## 1 Introduction  
Reducing emissions from deforestation and forest degradation (REDD+) implies the implementation of efficient monitoring methods for providing high-quality data on forest degradation and its changes, according to reporting standards and IPCC guidelines [1]. Since its beginnings, one of the main criticisms of REDD+ has been that its measuring, reporting and verification (MRV) systems focus mainly on deforestation because it is easier to measure, as compared to forest degradation [2]. The main issue and obstacle in attaining and implementing a strong MRV system are sometimes the lack of reliable data, over time, for some forest types and, therefore, the substantial uncertainty involved in the estimates [3]. Methods for monitoring the current state and changes of forest carbon stocks within a REDD+ regime exploit the advantages and the potential of satellite-borne or airborne remote sensing imagery [4]. There are a fair number of studies that explore the capabilities of remote sensing in carbon accounting techniques under REDD+ strategies using different remote sensing approachesand data to achieve the assessment [5; 6; 7; 8; 9]. One of the main advantages of using remote sensing data and procedures is that these have the potential to be decidedly instrumental in the assessment of forest degradation and deforestation processes at a much lower costs than any other methods [10; 11]. However, the reality of REDD+ projects is that the methods for MRV used and implemented are unique to each location and strongly depend on how forest degradation (i.e., its definition) is understood and applied practically [12]. Moreover, it is essential that the components of forest degradation are clearly identified and amenable to accurate measurement, together with understanding how these are used against country requirements [13]. Direct detection of forest degradation processes relates area changes to and focuses on forest canopy damage [14]. These changes in forest attributes occurring during a period of time can be detected using information from forest resources inventories (FRI) and some from remote sensing [5; 15]. Medium spatial resolution satellite remote sensing data such as Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper plus (ETM+) have proven capable of obtaining regional-scale forest variables [16]. Their main advantage over higher resolution imagery resides on their being freely available as archival data for a relatively long time, thus providing a long-term time series [17]. This makes these images a suitable data source in developing countries, where costs are usually the main concern. However, it must be borne in mind that there are some limitations in estimating biomass implicit in the phenomenon of saturation for biomass due to the inability of incident and reflected energy to penetrate the canopy. These limitations have to be considered while using optical remote data [18]. Indirect approaches to forest degradation assessment usually focus on its spatial distribution relative to proximity to human infrastructure and the effects that the latter has on the degradation of forests nearby. Often, these \"indirect\" factors are used as \"proxies\" for the assessment of newly degraded areas [19]. Considering the absence of a universal or standard methodology for REDD+ that may be adapted to the conditions of a specific developing country, the present study attempts to develop a methodological framework that supports the assessment and monitoring of forest degradation to be implemented as part of a national REDD+ strategy. The objective of this study is to implement an operational definition of what is understood by \"forest degradation\" and to identify measure and quantify its different degrees of intensity and its spatial extent in each landscape.