Arvin Fakhri

Verified @email.kntu.ac.ir

Fakultet for miljøvitenskap og naturforvaltning (MINA)
Postdoc fellow

Arvin Fakhri


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https://researchid.co/arvinf1995

RESEARCH, TEACHING, or OTHER INTERESTS

Space and Planetary Science, Forestry, Ecology, Evolution, Behavior and Systematics, Renewable Energy, Sustainability and the Environment
14

Scopus Publications

125

Scholar Citations

7

Scholar h-index

5

Scholar i10-index

Scopus Publications

  • Four decades of spatio-temporal trends in Miankaleh Wetland´s water body and vegetation as revealed by remote sensing time series
    Nima Arij, Hooman Latifi, Arvin Fakhri, Rohollah Esmaili
    Ecological Informatics, 2025
    Coastal wetlands offer essential ecosystem services, but are increasingly threatened by anthropogenic activities and climate change. These disrupt regional patterns, necessitating time series analyses to inform their long-term trends. Remote sensing provides cost-effective alternatives to demanding traditional wetland monitoring. Here, we employed a 40-year time series of Landsat data, supplemented by Sentinel-1 SAR imagery and Sentinel-2 multispectral data for enhanced recent-period analysis, and applied non-parametric trend analysis to examine changes in water bodies, vegetation, and climatic conditions in Miankaleh peninsula, encompassing an extensive Ramsar site in Iran. We utilized spectral indices and random forest classification to derive the area of water bodies and vegetation, followed by identifying significant trends using various trend analysis methods: Mann-Kendall (MK), Modified Mann-Kendall (MMK), Sequential Mann-Kendall (SeqMK), Seasonal Mann-Kendall (SMK), Sen’s Slope (SS), and LOcally Estimated Scatterplot Smoothing (LOESS). Findings showed a significant reduction in water area (30,700 ha, SS = -1.074) and an increase in vegetation cover (31,400 ha, SS = 1.365) from baseline levels. Among climatic factors, groundwater levels (SS = -0.214) and evaporation (SS = -0.312) were most influential on the wetland. The MMK, accounting for data autocorrelation, provided more accurate results compared to MK, while SeqMK detected important trend change points that were mostly missed by MMK. LOESS visualized local, nonlinear changes and identify subtle trend shifts. The results underscore significant ecological shifts, particularly the reduction of water bodies, which threaten the wetland's functionality. We provide general and case-specific considerations on the sole and complementary application of non-parametric trend analysis approaches, expanding insights into ecological processes in coastal wetlands with broader implications for similar ecosystems. • We compare trend analysis approaches of water bodies and vegetation cover in coastal wetlands. • Non-parametric trend analysis approaches complement each other to achieve accurate results, where using single methods is not recommended. • Advanced trend analysis methods, including MMK, SEQMK, and SMK, enhanced the accuracy and identified critical trend changes. • LOESS visualization highlighted subtle and nonlinear ecological changes essential for wetland management.
  • Introducing a computationally light approach to estimate forest height and fractional canopy cover from Sentinel-2 data
    Arvin Fakhri, Hooman Latifi, Kyumars Mohammadi Samani, Fabian Ewald Fassnacht
    Journal of Arid Environments, 2025
  • Improving the Accuracy of Forest Structure Analysis by Consumer-Grade UAV Photogrammetry Through an Innovative Approach to Mitigate Lens Distortion Effects
    Arvin Fakhri, Hooman Latifi, Kyumars Mohammadi Samani, F. Fassnacht
    Remote Sensing, 2025
    The generation of aerial and unmanned aerial vehicle (UAV)-based 3D point clouds in forests and their subsequent structural analysis, including tree delineation and modeling, pose multiple technical challenges that are partly raised by the calibration of non-metric cameras mounted on UAVs. We present a novel method to deal with this problem for forest structure analysis by photogrammetric 3D modeling, particularly in areas with complex textures and varying levels of tree canopy cover. Our proposed method selects various subsets of a camera’s interior orientation parameters (IOPs), generates a dense point cloud for each, and then synthesizes these models to form a combined model. We hypothesize that this combined model can provide a superior representation of tree structure than a model calibrated with an optimal subset of IOPs alone. The effectiveness of our methodology was evaluated in sites across a semi-arid forest ecosystem, known for their diverse crown structures and varied canopy density due to a traditional pruning method known as pollarding. The results demonstrate that the enhanced model outperformed the standard models by 23% and 37% in both site- and tree-based metrics, respectively, and can therefore be suggested for further applications in forest structural analysis based on consumer-grade UAV data.
  • CaR3DMIC: A novel method for evaluating UAV-derived 3D forest models by tree features
    Arvin Fakhri, Hooman Latifi, Kyumars Mohammadi Samani, Fabian Ewald Fassnacht
    ISPRS Journal of Photogrammetry and Remote Sensing, 2024
  • Combination of UAV Photogrammetry and Field Inventories Enables Description of Height–Diameter Relationship within Semi-Arid Silvopastoral Systems
    Seyed Arvin Fakhri, Hooman Latifi, Kyumars Mohammadi Samani, Zahed Shakeri, Hamed Naghavi, Fabian Ewald Fassnacht
    Remote Sensing, 2023
    Pollarding oak trees is a traditional silvopastoral technique practiced across wide areas of the northern Zagros mountains, a unique and vast semi-arid forest area with a strong cultural and ecological significance. So far, the effects of pollarding on tree structure in terms of DBH (diameter at breast height)~H (height) relationships within the typical pollarding cycle, which often lasts 4 years, has not been scientifically described. Here, we combine field inventories of DBH with H obtained from photogrammetric UAV flights for the first time to assess DBH~H relationships within this system. We conducted the research at six pollarded forest sites throughout the Northern Zagros. The sampling encompassed all three main species of coppice oak trees. In the case of multi-stem trees, we used the maximum DBH of each tree that formed a unique crown. A linear relationship between UAV and extracted H and the maximum DBH of pollarded trees explained a notable part of the variation in maximum DBH (R2 = 0.56), and more complex and well-known nonlinear allometries were also evaluated, for which the accuracies were in the same range as the linear model. This relationship proved to be stable across oak species, and the pollarding stage had a notable effect on the DBH~H relationship. This finding is relevant for future attempts to inventory biomass using remote sensing approaches across larger areas in northern Zagros, as well as for general DBH estimations within stands dominated by pollarded, multi-stem coppice structures.
  • Pavement crack detection through a deep-learned asymmetric encoder-decoder convolutional neural network
    Seyed Arya Fakhri, Mehran Satari Abrovi, Hamzeh Zakeri, Alireza Safdarinezhad, Seyed Arvin Fakhri
    International Journal of Pavement Engineering, 2023
    ABSTRACTCrack detection on roads' surfaces is an important issue in pavement management, as it provides an indication of the quality of the road and its deterioration over time. Pavement cracks are one of the most common types of damage observed on roads, and they can be seen visually. Despite the fact that it does not provide immediate resolution to the issue, understanding the extent of crack damage is essential for the upkeep of roads. This paper presents a novel approach to automatically detecting pavement cracks using the orthoimage generated by a consumer-grade photogrammetric Unmanned Aerial Vehicle (UAV) and a deep learning algorithm. We used an autoencoder Convolutional Neural Network (CNN) to train a dataset full of challenging factors such as road lines and marks, oil and colour spots, and water stains. The model was tested on a dataset of RGB patches of different patterns of cracks and achieved an overall accuracy (OA) and F1 score of about 0.98. The results demonstrate the effectiveness of the proposed method in accurately detecting pavement cracks in challenging real-world conditions. This approach provides an efficient and cost-effective solution for pavement crack detection, that can be used for measuring the road's quality and monitoring it.KEYWORDS: Pavement cracksdeep learningCNNorthoimageUAVpavement management system AcknowledgmentsThe authors are grateful to Dr. Masood Varshosaz, Dr. Mohammad Saadatseresht, and Mr. Ali Mahdinezhad Gargari for their assistance with UAV imaging and data collection.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availabilityThe dataset generated and analyzed during the current study are available from the corresponding author on reasonable request.Correction StatementThis article has been republished with minor changes. These changes do not impact the academic content of the article.
  • Estimation of heavy metal concentrations (Cd and Pb) in plant leaves using optimal spectral indicators and artificial neural networks
    Seyed Arvin Fakhri, Mohammad Javad Valadan Zoej, Alireza Safdarinezhad, Parvin Yavari
    Environmental Science and Pollution Research, 2022
  • Improved Road Detection Algorithm Based on Fusion of Deep Convolutional Neural Networks and Random Forest Classifier on VHR Remotely-Sensed Images
    Seyed Arvin Fakhri, Reza Shah-Hosseini
    Journal of the Indian Society of Remote Sensing, 2022
  • A novel vegetation index-based workflow for semi-arid, sparse woody cover mapping
    Seyed Arvin Fakhri, Sajad Sayadi, Hamed Naghavi, Hooman Latifi
    Journal of Arid Environments, 2022
  • ENHANCING CONTRAST OF IMAGES TO IMPROVE GEOMETRIC ACCURACY OF A UAV PHOTOGRAMMETRY PROJECT
    S. Motayyeb, S. A. Fakhri, M. Varshosaz, S. Pirasteh
    International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences ISPRS Archives, 2022
    In recent years, Unmanned Aerial Vehicles (UAVs) have become popular tools in mapping applications. In such applications, the image motion, bad lighting effects, and poor texture all directly affect the quality of the derived tie points, which in turn imposes constraints on image extraction and may lead to a low accuracy point cloud. This paper proposes a contrast enhancement technique to improve the accuracy of a photogrammetric model created using UAV images. The luminance component (Y) in the YIQ color space is normalized using the sigmoid function, and the low contrast images are enhanced using the Contrast-Limited Adaptive Histogram Equalization (CLAHE) on the luminosity component. To evaluate the proposed method, three-dimensional models were created using images acquired by the Phantom 4 Pro UAV in three distinct places and at altitudes of 20, 40, 60, 80, and 90 meters. The results showed that enhancing the contrast of images increased the number of tie points and reduced reprojection error by approximately 10%. It also improved the resolution of the digital elevation model by approximately 2cm/pixel while greatly improving the texture and quality with respect to that developed using the original images.
  • A consumer grade uav-based framework to estimate structural attributes of coppice and high oak forest stands in semi-arid regions
    Seyed Arvin Fakhri, Hooman Latifi
    Remote Sensing, 2021
  • Investigating the effect of PSO algorithm on reducing control points in camera calibration
    S. A. Fakhri, S. A. Fakhri
    International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences ISPRS Archives, 2019
  • Road crack detection using gaussian/prewitt filter
    S. A. Fakhri, S. A. Fakhri, M. Saadatseresht
    International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences ISPRS Archives, 2019
  • An optimized Enhanced Vegetation Index for Sparse Tree Cover Mapping across a Mountainous Region
    Seyed Arvin Fakhri, Sajad Sayadi, Hooman Latifi, Siddhartha Khare
    2019 IEEE International Workshop on Metrology for Agriculture and Forestry Metroagrifor 2019 Proceedings, 2019

RECENT SCHOLAR PUBLICATIONS

  • Four decades of spatio-temporal trends in Miankaleh Wetland´ s water body and vegetation as revealed by remote sensing time series
    N Arij, H Latifi, A Fakhri, R Esmaili
    Ecological Informatics, 103374 , 2025
    2025
    Citations: 6
  • Identifying Factors Influencing Wildfires in Khorramabad's Natural Areas Using a Decision Tree Model
    S Naserirad, H Naghavi, H Pourghasemi, A Fakhri
    Desert Ecosystem Engineering 14 (46), 1-16 , 2025
    2025
  • Introducing a computationally light approach to estimate forest height and fractional canopy cover from Sentinel-2 data
    A Fakhri, H Latifi, KM Samani, FE Fassnacht
    Journal of Arid Environments 228, 105343 , 2025
    2025
    Citations: 3
  • Improving the accuracy of forest structure analysis by consumer-grade UAV photogrammetry through an innovative approach to mitigate lens distortion effects
    A Fakhri, H Latifi, K Mohammadi Samani, FE Fassnacht
    Remote Sensing 17 (3), 383 , 2025
    2025
    Citations: 5
  • CaR3DMIC: A novel method for evaluating UAV-derived 3D forest models by tree features
    A Fakhri, H Latifi, KM Samani, FE Fassnacht
    ISPRS Journal of Photogrammetry and Remote Sensing 208, 279-295 , 2024
    2024
    Citations: 6
  • Pavement crack detection through a deep-learned asymmetric encoder-decoder convolutional neural network
    SA Fakhri, M Satari Abrovi, H Zakeri, A Safdarinezhad, A Fakhri
    International Journal of Pavement Engineering 24 (1), 2255359 , 2023
    2023
    Citations: 17
  • Combination of UAV photogrammetry and field inventories enables description of height–diameter relationship within semi-arid silvopastoral systems
    A Fakhri, H Latifi, K Mohammadi Samani, Z Shakeri, H Naghavi, ...
    Remote Sensing 15 (21), 5261 , 2023
    2023
    Citations: 7
  • Estimation of heavy metal concentrations (Cd and Pb) in plant leaves using optimal spectral indicators and artificial neural networks
    A Fakhri, MJ Valadan Zoej, A Safdarinezhad, P Yavari
    Environmental Science and Pollution Research 29 (50), 76119-76134 , 2022
    2022
    Citations: 8
  • Improved road detection algorithm based on fusion of deep convolutional neural networks and random forest classifier on VHR remotely-sensed images
    A Fakhri, R Shah-Hosseini
    Journal of the Indian Society of Remote Sensing 50 (8), 1409-1421 , 2022
    2022
    Citations: 11
  • A novel vegetation index-based workflow for semi-arid, sparse woody cover mapping
    A Fakhri, S Sayadi, H Naghavi, H Latifi
    Journal of Arid Environments 201, 104748 , 2022
    2022
    Citations: 17
  • A consumer grade UAV-based framework to estimate structural attributes of coppice and high oak forest stands in semi-arid regions
    A Fakhri, H Latifi
    Remote Sensing 13 (21), 4367 , 2021
    2021
    Citations: 16
  • An optimized enhanced vegetation index for sparse tree cover mapping across a mountainous region
    SA Fakhri, S Sayadi, H Latifi, S Khare
    2019 IEEE International Workshop on Metrology for Agriculture and Forestry … , 2019
    2019
    Citations: 8
  • Road crack detection using gaussian/prewitt filter
    SA Fakhri, M Saadatseresht
    The International Archives of the Photogrammetry, Remote Sensing and Spatial … , 2019
    2019
    Citations: 19
  • Investigating the Effect of Pso Algorithm on Reducing Control Points in Camera Calibration
    SA Fakhri
    The International Archives of the Photogrammetry, Remote Sensing and Spatial … , 2019
    2019
    Citations: 1
  • Economic Appraisal of the Rapid Catalytic Cracking Development Scheme for Municipal Solid Waste
    M RiyaziNejad, SA Fakhri, SM Moosavirad
    Environmental Energy and Economic Research 2 (4), 237-249 , 2018
    2018
    Citations: 1

MOST CITED SCHOLAR PUBLICATIONS

  • Road crack detection using gaussian/prewitt filter
    SA Fakhri, M Saadatseresht
    The International Archives of the Photogrammetry, Remote Sensing and Spatial … , 2019
    2019
    Citations: 19
  • Pavement crack detection through a deep-learned asymmetric encoder-decoder convolutional neural network
    SA Fakhri, M Satari Abrovi, H Zakeri, A Safdarinezhad, A Fakhri
    International Journal of Pavement Engineering 24 (1), 2255359 , 2023
    2023
    Citations: 17
  • A novel vegetation index-based workflow for semi-arid, sparse woody cover mapping
    A Fakhri, S Sayadi, H Naghavi, H Latifi
    Journal of Arid Environments 201, 104748 , 2022
    2022
    Citations: 17
  • A consumer grade UAV-based framework to estimate structural attributes of coppice and high oak forest stands in semi-arid regions
    A Fakhri, H Latifi
    Remote Sensing 13 (21), 4367 , 2021
    2021
    Citations: 16
  • Improved road detection algorithm based on fusion of deep convolutional neural networks and random forest classifier on VHR remotely-sensed images
    A Fakhri, R Shah-Hosseini
    Journal of the Indian Society of Remote Sensing 50 (8), 1409-1421 , 2022
    2022
    Citations: 11
  • Estimation of heavy metal concentrations (Cd and Pb) in plant leaves using optimal spectral indicators and artificial neural networks
    A Fakhri, MJ Valadan Zoej, A Safdarinezhad, P Yavari
    Environmental Science and Pollution Research 29 (50), 76119-76134 , 2022
    2022
    Citations: 8
  • An optimized enhanced vegetation index for sparse tree cover mapping across a mountainous region
    SA Fakhri, S Sayadi, H Latifi, S Khare
    2019 IEEE International Workshop on Metrology for Agriculture and Forestry … , 2019
    2019
    Citations: 8
  • Combination of UAV photogrammetry and field inventories enables description of height–diameter relationship within semi-arid silvopastoral systems
    A Fakhri, H Latifi, K Mohammadi Samani, Z Shakeri, H Naghavi, ...
    Remote Sensing 15 (21), 5261 , 2023
    2023
    Citations: 7
  • Four decades of spatio-temporal trends in Miankaleh Wetland´ s water body and vegetation as revealed by remote sensing time series
    N Arij, H Latifi, A Fakhri, R Esmaili
    Ecological Informatics, 103374 , 2025
    2025
    Citations: 6
  • CaR3DMIC: A novel method for evaluating UAV-derived 3D forest models by tree features
    A Fakhri, H Latifi, KM Samani, FE Fassnacht
    ISPRS Journal of Photogrammetry and Remote Sensing 208, 279-295 , 2024
    2024
    Citations: 6
  • Improving the accuracy of forest structure analysis by consumer-grade UAV photogrammetry through an innovative approach to mitigate lens distortion effects
    A Fakhri, H Latifi, K Mohammadi Samani, FE Fassnacht
    Remote Sensing 17 (3), 383 , 2025
    2025
    Citations: 5
  • Introducing a computationally light approach to estimate forest height and fractional canopy cover from Sentinel-2 data
    A Fakhri, H Latifi, KM Samani, FE Fassnacht
    Journal of Arid Environments 228, 105343 , 2025
    2025
    Citations: 3
  • Investigating the Effect of Pso Algorithm on Reducing Control Points in Camera Calibration
    SA Fakhri
    The International Archives of the Photogrammetry, Remote Sensing and Spatial … , 2019
    2019
    Citations: 1
  • Economic Appraisal of the Rapid Catalytic Cracking Development Scheme for Municipal Solid Waste
    M RiyaziNejad, SA Fakhri, SM Moosavirad
    Environmental Energy and Economic Research 2 (4), 237-249 , 2018
    2018
    Citations: 1
  • Identifying Factors Influencing Wildfires in Khorramabad's Natural Areas Using a Decision Tree Model
    S Naserirad, H Naghavi, H Pourghasemi, A Fakhri
    Desert Ecosystem Engineering 14 (46), 1-16 , 2025
    2025