Solar park detection from publicly available satellite imagery

References

• Achanta, R., and S. Süsstrunk. 2017. “Superpixels and Polygons Using Simple Non-Iterative Clustering.” Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 2017-January, Honolulu, HI, USA: 4895–4904. doi:10.1109/CVPR.2017.520.
   Google Scholar
• Alshehhi, R., P. Reddy Marpu, W. Lee Woon, and M. Dalla Mura. 2017. “Simultaneous Extraction of Roads and Buildings in Remote Sensing Imagery with Convolutional Neural Networks.” ISPRS Journal of Photogrammetry and Remote Sensing 130: 139–149. doi:10.1016/j.isprsjprs.2017.05.002.
   Web of Science ®Google Scholar
• Arel, I., D. C. Rose, and T. P. Karnowski. 2010. “Deep Machine Learning—A New Frontier.” IEEE November: 13–18.
   Google Scholar
• Ben-Dor, E. 2002. “Quantitative Remote Sensing of Soil Properties.“ Advances in Agronomy 75: 173–243. doi:10.1016/S0065-2113(02)75005-0
   Web of Science ®Google Scholar
• Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:10.1201/9780429469275-8.
   Web of Science ®Google Scholar
• Castello, R., S. Roquette, M. Esguerra, A. Guerra, and J. Louis Scartezzini. 2019. “Deep Learning in the Built Environment: Automatic Detection of Rooftop Solar Panels Using Convolutional Neural Networks.” Journal of Physics. Conference Series 1343 (1): 0–6. doi:10.1088/1742-6596/1343/1/012034.
   Google Scholar
• Centraal Bureau voor de Statistiek . 2019. “Zonnepanelen Automatisch Detecteren Met Luchtfoto’s.” https://www.cbs.nl/nl-nl/over-ons/innovatie/project/zonnepanelen-automatisch-detecteren-met-luchtfoto-s
   Google Scholar
• Chen, C., A. Liaw, and L. Breiman. 2004. Using Random Forest to Learn From Imbalanced Data Tech report 666. UC Berkeley: Department of Statistics. Accessed 4 February 2022, https://statistics.berkeley.edu/sites/default/files/tech-reports/666.pdf .
   Google Scholar
• Clement, M., C. Kilsby, and P. Moore. 2018. “Multi-Temporal Synthetic Aperture Radar Flood Mapping Using Change Detection.” Journal of Flood Risk Management 11 (2): 152–168. doi:10.1111/jfr3.12303.
   Web of Science ®Google Scholar
• Congalton, R. G. 1991. “A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data.” Remote Sensing of Environment 37 (1): 35–46. doi:10.1016/0034-4257(91)90048-B.
   Web of Science ®Google Scholar
• Czirjak, D. W. 2017. “Detecting Photovoltaic Solar Panels Using Hyperspectral Imagery and Estimating Solar Power Production.” Journal of Applied Remote Sensing 11 (2): 026007. doi:10.1117/1.JRS.11.026007.
   Google Scholar
• da Costa, M. V. C. V., O. L. F. de Carvalho, A. Gois Orlandi, I. Hirata, A. O. de Albuquerque, F. V. E Silva, R. Fontes Guimarães, R. A. T. Gomes, and O. A. de Carvalho Júnior. 2021. “Remote Sensing for Monitoring Photovoltaic Solar Plants in Brazil Using Deep Semantic Segmentation.” Energies 14 (10): 1–15. doi:10.3390/en14102960.
   Web of Science ®Google Scholar
• Defries, R. S., and J. R. G. Townshend. 1994. “NDVI-Derived Land Cover Classification at a Global Scale.” International Journal of Remote Sensing 15 (17): 3567–3586. doi:10.1080/01431169408954345.
   Web of Science ®Google Scholar
• Del Carmen Torres-sibille, A., V. A. Cloquell-Ballester, V. A. Cloquell-Ballester, and M. Á. Artacho Ramírez. 2009. “Aesthetic Impact Assessment of Solar Power Plants: An Objective and a Subjective Approach.” Renewable and Sustainable Energy Reviews 13 (5): 986–999. doi:10.1016/j.rser.2008.03.012.
   Web of Science ®Google Scholar
• Dunnett, S., A. Sorichetta, G. Taylor, and F. Eigenbrod. 2020. “Harmonised Global Datasets of Wind and Solar Farm Locations and Power.” Scientific Data 7 (1): 1–12. doi:10.1038/s41597-020-0469-8.
   PubMed Web of Science ®Google Scholar
• ESA. 2020. SNAP - ESA Sentinel Application Platform V8.0.0. https://step.esa.int/main/toolboxes/snap/.
   Google Scholar
• Frambach, M., and B. Schurer. 2019. “Indicatief Bodemonderzoek Onder Zonnepanelen.” Bodem 1: 34–36.
   Google Scholar
• Frantz, D., F. Schug, A. Okujeni, C. Navacchi, W. Wagner, S. van der Linden, and P. Hostert. 2021. “National-Scale Mapping of Building Height Using Sentinel-1 and Sentinel-2 Time Series.” Remote Sensing of Environment 252 (June 2020): 112128. doi:10.1016/j.rse.2020.112128.
   PubMedGoogle Scholar
• Georganos, S., T. Grippa, S. Vanhuysse, M. Lennert, M. Shimoni, S. Kalogirou, and E. Wolff. 2018. “Less Is More: Optimizing Classification Performance through Feature Selection in a Very-High-Resolution Remote Sensing Object-Based Urban Application.” GIScience and Remote Sensing 55 (2): 221–242. doi:10.1080/15481603.2017.1408892.
   Web of Science ®Google Scholar
• Haegel, N., T. Buonassisi, F. Armin, R. Garabedian, M. Green, S. Glunz, D. Feldman, et al. 2017. “Terawatt-Scale Photovoltaics: Trajectories and Challenges.” Science 356 (6334): 141–143. doi:10.1126/science.aal1288.
   PubMed Web of Science ®Google Scholar
• Haralick, R. M., and L. G. Shapiro. 1985. “Image Segmentation Techniques.” Computer Vision, Graphics, & Image Processing 29 (1): 100–132. doi:10.1016/S0734-189X(85)90153-7.
   Google Scholar
• Hernandez, R. R., S. B. Easter, M. L. Murphy-Mariscal, F. T. Maestre, M. Tavassoli, E. B. Allen, C. W. Barrows, et al. 2014. “Environmental Impacts of Utility-Scale Solar Energy.” Renewable and Sustainable Energy Reviews 29:766–779. doi:10.1016/j.rser.2013.08.041.
   Web of Science ®Google Scholar
• Hou, X., B. Wang, W. Hu, L. Yin, and H. Wu. 2019. “SolarNet: A Deep Learning Framework to Map Solar Power Plants in China from Satellite Imagery.” arXiv preprint. https://arxiv.org/abs/1912.03685
   Google Scholar
• International Energy Agency. 2021. “Solar PV.” https://www.iea.org/reports/solar-pv
   Google Scholar
• IPCC. 2014. “Climate Change 2014 Mitigation of Climate Change.” Climate Change 2014 Mitigation of Climate Change. doi:10.1017/cbo9781107415416.
   Google Scholar
• Karoui, M. S., F. Z. Benhalouche, Y. Deville, K. Djerriri, X. Briottet, T. Houet, A. Le Bris, and C. Weber. 2019. “Partial Linear NMF-Based Unmixing Methods for Detection and Area Estimation of Photovoltaic Panels in Urban Hyperspectral Remote Sensing Data.” Remote Sensing 11 (18): 2164. doi:10.3390/rs11182164.
   Web of Science ®Google Scholar
• Knopp, L., M. Wieland, M. Rättich, and S. Martinis. 2020. “A Deep Learning Approach for Burned Area Segmentation with Sentinel-2 Data.” Remote Sensing 12 (15): 2422. doi:10.3390/RS12152422.
   Web of Science ®Google Scholar
• Kok, L., N. Van Eekeren, W. Van Der Putten, G. J. van den Born, T. Schouten, and M. Rutgers. 2017. “Trade-Offs of Win-Win Bij Energieopwekking En Bodemfuncties?.” Bodem 4: 18–21.
   Google Scholar
• Kruitwagen, L., K. Story, J. Friedrich, L. Byers, S. Skillman, and C. Hepburn. 2021. “A Global Inventory of Photovoltaic Solar Energy Generating Units.” Nature 598 (October): 604–610. doi:10.1038/s41586-021-03957-7.
   PubMedGoogle Scholar
• Kuhn, M. 2021. “Classification and Regression Training, R Package Version 6.0-90.” https://cran.r-project.org/web/packages/caret/caret.pdf
   Google Scholar
• Li, M., E. Koks, H. Taubenböck, and J. van Vliet. 2020. “Continental-Scale Mapping and Analysis of 3D Building Structure.” Remote Sensing of Environment 245 111859. doi:10.1016/j.rse.2020.111859.
   Web of Science ®Google Scholar
• Li, X., Z. Li, S. Lv, J. Cao, M. Pan, Q. Ma, and H. Yu. 2021. “Ship Detection of Optical Remote Sensing Image in Multiple Scenes.” International Journal of Remote Sensing 1–29. doi:10.1080/01431161.2021.1931544.
   Web of Science ®Google Scholar
• Liaw, A., and M. Wiener. 2002. “Classification and Regression by RandomForest.” R News 2 (3): 18–22. http://cran.r-project.org/doc/Rnews
   Google Scholar
• Liu, D., and F. Xia. 2010. “Assessing Object-Based Classification: Advantages and Limitations.” Remote Sensing Letters 1 (4): 187–194. doi:10.1080/01431161003743173.
   Web of Science ®Google Scholar
• Lundberg, S. M., G. G. Erion, and S.-I. Lee. 2018. “Consistent Individualized Feature Attribution for Tree Ensembles.“ arXiv preprint 2. https://arxiv.org/abs/1802.03888
   Google Scholar
• Mahdianpari, M., B. Salehi, F. Mohammadimanesh, S. Homayouni, and E. Gill. 2019. “The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 M Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform.” Remote Sensing 11 (1). doi:10.3390/rs11010043.
   Web of Science ®Google Scholar
• Malof, J. M., R. Hou, L. M. Collins, K. Bradbury, and R. Newell. 2015. “Automatic Solar Photovoltaic Panel Detection in Satellite Imagery.” 2015 International Conference on Renewable Energy Research and Applications, ICRERA 2015, Palermo, April 2016, 1428–1431. doi:10.1109/ICRERA.2015.7418643.
   Google Scholar
• Maxwell, A. E., T. A. Warner, and F. Fang. 2018. “Implementation of Machine-Learning Classification in Remote Sensing: An Applied Review.” International Journal of Remote Sensing 39 (9): 2784–2817. doi:10.1080/01431161.2018.1433343.
   Web of Science ®Google Scholar
• McFeeters, S. K. 1996. “The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features.” International Journal of Remote Sensing 17 (7): 1425–1432. doi:10.1080/01431169608948714.
   Web of Science ®Google Scholar
• Nationaal Georegister. 2018. “Bestand Bodemgebruik 2015.” http://www.nationaalgeoregister.nl/geonetwork/srv/dut/catalog.search#/metadata/2d3dd6d2-2d2b-4b5f-9e30-86e19ed77a56
   Google Scholar
• Pesaresi, M., C. Corbane, A. Julea, A. J. Florczyk, V. Syrris, and P. Soille. 2016. “Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas.” Remote Sensing 8 (4): 299. doi:10.3390/rs8040299.
   Web of Science ®Google Scholar
• Pham-Duc, B., C. Prigent, and F. Aires. 2017. “Surface Water Monitoring within Cambodia and the Vietnamese Mekong Delta over a Year, with Sentinel-1 SAR Observations.” Water 9 (6): 1–21. doi:10.3390/w9060366.
   PubMed Web of Science ®Google Scholar
• Phan, T. N., V. Kuch, and L. W. Lehnert. 2020. “Land Cover Classification Using Google Earth Engine and Random Forest Classifier-the Role of Image Composition.” Remote Sensing 12 (15): 2411. doi:10.3390/RS12152411.
   Web of Science ®Google Scholar
• Rodriguez-Galiano, V. F., B. Ghimire, J. M. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez. 2012. “An Assessment of the Effectiveness of a Random Forest Classifier for Land-Cover Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 67 (1): 93–104. doi:10.1016/j.isprsjprs.2011.11.002.
   Web of Science ®Google Scholar
• ROM3D. 2021. “Zon Op Kaart.” http://zonopkaart.nl
   Google Scholar
• RVO and ROM3D. 2016. “Grondgebonden Zonneparken.” https://www.rvo.nl/sites/default/files/2016/09/GrondgebondenZonneparken-verkenningafwegingskadersmetbijlagen.pdf
   Google Scholar
• Sahu, A., N. Yadav, and K. Sudhakar. 2016. “Floating Photovoltaic Power Plant: A Review.” Renewable and Sustainable Energy Reviews 66: 815–824. doi:10.1016/j.rser.2016.08.051.
   Web of Science ®Google Scholar
• Sánchez-Pantoja, N., R. Vidal, and M. Carmen Pastor. 2018. “Aesthetic Impact of Solar Energy Systems.” Renewable and Sustainable Energy Reviews 98 (September): 227–238. doi:10.1016/j.rser.2018.09.021.
   Google Scholar
• Scott, K., J. Brown, and E. Culbertson. 2019. “Using Satellites to Track Solar Farm Growth.” https://medium.com/astraeaearth/astraea-solar-farm-study-8d1b3ec28361
   Google Scholar
• Song, C., C. E. Woodcock, and L. Xiaowen. 2002. “The Spectral/Temporal Manifestation of Forest Succession in Optical Imagery.” Remote Sensing of Environment 82 (2–3): 285–302. doi:10.1016/s0034-4257(02)00046-9.
   Web of Science ®Google Scholar
• Sun, H., L. Wang, R. Lin, Z. Zhang, and B. Zhang. 2021. “Mapping Plastic Greenhouses with Two-Temporal Sentinel-2 Images and 1d-Cnn Deep Learning.” Remote Sensing 13 (14): 1–22. doi:10.3390/rs13142820.
   Web of Science ®Google Scholar
• Trappey, A. J. C., C. V. Trappey, H. Tan, P. H. Y. Liu, S. Je Li, and L. Cheng Lin. 2016. “The Determinants of Photovoltaic System Costs: An Evaluation Using a Hierarchical Learning Curve Model.” Journal of Cleaner Production 112: 1709–1716. doi:10.1016/j.jclepro.2015.08.095.
   Web of Science ®Google Scholar
• Tsoutsos, T., N. Frantzeskaki, and V. Gekas. 2005. “Environmental Impacts from the Solar Energy Technologies.” Energy Policy 33 (3): 289–296. doi:10.1016/S0301-4215(03)00241-6.
   Web of Science ®Google Scholar
• van der Zee, F., J. Bloem, P. Galama, L. Gollenbeek, J. Van Os, A. Schotman, and S. De Vries. 2019. Zonneparken natuur en landbouw 2945. Wageningen, the Netherlands: Wageningen Environmental Research. Accessed 4 February 2022, https://library.wur.nl/WebQuery/wurpubs/alterra-reports/549942.
   Google Scholar
• Yamaguchi, Y., and C. Naito. 2003. “Spectral Indices for Lithologic Discrimination and Mapping by Using the ASTER SWIR Bands.” International Journal of Remote Sensing 24 (22): 4311–4323. doi:10.1080/01431160110070320.
   Web of Science ®Google Scholar
• Yu, J., Z. Wang, A. Majumdar, and R. Rajagopal. 2018. “DeepSolar: A Machine Learning Framework to Efficiently Construct A Solar Deployment Database in the United States.” Joule 2 (12): 2605–2617. doi:10.1016/j.joule.2018.11.021.
   Web of Science ®Google Scholar
• Yuan, J., H. Han Lexie Yang, O. A. Omitaomu, and B. L. Bhaduri. 2016. “Large-Scale Solar Panel Mapping from Aerial Images Using Deep Convolutional Networks.” Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016, Washington, DC, USA, 2703–2708. doi:10.1109/BigData.2016.7840915.
   Google Scholar
• Zhang, X., and M. Xu. 2020. “Assessing the Effects of Photovoltaic Powerplants on Surface Temperature Using Remote Sensing Techniques.” Remote Sensing 12 (11): 8–14. doi:10.3390/rs12111825.
   Web of Science ®Google Scholar
• Zhang, X., M. Zeraatpisheh, M. Rahman, S. Wang, and M. Xu. 2021. “Texture Is Important in Improving the Accuracy of Mapping Photovoltaic Power Plants: A Case Study of Ningxia Autonomous Region, China.” Remote Sensing 13 (19): 0–17. doi:10.3390/rs13193909.
   Web of Science ®Google Scholar