000136192 001__ 136192
000136192 005__ 20240719195438.0
000136192 0247_ $$2doi$$a10.1016/j.envres.2024.119432
000136192 0248_ $$2sideral$$a139121
000136192 037__ $$aART-2024-139121
000136192 041__ $$aeng
000136192 100__ $$aTanase, M.A.
000136192 245__ $$aLong-term annual estimation of forest above ground biomass, canopy cover, and height from airborne and spaceborne sensors synergies in the Iberian Peninsula
000136192 260__ $$c2024
000136192 5060_ $$aAccess copy available to the general public$$fUnrestricted
000136192 5203_ $$aThe Mediterranean Basin has experienced substantial land use changes as traditional agriculture decreased and population migrated from rural to urban areas, which have resulted in a large forest cover increase. The combination of Landsat time series, providing spectral information, with lidar, offering three-dimensional insights, has emerged as a viable option for the large-scale cartography of forest structural attributes across large time spans. Here we develop and test a comprehensive framework to map forest above ground biomass, canopy cover and forest height in two regions spanning the most representative biomes in the peninsular Spain, Mediterranean (Madrid region) and temperate (Basque Country). As reference, we used lidar-based direct estimates of stand height and forest canopy cover. The reference biomass and volume were predicted from lidar metrics. Landsat time series predictors included annual temporal profiles of band reflectance and vegetation indices for the 1985–2023 period. Additional predictor variables including synthetic aperture radar, disturbance history, topography and forest type were also evaluated to optimize forest structural attributes retrieval. The estimates were independently validated at two temporal scales, i) the year of model calibration and ii) the year of the second lidar survey. The final models used as predictor variables only Landsat based metrics and topographic information, as the available SAR time-series were relatively short (1991–2011) and disturbance information did not decrease the estimation error. Model accuracies were higher in the Mediterranean forests when compared to the temperate forests (R2 = 0.6–0.8 vs. 0.4–0.5). Between the first (1985–1989) and the last (2020–2023) decades of the monitoring period the average forest cover increased from 21 ± 2% to 32 ± 1%, mean height increased from 6.6 ± 0.43 m to 7.9 ± 0.18 m and the mean biomass from 31.9 ± 3.6 t ha−1 to 50.4 ± 1 t ha−1 for the Mediterranean forests. In temperate forests, the average canopy cover increased from 55 ± 4% to 59 ± 3%, mean height increased from 15.8 ± 0.77 m to 17.3 ± 0.21m, while the growing stock volume increased from 137.8 ± 8.2 to 151.5 ± 3.8 m3 ha−1. Our results suggest that multispectral data can be successfully linked with lidar to provide continuous information on forest height, cover, and biomass trends.
000136192 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2020-114062RA-I00$$9info:eu-repo/grantAgreement/ES/MICINN/CNS2022-135251$$9info:eu-repo/grantAgreement/ES/MICINN PRE2021-096894$$9info:eu-repo/grantAgreement/ES/MICINN/RYC-2017-22555$$9info:eu-repo/grantAgreement/ES/MICINN/RYC2018-024614$$9info:eu-repo/grantAgreement/ES/NextGenerationEU/MS-240621
000136192 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000136192 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000136192 700__ $$aMihai, M.C.
000136192 700__ $$aMiguel, S.
000136192 700__ $$aCantero, A.
000136192 700__ $$aTijerin, J.
000136192 700__ $$aRuiz-Benito, P.
000136192 700__ $$0(orcid)0000-0002-8362-7559$$aDomingo, D.
000136192 700__ $$0(orcid)0000-0003-2610-7749$$aGarcia-Martin, A.$$uUniversidad de Zaragoza
000136192 700__ $$aAponte, C.
000136192 700__ $$0(orcid)0000-0002-8954-7517$$aLamelas, M.T.$$uUniversidad de Zaragoza
000136192 7102_ $$13006$$2010$$aUniversidad de Zaragoza$$bDpto. Geograf. Ordenac.Territ.$$cÁrea Análisis Geográfico Regi.
000136192 773__ $$g259 (2024), 119432 [13 pp.]$$pEnviron. res.$$tEnvironmental Research$$x0013-9351
000136192 8564_ $$s6803254$$uhttps://zaguan.unizar.es/record/136192/files/texto_completo.pdf$$yVersión publicada
000136192 8564_ $$s2216467$$uhttps://zaguan.unizar.es/record/136192/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000136192 909CO $$ooai:zaguan.unizar.es:136192$$particulos$$pdriver
000136192 951__ $$a2024-07-19-18:29:00
000136192 980__ $$aARTICLE