Publication Date:
2019-03-01
Description:
The construction of transport infrastructure is often preceded by an environmentalimpact assessment procedure, which should identify amphibian breeding sites and migrationroutes. However, the assessment is very difficult to conduct because of the large number ofhabitats spread out over a vast expanse, and the limited amount of time available for fieldwork.We propose utilizing local environmental variables that can be gathered remotely using only GISsystems and satellite images together with machine learning methods. In this article, we introducesix new and easily extractable types of environmental features. Most of the features we proposecan be easily obtained from satellite imagery and spatial development plans. The proposed featurespace was evaluated using four machine learning algorithms, namely: a C4.5 decision tree,AdaBoost, random forest and gradient-boosted trees. The obtained results indicated that theproposed feature space facilitated prediction and was comparable to other solutions. Moreover,three of the new proposed features are ranked most important; these are the three dominantproperties of the surroundings of water reservoirs. One of the new features is the percentageaccess from the edges of the reservoir to open areas, but it affects only a few species. Furthermore,our research confirmed that the gradient-boosted trees were the best method for the analyzeddataset.
Electronic ISSN:
2220-9964
Topics:
Architecture, Civil Engineering, Surveying
,
Geosciences
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