Remote sensing of fractional green vegetation cover using spatially-interpolated endmembers

Remote Sensing

Volumn 4, Issue 9, 2012, Pages 2619-2634

  Johnson, Brian ^a   Tateishi, Ryutaro ^a   Kobayashi, Toshiyuki ^a  

^a CHIBA UNIVERSITY   (Japan)

Author keywords

Fractional vegetation cover; Kriging; Linear spectral unmixing; Msavis; Ndvi; Spatial interpolation; Spectral mixture analysis; Subpixel mapping

Indexed keywords

FRACTIONAL VEGETATION COVER; KRIGING; LINEAR SPECTRAL UNMIXING; MSAVIS; NDVI; SPATIAL INTERPOLATION; SPECTRAL MIXTURE ANALYSIS; SUB-PIXEL MAPPING;

AUTOCORRELATION; IMAGE RECONSTRUCTION; INTERPOLATION; PHOTOMAPPING; REMOTE SENSING; SOILS; VEGETATION;

ESTIMATION;

EID: 84870667903     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs4092619     Document Type: Article

References (40)

1. Adams, J.; Smith, M.; Johnson, P. Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander 1 Site. J. Geophys. Res. 1986, 91, 8098-8112.
2. de Asis, A.; Omasa, K.; Oki, K.; Shimizu, Y. Accuracy and applicability of linear spectral unmixing in delineating potential erosion areas in tropical watersheds. Int. J. Remote Sens. 2008, 29, 4151-4171.
3. Gutman, G.; Ignatov, A. The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models, Int. J. Remote Sens. 1998, 19, 1533-1543.
4. Zeng, X.; Dickinson, R.E.; Walker, A.; Shaikh, M.; Defries, R.S.; Qi, J. Derivation and evaluation of global 1-km fractional vegetation cover data for land modeling. J. Appl. Meteorol. 2000, 39, 826-839.
5. Montandon, L.; Small, E. The impact of soil reflectance on the quantification of the greenvegetation fraction from NDVI. Remote Sens. Environ. 2008, 112, 1835-1845.
6. James, K.; Stensrud, D.; Yussouf, N. Value of real-time vegetation fraction to forecasts of severe convection in high-resolution models. Weather Forecast. 2009, 24, 187-210.
7. Jiménez-Muñoz, J.; Sobrino, J.; Plaza, A.; Guanter, L.; Moreno, J.; Martinez, P. Comparison between fractional vegetation cover retrievals from vegetation indices and spectral mixture analysis: Case study of PROBA/CHRIS data over an agricultural area. Sensors 2009, 9, 768-793.
8. Gilalabert M.; Gonzalez-Piqueras, F.; Garcia-Haro, F.; Melia, J. A generalized soil-adjusted vegetation index. Remote Sens. Environ. 2002, 82, 303-310.
9. Gitelson, A.; Kaufman, Y.; Stark, R.; Rundquist, D. Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ. 2002, 80, 76-87.
10. Xiao, J.; Moody, A. A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA. Remote Sens. Environ. 2005, 98, 237-250.
11. Zhou, X.; Guan, H.; Xie, H.; Wilson, J. Analysis and optimization of NDVI definitions and arealfraction models in remote sensing of vegetation. Int. J. Remote Sens. 2009, 30, 721-751.
12. Qi, J.; Marsett, R.; Moran, M.; Goodrich, D.; Heilman, P.; Kerr, Y.; Dedieu, G. Spatial and temporal dynamics of vegetation in the San Pedro River basin area. Agr. For. Meteorol. 2000, 105, 55-68.
13. Guilfoyle, K.; Althouse, M.; Chang, C. A quantitative and comparative analysis of linear and nonlinear spectral mixture models using radial basis function neural networks. IEEE Trans. Geosci. Remote Sens. 2001, 39, 2314-2318.
14. Naksuntorn, N. Nonlinear spectral mixture analysis for hyperspectral imagery in an unknown environment. IEEE Geosci. Remote Sens. Lett. 2010, 7, 836-840.
15. Heylen, R.; Burazerovic, D.; Scheunders, P. Non-linear spectral unmixing by geodesic simplex volume maximization. IEEE J. Sel. Top. Signal Process. 2011, 5, 534-542.
16. Obata, K.; Yoshioka, H. Relationships between errors propogated in fraction of vegetation cover by algorithms based on a two-endmember linear mixture model. Remote Sens. 2010, 2, 2680-2699.
17. Theseira, M.; Thomas, G.; Sannier, A. An evaluation of spectral mixture modeling applied to a semi-arid environment. Int. J. Remote Sens. 2002, 23, 687-700.
18. Obata, K.; Yoshioka, H. Inter-algorithm relationships for the estimation of the fraction of vegetation cover based on a two endmember linear mixture model with the VI constraint. Remote Sens. 2010, 2, 1680-1701.
19. Merlin, O.; Duchemin, B.; Hagolle, O.; Jacob, F.; Coudert, B.; Chehbouni, G.; Dedieu, G.; Garatuza, J.; Kerr, Y. Disaggregation of MODIS surface temperature over an agricultural area using a time series of Formosat-2 images. Remote Sens. Environ. 2010, 114, 2500-2512.
20. Baumgardner, M.; Silva, L.; Biehl, L.; Stoner, E. Reflectance Properties of Soils. In Advances in Agronomy; Brady, N., Ed.; America Press Inc.: Orlando, FL, USA, 1985.
21. Jensen, J. Introductory Digital Image Processing: A Remote Sensing Perspective, 3rd ed.; Pearson Prentice Hall: Upper Saddle River, NJ, USA, 2004.
22. Richter, R.; Kellenberger, T.; Kaufmann, H. Comparison of topographic correction methods. Remote Sens. 2009, 1, 184-196.
23. Huete, A. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 1988, 25, 295-309.
24. Matsui, T.; Lakshmi, V.; Small, E. The effects of satellite-derived vegetation cover variability on simulated land-atmosphere interactions in the NAMS. J. Climate 2005, 18, 21-40.
25. Johnson, B.; Tateishi, R.; Xie, Z. Using geographically weighted variables for image classification. Remote Sens. Lett. 2012, 3, 491-499.
26. Rouse, J.; W., Haas, R.; Schell, J.; Deering, D.; Harlan, J. Monitoring the Vernal Advancement of Retrogradation of Natural Vegetation; Type III Final Report; NASA/GSFC: Greenbelt, MD, USA, 1974; p. 371.
27. Qi, J.; Chehbouni, A.; Huete, A.; Kerr, Y.; Sorooshian, S. A modified soil adjusted vegetation index (MSAVI). Remote Sens. Environ. 1994, 48, 119-126.
28. Kodani, J. Ecology and management of conifer plantations in Japan: control of tree growth and maintenance of biodiversity. J. Forest Res. 2006, 11, 267-274.
29. Seiwa, K.; Eto, Y.; Masaka, K. Effects of thinning intensity on species diversity and timber production in a conifer (Cryptomeria japonica) plantation in Japan. J. Forest. Res. 2011, doi: 10.1007/s10310-011-0316-z.
30. Japan Ministry of Agriculture, Forestry and Fisheries, Annual Report on Forest and Forestry in Japan: Fiscal Year 2010 (Summary). 2010. Available online: http://www.rinya.maff.go.jp/j/ kikaku/hakusyo/22hakusho/pdf/22_e.pdf (accessed on 20 June 2012).
31. Smith, M.; Milton, E. The use of the empirical line method to calibrate remotely sensed data to reflectance. Int. J. Remote Sens. 1999, 20, 2653-2662.
32. Chavez, P. Image-based atmospheric corrections-Revisited and improved. Photogramm. Eng. Remote Sensing 1996, 62, 1025-1036.
33. Baldridge, A.; Hook, S.; Grove, C.; Rivera, G. The ASTER Spectral Library Version 2.0. Remote Sens. Environ. 2009, 113, 711-715.
34. Moran, P. Notes on continuous stochastic phenomena. Biometrika 1950, 37, 17-23.
35. Lam, N. Spatial interpolation methods: A review. Cartogr. Geogr. Inform. 1983, 10, 129-150.
36. O'Sullivan, D.; Unwin, D. Geographic Information Analysis. John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2003.
37. Li, J.; Heap, A. A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors. Ecol. Inform. 2011, 6, 228-241.
38. Kleijnen, J. Kriging metamodeling in simulation: A review. Eur. J. Oper. Res. 2009, 192, 707-716
39. Kuemmerle, T.; Roder, A.; Hill, J. Separating grassland and shrub vegetation by multidate pixel-adaptive spectral mixture analysis. Int. J. Remote Sens. 2006, 27, 3251-3271.
40. Boardman, J. Geometric Mixture Analysis of Imaging Spectrometry Data. In Proceedings of International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 8-12 August 1994; Volume 4, pp. 2369-2371.