**3.3. Results and discussion**

The effect of water on plant photosynthesis *f(W)* was derived according to the algorithm

<sup>1</sup> ( ) <sup>1</sup> *LSWI f W LSWI*

*LSWI*

max

*nir swir nir swir*

 r

 r

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

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

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

The BFAST and BFAST monitor algorithms were applied as a trajectory analysis strategy. Canopy cover derived from Landsat from the period 1988 to 2014 was used to implement the time series analysis. The algorithms were implemented using the BfastSpatial package for R software available at http://github.com/dutri001/bfastSpatial [64, 89]. The steps followed to implement BfastSpatial were (a) pre‐processing of surface reflectance data, (b) inventorying and preparing data for analysis, and (c) analysis and formatting of change detection results.

Change detection on a bi‐temporal basis was implemented in NPP layers. The imaging differencing method allowed direct comparison between images and was used for two reasons: it is straightforward and allows an easy interpretation of the results [70]. The image differenc‐ ing method consisted of precisely co‐registered multi‐temporal images used to produce a residual image to represent changes. Although the USGS service provides Landsat imagery as

r

r

<sup>+</sup> <sup>=</sup> <sup>+</sup> (4)


suggested by Xiao et al. [88].

14 Land Degradation and Desertification - a Global Crisis

Landsat ETM+ images.

calculations (**Figure 3**).

*3.2.4. Trajectory analysis*

is described.

*3.2.4.1. BFAST*

*3.2.4.2. Bi-temporal change detection*

The procedures in this integrated methodology allowed for the identification of areas that have been degraded. The results allowed to highlight areas that have been degraded due to loss of net primary productivity and forest cover. Integration of the different elements in this methodology enabled the identification of areas that maintain a "stable" condition and areas that change over the period evaluated.

According to the results obtained here, Landsat‐derived indicators (forest canopy cover and net primary productivity) showed effectiveness in the identification and mapping of degraded forest landscapes. The results of this study also suggest that it is possible to produce explicit and high‐resolution canopy cover maps over relatively large areas.

The net primary productivity also showed its value in identifying and mapping forest degradation. NPP is a forest parameter that is difficult to estimate and can be subject to high levels of uncertainty [92–94]. NPP was estimated for the period 2007–2013 showing mean values in the range of 480–512 and maximum values of 742–936 gC/m2 /year. Although NPP estimations are difficult to perform and validate due to lack of field data, programs such the INIFAP meteorological network that register climatic variables every 15 minutes, and Eddy covariance tower networks along with remote sensing data, are promissory elements to support NPP modeling.

Finally, the results of the trajectory analysis of degradation indicators (NPP and CC) showed (overall timescale 28 years) a slight tendency toward forest degradation and decline, punctu‐ ated by cyclic oscillations of decline and recovery that indicate the cyclic nature of disturbances of the study area. These trends are shown in **Figure 4**.

**Figure 4.** Trajectories of means of net primary productivity, central Yucatan, Mexico.
