A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Spatial video remote sensing for urban vegetation mapping using vegetation indices
Abstract Urban vegetation is important because of the fast-growing urbanization. If we want cities to have sustainable growth and well-kept ecology, we need to develop a smart and efficient urban vegetation monitoring system. This paper examines the possibility of using a modified GoPro camera mounted on a car. The lens of a GoPro camera was replaced with the NDVI-7 lens to obtain blue, green and near-infrared band. The performance of four vegetation indices was tested: Blue normalized difference vegetation index (BNDVI), Green normalized difference vegetation index (GNDVI), Green-blue normalized difference vegetation index (GBNDVI), Blue-wide dynamic range vegetation index (BWDRVI). Based on those indices, binary classification was performed to classify objects in the scene as either vegetation or non-vegetation. Finally, the accuracy of each index was assessed on three different study sites. Results show that GBNDVI performs best for the given task with average classification accuracy of 95.10% for all study sites.
Spatial video remote sensing for urban vegetation mapping using vegetation indices
Abstract Urban vegetation is important because of the fast-growing urbanization. If we want cities to have sustainable growth and well-kept ecology, we need to develop a smart and efficient urban vegetation monitoring system. This paper examines the possibility of using a modified GoPro camera mounted on a car. The lens of a GoPro camera was replaced with the NDVI-7 lens to obtain blue, green and near-infrared band. The performance of four vegetation indices was tested: Blue normalized difference vegetation index (BNDVI), Green normalized difference vegetation index (GNDVI), Green-blue normalized difference vegetation index (GBNDVI), Blue-wide dynamic range vegetation index (BWDRVI). Based on those indices, binary classification was performed to classify objects in the scene as either vegetation or non-vegetation. Finally, the accuracy of each index was assessed on three different study sites. Results show that GBNDVI performs best for the given task with average classification accuracy of 95.10% for all study sites.
Spatial video remote sensing for urban vegetation mapping using vegetation indices
Rumora, Luka (author) / Majić, Ivan (author) / Miler, Mario (author) / Medak, Damir (author)
Urban Ecosystems ; 24
2020
Article (Journal)
Electronic Resource
English
BKL:
43.31
Naturschutz
/
42.90$jÖkologie: Allgemeines
/
43.31$jNaturschutz
/
42.90
Ökologie: Allgemeines
/
74.12
Stadtgeographie, Siedlungsgeographie
/
74.12$jStadtgeographie$jSiedlungsgeographie
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