Classifying Forest Species Using Hyperspectral Data in Balah Forest Reserve, Kelantan, Peninsular Malaysia

Zain, Ruhasmizan Mat;Ismail, Mohd Hasmadi;Zaki, Pakhriazad Hassan

  • 투고 : 2012.08.03
  • 심사 : 2012.09.20
  • 발행 : 2013.05.31


This study attempts to classify forest species using hyperspectral data for supporting resources management. The primary dataset used was AISA sensor. The sensor was mounted onboard the NOMAD GAF-27 aircraft at 2,000 m altitude creating a 2 m spatial resolution on the ground. Pre-processing was carried out with CALIGEO software, which automatically corrects for both geometric and radiometric distortions of the raw image data. The radiance data set was then converted to at-sensor reflectance derived from the FODIS sensor. Spectral Angle Mapper (SAM) technique was used for image classification. The spectra libraries for tree species were established after confirming the appropriate match between field spectra and pixel spectra. Results showed that the highest spectral signature in NIR range were Kembang Semangkok (Scaphium macropodum), followed by Meranti Sarang Punai (Shorea parvifolia) and Chengal (Neobalanocarpus hemii). Meanwhile, the lowest spectral response were Kasai (Pometia pinnata), Kelat (Eugenia spp.) and Merawan (Hopea beccariana), respectively. The overall accuracy obtained was 79%. Although the accuracy of SAM techniques is below the expectation level, SAM classifier was able to classify tropical tree species. In future it is believe that the most effective way of ground data collection is to use the ground object that has the strongest response to sensor for more significant tree signatures.


Classify tree species;hyperspectral;Spectral Angle Mapper;spectral signature;tropical forest;accuracy assessment


  1. Affendi S, Ainuddin NA, Awg Noor AG, Faridah Hanum I, Shafri HZM, Manohar M. 2005. Optimized selection of hyperspectral bandsets for mapping of tropical timber trees using hyperspectral imaging sensors. In proceedings of the 14th Malaysian Forestry Conference, Kota Kinablu, Sabah, Malaysia.
  2. Bo Zhou. 2007. Application of hyperspectral remote sensing in detecting and mapping sericea lespedeza in missouri. MS Thesis. University of Missouri-Columbia.
  3. Congalton RG, Green K. 1999. A Comparison of sampling schemes used in generating error matrices for assessing the accuracy of maps generated from remotely sensed data. Photogramm Eng Remote Sens 54: 593-600.
  4. Crosta AP, Sabine C, Taranik JV. 1998. Hydrothermal alteration mapping at bodie, california, using aviris hyperspectral data. Remote Sens Environ 65: 309-319.
  5. Felix NA, Binney DL. 1989. Accuracy Assessment of a landsat assisted vegetation map of the coastal plain of the artic national wildlife refuge. Photogramm Eng Remote Sens 55: 475-478.
  6. Goetz AF, Vane G, Solomon JE, Rock BN. 1985. Imaging spectrometry for Earth remote sensing. Science 228: 1147-1153.
  7. Helmi Zulhaidi MS, Affendi S, Shattri M. 2007. The Performance of maximum likelihood, spectral angle mapper, neural network and decision tree classifiers in hyperspectral image analysis. J Computer Sc 3: 419-423.
  8. Kruse FA, Lefkoff AB, Boardman JW, Heidebrecht KB, Shapiro AT, Barloon PJ, Goetz AFH. 1993. The Spectral image processing system (SIPS)-interactive visualization and analysis of imaging spectrometer data. Remote Sens Environ 44: 145-163
  9. Mohd Hasmadi I, Kamaruzaman J, Alias MS, Pakhriaza HZ, Manohar M. 2009. A review on application of hyperspectral imaging to forest resources in malaysia. J Sustainability Sc and Mgmt 4: 75-84.
  10. Pearce D, Putz FE, Vanclay JK. 2003. Sustainable forestry in the tropics: panacea or folly? Forest Ecology and Management 172: 229-247.
  11. Sayer J, Campbell B, Petheram L, Aldrich M, Ruiz Perez M, Endamana D, Dongmo ZN. 2007. Assessing environment and development outcomes in conservation landscapes. Biodivers Conserv 16: 2677-2694.
  12. Sohn Y, Rebello NS. 2002. Supervised and unsupervised spectral angle classifiers. Photogramm Eng Remote Sens 68: 1271-1280.
  13. Van Der Meer FD. 1994. Extraction of mineral absorption features from high-spectral resolution data using non-parametric geostatistical techniques. International Journal of Remote Sensing 15: 2193-2214.
  14. Woodcock CE, Collins JB, Gopal S, Jakabhazy VD, Li X, Macomber S, Rherd S, Harward VJ, Levitan J, Wu Y, Warbington R.1994. Mapping forest vegetation using landsat tm imagery and A canopy reflectance model. Remote Sensing of Environment 50: 240-254.