Research of forest fires using remote sensing data (on the example of the Сhornobyl exclusion zone)
dc.citation.epage | 43 | |
dc.citation.journalTitle | Геодезія, картографія і аерофотознімання | |
dc.citation.spage | 35 | |
dc.citation.volume | 94 | |
dc.contributor.affiliation | Національний університет “Львівська політехніка” | |
dc.contributor.affiliation | Lviv Polytechnic National University | |
dc.contributor.author | Бабушка, Андрій | |
dc.contributor.author | Бабій, Любов | |
dc.contributor.author | Четверіков, Борис | |
dc.contributor.author | Севрук, Андрій | |
dc.contributor.author | Babushka, Andriy | |
dc.contributor.author | Babiy, Lyubov | |
dc.contributor.author | Chetverikov, Borys | |
dc.contributor.author | Sevruk, Andriy | |
dc.coverage.placename | Львів | |
dc.coverage.placename | Lviv | |
dc.date.accessioned | 2023-04-11T07:58:33Z | |
dc.date.available | 2023-04-11T07:58:33Z | |
dc.date.created | 2021-02-23 | |
dc.date.issued | 2021-02-23 | |
dc.description.abstract | Дистанційне зондування Землі відіграє важливу роль у моніторингу та оцінюванні наслідків лісових пожеж. За допомогою різних методик опрацювання багатоспектральних космічних знімків можна визначати ризик поширення пожежі, виявляти гарячі точки та встановлювати теплові параметри, картографувати уражені території та оцінювати наслідки. Метоюроботи є оцінка ступеня тяжкості, пов’язаного з післяпожежноюфазою на прикладі лісів Чорнобильської зони відчуження. Задачами є визначення площ спалених територій за різночасовими космічними знімками, отриманими з супутника Sentinel-2 за допомогою нормалізованого коефіцієнта горіння (NBR) та методики контрольованої класифікації. Вхідними даними для дослідження слугували різночасові космічні знімки, отримані з супутника Sentinel-2 до та після пожежі. Знімки отримані з сервісу Copernicus Open Access Hub, і їхня просторова розрізненість становить 10 м для видимих та близького інфрачервоного каналів, та 20 м – для середніх інфрачервоних. Для автоматизованого підрахунку площі територій, пошкоджених пожежею, використано нормалізований індекс горіння (Normalized Burn Ratio (NBR)). Цей індекс призначений для ідентифікації ділянок, де відбувалось активне горіння. Для розрахунків цей індекс використовує близький та середній інфрачервоні канали. Додатково на досліджувану територію здійснено контрольовану класифікацію, при цьому були створені файли сигнатур для кожного класу. За результатами класифікації також обраховані площі територій, пошкоджених пожежею. Наукова новизна полягає в опрацюванні методики використання нормалізованого коефіцієнта горіння (NBR) та контрольованої класифікації для космічних знімків, отриманих до і після пожежі у Чорнобильській Зоні Відчуження. Практична значущість полягає у тому, що досліджені методи ГІС-технологій можуть бути застосовані для виявлення зон та обрахунку площ пошкодженої пожежами рослинності. Ці результати можуть бути використані місцевими організаціями, органами самоврядування та МНС для моніторингу стану та планування відновлення лісових насаджень. Нормалізований індекс горіння дає можливість швидко та ефективно виявити та обчислити площі територій, пошкоджених пожежами, що дозволяє оперативно оцінити наслідки таких пожеж та оцінити завдані збитки. Нормалізований індекс горіння дозволяє обчислити площу горілого лісу майже в 2 рази точніше, ніж контрольована класифікація. Сам процес обчислення також займає менше часу і не вимагає додаткових процедур (набору сигнатур). Контрольована класифікація в цьому випадку дає гіршу точність, сам процес є тривалішим, але дозволяє визначити площі декількох різних класів. | |
dc.description.abstract | Earth remote sensing and using the satellite images play an important role when monitoring the effects of forest fires and assessing damage. Applying different methods of multispectral space images processing, we can determine the risk of fire distribution, define hot spots and determine thermal parameters, mapping the damaged areas and assess the consequences of fire. The purpose of the work is the severity assessment connected with the post-fire period on the example of the forests in the Chornobyl Exclusion Zone. The tasks of the study are to define the area of burned zones using space images of different time which were obtained from the Sentinel-2 satellite applying the method of a normalized burn ratio (NBR) and method of supervised classification. Space images taken from the Sentinel-2 satellite before and after the fire were the input data for the study. Copernicus Open Access Hub service is a source of images and its spatial resolution is 10 m for visible and near infrared bands of images, and 20 m for medium infrared bands of images. We used method of Normalized Burn Ratio (NBR) and automatically calculated the area damaged with fire. Using this index we were able to identify areas of zones after active combustion. This index uses near and middle infrared bands for the calculations. In addition, a supervised classification was performed on the study area, and signature files were created for each class. According to the results of the classification, the areas of the territories damaged by the fire were also calculated. The scientific novelty relies upon the application of a method of using the normalized combustion coefficient (NBR) and supervised classification for space images obtained before and after the fire in the Chernobyl Exclusion Zone. The practical significance lies in the fact that the studied methods of GIS technologies can be used to identify territories and calculate the areas of vegetation damaged by fires. These results can be used by local organizations, local governments and the Ministry of Emergency Situations to monitor the condition and to plan reforestation. The normalized burned ratio (NBR) gives possibility efficiently and operatively to define and calculate the area which were damaged by fires, that gives possibility operatively assess the consequences of such fires and estimate the damage. The normalized burned ratio allows to calculate the area of burned forest almost 2 times more accurately than the supervised classification. The calculation process itself also takes less time and does not require additional procedures (set of signatures). Supervised classification in this case gives worse accuracy, the process itself is longer, but allows to determine the area of several different classes | |
dc.format.extent | 35-43 | |
dc.format.pages | 9 | |
dc.identifier.citation | Research of forest fires using remote sensing data (on the example of the Сhornobyl exclusion zone) / Andriy Babushka, Lyubov Babiy, Borys Chetverikov, Andriy Sevruk // Geodesy, Cartography and Aerial Photography. — Lviv : Lviv Politechnic Publishing House, 2021. — Vol 94. — P. 35–43. | |
dc.identifier.citationen | Research of forest fires using remote sensing data (on the example of the Сhornobyl exclusion zone) / Andriy Babushka, Lyubov Babiy, Borys Chetverikov, Andriy Sevruk // Geodesy, Cartography and Aerial Photography. — Lviv : Lviv Politechnic Publishing House, 2021. — Vol 94. — P. 35–43. | |
dc.identifier.doi | doi.org/10.23939/istcgcap2021.94.035 | |
dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/57949 | |
dc.language.iso | en | |
dc.publisher | Видавництво Львівської політехніки | |
dc.publisher | Lviv Politechnic Publishing House | |
dc.relation.ispartof | Геодезія, картографія і аерофотознімання (94), 2021 | |
dc.relation.ispartof | Geodesy, Cartography and Aerial Photography (94), 2021 | |
dc.relation.references | Boschetti, L., Roy, D. P., Giglio, L., Huang, H., | |
dc.relation.references | Zubkova, M., & Humber, M. L. (2019). Global | |
dc.relation.references | validation of the collection 6 MODIS burned area | |
dc.relation.references | product. Remote Sensing Environments, Vol. 235, 111490. https://doi.org/10.1016/j.rse.2019.111490. | |
dc.relation.references | Bowman, D. (2018). Wildfire science is at a loss for | |
dc.relation.references | comprehensive data. Nature. 560:7. https://doi.org/10.1038/d41586-018-05840-4. | |
dc.relation.references | Burshtynska, Kh., Denys, Yu., Polishchuk, B. & | |
dc.relation.references | Tymchyshyn, M. (2018). Monitoring of forest fires by | |
dc.relation.references | space images of medium resolution (on the example | |
dc.relation.references | of Arizona, USA). Modern achievements of geodetic | |
dc.relation.references | science and industry. No. 1 (35), 179–184 (in | |
dc.relation.references | Ukrainian). | |
dc.relation.references | DaCamara, C., Libonati, R., Pinto, M. & Hurduc, A. | |
dc.relation.references | (2018). Near- and Middle-Infrared Monitoring of | |
dc.relation.references | Burned Areas from Space. Satellite Information | |
dc.relation.references | Classification and Interpretation. http://dx.doi.org/10.5772/intechopen.82444. | |
dc.relation.references | Ertugrul, M., Ozel, H. B., Varol, T., Cetin, M. & Sevik, H. | |
dc.relation.references | (2019). Investigation of the relationship between | |
dc.relation.references | burned areas and climate factors in large forest fires | |
dc.relation.references | in the Canakkale region. Environmental Monitoring | |
dc.relation.references | and Assessment, 191 (12), 737. https://doi.org/10.1007/s10661-019-7946-6. | |
dc.relation.references | Filipponi, F. (2018). BAIS2: Burned Area Index | |
dc.relation.references | for Sentinel-2. Proceedings, 2 (7), 364. | |
dc.relation.references | https://doi.org/10.3390/ecrs-2-05177. | |
dc.relation.references | Giglio, L., Boschetti, L., Roy, DP., Humber, ML. & | |
dc.relation.references | Justice, CO. (2018). The collection 6 MODIS burned | |
dc.relation.references | area mapping algorithm and product. Remote | |
dc.relation.references | Sensing of Environment. 217, 72–85. https://doi.org/10.1016/j.rse.2018.08.005. | |
dc.relation.references | Hall, J., Argueta, F., & Giglio, L. (2021). Validation of | |
dc.relation.references | MCD64A1 and FireCCI51 cropland burned area | |
dc.relation.references | mapping in Ukraine. International Journal of | |
dc.relation.references | Applied Earth Observations and Geoinformation, | |
dc.relation.references | No. 102, 1–11. https://doi.org/10.1016/j.jag.2021.102443. | |
dc.relation.references | Kumar, S. S. & Roy, D. P. (2018). Global operational | |
dc.relation.references | land imager Landsat-8 reflectance-based active fire | |
dc.relation.references | detection algorithm. International Journal of Digital | |
dc.relation.references | Earth, Abingdon, Vol. 11, n. 2, 154–178. | |
dc.relation.references | https://doi.org/10.1080/17538947.2017.1391341. | |
dc.relation.references | Lanorte, R. Lasaponara, M. Lovallo and Telesca, L. | |
dc.relation.references | (2015). Fisher-Shannon information plane analysis of | |
dc.relation.references | SPOT/VEGETATION normalized difference | |
dc.relation.references | vegetation index (NDVI) time series to characterize | |
dc.relation.references | vegetation recovery after fire disturbance. | |
dc.relation.references | International Journal of Applied Earth Observation | |
dc.relation.references | and Geoinformation, Vol. 26, 441–446. https://doi.org/10.1016/j.jag.2013.05.008. | |
dc.relation.references | Lasaponara, R. and Tucci, B. (2019). Identification of | |
dc.relation.references | Burned Areas and Severity Using SAR Sentinel-1. | |
dc.relation.references | IEEE Geoscience and Remote Sensing Letters, | |
dc.relation.references | Vol. 16, no. 6, 917–921. https://doi.org/10.1109/LGRS.2018.2888641. | |
dc.relation.references | Lasko, K. (2019). Incorporating Sentinel-1 SAR imagery | |
dc.relation.references | with the MODIS MCD64A1 burned area product | |
dc.relation.references | to improve burn date estimates and reduce burn | |
dc.relation.references | date uncertainty in wildland fire mapping | |
dc.relation.references | Incorporating Sentinel-1 SAR imagery with the | |
dc.relation.references | MODIS. Geocarto International. https://doi.org/10.1080/10106049.2019.16085 92. | |
dc.relation.references | Ling, F., Du, Y., Zhang, Y., Li, X. & Xiao, F. (2015). | |
dc.relation.references | Burned-Area Mapping at the subpixel scale with | |
dc.relation.references | MODIS Images. IEEE Geoscience and Remote | |
dc.relation.references | Sensing Letters, Vol. 12, no. 9, 1963–1967. | |
dc.relation.references | https://doi.org/10.1109/LGRS.2015.2441135. | |
dc.relation.references | Padilla, M., Stehman, S. V., Ramo, R., Corti, D., Hantson, S., | |
dc.relation.references | Oliva, P., ... & Chuvieco, E. (2015). Comparing the | |
dc.relation.references | accuracies of remote sensing global burned area | |
dc.relation.references | products using stratified random sampling and | |
dc.relation.references | estimation. Remote sensing of environment, 160, 114–121. https://doi.org/10.1016/j.rse.2015.01.005. | |
dc.relation.references | Pereira. I. M. S. de Carvalho, E. V., Batista, A. C., | |
dc.relation.references | Machado, I. E. S., Tavares, M. E. F., & Giongo, M. | |
dc.relation.references | (2018). Identification of burned areas by special index | |
dc.relation.references | in a cerrado region of the state of tocantins, | |
dc.relation.references | Brazil. Floresta, 48(4), 553–562. https://doi.org/10.5380/rf.v48i4.57362. | |
dc.relation.references | Pleniou, M., & Koutsias, N. (2013). Sensitivity of | |
dc.relation.references | spectral reflectance values to different burn and | |
dc.relation.references | vegetation ratios: A multi- scale approach applied in a | |
dc.relation.references | fire affected area. ISPRS Journal of Photogrammetry and | |
dc.relation.references | Remote Sensing, Amsterdam, Vol. 79, 199–210. | |
dc.relation.references | https://doi.org/10.1016/j.isprsjprs.2013.02.016м | |
dc.relation.references | Quintano, C.; Fernandez-Manso, A.; Fernandez-Manso, O. | |
dc.relation.references | (2018). Combination of Landsat and Sentinel-2 MSI | |
dc.relation.references | data for initial assessing of burn severity. International | |
dc.relation.references | Journal of Applied Earth Observation and | |
dc.relation.references | Geoinformation, Vol. 64, 221–225. https://doi.org/10.1016/j.jag.2017.09.014 | |
dc.relation.references | Ramo, R., Roteta, E., Bistinas, I., Van Wees, D., | |
dc.relation.references | Bastarrika, A., Chuvieco, E. & Van der Werf, G. R. | |
dc.relation.references | (2021). African burned area and fire carbon | |
dc.relation.references | emissions are strongly impacted by small fires undetected | |
dc.relation.references | by coarse resolution satellite data. Proceedings of | |
dc.relation.references | the National Academy of Sciences, 118 (9). | |
dc.relation.references | https://doi.org/10.1073/pnas.2011160118, 2021. | |
dc.relation.references | Rasul, A., Ibrahim, G. R. F., Hameed, H. M. & Tansey, K. | |
dc.relation.references | (2021). A trend of increasing burned areas in Iraq | |
dc.relation.references | from 2001 to 2019. Environ Dev Sustain 23, 5739–5755. https://doi.org/10.1007/s10668-020-00842-7. | |
dc.relation.references | Stroppiana, D., Azar, R., Calò, F., Pepe, A., Imperatore, P., | |
dc.relation.references | Boschetti, M. & Lanari, R. (2015). Integration of | |
dc.relation.references | optical and SAR data for burned area mapping in | |
dc.relation.references | Mediterranean Regions. Remote Sensing, 7(2), 1320–1345. | |
dc.relation.referencesen | Boschetti, L., Roy, D. P., Giglio, L., Huang, H., | |
dc.relation.referencesen | Zubkova, M., & Humber, M. L. (2019). Global | |
dc.relation.referencesen | validation of the collection 6 MODIS burned area | |
dc.relation.referencesen | product. Remote Sensing Environments, Vol. 235, 111490. https://doi.org/10.1016/j.rse.2019.111490. | |
dc.relation.referencesen | Bowman, D. (2018). Wildfire science is at a loss for | |
dc.relation.referencesen | comprehensive data. Nature. 560:7. https://doi.org/10.1038/d41586-018-05840-4. | |
dc.relation.referencesen | Burshtynska, Kh., Denys, Yu., Polishchuk, B. & | |
dc.relation.referencesen | Tymchyshyn, M. (2018). Monitoring of forest fires by | |
dc.relation.referencesen | space images of medium resolution (on the example | |
dc.relation.referencesen | of Arizona, USA). Modern achievements of geodetic | |
dc.relation.referencesen | science and industry. No. 1 (35), 179–184 (in | |
dc.relation.referencesen | Ukrainian). | |
dc.relation.referencesen | DaCamara, C., Libonati, R., Pinto, M. & Hurduc, A. | |
dc.relation.referencesen | (2018). Near- and Middle-Infrared Monitoring of | |
dc.relation.referencesen | Burned Areas from Space. Satellite Information | |
dc.relation.referencesen | Classification and Interpretation. http://dx.doi.org/10.5772/intechopen.82444. | |
dc.relation.referencesen | Ertugrul, M., Ozel, H. B., Varol, T., Cetin, M. & Sevik, H. | |
dc.relation.referencesen | (2019). Investigation of the relationship between | |
dc.relation.referencesen | burned areas and climate factors in large forest fires | |
dc.relation.referencesen | in the Canakkale region. Environmental Monitoring | |
dc.relation.referencesen | and Assessment, 191 (12), 737. https://doi.org/10.1007/s10661-019-7946-6. | |
dc.relation.referencesen | Filipponi, F. (2018). BAIS2: Burned Area Index | |
dc.relation.referencesen | for Sentinel-2. Proceedings, 2 (7), 364. | |
dc.relation.referencesen | https://doi.org/10.3390/ecrs-2-05177. | |
dc.relation.referencesen | Giglio, L., Boschetti, L., Roy, DP., Humber, ML. & | |
dc.relation.referencesen | Justice, CO. (2018). The collection 6 MODIS burned | |
dc.relation.referencesen | area mapping algorithm and product. Remote | |
dc.relation.referencesen | Sensing of Environment. 217, 72–85. https://doi.org/10.1016/j.rse.2018.08.005. | |
dc.relation.referencesen | Hall, J., Argueta, F., & Giglio, L. (2021). Validation of | |
dc.relation.referencesen | MCD64A1 and FireCCI51 cropland burned area | |
dc.relation.referencesen | mapping in Ukraine. International Journal of | |
dc.relation.referencesen | Applied Earth Observations and Geoinformation, | |
dc.relation.referencesen | No. 102, 1–11. https://doi.org/10.1016/j.jag.2021.102443. | |
dc.relation.referencesen | Kumar, S. S. & Roy, D. P. (2018). Global operational | |
dc.relation.referencesen | land imager Landsat-8 reflectance-based active fire | |
dc.relation.referencesen | detection algorithm. International Journal of Digital | |
dc.relation.referencesen | Earth, Abingdon, Vol. 11, n. 2, 154–178. | |
dc.relation.referencesen | https://doi.org/10.1080/17538947.2017.1391341. | |
dc.relation.referencesen | Lanorte, R. Lasaponara, M. Lovallo and Telesca, L. | |
dc.relation.referencesen | (2015). Fisher-Shannon information plane analysis of | |
dc.relation.referencesen | SPOT/VEGETATION normalized difference | |
dc.relation.referencesen | vegetation index (NDVI) time series to characterize | |
dc.relation.referencesen | vegetation recovery after fire disturbance. | |
dc.relation.referencesen | International Journal of Applied Earth Observation | |
dc.relation.referencesen | and Geoinformation, Vol. 26, 441–446. https://doi.org/10.1016/j.jag.2013.05.008. | |
dc.relation.referencesen | Lasaponara, R. and Tucci, B. (2019). Identification of | |
dc.relation.referencesen | Burned Areas and Severity Using SAR Sentinel-1. | |
dc.relation.referencesen | IEEE Geoscience and Remote Sensing Letters, | |
dc.relation.referencesen | Vol. 16, no. 6, 917–921. https://doi.org/10.1109/LGRS.2018.2888641. | |
dc.relation.referencesen | Lasko, K. (2019). Incorporating Sentinel-1 SAR imagery | |
dc.relation.referencesen | with the MODIS MCD64A1 burned area product | |
dc.relation.referencesen | to improve burn date estimates and reduce burn | |
dc.relation.referencesen | date uncertainty in wildland fire mapping | |
dc.relation.referencesen | Incorporating Sentinel-1 SAR imagery with the | |
dc.relation.referencesen | MODIS. Geocarto International. https://doi.org/10.1080/10106049.2019.16085 92. | |
dc.relation.referencesen | Ling, F., Du, Y., Zhang, Y., Li, X. & Xiao, F. (2015). | |
dc.relation.referencesen | Burned-Area Mapping at the subpixel scale with | |
dc.relation.referencesen | MODIS Images. IEEE Geoscience and Remote | |
dc.relation.referencesen | Sensing Letters, Vol. 12, no. 9, 1963–1967. | |
dc.relation.referencesen | https://doi.org/10.1109/LGRS.2015.2441135. | |
dc.relation.referencesen | Padilla, M., Stehman, S. V., Ramo, R., Corti, D., Hantson, S., | |
dc.relation.referencesen | Oliva, P., ... & Chuvieco, E. (2015). Comparing the | |
dc.relation.referencesen | accuracies of remote sensing global burned area | |
dc.relation.referencesen | products using stratified random sampling and | |
dc.relation.referencesen | estimation. Remote sensing of environment, 160, 114–121. https://doi.org/10.1016/j.rse.2015.01.005. | |
dc.relation.referencesen | Pereira. I. M. S. de Carvalho, E. V., Batista, A. C., | |
dc.relation.referencesen | Machado, I. E. S., Tavares, M. E. F., & Giongo, M. | |
dc.relation.referencesen | (2018). Identification of burned areas by special index | |
dc.relation.referencesen | in a cerrado region of the state of tocantins, | |
dc.relation.referencesen | Brazil. Floresta, 48(4), 553–562. https://doi.org/10.5380/rf.v48i4.57362. | |
dc.relation.referencesen | Pleniou, M., & Koutsias, N. (2013). Sensitivity of | |
dc.relation.referencesen | spectral reflectance values to different burn and | |
dc.relation.referencesen | vegetation ratios: A multi- scale approach applied in a | |
dc.relation.referencesen | fire affected area. ISPRS Journal of Photogrammetry and | |
dc.relation.referencesen | Remote Sensing, Amsterdam, Vol. 79, 199–210. | |
dc.relation.referencesen | https://doi.org/10.1016/j.isprsjprs.2013.02.016m | |
dc.relation.referencesen | Quintano, C.; Fernandez-Manso, A.; Fernandez-Manso, O. | |
dc.relation.referencesen | (2018). Combination of Landsat and Sentinel-2 MSI | |
dc.relation.referencesen | data for initial assessing of burn severity. International | |
dc.relation.referencesen | Journal of Applied Earth Observation and | |
dc.relation.referencesen | Geoinformation, Vol. 64, 221–225. https://doi.org/10.1016/j.jag.2017.09.014 | |
dc.relation.referencesen | Ramo, R., Roteta, E., Bistinas, I., Van Wees, D., | |
dc.relation.referencesen | Bastarrika, A., Chuvieco, E. & Van der Werf, G. R. | |
dc.relation.referencesen | (2021). African burned area and fire carbon | |
dc.relation.referencesen | emissions are strongly impacted by small fires undetected | |
dc.relation.referencesen | by coarse resolution satellite data. Proceedings of | |
dc.relation.referencesen | the National Academy of Sciences, 118 (9). | |
dc.relation.referencesen | https://doi.org/10.1073/pnas.2011160118, 2021. | |
dc.relation.referencesen | Rasul, A., Ibrahim, G. R. F., Hameed, H. M. & Tansey, K. | |
dc.relation.referencesen | (2021). A trend of increasing burned areas in Iraq | |
dc.relation.referencesen | from 2001 to 2019. Environ Dev Sustain 23, 5739–5755. https://doi.org/10.1007/s10668-020-00842-7. | |
dc.relation.referencesen | Stroppiana, D., Azar, R., Calò, F., Pepe, A., Imperatore, P., | |
dc.relation.referencesen | Boschetti, M. & Lanari, R. (2015). Integration of | |
dc.relation.referencesen | optical and SAR data for burned area mapping in | |
dc.relation.referencesen | Mediterranean Regions. Remote Sensing, 7(2), 1320–1345. | |
dc.relation.uri | https://doi.org/10.1016/j.rse.2019.111490 | |
dc.relation.uri | https://doi.org/10.1038/d41586-018-05840-4 | |
dc.relation.uri | http://dx.doi.org/10.5772/intechopen.82444 | |
dc.relation.uri | https://doi.org/10.1007/s10661-019-7946-6 | |
dc.relation.uri | https://doi.org/10.3390/ecrs-2-05177 | |
dc.relation.uri | https://doi.org/10.1016/j.rse.2018.08.005 | |
dc.relation.uri | https://doi.org/10.1016/j.jag.2021.102443 | |
dc.relation.uri | https://doi.org/10.1080/17538947.2017.1391341 | |
dc.relation.uri | https://doi.org/10.1016/j.jag.2013.05.008 | |
dc.relation.uri | https://doi.org/10.1109/LGRS.2018.2888641 | |
dc.relation.uri | https://doi.org/10.1080/10106049.2019.16085 | |
dc.relation.uri | https://doi.org/10.1109/LGRS.2015.2441135 | |
dc.relation.uri | https://doi.org/10.1016/j.rse.2015.01.005 | |
dc.relation.uri | https://doi.org/10.5380/rf.v48i4.57362 | |
dc.relation.uri | https://doi.org/10.1016/j.isprsjprs.2013.02.016м | |
dc.relation.uri | https://doi.org/10.1016/j.jag.2017.09.014 | |
dc.relation.uri | https://doi.org/10.1073/pnas.2011160118 | |
dc.relation.uri | https://doi.org/10.1007/s10668-020-00842-7 | |
dc.rights.holder | © Національний університет “Львівська політехніка”, 2021 | |
dc.subject | Sentinel-2 | |
dc.subject | дані дистанційного зондування | |
dc.subject | нормалізований індекс горіння | |
dc.subject | території | |
dc.subject | пошкоджені пожежею | |
dc.subject | Чорнобильська зона відчуження | |
dc.subject | різночасові знімки | |
dc.subject | Sentinel-2 | |
dc.subject | Remote Sensing Data | |
dc.subject | NBR | |
dc.subject | burned area | |
dc.subject | Chernobyl Exclusion Zone | |
dc.subject | different time images | |
dc.subject.udc | 528.92 | |
dc.title | Research of forest fires using remote sensing data (on the example of the Сhornobyl exclusion zone) | |
dc.title.alternative | Дослідження лісових пожеж за даними дистанційного зондування Землі (на прикладі Чорнобильської зони відчуження) | |
dc.type | Article |
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