Remote sensing of illegal dumps through supervised classification of satellite images: application in Oaxaca, Mexico
DOI:
https://doi.org/10.18172/cig.6273Keywords:
Remote sensing, supervised classification, Urban solid waste, Artificial intelligence, illegal dumpsAbstract
Various economic, social, and cultural factors have contributed to the proliferation of illegal dumps, causing urban image degradation, population health impacts, and soil, air, and water contamination. Scientists developed remote sensing techniques to identify these red spots and thus contribute to their mitigation and control. They recently used these techniques to detect large areas of illegal waste dumping instead of using expensive field monitoring. Artificial intelligence algorithms have been used to process satellite images due to the availability of satellite images and the increase in the processing capacity of computer systems. This work presents the results of a satellite remote-sensing procedure to detect illegal dumps in one hydrographic subbasin in Oaxaca, Mexico, through a supervised land cover classification using a Random Forest classifier. Two hundred and fifty-six control polygons were used to train the classifier. The classification criteria were the twelve bands of the Sentinel 2A satellite images with a spatial resolution of 10x10 meters, the spectral indices NDVI, MNDWI, SAVI, NDBI, BSI, and the surface slope. Google Earth Engine platform was used to process satellite images. There were 288,100 hectares classified in this way: 65.4% classified as vegetation, 31.5% like bare soil, 2.7% was urban soil and the rest was classified as water or garbage. A confusion matrix calculated the accuracy of the model in 0.9517. The model was not able to accurately distinguish between urban soil, bare soil and garbage due to the similarity of their spectral fingerprints. NDVI and SAVI were the most important spectral indices for detecting litter, and those might contribute to building a spectral fingerprint of litter in the future. Poorly classified areas were discarded through photointerpretation work and post-processing. Finally, thirty-two probable illegal dumps were identified, twelve of which were confirmed on the territory.
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Ahmed, S. M., Muhammad, H., Sivertun, A., 2006, September. Solid waste management planning using GIS and remote sensing technologies case study Aurangabad City, India. In 2006 International Conference on Advances in Space Technologies (pp. 196-200). IEEE. https://doi.org/10.1109/ICAST.2006.313826 DOI: https://doi.org/10.1109/ICAST.2006.313826
Angelino, C. V., Focareta, M., Parrilli, S., Cicala, L., Piacquadio, G., Meoli, G., De Mizio, M., 2018. A case study on the detection of illegal dumps with GIS and remote sensing images. In Earth Resources and Environmental Remote Sensing/GIS Applications IX (Vol. 10790, pp. 165-171). SPIE. https://doi.org/10.1117/12.2325557 DOI: https://doi.org/10.1117/12.2325557
Cusworth, D. H., Duren, R. M., Thorpe, A. K., Tseng, E., Thompson, D., Guha, A., Newman, S., Koster, K.T., Miller, C. E., 2020. Using remote sensing to detect, validate, and quantify methane emissions from California solid waste operations. Environmental Research Letters 15(5), 054012. https://doi.org/10.1088/1748-9326/ab7b99 DOI: https://doi.org/10.1088/1748-9326/ab7b99
Dabholkar, A., Muthiyan, B., Srinivasan, S., Ravi, S., Jeon, H., Gao, J., 2017. Smart illegal dumping detection. In 2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService) (pp. 255-260). IEEE. https://doi.org/10.1109/BigDataService.2017.51 DOI: https://doi.org/10.1109/BigDataService.2017.51
Ferronato, N., Portugal Alarcón, G.P., Guisbert Lizarazu, E.G., Torretta, V., 2021. Assessment of municipal solid waste collection in Bolivia: Perspectives for avoiding uncontrolled disposal and boosting waste recycling options. Resources, Conservation and Recycling 167, 105234. 2021. https://doi.org/10.1016/j.resconrec.2020.105234 DOI: https://doi.org/10.1016/j.resconrec.2020.105234
Frost, B.R. Frost, C.D., 2019. Essentials of Igneous and Metamorphic Petrology. Second edition. Cambridge University Press, New York, 297 pages. DOI: https://doi.org/10.1017/9781108685047
Gandhi, U. 2021. End-to-End Google Earth Engine Course. Spatial Thoughts. Available: https://courses.spatialthoughts.com/end-to-end-gee.html
Glanville, K., Chang, H. C., 2015. Mapping illegal domestic waste disposal potential to support waste management efforts in Queensland, Australia. International Journal of Geographical Information Science, 29(6), 1042-1058. https://doi.org/10.1080/13658816.2015.1008002 DOI: https://doi.org/10.1080/13658816.2015.1008002
Gill, J., Faisal, K., Shaker, A., Yan, W. Y., 2019. Detection of waste dumping locations in landfill using multi-temporal Landsat thermal images. Waste Management and Research, 37(4), 386-393. https://doi.org/10.1177/0734242X18821808 DOI: https://doi.org/10.1177/0734242X18821808
Instituto Nacional de Estadística y Geografía (INEGI), 2021. Censo Nacional de Gobiernos Municipales y Demarcaciones Territoriales/Delegacionales, 2021. https://www.inegi.org.mx/programas/cngmd/2021/
Karimi, N., Ng, K. T. W., Richter, A., 2022. Development and application of an analytical framework for mapping probable illegal dumping sites using nighttime light imagery and various remote sensing indices. Waste Management, 143, 195-205. https://doi.org/10.1016/j.wasman.2022.02.031 DOI: https://doi.org/10.1016/j.wasman.2022.02.031
Kaza, S., Yao, L., Bhada-Tata P., Van Woerden, F., 2018. What a waste 2.0: a global snapshot of solid waste management to 2050. Urban Development Series. World Bank Group, Washington, D.C. https://doi.org/10.1596/978-1-4648-1329-0 DOI: https://doi.org/10.1596/978-1-4648-1329-0
Laonamsai, J., Julphunthong, P., Saprathet, T., Kimmany, B., Ganchanasuragit, T., Chomcheawchan, P., Tomun, N., 2023. Utilizing NDWI, MNDWI, SAVI, WRI, and AWEI for Estimating Erosion and Deposition in Ping River in Thailand. Hydrology 10(3), 70. https://doi.org/10.3390/hydrology10030070 DOI: https://doi.org/10.3390/hydrology10030070
Lehner, B., Grill G. 2013. Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrological Processes 27(15), 2171–2186. https://doi.org/10.1002/hyp.9740 DOI: https://doi.org/10.1002/hyp.9740
Mahmood, K., Iftikhar, W., Faizi, F., 2023. Geospatial indices as an alternative for environmental impact assessment of dumped waste. Acta Geophysica 71(1), 309-322. https://doi.org/10.1007/s11600-022-00974-6 DOI: https://doi.org/10.1007/s11600-022-00974-6
Matsumoto, S., Takeuchi, K., 2011. The effect of community characteristics on the frequency of illegal dumping. Environmental Economics and Policy Studies 13 (3), 177–193. https://doi.org/10.1007/s10018-011-0011-5 DOI: https://doi.org/10.1007/s10018-011-0011-5
Nguyen, C. T., Chidthaisong, A., Kieu Diem, P., Huo, L. Z., 2021. A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8. Land 10(3), 231. https://doi.org/10.3390/land10030231 DOI: https://doi.org/10.3390/land10030231
Niu, B., Feng, Q., Yang, J., Chen, B., Gao, B., Liu, J., Li, Y., Gong, J., 2023. Solid waste mapping based on very high-resolution remote sensing imagery and a novel deep learning approach. Geocarto International 38(1), 2164361. https://doi.org/10.1080/10106049.2022.2164361 DOI: https://doi.org/10.1080/10106049.2022.2164361
Papale, L. G., Guerrisi, G., De Santis, D., Schiavon, G., Del Frate, F., 2023. Satellite Data Potentialities in Solid Waste Landfill Monitoring: Review and Case Studies. Sensors, 23(8), 3917. https://doi.org/10.3390/s23083917 DOI: https://doi.org/10.3390/s23083917
Padubidri, C., Kamilaris, A., Karatsiolis, S., 2022. Accurate Detection of Illegal Dumping Sites Using High Resolution Aerial Photography and Deep Learning. 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), Pisa, Italy, pp. 451-456. https://doi.org/10.1109/PerComWorkshops53856.2022.9767451 DOI: https://doi.org/10.1109/PerComWorkshops53856.2022.9767451
Perumal, K., Bhaskaran, R., 2010. Supervised classification performance of multispectral images. arXiv preprint arXiv:1002.4046. https://doi.org/10.48550/arXiv.1002.4046
Quesada-Ruiz, L. C., Rodriguez-Galiano, V., Jordá-Borrell, R., 2019. Characterization and mapping of illegal landfill potential occurrence in the Canary Islands. Waste Management 85, 506-518. https://doi.org/10.1016/j.wasman.2019.01.015 DOI: https://doi.org/10.1016/j.wasman.2019.01.015
Girija, R. R., Mayappan, S., 2019. Mapping of mineral resources and lithological units: a review of remote sensing techniques. International Journal of Image and Data Fusion 10, 79–106. https://doi.org/10.1080/19479832.2019.1589585 DOI: https://doi.org/10.1080/19479832.2019.1589585
Rodríguez, A., 2023. Edoméx cuenta con al menos 50 tiraderos clandestinos. Capital Media. https://www.capitaledomex.com.mx/local/edomex-cuenta-con-al-menos-50-tiraderos-clandestinos/ (último acceso: 28/05/2024)
Secretaría de Medio Ambiente y Recursos Naturales (Semarnat), 2020. Diagnóstico básico para la gestión integral de los residuos. 2020. https://www.gob.mx/cms/uploads/attachment/file/554385/DBGIR-15-mayo-2020.pdf
Secretaría del Medio Ambiente de la Ciudad de México (Sedema), 2021. Inventario de Residuos Sólidos de la Ciudad de México 2021. https://www.sedema.cdmx.gob.mx/storage/app/media/DGCPCA/residuos/ InventariodeResiduosSolidos2021.pdf
Shi, D., Yang, X., 2016. An assessment of algorithmic parameters affecting image classification accuracy by random forests. Photogrammetric Engineering and Remote Sensing 82(6), 407-417. https://doi.org/10.14358/PERS.82.6.407 DOI: https://doi.org/10.14358/PERS.82.6.407
Shrivastava, P., Mishra, S., Katiyar, S. K., 2015. A review of solid waste management techniques using GIS and other technologies. In 2015 International conference on computational intelligence and communication networks (CICN) (pp. 1456-1459). IEEE. https://doi.org/10.1109/CICN.2015.281 DOI: https://doi.org/10.1109/CICN.2015.281
Silvestri, S., Omri, M., 2008. A method for the remote sensing identification of uncontrolled landfills: formulation and validation. International Journal of Remote Sensing 29 (4), 975-989. https://doi.org/10.1080/01431160701311317 DOI: https://doi.org/10.1080/01431160701311317
Torres, R. G., Bezada, M., Santalla, I. R., 2023. Cartografía de cobertura del suelo mediante datos de teledetección en la planicie de desborde del río Apure (Venezuela). Cuadernos de Investigación Geográfica (Geographical Research Letters) 49(1), 113-137. http://doi.org/10.18172/cig.5607 DOI: https://doi.org/10.18172/cig.5607
Torres, R. N., Fraternali, P., Biscontini, A., 2021. On the Use of Class Activation Maps in Remote Sensing: the case of Illegal Landfills. 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA). Porto, Portugal, 2021, pp. 1-10, https://doi.org/10.1109/DSAA53316.2021.9564243 DOI: https://doi.org/10.1109/DSAA53316.2021.9564243
Vigneshwaran, S., Vasantha Kumar, S., 2018. Extraction of built-up area using high resolution sentinel-2a and Google satellite imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-4/W9, 165–169. https://doi.org/10.5194/isprs-archives-xlii-4-w9-165-2018 . DOI: https://doi.org/10.5194/isprs-archives-XLII-4-W9-165-2018
Vu, H.L., Bolingbroke, D., Ng, K.T.W., Fallah, B., 2019. Assessment of waste characteristics and their impact on GIS vehicle collection route optimization using ANN waste forecasts. Waste Management 88, 118-130. https://doi.org/10.1016/j.wasman.2019.03.037 DOI: https://doi.org/10.1016/j.wasman.2019.03.037
Wang, Y., Zou, B., Zuo, X., Zou, H., Zhang, B., Tian, R., Feng, H., 2024. A remote sensing analysis method for soil heavy metal pollution sources at site scale considering source-sink relationships. Science of The Total Environment 946, 174021. https://doi.org/10.1016/j.scitotenv.2024.174021 DOI: https://doi.org/10.1016/j.scitotenv.2024.174021
Xi, Y., Thinh, N. X., Li, C., 2019. Preliminary comparative assessment of various spectral indices for built-up land derived from Landsat-8 OLI and Sentinel-2A MSI imageries. European Journal of Remote Sensing 52(1), 240–252. https://doi.org/10.1080/22797254.2019.1584737 DOI: https://doi.org/10.1080/22797254.2019.1584737
Xu, H., 2006. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033. https://doi.org/10.1080/01431160600589179 DOI: https://doi.org/10.1080/01431160600589179
Yan, W. Y., Mahendrarajah, P., Shaker, A., Faisal, K., Luong, R., Al-Ahmad, M., 2014. Analysis of multi-temporal Landsat satellite images for monitoring land surface temperature of municipal solid waste disposal sites. Environmental monitoring and assessment 186, 8161-8173. https://doi.org/10.1007/s10661-014-3995-z DOI: https://doi.org/10.1007/s10661-014-3995-z
You, T., Chen, W., Wang, H., Yang, Y., Liu, X., 2020. Automatic Garbage Scattered Area Detection with Data Augmentation and Transfer Learning in SUAV Low-Altitude Remote Sensing Images. Mathematical Problems in Engineering 2020, 1-13. https://doi.org/10.1155/2020/7307629 DOI: https://doi.org/10.1155/2020/7307629
Zhou, L., Luo, T., Du, M., Chen, Q., Liu, Y., Zhu, Y., He, C., Wang, S., Yang, K., 2021. Machine learning comparison and parameter setting methods for the detection of dump sites for construction and demolition waste using the google earth engine. Remote Sensing 13(4), 787. https://doi.org/10.3390/rs13040787 DOI: https://doi.org/10.3390/rs13040787
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