Uptake and barriers to the use of geospatial technologies in forest management

New Zealand Journal of Forestry Science

Volumn 43, Issue , 2013, Pages 1-9

  Morgenroth, Justin ^a   Visser, Rien ^a  

^a UNIVERSITY OF CANTERBURY   (New Zealand)

Author keywords

Aerial laser scanning; Aerial photography; GIS; GPS; LiDAR; Remote sensing; Satellite imagery; Survey

Indexed keywords

AERIAL PHOTOGRAPHY; ANTENNAS; GEOGRAPHIC INFORMATION SYSTEMS; GLOBAL POSITIONING SYSTEM; OPTICAL RADAR; PHOTOGRAPHIC EQUIPMENT; REMOTE SENSING; SATELLITE IMAGERY; SURVEYING; SURVEYS; TIMBER; TOPOGRAPHY;

CONTINUING EDUCATION; EMERGING TECHNOLOGIES; GEOSPATIAL TECHNOLOGY; HIGH SPATIAL RESOLUTION; INTERNATIONAL OPERATIONS; LASER SCANNING; PLANTATION FORESTRY; REMOTELY SENSED DATA;

FORESTRY;

AERIAL PHOTOGRAPHY; FOREST MANAGEMENT; GIS; GPS; LASER METHOD; LIDAR; PLANTATION FORESTRY; REMOTE SENSING; SATELLITE IMAGERY; SPATIAL DATA;

AIR CRAFT; FORESTRY; FORESTS; GIS; PHOTOGRAPHY; RADAR; REMOTE SENSING; SURVEYING; SURVEYS; TOOLS;

NEW ZEALAND;

EID: 84891429234     PISSN: 00480134     EISSN: 11795395     Source Type: Journal    
DOI: 10.1186/1179-5395-43-16     Document Type: Article

References (40)

1. Abdi, E, Majnounian, B, Darvishsefat, A, Mashayekhi, Z, & Sessions, J. (2009). A GIS-MCE based model for forest road planning. Journal of Forest Science, 55(4), 171-176.
2. Adams, T, Brack, C, Farrier, T, Pont, D, & Brownlie, R. (2011). So you want to use LiDAR?-a guide on how to use LiDAR in forestry. New Zealand Journal of Forestry, 55(4), 19-23.
3. Akay, AE, Oǧuz, H, Karas, IR, & Aruga, K. (2009). Using LiDAR technology in forestry activities. Environmental Monitoring and Assessment, 151(1-4), 117-125.
4. Balzter, H. (2001). Forest mapping and monitoring with interferometric synthetic aperture radar (InSAR). Progress in Physical Geography, 25(2), 159-177.
5. Beets, PN, Brandon, AM, Goulding, CJ, Kimberley, MO, Paul, TSH, & Searles, N. (2012). The national inventory of carbon stock in New Zealand's pre-1990 planted forest using a LiDAR incomplete-transect approach. Forest Ecology and Management, 280, 187-197.
6. Chen, Y, & Zhu, X. (2012). Site quality assessment of a Pinus radiata plantation in Victoria, Australia, using LiDAR technology. Southern Forests, 74(4), 217-227.
7. Coops, NC, Johnson, M, Wulder, MA, & White, JC. (2006). Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation. Remote Sensing of Environment, 103(1), 67-80.
8. Dassot, M, Constant, T, & Fournier, M. (2011). The use of terrestrial LiDAR technology in forest science: application fields, benefits and challenges. Annals of Forest Science, 68(5), 959-974.
9. Devlin, GJ, McDonnell, K, & Ward, S. (2008). Timber haulage routing in Ireland: an analysis using GIS and GPS. Journal of Transport Geography, 16(1), 63-72.
10. Dobson, MC, Ulaby, FT, LeToan, T, Beaudoin, A, Kasischke, ES, & Christensen, N. (1992). Dependence of radar backscatter on coniferous forest biomass. IEEE Transactions on Geoscience and Remote Sensing, 30(2), 412-415.
11. Donoghue, DNM, & Watt, PJ. (2006). Using LiDAR to compare forest height estimates from IKONOS and Landsat ETM + data in Sitka spruce plantation forests. International Journal of Remote Sensing, 27(11), 2161-2175.
12. Fossum, ER. (1997). CMOS image sensors: electronic camera-on-a-chip. IEEE Transactions on Electron Devices, 44(10), 1689-1698. doi:10.1109/16.628824.
13. 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.
14. Greer, JD, Hoppus, ML, & Lachowski, HM. (1990). Color infrared photography for resource management: unique attributes improve vegetation mapping and assessment. Journal of Forestry, 88(7), 12-17.
15. Hill, RA, Boyd, DS, & Hopkinson, C. (2011). Relationship between canopy height and Landsat ETM + response in lowland Amazonian rainforest. Remote Sensing Letters, 2(3), 203-212.
16. Imhoff, ML. (1995). Radar backscatter and biomass saturation: ramifications for global biomass inventory. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 511-518.
17. Jupp, DLB, Culvenor, DS, Lovell, JL, Newnham, GJ, Strahler, AH, & Woodcock, CE. (2009). Estimating forest LAI profiles and structural parameters using a ground-based laser called 'Echidna®. Tree Physiology, 29(2), 171-181.
18. Koch, B. (2010). Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment. ISPRS Journal of Photogrammetry and Remote Sensing, 65(6), 581-590.
19. Laamanen, R, & Kangas, A. (2011). Large-scale forest owner's information needs in operational planning of timber harvesting-some practical views in Metsähallitus, Finnish state-owned enterprise. Silva Fennica, 45(4), 711-727.
20. Land Information New Zealand. (2009). Spatial information in the New Zealand economy. NZ: Wellington.
21. Leathwick, J, Morgan, F, Wilson, G, Rutledge, D, McLeod, M, & Johnston, K. (2002). Land environments of New zealand: a technical guide. NZ: Wellington.
22. Merry, KL, Bettinger, P, Clutter, M, Hepinstall, J, & Nibbelink, NP. (2007). An assessment of geographic information system skills used by field-level natural resource managers. Journal of Forestry, 105(7), 364-370.
23. Ministry for Primary Industries. (2011). National exotic forest regional yield tables. Wellington, NZ.
24. Murphy, G. (2008). Determining stand value and log product yields using terrestrial lidar and optimal bucking: a case study. Journal of Forestry, 106(6), 317-324.
25. NZ FOA. (2012). New Zealand plantation forest industry facts and figures. Wellington, NZ: New Zealand Forest Owners Association Inc.
26. Olthof, I, & King, DJ. (2000). Development of a forest health index using multispectral airborne digital camera imagery. Canadian Journal of Remote Sensing, 26(3), 166-176.
27. Palmer, DJ, Watt, MS, Kimberley, MO, Hock, BK, Payn, TW, & Lowe, DJ. (2010). Mapping and explaining the productivity of Pinus radiata in New Zealand. New Zealand Journal of Forestry, 55(1), 15-21.
28. Patenaude, G, Milne, R, & Dawson, TP. (2005). Synthesis of remote sensing approaches for forest carbon estimation: reporting to the Kyoto Protocol. Environmental Science and Policy, 8(2), 161-178.
29. Puttonen, E, Suomalainen, J, Hakala, T, Räikkönen, E, Kaartinen, H, Kaasalainen, S, & Litkey, P. (2010). Tree species classification from fused active hyperspectral reflectance and LIDAR measurements. Forest Ecology and Management, 260 (10), 1843-1852.
30. Rieman, BE, Lee, DC, Thurow, RF, Hessburg, PF, & Sedell, JR. (2000). Toward an integrated classification of ecosystems: defining opportunities for managing fish and forest health. Environmental Management, 25(4), 425-444.
31. Rosenqvist, Å, Shimada, M, Chapman, B, Freeman, A, De Grandi, G, Saatchi, S, & Rauste, Y. (2000). The global rain forest mapping project-a review. International Journal of Remote Sensing, 21(6-7), 1375-1387.
32. Sader, SA, & Vermillion, S. (2000). Remote sensing education: an updated survey. Journal of Forestry, 98(4), 31-37.
33. Sader, SA, Hoffer, RM, & Johnson, EW. (1989). The status of remote-sensing education: a 1988 survey of US and Canadian forestry schools. Journal of Forestry, 87(10), 25-30.
34. Sample, VA, Ringgold, PC, Block, NE, & Giltmier, JW. (1999). Forestry education: adapting to the changing demands. Journal of Forestry, 97(9), 4-10.
35. Stone, C, Penman, T, & Turner, R. (2011). Determining an optimal model for processing lidar data at the plot level: results for a Pinus radiata plantation in New South Wales, Australia. New Zealand Journal of Forestry Science, 41, 191-205.
36. Tomppo, E, Goulding, C, & Katila, M. (1999). Adapting finnish multi-source forest inventory techniques to the New Zealand preharvest inventory. Scandinavian Journal of Forest Research, 14(2), 182-192. doi:10.1080/02827589950152917.
37. Turcotte, P. (2003). Multidata and opti-grade: two innovative solutions to better manage forestry operations. Paper presented at the The Proceedings of the 2nd International Precision Forestry Symposium: Seattle, WA.
38. Watt, P, & Watt, M. (2011). Applying satellite imagery for forest planning. New Zealand Journal of Forestry, 56(1), 23-25.
39. Wing, BM, Ritchie, MW, Boston, K, Cohen, WB, Gitelman, A, & Olsen, MJ. (2012). Prediction of understory vegetation cover with airborne lidar in an interior ponderosa pine forest. Remote Sensing of Environment, 124, 730-741.
40. Wulder, M. (1998). Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters. Progress in Physical Geography, 22(4), 449-476.