## Materials and Methods
### Estimation of Forest Degradation Indicators  
#### 2.4.1 Canopy Cover  
The CLASlite(tm) image processing system [34] was used to develop the fractional cover and forest cover maps for the Landsat imagery dates over the study area. CLASlite(tm) can distinguish and separate Photosynthetic Vegetation (PV), Non-Photosynthetic Vegetation (NPV), and Bare Soil (S) into layers from the core process within CLASLite(tm) called \"Automated Monte Carlo Unmixed Process\" (AutoMCU). These outputs provide a quantitative analysis of the fractional or percentage cover (0-100%) of live and dead vegetation and bare substrate within each Landsat pixel [34]. The AutoMCU sub-model is based on a probabilistic algorithm designed for savanna, woodland, and shrubland  
\begin{table}
\begin{tabular}{c c c c} \hline
**Name** & **Location** & **Source** & **Coordinates (Lat, Long)** \\ \hline “Olchen” & Campeche & CONAGUA & \(20^{\circ}00^{\prime}13^{\prime\prime}\) N, \(89^{\circ}44^{\prime}1^{\prime\prime}\) W \\ “Opelchen” & Campeche & CONAGUA & \(19^{\circ}45^{\prime}28^{\prime\prime}\) N, \(89^{\circ}50^{\prime}35^{\prime\prime}\) W \\ “Xul” & Yucatan & CONAGUA & \(20^{\circ}06^{\prime}03^{\prime\prime}\) N, \(89^{\circ}27^{\prime}45^{\prime\prime}\) W \\ “Tabi” & Yucatan & CONAGUA & \(20^{\circ}13^{\prime}39^{\prime\prime}\) N, \(89^{\circ}31^{\prime}55^{\prime\prime}\) W \\ “Santa Elena” & Yucatan & CONAGUA & \(20^{\circ}19^{\prime}39^{\prime\prime}\) N, \(89^{\circ}38^{\prime}22^{\prime\prime}\) W \\ “San Diego B.” & Yucatan & CONAGUA & \(19^{\circ}51^{\prime}22^{\prime\prime}\) N, \(089^{\circ}17^{\prime}52^{\prime\prime}\) W \\ “Becanchen” & Yucatan & CONAGUA & \(20^{\circ}00^{\prime}13^{\prime\prime}\) N, \(89^{\circ}44^{\prime}1^{\prime\prime}\) W \\ “Aki\({}^{\prime\prime}\) & Yucatan & CONAGUA & \(20^{\circ}14^{\prime}39^{\prime\prime}\) N, \(089^{\circ}19^{\prime}29^{\prime\prime}\) W \\ “Pozo 5” & Yucatan & INIFAP & \(20^{\circ}21^{\prime}53^{\prime\prime}\) N, \(89^{\circ}28^{\prime}39^{\prime\prime}\) W \\ “El Chinal” & Yucatan & INIFAP & \(20^{\circ}16^{\prime}39^{\prime\prime}\) N, \(89^{\circ}228^{\prime\prime}6^{\prime\prime}\) W \\ “Inchek” & Campeche & INIFAP & \(19^{\circ}44^{\prime}13^{\prime\prime}\) N, \(89^{\circ}57^{\prime}59^{\prime\prime}\) W \\ “Uxmal” & Yucatan & INIFAP & \(20^{\circ}24^{\prime}40.1^{\prime\prime}\) N, \(89^{\circ}45^{\prime}24.9^{\prime\prime}\) W \\ “Parcela 28” & Yucatan & INIFAP & \(20^{\circ}26^{\prime}38.1^{\prime\prime}\) N, \(89^{\circ}37^{\prime}36.5^{\prime\prime}\) W \\ \hline \end{tabular}
\end{table}
Table 1: Meteorological stations used in this study.  
ecosystems and later modified for tropical forests [35; 36]. The method uses three spectral endmember libraries to decompose each image pixel into three different elements that can be used to infer the percentage of forest canopy cover for each pixel of the Landsat imagery. The Photosynthetic Vegetation (PV) layer (0-100%) was used as an equivalent of field forest canopy cover (0-100%) for subsequent analysis.  
Overall accuracy of the forest canopy cover layers was measured by computing the Kappa coefficient [37] from the percentage of pixels classified correctly in contingency tables, and by computing cross-tabulation quantities and allocation disagreement indices [38] against data from the field and forest inventory data.