Remote sensing of upland vegetation: the potential of high spatial resolution satellite sensors

Aim  Traditional methodologies of mapping vegetation, as carried out by ecologists, consist primarily of field surveying or mapping from aerial photography. Previous applications of satellite imagery for this task (e.g. Landsat TM and SPOT HRV) have been unsuccessful, as such imagery proved to have insufficient spatial resolution for mapping vegetation. This paper reports on a study to assess the capabilities of the recently launched remote sensing satellite sensor Ikonos, with improved capabilities, for mapping and monitoring upland vegetation using traditional image classification methods. Location  The location is Northumberland National Park, UK. Methods  Traditional remote sensing classification methodologies were applied to the Ikonos data and the outputs compared to ground data sets. This enabled an assessment of the value of the improved spatial resolution of satellite imagery for mapping upland vegetation. Post-classification methods were applied to remove noise and misclassified pixels and to create maps that were more in keeping with the information requirements of the NNPA for current management processes. Results The approach adopted herein for quick and inexpensive land cover mapping was found to be capable of higher accuracy than achieved with previous approaches, highlighting the benefits of remote sensing for providing land cover maps. Main conclusions  Ikonos imagery proved to be a useful tool for mapping upland vegetation across large areas and at fine spatial resolution, providing accuracies comparable to traditional mapping methods of ground surveys and aerial photography.

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

[2]  Des B. A. Thompson,et al.  Upland heather moorland in Great Britain: A review of international importance, vegetation change and some objectives for nature conservation , 1995 .

[3]  H. E. Dresser.  Society for the Protection of Birds , 1896 .

[4]  G. M. Foody.  The Continuum of Classification Fuzziness in Thematic Mapping , 1999 .

[5]  Zhenkui Ma,et al.  Tau coefficients for accuracy assessment of classification of remote sensing data , 1995 .

[6]  R. Lucas,et al.  Non-linear mixture modelling without end-members using an artificial neural network , 1997 .

[7]  Clive S. Fraser,et al.  Geopositioning Accuracy of Ikonos Imagery: Indications from Two Dimensional Transformations , 2001 .

[8]  P. Chavez.  An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data , 1988 .

[9]  Colin J. McClean,et al.  The reliability of ‘Phase 1’ habitat mapping in the UK: the extent and types of observer bias , 1999 .

[10]  A J Morton.  Moorland plant community recognition using Landstat MSS data , 1986 .

[11]  P. Atkinson,et al.  Mapping sub-pixel proportional land cover with AVHRR imagery , 1997 .

[12]  Erik Næsset,et al.  Conditional tau coefficient for assessment of producer's accuracy of classified remotely sensed data , 1996 .

[13]  Nigel M. Trodd.  Analysis and Representation of Heathland Vegetation from Near-Ground Level Remotely-Sensed Data , 1996 .

[14]  P. Chavez.  Image-Based Atmospheric Corrections - Revisited and Improved , 1996 .

[15]  I. Kanellopoulos,et al.  Land-cover discrimination in SPOT HRV imagery using an artificial neural network - a 20-class experiment , 1992 .

[16]  J. Cihlar.  Land cover mapping of large areas from satellites: Status and research priorities , 2000 .

[17]  T. W. Ray,et al.  Remote monitoring of shifting sands and vegetation cover in arid regions , 1994, Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium.

[18]  N. Veitch,et al.  Habitat mapping from satellite imagery and wildlife survey data using a Bayesian modeling procedure in a GIS , 1993 .

[19]  R. Fuller,et al.  A comparison of land cover types in an ecological field survey in Northern England and a remotely sensed land cover map of Great Britain , 1995 .

[20]  Giles M. Foody,et al.  On the compensation for chance agreement in image classification accuracy assessment, Photogram , 1992 .

[21]  J. Slater,et al.  Changing landscapes: Monitoring Environmentally Sensitive Areas using satellite imagery , 2000 .

[22]  A. Jones,et al.  The Land Cover Map of Great Britain: an automated classification of Landsat Thematic Mapper data , 1994 .