رقیه جهدی

Wildfires reveal evidence of forest soil, water, and vegetation disturbances resulting from various interacting natural and human factors that create patterns that vary spatially and temporally. Fire risk assessment allows for identifying these factors and estimating their area of influence, thereby determining locations at high fire risk. Fire risk assessment typically involves the ignition probability (IP) and burn probability (BP) modeling of natural and human-made resources, as well as identifying resource responses to fires of varying severity. In recent decades, wildfires have caused significant damage to the Hyrcanian forests in northern Iran, even in the protected areas. Therefore, this study focuses on the spatial distributions of fire size, fire frequency, IP, and BP as essential components of the fire risk framework. First, a historical fire database (including ignition points, burned area, etc.) was prepared using available resources and field surveying. Second, a modeling approach using a limited number of auxiliary variables representing the fire environment (fuel, topography, and weather) and the historical fires (1992-2022) was implemented to calculate IP and BP. The spatial distribution of these parameters helps improve decision-making in fire prevention and control strategies.

Guilan Province is located in northern Iran and has an area of 14,044 km2 with an average elevation of 741 m above sea level. This province has 20 natural protected areas, which cover a total of 256,488 ha. ArcGIS 10.8 was used to create a historical fire database in the study area by digitizing fires between 1992 and 2022 from maps or by importing information directly from the previous GIS datasets. Point process models (PPMs) were used to analyze the spatial distribution of fire frequency. PPMs are a regression approach to model point data (i.e., geographic coordinates) for the number of times a 100-m pixel burns between 1992 and 2022. An existing raster map of the study area was converted to points by calculating the center of each pixel, and each point was assigned a frequency. Furthermore, IP was calculated as the average ignition probability occurring over a year in a raster pixel. To help use the fire ignition density to plan preventive activities, the output values were classified into five classes reflecting ignition occurrences (from very low to very high). Finally, the fire risk using BP was assessed by considering topography, fuel loads, and weather using FlamMap. To calculate BP, 1000 random ignition points were created based on the distribution of historical ignition points in the study area. The maximum fire simulation time was set to 6 hr (the average fire duration in the area).

There are 186 recorded fires (total burned area of 2232 ha) with an average annual number of 6 fires (average burned area of 12 ha) in the protected areas. Fires <10 ha accounted for 62.4% and 30.8% of the fire number and the burned area, respectively. Fires (10-50 ha) accounted for 32.8% and 46.2% of the fire number and the burned area, respectively. Fires (50-100 ha) accounted for 2.7% and 14.5% of the fire number and the burned area, respectively. Finally, fires >100 ha accounted for <0.5% of the fire number but alone accounted for 8.4% of the burned area. The distribution of fire frequency ranged from 0 to 6. The largest protected areas (40%) experienced no fires. 13% of these areas had 1-2 fire frequencies. Furthermore, 48% of this area had more than two fire frequencies. About 35% of the study area had very low and low IP values, 36% had medium IP, and 18 and 11% had high and very high IP, respectively. 88% of the study area had low and moderate BP values, and 12% had high and very high predicted values. Two fire regimes can be distinguished in the area, one with relatively high fire frequency and BP (mainly at higher elevations) and the other with low fire frequency and BP (at lower elevations). High fire frequency and BP is very limited in extent and occurs in the patches in the southern area. In contrast, low fire frequency and BP regime is the most widespread regime in the area (except for the southern part).

According to the simulated patterns of fire frequency, IP, and BP in the study area, a clear distinction between the actual historical fire perimeters and the predicted burn pattern is that there are areas of moderate to high IP where fires have not occurred in the past 30 years. This is particularly evident in the southern and central parts of the area, where fires have either not occurred or have been very limited in extent. Therefore, a justifiable assessment could be that the likelihood of fire spread and vegetation communities undergoing extensive and long-term changes following the fire is high shortly. Although this study focuses on the protected forest areas, this approach can be applied to fire risk modeling at larger scales. This allows for broader application in natural resources management and planning at regional and national levels. It also provides a comprehensive tool for assessing and managing forest vegetation, soil, and water vulnerability.

In recent decades, intentional and unintentional forest fires have posed a serious threat to natural resources. These fires not only lead to direct human and financial losses but also contribute to the destruction of ecosystems, soil degradation, and loss of biodiversity. Additionally, they disrupt the balance of watersheds by reducing vegetation cover, increasing the risk of soil erosion, altering hydrological cycles, and ultimately affecting water quality and availability. Understanding fire-prone areas and their contributing factors is essential for effective fire management, risk reduction, and the development of preventive strategies. Fire risk mapping serves as a valuable tool for identifying critical areas and implementing targeted mitigation measures.

Fire risk mapping is an effective tool for identifying critical areas and mitigating fire hazards. This study evaluates the efficiency of the Geographic Information System (GIS), Analytical Hierarchy Process (AHP), and Frequency Ratio (FR) methods in mapping fire-prone areas in Astara, a city in western Gilan province covering 42,000 hectares. Astara is part of the Hyrcanian forests and has a humid climate with an average annual rainfall of 650 mm and a mean temperature of 13.1°C. In this study, the AHP method assigned weights to fire risk criteria based on expert surveys, while the FR method used the distribution of 70% of observed fire incidents. The obtained weights were applied to the study criteria to generate weighted maps. These maps were then integrated to produce a fire susceptibility map, categorizing fire risk into five classes, from very low to very high. To validate the results, 30% of recorded fire incidents and the ROC curve were used. The validation data were not included in the modeling phase.

In the AHP method, the consistency index (CI) was below 0.1, indicating reliable parameter selection. Model validation showed that the AHP method had an area under the curve (AUC) of 0.826, while the FR method achieved an AUC of 0.894. These results suggest that the FR model is more accurate for mapping fire-prone areas in Astara. The lower accuracy of the AHP model is likely due to potential errors in the pairwise comparison of criteria.Among the evaluated factors, proximity to villages (weight: 0.436) and roads (weight: 0.314) were the most influential in fire occurrence. In contrast, population density had the least impact (weight: 0.062).

In Astara, fires are concentrated in the northern and southeastern regions, particularly in watersheds No. 1, 4, and 7. The fire susceptibility map indicates that 1.82% of the study area is highly fire-prone, while 22.9% has moderate sensitivity. With proper management, fires in these areas can be controlled effectively. High-risk basins have a dense village distribution with short distances to roads, making them more susceptible to fires. Therefore, promoting public participation and equipping villagers with fire extinguishers can help prevent rapid fire spread.

In Iran, various fire spread models have been used to predict fire behavior and spread at regional and local scales (Alhaj Khalaf, Shataee Joibary, Jahdi & Bacciu, 2021; Jahdi, Salis, Alcasena & Del Giudice, 2023). However, the calculation of an integrated fire risk index using these fire spread models has not yet been conducted. Typically, fire risk assessments are based on a set of fire spread descriptors that are used to map fire-prone areas and identify the most appropriate fire and fuel management measures in line with predefined goals (e.g., reducing fire intensity or minimizing fire size).Fire and fuel management involves a comprehensive approach that integrates land management practices, considering fire regimes, impacts, values at risk, and multiple resource use activities. Despite the merits of each approach, limited resources often prevent the implementation of management measures in all areas with significant fire risk. Therefore, it is essential to prioritize high-risk areas and identify appropriate management strategies that maximize ecosystem protection. This study aims to develop an integrated method for identifying areas with the highest fire risk in the forests and rangelands of Guilan Province. The method combines simulation models of fire spread with landscape data (topography and fuel), fire weather conditions, and historical ignition point data. Additionally, anthropogenic factors, such as proximity to roads and settlements, are included in the fire risk modeling process, given their significant influence on fire behavior and regimes (Twidwell et al., 2016). The results of this research can support land-use planning, management, and future studies on fire hazards.

Study Area: This research was conducted in Guilan Province, which spans 1,404,400 hectares in northern Iran. The area ranges from -74 meters in the north to approximately 3,700 meters in the southeast. The province receives an average of 1,100 mm of annual rainfall, and the relative humidity averages 80%, decreasing during the summer months with 185 mm of rainfall during this period.

Three Landsat-8 OLI/TIRS L1TP images from October 2021 were used to identify the main land cover types and produce a land cover map of the study area using ENVI 5.6 software. A map of surface fuel models was created by assigning local fuel models (Jahdi et al., 2020, 2023) and standard fuel models (Scott & Burgan, 2005) to the land cover types. Additionally, a vegetation density map and forest canopy density map (in percentage) for 2021 were generated from Landsat-8 images using the FCD model in ENVI software.Historical fire ignition data were used to calculate ignition probability by interpolating ignition points using the Inverse Distance Weighting (IDW) algorithm with a fixed radius of 5 km.The frequency of fire recurrence was determined from historical fire perimeters. Weather data, including temperature, relative humidity, wind speed, and wind direction, were collected for the fire season in the study area.To prepare input data for fire risk modeling:FlamMap 6 fire simulator (Finney, 2006) was used to model fire behavior, including fireline intensity and fire rate of spread, at a 100-meter resolution across the study area.Raster maps showing Euclidean distance from roads and settlements were created using ArcMap 10.8 software.Kernel density estimation was applied to convert the historical ignition point map into a fire density raster map.The fireline intensity, fire rate of spread, human factor maps, and fire history maps were rescaled to values between 0 and 1. These layers were combined to calculate the fire risk index (Xofis, Konstantinidis, Papadopoulos & Tsiourlis, 2020). The fire risk index values ranged from 0 (low risk) to 1 (highest risk).

Fifteen land cover classes were identified in the study area (Fig. 2, Table 4). The overall accuracy of the classification was 91%, with a Kappa statistic of 0.88. Dense broadleaf forest (30.6%) and orchard-irrigation farming (26%) were the dominant land cover types. Approximately 65% of the study area comprised natural forests, plantations, woodlands, shrublands, and rangelands. The remaining area included agricultural lands, built-up areas, water bodies, and bare lands.Based on the simulation outputs for fireline intensity and fire rate of spread (Fig. 4):In over 93% of the study area, the fire rate of spread did not exceed 0.25, and less than 7% of the area had higher values.Similarly, in over 90% of the area, the fireline intensity remained below 0.25, with about 10% exceeding this threshold.The highest values for fireline intensity and fire rate of spread were observed at high altitudes with dense vegetation, consistent with findings by Okano & Yamano (2015), which showed topography and slope significantly influenced fire behavior in broadleaf forests.Human factors, such as proximity to roads and the fire kernel density/fire history (Fig. 5), were found to impact a smaller proportion of the study area. This finding aligns with the study by Xofis et al. (2020).The integration of these components (Fig. 4 and Fig. 5) produced the final fire risk index map (Fig. 6). Fire risk values ranged from 0.0 to 0.68, with an average value of 0.11. Lowlands covered by non-burnable fuel models had the lowest risk (0.0), while the highest fire risk values were observed in the highlands with dense forest cover.

This study demonstrates how landscape information (topography and fuel), weather conditions, and historical fire data, combined with human factors, can be used to estimate fire risk spatially using fire behavior modeling. Moderate and high-risk areas account for 21% of Guilan Province, where fire management efforts should be prioritized. Specific measures include:Strengthening fire prevention infrastructure.Conducting fire prevention training to raise public awareness.Enhancing ecosystem resilience in vulnerable areas.While many fires remain random and difficult to fully control, estimating fire risk and developing prediction models can help mitigate fire hazards, reduce potential damages, and improve the allocation of fire prevention resources.

Underground dams, as one of the water flow control and storage structures along seasonal waterways, can be the most effective way to solve water-related issues. In the current research, using the integrated QSPM-SWOT model, suitable places for the development of underground dams in Khorramabad County were examined. First, to provide the required information, the study area was visited and a questionnaire was prepared to prepare the SWOT (strengths, weaknesses, opportunities, and threats) in the construction of underground dams. This questionnaire was completed by 25 water resource management experts and residents of the study area. Then, using the SWOT model and QSPM matrix, a thorough analysis and convenient problem-solving method were selected to locate underground dams. The results showed that among the internal factors, vulnerability and pollution are less due to human and environmental factors (0.458) and extraction of high-quality water (0.372), and among external factors, restoration, stabilization, and increase of vegetation (0.304) and damage to underground water tables in case of construction without conducting sufficient studies (0.276), have been most effective in choosing strategies. Based on the obtained results, the use of a defensive strategy for the management of underground water resources, which is done by covering the weaknesses, overcoming the threats, and taking advantage of the strengths and the opportunities to locate the underground dams suitably, has a higher priority than other strategies (competitive-aggressive, diversity, and revision) in the study area. In addition, solutions were presented along the supply measures and then for the spatial prioritization of underground dams using the QSPM quantitative matrix. Finally, the solution to increasing the level of regional water flow by selecting educated managers in the field of underground dams water before and after construction was given priority with a final score of 6.074.

The characteristics of drought as one of the environmental events may affect climate changes in the future. The purpose of this study is to evaluate the effect of meteorological drought on vegetation dynamics from 2001 to 2021 in the Golestan province, northern Iran, using remote sensing satellite images. First, using MATLAB software, the Standardized Precipitation Evapotranspiration Index (SPEI) was calculated in time scales of 3, 6, 9, and 12 months for the Golestan province with a statistical period of 20 years and zoned in ArcGIS software with IDW (Inverse Distance Weighting) Interpolation Method. In the next step, we obtained the maximum value of the Enhanced Vegetation Index (EVI) per month from the MODI3Q1 product of the MODIS spectrometer. Also, using Tersest software, the correlation and line slope of EVI index changes were calculated based on SPEI index changes. The results of correlation analysis showed that in 54.19% of the area of Golestan province, 6-month SPEI changes have the maximum correlation with the EVI index compared to SPEI in other periods. As a result, the highest impact of drought on vegetation in most parts of the study area is related to the 6-month SPEI in May. The results of vegetation sensitivity to meteorological drought showed that in the south of Golestan province, which is covered with dense forest conditions and includes the eastern slopes of Alborz spread in the form of a strip from the west to the east of the province, the vegetation has relatively low sensitivity to the phenomenon of drought due to suitable climatic conditions and the relative humidity of the air. The research results can be used in long-term planning for sustainable natural resources management.

Natural ecosystems in Iran are under the influence of unprecedented disturbances, including climate changes such as drought, pests and diseases, invasive species, and as a result, vegetation changes. Trend analysis of vegetation and ecosystem health monitoring on a regional scale is necessary to evaluate the changing status of ecosystems and inform the management of environmental disturbance factors. However, these analyses and monitoring are difficult only based on field investigations. Remote sensing provides the possibility of comprehensive evaluations of ecosystems in large areas, and their changes can be observed over time using vegetation indicators in multi-temporal satellite images.

This research is based on the time series of Landsat-7 ETM+ and Landsat 8 OLI/TIRS images related to the years 2003 to 2023 and Geographic Information Systems used for analyzing vegetation trends and monitoring the health of the ecosystems in the Fandoghlo region (farthest end of the western Hyrcanian region located in the east of Ardabil). The non-parametric Mann-Kendall (M-K) trend test was used to analyze the spatial-temporal changes of vegetation in the study area.

Based on the results of land cover changes in the study area, the reduction of vegetation cover was found in all natural ecosystems, including forests (-8.5 %), rangeland (-16.5 %), and water bodies (-17.2 %). On the other hand, agriculture (14.7 %) and built-up (30.9 %) areas showed an increase during the study period. Furthermore, the results of the changes in health index (NDVI) during the period 2003-2023 indicate a trend of vegetation browning (decrease) in the Fandoghlo region. In addition, the results of the M-K trend test show that NDVI had a significant linear increasing trend from 2003 to 2011, a non-significant linear increasing trend from 2011 to 2016, and a non-significant linear decreasing trend from 2016 to 2023.

In this study, the analysis of the trend of vegetation cover and health index based on NDVI can provide valuable indications about some processes that are relevant for ecological assessments such as the simplification and destruction of ecosystems or the intensification of agricultural activities. Considering the declining trend of natural ecosystems and the threat to health, it is recommended to implement conservation programs in line with nature to increase the vegetation cover and its health.

Investigating forest structure is an important and necessary topic, especially in line with the goals of close-to-nature silviculture. This research aimed to quantitatively analyze the complexity of stand structure during the transformational phase of understory formation in the oriental beech (Fagus orientalis Lipsky) forests of Kheyrud, Nowshahr. After field inspections, three one-hectare sample plots in the transformational phase of understory formation were selected, and a full inventory of all stand parameters, including tree diameter at breast height, natural regeneration, size and distribution of gaps, and deadwood, was carried out. To analyze the structural complexity in forest stands, the structure complexity indices, Cox coefficient, coefficient of variation, and Gini coefficient of tree diameter were used. According to the research results, in this evolutionary phase, the highest frequency is in small diameter classes, while the lowest frequency is in large trees. The distribution curve of trees forms a decreasing exponential with a steep slope in small diameter classes. The average deadwood volume was 30 m³, and the average gap factor of crown coverage was 6.4%. Additionally, the mean Gini coefficient and diameter change coefficient were 0.7 and 6, respectively. These values, along with the Cox coefficient, indicate optimal complexity in the stand structure during this phase. In the understory, the average abundance was 306 stems per hectare, with beech being the most abundant species. Based on these results, to maintain heterogeneity in the managed forests while preserving large trees in the stand, it is suggested to implement operations to increase the complexity of the forest stand structure and perform thinning from below.

Due to the effect of climate change, the frequency and intensity of wildfires is continuously increasing. Wildfire risk assessment is an important part of fire prevention because pre-fire planning requires tools to monitor an area in terms of when and where fire is most likely to occur, or when a fire will have the most severe negative effects. Wildfires are one of the most important natural hazards in Zagros forest ecosystems in western Iran. This study was conducted to analyze the risk of fire and investigate the factors affecting fire in the forest area of Dore Chegeni County. The main goal of the current study is to try to prepare a wildfire risk zoning map by combining topographic data and other auxiliary data through the geographic information system and using the Best-Worst Method for the study area. The fire risk map was obtained by scoring and weighing the layers of effective fire factors (vegetation, slope, aspect, elevation, climate, and distance from settlements and roads). Based on the results, the study area was classified into four fire risk classes. Very high (7%) and high (11%) fire risk areas were identified in the study area. Furthermore, 8% and 74% of the study area were placed in medium and low fire risk classes, respectively. This analysis helps land managers to understand patterns of vulnerability and fire risk in the landscape. By using the analysis of the results, investments in conditions of limited resources, forest restoration, fuel treatments and other mitigation measures to reduce fire risk can be targeted in areas with high fire risk.

 Forest health indicators encompass a wide range of physical environmental indicators, including climatic and ecological measurements, as well as records on human actions and management practices in forest ecosystems. This study aims to investigate the relationship among the forest health indices in the mixed-beech forest type, including tree diversity, crown condition, stem condition, deadwood, and regeneration in the forest landscape in the Masouleh watershed, Guilan province.

 In this study, the relationship among indices of forest health in the study area was investigated using an integrated approach of DEMATEL-ANP (DANP). Data collection was conducted through two researcher-made questionnaires, whose validity and reliability were examined through content validity and the retest method, respectively. Finally, the most important input indicators, based on experts’ views, were ranked by DEMATEL and output indicators by ANP. A sample of 12 experts was used in this study.

 The results of DEMATEL indicate the importance of the regeneration index in the forest health status in the study area. This index (regeneration) was recognized with the highest influence ratio among others. In addition, two indicators, tree diversity and deadwood, had a high influence ratio among other investigated indices. Also, tree diversity and crown condition indices were the most influential ones. Based on the ranking results of criteria in ANP, the tree diversity criterion has a more important effect on forest health compared to other criteria. In addition, healthy seedling, crown dieback, and standing deadwood are among the most important sub-criteria compared to other sub-criteria in the study area.

 These results can play a significant role in increasing awareness about the factors affecting the health of forests and the causes of unhealthy forests. They can also assist policymakers in identifying appropriate measures to maintain and improve forest health, thereby increasing the sustainability of forest ecosystems.

The presence of trees of varying sizes contributes to the creation of diverse and complex forest structures. However, human disturbances can disrupt these structures and negatively impact tree size diversity. This study investigates the effects of human disturbances on tree size diversity in the Arasbaran forests of northwestern Iran. The findings provide valuable insights for managing existing forests under population pressure and diverse land uses, as well as for developing conservation measures that optimize the utilization of limited forest resources.

To assess the impact of human disturbances, three areas with varying disturbance levels were selected based on forest history, observations, and proximity to the primary disturbance source. Within each area, four 1-hectare (100 × 100 m²) sample plots were established. A census of all trees and shrubs was conducted, and their diameter at breast height (DBH; ≥5 cm), height, and species number per hectare were recorded. Tree size characteristics in these areas were quantified using the plotless nearest neighbor method. Three transects were laid parallel and perpendicular to the secondary road in each area, with samples taken at regular 25-meter intervals. Using random-systematic selection, the nearest tree to the sample line was selected as a reference tree, and four neighboring trees were identified. DBH, height, and tree-to-tree distance were measured for each tree. Tree size diversity was analyzed using indices including distance from the nearest neighbor, diameter mean and coefficient of variation, diameter differentiation, height differentiation, Gini coefficient, and Shannon index of tree size diversity. Pearson's correlation test was used to determine the correlation between each tree size diversity index and the disturbance intensity index in the region.

 An average of 399 trees and shrubs were recorded per hectare in the study area, with the highest density observed in the low disturbance area. Mean diameter and total tree height were higher in the high disturbance area compared to the low and medium disturbance areas. The distance index from the nearest neighbor was significantly higher in the high disturbance area compared to the other two areas (F=41.49, sig<0.001). The coefficient of diameter variation was also significantly higher in the low disturbance area compared to the other two areas (F=17.11, sig<0.001). There was a significant negative correlation (r=-0.682, sig<0.001) between the coefficient of diameter variations and the disturbance intensity index. Average DBH was higher in the high disturbance area, but the difference between the three areas was not significant (F=1.06, sig=0.356). The Gini coefficient index was significantly higher in the low disturbance area compared to the other two areas (F=17.89, si <0.001). Tree diameter differentiation was also significantly higher in the low disturbance area compared to the other two areas (F=13.86, sig<0.001).

Human disturbances negatively impact tree size diversity and forest stand structure, leading to reduced structural complexity. Mitigation strategies and protection plans are recommended to address human-caused disturbances in areas lacking size diversity. Planting pioneer trees can enrich structural complexity. Additionally, a conservation plan tailored to habitat conditions is required to promote size diversity and enhance forest resilience. Local organizations should prioritize this plan.

Analyzing changes in land use/cover and identifying their drivers is essential for developing sustainable land management approaches that restore ecosystem resources and services. This research analyzes land use/land cover changes in the Fandoghlo region in Ardabil province in the last two decades based on Landsat 8 (OLI/TIRS L1TP) satellite images and investigates governance and socio-economic drivers based on household surveys and key informants interviews. The results recorded a negative net change in forest (187.5 ha) and rangeland (662.5 ha) ecosystems and a positive net change in agriculture (587.1 ha) and man-made (267.6 ha). Analysis of the 20-year (2003-2023) change detection matrix showed that about 26% of the study area experienced a transition. Based on the results of the investigation of 11 governance and socio-economic drivers, the observed change in ecosystems caused by the expansion of agriculture (91%), poverty (71%), livestock pressure (68%), weak law enforcement (65%), wood smuggling (62%), increasing demand for firewood (60%), population growth (44%), fires (41%), incompatible forms of tourism (40%), distance to the road (36%), and mining (11%). These socio-economic drivers have a significant impact on the availability of ecosystem services, community welfare, biodiversity, and climate. Therefore, this study recommends coordinated efforts by stakeholders to develop participatory ecosystem management and public awareness on the coexistence of nature conservation and sustainable livelihoods. In order to protect ecosystems in this region, it is crucial to empower local communities, understand the dynamics of agricultural lands, and manage the causes and consequences of recent demographic changes.

Ecosystem health is a concept that is often used to evaluate ecosystems (Lu et al., 2015). A healthy ecosystem can be considered as resilient against disturbance. On the other hand, degradation occurs when different states of ecosystems related to energy flow, nutrient cycling, and hydrological regimes are negatively affected. Especially for forest areas, it is necessary to protect the structure and function of ecosystems and actively restore destroyed ecosystems to ensure their integrity and maintain their health. As a result, a wide range of forest ecosystem health indicators has been developed with an emphasis on the use of quantitative and qualitative indicators (Chen et al., 2016; Fei et al., 2013). Assessing the health of ecosystems based on these indicators is a prerequisite for ecological restoration and sustainable development.Forest management requires information about the state of the forest with a focus on forest health. Because healthy forests are able to perform well compared to unhealthy forests. Forest health monitoring is especially important in creating sustainable forest management and also the integrated watershed management. However, today, the protection and monitoring of forests concerning forest health still do not exist and are limited to small scale studies. Therefore, this study assesses the health conditions of forest ecosystems at the watershed level in the north of Iran. In this research, by using remote sensing data and preparation of vegetation indices along with field data obtained from forest health monitoring in the Siahkol Shenrud watershed, the analysis of these indices and their grading at the forest health level is discussed. The protection and monitoring of forest conditions requires an extensive investigation of different forest types that are located inside a watershed. Therefore, in this study, a method suitable to the conditions of the forests of northern Iran is presented, which is necessary for detecting forest health levels based on the different types in a watershed. This study is conducted to provide useful knowledge in the field of creating a sustainable management strategy in the forests of northern Iran, which is based on watershed management.

Study area This research was carried out in the Siahkol Shenrod watershed located in Gilan province with a total area of 190 km2 (Figure 1). This basin is located in a plain where a small river named Shamrod passes through the middle and its height increases from north to south. This region has a mild and humid climate, which is also influenced by the cooler climate of the hills. The average annual temperature is 18°C. The average rainfall varies between 800 mm (southern part) and 1200 mm (northern part) per year.

This research is a combination of remote sensing survey and analysis methods and a geographic information system (GIS) to describe the health status of forest ecosystems in the Siahkol Shenrod watershed. Land use classification was done using the Landsat 8 OLI satellite image of 2021. Supervised classification method and maximum likelihood algorithm were used to prepare land use maps. In order to evaluate the accuracy of the classification, Google-Earth images were used by selecting 30 educational samples. For accuracy assessment, the high value of the Kappa coefficient (0.92) was obtained.  Vegetation indices calculation After pre-processing, the Landsat 8 image was used to calculate vegetation indices according to Table 1. Reference Equation Vegetation Index Frampton, Dash, Watmough & Milton (2013) NDVI Huete et al. (2002) EVI Bannari et al. (1995) L=0.5 SAVI Gao, 1996 NDWI Gitelson et al. (2001) ARI1 Gitelson et al. (2002) CRI1  Forest health index extraction The six calculated vegetation indices (Table 1) were combined and classified for the input of forest health index (FHI) in ENVI software. In this classification, ten classes were defined for all six indices, and then in calculating the FHI index, it was reduced to five classes by combining both related classes. The FHI index (Gupta and Pandey, 2021) is available in the ENVI forest health toolbox, as follows:• Field data collection To validate the forest health levels in different forest types of the watershed obtained from remote sensing data, it is necessary to conduct a field survey. Field survey data were obtained from the study area in September and October 2021. A stratified sampling method was used before the field survey in the GIS environment. Based on this method, 40 square-shaped samples (30 m * 30 m) were built. Observational activities were conducted using the Forest Health Monitoring (FHM) method (USDA Forest Service, 2020). The area with less than 10% damage was classified as a very healthy forest, and the forest damage between 10 and 25% was classified as a healthy forest. 25-50% damage was classified as medium health forest, 50-75% as poor forest health, and 75-100% damage was classified as very poor forest health.

 Vegetation indicators Six vegetation indices extracted for forests in the study area are presented in Figure 3. Based on the results, 95% of the forests in the study area showed an NDVI index of more than 0.5, which indicates healthy vegetation. Also, healthy vegetation with high chlorophyll content was shown in 91% of the studied area with an EVI index between 0.2-0.8. In this research, according to the multiple linear regression analysis of the effect of vegetation indices used in calculating forest health, the NDVI index with an R2 value of 0.77 has the greatest effect on the level of forest health in the watershed, which is the greenness level. It also shows the density level of forest stands. Similar results of the influence of vegetation indices have been presented by Tuominen et al. (2009). Furthermore, the higher effectiveness of NDVI in this study is similar to the findings of Barkey and Nursaputra (2017) and Oliech (2019) in assessing forest health. After NDVI, R2 values for EVI, SAVI, and AR1 indices were 0.75, 0.73, and 0.72, respectively, and for NDWI and CRI1 indices were 0.63. Forest health index The relationship of six vegetation indices calculated from the Landsat 8 image was used to determine the level of forest health in the Shenrod watershed. Forest health was divided into five categories: 1) very good (very healthy); 2) good (healthy); 3) moderate (under stress); 4) poor (unhealthy); 5) very poor (dead). Based on the results, the overall accuracy was 86%. Also, the kappa value of 0.81 was obtained. Figure 4 presents the health status of forest stands in the Shenrod watershed.Based on Figure 4, the distribution of forest health classes in the study area is not uniform. The central areas of the watershed have higher levels of forest health, while the southern areas of the watershed have lower levels of forest health. This can be due to the history of heavy wood harvesting, selective cutting, and as a result, the widespread opening of forest stands (Jahdi and Arabi, 2023), in the form of forestry plans implemented in the southern areas of the watershed during the last two decades. Wildfires in this area also affect the structure and composition of the vegetation and are a great threat to the long-term productivity and overall health of the forest. In general, 56.8% of the forest area is in the medium health category. Healthy and unhealthy forests with levels almost close to each other make up 41.7% of the total forest area. 1.5% of the forest area is also in the very poor health category (Figure 4).The information on the forest health levels in the forest types of the watershed is presented in Table 3. Plantation, especially broadleaf plantations, has the largest forest area in unhealthy conditions (approximately 33%). In contrast, natural forests, including mixed broadleaf forest and beech forest, have the largest forest area in healthy conditions (approximately 21%). On average, almost 60% of all existing forest types in the study area are in the medium health category.In general, many parameters affect the health conditions of forest types in the watershed, so it is important to identify the factors that caused the forest health levels in the survey with more observational activities.

In this study, the quantitative index of forest health was evaluated using broadband spectral indices and stress-related pigments before tree fall using remote sensing data. Using forest health analysis, the quantitative health conditions of the forest were described in 2021 with a forest area of 122.2 km2. Of this amount, there are healthy forests with an area of 23.8 km2 and unhealthy forest conditions with an area of 29 km2. The maximum area of the watershed, i.e. 69.4 km2, is also in medium health conditions. The health status of forest ecosystems is mostly average health, which is the result of forest destruction, soil erosion, and human overexploitation. Forest conservation programs can effectively control forest degradation and improve the health of local ecosystems. Forest health detection based on remote sensing in the study area was consistent with the field survey results. This study guided the systematic approach and provided remote sensing techniques for forest health monitoring programs and sustainable forest management.

A healthy forest ecosystem is essential to provide a wide range of environmental, social, and economic services. To evaluate forest health level, it is necessary to quantify ecosystem conditions using a variety of indicators. In this study, a combination of six indices: NDVI, EVI, SAVI, NDWI, ARI1, and CRI1 were used to derive the forest health condition map around Shenroud watershed, Siahkal using Landsat 8 OLI image of 2021 and ENVI Forest Health Tool. We evaluated the forest health with five grades (A; very good to E; very poor). Forest health data validation in various forest types in the study area is conducted via field crosscheck and assessment regarding the forest health level using 40 sample plots and FHM (Forest Health Monitoring) method. In this work, based on the multiple linear regression model of the effect of vegetation indices in forest health assessment, the NDVI vegetation index with an R2 value of 0.77 has the highest effect on the forest health level in the watershed, which shows the greenness level and the density level of forest stands. The results of the analysis show that most of the forests in the study area were moderately healthy. 19.4% of the forest areas were classified as “Healthy”, 56.8% as “Moderately healthy”, and 23.8% as “Unhealthy”. Furthermore, different forest types in the study area have different percentages of healthy forests. Plantations, especially broadleaf plantation, has the largest forest area in unhealthy conditions (weak and very weak) (approximately 33%). In contrast, natural forests, including mixed broadleaf forest and beech forest, have the largest forest area in healthy conditions (very good and good) (approximately 21%). In general, the current state of the ecosystem in the study area is mainly in middle health, which is a result of long-term deforestation, soil erosion, and inappropriate human exploitation. Our RS-based health diagnosis of the area was consistent with field survey results. The process could be very useful in mapping the health conditions of the forests of the country accurately. It is suggested to study the spatial and temporal variation of forest health in the watershed under future climate change.

Under rapid climate change, desertification, and land degradation, a critical management issue in forests of northern Iran is how to increase forest resilience to wildfires. For decades, forest managers have been employing silvicultural treatments to restore the Hyrcanian forest stand's structure, composition, and diversity, but there is no scientific evidence to support that these treatments reduce the wildfire risk in the area. In this study, based on the wildfire behavior modeling approach using FlamMap MTT, we evaluated spatial relationships between silvicultural fuel reduction treatment (e.g., thinning) and surface and crown burn probabilities in the 6 and 7 Malekroud districts of Siahkal forest. We first analyzed fuel characteristics after the development of silvicultural treatments in the period 2007-2017 in the study area to evaluate low thinning and heavy thinning as restoration tools for similar fuel conditions. Furthermore, we conducted analyses of variance to determine the influence of the silvicultural thinning treatment on surface and canopy burn probabilities. Modeling results at the landscape scale confirm the important role of thinning in the mitigation of crown burn probability (almost 11%) in the study area. However, the effect of thinning on the surface burn probability was not statistically significant (P> 0.05). Comparing the two types of thinning in terms of intensity, heavy thinning had a greater effect on changing the surface and crown (i.e., 10% increase and 39% decrease, respectively, in conifer plantation) burn probability than the low thinning. The modeling approach in the study offers new insights to improve the spatial mapping of wildfire likelihood, include variables sensitive to thinning operation in the models, and provide useful information to prevent extreme wildfire behavior through effective silvicultural treatments.

In this research, in order to identify changes, Landsat-7 ETM+ and Landsat-8 OLI/TIRS L1TP images were used for 2003 (August) and 2023 (June), respectively, under supervised classification using Support Vector Machine (SVM) classification algorithm. Five main land use classes including agriculture, forest, rangeland, built-up, and water areas were extracted. The accuracy evaluation results for the classified images showed an overall accuracy of > 92% and a Kappa coefficient of > 0.89 for both years. During the period, agriculture and built-up areas increased, while forests, rangelands, and water bodies decreased. The highest rate of change (62.3 hectares per year) was observed in rangelands. The results of forest cover change maps after classification show a significant decrease of 8.5 percent in forest cover over a period of 20 years. The main reason for this change is the observed replacement of native forest cover with rangelands (16.9 %), built-up areas (4.3 %), and agricultural areas (0.8 %). Most of these land use changes are unplanned and mainly a result of human activities with a wide range of negative effects on natural ecosystems. Therefore, effective policies and land use management are needed to protect the environmental and aesthetic benefits of ecosystems, as well as to manage natural hazards caused by these changes. Apart from the assessment of land use changes, this research provides insight into the dynamics of forest cover, along with its causes and consequences, and in this way shows the hot spots of forest loss.

In this study, we analyzed the spatial–temporal trends of land use dynamics from 2000 to 2021 using remote sensing data. The image classification was based on three main land use classes, i.e. forest, artificial areas (agriculture and built-up), and other natural areas (bare lands, grassland, plantation, shrubland, water bodies, and woodlands). Maps of land use changes in the area for 2000-2021 show that built-up areas have increased by 9.3%. In contrast, forest and other natural areas decreased by 7.1% and 2.2%, respectively. In addition, an integrated CA-ANN (Artificial Neural Networks- Cellular Automata) model was used to predict land use changes from 2021–2042. The percentage of correctness for the simulation was 91%, and the overall kappa value was 0.86. Similar to the classified maps in 2000-2021, the prediction maps from 2021–2042 illustrated increasing trends in built-up areas by 4.7% and decreasing trends in the forest by 4.26% and other natural areas by 0.4%. In this work, we implemented ordinary least squares (OLS) regression models to predict land cover changes in the study area as a function of explanatory variables [elevation, slope, and proximity variables - distance to the city center, roads, villages, and streams-]. The results of the OLS models showed a relatively good performance for predicting land use changes with an R-squared value greater than 0.5. These results provide important knowledge that can help develop future sustainable management and planning and help managers make informed decisions to improve environmental and ecological conditions.

Forest fires are a natural part of the ecosystem, but their uncontrolled spread can result in severe economic and environmental damage. Therefore, it is vital to take measures to prevent and manage their spread. However, given the intricate and complex nature of fires, studying real fires in their environmental context can be challenging, if not impractical. As an alternative, researchers can use computer simulation models based on field studies to better understand fire behavior and its effects on the environment. The FARSITE simulator, based on the Rothermel model, was used to conduct numerical simulations of fire spread with respect to fuel, topography, and weather conditions such as humidity. Simulation results showed that the Sorensen and kappa coefficients were at their highest at 0.80 and 0.77, respectively, in air humidity scenarios; with average values of fire spread rate, flame length, and fireline intensity being 0.58 m/min, 0.54 m, and 74.54 kW/m, respectively. Based on these results, it can be concluded that increasing air humidity is a natural way to prevent fire spread. Furthermore, numerical studies and simulations of fire behavior and spread are appropriate alternatives to experimental studies in this field due to their lower costs and fewer limitations.

Silvicultural treatments have been applied in the last decades to broadleaf and conifer plantations to increase resilience by enhancing structural and age-class diversity in northern Iran. The treatments included mechanical thinning manipulations, not followed by surface fuel reduction treatments like prescribed burning. The goal of this study was analyzing  the impact of stand-level silvicultural thinning on landscape-scale wildfire behavior. FlamMap minimum travel time (MTT) fire modeling system was used to simulate the impacts of thinning on fire growth and behavior based on spatial and temporal patterns of historical fire ignitions and associated weather and fuel moisture conditions within a forest landscape. Simulations were done by setting two different fuel moisture scenarios: 1) fuel moisture is fixed as the stands thinned, and 2) fuel moisture is reduced with the same degree of thinning. The results showed that thinning alone can be somewhat effective at mitigating the wildfires; as the landscape proportion with low burn probabilities (BP), conditional flame lengths (CFL), and fire sizes (FS) increases. This trend was not the same at higher values of these parameters. Although fine fuel moisture in thinned stands was lower than in unthinned stands, the fuel moisture difference resulting from stand thinning did not substantially influence fire behavior at the landscape level. The findings of this study can be used for better fire and fuel management and other land-use objectives in fire-prone areas.

The main purpose of this study was to determine the quantitative and qualitative characteristics of maple trees in the plantation of Lomar Asalem,. The measurement and comparison of quantitative and qualitative characteristics of maple trees and regeneration status in plantation and natural forests and compared to other species is concerned. This study was conducted in Maple plantations at the age of 25 based on a systematic random sampling method with 30 sample plots of 400 m2. The results of t-test showed a significant difference between the number of trees per hectare, basal area, volume, slenderness coefficient and diameter growth of the maple trees in the two areas. However, there was no significant difference in the mean height of trees in the two regions and their height growth. In terms of qualitative characteristics, there was no significant difference in the trunk health of maple trees in the two areas. However, there was a significant difference at the level of 0.05 compared to the trunk form of these trees between the two areas. Significant differences were observed in the crown symmetry of the maple trees in the two areas, but there was no significant difference in the crown health and vitality of these trees. Regeneration density in the natural forest was higher than in the plantation. While the frequency of healthy seedlings and single trunk seedlings in the plantation was significantly higher than natural forest. Plantation in the area had a suitable condition in terms of quantity and quality than natural forest.

Siahkal forests in northern Iran experienced extended and devastating fire seasons in recent decades, in most cases, driven by dry, warm/hot, windy weather. Identifying forest fuel and wildfire risk dynamics is important for an integrated fire-forest management strategy. To understand how forest structure controls these dynamics, we quantified forest fuels and fire behaviour across different forest types in the study area. In this work, we present the results of an analysis of wildfire behaviour from historical fire records, vegetation type, weather, and fuel moisture. We used a modelling approach to estimate the physical parameters of surface fire behaviour: flame length and fire size; as well as the potential crown fire occurrence. We selected a large forest watershed area in Siahkal County of north Iran, under temperate broadleaf forests and mixed plantations with variable compositions. To run the model, we gathered data on forest structure and composition, and physical information. We detected moderate to high levels of fire size and flame length, ascribed to the high availability of very dry fine biomass. The crown fire potential varied greatly throughout the landscape. Low stands were more prone to crowning. The results show that crown fire potential in plantation stands especially conifers are higher compared to natural stands. In addition, the early stages of planted stands are more likely to experience crown fire due to their low mean height and low CBH. The findings can assist in the identification of priority areas where forest structure needs to be managed.

Quantifying land use change dynamics is critical in tackling environmental and socio-economic challenges such as climate change in recent years. This study takes Siahkal County in Guilan Province as the research subject and analyzes the land use changes in two different years: 2000 and 2021, and predicts the change in 2031. We carried out land use change analysis using LANDSAT-7 ETM+ and LANDSAT-8 OLI multitemporal data pertaining to the years 2000 and 2021. For land use data extraction, a pixel-based digital classification using an ISODATA algorithm with a high Kappa index of 0.97 was applied to Landsat images. In addition, the MOLUSCE plugin in QGIS software was used to model land use change for 2000-2021, produce a transition probability matrix, and detect the future of land use for 2042 by using Cellular Automata Simulation. During 2000-2021, the result showed that the areas under agricultural areas, bare, built-up, plantation, shrubland, and woodland classes are found to increase with growth rates of about 31%, 39%, 67%, 88%, 20%, and 5%, respectively. The area under forest, grassland, and water bodies is found to decrease with decrease rates of about 22%, 16%, and 18%, respectively. Mountainous meadows did not change significantly. In terms of projected land use, the result also indicates fluctuations in land use change, especially for built-up land, showing a steady increase over time. We saw evidence of the local expansion of forest plantations, but the continuous decrease of natural forests may negatively impact the natural environment and landscape patterns regionally. In conclusion, the results of monitoring and modeling land use changes can be seen as a warning to managers, policy-makers, and planners. It is also advisable to use more data to analyze the impacts of land use changes on landscape patterns in future studies.

Public-Private-People Partnership (4P) is important to promote and improve wildfire prevention and control through research and evaluation. Understanding and collaboration among local residents and different jurisdictions are needed, such as municipalities and provincial land management agencies. The collaborative action requires the adoption and acceptance of fire management plans and the participation of local communities and residents in fire risk mitigation measures. In 2019, we conducted a field survey of a random sample (n = 120) of local communities in one of the greatest fire-risk areas in Siahkal County in Guilan province. The survey asked about respondents’ risk perceptions, wildfire experiences, knowledge of wildfire, awareness of mitigation, adoption of mitigation measures, and demographic factors and social characteristics. Based on the results, with respect to fire causes, the respondent's perceptions was realistic at the general level, although the majority of respondents rated the wildfire risk to their environment as low to moderate. The studied community was less familiar with fire mitigation activities and a general lack of awareness about the wildfire risk and fire mitigation behaviors has emerged from the study. At a broad level, the results of this study visualize the importance of specifying the human dimension of wildfire risk. Assessing human perceptions and detailed information on fire causes, impacts, and governance policies are crucial to improving the effectiveness of fire management activities. The results of these studies will support effective fire management plans that are more adapted to local conditions and more general acceptance by society.

Due to rapid population growth and increasing demand for forest resources, large areas of forest have been subjected to different degrees of degradation, and this degradation is increasing at an alarming rate. Large areas of forest have been varied by various degrees of degradation, and this degradation is increasing with an alarming rate due to rapid population growth and increasing demand for forest resources. Only 15 percent of the world's forests are undegraded, 20 percent have been destroyed and 28 percent have been deforested. Forest degradation is a global phenomenon that destroy10.6 million hectares of the world's forests each year. Therefore, studies focus on the effects of forest degradation impacts on the structure and composition of forests around the world. The goal of this study was to compare the structure of oak-hornbeam stands in different degradation intensities in Arasbaran forest.

The distance method was used to calculate the structural diversity index in Arasbaran forests, using three indicators, including uniform angle, tree diameter differentiation, and mingling indices. Within the study area with the different intensity of human interventions, three one-hectare sampling areas were selected. In order to select the study points in the regular systematic way 25 *25 m grid was laid out and the nearest tree to the intersection point of gird as reference tree was selected and measured with the four nearest neighbor trees. the diameter at the breast height (DBH), distance, and angle between trees in structural groups were measured.

The results of calculating the uniform angle index showed a change in the distribution pattern of stands due to the increase in the intensity of destruction from random to clump and uniform distribution. The diameter diffrentation index of trees showed that the value of this index was the lowest (0.12) in the moderate and was the highest (0.38) in the severe degradation intensity. The results of the study of the mingling species index showed that the highest value of this index was in the low degradation intensity (0.61) and the lowest value was in the moderate degradation intensity (0.08). Statistical analysis showed a significant difference (p≤ 0.05) between these indices in all stands. Calculation of the structural diversity index also showed that the highest structural diversity was in the stands with low degradation intensity (0.5) and the stands under medium degradation intensity had the lowest amount of structural diversity index (0.14).

Planning to reduce the human interference should be prioritized considering the effects of human degradation on the structure of the Arasbaran forest.

Forest health monitoring is essential to assess the changing state of forest resources and create awareness of forest health issues. This study was conducted to monitor forest health using two indicators of regeneration and deadwood in the Masouleh watershed,. To collect data in the study area, using Forest Health Monitoring method 5 clusters of plots including 20 plots in two different altitudes were considered. In each cluster, the regeneration status of species was determined based on the quality of seedlings and their division into two categories: healthy and unhealthy. The characteristics of the deadwood including diameter, and quality characteristics (species type, degree of decay) were measured and recorded. In the study area, 80% of the seedlings were healthy and 20% unhealthy. The results of the analysis of variance of the number of regenerations per hectare in the altitude classes showed that there is a significant difference between the number of healthy seedlings and unhealthy seedlings in the studied altitude classes. According to the results of the study, the volume of deadwood is 44 m3/ha, which is 34 and 10 m3/ha of down logs and standing deadwood, respectively. Analysis of variance showed that there was no significant difference between the volumes of deadwood per hectare in the two altitude classes in the study area. But there is a significant difference in the number of deadwood per hectare in the altitude classes. Study of the quality of deadwood showed that the highest percentage of deadwood belongs to the first-level decay (81%).

This study aimed to analyze tree diversity and tree crown and stem condition as two indicators of forest health in the Masouleh forest watershed, Guilan province. Field data collection was performed using cluster sampling based on Forest Health Monitoring (FHM) method. A total of 5 clusters including 20 subplots in the mixed beech (Fagus orientalis Lipsky.) forest stand were established. Within each cluster, quantitative and qualitative characteristics of existing trees such as diameter at breast height (dbh), total height, canopy diameter, density, and percentage of crown dieback and stem quality were studied and measured. To assess the tree species diversity, species biodiversity indices, richness and evenness were calculated in each cluster. Based on the results of the analysis of variance in 95% confidence level, there are no significant differences in the species diversity indices between the five different clusters (P>0.05). The highest and lowest value of the species diversity indices were measured in clusters 2 and 3, respectively. The largest change in the species diversity occurred at cluster 4. The results showed that all clusters in the study area had good status in terms of the species diversity indices. The amount of canopy drought in some species was proportional to the trend of canopy diameter changes and Buxus hyrcana Pojark. and Diospyros lotus L. species had the highest percentage of canopy drought (approximately 17%). Regarding the quality of stems, more than 80% of the trees in the forest stand have good quality. It is concluded that the health status of the studied ecosystem in terms of the level of tree diversity and tree crown and stem conditions is desirable.