*3.2.3.1. Canopy cover*

The CLASlite™ image processing system [78] was used to develop the fractional cover and forest cover maps for the Landsat dates. CLASlite™ produces photosynthetic vegetation, non‐photosynthetic vegetation and, bare soil layers from the core process within CLAS‐ Lite™ 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 vege‐ tation, and bare substrate within each Landsat pixel [78]. The Auto MCU submodel is based on a probabilistic algorithm designed for savanna, woodland, and shrubland ecosystems, and later modified for the tropical forest [79, 80].

Photosynthetic vegetation layers (0–100%) were used as an equivalent of field forest cover (0– 100%) for subsequent analysis. To validate this assumption, the direct relationship between the PV and CC was measured. Canopy cover derived from LiDAR data was used to support the PV layers. CC LiDAR was estimated using the ratio of the pulse returned from the upper layer of tree crown (sum of all pulses > pre‐defined threshold) to total returns. Hence

$$CC = \frac{nh}{n} \tag{1}$$

where

*CC*: canopy cover

*nh*: ∑all returns > predefined height

*n*: total returns.

The predefined height was set to 1.5 m. Range between 1.0 and 2.0 m is appropriate and has no substantial variation in the correlation between canopy cover measured in the field and the one estimated from Lidar data [81, 82].

Validation of the estimated Landsat CC was achieved by computing a residual mean of squares (RMS) of differences between Landsat CC and the Lidar CC product. This comparison was made possible by aggregating Lidar CC to 30 m to correspond to Landsat products spatially.

NPP in this study was calculated according to the theory of light use efficiency (LUE) as follows [46, 83]:

The Assessment of Land Degradation and Desertification in Mexico: Mapping Regional Trend Indicators with... http://dx.doi.org/10.5772/64241 13

$$NPP = \varepsilon \cdot fPAR \cdot PAR \tag{2}$$

where

can recover efficiently the pixels missing due to SLC failure, and its outputs are suitable for forest monitoring applications [77]. Landsat imagery was separated according to the date taken (i.e. wet or dry season), and an initial cloud filter was applied. Imagery with more than 10% of cloud cover was avoided for the analysis to focus on high‐quality imagery (cloud free).

The CLASlite™ image processing system [78] was used to develop the fractional cover and forest cover maps for the Landsat dates. CLASlite™ produces photosynthetic vegetation, non‐photosynthetic vegetation and, bare soil layers from the core process within CLAS‐ Lite™ 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 vege‐ tation, and bare substrate within each Landsat pixel [78]. The Auto MCU submodel is based on a probabilistic algorithm designed for savanna, woodland, and shrubland ecosystems,

Photosynthetic vegetation layers (0–100%) were used as an equivalent of field forest cover (0– 100%) for subsequent analysis. To validate this assumption, the direct relationship between the PV and CC was measured. Canopy cover derived from LiDAR data was used to support the PV layers. CC LiDAR was estimated using the ratio of the pulse returned from the upper

The predefined height was set to 1.5 m. Range between 1.0 and 2.0 m is appropriate and has no substantial variation in the correlation between canopy cover measured in the field and the

Validation of the estimated Landsat CC was achieved by computing a residual mean of squares (RMS) of differences between Landsat CC and the Lidar CC product. This comparison was made possible by aggregating Lidar CC to 30 m to correspond to Landsat products spatially.

NPP in this study was calculated according to the theory of light use efficiency (LUE) as

*nh CC <sup>n</sup>* <sup>=</sup> (1)

layer of tree crown (sum of all pulses > pre‐defined threshold) to total returns. Hence

**Figure 2** shows in a very generic way the pre‐processing process.

*3.2.3. Forest degradation indicators estimation*

12 Land Degradation and Desertification - a Global Crisis

and later modified for the tropical forest [79, 80].

*3.2.3.1. Canopy cover*

where

*CC*: canopy cover

*n*: total returns.

follows [46, 83]:

*nh*: ∑all returns > predefined height

one estimated from Lidar data [81, 82].

*PAR* is photosynthetically active radiation (MJ/(m2 month))

*fPAR* is the fraction of PAR absorbed by vegetation canopy,

*ε* is the light use efficiency coefficient (g of C/MJ) and includes the plant respiration costs [84].

The light use efficient coefficient *ε* was derived following the MODIS‐GPP approach [85] where ε is calculated using two factors: the biome‐specific maximum conversion efficiency *εmax*, and the effect of temperature *f(T)* and water on plant photosynthesis *f(W)* [83]. The *εmax* used in this study was 1.044 g of C/MJ according to the lookup tables [84].

*f(T)* was estimated on a monthly basis using the equation developed for the terrestrial ecosys‐ tem model (TEM) [86], as:

$$f\left(T\right) = \frac{(T - Tmin)(T - Tmax)}{(T - Tmin)(T - Tmax) - (T - Topt)^2} \tag{3}$$

where *T* is the atmospheric temperature (°C); and *Tmin, Tmax*, and *Topt* are the minimum, maximum, and optimal temperatures for photosynthetic activities, respectively. Values of 2°C, 39°C, and 26°C were used for *Tmin, Tmax*, and *Topt*, respectively [47, 87].

**Figure 3.** Daily solar radiation from the meteorological network (www.inifap.gob.mx).

The effect of water on plant photosynthesis *f(W)* was derived according to the algorithm suggested by Xiao et al. [88].

$$f(W) = \frac{1 + LSWI}{1 + LSWI\_{\text{max}}} \tag{4}$$

$$LSWI = \frac{\rho\_{n\nu} - \rho\_{sair}}{\rho\_{n\nu} + \rho\_{sair}} \tag{5}$$

where *LSWI* is the land surface water index, and *LSWImax* is the maximum *LSWI* within the plant growing season. *ρnir* and *ρswir* are the surface reflectance of the NIR and MIR bands in Landsat ETM+ images.

Meteorological data from the national meteorological network from the National Institute of Forestry, Agriculture, and Livestock Research (INIFAP) were used as inputs for the NPP calculations (**Figure 3**).

#### *3.2.4. Trajectory analysis*

Trajectory analysis and change detection on degradation indicators were performed using two different approaches: a time series and a bi‐temporal approach. The BFAST [63] model was selected as the time series analysis approach. Canopy cover was the only indicator that went into the BFAST time series analysis because of the high frequency of data available. Change detection on above‐ground biomass and NPP were performed using a bi‐temporal approach as a result of the low frequency in data available. Next, the implementation of both methods is described.
