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Use of ASTER Optical Indices to Estimate Spatial Variation in Tropical Seasonal Forests on the West Bank of the Mekong River, Cambodia

  • Eriko Ito
  • Sopheap Lim
  • Sopheavuth Pol
  • Bora Tith
  • Phearak Pith
  • Saret Khorn
  • Akihiro Tani
  • Mamoru Kanzaki
  • Takayuki Kaneko
  • Youichirou Okuda
  • Makoto Araki

Abstract

Forest ecosystem parameters related to the amount of evapotranspiration and rain interception are key elements to successful hydrological modeling. Thus, we evaluated ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer) reflectance bands and optical indices for qualitative and quantitative estimation of various characteristics of tropical seasonal forests. Ground conditions were measured in 14 sites in Kampong Thom Province, Cambodia, representing six major tropical seasonal forest types: dry evergreen forest, mixed evergreen-deciduous forest, dry deciduous forest, regrowth of dry evergreen forest, moist evergreen forest, and swamp forest. We performed a discriminant analysis to classify forest types using ASTER reflectance bands and optical indices. We used Visible and near infrared Radiometer (VNIR) and Shortwave Length Infrared Radiometer (SWIR) surface reflectance, four vegetation indices: NDVI (Normalized Difference Vegetation Index); SR (Simple Ratio); DVI (Difference Vegetation Index), and MSAVI2 (Second Modified Soil Adjustment Vegetation Index), and three water content indices: SRWI (Simple Ratio Water Index); NDWI (Normalized Difference Water Index); and LWCI (Leaf Water Content Index), for the discriminant analysis. ASTER image products were acquired on January 12, 2002 in the dry season. We also performed regression analyses to identify an optical index closely correlated with forest qualitative characteristics such as tree density, tree height, basal area, and leaf area index (LAI). Each forest type showed a distinctive pattern in reflectance bands, demonstrating that satellite images can potentially be used for regional forest type classification.

Keywords

Normalize Difference Vegetation Index Forest Type Leaf Area Index Normalize Difference Water Index Optical Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Aber JD, Federer CA (1992) A generalized, lumped-parameter model of photosynthesis, evapotranspiration and net primary production in temperate and boreal forest ecosystems. Oecologia (Berl) 92:463–474CrossRefGoogle Scholar
  2. Anasawa M, Sawada H (2001) Moisture environmental map using leaf water content index (LWCI) (in Japanese). Water Sci 257:1–22Google Scholar
  3. Cohen WB, Maiersperger TK, Gower ST, Turner DP (2003) An improved strategy for regression of biophysical variables and Landsat ETM+ data. Remote Sens Environ 84:561–571CrossRefGoogle Scholar
  4. Engel VC, Stieglitz M, Williams M, Griffin KL (2002) Forest canopy hydraulic properties and catchment water balance: observations and modeling. Ecol Model 154:263–288CrossRefGoogle Scholar
  5. Gao BC (1996) NDWI: a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58:257–266CrossRefGoogle Scholar
  6. Hiramatsu R, Kanzaki M, Toriyama J, Kaneko T, Okuda Y, Ohta S, Khorn S, Pith P, Lim S, Pol S, Ito E, Araki M (2007) Open woodland patches in an evergreen forest of Kampong Thom, Cambodia: correlation of structure and composition with microtopography. In: Sawada H, Araki M, Chappell NA, LaFrankie JV, Shimizu A (eds) Forest Environments in the Mekong River Basin. Springer, Tokyo, 222–230Google Scholar
  7. Hunt ER Jr, Rock BN, Nobel PS (1987) Measurement of leaf relative water content by infrared reflectance. Remote Sens Environ 22:429–435CrossRefGoogle Scholar
  8. Ito E, Khorn S, Lim S, Pol S, Tith B, Pith P, Tani A, Kanzaki M, Kaneko T, Okuda Y, Kabeya N, Nobuhiro T, Araki M (2007) Comparison of the leaf area index (LAI) of two types of dipterocarp forest on the West Bank of the Mekong River, Cambodia. In: Sawada H, Araki M, Chappell NA, LaFrankie JV, Shimizu A (eds) Forest Environments in the Mekong River Basin. Springer, Tokyo, 214–221Google Scholar
  9. Jordan CF (1969) Derivation of leaf area index from quality of light on the forest floor. Ecology 50:663–666CrossRefGoogle Scholar
  10. Kang S, Lee D, Kimball JS (2004) The effects of spatial aggregation of complex topography on hydroecological process simulations within a rugged forest landscape: development and application of a satellite-based topoclimatic model. Can J For Res 34:519–530CrossRefGoogle Scholar
  11. Landsberg JJ, Gower ST (1997) Applications of physiological ecology to forest management. Academic Press, San DiegoGoogle Scholar
  12. Lillesand TS, Kiefer RW, Chipman JW (2004) Remote sensing and image interpretation, 5th edn. Wiley, New YorkGoogle Scholar
  13. Mo X, Liu S, Lin Z, Zhao W (2004) Simulating temporal and spatial variation of evapotranspiration over the Lushi basin. J Hydrol 285:125–142CrossRefGoogle Scholar
  14. Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S (1994) A modified soil adjusted vegetation index. Remote Sens Environ 48:119–126CrossRefGoogle Scholar
  15. Richardson AJ, Everitt JH (1992) Using spectral vegetation indices to estimate rangeland productivity. Geocartogr Int 1:63–69CrossRefGoogle Scholar
  16. Rouse JW, Haas RH, Schell JA, Deering DW (1974) Monitoring vegetation systems in the Great Plains with ERTS. Publication SP-351. NASA, Greenbelt, MDGoogle Scholar
  17. Running SW, Coughlan JC (1988) A general-model of forest ecosystem processes for regional applications. I. Hydrologic balance, canopy gas-exchange and primary production processes. Ecol Model 42:125–154CrossRefGoogle Scholar
  18. Tanaka K, Takizawa H, Tanaka N, Kosaka I, Yoshifuji N, Tantasirin C, Piman S, Suzuki M, Tangtham N (2003) Transpiration peak over a hill evergreen forest in northern Thailand in the late dry season: assessing the seasonal changes in evapotranspiration using a multilayer model. J Geophys Res 108: D17, 4533, doi:10.1029/2002JD003028Google Scholar
  19. Tani A, Ito E, Kanzaki M, Ohta S, Khorn S, Pith P, Tith B, Pol S, Lim S (2007) Principal forest types of three regions of Cambodia: Kampong Thom, Kratie, and Mondolkiri. In: Sawada H, Araki M, Chappell NA, LaFrankie JV, Shimizu A (eds) Forest Environments in the Mekong River Basin. Springer, Tokyo, pp 201–213Google Scholar
  20. Toriyama J, Ohta S, Araki M, Kanzaki M, Khorn S, Pith P, Lim S, Pol S (2007) Soils under different forest types in the dry evergreen forest zone of Cambodia: morphology, physicochemical properties and classification. In: Sawada H, Araki M, Chappell NA, LaFrankie JV, Shimizu A (eds) Forest Environments in the Mekong River Basin. Springer, Tokyo, 241–253Google Scholar
  21. Wang Q, Adiku S, Tenhunen J, Granier A (2005) On the relationship of NDVI with leaf area index in a deciduous forest site. Remote Sens Environ 94:244–255CrossRefGoogle Scholar
  22. Yamazaki T, Yabuki H, Ishii Y, Ohta T, Ohata T (2004) Water and energy exchanges at forests and a grassland in eastern Siberia evaluated using a one-dimensional land surface model. J Hydrometeorol 5:504–515CrossRefGoogle Scholar
  23. Zarco-Tejada PJ, Rueda CA, Ustin SL (2003) Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sens Environ 85:109–124CrossRefGoogle Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • Eriko Ito
    • 1
  • Sopheap Lim
    • 2
  • Sopheavuth Pol
    • 2
  • Bora Tith
    • 2
  • Phearak Pith
    • 2
  • Saret Khorn
    • 2
  • Akihiro Tani
    • 3
  • Mamoru Kanzaki
    • 3
  • Takayuki Kaneko
    • 3
  • Youichirou Okuda
    • 3
  • Makoto Araki
    • 1
  1. 1.Forestry and Forest Products Research Institute (FFPRI)TsukubaJapan
  2. 2.Forest and Wildlife Science Research Institute (FWSRI)Forestry AdministrationPhnom PenhCambodia
  3. 3.Graduate School of AgricultureKyoto UniversityKyotoJapan

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