Geodaysit 2023

Claudio Ladisa

I am a PhD student at the AG Lab (Applied Geomatics Laboratory) of the Polytechnic of Bari. My research focuses on the development of new algorithms and techniques for the Object-Based Image Analysis (OBIA) approach. Specifically, my work aims to enhance the accuracy and efficiency of environmental monitoring and management through the integration of OBIA with machine learning and data fusion techniques. In my research, I have specifically focused on the detection of photovoltaic plants from Sentinel-2 satellite images, using a combination of spectral, spatial, and contextual information.


Development of a Photovoltaic System Extraction Index for the detection of large PV plants using Sentinel-2 images
Alessandra Capolupo, Eufemia Tarantino, Claudio Ladisa, Fernando J. Aguilar

Authors: Claudio Ladisa, Manuel A. Aguilar, Alessandra Capolupo, Eufemia Tarantino, Fernando J. Aguilar.
The use of renewable energy sources in power generation is increasing due to environmental awareness and technological advancements. Solar energy, with its extensive availability and minimal greenhouse gas emissions, is a promising source. However, large photovoltaic (PV) plants require constant monitoring to ensure efficiency and reliability. Remote sensing technology can be beneficial in providing accurate information on the plant's size, shape, and location, reducing costs and increasing monitoring efficiency. The detection of large PV plants can be carried out using various technologies, including the use of satellite imagery, drones imagery or observation from aircraft. However, the use of satellite imagery is advantageous for the detection of large PV plants because it allows to acquire data on a large area without having to move around the site and to monitor the plant over time without interfering with its activity. Open-source imagery from satellites like Sentinel-2 (S2) and Landsat 9 has led to a significant increase in remote sensing research related to extracting (PV) systems. This is because the free and public availability of high-quality images with extensive spatial coverage has eliminated the need to buy costly private satellite images. Additionally, the frequency of image acquisition, which can occur every few days, has allowed for quick and accurate monitoring of areas of interest. Several research have recently merged remote sensing with machine learning (ML) methods to develop automatic classification algorithms for PV systems. Most of these algorithms employ different spectral indices, such as the Normalized Difference Water Index (NDWI), the Normalized Difference Vegetation Index (NDVI), and Normalized Difference Bare Index (NDBI), as input. These spectral indices provide useful information on the presence of water, vegetation and bare soil, respectively, which can be used to identify PV systems more accurately, thus improving classification accuracy. However, there is no specific spectral index that has been tested exclusively for the extraction of PV. This is partially because PV arrays may be constructed on many kinds of surfaces, in various environmental and climatic circumstances, and with different solar panel sizes and types. In this regard, the goal of this work was to suggest a Photovoltaic Systems Extraction Index (PVSEI) for the detection of PV installations from S2 images in two distinct study areas characterized by the persistent presence of large PV installations: The province of Viterbo (Italy) and the province of Seville (Spain). The development of the PVSEI was based on the combination of different bands provided by S2, in order to maximise the spectral difference between the solar panels and their surroundings. For each study area, two S2 images, one taken in February and the other one in August, were used to analyse the seasonal variation of the solar panels' spectral signature and test the PVSEI's accuracy in each of the four scenarios. The image analysis was carried out using an Object-Based Image Analysis (OBIA) method since it allowed for a more accurate identification of PV systems than the pixel-based method, which analyzes individual elements without taking their spatial arrangement and semantic significance into account. Multi-resolution segmentation was used to create segments with different dimensions based on scale, shape and compactness parameters. The Decision Tree (DT) classifier was used to evaluate the effectiveness of the PVSEI and its importance in comparison to the other indices used in the literature in both locations and for both periods after the objects had been labelled as "PV" and "No-PV”. The effectiveness of the new index was demonstrated through the results obtained from the DT analysis. In three out of four scenarios, the PVSEI was selected as the first cut in the DT analysis. In the remaining scenario where it was not ranked first, it still maintained a high level of significance, being the second index in importance. The accuracy was assessed using an error matrix calculated on both the entire segmentation dataset (i.e. using all the objects) and with TTA mask with 2 m pixel size. Four metrics were used to evaluate accuracy of the PVSEI, including Overall Accuracy (OA), Kappa Index of Agreement (KIA), Producer Accuracy (PA), and User Accuracy (UA) for both classes. OA exceeded 98% in all scenarios, both for the segmentation dataset and the TTA mask. KIA values for the TTA mask ranged from 0.81 to 0.86, while values for the segmentation objects ranged from 0.74 to 0.82. In conclusion, the new index has demonstrated favourable outcomes in both study areas, with only a limited number of misclassifications involving bare soil objects that have a spectral signature resembling certain photovoltaic systems.

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