Abstract
We estimated leaf area index (LAI) and canopy openness of broad-leaved forest using discrete return and small-footprint airborne laser scanner (ALS) data. We tested four ALS variables, including two newly proposed ones, using three echo types (first, last, and only) and three classes (ground, vegetation, and upper vegetation), and compared the accuracy by means of correlation and regression analysis with seven conventional vegetation indices derived from simultaneously acquired high-resolution near-infrared digital photographs. Among the ALS variables, the ratio of the “only-and-ground” pulse to “only” pulse (OGF) was the best estimator of both LAI (adjusted R 2 = 0.797) and canopy openness (adjusted R 2 = 0.832), followed by the ratio of the pulses that reached the ground to projected lasers (GF). Among the vegetation indices, the normalized differential vegetation index (NDVI) was the best estimator of both LAI (adjusted R 2 = 0.791) and canopy openness (adjusted R 2 = 0.764). Resampling analysis on ALS data to examine whether the estimation of LAI and canopy openness was possible with lower point densities revealed that GF maintained a high adjusted R 2 until a fairly low density of about 0.226 points/m2, while OGF performed marginally when the point density was reduced to about 1 point/m2, the standard density of high-density products on the market as of February 2008. Consequently, the ALS variables proposed in the present study, GF and OGF, seemed to have great potential to estimate LAI and canopy openness of broad-leaved forest, with accuracy comparable to NDVI, from high-resolution near-infrared imagery.
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References
Barilotti A, Turco S (2006) LAI determination in forestry ecosystem by LiDAR data analysis. http://geomatica.uniud.it/progetti/laserscan/lpi/Accessed 8 April 2007
Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ 62:241–251
Carreiras JMB, Pereira JMC, Pereira JS (2006) Estimation of tree canopy cover in evergreen oak woodlands using remote sensing. Forest Ecol Manag 224:45–53
Chen JM, Cihlar J (1996) Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sens Environ 55:153–162
Cohen WB, Maiersperger TK, Gower ST, Turner DP (2003) An improved strategy for regression of biophysical variables and Landsat ETM+ data. Remote Sens Environ 84:561–571
Colombo R, Bellingeri D, Fasolini D, Marino CM (2003) Retrieval of leaf area index in different vegetation types using high resolution satellite data. Remote Sens Environ 86:120–131
Frazer GW, Canham CD, Lertzman KP (1999) Gap Light Analyzer (GLA) Imaging software to extract canopy structure and gap light transmission indices from true-colour fisheye photographs, Users Manual and Program Documentation, Version 2.0. Simon Fraser University, Burnaby, British Columbia, CANADA, and Institute of Ecosystem Studies, Millbrook
Hoshi N, Tatsuhara S, Abe N (2001) Estimation of leaf area index in natural deciduous broad-leaved forests using Landsat TM data. J Jpn For Soc 83:315–321 (in Japanese with English abstract)
Huete AR (1988) A soil adjusted vegetation index (SAVI). Remote Sens Environ 25:295–309
Jonckheere I, Fleck S, Nackaerts K, Muys B, Coppin P, Weiss M, Baret F (2004) Review of methods for in situ leaf area index determination: Part I. Theories, sensors and hemispherical photography. Agric For Meteorol 121:19–35
Jordan CF (1969) Derivation of leaf area index from quality of light on the forest floor. Ecology 50:663–666
Kusakabe T, Tsuzuki H, Sueda T (2006) Long-range estimation of leaf area index using airborne laser altimetry in Siberian Boreal forest. J Jpn For Soc 88:21–29 (in Japanese with English abstract)
Lefsky MA, Hudak AT, Cohen WB, Acker SA (2005) Geographic variability in lidar predictions of forest stand structure in the Pacific Northwest. Remote Sens Environ 95:532–548
Maltamo M, Eerikainen K, Pitkanen J, Hyyppa J, Vehmas M (2004) Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions. Remote Sens Environ 90:319–330
Morsdorf F, Kotz B, Meier E, Itten KI, Allgower B (2006) Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction. Remote Sens Environ 104:50–61
Muraoka H, Koizumi H (2005) Photosynthetic and structural characteristics of canopy and shrub trees in a cool-temperate deciduous broadleaved forest. Agric For Methodol 134:39–59
Naesset (1997) Estimating timber volume of forest stands using airborne laser scanner data. Remote Sens Environ 61:246–253
Nakamura A, Morimoto Y, Mizutani Y (2005) Adaptive management approach to increasing the diversity of a 30-year-old planted forest in an urban area of Japan. Landsc Urban Plan 70:291–300
Nemani RR, Running SW (1989) Testing a theoretical climate-soil-leaf area hydrologic equilibrium of forests using satellite data and ecosystem simulation. Agric For Methodol 44:245–260
Pearson RL, Miller LD (1972) Remote sensing of standing crop biomass for estimation of the productivity of the short-grass prairie, Pawnee National Grasslands, Colorado. In: Proceeding of the 8th international symposium on remote sens. of environ. ERIM, Ann Arbor, pp 1357–1381
Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S (1994) A modified soil adjusted vegetation index (MSAVI). Remote Sens Environ 48:119–126
Riano D, Valladares F, Condes S, Chuvieco E (2004) Estimation of leaf area index and covered ground from airborne laser scanner (Lidar) in two contrasting forests. Agric For Methodol 124:269–275
Rondeaux G, Steven M, Varet F (1996) Optimization of soil-adjusted vegetation indices. Remote Sens Environ 55:95–107
Roujean JL, Breo FM (1995) Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens Environ 51:375–384
Rouse JW, Haas RH, Schell JA, Deering DW, Harlan JC (1974) Monitoring the vernal advancement of retrogradation of natural vegetation, NASA/GSFC, Type III, Final Report, Greenbelt, pp 371
Sasaki T, Morimoto Y, Imanishi J (2007) The stand structure and soil properties of the forested area in a large scale reclamation site for 30 years after construction. J Jpn Inst Landsc Arch 70(5):413–418 (in Japanese with English abstract)
Satake Y, Hara H, Watari S, Tominari T (1989) Wild flowers of Japan: woody plants I and II, 1st edn. Heibonsha, Tokyo
Setojima M, Akamatsu Y, Funabashi M, Imai Y, Amano M (2002) Measurement of forest area by airborne laser scanner and its applicability. J Jpn Soc Photogram Renote Sens 41(2):15–26 (in Japanese with English abstract)
Spanner MA, Pierce LL, Running SW, Peterson DL (1990) The seasonality of AVHRR data of temperate coniferous forests: relationship with leaf area index. Remote Sens Environ 33:97–112
Weiss M, Baret F, Smith GJ, Jonckheere I, Coppin P (2004) Review of methods for in situ leaf area index determination: part II. Estimation of LAI, errors and sampling. Agric For Meteorol 121:37–53
Acknowledgments
We wish to acknowledge the staff of the Commemorative Organization for the Japan World Exposition ’70 for their support in field observation. This research was partly funded by the Organization for Landscape and Urban Green Technology Development, Japan.
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Sasaki, T., Imanishi, J., Ioki, K. et al. Estimation of leaf area index and canopy openness in broad-leaved forest using an airborne laser scanner in comparison with high-resolution near-infrared digital photography. Landscape Ecol Eng 4, 47–55 (2008). https://doi.org/10.1007/s11355-008-0041-8
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DOI: https://doi.org/10.1007/s11355-008-0041-8