Mobile Pollution Data Sensing Using UAVs
Author:
Affiliation:
1. Department of Computer Engineering, Universitat Politècnica de València, Camino de Vera, S/N 46022 Valencia, Spain, Tel.: +34.963877007
Funder
Ministerio de Economía y Competitividad, Spain
Generalitat Valenciana, Spain
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/2837126.2843842
Reference19 articles.
1. European Topic Centre for Air Pollution and Climate Change Mitigation (ETC/ACM). http://acm.eionet.europa.eu/ Accessed: October 3 2015. European Topic Centre for Air Pollution and Climate Change Mitigation (ETC/ACM). http://acm.eionet.europa.eu/ Accessed: October 3 2015.
2. United States Environmental Protection Agency (EPA). http://www.epa.gov/ Accessed: October 3 2015. United States Environmental Protection Agency (EPA). http://www.epa.gov/ Accessed: October 3 2015.
3. A. Adam-Poupart A. Brand M. Fournier M. Jerrett and A. Smargiassi. Spatiotemporal modeling of ozone levels in Quebec (Canada): a comparison of kriging land-use regression (LUR) and combined Bayesian maximum entropy--LUR approaches. Environmental health perspectives 122(9):970 2014. A. Adam-Poupart A. Brand M. Fournier M. Jerrett and A. Smargiassi. Spatiotemporal modeling of ozone levels in Quebec (Canada): a comparison of kriging land-use regression (LUR) and combined Bayesian maximum entropy--LUR approaches. Environmental health perspectives 122(9):970 2014.
4. Lightweight unmanned aerial vehicles will revolutionize spatial ecology
5. Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle
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