Testing Ikonos and Landsat 7 ETM+ Potential for Stand-Level Forest Type Mapping by Soft Supervised Approaches

Forest types can be adopted as a suitable reference for classifying survey units within multipurpose forest resources inventories, at the properly considered level. This kind of hierarchical classification approach integrates an ecologically meaningful per-habitat perspective with practical survey, planning and management requirements. Advanced remote sensing technologies can be valuable tools for a cost-effective implementation of such an approach. In the present paper, data from high (Landsat 7 ETM+) and very high (Ikonos) spatial resolution satellite sensors were tested to understand their potential contribution supporting stand-level forest type mapping under Mediterranean conditions. Ikonos and Landsat images were used to differentiate forest coverages by so called soft classifiers: fuzzy maximum likelihood procedure for Ikonos and subpixel unmixing procedure for Landsat. Fuzzy classified images are then contrasted with forest type map made by photointerpretation of Ikonos imagery. Perfomances are showed and drawbacks discussed.

[1]  R. Mccoy,et al.  Mapping Desert Shrub Rangeland Using Spectral Unmixing and Modeling Spectral Mixtures with TM Data , 1997 .

[2]  Giles M. Foody,et al.  Fuzzy modelling of vegetation from remotely sensed imagery , 1996 .

[3]  Yosio Edemir Shimabukuro,et al.  The least-squares mixing models to generate fraction images derived from remote sensing multispectral data , 1991, IEEE Trans. Geosci. Remote. Sens..

[4]  Maria Petrou,et al.  Recovering more classes than available bands for sets of mixed pixels in satellite images , 2000, Image Vis. Comput..

[5]  M. Bauer,et al.  Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method , 2001 .

[6]  William J. Emery,et al.  Unmixing multiple land-cover type reflectances from coarse spatial resolution satellite data , 1995 .

[7]  John A. Richards,et al.  Remote Sensing Digital Image Analysis: An Introduction , 1999 .

[8]  S. Franklin.  Remote Sensing for Sustainable Forest Management , 2001 .

[9]  J. Hyyppä,et al.  Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes , 2000 .

[10]  J. Settle,et al.  Linear mixing and the estimation of ground cover proportions , 1993 .

[11]  Leonardo Filesi,et al.  Il ruolo della Riserva del Litorale nella pianificazione delle risorse naturalistiche dell’area metropolitana di Roma , 1999 .

[12]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[13]  Xavier Pons,et al.  On the applicability of Landsat TM images to Mediterranean forest inventories , 1998 .

[14]  Giles M. Foody,et al.  Estimation of sub-pixel land cover composition in the presence of untrained classes , 2000 .

[15]  Fangju Wang,et al.  Fuzzy supervised classification of remote sensing images , 1990 .

[16]  John A. Richards,et al.  Remote Sensing Digital Image Analysis , 1986 .

[17]  J. Campbell.  Introduction to remote sensing , 1987 .