Global tree cover percentage is an important parameter used to understand the global environment. However, the available global percent tree cover products are few, and efforts to validate these maps have been limited. Therefore, producing a new broad-scale percent tree cover dataset is valuable. Our study was undertaken to map tree cover percentage, on a global scale, with better accuracy than previous studies. Using a modified supervised regression tree algorithm from Moderate Resolution Imaging Spectroradiometer (MODIS) data of 2008, the tree cover percentage was estimated at 500 m resolution in Eurasia. Training data were created by simulation using reference data interpreted from Google Earth. We collected approximately 716 high-resolution images from Google Earth. The regression tree model was modified to fit those images for improved accuracy. Our estimation result was validated using 307 points. The root mean square error (RMSE) between estimated and observed tree cover was 11.2%, and the weighted RMSE between them, in which five tree cover strata (0%–20%, 21%–40%, 41%–60%, 61%–80%, and？81%–100%) were weighted equally, was 14.2%. The result was compared to existing global percent-scale tree cover datasets. We found that existing datasets had some pixels with estimation error of more than 50% and each map had different characteristics. Our？map could be an alternative dataset and other existing datasets could be modified using our resultant map.
References
[1]
FAO (Food and Agriculture Organization of the United Nations). Forest Resources Assessment Programme Working Paper 33: FRA 2000 on Definitions of Forest and Forest Change; FAO: Rome, Italy, 2000.
[2]
FAO (Food and Agriculture Organization of the United Nations). Global Forest Resources Assessment 2010 Main Report; FAO: Rome, Italy, 2010.
[3]
DeFries, R.S.; Hansen, M.C.; Townshend, J.R.G. Global continuous fields of vegetation characteristics: A linear mixture model applied to multi-year 8 km AVHRR data. Int. J. Remote Sens 2000, 21, 1389–1414.
[4]
DeFries, R.S.; Hansen, M.C.; Townshend, J.R.G.; Janetos, A.C.; Loveland, T.R. A new global 1-km dataset of percentage tree cover derived from remote sensing. Glob. Chang. Biol 2000, 6, 247–254.
[5]
Geospatial Information Authority of Japan, Chiba University and Collaborating Organizations. Global Map—Global Land Cover (GLCNMO), Global Map—Percent Tree Cover, 2008. Available online: http://www.iscgm.org/login.html (accessed on 1 April 2013).
[6]
Hansen, M.C.; DeFries, R.S.; Townshend, J.R.G.; Carroll, M.; Dimiceli, C.; Sohlberg, R.A. Global percent tree cover at a spatial resolution of 500 meters: First results of the MODIS vegetation continuous fields algorithm. Earth Interact 2003, 7, 1–15.
[7]
Rokhmatuloh; Nitto, D.; Al-Bilbisi, H.; Arihara, K.; Tateishi, R. Estimating percent of tree cover using regression tree method with very-high-resolution QuickBird images as training data. J. Remote Sens. Soc. Jpn 2007, 27, 1–12.
[8]
Rokhmatuloh, R.; Tateishi, R.; Al-Bilbisi, H.; Arihara, K.; Kobayashi, T.; Nitto, D.; Lee, S.A.; Hirabayashi, K.; Lu, Y.Q.; Lu, C.; et al. Global percent tree cover mapping using regression tree method: An advantage of QuickBird images as training data. Asian J. Geoinform 2010, 10, 21–28.
[9]
Berberoglu, S.; Satir, O.; Atkinson, P.M. Mapping percentage tree cover from Envisat MERIS data using linear and nonlinear techniques. Int. J. Remote Sens 2009, 30, 4747–4766.
[10]
Heiskanen, J.; Kivinen, S. Assessment of multispectral, -temporal and -angular MODIS data for tree cover mapping in the tundra-taiga transition zone. Remote Sens. Environ 2008, 112, 2367–2380.
[11]
Schwarz, M.; Zimmermann, N.E. A new GLM-based method for mapping tree cover continuous fields using regional MODIS reflectance data. Remote Sens. Environ 2005, 95, 428–443.
[12]
Hansen, M.C.; DeFries, R.S.; Townshend, J.R.G.; Marufu, L.; Sohlberg, R. Development of MODIS tree cover validation data set for Western Province, Zambia. Remote Sens. Environ 2002, 83, 320–335.
[13]
Hansen, M.C.; DeFries, R.S.; Townshend, J.R.G.; Sohlberg, R.; Dimiceli, C.; Carroll, M. Towards an operational MODIS continuous field of percent tree cover algorithm: Examples using AVHRR and MODIS data. Remote Sens. Environ 2002, 83, 303–319.
[14]
White, M.A.; Shaw, J.D.; Ramsey, R.D. Accuracy assessment of the vegetation continuous field tree cover product using 3954 ground plots in the south-western USA. Int. J. Remote Sens 2005, 26, 2699–2704.
[15]
Lefsky, M.A. A global forest canopy height map from the Moderate Resolution Imaging Spectroradiometer and the Geoscience Laser Altimeter System. Geophys. Res. Lett 2010, 37, doi:10.1029/2010GL043622.
[16]
Heiskanen, J. Evaluation of global land cover data sets over the tundra-taiga transition zone in northernmost Finland. Int. J. Remote Sens 2008, 29, 3727–3751.
[17]
Montesano, P.M.; Nelson, R.; Sun, G.; Margolis, H.; Kerber, A.; Ranson, K.J. MODIS tree cover validation for the circumpolar taiga-tundra transition zone. Remote Sens. Environ 2009, 113, 2130–2141.
[18]
Arino, O.; Bicheron, P.; Achard, F.; Latham, J.; Witt, R.; Weber, J.L. GLOBCOVER: The most detailed portrait of Earth. Eur. Space Agency Bull 2008, 136, 24–31.
[19]
Friedl, M.A.; Sulla-Menashe, D.; Tan, B.; Schneider, A.; Ramankutty, N.; Sibley, A.; Huang, X. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ 2010, 114, 168–182.
[20]
Herold, M.; Mayaux, P.; Woodcock, C.E.; Baccini, A.; Shmullius, C. Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets. Remote Sens. Environ 2008, 112, 2538–2556.
[21]
Pflugmacher, D.; Krankina, O.N.; Cohen, W.B.; Friedl, M.A.; Sulla-Menashe, D.; Kennedy, R.E.; Nelson, P.; Loboda, T.V.; Kuemmerle, T.; Dyukarev, E.; et al. Comparison and assessment of coarse resolution land cover maps for Northern Eurasia. Remote Sens. Environ 2011, 115, 3539–3553.
[22]
Hansen, M.C.; DeFries, R.S.; Townshend, J.R.G.; Carroll, M.; Dimiceli, C.; Sohlberg, R.A. Vegetation Continuous Fields MOD44B, 2008 Percent Tree Cover, Collection 5, 2011. Available online: http://www.glcf.umd.edu/data/vcf/ (accessed on 1 April 2013).
[23]
FAO (Food and Agriculture Organization of the United Nations). Global Forest Resources Assessment Update 2005: Terms and Definitions; FAO: Rome, Italy, 2004.
[24]
Hallé, F.; Oldeman, R.A.A.; Tomlinson, P.B. What is a Tree? In Tropical Trees and Forests: An Architectural Analysis; Springer-Verlag: Berlin, Germany, 1978; pp. 1–6.
[25]
Nicholson, B.E.; Clapham, A.R. The Oxford Book of Trees; Oxford University Press: London, UK, 1975.
[26]
Oldeman, R.A.A. Second Part: Forest Components. In Forests: Elements of Silvology; Springer-Verlag: Berlin, Germany, 1990; pp. 27–154.
[27]
Thomas, P. Trees: Their Natural History; Cambridge University Press: Cambridge, UK, 2000.
[28]
Peel, M.C.; Finlayson, B.L.; McMahon, T.A. Updated world map of the Koppen-Geiger climate classification. Hydrol. Earth Syst. Sci 2007, 11, 1633–1644.
[29]
Hoan, N.T.; Tateishi, R.; Al-Bilbisi, H. Global MODIS 2008 Data User’s Manual; Chiba University: Chiba, Japan, 2011.
[30]
Schaaf, C.B.; Gao, F.; Strahler, A.H.; Lucht, W.; Li, X.; Tsang, T.; Strugnell, N.C.; Zhang, X.; Jin, Y.; Muller, J.; et al. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens. Environ 2002, 83, 135–148.
[31]
Schaaf, C. MODIS BRDF/Albedo Product (MCD43) User’s Guide, 2004. Available online: http://www-modis.bu.edu/brdf/userguide/intro.html (accessed on 1 April 2013).
[32]
Schaaf, C.; Liu, J.; Gao, F.; Jiao, Z.; Shuai, Y.; Strahler, A. Collection 005 Change Summary for MODIS BRDF/Albedo (MCD43) Algorithms. Available online: http://landweb.nascom.nasa.gov/QA_WWW/forPage/C005_Change_BRDF.pdf (accessed on 1 April 2013).
[33]
Clark, M.L.; Aide, T.M.; Grau, H.R.; Riner, G. A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco ecoregion of South America. Remote Sens. Environ 2010, 114, 2816–2832.
[34]
Joshi, C.; Leeuw, J.D.; Skidmore, A.K.; van Duren, I.C.; van Oosten, H. Remotely sensed estimation of forest canopy density: A comparison of the performance of four methods. Int. J. Appl. Earth Obs 2006, 8, 84–95.
[35]
Hansen, M.C.; Townshend, J.R.G.; DeFries, R.S.; Carroll, M. Estimation of tree cover using MODIS data at global, continental and regional/local scales. Int. J. Remote Sens 2005, 26, 4359–4380.
[36]
Rikimaru, A.; Roy, P.S.; Miyatake, S. Tropical forest cover density mapping. Trop. Ecol 2002, 43, 39–47.
[37]
Pal, M. M5 model tree for land cover classification. Int. J. Remote Sens 2006, 27, 825–831.
[38]
Quinlan, J.R. Learning with Continuous Classes. Proceedings of Australian Joint Conference on Artificial Intelligence, Hobart, Australia, 16–18 November 1992; World Scientific Press: Singapore, Singapore, 1992; pp. 343–348.
[39]
Li, A.; Huang, C.; Sun, G.; Shi, H.; Toney, C.; Zhu, Z.; Rollins, M.G.; Goward, S.N.; Zhu, Z. Modeling the height of young forests regenerating from recent disturbances in Mississippi using Landsat and ICESat data. Remote Sens. Environ 2011, 115, 1837–1849.
