Ecosystem functional assessment based on the “optical type” concept and self-similarity patterns: An application using MODIS-NDVI time series autocorrelation

摘要: Remote sensing (RS) time series are an excellent operative source for information about the land surface across several scales and different levels of landscape heterogeneity. Ustin Gamon (2010) proposed new concept "optical types" (OT), meaning "optically distinguishable functional types", as a way to better understand remote signals related actual behavior species that share common physiognomic forms but differ in functionality. Whereas OT approach seems be promising consistent with ecological theory monitor vegetation derived from RS, it received little implementation. This work presents method implementing efficient monitoring ecosystems based on RS series. We propose relying ecosystem's repetitive pattern temporal domain (self-similarity) assess its dynamics. Based this approach, our main hypothesis is distinct dynamics intrinsic specific OT. Self-similarity level within broadleaf forest class was quantitatively assessed using auto-correlation function (ACF), statistical analysis. A vector comparison classification method, spectral angle mapper, principal component analysis were used identify general patterns Phenological metrics MOD IS NDVI TIMESAT software, together National Forest Map explain found. Results showed significant highly stable self-similarity OTs corresponded forests under non-moisture-limited environments adaptation strategy strong phenological synchrony climate seasonality. These characterized by dense closed canopy deciduous associated high productivity low biodiversity terms dominant species. Forests transitional areas less stability probably due mixtures strategies (i.e., deciduous, marcescent evergreen species) higher diversity variability at long short terms. seasonality even double season appear Mediterranean open dominated evergreen-sclerophyllous formations. Within forest, understory overstory maximize resulting contrasting traits adapted summer drought, winter frosts, precipitation variability. (C) 2015 Elsevier B.V. All rights reserved. (Less)