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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.

Carbon storage (CS) and consequently carbon sequestration by plants is the most natural and least expensive method for atmospheric CO2 mitigation. Areas with high CS potential should be identified, maintained and subject to investment. CS depends strongly on status of vegetation and soil surface cover characteristics. Therefore, the objectives of the present study was an assessment of the relationship between aridisols CS and characteristics of soil cover in some plant types in western rangelands of Isfahan in Iran. Three different plant types including Scariola orientalis-Astragalus gossypinus, Hordeum fragile-Astragalus gossypinus and Cousinia bachtiarica-Astragalus gossypinus were identified. Then, some sampling plots were placed along the four transects in each plant type, which were placed obliquely to the general and the lateral slope of the region. Plants' characteristics and soil surface characteristics were recorded. The soil samples were taken along transects at depths of 0–20 and 20–75 cm. Soil samples were transferred to the soil laboratory and analysed. The results demonstrated that the soil carbon storge (SCS) is different between three plant types. SCS in Sc. or-As. go, Ho.fr-As. go and Co. ba-As.go plant types were 340, 182 and 173 kg/ha, respectivly. Moreover, there was a positive and significant relationship (R2 = 0.88|0.42, p

Studying the phenology, which is an important issue of plants, can be used for determining the best time of all kinds of utilizations, and also conservation and stability of plants’ growth and breeding will result. Therefore, this study was performed in Tezkharab rangelands, where Puccinella distans is an important vegetative element that is consumed by livestock, to determine the suitable grazing time for this plant based on its phenology, for about a 4-year period (2007–2010). For this purpose, plant phenology and changes in its growth characteristics were investigated weekly. Moreover, during this period, all phenological stages were adjusted to the monthly precipitation and temperature. The results indicated that the time of phenological stages had fluctuations which were related to environmental conditions. The best time to start grazing is early May and the best time for collecting the seeds of this species is late July to early August.

Traditional and indigenous knowledge on plants usage is a valuable source of information from cultural and natural perspectives, reflecting society’s complicated and close relationship with the environment. Communities have a valuable source of traditional knowledge on the utilization of natural resources, and it is worth to be documented and preserved for current and future applications. We conducted this research to collect and identify plant species of Karvan District and document the traditional knowledge on their use and consumption values. Regarding the results, 150 plant species are used by local communities, more than 30 % of which are directly consumed as food, 24 % (37 species) are used as medicinal plants, and 16.3 % are applied for decoration purposes; 58.6 % of the species are consumed in the raw form, and the remaining is processed before consumption. Leaves (35 %), seeds (21 %) and flowers (21 %) are the most frequent parts of the plants that are used. High number of young emigrants to industrialized areas in seek of job opportunities is threatening this precious source of indigenous knowledge. Attempts to preserve this empirical source of information by encouraging trans-generational knowledge transmission would help to maintain it for future applications.