Predicting bird species richness and micro-habitat diversity using satellite data

Ozdemir I., Mert A., Ozkan U. Y., Aksan S., Unal Y.

FOREST ECOLOGY AND MANAGEMENT, vol.424, pp.483-493, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 424
  • Publication Date: 2018
  • Doi Number: 10.1016/j.foreco.2018.05.030
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.483-493
  • Süleyman Demirel University Affiliated: Yes


Effective biodiversity management in forest ecosystems relies on the assessment of environmental indicators (surrogates) when attempting to measure total biodiversity. Bird species (BS) richness and micro-habitat (MH) diversity are two key features that are easily measured and can be utilized as biodiversity surrogates. Remote sensing technologies may be employed as a cost-efficient measure as well as accurately mapping diversity features across broad geographical areas. This study examined the possibilities of predicting BS richness and MH diversity using variables derived from satellite data in a brutian pine (Pinus brutia Ten.) forest ecosystem located in the Southwestern Mediterranean Region of Turkey. The study utilized 40 (90 x 90 m, 0.81 ha) sample plots. We used first, and second-order image texture measures calculated from RapidEye, SPOT-5 and Aster Normalized Difference Vegetation Index (NDVI) as explanatory variables for predicting these biodiversity surrogates at alpha (alpha) level. Stepwise linear regression analyses showed that BS richness and MH diversity can be estimated using image texture measures. According to the cross-validation test; BS richness was best predicted using standard deviation of Gray levels (STD) and Gray-Level Co-Occurrence Matrix (GLCM) Homogeneity of RapidEye NDVI (R-CV(2) = 0.73, STEcT = 4.321), while MH diversity was best predicted using STD and GLCM Correlation of SPOT NDVI (R-CV(2) = 0.73, STECV = 0.148). In conclusion, the satellite-based diversity maps produced in this study can provide valuable data for forest managers and assist in formulating adaptive management plans for the ecologically sustainable management of brutian pine forest ecosystems.