RELATIONSHIPS BETWEEN LANDSAT 8 OLI PRODUCTS, MOISTURE, AND SURFACE FUEL LOAD IN CERRADO GRASSLAND ENVIRONMENTS
DOI:
https://doi.org/10.5380/rf.v55i1.97510Keywords:
fuel load, moisture, linear correlations, satellite images, image processingAbstract
This study evaluated the relationships among Landsat 8 OLI products, surface fuel load, and moisture content in Cerrado grassland environments. Sixty-eight sampling units, with a minimum distance of 100 m between them, were used in this study. The pixel values associated with the field plots were obtained from a 3 x 3-pixel window by extracting the median values of each polygon. After receiving the satellite images corresponding to the in situ data collection period and tabulating the information, Pearson's linear correlation analyses were performed between the fuel load and independent variables derived from image processing. Dead herbaceous fuel and the soil fraction of the spectral mixture analyses showed strong correlations, with r = -0.81 for load and r = -0.60 for moisture content. For reflectance of the Landsat 8 bands, the highest correlations were observed between dead herbaceous fuel and near-infrared, with r = -0.68 (load) and r = -0.61 (moisture). The correlations between dead herbaceous fuel and the MSI (r = 0.77), NDII6, and GVMI (r = -0.77) indices for fuel load stood out among the vegetation indices. Fuel moisture was negatively correlated with the DER23 (r = -0.57) and MNDWI (r = -0.56) indices and positively correlated with MSI (r = 0.47). The relationships between load and moisture in the dead fuel classes were more substantial, particularly for the dead herbaceous fuel load.
References
CHRYSAFIS, I.; MALLINIS, G.; GITAS, I.; TSAKIRI-STRATI, M. Estimating Mediterranean forest parameters using multi seasonal Landsat 8 OLI imagery and an ensemble learning method. Remote Sensing of Environment, [S. l.], v. 199, p. 154–166, 2017. DOI: 10.1016/J.RSE.2017.07.018.
CHUVIECO, E.; RIAÑO, D.; AGUADO, I.; COCERO, D. Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: Applications in fire danger assessment. International Journal of Remote Sensing, [S. l.], v. 23, n. 11, p. 2145–2162, 2002. DOI: 10.1080/01431160110069818.
COSTA-SAURA, J.M.; BALAGUER-BESER, A.; RUIZ, L.A.; PARDO-PASCUAL, J.E.; SORIANO-SANCHO, J.L. Empirical models for spatio-temporal live fuel moisture content estimation in mixed mediterranean vegetation areas using sentinel-2 indices and meteorological data. Remote Sensing, [S. l.], v. 13, n. 18, p. 3726, 2021. DOI: 10.3390/rs13183726.
DANSON, F. M.; BOWYER, P. Estimating live fuel moisture content from remotely sensed reflectance. Remote Sensing of Environment, [S. l.], v. 92, n. 3, p. 309–321, 2004. DOI: 10.1016/J.RSE.2004.03.017.
FORBES, B.; REILLY, S.; CLARK, M.; FERRELL, R.; KELLY, A.; KRAUSE, P.; MATLEY, C.; O’NEIL, M.; VILLASENOR, M.; DISNEY, M.; WILKES, P.; BENTLEY, L.P. Comparing Remote Sensing and Field-Based Approaches to Estimate Ladder Fuels and Predict Wildfire Burn Severity. Frontiers in Forests and Global Change, [S. l.], v. 5, p. 818713, 2022. DOI: 10.3389/ffgc.2022.818713.
FRANKE, J.; BARRADAS, A.C.S.; BORGES, M.A.; MENEZES COSTA, M.; DIAS, P.A.; HOFFMANN, A.A.; OROZCO FILHO, J.C.; MELCHIORI, A.E.; SIEGERT, F. Fuel load mapping in the Brazilian Cerrado in support of integrated fire management. Remote Sensing of Environment, [S. l.], v. 217, p. 221–232, 2018. DOI: 10.1016/J.RSE.2018.08.018.
GALE, M. G.; CARY, G. J.; VAN DIJK, A. I. J. M.; YEBRA, M. Forest fire fuel through the lens of remote sensing: Review of approaches, challenges and future directions in the remote sensing of biotic determinants of fire behaviour. Remote Sensing of Environment, v. 255, p. 112282, 2021. DOI: 10.1016/j.rse.2020.112282.
GAO, X.; DONG, S.; LI, S.; XU, Y.; LIU, S.; ZHAO, H.; YEOMANS, J.; LI, Y.; SHEN, H.; WU, S.; ZHI, Y. Using the random forest model and validated MODIS with the field spectrometer measurement promote the accuracy of estimating aboveground biomass and coverage of alpine grasslands on the Qinghai-Tibetan Plateau. Ecological Indicators, [S. l.], v. 112, p. 106114, 2020. DOI: 10.1016/J.ECOLIND.2020.106114.
GARCÍA, M.; RIAÑO, D.; YEBRA, M.; SALAS, J.; CARDIL, A.; MONEDERO, S.; RAMIREZ, J.; MARTÍN, M.P.; VILAR, L.; GAJARDO, J. A.; USTIN, S. Live Fuel Moisture Content Product from Landsat TM Satellite Time Series for Implementation in Fire Behavior Models. Remote Sensing, [S. l.], v. 12, n. 11, p. 1714, 2020. DOI: 10.3390/RS12111714.
GORELICK, N.; HANCHER, M.; DIXON, M.; ILYUSHCHENKO, S.; THAU, D.; MOORE, R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, [S. l.], v. 202, p. 18–27, 2017. DOI: 10.1016/J.RSE.2017.06.031.
HADI; KORHONEN, L.; HOVI, A.; RÖNNHOLM, P.; RAUTIAINEN, M. The accuracy of large-area forest canopy cover estimation using Landsat in boreal region. International Journal of Applied Earth Observation and Geoinformation, [S. l.], v. 53, p. 118–127, 2016. DOI: 10.1016/J.JAG.2016.08.009.
ICMBIO. Management plan for Serra Geral do Tocantins Ecological Station [Plano de manejo para Estação Ecológica Serra Geral do Tocantins (EESGT)]. 1. ed. Brasília. v. 1. 2014.
KENNEDY, M.C.; PRICHARD, S.J.; MCKENZIE, D.; FRENCH, N.H.F. Quantifying how sources of uncertainty in combustible biomass propagate to prediction of wildland fire emissions. International Journal of Wildland Fire, [S. l.], v. 29, n. 9, p. 793–806, 2020. DOI: 10.1071/WF19160.
LECINA-DIAZ, J.; MARTÍNEZ-VILALTA, J.; ALVAREZ, A.; VAYREDA, J.; RETANA, J. Assessing the Risk of Losing Forest Ecosystem Services Due to Wildfires. Ecosystems, [S. l.], v. 24, n. 7, p. 1687–1701, 2021. DOI: 10.1007/s10021-021-00611-1.
LI, Z.; ANGERER, J.P.; JAIME, X.; YANG, C.; WU, X.B. Estimating Rangeland Fine Fuel Biomass in Western Texas Using High-Resolution Aerial Imagery and Machine Learning. Remote Sensing, [S. l.], v. 14, n. 17, p. 4360, 2022. DOI: 10.3390/RS14174360.
MARINO, E.; YEBRA, M.; GUILLÉN-CLIMENT, M.; ALGEET, N.; TOMÉ, J.L.; MADRIGAL, J.; GUIJARRO, M.; HERNANDO, C. Investigating Live Fuel Moisture Content Estimation in Fire-Prone Shrubland from Remote Sensing Using Empirical Modelling and RTM Simulations. Remote Sensing, v. 12, n. 14, p. 2251, 2020. DOI: 10.3390/RS12142251.
MERRILL, E.H.; BRAMBLE-BRODAHL, M.K.; MARRS, R.W.; BOYCE, M.S. Estimation of green herbaceous phytomass from Landsat MSS data in Yellowstone National Park. Journal of Range Management, [S. l.], v. 46, n. 2, p. 151–157, 1993. DOI: 10.2307/4002273.
PONZONI, F.J.; SHIMABUKURO, Y.E.; KUPLICH, T.M. Sensoriamento remoto da vegetação. 2. ed. [s.l.] : Oficina de Textos, 2012. v. 1
RIBEIRO, J.F.; WALTER, B.M.T. As principais fitofisionomias do Bioma Cerrado. In: Cerrado: ecologia e flora. 1. ed. Brasília-DF: Embrapa Cerrados, 2008. v. 1p. 152–212.
ROTHERMEL, R.C. A mathematical model for predicting fire spread in wildland fuels. Forest Service - Rocky Mountain Research Station, Usda, [S. l.], n. Research Paper INT-115, p. 1–48, 1972.
SHARMA, S.; DHAKAL, K. Boots on the Ground and Eyes in the Sky: A Perspective on Estimating Fire Danger from Soil Moisture Content. Fire, [S. l.], v. 4, n. 3, p. 45, 2021. DOI: 10.3390/fire4030045.
SCHROEDER, M.; BUCK, C. Fire Weather: A Guide for Application of Meteorological Information to Forest Fire Control Operations. USDA Forest Service, Agriculture Handbook 360, [S. l.], 1970.
VERMOTE, E.; ROGER, J. C.; FRANCH, B.; SKAKUN, S. LASRC (Land Surface Reflectance Code): Overview, application and validation using MODIS, VIIRS, LANDSAT and Sentinel 2 data’s. International Geoscience and Remote Sensing Symposium (IGARSS), [S. l.], v. 2018- July, p. 8173–8176, 2018. DOI: 10.1109/IGARSS.2018.8517622.
YEBRA, M.; QUAN, X.; RIAÑO, D.; ROZAS LARRAONDO, P.; VAN DIJK, A.I.J.M.; CARY, G.J. A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing. Remote Sensing of Environment, [S. l.], v. 212, p. 260–272, 2018. DOI: 10.1016/J.RSE.2018.04.053.
ZHANG, C.; DENKA, S.; COOPER, H.; MISHRA, D.R. Quantification of sawgrass marsh aboveground biomass in the coastal Everglades using object-based ensemble analysis and Landsat data. Remote Sensing of Environment, [S. l.], v. 204, p. 366–379, 2018. DOI: 10.1016/J.RSE.2017.10.018.
ZORMPAS, K.; VASILAKOS, C.; ATHANASIS, N.; SOULAKELLIS, N.; KALABOKIDIS, K. Dead fuel moisture content estimation using remote sensing. European Journal of Geography, [S. l.], v. 8, n. 5, p. 17–32, 2017.
Downloads
Published
How to Cite
Issue
Section
License
Direitos Autorais para artigos publicados nesta revista são do autor, com direitos de primeira publicação para a revista. Em virtude da aparecerem nesta revista de acesso público, os artigos são de uso gratuito, com atribuições próprias, em aplicações educacionais e não-comerciais.A revista, seguindo a recomendações do movimento Acesso Aberto, proporciona acesso publico a todo o seu conteudo, seguindo o principio de que tornar gratuito o acesso a pesquisas gera um maior intrcambio global de conhecimento.
Conteúdos do periódico licenciados sob uma CC BY-NC-SA 4.0

