TREEs provide a wide range of benefits to humans and other living organisms. An accurate method TREE SPECIES IDENTIFICATION will improve their management and conservation. Also, TREE IDENTIFICATION and description are crucial for genetic study, biodiversity conservation, management and regeneration strategies. The conventional methods of TREE IDENTIFICATION are time-consuming and requires a high level of expertise, necessitating development of a more efficient TREE IDENTIFICATION means. In this research, a QR code system for TREE IDENTIFICATION was developed. TREE data were collected from campuses of two tertiary institutions in Akure, Nigeria: Federal University of Technology and Federal College of Agriculture. System design was built around a three-tier architectural model. PostgresSQL was used as the Database System, the lowest tier. The Middle tier is the Web Server, Apache HTTP Server. Php 8. 1 was the scripting language that communicates with the database. For the Client tier, HTML, CSS and Javascript were used. The QR code generator was developed using PHP 8. 1. The PHP script used a QR code library to generate the QR code image. The QR code is linked to the website database containing all TREE SPECIES information. The generated QR codes were attached to TREEs, and when scanned, the website is automatically launched and the TREE information is retrieved. A survey was conducted to get end-users’ feedback within the study sites. The results obtained revealed that the QR codes are easy to use, and can make TREE IDENTIFICATION more interesting, thus increasing people’s knowledge about TREEs and improving TREEs management.

Detailed information on forest combination is required for many environmental, monitoring, and forest protection purposes. The link between ecology and remote sensing provides valuable information for the study of forest TREEs to facilitate the study of ecosystem performance and to measure the spatial distribution of vegetation. In recent years, the use of modern remote sensing methods and techniques based on UAVs have been used for regular updating of forest inventory. In this research, different data sources including multi-spectral and RGB images with very high spatial resolution, were used for TREE SPECIES recognition in plain forests of Noor City located in Mazandaran province. Also, taking images was performed in the growing season to prepare a time series of UAV-RGB images for investigating the effect of TREE crown phonological changes on classification accuracy.  Following orthomosaic generation, RGB (NGB, NRB) and multi-spectral (NDVI, CIgreen) indices were calculated and the random forest classification method was used for forest SPECIES classification. Based on single-time images, late April images provided the highest overall accuracy (75%). However, the results of the time series obtained from RGB images showed an increase in accuracy of up to 86%. SPECIES IDENTIFICATION based on multispectral images obtained from the Sequoia sensor also provided 85% accuracy. The results showed that the single-time image at the appropriate time using a UAV-RGB, compared to taking a time series and using a UAV equipped with multispectral sensors, has acceptable and less expensive results for TREE recognition in the study area.

The Clearwing moths (Lep.: Sesiidae) are important and destructive pests of fruitful and non- fruit bearing TREEs in different countries of the world. Considering the high damage of sesiid moths in Isfahan landscape, the infestation percentage and intensity were studied on 4768 TREEs of different SPECIES in 14 Isfahan municipality regions, Isfahan University of Technology campus and Nazhvan parks as 15th and 16th regions, respectively. To identify sesiid moth SPECIES, DNA was extracted from larvae and adults. Polymerase Chain Reaction (PCR) was used to amplify a 709-bp region of the mitochondrial DNA cytochrome oxidase I and samples were sequenced. Results of COI sequences revealed that the moth Synanthedon caucasica (Gorbunov) is the damaging SPECIES on Platanus orientalis, Ulmus carpinifolia and U. carpinifolia var umbraculifera and the sesiid moth Sesia pimplaeformis (Oberthür) (=Eusphecia pimplaeformis) is harmful to Salix alba and Populus alba.Percentage of infestation was calculated based on the number of infested TREEs divided by total TREEs and intensity of infestation was determined based on the total number of pupal exuviae, active larval holes and inactive larval holes per 10 centimeters of trunk diameter. The combined analysis was performed in a completely randomized design with 10 replicates. The regions and hosts were considered as main and secondary factors, respectively. Using linear regression between TREE diameter and infestation rate, the effect of trunk diameter on the infestation rate was determined. Results showed that 53.8 % of Platanus orientalis, 65.6% of U. carpinifolia, 64.4% of U. carpinifolia var umbraculifera, 52.6% of S. alba and 45.2% of P. alba and totally 53.2% of all TREEs were infested with sesiid larvae. The highest infestation intensity (4.2) was observed on U. carpinifolia var umbraculifera. The SPECIES U. carpinifolia, Platanus orientalis, salix alba and P. alba had lower infestation intensity. No sesiid damage symptoms were observed on Morus spp., Rubinia pseudoacacia and Fraxinus rotundifolia subsp. persica. On all host TREE SPECIES, the correlation between trunk diameter and infestation rate was positive but a significant correlation (p<0.05) was only occurred in U. carpinifolia var umbraculifera and salix alba.

Remotely sensed data, especially high spatial and radiometric resolution data can be useful to identify TREE SPECIES. In this research, the capability of digital aerial images for this purpose was investigated by object-based classification method. About 5.8 ha of Taleghani park in Tehran, including SPECIES like platanus orientalis, Morus alba, Cupressus arisonica, Robinia pseudoacacia, Pinus eldarica, Ailanthus altissima, Cedrus atlantica, was studied. Four pan-sharpened multispectral images of UltraCam-D camera with the spatial and radiometric resolution,  0.07×0.07m and 16 bit respectively, were analyzed. These data were geometrically corrected by aero-triangulation method using GCPs and IMU. The images classified using object-based method with the main and synthesic bands of Ratioing, PCA and HIS transformations. Firstly, segmentation was done with different parameters in order to avoid exceeding the maximum allowable number of objects. Finally, the classification was performed using appropriate features and layers by Nearest Neighbor method. In order to assess the accuracy of result, a ground truth map was produced based on field survey. This map has included 688 points, which each represents a TREE on the ground. The result of accuracy assessment showed that overall accuracy and Kappa coefficient were 78% and 0.73 respectively. Platanus and Aillan showed the highest and the lowest Kappa 0.81700.2481, respectively. The result of this study showed that the UltraCam-D and object-based method have relatively good capability to recognize TREE SPECIES. To reach a certainty about this result, it is essential to evaluate UltraCam-D data in other sites with different SPECIES.

This study is a contribution to assess the high resolution digital aerial imagery for semi-automatic analysis of TREE SPECIES IDENTIFICATION. To maximize the benefit of such data, the object-based classification was conducted in a mixed forest plantation. Two subsets of an UltraCam D image were geometrically corrected using aero-triangulation method. Some appropriate transformations were performed and utilized. Segmentation was conducted stepwise at two levels and a hierarchical image object network was constructed. The classification hierarchy was developed and Nearest Neighbor classifier, using integration of different features was performed. Training samples and ground truth map were prepared through fieldwork. Accuracy assessment of the resulting maps in comparison with reference data showed overall accuracies and Kappa Index of Agreement of 90.2%, 0.82 (Area1) and 69.8%, 0.49 (Area2), respectively. Transformed images were advantageous to improve the results. The lower accuracy in Area2 can be attributed to high diversity and heterogeneous mixture of SPECIES. More detailed and accurate mapping of TREE SPECIES would be fulfilled applying precise 3D data. The accuracy of detailed vegetation classification with very high-resolution imagery is highly dependent on the segmentation quality, sample size, sampling quality, classification framework and ground vegetation distribution and mixture.

The present study is focused on the SPECIES belonging to the genus Maiestas Distant, 1917 (Hemiptera: Cicadellidae: Deltocephalinae, Deltocephalini) in Iran, based on some specimens collected during 2016 to 2019 from Southeast Iran and SPECIES that have been previously reported: M. horvathi (Then, 1896), M. pruthii (Metcalf, 1967), M. schmidtgeni (Wagner, 1939), and M. trifasciata (Lindberg, 1954). M. pruthii, previously known from the Oriental region (India and Pakistan), is recorded for the first time in the Palaearctic region from Southeast Iran and M. horvathi is a new record for the fauna of Sistan and Baluchestan Province. An IDENTIFICATION key and distribution maps for the Iranian Maiestas SPECIES are provided.

One important point in using all the capabilities of digital aerial images in forest management is the implementation of optimized classification methods and extraction of useful information from the forest. Object-based classification reduces local spectral variations caused by crown textures, gaps and shadows. In addition, with spectrally homogeneous segments of images, both spectral values and spatial properties, such as size, shape and texture can be explicitly utilized as features for further classification. The aim of this study is to evaluate the possibility of object-based method for the IDENTIFICATION of major TREE SPECIES and mapping those using aerial digital (UltraCam-D) images in forested areas. After pre-processing of images, a trial and error method was employed to achieve the ideal segmentation results. In order to construct the hierarchical image object network, segmentation was conducted stepwise at two levels. The classification hierarchy was developed and NN classification was performed. Accuracy assessment of the produced maps, comparing with field reference data showed the overall accuracy and Kappa coefficient of 90% and 0.82, respectively. To evaluate the data and the method, similar studies seem to be necessary in different forest structures. Furthermore, classification of TREE SPECIES composition with such high level of detail and accuracy would be suitable to combine with DSM, DTM or LiDAR data.

Nemacheilian loaches are a group of freshwater fishes which are distributed in many inland waters of Iran. They are complex SPECIES from aspects of systematic with many SPECIES of this family being morphologically undistinguishable. Due to limitations behind the morphology-based IDENTIFICATION of such SPECIES, genetic approaches such as DNA barcoding can be helpful to distinguish SPECIES, although their genetic relationships are not well determined yet. The present investigation provides data on genetic structure of some SPECIES of Nemachilidae including Paraschistura bampurensis, Oxynoemacheilus kiabii and Turcinemacheilus saadii from inland waters of Iran. These SPECIES were sequenced for a 652 base pair region of the mitochondrial cytochrome oxidase subunit I gene. All SPECIES were identified by cytochrome oxidase subunit I gene sequence. The sequences of P. bampurensis showed that specimens collected from two streams (Shapour and Fahlian, south-west of Iran), with 1. 89% within-SPECIES Kimura two parameter distance, shared haplotype in neighbor joining TREE. The results provided the first genetic evidence for O. kiabii, which has recently been recorded as a new SPECIES in Iran, and also cytochrome oxidase subunit I gene data showed that O. kiabii is completely different from other SPECIES of Nemacheilidae. The results suggested that cytochrome oxidase subunit I gene sequencing can be used to identify SPECIES of Nemachilidae from the Iranian streams. The picture of Nemachilidae is yet incomplete, and this study is one of the first steps towards enhancing genetic understanding of associations among the SPECIES of Nemachilidae in Iran. There should also be further attempts in genetic or systematic research to fill data gaps among fish SPECIES in Iran to help establishing a global COI barcode database.

One of the most important ideas in forest management is maintaining and developing of biodiversity. Biodiversity measuring is one of the most difficult works, time consuming, high cost and in this context; there is not exist comprehensive and standard recipe toward biodiversity measuring. This study has been accomplished in order to preserve of biodiversity in Darabkola forests (area 280 ha) with measuring TREE SPECIES diversity in two geographic aspects (northern and southern). In order to investigate of TREE, eighteen plots (1000 m2), were taken by a systematic random sampling method. Values of SPECIES diversity was calculated using Simpson, Shanno - wiener and SPECIES richness Margalef, Menhenich and evenness (Pit and Hill) indices at each plot. SPECIES diversity, SPECIES richness and evenness indices Value maximum, at north and southern aspects is related to Shanon-Viner, Margalef and Hill indices, respectively. Results indicated that SPECIES diversity indices at northern aspect is more than southern aspect, but richness SPECIES indices was the same in the both aspects. For northern and southern aspects Pit and Hill indices were higher respectively. Also results indicated that SPECIES diversity indices and evenness have significant correlation with together, but there wasn’t a significant correlation within SPECIES diversity indices and evenness. As a result, evenness indices have higher effect to increase biodiversity in comparison with richness.

In order to identify fungal agents associated with juniper TREEs in the highlands of Tarm County, samples of various tissues from affected TREEs were collected and subjected to cultivation and isolation using conventional methods. For SPECIES IDENTIFICATION, both morphological and molecular methods were employed. To achieve this, DNA was extracted from 11 fungal SPECIES isolated. The internal transcribed spacer (ITS) region of their ribosomal DNA was amplified using universal primers (ITS-1 and ITS-4). Subsequently, their sequences were determined and compared to sequences available in databases to confirm the IDENTIFICATION of SPECIES. In total, more than 150 fungal isolates were separated from diseased juniper TREEs over a span of three years. Based on morphological characteristics and ITS sequence analysis, the following fungal SPECIES were confirmed: Nigrospora oryzae (31 isolates), Penicillium spp. (9 isolates), Aspergillus spp. (4 isolates), Trichoderma spp. (5 isolates), Aureobasidium pullulans (33 isolates), Polyporus sp. (3 isolates), Phoma sp. (14 isolates), Phoma medicaginesis (3 isolates), Alternaria alternata (5 isolates), Alternaria sp. (9 isolates), Alternaria consortialis (6 isolates), Hormonema carpetanum (5 isolates), Hormonema sp. (9 isolates), Peyronellaea pinodella (4 isolates), Kabatiella microsticta (3 isolates), and Microsphaeropsis spp. (8 isolates). Most of these SPECIES are opportunistic pathogens, reported for the first time on juniper TREEs in Iran. These agents, in conjunction with host vulnerability due to infestations by phytophagous insects, drought stress, and pest attacks, accelerate the decline of the host TREEs.