Interpretation of vegetation was implemented with the use of high resolution imagery from satellites QuickBird-2 (2.4 and 0.6 m), GeoEye-1 (2 and 0.5 m) and WorldView-2 (2 and 0.5 m). The following morphological and physiological characteristics of objects were used as indicators for interpretation: morphometric features of cover projection of vegetation (tree crown) on snow, estimated by reflection of vegetation in the early spring image, and integral index of photosynthetic activity of vegetation, estimated by NDVI from summer image. Conceptual and methodological aspects of direct expert interpretation of vegetation by methods of classification with the use of raster algebra are considered. Validation of interpretation results by field observations showed 70−100% precision of mapping different types of vegetation (6 classes for level of formations and groups of formation). Accounting more morphological and physiological characteristics allows to increase the accuracy of interpretation. However, some problems of using high resolution images of (<1 m) should be noted. For example, the problem of inaccurate geometric correction of high resolution images, provided for the research, and different camera angles during acquisition of images. These factors do not allow to conduct precise comparison of multitemporal images and to use the abilities of change detection by texture features of vegetation cover on the pixel level. This raises generalization as the necessary step in interpretation of high resolution images by classification methods of raster algebra.

Original languageEnglish
Pages (from-to)147-156
Number of pages10
JournalSovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa
Volume15
Issue number1
DOIs
StatePublished - 1 Jan 2018

    Scopus subject areas

  • Computer Science Applications
  • Computers in Earth Sciences
  • Computer Networks and Communications

    Research areas

  • Classification, Generalization, GIS, High resolution imagery, Interpretation, Interpretation indicators, Methods, Morphological, Physiological indicators, Raster algebra, Remote sensing data, Vegetation

ID: 35961006