Premium
Using remotely sensed data to model suitable habitats for tree species in a desert environment
Author(s) -
Campos Valeria E.,
Cappa Flavio M.,
Viviana Fernández Maldonado,
Giani Stella M.
Publication year - 2016
Publication title -
journal of vegetation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 115
eISSN - 1654-1103
pISSN - 1100-9233
DOI - 10.1111/jvs.12328
Subject(s) - habitat , digital elevation model , ecology , vegetation (pathology) , remote sensing , physical geography , geography , environmental science , biology , medicine , pathology
Questions Can the species–environment relationship be understood using current remote sensing techniques? Can the derived indicators of remotely sensed data 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 of remotely sensed data predict the occurrence of these species? What are the potential distributions of Ramorinoa girolae , Prosopis spp. and Bulnesia retama in the study area? Location Ischigualasto Provincial Park, San Juan province, Argentina. Methods We selected random field points from a Landsat 8 OLI to determine presence/absence of trees species. We calculated Brightness index (BI) using the same image and used this index to calculate texture measures on a 3 × 3 moving window size. We used the following subset of texture measures: (1) first‐order: range, (2) second‐order: mean, variance, contrast, entropy, second moment 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 on the occurrence of target trees species. TWI was an important variable for Prosopis spp. and B. retama , whereas slope angle was important for R. girolae and B. retama . In addition, the occurrence of R. girolae was affected by second‐order variance of BI and slope aspect; and the presence of B. retama was affected by second‐order contrast of BI. All the variables that had important effects on the occurrence 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 study area.