Skip to main content

Fine-scale mapping of a grassland from digitized aerial photography: an approach using image segmentation and discriminant analysis

Buy Article:

$63.00 plus tax (Refund Policy)

Conventional methods of classification from remotely-sensed images seldom discriminate accurately among the land cover categories that are relevant in ecological applications. In the present study, we apply an image segmentation technique to a high-spatial-resolution (13.5cm), digitized, aerial, colour-infrared photograph of an annual grassland and subsequently identify land cover categories through field inspection and linear canonical discriminant analysis of the image. We show that per-segment statistics can be used to discriminate among four land cover categories bunch grasses, dense cover of annuals, sparse cover of annuals and bare ground while conventional per-pixel statistics produce low separabilities for the same categories. We also show that soil disturbances by pocket gophers (Thomomys bottae) can be identified and they are significantly concentrated in areas covered by bunch grasses at the time of image acquisition. We conclude that image segmentation and linear canonical discriminant analysis of high spatial resolution imagery provides an adequate tool for monitoring a grassland's patch dynamics at a scale and with a legend that are compatible with outputs of spatially-explicit ecological models.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Data/Media
No Metrics

Document Type: Research Article

Publication date: 1998-01-10

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more