Hg and As pollution in the soil-plant system evaluated by combining multispectral UAV-RS, geochemical survey and machine learning

Salgado, Luis G.; López-Sánchez, Carlos A.; Colina, Arturo; Baragaño, Diego; Forján, Rubén; Gallego, José Luis

doi:10.1016/j.envpol.2023.122066

Cited by 9 publications

(6 citation statements)

References 109 publications

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“…Exploring the Spatiotemporal Distribution Patterns of Pollutants from the Perspective of Climate Change involves utilizing meteorological, remote sensing, atmospheric pollutant concentration, and socio-economic data. Techniques such as Visible [ [205] , [214] , [215] ] and Near-Infrared Reflectance Spectroscopy (VNIR) coupled with Random Forest machine learning (RF) [ 162 ], Support Vector Machine (SVM), and Deep Neural Networks (DNN) [ 163 ] are applied to regional, urban, and neighbourhood environments. This research investigates pollutant distribution characteristics across various spatial and temporal scales, uncovering transmission pathways, rates, and the contributions of different factors.…”

Section: Discussionmentioning

confidence: 99%

Brownfield-related studies in the context of climate change: A comprehensive review and future prospects

Wan,

Chen,

Liu

et al. 2024

Heliyon

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Section: Discussionmentioning

confidence: 99%

Brownfield-related studies in the context of climate change: A comprehensive review and future prospects

Wan,

Chen,

Liu

et al. 2024

Heliyon

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“…An unmanned aerial vehicle is capable of recording the area in which air pollution concentration is the highest, collecting a sample and analyzing it. The results are then transmitted by a mobile outdoor unit [8]. Exceeded levels of gaseous air pollutants, such as CO 2 , SO 2 , NxOy, and particulate matter, such as PM 1 , PM 2.5 , and PM 10 , demonstrate the validity of such measures and provide important information for environmental protection services.…”

Section: The Use Of Uavs For Pollution Mappingmentioning

confidence: 99%

“…Exceeded levels of gaseous air pollutants, such as CO 2 , SO 2 , NxOy, and particulate matter, such as PM 1 , PM 2.5 , and PM 10 , demonstrate the validity of such measures and provide important information for environmental protection services. In addition, a special drone equipped with a sensor can create a smog map showing the most polluted areas [8][9][10][11].…”

Section: The Use Of Uavs For Pollution Mappingmentioning

confidence: 99%

An Assessment of the Possibility of Using Unmanned Aerial Vehicles to Identify and Map Air Pollution from Infrastructure Emissions

Jaroń,

Borucka,

Deliś

et al. 2024

Energies

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Sustainable development and the creation of smart, green cities requires cooperation in many scientific fields, including those related to ecology, mobility, or sustainable management, among others. Environmental protection is a particularly important element here. Atmospheric pollution, due to air movements, spreads over very large areas; therefore, air quality monitoring is crucial to ensure protection from harmful substances. One of the most severe sources of air pollution, accounting for as much as approximately 25% of total annual emissions within the EU, is road transport. Therefore, the European Union has set an ambitious target to reduce total emissions to 55% for cars and 50% for vans by 2030. In recent years, unmanned aerial vehicles (UAVs) have become increasingly popular in many scientific fields, including environmental protection and photogrammetry. The use of UAVs to identify harmful pollutants allows them to gain an advantage over conventional detection methods, due to the possibility of remote (therefore safe for humans), faster, and area-based measurements. Given the ever-expanding scale of the use of this technology, this paper presents the possibilities of using UAVs to identify and visualize (map) pollution. The examples presented in the foreign literature, as well as our own research, in imaging the altitude distribution of air pollutants; gaseous pollutants: C6H6, HCHO, SO2; and particulate matter: PM1, PM2.5, PM10 demonstrate the validity of such measures. This research was carried out in the area of one of Poland’s key A4 highways. The maps obtained allow for an area-wise and altitude-wise presentation of one of the significant air pollutants in the EU. In addition, they can be a valuable source of information for the implementation of future projects and the improvement of road infrastructure, thus contributing to the reduction of air pollution and the creation of so-called “green cities”.

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“…(("img@1" + "img@2" + "img@3") * ("img@3)/("img@2" * ("img@1" − "img@3" + 256)) > 1.5) (7) The obtained clusters for the two images are presented in Figure 10 and the confusion matrices from the two classifications-in Tables 4 and 5.…”

mentioning

confidence: 95%

“…The application of unmanned aerial vehicles (UAVs) for remote monitoring of the growth of crops has become a widely adopted practice in modern agriculture. The use of UAVs in agriculture allows farmers to gather a large amount of information about vegetation indices [1][2][3][4], content of nitrogen (N), phosphorus (P), and potassium (K) in plant fodder [5], soil salinity [6] contamination with hazardous toxic elements in soil, and vegetation [7], soil water deficit [8,9], plant water status [10], etc.…”

Section: Introductionmentioning

confidence: 99%

A Novel Algorithm to Detect White Flowering Honey Trees in Mixed Forest Ecosystems Using UAV-Based RGB Imaging

Atanasov,

Evstatiev,

Vladut

et al. 2024

AgriEngineering

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Determining the productive potential of flowering vegetation is crucial in obtaining bee products. The application of a remote sensing approach of terrestrial objects can provide accurate information for the preparation of maps of the potential bee pasture in a given region. The study is aimed at the creation of a novel algorithm to identify and distinguish white flowering honey plants, such as black locust (Robinia pseudo-acacia) and to determine the areas occupied by this forest species in mixed forest ecosystems using UAV-based RGB imaging. In our study, to determine the plant cover of black locust in mixed forest ecosystems we used a DJI (Da-Jiang Innovations, Shenzhen, China) Phantom 4 Multispectral drone with 6 multispectral cameras with 1600 × 1300 image resolution. The monitoring was conducted in the May 2023 growing season in the village of Yuper, Northeast Bulgaria. The geographical location of the experimental region is 43°32′4.02″ N and 25°45′14.10″ E at an altitude of 223 m. The UAV was used to make RGB and multispectral images of the investigated forest massifs, which were thereafter analyzed with the software product QGIS 3.0. The spectral images of the observed plants were evaluated using the newly created criteria for distinguishing white from non-white colors. The results obtained for the scanned area showed that approximately 14–15% of the area is categorized as white-flowered trees, and the remaining 86–85%—as non-white-flowered. The comparison of the developed algorithm with the Enhanced Bloom Index (EBI) approach and with supervised Support Vector Machine (SVM) classification showed that the suggested criterion is easy to understand for users with little technical experience, very accurate in identifying white blooming trees, and reduces the number of false positives and false negatives. The proposed approach of detecting and mapping the areas occupied by white flowering honey plants, such as black locust (Robinia pseudo-acacia) in mixed forest ecosystems is of great importance for beekeepers in determining the productive potential of the region and choosing a place for an apiary.

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Hg and As pollution in the soil-plant system evaluated by combining multispectral UAV-RS, geochemical survey and machine learning

Cited by 9 publications

References 109 publications

Brownfield-related studies in the context of climate change: A comprehensive review and future prospects

Brownfield-related studies in the context of climate change: A comprehensive review and future prospects

An Assessment of the Possibility of Using Unmanned Aerial Vehicles to Identify and Map Air Pollution from Infrastructure Emissions

A Novel Algorithm to Detect White Flowering Honey Trees in Mixed Forest Ecosystems Using UAV-Based RGB Imaging

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