FIG Peer Review Journal


Mapping and Modelling of Animal Diversity Index in Green Campus Using Integrated Geospatial Technique and In-Situ Camera Trapping (6807)

Mazlan Hashim, Mohd Syafiq Shukor (Malaysia), Shinya Numata (Japan), Samsudin Ahmad and Syarifuddin Misbari (Malaysia)
Dr. Mazlan Hashim
Inst of Geopatial Science & Tehnology (INSTeG)
Universiti Teknologi Malaysia (UTM)
Faculty of Geoinformation
Unversiti Teknologi Malaysia
Johor Bahru
Corresponding author Dr. Mazlan Hashim (email: mazlanhashim[at], tel.: 607 5557622)

[ abstract ] [ paper ] [ handouts ]

Published on the web 2014-03-21
Received 2013-11-15 / Accepted 2014-02-06
This paper is one of selection of papers published for the FIG Congress 2014 in Kuala Lumpur, Malaysia and has undergone the FIG Peer Review Process.

FIG Congress 2014
ISBN 978-87-92853-21-9 ISSN 2308-3441


This paper reports a biodiversity index of ground animal species using the indirect remote sensing approach for large-scale mapping. Remotely sensed data acquired from World View 2 satellite data were used to obtain biophysical parameters, where all these parameters are then utilized for modelling of animal biodiversity mapping in a green landscape of Universiti Teknologi Malaysia campus. Three biodiversity indices, namely, species richness, evenness, and diversity were mapped and analysed against ground truth obtained from unmanned sensor-camera trappings. The biophysical parameters derived from the remote sensing and ancillary information for the mammal habitat heterogeneity was categorized based on relevancy to vegetation density and moisture presence within the canopy and vegetated areas. Results of this study demonstrate the utility of satellite remote sensing, especially with the new generation of fine spatial and spectral data such as World View2 data, for mapping animal biodiversity at large scale. The derived richness, diversity and evenness indices were shown to agree fully with the in-situ observations.
Keywords: Geoinformation/GI; Animal, biodiversity index; remote sensing, World View2