000121086 001__ 121086
000121086 005__ 20230519145544.0
000121086 0247_ $$2doi$$a10.1016/j.rse.2021.112521
000121086 0248_ $$2sideral$$a127060
000121086 037__ $$aART-2021-127060
000121086 041__ $$aeng
000121086 100__ $$aGelabert P.J.
000121086 245__ $$aLandTrendr smoothed spectral profiles enhance woody encroachment monitoring
000121086 260__ $$c2021
000121086 5060_ $$aAccess copy available to the general public$$fUnrestricted
000121086 5203_ $$aSecondary succession (SS) is one of the main consequences of the abandonment of agricultural and forestry practices in rural areas, causing -among other processes- woody encroachment on former pastures and croplands. In this study we model and monitor the spatial evolution of SS over semi-natural grassland communities in the mountain range of the Pyrenees in Spain, during the last 36 years (1984-2019). Independent variables for ‘annual-based’ and ‘period-based’ modeling were drawn from a suite of Surface Reflectance Landsat images, LandTrendr (LT)-algorithm-adjusted images and LT outputs. Support vector machine (SVM) classifiers were trained and tested using all possible variable combinations of all the aforementioned datasets. The best modeling strategy involved yearly time series of LT-adjusted Tasseled Cap Brightness (TCB) and Wetness (TCW) axes as predictors, attaining a F1-score of 0.85, a Matthew Correlation Coefficient (MCC) of 0.67 and an AUC 0.83. Woodlands encroached above 480, 000 ha of grasslands and crops during the study period. A model using LT outputs for the whole period also denoted good performance (F1-score = 0.85, MCC = 0.75) and estimated a similar area of woodland expansion (~509, 000 ha), but this ‘period’ approach was unable to provide temporal information on the year or the encroachment dynamics. Our results suggest an overall proportion of 66% for the Pyrenees being affected by SS, with higher intensity in the west-central part, decreasing towards the eastern end. © 2021 The Authors
000121086 536__ $$9info:eu-repo/grantAgreement/ES/FECYT/IMAGINE CGL2016-80400-R
000121086 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000121086 590__ $$a13.85$$b2021
000121086 591__ $$aIMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY$$b2 / 28 = 0.071$$c2021$$dQ1$$eT1
000121086 591__ $$aREMOTE SENSING$$b2 / 34 = 0.059$$c2021$$dQ1$$eT1
000121086 591__ $$aENVIRONMENTAL SCIENCES$$b10 / 279 = 0.036$$c2021$$dQ1$$eT1
000121086 592__ $$a3.862$$b2021
000121086 593__ $$aGeology$$c2021$$dQ1
000121086 593__ $$aComputers in Earth Sciences$$c2021$$dQ1
000121086 594__ $$a20.7$$b2021
000121086 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000121086 700__ $$0(orcid)0000-0002-0477-0796$$aRodrigues M.$$uUniversidad de Zaragoza
000121086 700__ $$0(orcid)0000-0003-2615-270X$$ade la Riva J.$$uUniversidad de Zaragoza
000121086 700__ $$aAmeztegui A.
000121086 700__ $$aSebastià M.T.
000121086 700__ $$aVega-Garcia C.
000121086 7102_ $$13006$$2010$$aUniversidad de Zaragoza$$bDpto. Geograf. Ordenac.Territ.$$cÁrea Análisis Geográfico Regi.
000121086 773__ $$g262 (2021), 112521 [10 pp]$$pRemote sens. environ.$$tRemote Sensing of Environment$$x0034-4257
000121086 8564_ $$s3261679$$uhttps://zaguan.unizar.es/record/121086/files/texto_completo.pdf$$yVersión publicada
000121086 8564_ $$s2467819$$uhttps://zaguan.unizar.es/record/121086/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000121086 909CO $$ooai:zaguan.unizar.es:121086$$particulos$$pdriver
000121086 951__ $$a2023-05-18-15:42:59
000121086 980__ $$aARTICLE