000164986 001__ 164986
000164986 005__ 20251204150238.0
000164986 0247_ $$2doi$$a10.1016/j.rse.2020.112025
000164986 0248_ $$2sideral$$a119862
000164986 037__ $$aART-2020-119862
000164986 041__ $$aeng
000164986 100__ $$0(orcid)0000-0001-7403-1764$$aMontorio, R.$$uUniversidad de Zaragoza
000164986 245__ $$aUnitemporal approach to fire severity mapping using multispectral synthetic databases and Random Forests
000164986 260__ $$c2020
000164986 5060_ $$aAccess copy available to the general public$$fUnrestricted
000164986 5203_ $$aFire severity assessment is crucial for predicting ecosystem response and prioritizing post-fire forest management strategies. Although a variety of remote sensing approaches have been developed, more research is still needed to improve the accuracy and effectiveness of fire severity mapping. This study proposes a unitemporal simulation approach based on the generation of synthetic spectral databases from linear spectral mixing. To fully exploit the potential of these training databases, the Random Forest (RF) machine learning algorithm was applied to build a classifier and regression model. The predictive models parameterized with the synthetic datasets were applied in a case study, the Sierra de Luna wildfire in Spain. Single date Landsat-8 and Sentinel-2A imagery of the immediate post-fire environment were used to develop the validation spectral datasets and a Pléiades orthoimage, providing the ground truth data. The four defined severity categories – unburned (UB), partial canopy unburned (PCU), canopy scorched (CS), and canopy consumed (CC) – demonstrated high accuracy in the bootstrapped (about 95%) and real validation sets (about 90%), with a slightly better performance observed when the Sentinel-2A dataset was used. Abundance of four ground covers (green vegetation, non-photosynthetic vegetation, soil, and ash) was also quantified with moderate (~45% for NPV) or high accuracy (higher than 75% for the remaining covers). No specific pattern in the comparison of sensors was observed. Variable importance analysis highlighted the complementary behavior of the spectral bands, although the contrast between the near and shortwave infrared regions stood out above the rest. Comparison of procedures reinforced the usefulness of the approach, as RF image-derived models and the multiple endmember spectral unmixing technique (MESMA) showed lower accuracy. The capabilities for detailed mapping are reflected in the development of different types of cartography (classification maps and fraction cover maps). The approach holds great potential for fire severity assessment, and future research needs to extend the predictive modeling to other burned areas – also in different ecosystems – and analyze its competence and the possible adaptations needed.
000164986 536__ $$9info:eu-repo/grantAgreement/ES/DGA/S51-17R
000164986 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
000164986 590__ $$a10.164$$b2020
000164986 591__ $$aENVIRONMENTAL SCIENCES$$b12 / 273 = 0.044$$c2020$$dQ1$$eT1
000164986 591__ $$aIMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY$$b1 / 29 = 0.034$$c2020$$dQ1$$eT1
000164986 591__ $$aREMOTE SENSING$$b1 / 32 = 0.031$$c2020$$dQ1$$eT1
000164986 592__ $$a3.611$$b2020
000164986 593__ $$aComputers in Earth Sciences$$c2020$$dQ1
000164986 593__ $$aSoil Science$$c2020$$dQ1
000164986 593__ $$aGeology$$c2020$$dQ1
000164986 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000164986 700__ $$0(orcid)0000-0003-4831-4060$$aPérez-Cabello, F.$$uUniversidad de Zaragoza
000164986 700__ $$0(orcid)0000-0001-6658-7017$$aBorini Alves, D.
000164986 700__ $$0(orcid)0000-0003-2610-7749$$aGarcía-Martín, A.$$uUniversidad de Zaragoza
000164986 7102_ $$13006$$2010$$aUniversidad de Zaragoza$$bDpto. Geograf. Ordenac.Territ.$$cÁrea Análisis Geográfico Regi.
000164986 773__ $$g249, 112025 (2020), [19 pp]$$pRemote sens. environ.$$tRemote Sensing of Environment$$x0034-4257
000164986 8564_ $$s3540929$$uhttps://zaguan.unizar.es/record/164986/files/texto_completo.pdf$$yPostprint
000164986 8564_ $$s2313379$$uhttps://zaguan.unizar.es/record/164986/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000164986 909CO $$ooai:zaguan.unizar.es:164986$$particulos$$pdriver
000164986 951__ $$a2025-12-04-14:38:46
000164986 980__ $$aARTICLE