Work

Detection of urban tree canopy from very high resolution imagery using an object based classification

Year: 2022

Type: article

Abstract: Tree that grows within a town, city and suburban areas, collection of these trees makes the urban forest. These urban forest and urban trees have impact on urban water, pollution and heat. Nowadays we are experiencing drastic climatic changes because of cutting of trees for our growth and increasing population which leads to expansion of roads, towers, and airports. Individual tree crown detection is necessary to map the forest along with feasible planning for urban areas. In this study, using WorldView-2imagery, trees in specific area are detected with object-based image analysis (OBAI) approach. Therefore with improvement in spatial and spectral resolution of an image, extracted from WorldView-2 carried out urban features with better accuracy. The aim of this research is to illustrate how object-based method can be applied to the available data to accurately find out vegetation, which can be further sub-classified to obtain area under tree canopy. The result thus obtained gives area under tree canopy with an accuracy of 92.43 % and a Kappa coefficient of 0.80.

Source: International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering

DOI: 10.11591/ijece.v12i4.pp3665-3673

Authors: Sujata R. Kadu, Balaji G. Hogade, Imdad Rizvi

Institution: University of Sharjah

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: diamond

Funder: Indian Institute of Technology Bombay