NDVI Based Change Analysis of Vegetation Cover in Ordu Province

ARCHITECTURAL SCIENCES AND SUSTAINABLE APPROACHES: URBAN RESILIENCE Editors Prof. Dr. Ömer ATABEYOĞLU Prof. Dr. Ertan DÜZGÜNEŞ October 15, 2025 Copyright © 2025 by İKSAD publishing house All rights reserved. No part of this publication may be reproduced, distributed or transmitted in any form or by any means, including photocopying, recording or other electronic or mechanical methods, without the prior written permission of the publisher, except in the case of brief quotations embodied in critical reviews and certain other noncommercial uses permitted by copyright law. Institution of Economic Development and Social Researches (The Licence Number of Publicator: 2014/31220) TÜRKİYE TR: +90 342 606 06 75 USA: +1 631 685 0 853 E mail: [email protected] www.iksadyayinevi.com It is responsibility of the author to abide by the publishing ethics rules. Iksad Publications – 2025© Architectural Sciences and Sustainable Approaches: Urban Resilience ISBN: 978-625-378-337-2 Cover Design: Prof. Dr. Ertan DÜZGÜNEŞ October 15, 2025 Ankara / Türkiye Size = 16x24 cm PREFACE Dear Professors and Colleagues, We are pleased bring to life that Architectural Sciences and Sustainable Approaches: Urban Resilience, which was published as an e-book by IKSAD Publishing House with the editors Prof. Dr. Ömer ATABEYOĞLU and Prof. Dr. Ertan DÜZGÜNEŞ. This book project, entitled “Architectural Sciences and Sustainable Approaches: Urban Resilience,” aims to address sustainability-oriented approaches to urban resilience from theoretical, methodological, and practical perspectives. The volume seeks to establish a multi-layered platform of discussion, ranging from the scale of individual buildings to the entirety of the urban fabric. 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In addition, we would like to express our sincere appreciation to Prof. Dr. Atila GÜL, the book coordinator of IKSAD Publishing House, for his guidance and support throughout the publication process. We hope that our book ‘Architectural Sciences and Sustainable Approaches: Urban Resilience’ will be helpful to the readers. Best regards. 15.10.2025 EDITORS Prof. Dr. Ömer ATABEYOĞLU Prof. Dr. Ertan DÜZGÜNEŞ EDITORS Prof. Dr. Ömer ATABEYOĞLU Prof. Dr. Ertan DÜZGÜNEŞ AUTHORS The authors were listed in alphabetical order Alper ÇABUK Ayça GÜLTEN Ayşe ÖZYETGİN ALTUN Ayşe Özge ŞİMŞEK SOYSAL Ayşegül TANRIVERDİ KAYA Demet EROL Deniz DEMİRARSLAN Ebru Vesile ÖCALIR Eda ŞENTÜRK Elif Kübra ÖZTÜRK Emine BAYDAN Esra KESKİN Feran AŞUR Feyza Sena ŞENOCAK Filiz KARAKUŞ Furkan AKDEMİR Gencay ÇUBUK Gülşah BİLGE ÖZTÜRK Halil DUYMUŞ Hamza ALTAŞ Hande AKARCA İnci OLGUN Kemal Mert ÇUBUKÇU Kumru ÇILGIN Mehmet Akif IRMAK Mehmet Emin DAŞ Mehtap ÖZENEN KAVLAK Merve ALICI AKA Mesut GÜZEL Muhammed Akif AÇIKGÖZ Muhammed Emir GÖRAL Murat YEŞİL Olcay Türkan YURDUGÜZEL Özge DÜZGÜN EREKİNCİ Pervin YEŞİL Rabia Nurefsan ACIKGOZ Sedef ŞENDOĞDU Seher Simay KUŞOĞLU Serim DİNÇ Sevilay YILDIZ Sinem SEYHAN Şevval ERGİNDOĞAN Şuheda ALTUNOK Temuçin Göktürk SEYHAN Tuba Nur OLĞUN Tuna BATUHAN Ufuk Teoman AKSOY Yusuf Eminoğlu REVIEWER LIST The authors were listed in alphabetical order Aslıhan TIRNAKÇI Nevşehir Hacı Bektaş Veli University Atila GÜL Süleyman Demirel University Ayşe Kalaycı ÖNAÇ İzmir Katip Çelebi University Bige ŞİMŞEK İLHAN İstanbul Medipol University Burcu YILMAZEL Eskişehir Technical University Eda KOÇAK Siirt University Ekrem BAHADIR Ankara Yıldırım Beyazıt University Elif KUTAY KARAÇOR İstanbul Technical University Hakan ARSLAN Ondokuz Mayıs University Hilal TURGUT Karadeniz Technical University Meliha AKLIBAŞINDA Nevşehir Hacı Bektaş Veli University Murat AKTEN Süleyman Demirel University Nihan Sümeyye GÜNDOĞDU Atlas University Okan Murat DEDE Amasya University Ömer Lütfü ÇORBACI Recep Tayyip Erdoğan University Selcen Nur Erikci Çelik Beykoz University Sibel AKTEN Isparta Unıversıty Of Applıed Scıences Sinem ÖZDEDE Pamukkale University Şeyma ŞENGÜR Ordu University Turgut KALAY Kütahya Dumlupınar University Tendü Hilal GÖKTUĞ Aydın Adnan Menderes University 569 1. Introduction Vegetation cover is a biophysical variable that represents the distribution of living biomass on the land surface. It has a direct impact on the energy balance, carbon cycle, and water cycle of Earth's systems (Jones & Vaughan, 2010). Vegetation absorbs carbon from the atmosphere through photosynthesis, contributes to energy conversion processes through evaporation, and helps preserve the physical land structure by limiting soil erosion (Bahre, 1991). Monitoring changes in vegetation provides an analytical basis for assessing environmental processes such as desertification, land degradation, urbanization and land use change (Bai et al., 2008; Yengoh et al., 2015). Performing such assessments requires the integrated use of high-resolution satellite data and numerical approaches that enable the analysis of these data (Verón, Paruelo & Oesterheld, 2006). At this point, Remote Sensing (RS) technologies provide a valuable data source for monitoring environmental changes, while Geographic Information Systems (GIS) offer a strong infrastructure for processing, analyzing and visualizing these data (Jensen, 2009; Goodchild, 1992). The combined use of RS and GIS has become a widely used method, especially in studies aimed at assessing the environmental impacts of urbanization processes and land use changes (Moore & McCutcheon, 2022; Kapluhan, 2014). In remote sensing-based studies on vegetation, vegetation indices derived from spectral bands produce effective results in terms of spatial and temporal analyses. Among these indices, the Normalized Difference Vegetation Index (NDVI) is preferred especially for monitoring photosynthetic activity and evaluating plant vitality (Ateşoğlu, 2021; Sobrino & Julien, 2011). NDVI is a dimensionless indicator calculated 570 based on reflectance values recorded in the red (RED) and near infrared (NIR) bands and makes it possible to objectively measure vegetation density (Wang et al., 2004). NDVI data can be applied at many different scales, such as monitoring agricultural areas, monitoring changes in forest cover, and assessing urban green spaces (Ivanova, Kovaley & Soukhovolsky, 2020; Zhu et al., 2021). NDVI series obtained from multispectral satellite images reveal not only plant presence but also important ecological indicators such as vitality level and growth dynamics (Xue, Wang & Hou, 2023). In this study, it was aimed to evaluate the temporal change in vegetation cover at the spatial level by using NDVI values for the years 2016, 2019, 2022 and 2025 of Ordu province. The study area constitutes a meaningful sample for NDVI-based analyses because it is a region that attracts attention with its topographic diversity and urbanization pressure. Sentinel-2 satellite images were processed in ArcMap 10.5 environment; NDVI rasters were produced, classified and difference analyses were performed. The findings reveal temporal trends in vegetation transformation and provide spatial-thematic indicators that can be used in land use decisions. Such spatial analyses produce functional data at many planning and management levels, from the assessment of environmental impacts of urbanization processes to the protection of natural areas. Quantitative indicators provided by NDVI-based analyses contribute to the formation of a scientific infrastructure for green space management, the development of land use scenarios, and the strengthening of environmental monitoring mechanisms. Such spatial analyses, especially those performed 571 over time series, enable decision makers to interpret spatial change patterns more systematically. 2. Material and Method 2.1.Study Area and Data Sources Ordu province, where this study was conducted, is a coastal city located on the Black Sea coast in the north of Türkiye and consists of 19 districts in total (Figure 1). The city center is located between the 41° north latitude and the 37°–38° east meridians, in the Eastern Black Sea region. The province is surrounded by Giresun to the east, Samsun to the west, and Tokat and Sivas to the south. Figure 1. Study Area The topography of Ordu province is generally rugged and mountainous, with an increasing slope from the coastline towards the inland areas. The humid Black Sea climate prevailing in the province ensures regular rainfall throughout the year and allows the preservation of a high percentage of vegetation. This climatic structure supports various agricultural activities, especially hazelnuts, and shapes the natural landscape character with 572 widespread meadow-pasture areas and wide forest belts. Increasing urbanization and transportation infrastructure, particularly along the coastline, have led to significant changes in land cover in recent years. In this context, Ordu province offers a suitable sampling area for NDVIbased vegetation change analyses. Sentinel-2 10m resolution satellite images provided by the European Space Agency (ESA) were used in the analysis process. Sentinel-2 is a high spatial and temporal resolution satellite system with 13 spectral bands. For NDVI calculations, the red band (Band 4) and near-infrared band (Band 8) were chosen. The images were obtained from the Copernicus Open Access Hub platform. In order to minimize the effect of seasonal variations and increase interannual comparability in the study, satellite images of July for the years 2016, 2019, 2022 and 2025 with cloud cover below 5% and suitable atmospheric conditions were preferred. 2.2. Image Processing and NDVI Calculation Process Satellite imagery was processed using ArcMap 10.5 software. Multiple scenes from the same year were combined using raster mosaicing and then clipped based on the administrative boundaries of Ordu province. The Raster Calculator tool was used to generate NDVI rasters, using the following equation: NDVI = (NIR - RED) / (NIR + RED) Based on this formula, NDVI rasters were created for each year, and then normalized to range from -1 to +1. NDVI values were divided into six categories according to the classification system described below. This classification is based on the ranges recommended by Aquino et al. (2018) (Table 1). 573 Table 1. NDVI Value Ranges and Vegetation Coverage Ratios (Aquino et al., 2018) NDVI Value Range Vegetation Cover Rate NDVI ≤ 0 Bare soil or water surface 0 < NDVI ≤ 0.2 Very low 0.2 < NDVI ≤ 0.4 Low 0.4 < NDVI ≤ 0.6 Medium-Low 0.6 < NDVI ≤ 0.8 Medium-High 0.8 < NDVI ≤ 1 High 2.3. Temporal Comparative NDVI Analysis NDVI rasters from four different years (2016, 2019, 2022, and 2025) were analyzed to statistically compare temporal changes. Each raster dataset was processed in ArcMap 10.5 software, and pixel-based mean, minimum (min), maximum (max), and standard deviation (std. dev) values were calculated. These operations were obtained through the “Raster Properties > Statistics” tab, which is applied directly to raster data, and were also supported by the Zonal Statistics as Table tool when necessary. NDVI rasters for each year were reclassified with the Reclassify tool according to previously determined classification thresholds (Aquino et al., 2018). Classified rasters were then converted to vector data format using the Raster to Polygon tool and the area amount of each class was calculated in hectares (ha) with the “Calculate Geometry” tool. As a result of this analysis process, area sizes corresponding to six NDVI classes (bare soil/water, very low, low, medium-low, medium-high, high) 574 were obtained for each year, and thus it was determined how the vegetation cover changed spatially and quantitatively over the four-year period. 2.4. Temporal Comparative NDVI Analysis To more clearly observe temporal changes in the spatial domain, change detection was applied to NDVI rasters. Using 2016 as a reference, the Raster Calculator tool was used to generate pixel-based difference rasters for 2019, 2022, and 2025, respectively. The formula used is as follows: NDVI Difference=NDVIyear−NDVI2016 This process created three different difference rasters: NDVI_2019 – NDVI_2016 NDVI_2022 – NDVI_2016 NDVI_2025 – NDVI_2016 The generated difference rasters were visualized based on the distribution of positive and negative values, and areas of increase and decrease were clearly mapped using a color scale. For numerical interpretation, statistical summary values were extracted from these rasters, and the mean difference, variance, and distribution density were calculated. In addition, the difference rasters were reclassified with the Reclassify tool, and areas of increase (positive difference), unchanged (neutral difference) and decrease (negative difference) were determined; the number of pixels and area amounts falling into each category were calculated and comparative analysis tables were created. 2.5. Statistical Analysis Analysis of variance (ANOVA) was first applied to determine significant differences between years in NDVI averages. However, due to the assumption of normality not being met, the Kruskal-Wallis test, a non- 575 parametric method, was preferred. This analysis statistically assessed the differences in distribution between NDVI values across four different years. Statistical analyses were performed using the Jamovi 2.3.28 program. 3. Findings and Discussion In this study, spatial and temporal changes in NDVI values for Ordu province were analyzed for different years. NDVI values for the study area for the years 2016, 2019, 2022, and 2025 were examined and their distributions were compared. The minimum, maximum, mean, and standard deviation values of NDVI values for the years are presented in Table 2. The 2016 average NDVI was 0.571, the highest compared to other years. In contrast, the average NDVI values in 2019 and 2025 were found to be 0.485 and 0.490, respectively, and the lowest average NDVI value was measured at 0.389 in 2022. Standard deviation values were within a similar range for all years, indicating that the changes were relatively homogeneous. Table 2. Basic Statistics of NDVI Values by Year Year Min. Max. Average Standard Deviation 2016 -1 0.823 0.571 0.117 2019 -1 1.000 0.485 0.116 2022 -1 1.000 0.389 0.102 2025 -0.339 0.769 0.490 0.110 The Kruskal-Wallis test (χ² = 0.179, p = 0.981) revealed no statistically significant difference between the NDVI averages of different years. However, the boxplot shown in Figure 2 shows that there are variations in 576 the NDVI distribution across years, with values being more concentrated in certain ranges, particularly in 2019 and 2022. Figure 2. Boxplot Chart of NDVI Values by Year To assess these general distribution trends in more detail, surface areas related to land cover changes across years, based on NDVI classes, were calculated and presented comparatively. The distribution of NDVI classes in the study area in hectares (ha) by year is presented in Table 3. These data provide the opportunity to quantitatively assess temporal changes in vegetation density. While the lowest NDVI class, NDVI ≤ 0, covered a very limited area in all years, NDVI values in the range of 0.4–0.6 stood out as the most dominant class in 2019 and 2025. It is particularly noteworthy that the class in the range of 0.2–0.4 experienced a significant increase in surface area in 2022. While the dense vegetation area with an NDVI range of 0.6–0.8 was measured at approximately 336,441.53 ha in 2016, this value decreased dramatically to 8,224.97 ha in 2022, and a partial recovery reached 67,236.05 ha in 2025. The highest class, the 0.8– 577 1 range, remained negligible in all years, and no surface area belonging to this class was detected in 2025. Table 3. Area Distribution According to NDVI Classification (ha) NDVI Class 2016 2019 2022 2025 NDVI ≤ 0 1,563.51 1,513.67 953.68 1,544.63 0 < NDVI ≤ 0.2 9,395.25 19,585.05 29,409.06 13,028.05 0.2 < NDVI ≤ 0.4 42,577.88 87,864.86 282,076.16 81,055.44 0.4 < NDVI ≤ 0.6 202,946.29 426,321.45 272,260.56 430,060.30 0.6 < NDVI ≤ 0.8 336,441.53 57,639.20 8,224.97 67,236.05 0.8 < NDVI ≤ 1 0.01 0.24 0.04 — In line with these quantitative findings, maps visualizing the distribution of NDVI classes in the spatial plane are presented in Figure 3 and the temporal change patterns are detailed. Figure 3. Time Series-Based NDVI Distribution Maps (2016–2025) 578 Following the general distribution trends of NDVI values over the years, in order to reveal this change more clearly and comparatively, difference analyses were conducted with NDVI data from 2019, 2022, and 2025, using the 2016 NDVI raster data as a reference. The difference maps obtained as a result of the raster calculations visualize in detail the reflection of increases or decreases in vegetative cover on the spatial plane (Figure 4). Figure 4. NDVI Difference Rasters (2019–2016, 2022–2016, 2025–2016) According to these difference maps: • The 2019–2016 difference ranges between +0.992722 and –0.982521, indicating an increase in vegetation density in some regions during this period, while significant decreases are noted in others. 585 Sobrino, J. A., & Julien, Y. (2011). Global trends in NDVI-derived parameters obtained from GIMMS data. International journal of remote sensing, 32(15), 4267-4279. Verón, S. R., Paruelo, J. M., & Oesterheld, M. (2006). Assessing desertification. Journal of arid environments, 66(4), 751-763. Wang, Q., Tenhunen, J., Dinh, N. Q., Reichstein, M., Vesala, T., & Keronen, P. (2004). 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Use of the Normalized Difference Vegetation Index (NDVI) to assess Land degradation at multiple scales: current status, future trends, and practical considerations. Springer. Zhu, X., Xiao, G., Zhang, D., & Guo, L. (2021). Mapping abandoned farmland in China using time series MODIS NDVI. Science of the Total Environment, 755, 142651. 586 Eda ŞENTÜRK E-mail: [email protected] Educational Status License:Ordu University Degree: Ordu University-Continues Res. Assist. Rabia Nurefsan AÇIKGÖZ E-mail:[email protected] Educational Status License: Eskişehir Osmangazi University Degree:Ordu University Doctorate:Ordu University-Continues Prof. Dr. Murat YEŞİL E-mail: [email protected] Educational Status License:Atatürk University Degree:Atatürk University Doctorate:Atatürk University