Ecological condition indicators for dry forest: Forest structure variables estimation with NDVI texture metrics and SAR variables

The ecological condition of forest ecosystems is degraded. Limited prior research in vegetation has focused on monitoring ecological condition levels in dry forest at fine scale. We proposed a novel approach to obtain accurate indicators of the ecological condition of the Chaco Serrano forest (Córdoba, Argentina) by estimating forest structure variables (canopy cover ($CC$), diameter breast height ($DBH\text{\_}sum$), number of woody individuals ($NW$) and two first axes of a principal component analysis ($PC1$ and $PC2$)) as a measure of forest degradation. To achieve this, first the correlation with two complementary groups of remote sensing derived data (texture metrics over Normalised difference vegetation index and SAR-derived data) was explored. Then, General linear models (GLM) were constructed using the most correlated remote sensing derived variables with forest structure variables as predictor variables. The best estimation was obtained to $CC$ ($r^2$=0.58, rmse=14,5%), followed by $DB{H}_{sum}$ ($r^2$=0.37, rmse=156.6) and $NW$ ($r^2$=0.22, rmse=14.6), with an spatial arrangement consistent with field observations. Moreover, $CC$ estimation was more accurate than those at regional and global scale, and highlights the importance of developing local models in areas that exhibit high ecological, geological, and human heterogeneity. In addition, other forest variables could also be evaluated, like floristic composition or others associated with functioning. Results offer valuable insights for developing management strategies suitable for each condition, and for future studies regarding the relationship of the mentioned condition and associated natural and anthropic factors.