Automatic environmental quality assessment for mixed-land zones using lidar and intelligent techniques

► LiDAR can provide excellent information to improve thematic maps in especially interesting areas. ► Intelligent techniques are a key factor to provide fast and accurate results when lidar is applied to the study of the natural environment. ► Our experimentation on real data from a riparian area in...

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Bibliographic Details
Published in:Expert systems with applications 2011-06, Vol.38 (6), p.6805-6813
Main Authors: Garcia-Gutierrez, Jorge, Gonçalves-Seco, Luis, Riquelme-Santos, Jose C.
Format: Article
Language:English
Subjects:
Algorithms
Assessments
Classification
Clouds
Cost engineering
Decision trees
Intelligent techniques
Land cover
Land use
Land uses
Lidar
LULC
Mouth
Natural resources
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Summary:► LiDAR can provide excellent information to improve thematic maps in especially interesting areas. ► Intelligent techniques are a key factor to provide fast and accurate results when lidar is applied to the study of the natural environment. ► Our experimentation on real data from a riparian area in the south of Spain shows decision trees (C4.5) provide the best results with the highest level of clarity for the final model. Human impact on the natural environment is an evident global fact. Natural, industrial and touristic areas coexist in a more than delicate balance. In Andalusia, in the south of Spain, the Regional Ministry for the Environment is responsible for the control and preservation of natural resources. This task bears a high cost in time and money. Remote sensing and the use of intelligent techniques are excellent tools to reduce such costs. This work explores the joint use of the lidar sensor, which provides a great quantity of information describing three dimensional space, and the application of intelligent techniques for rapid and efficient land use and land cover classification with the objective of differentiating urban land from natural ground close to protected areas of Huelva province. For this, seven types of land use and land cover have been studied for a riparian area next to the mouth of the rivers Tinto and Odiel, extracting 33 distinct features from the lidar point cloud. Subsequently, a supervised learning algorithm is applied to construct a model which, with a resolution of 4m2, obtained relative precision between 71% and 100% and an average total precision of 85%.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2010.12.065