*3.2.1. Study area: Yucatan Peninsula, Mexico*

Mexico, within its ecological and climatic conditions, offers an excellent site for experimenta‐ tion and application of this methodology. Although the method can be implemented in any part of the country, it has been decided to use a region from the southeast of Mexico, in the Yucatan Peninsula as the experimental site.

The methodological framework for land degradation assessment in drylands [21] is used to support methods to evaluate degradation within a tropical dry forest area located in the Yucatan peninsula, Mexico. Landsat imagery was used as the main source to estimate indica‐ tors such as canopy cover (CC) and net primary productivity (NPP). Use of Landsat imagery enables to see changes over time [68] within a pixel 30 m resolution over 28 years (1986–2014). The methods enabled selection of priority areas and spatial patterns. The MENDA‐1 water‐ shed [73] in the Yucatan Peninsula, Mexico, was selected as experimental area (**Figure 1**).

**Figure 1.** Study area.

The integration of the methodology is described as follows:

Selection of the indicators to monitor and assess degradation was the first step. Each one of the indicators selected was estimated using remote sensing as the primary source of data input. Because of the characteristics and free availability of Landsat archive [13], Landsat imagery is suggested as the major contribution. The indicators were estimated for the period of time required according to particular needs. Although in many tropical regions cloud cover is a significant issue, the probability of acquiring at least one cloud‐free or reasonably cloud‐free Landsat image per season is relatively high [74]. At least one Landsat image per season ensures continuity in historical estimations of the forest landscape dynamics based on Landsat archive.

Very high‐resolution satellite imagery or LiDAR data is recommended as auxiliary data to validate calculations. Another data set crucial for the implementation of this framework was forest inventory databases. Many developing countries (e.g. Mexico) carried out periodical forest inventories on a regional scale. Forest inventory data were the base for knowing the actual state of the forest and natural resources.

Once each one of the indicators has been calculated, the selection of a strategy for monitoring changes has to be made. As described before, the methods for change detection can be a time series approach (in the case of high frequency of data) or a bi‐temporal change detection approach (in the case of low frequency of data). The implementation of this step allows identifying spatial and temporal patterns of the indicators used.

The establishment of a baseline and the definition of the threshold for comparisons was the next step toward the final integration. This was done using field data or high‐resolution auxiliary imagery available (e.g. Google Earth™). The comparison of the spatial and temporal trends in the baseline scenario allowed identification of degraded areas regarding the indica‐ tors used.
