Volume XL-1/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 43-47, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W3-43-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 43-47, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W3-43-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

  24 Sep 2013

24 Sep 2013

EVALUATION OF DIFFERENT CHANGE DETECTION TECHNIQUES IN FORESTRY FOR IMPROVEMENT OF SPATIAL OBJECTS EXTRACTION ALGORITHMS BY VERY HIGH RESOLUTION REMOTE SENSING DIGITAL IMAGERY

N. Amiri N. Amiri
  • Dept. of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden

Keywords: Change detection, pixel based, object based, VHRS optical images, Forestry

Abstract. Earth observations which are being useable by spatial analysis ability play an important role in detecting, management and solving environmental problems such as climate changes, deforestation, disasters, land use, water resource and carbon cycle. Remote sensing technology in combination with geospatial information system (GIS) can render reliable information on vegetation cover. Satellite Remote sensed data and GIS for land cover/use with its changes is a key to many diverse applications such as Forestry.

Change detection can be defined as the process of identifying differences in the state of an object or phenomenon by observing it at different times. The analysis of the spatial extent and temporal change of vegetation cover (Forest) by using remotely sensed data is critically importance to natural resource management sciences.

The main aim of this review paper is to go through the different change detection methods and algorithms based on very high resolution remote sensing imagery data, evaluate the quality of the spatial individual crown cover extraction in forests with high density, analyse, compare the results by optimized performance of control data for the same objects to provide the improvement in technique for detection and improve the mathematical sides of the change detection algorithms for high dense forests regions with different boundaries.