Análise de acurácia para o mapeamento de áreas queimadas utilizando uma cena VIIRS 1Km e classificação por Random Forest
DOI:
https://doi.org/10.26848/rbgf.v14.6.p3225-3240Abstract
A disponibilidade gratuita de dados de sensoriamento remoto em áreas atingidas por incêndios florestais em escala global oferece a oportunidade de geração sistemática de produtos terrestres de média resolução espacial, porém as conhecidas limitações de precisão é objeto de estudo em todo o mundo. Este artigo tem como objetivo analisar a acurácia da detecção de áreas queimadas utilizando o classificador Random Forest (RF) por meio de uma cena do sensor Radiômetro de Imagem Infravermelho Visível (VIIRS) (1Km) em quatro pontos da savana brasileira. Os resultados foram validados através dos produtos de referência espacial de áreas queimadas: Aq30m, Fire_cci e MCD64A1 por meio de uma abordagem estratificada possibilitando a amostragem dos dados no espaço e tempo. Os modelos de RF avaliados com seus parâmetros de entrada, em que, incluiu-se 400 árvores e um atributo, fornecendo uma taxa de erro abaixo de 4%. Os resultados mostraram que o mapeamento validado com o produto Aq30m apresentou importantes estimativas de Coeficiente de Sorensen-Dice enquanto a validação realizada entre os modelos globais, o MCD64A1 mostrou-se com maior exatidão (>50%) principalmente em feições de áreas queimadas de grandes proporções (> 200Km²). Em particular, a análise sugere que a validação de produtos de área queimada sempre deve estar ligada ao tempo mínimo da data dos dados de validação e o tamanho da área atingida pelo fogo. Os resultados mostram que esta abordagem é muito útil para ser usado para determinar áreas de floresta queimada.
Accuracy analysis for mapping burnt areas using a 1Km VIIRS scene and Random Forest classification
A B S T R A C T
The availability of remote sensing data with medium spatial resolution has offered several mapping possibilities for areas affected by forest fires on the Earth's surface. In this context, the analysis of sensor spatial accuracy limitations has been the subject of global research. The objective of this study was to analyze the mapping accuracy of the VIIRS sensor on board the NOAA satellite, using the Random Forest (RF) classifier for the detection of burned areas, in four points of the Chapada dos Veadeiros National Park - Goiás, inserted in the Brazilian savanna. The methodology consisted in validating the classification using the Sorensen-Dice coefficient (SD) in a stratified approach, using as reference the products: Aq30m, Fire_cci and MCD64A1. As a result, the RF models, included 400 trees and one attribute, with an error of less than 4%. Among the global models, the MCD64A1 presented a significant accuracy, greater than 50%, especially in features of burned areas greater than 200Km². Thus, the data suggest that the quality of accuracy of the validation process of mapping products for burned areas is associated with the minimum time interval of availability of validation data and the size of the area affected by fire. Based on this, the results show effectiveness in using the RF algorithm on medium spatial resolution images for fire detection in seasonally dry forests, such as the Cerrado.
Keywords: Cerrado, fires, Random Forest.
Downloads
References
Archibald, S.A.; Roy, D.P.; Van Wilgen, B.W.; Scholes, R.J. What limits fire? An examination of drivers of burnt area in southern africa. Glob. Chang. Biol. 2009, 15, 613–630. https://doi.org/10.1111/j.1365-2486.2008.01754.x
Aldersley, A.; Murray, S.J.; Cornell, S.E. Global and regional analysis of climate and human drivers of wildfire. Sci. Total Environ. 2011, 409, 3472–3481. https://doi.org/10.1016/j.scitotenv.2011.05.0
A. Alencar, J.Z. Shimbo, F. Lenti, C. Balzani Marques, B. Zimbres, M. Rosa, V. Arruda, I. Castro, J.P. Fernandes Márcico Ribeiro, V. Varela, I. Alencar, V. Piontekowski, V. Ribeiro, M.M.C. Bustamante, E. Eyji Sano, M. Barroso Mapping three decades of changes in the Brazilian savanna native vegetation using landsat data processed in the google earth engine platform Remote Sensing, 12 (6) (2020), p. 924.
Alonso-Canas, I.; Chuvieco, E. Global burned area mapping from envisat-meris data. Remote Sens. Environ. 2015, 163, 140–152. https://doi.org/10.1016/j.rse.2015.03.011.
Arvor, Damien; Betbeder, Julie; Daher, Felipe R.G.; Blossier, Tim; Roux, Renan Le; Corgne, Samuel; Corpetti, Thomas; Silgueiro, Vinicius De Freitas; Silva Junior, Carlos Antonio da. Towards user-adaptive remote sensing: knowledge-driven automatic classification of sentinel-2 time series. Remote Sensing Of Environment, [S.L.], 264, 112615-112630, out. 2021.
Baret et al., "Evaluation of the representativeness of networks of sites for the global validation and intercomparison of land biophysical products: proposition of the CEOS-BELMANIP," in IEEE Transactions on Geoscience and Remote Sensing, 44, no. 7, pp. 1794-1803, July 2006, doi: 10.1109/TGRS.2006.876030.
Bacour, C.; Baret, F.; Jacquemoud, S. Information Content of HyMap Hyperspectral Imagery. Proceedings of 1st International Symposium on Recent Advances in Quantitative Remote Sensing, Valencia, Spain, 27 September–1 October 2002; pp. 503–508.
Bispo, Polyanna Da Conceição; Rodríguez-Veiga, Pedro; Zimbres, Barbara; Miranda, Sabrina Do Couto De; Cezare, Cassio Henrique Giusti; Fleming, Sam; Baldacchino, Francesca; Louis, Valentin; Rains, Dominik; Garcia, Mariano. Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach. Remote Sensing, [S.L.], 12, 2685-2640, 19 ago. 2020.
Breiman, L. Random Forests. Machine Learning 45, 5–32 (2001). https://doi.org/10.1023/A:1010933404324
Breiman, L .; Friedman, J .; Stone, CJ; Olshen, RA Classification and Regression Trees; CRC Press: Florida, FL, USA, 1984.
Brewer, C.K.; Winne, J.C.; Redmond, R.L.; Opitz, D.W.; Mangrich, M.V. Classifying and mapping wildfire severity: A comparison of methods. Photogramm. Eng. Remote Sens. 2005, 71, 1311–1320.
Briones-Herrera, C.I.; Vega-Nieva, D.J.; Monjarás-Vega, N.A.; Briseño-Reyes, J.; López-Serrano, P.M.; Corral-Rivas, J.J.; Alvarado-Celestino, E.; Arellano-Pérez, S.; Álvarez-González, J.G.; Ruiz-González, A.D.; Jolly, W.M.; Parks, S.A. Near Real-Time Automated Early Mapping of the Perimeter of Large Forest Fires from the Aggregation of VIIRS and MODIS Active Fires in Mexico. Remote Sensing. 2020, 12, 2061. https://doi.org/10.3390/rs12122061
Belgiu, M.; Drăguţ, L. Random forest in remote sensing: A review of applications and future directions. ISPRS J. Photogramm. Remote Sens. 2016, 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
Boschetti L, David P. Roy, Louis Giglio, Haiyan Huang, Maria Zubkova, Michael L. Humber, Global validation of the collection 6 MODIS burned area product, Remote Sensing of Environment, Volume 235, 2019, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2019.111490.
Bowman, David & Perry, George & Marston, J. (2015). Feedbacks and landscape-level vegetation dynamics. Trends in ecology & evolution. 30. https://doi.org/10.1016/j.tree.2015.03.005
Boschetti L, P.A. Brivio, J.M. Gregoire The use of Meteosat and GMS imagery to detect burned areas in tropical environments. Remote Sens. Environ., 85 (1) (2003), pp. 78-91
Campagnolo M.L., R. Libonati, J.A. Rodrigues, J.M.C. Pereira. A comprehensive characterization of MODIS daily burned area mapping accuracy across fire sizes in tropical savannas, Remote Sensing of Environment, Volume 252,2021,ISSN 0034-425. https://doi.org/10.1016/j.rse.2020.112115
Carlos C. DaCamara, Renata Libonati, Miguel M. Pinto and Alexandra Hurduc (2019). Near- and Middle-Infrared Monitoring of Burned Areas from Space, Satellite Information Classification and Interpretation, Rustam B. Rustamov, IntechOpen, DOI: 10.5772/intechopen.82444.
Chuvieco, E.; Pettinari, M.L.; Lizundia-Loiola, J.; Storm, T.; Padilla Parellada, M. (2018): ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Pixel product, version 5.1. Centre for Environmental Data Analysis. (2018) https://doi.org/10.5285/58f00d8814064b79a0c49662ad3af537
CONCIANI, Dhemerson E.; SANTOS, Lucas Pereira dos; SILVA, Thiago Sanna Freire; DURIGAN, Giselda; ALVARADO, Swanni T.. Human-climate interactions shape fire regimes in the Cerrado of São Paulo state, Brazil. Journal For Nature Conservation, [S.L.], 61, 126006-126015, jun. 2021.
Comert, Resul & Matci, Dilek & Avdan, Ugur. Object Based Burned Area Mapping with Random Forest Algorithm. International Journal of Engineering and Geosciences. 4. 78-87. (2019). Doi:10.26833/ijeg.455595
DaCamara C.C, Renata Libonati, Miguel M. Pinto and Alexandra Hurduc (May 2nd 2019). Near- and Middle-Infrared Monitoring of Burned Areas from Space, Satellite Information Classification and Interpretation, Rustam B. Rustamov, IntechOpen, DOI: 10.5772/intechopen.82444.
Duro, D.C.; Franklin, S.E.; Dubé, M.G. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing. Environment. 2012, 118, 259–272.
Fornacca, D.; Ren, G.; Xiao, W. Performance of Three MODIS Fire Products (MCD45A1, MCD64A1, MCD14ML), and ESA Fire_CCI in a Mountainous Area of Northwest Yunnan, China, Characterized by Frequent Small Fires. Remote Sensing. 2017, 9, 1131.
Giglio, L., Justice, C., Boschetti, L., Roy, D. (2015). MCD64A1 MODIS/Terra+Aqua Burned Area Monthly L3 Global 500m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MCD64A1.006
Ghimire B, J. Rogan, J. Miller. Contextual land-cover classification: incorporating spatial dependence in land-cover classification models using random forests and the Getis statistic. Remote Sensing Letters, 1 (2010), pp. 45-54.
Giglio, L.; Loboda, T.; Roy, D.P.; Quayle, B.; Justice, C.O. An active-fire based burned area mapping algorithm for the MODIS sensor. Remote Sensing. Environment. 2009, 113, 408–420
García, V. Caselles M.J.L. Mapping burns and natural reforestation using thematic Mapper data Geocarto Int., 6 (1991), pp. 31-37, 10.1080/10106049109354290
Hantson, S. Pueyo, E. Chuvieco Global fire size distribution is driven by human impact and climate. Glob. Ecol. Biogeogr., 24 (1) (2015), pp. 77-86
Holden, Z.A.; Morgan, P.; Evans, J.S. A predictive model of burn severity based on 20-year satellite-inferred burn severity data in a large southwestern us wilderness area. For. Ecol. Manag. 2009, 258, 2399–2406.
Humber, M.L.; Boschetti, L.; Giglio, L.; Justice, C.O. Spatial and temporal intercomparison of four global burned area products. Int. J. Digital Earth. 2019, 12, 460–484.
Justice C., A. Belward, J. Morisette, P. Lewis, J. Privette, F. Baret Developments in the 'validation' of satellite sensor products for the study of the land surface Int. J. Remote Sens., 21 (17) (2000), pp. 3383-3390
Jeffrey T. Morisette, Jeffrey L. Privette, Christopher O. Justice A framework for the validation of MODIS land products Remote Sensing of Environment, 83 (1-2) (2002), pp. 77-96.
Jacquemoud, S.; Verhoef, W.; Baret, F.; Bacour, C.; Zarco-Tejada, P.J.; Asner, G.P.; François, C.; Ustin, S.L. PROSPECT+SAIL models: A review of use for vegetation characterization. Remote Sensing. Environment (2009), 113, S56–S66.
Kantola, T.; Vastaranta, M.; Yu, X.; Lyytikainen-Saarenmaa, P.; Holopainen, M.; Talvitie, M.; Kaasalainen, S.; Solberg, S.; Hyyppa, J. Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images. Remote Sensing. 2010, 2, 2665-2679
Key, C.H.; Benson, N.C. Landscape Assessment: Ground measure of severity, the Composite Burn Index; and Remote sensing of severity, the Normalized Burn Ratio; 2006; Other Government Series; RMRS-GTR-164-CD: LA 1-51; FIREMON: Fire Effects Monitoring and Inventory System.
Key, N C.H.. Benson The Normalized Burn Ratio (NBR): A Landsat TM Radiometric Measure of Burn Severity. US Geol. Surv. North. Rocky Mt. Sci. Center (1999)
Kganyago, Mahlatse; Shikwambana, Lerato. Assessment of the Characteristics of Recent Major Wildfires in the USA, Australia and Brazil in 2018–2019 Using Multi-Source Satellite Products. Remote Sensing, [S.L.], 12, 1803-1815, 3 jun. 2020.
Lawrence, R.L.; Wood, S.D.; Sheley, R.L. Mapping invasive plants using hyperspectral imagery and breiman cutler classifications (randomforest). Remote Sens. Environ. 2006, 100, 356–362.
Lasaponara, R. Estimating spectral separability of satellite derived parameters for burned areas mapping in the Calabria region by using SPOT-Vegetation data. Ecol. Model (2006), 196, 265–270.
López-García, M.J.; Caselles, V. Mapping burns and natural reforestation using Thematic Mapper data. Geocarto Int (1991), 6, 31–37.
Libonati, R.; DaCamara, C.C.; Setzer, A.W.; Morelli, F.; Melchiori, A.E. An Algorithm for Burned Area Detection in the Brazilian Cerrado Using 4 µm MODIS Imagery. Remote Sens. 2015, 7, 15782-15803.
Liaw, A.; Wiener, M. Classification and regression by random Forest. R News (2002), 2, 18–22.
Mallinis G.; Mitsopoulos I.; Chrysafi I. Evaluating and comparing sentinel 2a and landsat-8 operational land imager (oli) spectral indices for estimating fire severity in a mediterranean pine ecosystem of greece. GIScience & Remote Sensing 2018, 55, 1–18.
Mouillot F., M.G. Schultz, C. Yue, P. Cadule, K. Tansey, P. Ciais, E. Chuvieco Ten years of global burned area products from spaceborne remote sensing—a review: analysis of user needs and recommendations for future developments Int. J. Appl. Earth Obs. Geoinf., 26 (2014), pp. 64-79S.
Martín M.P., E. Chuvieco Cartografía de grandes insendios forestales en la península Ibérica a partir de imágenes NOAA-AVHRR. Teledetección Av. y Apl (1998), pp. 248-251
Martin P., I. Gómez, E. Chuvieco. Performance of a burned-area index (BAIM) for mapping Mediterranean burned scars from MODIS data Proceedings of the 5th International Workshop on Remote Sensing and GIS Application to Forest Fire Management: Fire Effects Assessment (2005), pp. 193- 197.
Morisette, F. Baret, J.L. Privette, R.B. Myneni, J.E. Nickeson, S. Garrigues, ..., M. Kalacska Validation of global moderate-resolution LAI products: a framework proposed within the CEOS land product validation subgroup IEEE Trans. Geosci. Remote Sensing., 44 (7) (2006), pp. 1804-1817
Ngadze F, Mpakairi KS, Kavhu B, Ndaimani H, Maremba MS (2020) Exploring the utility of Sentinel-2 MSI and Landsat 8 OLI in burned area mapping for a heterogenous savannah landscape. PLoS ONE 15(5): e0232962. https://doi.org/10.1371/journal.pone.0232962.
Oliveira, A.s.; Soares-Filho, B.s.; Oliveira, U.; Hoff, R. van Der; Carvalho-Ribeiro, S.M.; Oliveira, A.R.; Scheepers, L.C.; Vargas, B.A.; Rajão, R.G.. Costs and effectiveness of public and private fire management programs in the Brazilian Amazon and Cerrado. Forest Policy And Economics, [S.L.], 127, p. 102447-102450, jun. 2021.
Oliveira, S.; Oehler, F.; San-Miguel-Ayanz, J.; Camia, A.; Pereira, J.M. Modeling spatial patterns of fire occurrence in mediterranean europe using multiple regression and random forest. For. Ecol. Manag. 2012, 275, 117–129.
Pereira, J.M.C.; Sá, A.C.L.; Sousa, A.M.O.; Silva, J.M.N.; Santos, T.N.; Carreiras, J.M.B. Spectral Characterisation and Discrimination of Burnt Areas. In Remote Sensing of Large Wildfires in the European Mediterranean Basin; Springer-Verlag: Berlin, Germany, 1999; pp. 123–138.
Padilla, M.; Stehman, S.V.; Ramo, R.; Corti, D.; Hantson, S.; Olive, P.; Alonso-Canas, I.; Bradley, A.V.; Tansey, K.; Mota, B.; et al. Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation. Remote Sens. Environ. 2015, 160, 114–121.
Pereira, J.M.C. A comparative evaluation of NOAA/AVHRR vegetation indexes for burned surface detection and mapping. IEEE Trans. Geosci. Remote Sens .1999, 37, 217–226.
Pleniou, M.; Koutsias, N. Sensitivity of spectral reflectance values to different burn and vegetation ratios: A multi-scale approach applied in a fire affected area. ISPRS J. Photogramm. Remote Sensing 2013, 79, 199–210.
Ramo, R.; Chuvieco, E. Developing a Random Forest Algorithm for MODIS Global Burned Area Classification. Remote Sensing. 9, 1193. 2017. https://doi.org/10.3390/rs9111193
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J. P. (2012). An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 93–104. doi:10.1016/j.isprsjprs.2011.11.002
Rodrigues, M.; Riva, J. An insight into machine-learning algorithms to model human-caused wildfire occurrence. Environ. Model. Softw. 2014, 57, 192–201.
Ramo, R.; Chuvieco, E. Developing a Random Forest Algorithm for MODIS Global Burned Area Classification. Remote Sensing. 2017, 9, 1193
Roy D.P., P.E. Lewis, C.O. Justice Burned area mapping using multi-temporal moderate spatial resolution data—a bi-directional reflectance model-based expectation approach Remote Sensing. Environment., 83 (1–2) (2002), pp. 263-286
Rodriguez-Galiano, V.F.; Ghimire, B.; Rogan, J.; Chica-Olmo, M.; Rigol-Sanchez, J.P. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogramm. Remote Sens. 2012, 67, 93–104.
Roy D. P, Haiyan Huang, Luigi Boschetti, Louis Giglio, Lin Yan, Hankui H. Zhang, Zhongbin Li, Landsat-8 and Sentinel-2 burned area mapping - A combined sensor multi-temporal change detection approach, Remote Sensing of Environment, Volume 231, 2019, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2019.111254.
Roy D.P., P.E. Lewis, C.O. Justice Burned area mapping using multi-temporal moderate spatial resolution data—a bi-directional reflectance model-based expectation approach. Remote Sensing. Environment., 83 (1–2) (2002), pp. 263-286
Story, M.; Congalton, R.G. Accuracy assessment: A user’s perspective. Photogramm. Eng. Remote Sens. 1986, 52, 397–399. [Google Scholar]
Su, Z.; Hu, H.; Wang, G.; Ma, Y.; Yang, X.; Guo, F. Using GIS and random forests to identify fire drivers in a forest city, Yichun, China. Geomat. Nat. Hazards Risk, 2018, 9, 1207–1229.
Schepers, L.; Haest, B.; Veraverbeke, S.; Spanhove, T.; Vanden Borre, J.; Goossens, R. Burned Area Detection and Burn Severity Assessment of a Heathland Fire in Belgium Using Airborne Imaging Spectroscopy (APEX). Remote Sensing. 2014, 6, 1803-1826.
Smith, A.M.S.; Drake, N.A.; Wooster, M.J.; Hudak, A.T.; Holden, Z.A.; Gibbons, C.J. Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: Comparison of methods and application to MODIS. Int. J. Remote Sens. 2007. 28, 2753–2775.
Shimabukuro, Yosio Edemir; Dutra, Andeise Cerqueira; Arai, Egidio; Duarte, Valdete; Cassol, Henrique Luís Godinho; Pereira, Gabriel; Cardozo, Francielle da Silva. Mapping Burned Areas of Mato Grosso State Brazilian Amazon Using Multisensor Datasets. Remote Sensing, [S.L.], 12, 3827-3440, 21 nov. 2020.
Veraverbeke, S.; Harris, S.; Hook, S. Evaluating spectral indices for burned area discrimination using MODIS/ASTER (MASTER) airborne simulator data. Remote Sensing. Environment 2011, 115, 2702–2709.
Vermote, E., Franch, B., Claverie, M. (2016). VIIRS/NPP Surface Reflectance 8-Day L3 Global 1km SIN Grid V001 [Data set]. NASA EOSDIS Land Processes DAAC. Accessed 2021-01-07 from https://doi.org/10.5067/VIIRS/VNP09A1.001
Van der Werf, J.T G.R.. Randerson, L. Giglio, T.T.van Leeuwen, Y. Chen, B.M. Rogers, et al. Global fire emissions estimates during 1997-2016 Earth Syst. Sci. Data., 9 (2017), pp. 697-720
Tanase, Mihai A.; Belenguer-Plomer, Miguel A.; Roteta, Ekhi; Bastarrika, Aitor; Wheeler, James; Fernández-Carrillo, Ángel; Tansey, Kevin; Wiedemann, Werner; Navratil, Peter; Lohberger, Sandra. Burned Area Detection and Mapping: intercomparison of sentinel-1 and sentinel-2 based algorithms over tropical africa. Remote Sensing, [S.L.], 12, 334-345, 20 jan. 2020.
Trigg S.N., D.P. RoyA focus group study of factors that promote and constrain the use of satellite-derived fire products by resource managers in southern Africa J. Environ. Manag., 82 (1) (2007), pp. 95-110
Vermote, E., Franch, B., Claverie, M. (2016). VIIRS/NPP Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V001. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/VIIRS/VNP09GA.001
Veraverbeke, S.; Harris, S.; Hook, S. Evaluating spectral indices for burned area discrimination using MODIS/ASTER (MASTER) airborne simulator data. Remote Sens. Environ 2011, 115, 2702–2709.
Zhang, H.K.; Roy, D.P. Using the 500 m MODIS land cover product to derive a consistent continental scale 30 m Landsat land cover classification. Remote Sens. Environ. 2017, 197, 15–34.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Brazilian Journal of Physical Geography
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with Revista Brasileira de Geografia Física agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted to make their work available online before or during the editorial process, on academic social networks, digital repositories, or preprint servers. After publication in Revista Brasileira de Geografia Física, authors are expected to update the preprint or postprint versions on the platforms where they were originally made available, providing a link to the final published version and any other relevant information, with proper recognition of authorship and the initial publication in this journal.
You are free to:
Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
Adapt — remix, transform, and build upon the material for any purpose, even commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
