Inversion Study of Heavy Metals in Soils of Potentially Polluted Sites Based on UAV Hyperspectral Data and Machine Learning Algorithms

Zhang, Yaqiong; Xu, Youtao; Xiong, Weixing; Qu, Ran; Ten, Jiahua; Lou, Qijia; Lv, Na

doi:10.1109/whispers52202.2021.9484047

Cited by 8 publications

(3 citation statements)

References 4 publications

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“…The undisputed widespread use of such technologies across a wide range of soil monitoring tasks is strictly correlated with the progresses in the miniaturization and portability of RGB cameras and multispectral/hyperspectral imaging technologies, which allow to bridge the gaps with conventional satellite or airborne remote sensing platforms while providing a cost-effective way to obtain data at high spatial and temporal resolution [156,157]. By inspecting target areas at a very fine scale, UAV platforms are foreseen as potential tools to detect and counteract the illegal dumping of solid/liquid waste in the environment [158,159], for actively monitoring the operations in existing landfills [160], and for general prevention of soil contamination [161]. Preventing the diffusion of unauthorized constructions or their illegal demolition is another important application field for UAV-based monitoring systems [162].…”

Section: Uav For Land Monitoringmentioning

confidence: 99%

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

Fascista

2022

Sensors

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Fighting Earth’s degradation and safeguarding the environment are subjects of topical interest and sources of hot debate in today’s society. According to the United Nations, there is a compelling need to take immediate actions worldwide and to implement large-scale monitoring policies aimed at counteracting the unprecedented levels of air, land, and water pollution. This requires going beyond the legacy technologies currently employed by government authorities and adopting more advanced systems that guarantee a continuous and pervasive monitoring of the environment in all its different aspects. In this paper, we take the research on integrated and large-scale environmental monitoring a step further by providing a comprehensive review that covers transversally all the main applications of wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs), and crowdsensing monitoring technologies. By outlining the available solutions and current limitations, we identify in the cooperation among terrestrial (WSN/crowdsensing) and aerial (UAVs) sensing, coupled with the adoption of advanced signal processing techniques, the major pillars at the basis of future integrated (air, land, and water) and large-scale environmental monitoring systems. This review not only consolidates the progresses achieved in the field of environmental monitoring, but also sheds new lights on potential future research directions and synergies among different research areas.

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Section: Uav For Land Monitoringmentioning

confidence: 99%

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

Fascista

2022

Sensors

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“…Tan et al [8] proposed estimating the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest. Zhang et al [9] made contributions on the issue in soils of potentially polluted sites based on unmanned aerial vehicle (UAV) hyperspectral imagery. The authors [10,11] summarized previous research on soil heavy metal content estimation using different data sources and analyzed the ongoing challenges and existing issues.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, choosing an effective regression model also is vital to improve the precision of the retrieval of soil heavy metal content. According to the previous related publications, e.g., [9,[13][14][15], statistical and machine learning models such as partial least squares regression (PLSR), support vector regression (SVR), M5 model tree, extreme learning machines, random forest, or back propagation are popular for modeling the complex quantitative retrieval problems due to their advantages of simple structure and low training cost compared with popular deep learning networks. However, it is not enough to depend on common factors and popular estimation models; more factors reflecting the relationship of the adsorption or occurrence among organic matter, clay minerals, and other soil parameters should be incorporated.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Soil Heavy Metal Content for Environment Monitoring in Mining Area via Transfer Learning

Yang,

Cui,

Cheng

et al. 2023

Sustainability

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Monitoring environmental pollution sources is an ongoing issue that must be addressed to reduce risks to public health, food safety, and the environment. However, retrieving topsoil heavy metal content at a low cost for environmental monitoring in mining areas is challenging. Therefore, this study proposes a network model based on transfer learning theory and a back propagation (BP) network optimized by a genetic algorithm (GA), taking the Daxigou mining area in Shaanxi Province, China, as a case study. Firstly, visible and near-infrared spectrum data from Landsat8 satellite images, digital elevation models, and geochemical data from field-collected soil samples were used to extract environmental factor candidates indicating the content and spatial distribution of certain heavy metals, including copper (Cu) and lead (Pb). Secondly, each element was correlated with environmental factors and a multicollinearity test was performed to determine the optimal factor set. Then, the BP network optimized by GA was pre-trained with sample data collected in 2017 and retrained with minimal sample data from 2019 using the parameter transfer learning method, allowing spatial distribution mapping of the Cu and Pb content in topsoil of the Daxigou mining area in 2019. From the validation results using field-collected data, the root mean square error (RMSE) and mean relative error (MRE) values using the proposed model, respectively, reduced by 4.688 mg/kg and 1.533 mg/kg for Cu and reduced by 1.586 mg/kg and 1.232 mg/kg for Pb compared to the traditional GA-BP model. Thus, conclusions can be drawn that our proposed Tr-GA-BP network performs well, requiring 16 training samples collected in 2019. In addition, the content of Cu is the highest; Pb is the second highest in the study area. Both of them were spatially distributed mainly in the exploitation, slag stacking, roadside, etc., consistent with field investigation results.

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Machine learning approaches for monitoring environmental metal pollutants: Recent advances in source apportionment, detection, quantification, and risk assessment

Nkinahamira,

Feng,

Zhang

et al. 2024

TrAC Trends in Analytical Chemistry

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Inversion Study of Heavy Metals in Soils of Potentially Polluted Sites Based on UAV Hyperspectral Data and Machine Learning Algorithms

Cited by 8 publications

References 4 publications

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

Retrieval of Soil Heavy Metal Content for Environment Monitoring in Mining Area via Transfer Learning

Machine learning approaches for monitoring environmental metal pollutants: Recent advances in source apportionment, detection, quantification, and risk assessment

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