Monitoring Long-term Mangrove Shoreline Changes along the Northern Coasts of the Persian Gulf and the Oman Sea

Generally, investigating changes in mangrove shorelines is an important step to evaluate whether mangrove ecosystems are expanding or contracting. In this study, the rates of change of mangrove boundaries were investigated along the coasts of the Persian Gulf and the Oman Sea, over a 30-year period. Seaward edges of mangrove forests were extracted from Landsat images of the years 1986, 2000 and 2016 and the Digital Shoreline Analysis System (DSAS) Software was used to implement the Linear Regression Rate (LRR) method to quantify the rates of boundary changes. On average, areas that experienced boundary expansion progressed by 2.55 m yr-1 and those that experienced contraction regressed by -0.38 m yr-1. The maximum rate of expansion was 25.91 m yr-1 and the maximum rate of contraction was -22.45 m yr-1. Mangroves located on the coasts of the Persian Gulf exhibited differential rates of progression and regression at their borders, with expansion rates increasing in an eastward direction toward the coasts of the Oman Sea. However, on the eastern coasts of the Oman Sea, mangroves are characterized by contraction and erosion

Evaluation of mangrove rehabilitation and afforestation in the southern coasts of Iran

The increasing multiple ecosystem services of mangrove forests, especially in the coastal regions have highlighted a need for conservation and afforestation of these forests. However, economic development and activities on the coasts have generated severe pollution issues that caused irreparable damages to the areas and quality of mangrove forests. As a result, rehabilitating the affected areas and forest planting are increasingly important, whereby some form of an assessment is needed to determine their sustainable performance and effectives. This study has used the indicators of forest resource sustainability, and the sustainability of planting sites to evaluate mangrove plantings in Iran’s southern coast. Findings showed that there was a total of 47 mangrove planting sites on the coasts of the three provinces studied with an area of 9584.5 ha. There were 26 afforestation practice sites with an area of 5724 ha, and 21 combined rehabilitation and afforestation practice sites with an area of 3860.5 ha identified in this study. Approximately 76.6% of planting sites had been lost and the remaining areas had experienced an average density drop of 44%. Results of the stability class analysis revealed that 23 planting sites were in an extremely unsustainable state, 15 sites were considered as highly unsustainable, six sites were in a state of tendency to be unsustainable, whereas only three sites were regarded as sustainable. Findings from this study can assist managers and decision makers to review the site selection processes and pattern of successful planting sites, to facilitate better site selection and enhance the monitoring of mangrove rehabilitation or afforestation

Forest Dwellers’ Dependence on Forest Resources in Semi-Arid Environments

Forests remain an important resource in Iran, as most of the livelihood activities of local communities, especially in the semi-arid environment of the Zagros forests, are dependent on forest resources. The aim of this study was to identify the type and extent of forest dependency. Semi-structured interviews and questionnaires were used to collect data from 170 households in Central Zagros. Results show that using firewood for fuel and non-fuel uses, harvesting edible and medicinal plants, agriculture and horticulture, and livestock grazing were the main forest livelihood activities undertaken by the households in the study area. On average, each household harvested 18.08 cubic meters of oak per year for water heating (bathing), baking bread, heating, cooking, heating milk and buttermilk, agricultural tools, house building, warehouses and shelters, fencing, branches for livestock, charcoal and harvesting firewood for sale. Of rural households, 72% used edible plants, and 86% used medicinal plants. Age, job, residence status, number of livestock, crop farming and household size were found to be correlated with forest dependency. Findings from this study contribute broadly to an integrated understanding of the bio-human dimensions of forest ecosystems, with specific reference to the study area

Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics

Wildfires are one of the most common natural hazards worldwide. Here, we compared the capability of bivariate and multivariate models for the prediction of spatially explicit wildfire probability across a fire-prone landscape in the Zagros ecoregion, Iran. Dempster–Shafer-based evidential belief function (EBF) and the multivariate logistic regression (LR) were applied to a spatial dataset that represents 132 fire events from the period of 2007–2014 and twelve explanatory variables (altitude, aspect, slope degree, topographic wetness index (TWI), annual temperature, and rainfall, wind effect, land use, normalized difference vegetation index (NDVI), and distance to roads, rivers, and residential areas). While the EBF model successfully characterized each variable class by four probability mass functions in terms of wildfire probabilities, the LR model identified the variables that have a major impact on the probability of fire occurrence. Two distribution maps of wildfire probability were developed based upon the results of each model. In an ensemble modeling perspective, we combined the two probability maps. The results were verified and compared by the receiver operating characteristic (ROC) and the Wilcoxon Signed-Rank Test. The results showed that although an improved predictive accuracy (AUC = 0.864) can be achieved via an ensemble modeling of bivariate and multivariate statistics, the models fail to individually provide a satisfactory prediction of wildfire probability (EBFAUC = 0.701; LRAUC = 0.728). From these results, we recommend the employment of ensemble modeling approaches for different wildfire-prone landscapes

Forestry Research in the Middle East: A Bibliometric Analysis

Research trends in the field of forestry have experienced a significant evolution in recent years. However, there has been little use of bibliometric analyses to assess academic organizations and individual researchers in this field of science. This study investigates the progress of forestry research in Iran, Israel, and Turkey based on a bibliometric analysis of 2482 documents published between 2005 and 2019 and indexed in the Web of Science (WoS) scientific information platform. The countries were analyzed and compared in terms of the number of documents, the number of citations, the mean number of citations per document, the h-index, the share of funded articles, and several other metrics. A complete keyword network with graphical visualization and cluster analysis was also used for depicting the most frequent keywords used by the authors from these three countries. The results showed that the number of publications on forestry research grew steadily during the study period. Turkey, with 1529 documents, was the most active in publishing research in the field of forestry, followed by Iran (726 documents) and Israel (219 documents). Turkey’s publications received 11,220 citations with a cooperation coefficient (CC) of 0.587 that revealed a strong relationship between international collaboration with the USA, Germany, and Italy, and the number of citations, such that the articles with co-authors affiliated to foreign institutions were cited far more often than the articles with Turkish authorship. Although Iran (CC = 0.680) and Israel (CC = 0.706) recorded more activities in international collaboration than Turkey, their publications received much lower citations (Iran’s citations = 4433, Israel’s citations = 3939). Israel had 136 articles (62%) that received research funding, followed by Turkey and Iran with 604 (39%) and 284 (38%) articles. Nine out of the ten most popular journals among Israeli researchers were ranked as quartiles 1 and 2 in the forestry category, whereas Iranian and Turkish researchers mostly published in fewer journals ranked as quartiles 1 and 2. The most frequent keywords (i.e., topics) were species, condition, forest, and tree. Insights provided here can help balance research activities towards publishing more informed and effective scientific articles

Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction

Predicting and mapping fire susceptibility is a top research priority in fire-prone forests worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB), Decision Tree (DT), and Multivariate Logistic Regression (MLP) machine learning methods for the prediction and mapping fire susceptibility across the Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing the information from the 57 historical fires and a set of nine spatially explicit explanatory variables, namely elevation, slope degree, aspect, average annual temperate, drought index, river density, land cover, and distance from roads and residential areas. Using the area under the receiver operating characteristic curve (AUC) and seven other performance metrics, the models were validated in terms of their abilities to elucidate the general fire behaviors in the Pu Mat National Park and to predict future fires. Despite a few differences between the AUC values, the BN model with an AUC value of 0.96 was dominant over the other models in predicting future fires. The second best was the DT model (AUC = 0.94), followed by the NB (AUC = 0.939), and MLR (AUC = 0.937) models. Our robust analysis demonstrated that these models are sufficiently robust in response to the training and validation datasets change. Further, the results revealed that moderate to high levels of fire susceptibilities are associated with ~19% of the Pu Mat National Park where human activities are numerous. This study and the resultant susceptibility maps provide a basis for developing more efficient fire-fighting strategies and reorganizing policies in favor of sustainable management of forest resources.Validerad;2020;Nivå 2;2020-06-18 (alebob)</p