 39 (2013), 251–262, 10.5589/m13-032.
46. Persson, H.J., Estimation of boreal forest attributes from very high resolution Pléiades Data. Remote Sens., 8, 2016, 736, 10.3390/rs8090736 .
47. Persson, H.J., Perko, R., Assessment of boreal forest height from WorldView-2 satellite stereo images. Remote Sens. Lett. 7 (2016), 1150–1159, 10.1080/2150704X.2016.1219424.
48. Peuhkurinen, J., Maltamo, M., Vesa, L., Packalén, P., Estimation of forest stand characteristics using spectral histograms derived from an Ikonos Satellite Image. Photogramm. Eng. Remote Sens. 74 (2008), 1335–1341.
49. Pitt, D.G., Woods, M., Penner, M., A comparison of point clouds derived from stereo imagery and airborne laser scanning for the area-based estimation of forest inventory attributes in Boreal Ontario. Can. J. Remote Sens. 40 (2014), 214–232.
50. Pont, D., Kimberley, M.O., Brownlie, R.K., Sabatia, C.O., Watt, M.S., Calibrated tree counting on remotely sensed images of planted forests. Int. J. Remote Sens., 2015 .
51. Popescu, S.C., Wynne, R.H., Nelson, R.F., Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass. Can. J. Remote Sens. 29 (2003), 564–577, 10.5589/m03-027.
52. Popescu, S.C., Wynne, R.H., Scrivani, J.A., Fusion of small-footprint lidar and multispectral data to estimate plot-level volume and biomass in deciduous and pine forests in Virginia, USA. Forest Sci. 50 (2004), 551–565.
53. Popescu, S.C., Zhao, K., A voxel-based lidar method for estimating crown base height for deciduous and pine trees. Remote Sens. Environ. 112 (2008), 767–781 .
54. R Core Team, 2017. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. .
55. Rahlf, J., Breidenbach, J., Solberg, S., Næsset, E., Astrup, R., Comparison of four types of 3d data for timber volume estimation. Remote Sens. Environ. 155 (2014), 325–333.
56. Roberts, S.D., Dean, T.J., Evans, D.L., McCombs, J.W., Harrington, R.L., Glass, P.A., Estimating individual tree leaf area in loblolly pine plantations using LiDAR-derived measurements of height and crown dimensions. For. Ecol. Manage. 213 (2005), 54–70.
57. Stepper, C., Straub, C., Pretzsch, H., 2014a. Assessing height changes in a highly structured forest using regularly acquired aerial image data. Forestry (p. cpu050).
58. Stepper, C., Straub, C., Pretzsch, H., Using semi-global matching point clouds to estimate growing stock at the plot and stand levels: application for a broadleaf-dominated forest in central Europe. Can. J. For. Res. 45 (2014), 111–123.
59. Straub, C., Tian, J., Seitz, R., Reinartz, P., Assessment of Cartosat-1 and WorldView-2 stereo imagery in combination with a LiDAR-DTM for timber volume estimation in a highly structured forest in Germany. Forestry 86 (2013), 463–473.
60. St-Onge, B., Hu, Y., Vega, C., Mapping the height and above-ground biomass of a mixed forest using lidar and stereo Ikonos images. Int. J. Remote Sens. 29 (2008), 1277–1294.
61. Tian, J., Schneider, T., Straub, C., Kugler, F., Reinartz, P., Exploring digital surface models from nine different sensors for forest monitoring and change detection. Remote Sens., 9, 2017, 287, 10.3390/rs9030287 .
62. Trotter, C.M., Dymond, J.R., Goulding, C.J., Estimation of timber volume in a coniferous plantation forest using Landsat TM. Int. J. Remote Sens. 18 (1997), 2209–2223, 10.1080/014311697217846.
63. Vastaranta, M., Niemi, M., Wulder, M.A., White, J.C., Nurminen, K., Litkey, P., Honkavaara, E., Holopainen, M., Hyyppä, J., Forest stand age classification using time series of photogrammetrically derived digital surface models. Scand. J. For. Res. 31 (2016), 194–205.
64. Vastaranta, M., Wulder, M.A., White, J.C., Pekkarinen, A., Tuominen, S., Ginzler, C., Kankare, V., Holopainen, M., Hyyppä, J., Hyyppä, H., Airborne laser scanning and digital stereo imagery measures of forest structure: Comparative results and implications to forest mapping and inventory update. Can. J. Remote Sens. 39 (2013), 382–395.
65. Vauhkonen, J., Maltamo, M., McRoberts, R.E., Næsset, E., Introduction to forestry applications of airborne laser scanning. Forestry Applications of Airborne Laser Scanning, 2014, Springer, 1–16.
66. Wallace, L., Lucieer, A., Malenovský, Z., Turner, D., Vopěnka, P., Assessment of forest structure using two UAV techniques: a comparison of airborne laser scanning and structure from motion (SfM) Point Clouds. Forests, 7, 2016, 62.
67. Wang, Z., Ginzler, C., Waser, L.T., A novel method to assess short-term forest cover changes based on digital surface models from image-based point clouds. Forestry 88 (2015), 429–440.
68. Watt, M.S., Meredith, A., Watt, P., Gunn, A., The influence of LiDAR pulse density on the precision of inventory metrics in young unthinned Douglas-fir stands during initial and subsequent LiDAR acquisitions. NZ J. Forest. Sci., 44, 2014, 18, 10.1186/s40490-014-0018-3 .
69. White, J.C., Coops, N.C., Wulder, M.A., Vastaranta, M., Hilker, T., Tompalski, P., Remote sensing technologies for enhancing forest inventories: a review. Can. J. Remote Sens. 42 (2016), 619–641.
70. White, J.C., Wulder, M.A., Vastaranta, M., Coops, N.C., Pitt, D., Woods, M., The utility of image-based point clouds for forest inventory: a comparison with airborne laser scanning. Forests 4 (2013), 518–536, 10.3390/f4030518 .
71. Woods, M., Pitt, D., Penner, M., Lim, K., Nesbitt, D., Etheridge, D., Treitz, P., Operational implementation of a LiDAR inventory in Boreal Ontario. Forest. Chronicle 87 (2011), 512–528.
72. Wulder, M.A., Hall, R.J., Coops, N.C., Franklin, S.E., High spatial resolution remotely sensed data for ecosystem characterization. Bioscience 54 (2004), 511–521.
73. de Wulf, R.R., Goossens, R.E., de Roover, B.P., Borry, F.C., Extraction of forest stand parameters from panchromatic and multispectral SPOT-1 data. Int. J. Remote Sens. 11 (1990), 1571–1588.
74. Yu, X., Hyyppä, J., Karjalainen, M., Nurminen, K., Karila, K., Vastaranta, M., Katoh, M., Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes. Remote Sens. 7 (2015), 15933–15954, 10.3390/rs71215809 .
75. Yu, X.W., Hyyppa, J., Kaartinen, H., Maltamo, M., Automatic detection of harvested trees and determination of forest growth using airborne laser scanning. Remote Sens. Environ. 90 (2004), 451–462.
76. Zhao, K., Popescu, S., Lidar-based mapping of leaf area index and its use for validating GLOBCARBON satellite LAI product in a temperate forest of the southern USA. Remote Sens. Environ. 113 (2009), 1628–1645.
77. Zou, H., Hastie, T., Regularization and variable selection via the elastic net. J. Roy. Stat. Soc.: Ser. B (Stat. Methodol.) 67 (2005), 301–320, 10.1111/j.1467-9868.2005.00503.x .