Using Landsat Time Series to detect forest degradation in semi-arid areas

According to recent studies, many semi-arid forests are rapidly declining, which necessitates a profound understanding of the processes and causes of degradation. The Zagros Forest in Iran has been degraded during the past few decades. The analysis of forest degradation in this region is still in its initial phases, with no detailed investigation of the underlying causes. Understanding forest degradation is crucial for more effective forest management, particularly in arid and semi-arid regions.
Since one core principle of remote sensing is to identify changes in signals from multiple acquisitions that relate to the status of vegetation in a specific area, they offer efficient techniques for assessing forest degradation across large and rarely accessible forests. Frequently used remote sensing metrics to assess vegetation health are measures of vegetation greenness. For example, vegetation indices may be computed from optical remote sensing data and used to quantify forest degradation over time. Time series of vegetation indices can track forest degradation across large areas by identifying the decrease in photosynthetic activity caused by leaf loss, defoliation, and structural changes in trees.
However, numerous studies examining forest degradation either focus on dense forests or use very high-resolution remote sensing data, which is often expensive and generally difficult to obtain for large regions. Furthermore, most forest monitoring studies using remote sensing have focused on deforestation rather than forest degradation. Detecting forest degradation is challenging compared with detecting tree mortality induced by abrupt disturbances because degradation processes last longer and have a more subtle signal. 
Because of the free accessibility, relatively high spatial resolution, and long and consistent acquisition record, Landsat time series are a viable source of data for monitoring and assessing forest degradation and disturbances, as well as providing continuous reporting on forest changes. There are several methods to monitor forest disturbances, but most of these are better suited to monitoring large-scale deforestation than subtle changes in forest status. These include Landsat-based detection of trends in disturbance and recovery (LandTrendr) and breaks for additive season and trend (BFAST).
The aim of the study is to compare the mentioned algorithms with other methods, such as random forest classification, anomaly analysis, and Sen's slope. We applied the aforementioned methodologies to Landsat time series data from 1986 to 2021 to separate healthy from declining forest patches in a representative portion of the Zagros.
The highest random forest accuracy result returned an overall accuracy and kappa value of 0.77 and 0.54, respectively. The most accurate results of the anomaly analysis were an overall accuracy and kappa value of 0.58 and 0.005, respectively. Sen's slope had the lowest accuracy among the applied methods, with the highest overall accuracy and kappa values of 0.53 and 0.0039, respectively. These results indicate that the detection of degraded forest regions using Landsat data is challenging and may only be possible if additional information is added to the analysis. We hypothesize that a particularly weak vegetation signal of sparse canopy cover before the bright soil background hampers the detectability of subtle degradation processes.