یوسف عرفانی فرد

Mangrove forests are coastal ecosystems that provide various benefits for ecology, economy and society. To manage these forests sustainably, accurate mapping of their distribution using modern techniques is crucial. This study aimed to map the mangrove forests along the southern coast of Iran from Khuzestan province to Sistan and Baluchestan province and identify new sites using remote sensing. The Mangrove vegetation index (MVI) was applied to Sentinel-2 images on the Google Earth Engine (GEE) cloud computing platform. The MVI images were classified by the random forest algorithm. The final maps were evaluated by accuracy, F1-score and other common indices. The results indicated that the total area of mangrove forests in southern Iran was about 12471.8 ha (accuracy 0.98, F1-score 0.97) in October 2021 in 40 sites across four provinces: Khuzestan (286.7 ha), Bushehr (296.9 ha), Hormozgan (11281.9 ha) and Sistan and Baluchestan (606.3 ha). Moreover, some new sites, such as the mangroves of Mahshahr Harbor with an area of around 290 ha in Khuzestan province, were discovered in this study.

Application of unmanned aerial vehicles (UAVs) is widespread in measurement of quantitative characteristics of single trees such as crown area. However, the efficiency of different types of data collected by UAVs are less compared. Therefore, the aim of this study was comparison of UAV RGB imagery and point clouds in crown area estimation of individual pine trees within a man-made forest in Pardisan Park, Northern Khorasan province, Iran. Both datatypes were analyzed by similar segmentation method (Multiresolution segmentation on the RGB images and Marker-Controlled Watershed algorithm in the point clouds) to estimate crown area of 324 sample pine trees. The results showed that the crown area measured on the point clouds had higher accuracy and precision compared to RGB imager (Spearman correlation of 0.95 and 0.81, coefficient of determination of 0.97 and 0.59, p-value of 0.97 and 0.001, respectively). Additionally, crown area of pine trees with large crown (> 18 m2) delineated on the point clouds showed the highest accuracy in comparison to trees with small and medium crowns. In general, it was concluded that segmentation of UAV point clouds was more efficient than RGB imagery segmentation in quantification of crown area of pine trees within the study area.

Mangrove forests are one of the important plant ecosystems established across the intertidal zones and consist of evergreen species. According to Food and Agriculture Organization (FAO) reports, the area of world mangrove forests is almost 14.6 million ha and more than 40% of them are located in Asia. Indonesia has the largest mangrove forests with 2.3 million ha with the highest richness. Moreover, Iran with approximately 10,000 ha of mangrove forests in northern parts of the Persian Gulf and Oman Sea is one of the countries with mangrove ecosystems. The ecological and socio-economic importance of mangrove forests is evident to researchers and managers, however, an annual quantitative and qualitative decrease in these forests happens due to natural (e.g., storm) and anthropogenic (e.g., overexploitation) factors. Therefore, it seems essential to develop a practical approach in order to protect the present sites and improve the management, monitoring, and assessment of mangrove forests. The first step in every management and conservation plan in mangrove forests is mapping their spatial distribution and monitoring the spatial changes. It is important to find efficient methods for mensuration and assessment of temporal and spatial changes of mangrove forests for their efficient management and conservation. Field measurement difficulties in these ecosystems result in the rapid development of remote sensing data in mangrove mapping. However, previous studies have shown that common vegetation indices are not efficient in mangrove classification because of the high greenness and moisture content of leaves. Assessing the spectral signature of mangrove forests, researchers have designed specific indices for mangrove classification on satellite imagery. Since the mangrove indices have been recently developed, their efficiency in similar conditions has not been investigated, while they have been compared to some vegetation indices or individually investigated in case studies. Additionally, the mangrove indices have not been applied in mapping mangrove forests of southern Iran. Therefore, the aim of this study was a comparison of eight mangrove indices in mapping mangrove forests of Nayband Gulf (Bushehr province), Sirik (Hormozgan province), and Govatr Gulf (Sistan-Baluchestan province) on Landsat 8 imagery.

 Previous studies have shown that mangrove forests in Iran are distributed in 21 sites in 10 cities in Bushehr, Hormozgand, and Sistan-Baluchestan provinces. In order to assess the mangrove indices, a region was selected in each province. Mangroves in Nayband Gulf are concentrated in Bidkhun and Basatin Creeks. In Sirik, mangroves are located in the Azini wetland, and in Govatr Gulf, they are established in Baho and Govatr Creeks. Low- and high-tide Landsat imagery of each study area related to 2020 was downloaded. After pre-processing, the images were used to compute MI (Mangrove Index), NDMI (Normalized Difference Mangrove Index), CMRI (Combined Mangrove Recognition Index), MDI (Mangrove Discrimination Index), MMRI (Modular Mangrove Recognition Index), L8MI (Landsat 8 Mangrove Index), and MVI (Mangrove Vegetation Index). Moreover, low- and high-tide images were implemented in making SMRI (Submerged Mangrove Recognition Index). The classification of soil, water, and mangrove was performed by a support vector machine (SVM) algorithm. In addition to common accuracy criteria (i.e., overall accuracy, Kappa coefficient, mangrove producer's and user's accuracies), the results were evaluated by area under the curve (AUC) of receiver operating characteristic (ROC).

 The efficiency of 10 mangrove indices was evaluated in similar conditions. The number of selected indices was eight; however, two of them (i.e., L8MI, MDI) were calculated two times, once with SWIR1 and once with SWIR2, and in total, 10 mangrove indices were used in three regions to classify mangrove forests. Between the indices, SMRI was selected as the most efficient mangrove index. One of the likely reasons for the efficiency of the index can be the application of low- and high-tide imagery to detect mangroves. In addition to PAmangrove and UAmangrove, the overall accuracy and kappa coefficient of soil, water, and mangrove of SMRI were more than other indices. The results of MDI and L8MI showed that they were more efficient with SWIR2 in Nayband Gulf. One of the reasons that likely caused the result can be urban areas and non-mangrove vegetation cover in Nayband Gulf. However, both indices were more accurate in mangrove discrimination when calculated with SWIR1 in Govatr Gulf. Investigation of AUC values proved that SMRI was the most efficient index between all studied indices in mangrove mapping within three study areas. The AUC of mangroves in Nayband Gulf, Sirik, and Govatr Gulf were 0.94, 0.92, and 0.93, respectively. The area of mangrove forests was estimated in Nayband Gulf (260.1 ha), Sirik (1049.2 ha), and Govatr Gulf (649.5 ha) using SMRI.

 In general, the results showed that all mangrove indices were reliable in mangrove discrimination in three study areas and no weak results were achieved. The AUC values of mangroves using SMRI were more than 0.9 in three regions and the index was known as the most reliable index in all regions. The outcome in the study areas revealed that the efficiency of mangrove indices was less in Nayband Gulf compared to two other regions (The AUC of 0.6 for NDMI and L8MI-1). The area of mangrove forests in Nayband Gulf, Sirik, and Govatr Gulf was estimated on Landsat 8 imagery of 2020. The results indicated that between the study sites Sirik (1049.2 ha) and Basatin Creek (43.3 ha) had the highest and the lowest area covered by mangroves. It is suggested to use SMRI in other mangrove forests in southern Iran to approve the achievements of the present study.

Effective mangrove mapping needs reliable indices that can characterize mangroves from other land cover types on remote sensing data. Currently, a variety of vegetation- and mangrove indices are available, while a comparative assessment of their efficiency seems essential. The aim of this study was to evaluate six vegetation indices and seven mangrove indices developed for Sentinel-2 imagery to obtain a robust approach in mangrove mapping within Google Earth Engine (GEE) cloud computing platform. The rasterized indices were classified by support vector machine. The final maps were evaluated by area under curve (AUC) of receiver operating characteristic (ROC) in addition to common accuracy assessment criteria. Results showed that mangrove indices were more reliable than vegetation indices. Amongst the vegetation indices, Modified Chlorophyll Absorption in Reflectance Index (MCARI) (AUCmangrove from 0.91 to 0.92) achieved the highest AUC values, while MFI (Mangrove Forest Index) returned the highest values amongst the mangrove indices (AUCmangrove from 0.93 to 0.95). All in all, results revealed that MFI on Sentinel-2 imagery in GEE was efficient in mangrove mapping within the study sites.

Estimation of qualitative and quantitative characteristics of plants on UAV images is considered a challenge in remote sensing. Therefore, this study aimed to present a method to estimate crown area, height, and species in a mixed Pistacia-Amygdalus stand in UAV-derived point clouds. To this aim, 100 Pistacia atlantica Desf. trees and 100 Amygdalus spp < em>. shrubs were randomly selected. Point cloud was obtained by UAV-derived imagery with 50 points per m2 in a 64-ha study area in Baneh Research Forest, Fars province. The quantitative characteristics were then estimated on the point cloud. Additionally, species type was classified using random forest and 37 quantitative attributes measured on point cloud, canopy height model, and orthomosaic. Crown area and height of Pistacia (R2= 0.91 and 0.83, PRMSE=4.7% and 3.2%, respectively) and Amygdalus (R2= 0.89 and 0.47, PRMSE=22.1% and 21.5%, respectively) were also estimated. By application of quantitative attributes and random forest, species type was classified with an accuracy of 0.92 and κ of 0.98. All in all, results indicated that UAV point clouds can be efficiently applied to estimate a set of qualitative and quantitative attributes of Pistacia and Amygdalus within the study area. However, inaccurate and imprecise results were observed for estimated heights of Amygdalus.

One of the most common uses of forestry is to identify individual trees and compassions tree species using image analysis and satellite or aerial image classification. The aim of this study is to evaluate support vector machines (SVM) and Decision Tree (DT) classification methods in estimating the quantitative characteristics of oak trees in WorldView-2 images and UAVs. In this study, Haft-Barm forest, Shiraz, was considered as a study area to assess the potential of Worldview-2 satellite images in 2015. Estimation of forest parameters with a focus on single tree extraction using SVM and DT classification methods with complex matrix evaluation and area under operational characteristic curve (AUC) with the help of UAV4 Phantom Bird image was evaluated in two separate areas. Data were analyzed using independent T-tests, multivariate regression analysis, using SPSS 25, Excel 2016, eCognation v. 8.7, ENVI, 5, PCI Geomatica 16 and Google Earth 7.3 software. The SVM classification had the highest and best accuracy in estimating single tree parameters. The SVM classification method was a very useful way to identify the oak tree Zagros mountain forest. Using WV-2 data, the parameters of single trees in the forest can be extracted. The SVM algorithm using WV-2 also produced very promising result, with 96% overall accuracy and a Kappa index of 0.97. The results of regression analysis indicated that using WV-2 images (R2= 0.97) was suitable for estimating canopy area on the forest.  While UAVs have the potential to provide flexible and practical solutions for mapping forests, some issues related to image quality still need to be addressed to improve classification performance.

Zagros arid woodlands are among the most important and valuable areas of Iran΄s natural resources that due to the dieback of trees in recent years, it seems necessary to manage and rehabilitate this vegetation. This research was aimed to study the spatial distribution map dieback of Persian oak trees (Quercus brantii Lindl), analyze and describe the spatial distribution using a combination of geostatistical techniques and remote sensing in Barm plain, Fars province. First, the RapidEye satellite image was classified into two categories of healthy and dried trees with the supervised classified algorithm including maximum likelihood. The overall accuracy and Kappa coefficients were 80% and 73%, respectively. The data were then collected in circular sample plots of 2000 m2 (with a radius of 25.24 m) based on a 300×300 meter network in a randomized manner. After preparing the point map, the percentage of drying of the classified image was determined by simple, ordinary, and universal Kriging  interpolation method, which were evaluated using three models: Exponential, spherical, Gaussian. After evaluating the data using the cross-evaluation results, the most accurate fitting was shown by the simple Kriging method with the exponential model (mean estimation error of 0.023). Dieback map was obtained with classes of zero to 10, 10-20, 20-30, 30-40 and more than 40%. The largest area was related to class 20% to 30% with 493.9 ha and the smallest area was for zero to 10%, with 70.46 ha. The present study showed that it is possible to obtain maps of the spatial distribution of Persian oak dieback and recognize the focal points using geostatistical techniques and remote sensing.

The development and implementation of practical natural resources and catchment management policies require a comprehensive knowledge of the system processes (biological, physical, and socio-economic), their complicated interactions, and how they react to different changes. The current research assessed the ecological, physical, and socio-economic consequences of biologically-based management scenarios targeting runoff and soil erosion problems in the Darenari catchment. The Darenari catchment with an approximate area of 554 ha is located in Fars province, Iran. Three biological activities and 8 management scenarios were considered. Ecological consequences were studied using the weighted land cover area index (WLCAI). Physical effects were investigated applying the runoff curve number (SCS-CN) hydrologic model. Economic and social effects were assessed applying the cost/benefit analysis as well as examining the outcomes of a social survey. Then, a fuzzy AHP approach was applied to weigh the criteria and ultimately, the best management option was chosen using FTOPSIS model. The results showed that social criteria with the highest weight and scenario No 8 was the best scenario and had first priority. The results idicated that the multi-criteria decision making techniques included capability of expressing different aspects of the problem and are the perfect tool for watershed resources management.