Volume III-8
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-8, 145-151, 2016
https://doi.org/10.5194/isprs-annals-III-8-145-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-8, 145-151, 2016
https://doi.org/10.5194/isprs-annals-III-8-145-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  07 Jun 2016

07 Jun 2016

MEASURE OF LANDSCAPE HETEROGENEITY BY AGENT-BASED METHODOLOGY

E. Wirth1, Gy. Szabó1, and A. Czinkóczky2 E. Wirth et al.
  • 1Dept. of Photogrammetry and Geoinformatics, Budapest University of Technology and Economics, 1111 Budapest, Hungary
  • 2Lab. of Spatial Analysis and Environment, Szent István University, 2103 Gödöllő, Hungary

Keywords: agent-based modelling, greening, heterogeneity, simulation, Monte Carlo method, diversity, landscape, potential

Abstract. With the rapid increase of the world's population, the efficient food production is one of the key factors of the human survival. Since biodiversity and heterogeneity is the basis of the sustainable agriculture, the authors tried to measure the heterogeneity of a chosen landscape. The EU farming and subsidizing policies (EEA, 2014) support landscape heterogeneity and diversity, nevertheless exact measurements and calculations apart from statistical parameters (standard deviation, mean), do not really exist. In the present paper the authors’ goal is to find an objective, dynamic method that measures landscape heterogeneity. It is achieved with the so called agent-based modelling, where randomly dispatched dynamic scouts record the observed land cover parameters and sum up the features of a new type of land. During the simulation the agents collect a Monte Carlo integral as a diversity landscape potential which can be considered as the unit of the ‘greening’ measure. As a final product of the ABM method, a landscape potential map is obtained that can serve as a tool for objective decision making to support agricultural diversity.