Solar photovoltaic module detection using laboratory and airborne imaging spectroscopy data

Chaonan Ji,Martin Bachmann,Thomas Esch,Hannes Feilhauer,Uta Heiden,Wieke Heldens,Andreas Hueni,Tobia Lakes,Annekatrin Metz-Marconcini,Marion Schroedter-Homscheidt,Susanne Weyand,Julian Zeidler

doi:10.1016/j.rse.2021.112692

Abstract

Over the past decades, solar panels have been widely used to harvest solar energy owing to the decreased cost of silicon-based photovoltaic (PV) modules, and therefore it is essential to remotely map and monitor the presence of solar PV modules. Many studies have explored on PV module detection based on color aerial photography and manual photo interpretation. Imaging spectroscopy data are capable of providing detailed spectral information to identify the spectral features of PV, and thus potentially become a promising resource for automated and operational PV detection. However, PV detection with imaging spectroscopy data must cope with the vast spectral diversity of surface materials, which is commonly divided into spectral intra-class variability and inter-class similarity. We have developed an approach to detect PV modules based on their physical absorption and reflection characteristics using airborne imaging spectroscopy data. A large database was implemented for training and validating the approach, including spectra-goniometric measurements of PV modules and other materials, a HyMap image spectral library containing 31 materials with 5627 spectra, and HySpex imaging spectroscopy data sets covering Oldenburg, Germany. By normalizing the widely used Hydrocarbon Index (HI), we solved the intra-class variability caused by different detection angles, and validated it against the spectra-goniometric measurements. Knowing that PV modules are composed of materials with different transparencies, we used a group of spectral indices and investigated their interdependencies for PV detection with implementing the image spectral library. Finally, six well-trained spectral indices were applied to HySpex data acquired in Oldenburg, Germany, yielding an overall PV map. Four subsets were selected for validation and achieved overall accuracies, producer's accuracies and user's accuracies, respectively. This physics-based approach was validated against a large database collected from multiple platforms (laboratory measurements, airborne imaging spectroscopy data), thus providing a robust, transferable and applicable way to detect PV modules using imaging spectroscopy data. We aim to create greater awareness of the potential importance and applicability of airborne and spaceborne imaging spectroscopy data for PV modules identification.

Highlights

Due to the increasing energy demand (Wolfram et al, 2012; Sorrell, 2015), the need of cutting down greenhouse gas emissions (Zhang et al., 2019) and the ongoing energy transition process with substantial sub sidies (Markard, 2018), the number of solar photovoltaic (PV) modules in operation has increased rapidly in recent years (Tao and Yu, 2015; Green, 2019)
Identifying solar PV modules across large regions remains challenging due to the requirement of high-resolution imagery, difficult identification of solar PV mod ules in many situations, and confusion of many other types of structures to PV modules. This is because PV modules are composed of materials that typically include fully transparent glass covers for protection, highly transparent Ethylene Vinyl Acetate (EVA) films, and the core PV cell
We aim to address 1) the spectral intra-class variability caused by different viewing and illumi nation angles, which is always present in PV detection; 2) the spectral inter-class similarity that occurs mainly between PV modules and other hydrocarbon-bearing materials; 3) as well as to apply and validate the developed spectral indices on the city of Oldenburg, Germany

Summary

Introduction

Due to the increasing energy demand (Wolfram et al, 2012; Sorrell, 2015), the need of cutting down greenhouse gas emissions (Zhang et al., 2019) and the ongoing energy transition process with substantial sub sidies (Markard, 2018), the number of solar photovoltaic (PV) modules in operation has increased rapidly in recent years (Tao and Yu, 2015; Green, 2019). Identifying solar PV modules across large regions remains challenging due to the requirement of high-resolution (typically 0.3 m/pixel or finer) imagery, difficult identification of solar PV mod ules in many situations (such as dark PV modules on dark roofs), and confusion of many other types of structures (such as solar hot water systems, roads, and even pools) to PV modules (de Hoog et al, 2020) This is because PV modules are composed of materials that typically include fully transparent glass covers for protection, highly transparent Ethylene Vinyl Acetate (EVA) films, and the core PV cell. In addition to these reasons, these methods require large, elaborated and pixel-accurate labeled data sets for training and validation (Malof et al, 2016a,b; Yuan et al, 2016; Camilo et al, 2018)

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