{"title":"Digital image processing for atmospheric monitoring at Colombian Andes","authors":"Yhesly López, E. Pawelko, Daniel Nisperuza","doi":"10.1080/19479832.2023.2252817","DOIUrl":null,"url":null,"abstract":"ABSTRACT As an alternative to the current technologies, we explored the feasibility of using low cost and massive use of digital cameras as photometric sensors to retrieve the atmospheric total optical depth (τ) in the urban area of a city in the Colombian Andes. This study proposes a simple way to estimate τ from digital processing of images of the Sun based on the Beer-Bouguer-Lambert law Langley’s linear fitting for the colour levels in channels red, green, and blue registered by the pixels of cameras’ sensors. From February to March 2022, the τ values retrieved from the images were correlated to the retrieved values from a solar spectral radiometer (SSR). We found that τ is sensible to the featured changes in the local atmosphere and to the cameras’ exposure parameters setup. Under conditions of partly clear sky, around 80% (r > 0.8) of the τ values from cameras showed a linear correspondence to those retrieved from SSR system. Its spectral dependency (τ _red < τ _green < τ _blue) is in accordance with the physical phenomena in light-atmosphere interaction. The results suggest that the methodology applied can be used for monitoring the atmosphere at any geographical location in the world.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"14 1","pages":"324 - 335"},"PeriodicalIF":1.8000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Image and Data Fusion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/19479832.2023.2252817","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"REMOTE SENSING","Score":null,"Total":0}
引用次数: 0
Abstract
ABSTRACT As an alternative to the current technologies, we explored the feasibility of using low cost and massive use of digital cameras as photometric sensors to retrieve the atmospheric total optical depth (τ) in the urban area of a city in the Colombian Andes. This study proposes a simple way to estimate τ from digital processing of images of the Sun based on the Beer-Bouguer-Lambert law Langley’s linear fitting for the colour levels in channels red, green, and blue registered by the pixels of cameras’ sensors. From February to March 2022, the τ values retrieved from the images were correlated to the retrieved values from a solar spectral radiometer (SSR). We found that τ is sensible to the featured changes in the local atmosphere and to the cameras’ exposure parameters setup. Under conditions of partly clear sky, around 80% (r > 0.8) of the τ values from cameras showed a linear correspondence to those retrieved from SSR system. Its spectral dependency (τ _red < τ _green < τ _blue) is in accordance with the physical phenomena in light-atmosphere interaction. The results suggest that the methodology applied can be used for monitoring the atmosphere at any geographical location in the world.
期刊介绍:
International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications. Image and data fusion techniques are important for combining the many sources of satellite, airborne and ground based imaging systems, and integrating these with other related data sets for enhanced information extraction and decision making. Image and data fusion aims at the integration of multi-sensor, multi-temporal, multi-resolution and multi-platform image data, together with geospatial data, GIS, in-situ, and other statistical data sets for improved information extraction, as well as to increase the reliability of the information. This leads to more accurate information that provides for robust operational performance, i.e. increased confidence, reduced ambiguity and improved classification enabling evidence based management. The journal welcomes original research papers, review papers, shorter letters, technical articles, book reviews and conference reports in all areas of image and data fusion including, but not limited to, the following aspects and topics: • Automatic registration/geometric aspects of fusing images with different spatial, spectral, temporal resolutions; phase information; or acquired in different modes • Pixel, feature and decision level fusion algorithms and methodologies • Data Assimilation: fusing data with models • Multi-source classification and information extraction • Integration of satellite, airborne and terrestrial sensor systems • Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets (e.g. geometric information, topological information, statistical information, etc.).