Using remotely sensed data to model suitable habitats for tree species in a desert environment.

CAMPOS VALERIA; CAPPA FLAVIO; FERNANDEZ, VIVIANA.; GIANNONI STELLA M

JOURNAL OF VEGETATION SCIENCE

WILEY-BLACKWELL PUBLISHING, INC

Lugar: Londres; Año: 2016 vol. 27 p. 1 - 2

1100-9233

Questions:Can the species?environment relationship be understood using current remote sensing techniques? Can the derived indicators of remotely senseddata serve as a proxy for variables that affect habitat suitability of plant species?Which remote sensing predictors are best associated with woody species occurrence in a desert environment? How well do models with derived indicators ofremotely sensed data predict the occurrence of these species? What are thepotential distributions ofRamorinoa girolae, Prosopisspp. andBulnesia retamainthe study area?Location:Ischigualasto Provincial Park, San Juan province, Argentina.Methods:We selected random field points from a Landsat 8 OLI to determinepresence/absence of trees species. We calculated Brightness index (BI) using thesame image and used this index to calculate texture measures on a 393 moving window size. We used the following subset of texture measures: (1) firstorder: range, (2) second-order: mean, variance, contrast, entropy, secondmoment and correlation. We also calculated Topographic Wetness Index (TWI),slope angle and slope aspect from Global Digital Elevation Model.Results and Conclusion:Second-order mean of BI had an important effect onthe occurrence of target trees species. TWI was an important variable forProsopisspp. andB. retama, whereas slope angle was important for R. girolaeandB. retama. In addition, the occurrence ofR. girolaewas affected by second-order variance of BI and slope aspect; and the presence ofB. retamawas affected bysecond-order contrast of BI. All the variables that had important effects on theoccurrence of tree species provide environmental information about their different habitat requirements; therefore, our findings indicate that the remote sensing data are reliable to derive indicators of tree species presence in our studyarea.