The Application of LiDAR to Assessment of Rooftop Solar Photovoltaic Deployment Potential in a Municipal District Unit

Sensors

Ha T. Nguyen²  Joshua M. Pearce¹  Rob Harrap³ 

[1] Department of Materials Science & Engineering and Department of Electrical & Computer Engineering, Michigan Technological University, 1400 Townsend Dr., Houghton, MI 49931, USA;Department of Geography and Environment, Boston University, Boston, MA 02215, USA; E-Mail:;Department of Geological Sciences & Geological Engineering, Queen's University, Kingston, ON K7L 3N6, Canada; E-Mail:

关键词: airborne laser scanning;    ALS;    Digital Surface Model;    DSM;    Light Detection and Ranging;    LiDAR;    roof extraction;    photovoltaic;   

DOI  :  10.3390/s120404534

来源: mdpi

【 摘 要 】

A methodology is provided for the application of Light Detection and Ranging (LiDAR) to automated solar photovoltaic (PV) deployment analysis on the regional scale. Challenges in urban information extraction and management for solar PV deployment assessment are determined and quantitative solutions are offered. This paper provides the following contributions: (i) a methodology that is consistent with recommendations from existing literature advocating the integration of cross-disciplinary competences in remote sensing (RS), GIS, computer vision and urban environmental studies; (ii) a robust methodology that can work with low-resolution, incomprehensive data and reconstruct vegetation and building separately, but concurrently; (iii) recommendations for future generation of software. A case study is presented as an example of the methodology. Experience from the case study such as the trade-off between time consumption and data quality are discussed to highlight a need for connectivity between demographic information, electrical engineering schemes and GIS and a typical factor of solar useful roofs extracted per method. Finally, conclusions are developed to provide a final methodology to extract the most useful information from the lowest resolution and least comprehensive data to provide solar electric assessments over large areas, which can be adapted anywhere in the world.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.