*3.1.1.1. Canopy cover*

Canopy cover is recognized as a significant biophysical and structural attribute of the forest [40]. It affects terrestrial energy and water exchanges, photosynthesis and transpiration, net primary production, and carbon and nutrient fluxes, and is the key element for defining forests in international and national accords [41]. Canopy cover provides an attribute that is measur‐ able and can be used to monitor and retrieve site‐specific histories of different stages within the forest landscape dynamics [41].

Canopy cover has already been used as an indicator to monitor and map forest degradation in various contexts [32, 35]. Some studies [42] evaluated forest degradation based on canopy closure classes, namely non‐degraded (>70%), moderately degraded (40–70%), degraded (10– 40%), and severely degraded (<10%). Another study [43] assessed forest degradation using canopy disturbance as a result of gaps produced by logging, road construction, and skid trails as an indication of forest degradation. Another approach suggested for mapping forest degradation and deforestation was the use of canopy cover combined with spectral mixture analysis, normalized difference fraction index, and a decision tree classification [44].

#### *3.1.1.2. Net primary productivity*

NPP determines the rate of atmospheric carbon sequestration and storage by vegetation [45, 46]. NPP has been used previously as an indicator of ecosystems' decline [47–49]. These approaches open the door to the possibility of using NPP as both a baseline and indicator of forest degradation [50], based on the assumptions that losses of canopy cover will affect the capacity of the forest to fix carbon and reduce NPP rates.

NPP estimations are regularly based on the light use efficiency (LUE) theory [51]. The LUE theory is estimated on two broad assumptions. First, NPP is related to the absorbed photo‐ synthetically active radiation, APAR, where LUE determines the amount of dry matter produced per unit of APAR. Second, environmental stresses such as low temperature or water shortage have an adverse impact over LUE [52, 53]. Production efficiency models (PEM) are developed from the LUE theory. They require inputs of meteorological data and take advant‐ age of available satellite data to derive the fraction of absorbed photosynthetically active radiation, fPAR [53]. Examples of production efficiency models include the CASA model (Carnegie‐Ames‐Stanford approach) [54], C‐Fix [55–57], and MOD17 [48] used for monitoring NPP at regional and global scale from satellite remote sensing data.

Net primary productivity is employed by the global land degradation assessment in Drylands (LADA) project [21], where NPP is highly relevant to the assessment of degradation. NPP can be readily used as a direct indicator of the condition and trend of changes in the state of ecosystems over time, whereby the decrease in NPP over time would signal the degradation of ecosystems. Through the LADA project conducted by the FAO [18] and within the UNCCD framework [58], mapped out land degradation at national, regional, and local scales in Ethiopia using NPP as one of the major indicators in their studies.

#### *3.1.2. Trajectory analysis and change detection*

One of the most frequent uses of remote sensing is change detection [59]. The stock pile of optical satellite imagery freely available (e.g. Landsat program) [13] offers opportunities for the reconstruction and understanding of landscape dynamics. Direct comparison of pairs of images (bi‐temporal analysis) is perhaps the most common approach to change detection [60].

Although many change detection methods have been developed [61–63], the question of how to reliably map land‐use change remains a central challenge. Land‐use change (LUC) can result in both land cover conversions and land cover modifications, but remote sensing mainly focuses on mapping the former. However, land cover changes may be more prevalent, meaningful, and significant to forest degradation than conversions. Forest degradation is more likely to be the reflection of a land cover change with its particular degree of intensity and duration.

Temporal trajectory analysis is understood in this context as the analysis of the sequence of changes in detection in every pixel of the image part of a stock pile of imagery over a continuous timescale. This type of analysis has been shown particularly useful in characterizing land ecosystem dynamics since it exploits the multi‐temporal sequence of images to reveal temporal patterns over several temporal scales [62, 64, 65].

Trajectory analysis from multispectral and optical remote sensing is commonly employed for detecting changes of a set of forest degradation indicator variables over time that can be readily computed from satellite images and that are associated with the state and condition of forests [66, 67].

Examples from the literature have proved the value of the trajectory analysis in forest assess‐ ments, especially those that take advantage of the stock pile of Landsat imagery [61, 68, 69]. This methodology incorporates this type of analysis as a part of the degradation assessment.
