Mapping Green space, Roads & Built-up Areas with Optical Remote Sensing and Polarimetric SAR (A Novel Approach)

Mapping Green space, Roads & Built-up Areas with Optical Remote Sensing and Polarimetric SAR (A Novel Approach) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mapping Green space, Roads & Built-up Areas with Optical Remote Sensing and Polarimetric SAR (A Novel Approach) Majid Aghlmand, Mehmet İnanç Onur, Reza Talaei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4850131/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Recent progress in remote sensing and Geographical Information System (GIS) has revolutionized the research studies on urban space. Satellites that image on daily basis and cloud platforms that increase mathematical modelling precision and speed have given rise to extraction of high-quality data. In this study, we propose a novel approach in extracting information on green space, buildings, and roads in Ankara and Eskişehir cities in Türkiye. In this approach, optical and SAR images are utilized. Modelling is also fulfilled in google earth engine cloud platform using machine learning algorithm. We show how optical and SAR images with varying indexes may lead to a Land use/Land cover map with the highest overall accuracy (98.94 for Ankara and 93.97 for Eskişehir). Additionally, techniques offered in this study can help to extract other classes other than the present study ones. Land use/Land cover map is the basis of many studies and can benefit urban management, planning, urban policy making, protection and renovation, and environmental sustainment. remote sensing in urban studies google earth engine machine learning algorithms land use/land cover urban form Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Urbanization and quick increase of population have negatively affected urban space. Each city has its own challenges and evaluation of existing conditions always take priority when implementing a program. Technology helps to constantly monitor cities and shortens the process of data extraction for critical conditions. Being cognizant of the conditions, examining the factors, and trying to plan and resolve them make it possible to offer better urban services, increase urban life quality, and make urban space livable. Recent advances in remote sensing and GIS have revolutionized the access to data, regional modelling, and spatial analysis. New satellites can take images of cities daily. Moreover, cloud systems that have been devised to process these data are able to analyze and examine data quickly and accurately. Using remote sensing satellites is one the methods of data collection for urban studies. Optical and SAR satellites have been employed in different studies. Landsat, Sentinel-2, and PlanetScope satellites are optical and Sentinel-1 satellite is a SAR satellite. Landsat-series satellite has been monitoring the earth for 50 years (since 1972). Its images are the richest archive of the earth and one of its purposes is monitoring the regional changes over time. The information from this satellite are the basis of many studies regarding forest monitoring (Townshend et al., 2012 ; Banskota et al., 2014 ), change detection (Awty-Carroll et al., 2019 ; Hemati et al., 2021 ), water quality (Peterson, Sagan and Sloan, 2020 ; Al-Shaibah et al., 2021 ), land surface temperature (Balew and Korme, 2020 ; Ermida et al., 2020 ) and mapping vegetation (Schwieder et al., 2016 ; Peterson, Sagan and Sloan, 2020 ). The images from Landsat 8 satellite have also been used in urban studies such as urban heat island (Elmes et al., 2020 ), ecological evaluation of urban heat island (Dissanayake, Kurugama and Ruwanthi, 2020 ), quantification of carbon sequestration by urban forest (Uniyal et al., 2022 ), extracting urban impervious surfaces (Deliry, Avdan and Avdan, 2021 ),urban green infrastructure health assessment (Chang et al., 2021 ), and producing land use/land cover (LULC) maps (Nasiri et al., 2022 ; Theres and Selvakumar, 2022 ). Satellites differ in terms of spatial, spectral, radiometric, and temporal resolution. Landsat 8 satellite has 16-day temporal resolution, 30-meter spatial resolution, and 15-meter panchromatic band. Compared with Landsat 8 satellite, the Sentinel-2 satellite has 10-day temporal resolution that becomes 5 days because of using two repeat cycle satellites. This satellite also has bands with 10, 20, and 60-meter resolutions. Data produced by this satellite are typically used in studies regarding climate change, land monitoring, emergency, management, and security (Sentinel-2 User Handbook, 2015; Gibson et al., 2020 ; Mngadi, Odindi and Mutanga, 2021 ). Some studies show that Sentinel-2 satellite performs better than Landsat 8 satellite (J. Wang et al., 2020 ; Q. Wang et al., 2020 ; Ghayour et al., 2021 ). Landsat 8 satellite and Sentinel-2 satellite images can be combined (Mandanici and Bitelli, 2016 ). Using the images of these two satellites can remarkably increase the phenological variation and, consequently, precision of maps (Nasiri et al., 2022 ; Pouya, Aghlmand and Karsli, 2022 ). PlanetScope satellite has better temporal resolution than Landsat 8 and Sentinel-2 satellites. This satellite, which consists of 130 small satellites, takes images of the earth surface every day and may even take images of some regions more than once. What distinct this satellite from Landsat 8 and Sentinel-2 satellites is the 3-meter resolution of its images (Planet Team, 2018 ). Its images can play a critical role in urban studies. However, few studies have been conducted in this regard. sentinel − 1 satellite offers ordinary and systematic data to monitor sea and land, emergency reaction, climate change, and security. Two underlying limitations of optical satellites are lack of imaging at night and bad weather conditions (existence of cloud) (Sherpa and Shirzaei, 2022 ). Sentinel-1 satellite does not have these restrictions. Combining its images with those of Sentinel-2 and Landsat 8 satellites has been utilized in different studies on mapping of crop types and crop sequences (Blickensdörfer et al., 2022 ), eliminating leaf area index and aboveground biomass of grazing pastures (Wang et al., 2019 ), surface moisture and vegetation cover analysis (Urban et al., 2018 ), urban change detection (Benedetti, Picchiani and Del Frate, 2018 ; Hafner, 2022 ), and river water mapping (Liu et al., 2022 ) that provided higher quality results. As yet Sentinel-1 satellite images alone have not been used in producing land use maps. However, many studies have combined them with optical satellites images such as Landsat 8 and Sentinel-2 that has led to increased accuracy of the maps (Brandmeier et al., 2022 ; De Luca et al., 2022 ). Regarding monitoring urban growth, extensive studies have been carried out using remote sensing satellites images and GIS. Most of these studies have been based on Landsat series images with 30-meter resolution and deal with urban growth. In these studies, archived images are received to examine changes and growth `extent of urban space to propose strategies for planning to associative officials (Dhanaraj and Angadi, 2020 ; Roy and Kasemi, 2021 ; Wang, Murayama and Morimoto, 2021 ; Elhamdouni, Arioua and Karaoui, 2022 ). Most of the studies have generally dealt with urban growth issue because of restrictions such as lack of access to images with high spatial resolution and platforms with high processing power and have not examined the growth of roads, paths, and urban green space separately. The results obtained with these images are based on their spatial resolution. Thus, small streets and paths cannot be mapped. Additionally, green spaces that have a smaller area than spatial resolution do not appear in the results. For example, in Landsat 8 satellite, the area of each pixel is 900 square meters. Thus, the three main classes of green space, roads, and buildings are not clear well in these images. In Fig. 1 , which shows one neighborhood of Eskişehir city in Türkiye, some parts of urban space that has low-area buildings, roads, and green space can be observed. In Fig. 1 , three images of PlanetScope satellite with 3-meter resolution (a), Sentinel-2 satellite with 10-meter resolution (b), and Landsat 8 satellite with 30-meter resolution (c) can be seen. The main image position in this figure has been shown by red. This figure clearly indicates the spatial resolution of these three satellites. In the image with 3-meter resolution, different elements such as green space, roads, and buildings can be easily seen; but in image b, separation of classes is hard and in image c, is impossible. Using new satellites with better spatial, spectral, radiometric, and temporal resolution has helped to gain more information on urban space to produce more detailed LULC maps. Although images with higher resolution produce more details of urban space, their spatial resolution is low, and it is necessary to combine the images of satellites to reach better results. Combining images can also increase the bulk of data and process of them. All the process needs to be done in cloud platforms. Google earth engine (GEE) cloud platform can be referred to as one of the most important progress in GIS and remote sensing area that not only offer GIS data but also makes it possible to do different analyses on them. GEE data do not require pre-processing. Moreover, not only data of its database can be used but also those of other sources can be uploaded. This, in turn, makes it possible to analyze a great deal of data in a short time simultaneously. Another advantage of GEE platform is the existence of strong libraries for processing of satellite images. These libraries offer important machine learning algorithms for classification and production of LULC maps. In this research, we aimed to produce a 3-meter LULC map and then extract data from green space, roads, and buildings. The study was conducted by using optical images (PlanetScope and Sentinel-2 ), varying indexes estimated from the bands of these two satellites, and also images of SAR satellite in GEE cloud platform with machine learning algorithm. The study was done on two cities in Türkiye, that is, Ankara and Eskişehir to examine data and offer the best combination of data to achieve the study purposes. Materials and Method Research Area Ankara and Eskişehir, as two cities of Türkiye, were selected as the research area of this study. They are located in the central Anatolian region of Türkiye (Fig. 2 ). According to Koppen-Geiger climate taxonomy, the studied area has Mediterranean summer warm and dry weather with cool winter. Ankara was selected as the capital of Türkiye in 1923. Its population was 2,747,325 in 1980 and is 5,747,325 now. In terms of population, Ankara is the second city of Türkiye and 57th of the world. Eskişehir population was 543,802 in 1980 and 898,369 now. It is ranked as the 25th city of Türkiye in terms of population. Ankara and Eskişehir are different from each other in social changes, environment, economic conditions, technology, planning, urban housing and policy, and transportation. This has caused them to have thoroughly different urban forms and spatial features. Methodology Figure 3 shows the process of the present study. It was conducted in two stages: data collection and criterion, and data processing. A detailed description of the two stages are provided in the following. Data collection and creation In this study, Sentinel-2, Sentinel-1, and PlanetScope images were utilized. Image selection was done based on the normalized difference vegetation index (NDVI). GEE provides the best possibility for extracting NDVI time series so that the months when this index is minimum, and maximum would be identified. In the study, the dataset of Ankara city was set based on the month when NDVI index was maximum (August) and that of Eskişehir was based on the month when the NDVI index was minimum (January). Sentinel-2 and senbtinel-1 images are accessible in pre-processed forms in GEE platform. Images of PlanetScope were also downloaded in Tiff file and were then uploaded in GEE. The image of Sentinel-2 was set with 10 bands and resolutions of 10 and 20 meters in the platform. Further, NDVI, the normalized difference water index (NDWI), the Pigment Specific Simple Ratio (PSSR), Urban Index (Ui) and the Normalized Difference Built-up Index (NDBI) indexes from Sentinel-2 were estimated in GEE and were set in dataset. NDWI, NDVI, the Green Normalized Difference Vegetation Index (GNDVI) and PSSR indexes from PlanetScope satellite and two images from Sentinel-1 were also opted with ascending (A) and descending (D) directions. Each of the images had vertical transmit/horizontal receive (VH) and vertical transmit/vertical receive (VV) bands that were put in the dataset as well. Thus, 27 bands were included in the dataset 15 of which related to Sentinel-2, 8 ones related to PlanetScope, and 4 ones were related to Sentinel-1 (Fig. 3 and Table 1 ). Table 1 Research dataset No. Satellite Combination The number of bands 1 PlanetScope B4, B3, B2 & B1 4 2 PlanetScope NDVI, NDWI, PSSR & GNDVI 4 3 Sentinel-2 B12, B11, B8A, B8, B7, B6, B5, B4, B3 & B2 10 4 Sentinel-2 PSSR, NDVI, NDWI, NDBI & Ui 5 5 Sentinel-1 VVA & VHA 2 6 Sentinel-1 VVD & VHD 2 Total bands 27 Data processing After preparing dataset, the process of producing LULC map via Support Vector Machines Library (LibSVM) (Chang and Lin, 2011 ) was fulfilled. For Ankara, classes Build-up 1, Build-up 2, Vegetation-1, Vegetation-2, Road, Bare land and water; and for Eskişehir classes Build-up 1, Build-up 2, Vegetation-1, Road, Bare land and water were considered. Vegetation-1 pertained to trees and vegetation-2 pertained to bushes. Build-up 1 was related to buildings with Roof tiles and Build-up 2 was related to buildings with non-Roof tile. After selecting classes, 400 points were chosen for each class using GEE. Then, the selected points were checked based on RGB picture of PlanetScope, Sentinel-2, and indexes of NDBI, NDWI, AND NDVI. 70 percent of the points was considered for modelling (training points) and 30 percent (test points) for examining the accuracy of the model (overall Kappa coefficient & accuracy). Results The result of classification is production of urban LULC map. This map was estimated with 3-meter resolution with Table 1 dataset that included images of the three above-mentioned satellite with different indexes from PlanetScope and Sentinel-2 images. It was done in GEE platform using LibSVM method. The accuracy and efficiency of the classification can be calculated through overall accuracy and Kappa coefficient. It turned out to be 98.94 and 98.34 for Ankara and 93.97 and 93.1 for Eskişehir, which are high accuracy. Table 2 Results of classification by using LibSVM method Ankara Eskişehir Overall accuracy Kappa coefficient Overall accuracy Kappa coefficient 98.94 98.34 93.97 93.1 Figure 4 is Ankara’s LULC. In this figure, two areas (a and b) have been highlighted. As the figure shows, the two classes are clearly distinct from each other. Figure 5 also displays the results of extraction of each of the classes of urban green space, roads, and buildings from LULC map. As the Figs. 4 and 5 show, in some streets of Ankara, tall trees have caused the roads class not to be clearly visible. Figures 6 and 7 also uncover the result of classification and production of LULC map for Eskişehir. They indicate that each of the classes are clearly distinct from others. The dataset of this city was based on the month when NDVI was minimum (January). The green space is the least in this month but the accuracy rate of ways (roads and streets) was high. Figure 7 clearly indicates the effect of using this method on accuracy of classification. Additionally, the urban green space in this figure is the least. Thus, the farmlands around the city are also visible in this figure. Discussion and conclusion Urban planning typically refers to activities that provide development plans for monitoring the use of urban spaces with the purpose of living conditions development and welfare of dwellers in urban areas. Urban planning plays a key role in urban dwelling and economic development of cities. It is taken as an important tool in decision making by local governments. Urban planning needs to be supported by different sciences. One of them is related to data extraction science. In order to pinpoint the current problems and needs and have a comprehensive understanding of the interactive components and their impacts, urban planners must take a wide range of information to analyze with some techniques and tools. They often obtain this information by field operations, document analysis, and typographic maps, which are costly, cumbersome, and time consuming. In recent years, remote sensing technology has considerably been used to obtain information on urban areas. Fundamental urban planning needs to include urban plan strategies, city development and expansion purposes, standards and criteria for general urban building and land uses, comprehensive transportation system, and green space system. Comprehensive urban planning has been a complicated periodic program that needs a wide range of information that is prepared partly by GIS and remote sensing. Some merits of remote sensing and GIS systems include wide cover of satellite images, multi-time and multi-range imaging, image receive at different weather conditions, being economical, and easy access. Data obtained from GIS and remote sensing can be used to examine and analyze cities natural conditions, resource distribution, roads network, and land use changes. Green space, roads, and buildings are among important elements of cities. Recent progress in remote sensing and GIS has made it possible to monitor them. New processing systems such as GEE and remote sensing satellite images have made it possible to analyze them freely and quickly. There are, however, some limitations such as technology-related constraints and weather condition-related restrictions. In urban space, we need images that have high resolution, because spatial resolution is a determinant in mapping of green space, roads, and buildings. For example, in a image with 3-meter resolution, the green space that has 9 square meter area can form a complete one pixel in satellite image and this one pixel can be a tree or a small green space in a yard, school, or street. Thus, the images of PlanetScope satellite were used in the present study as they have 3-meter resolution with four bands. Although spatial resolution is high, spectral resolution is low and it can be stated that four bands of Green, Blue, Red, and NIR are not enough for classification. Therefore, four indexes of NDVI, NDWI, PSSR, and GNDVI were also estimated and added to the dataset. Increasing dataset leads to increasing machine learning algorithms efficiency that had remarkable classification accuracy by adding bands of Sentinel-2 along with PSSR, NDVI, NDWI, NDBI, and Ui indexes and VH and VI bands of Sentinel-1 images (Table 2 ). Adding Sentinel-1 bands amended errors such as tree shadows and improved classification accuracy in vegetation classes (trees and lawn). Another positive impact of Sentinel-1 was related to extraction of data from roads. Using the ascending and descending bands helped to increase their related classification (Figs. 4 and 5 ). One of the errors that generally happen in classification is erroneous categorization of phenomena. For example, a point that has been identified as building is categorized in roads classification or a point selected as road may be placed in bare land classification. This error is the least in Ankara classification because the scope of urban green space, roads, and buildings is considerable which, in turn, increases the accuracy of classification and mapping. In small cities such as Eskişehir with smaller green space, roads, and buildings, it is more likely to see errors. Combining other data can minimize these errors. In fact, combing data and putting SAR data and different indexes can increase the features of each pixel of image. GEE cloud platform is a revolution in remote sensing and GIS system. Almost all remote sensing data are present in it in pre-processed form. Additionally, if specific data does not exist in it, it can be uploaded from other sources. Another advantage of GEE is the possibility of parallel analysis. This causes the calling of data, calculation of indexes, classification, and accuracy assessment to be fulfilled in less than 60 seconds. In this study, supervised classification offered by GEE was used. They are free to access. In addition, they can be revised as well. The other merit of this platform is the existence of aerial photos in base map and possibility of simultaneous show of varying satellites and indexes that eases the selection of training points for supervised classification in the shortest possible time. Declarations Conflict of interest disclosure The authors declare no conflict of interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution This research is related to the doctoral thesis of Majid Aghlmand, who was the second and third author of the supervisors. 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(2020) ‘Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI’, Science of the Total Environment, 707, p. 136092. Wang, Q. et al. (2020) ‘Comparative analysis of Landsat-8, Sentinel-2, and GF-1 data for retrieving soil moisture over wheat farmlands’, Remote Sensing, 12(17), p. 2708. Wang, R., Murayama, Y. and Morimoto, T. (2021) ‘Scenario simulation studies of urban development using remote sensing and GIS’, Remote Sensing Applications: Society and Environment, 22, p. 100474. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Aghlmand","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYLCCBAYGORB94AHxWhIMjMFaEkiwxiCxAWodYaDb3nzww8Mff9Lnhx1+CLTFTk63gYAWszPHkiWADsvdeDvNAKgl2djsACEtN3LMGMBaZieAtBxI3EZQy/3330Ba0g1np38gUssNHjaQlgR56RxibTmTZiyRkGZsuEE6p+BAggExfjl++OHHHzZy8vKz0zd/+FBhJ0dQCxwYgFUaEKscBOQbSFE9CkbBKBgFIwoAAKxORu+KiChuAAAAAElFTkSuQmCC","orcid":"","institution":"Eskisehir Technical University","correspondingAuthor":true,"prefix":"","firstName":"Majid","middleName":"","lastName":"Aghlmand","suffix":""},{"id":341593625,"identity":"9d27eba4-d36f-4c18-8b35-6a29ca57b570","order_by":1,"name":"Mehmet İnanç Onur","email":"","orcid":"","institution":"Eskisehir Technical University","correspondingAuthor":false,"prefix":"","firstName":"Mehmet","middleName":"İnanç","lastName":"Onur","suffix":""},{"id":341593626,"identity":"af8faa7e-2d56-4323-8501-befe0d70201f","order_by":2,"name":"Reza Talaei","email":"","orcid":"","institution":"Soil Conservation and Watershed Management Research Department, Ardabil","correspondingAuthor":false,"prefix":"","firstName":"Reza","middleName":"","lastName":"Talaei","suffix":""}],"badges":[],"createdAt":"2024-08-02 18:38:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4850131/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4850131/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64621873,"identity":"63d9b9c2-76ef-402e-8985-85225d370c4e","added_by":"auto","created_at":"2024-09-16 16:46:39","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":796722,"visible":true,"origin":"","legend":"\u003cp\u003eA) PlanetScope satellite image with 3-meter spatial resolution B) Sentinel-2 satellite image with 10-meter spatial resolution C) Landsat 8 satellite image with 30-meter resolution\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4850131/v1/20dcb7f8bc467de8691d80c7.jpeg"},{"id":64621872,"identity":"c42ba914-63d8-425f-9f43-c60401ce6753","added_by":"auto","created_at":"2024-09-16 16:46:39","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":219423,"visible":true,"origin":"","legend":"\u003cp\u003eResearch area\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4850131/v1/23e89bce72ebd6554ee9bbcd.jpeg"},{"id":64621870,"identity":"af71bb40-0703-47e3-b788-f07801c24aff","added_by":"auto","created_at":"2024-09-16 16:46:39","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":204287,"visible":true,"origin":"","legend":"\u003cp\u003eResearch process\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4850131/v1/5315d2c1f0fb320d7539fc9d.jpeg"},{"id":64621877,"identity":"0b704fab-e08a-4b85-9864-2f70ccd8036c","added_by":"auto","created_at":"2024-09-16 16:46:39","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1476307,"visible":true,"origin":"","legend":"\u003cp\u003eAnkara land use/land cover\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4850131/v1/721190de8e4005a71bd1b2b0.jpeg"},{"id":64621874,"identity":"a74a25d0-49b3-4381-bb4a-d58bdc833db7","added_by":"auto","created_at":"2024-09-16 16:46:39","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":934449,"visible":true,"origin":"","legend":"\u003cp\u003ea) roads b) buildings c) urban green space\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4850131/v1/ad63bb20b3441f62283af689.jpeg"},{"id":64621876,"identity":"e8a4b8ca-2006-46af-90d4-6b5f158e8213","added_by":"auto","created_at":"2024-09-16 16:46:39","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1132646,"visible":true,"origin":"","legend":"\u003cp\u003eEskişehir LULC\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4850131/v1/a8abafd16a6d6ae05ef18b11.jpeg"},{"id":64621875,"identity":"078644a4-48be-4794-9776-35c4f5367725","added_by":"auto","created_at":"2024-09-16 16:46:39","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":912959,"visible":true,"origin":"","legend":"\u003cp\u003eEskişehir Green space and roads\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4850131/v1/9e51eedda11f7d4f2f08d1f0.jpeg"},{"id":78272446,"identity":"4d1e725a-87b5-4a0c-8103-107037eb3bd9","added_by":"auto","created_at":"2025-03-11 13:31:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6163392,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4850131/v1/d965807b-2f48-46af-b119-7003f7e30ca9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mapping Green space, Roads \u0026 Built-up Areas with Optical Remote Sensing and Polarimetric SAR (A Novel Approach)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUrbanization and quick increase of population have negatively affected urban space. Each city has its own challenges and evaluation of existing conditions always take priority when implementing a program. Technology helps to constantly monitor cities and shortens the process of data extraction for critical conditions. Being cognizant of the conditions, examining the factors, and trying to plan and resolve them make it possible to offer better urban services, increase urban life quality, and make urban space livable. Recent advances in remote sensing and GIS have revolutionized the access to data, regional modelling, and spatial analysis. New satellites can take images of cities daily. Moreover, cloud systems that have been devised to process these data are able to analyze and examine data quickly and accurately.\u003c/p\u003e \u003cp\u003eUsing remote sensing satellites is one the methods of data collection for urban studies. Optical and SAR satellites have been employed in different studies. Landsat, Sentinel-2, and PlanetScope satellites are optical and Sentinel-1 satellite is a SAR satellite. Landsat-series satellite has been monitoring the earth for 50 years (since 1972). Its images are the richest archive of the earth and one of its purposes is monitoring the regional changes over time. The information from this satellite are the basis of many studies regarding forest monitoring (Townshend et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Banskota et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), change detection (Awty-Carroll et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hemati et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), water quality (Peterson, Sagan and Sloan, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Al-Shaibah et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), land surface temperature (Balew and Korme, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ermida et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and mapping vegetation (Schwieder et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Peterson, Sagan and Sloan, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The images from Landsat 8 satellite have also been used in urban studies such as urban heat island (Elmes et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), ecological evaluation of urban heat island (Dissanayake, Kurugama and Ruwanthi, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), quantification of carbon sequestration by urban forest (Uniyal et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), extracting urban impervious surfaces (Deliry, Avdan and Avdan, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e),urban green infrastructure health assessment (Chang et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and producing land use/land cover (LULC) maps (Nasiri et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Theres and Selvakumar, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSatellites differ in terms of spatial, spectral, radiometric, and temporal resolution. Landsat 8 satellite has 16-day temporal resolution, 30-meter spatial resolution, and 15-meter panchromatic band. Compared with Landsat 8 satellite, the Sentinel-2 satellite has 10-day temporal resolution that becomes 5 days because of using two repeat cycle satellites. This satellite also has bands with 10, 20, and 60-meter resolutions. Data produced by this satellite are typically used in studies regarding climate change, land monitoring, emergency, management, and security (Sentinel-2 User Handbook, 2015; Gibson et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mngadi, Odindi and Mutanga, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Some studies show that Sentinel-2 satellite performs better than Landsat 8 satellite (J. Wang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Q. Wang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ghayour et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Landsat 8 satellite and Sentinel-2 satellite images can be combined (Mandanici and Bitelli, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Using the images of these two satellites can remarkably increase the phenological variation and, consequently, precision of maps (Nasiri et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pouya, Aghlmand and Karsli, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). PlanetScope satellite has better temporal resolution than Landsat 8 and Sentinel-2 satellites. This satellite, which consists of 130 small satellites, takes images of the earth surface every day and may even take images of some regions more than once. What distinct this satellite from Landsat 8 and Sentinel-2 satellites is the 3-meter resolution of its images (Planet Team, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Its images can play a critical role in urban studies. However, few studies have been conducted in this regard. sentinel \u0026minus;\u0026thinsp;1 satellite offers ordinary and systematic data to monitor sea and land, emergency reaction, climate change, and security. Two underlying limitations of optical satellites are lack of imaging at night and bad weather conditions (existence of cloud) (Sherpa and Shirzaei, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Sentinel-1 satellite does not have these restrictions. Combining its images with those of Sentinel-2 and Landsat 8 satellites has been utilized in different studies on mapping of crop types and crop sequences (Blickensd\u0026ouml;rfer et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), eliminating leaf area index and aboveground biomass of grazing pastures (Wang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), surface moisture and vegetation cover analysis (Urban et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), urban change detection (Benedetti, Picchiani and Del Frate, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hafner, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and river water mapping (Liu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) that provided higher quality results. As yet Sentinel-1 satellite images alone have not been used in producing land use maps. However, many studies have combined them with optical satellites images such as Landsat 8 and Sentinel-2 that has led to increased accuracy of the maps (Brandmeier et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; De Luca et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding monitoring urban growth, extensive studies have been carried out using remote sensing satellites images and GIS. Most of these studies have been based on Landsat series images with 30-meter resolution and deal with urban growth. In these studies, archived images are received to examine changes and growth `extent of urban space to propose strategies for planning to associative officials (Dhanaraj and Angadi, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Roy and Kasemi, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang, Murayama and Morimoto, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Elhamdouni, Arioua and Karaoui, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Most of the studies have generally dealt with urban growth issue because of restrictions such as lack of access to images with high spatial resolution and platforms with high processing power and have not examined the growth of roads, paths, and urban green space separately. The results obtained with these images are based on their spatial resolution. Thus, small streets and paths cannot be mapped. Additionally, green spaces that have a smaller area than spatial resolution do not appear in the results. For example, in Landsat 8 satellite, the area of each pixel is 900 square meters. Thus, the three main classes of green space, roads, and buildings are not clear well in these images. In Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which shows one neighborhood of Eskişehir city in T\u0026uuml;rkiye, some parts of urban space that has low-area buildings, roads, and green space can be observed. In Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, three images of PlanetScope satellite with 3-meter resolution (a), Sentinel-2 satellite with 10-meter resolution (b), and Landsat 8 satellite with 30-meter resolution (c) can be seen. The main image position in this figure has been shown by red. This figure clearly indicates the spatial resolution of these three satellites. In the image with 3-meter resolution, different elements such as green space, roads, and buildings can be easily seen; but in image b, separation of classes is hard and in image c, is impossible.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing new satellites with better spatial, spectral, radiometric, and temporal resolution has helped to gain more information on urban space to produce more detailed LULC maps. Although images with higher resolution produce more details of urban space, their spatial resolution is low, and it is necessary to combine the images of satellites to reach better results. Combining images can also increase the bulk of data and process of them. All the process needs to be done in cloud platforms. Google earth engine (GEE) cloud platform can be referred to as one of the most important progress in GIS and remote sensing area that not only offer GIS data but also makes it possible to do different analyses on them. GEE data do not require pre-processing. Moreover, not only data of its database can be used but also those of other sources can be uploaded. This, in turn, makes it possible to analyze a great deal of data in a short time simultaneously. Another advantage of GEE platform is the existence of strong libraries for processing of satellite images. These libraries offer important machine learning algorithms for classification and production of LULC maps.\u003c/p\u003e \u003cp\u003eIn this research, we aimed to produce a 3-meter LULC map and then extract data from green space, roads, and buildings. The study was conducted by using optical images (PlanetScope and Sentinel-2 ), varying indexes estimated from the bands of these two satellites, and also images of SAR satellite in GEE cloud platform with machine learning algorithm. The study was done on two cities in T\u0026uuml;rkiye, that is, Ankara and Eskişehir to examine data and offer the best combination of data to achieve the study purposes.\u003c/p\u003e"},{"header":"Materials and Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch Area\u003c/h2\u003e \u003cp\u003eAnkara and Eskişehir, as two cities of T\u0026uuml;rkiye, were selected as the research area of this study. They are located in the central Anatolian region of T\u0026uuml;rkiye (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). According to Koppen-Geiger climate taxonomy, the studied area has Mediterranean summer warm and dry weather with cool winter. Ankara was selected as the capital of T\u0026uuml;rkiye in 1923. Its population was 2,747,325 in 1980 and is 5,747,325 now. In terms of population, Ankara is the second city of T\u0026uuml;rkiye and 57th of the world. Eskişehir population was 543,802 in 1980 and 898,369 now. It is ranked as the 25th city of T\u0026uuml;rkiye in terms of population. Ankara and Eskişehir are different from each other in social changes, environment, economic conditions, technology, planning, urban housing and policy, and transportation. This has caused them to have thoroughly different urban forms and spatial features.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMethodology\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the process of the present study. It was conducted in two stages: data collection and criterion, and data processing. A detailed description of the two stages are provided in the following.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData collection and creation\u003c/h2\u003e \u003cp\u003eIn this study, Sentinel-2, Sentinel-1, and PlanetScope images were utilized. Image selection was done based on the normalized difference vegetation index (NDVI). GEE provides the best possibility for extracting NDVI time series so that the months when this index is minimum, and maximum would be identified. In the study, the dataset of Ankara city was set based on the month when NDVI index was maximum (August) and that of Eskişehir was based on the month when the NDVI index was minimum (January).\u003c/p\u003e \u003cp\u003eSentinel-2 and senbtinel-1 images are accessible in pre-processed forms in GEE platform. Images of PlanetScope were also downloaded in Tiff file and were then uploaded in GEE. The image of Sentinel-2 was set with 10 bands and resolutions of 10 and 20 meters in the platform. Further, NDVI, the normalized difference water index (NDWI), the Pigment Specific Simple Ratio (PSSR), Urban Index (Ui) and the Normalized Difference Built-up Index (NDBI) indexes from Sentinel-2 were estimated in GEE and were set in dataset. NDWI, NDVI, the Green Normalized Difference Vegetation Index (GNDVI) and PSSR indexes from PlanetScope satellite and two images from Sentinel-1 were also opted with ascending (A) and descending (D) directions. Each of the images had vertical transmit/horizontal receive (VH) and vertical transmit/vertical receive (VV) bands that were put in the dataset as well. Thus, 27 bands were included in the dataset 15 of which related to Sentinel-2, 8 ones related to PlanetScope, and 4 ones were related to Sentinel-1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResearch dataset\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSatellite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCombination\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe number of bands\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlanetScope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB4, B3, B2 \u0026amp; B1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlanetScope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNDVI, NDWI, PSSR \u0026amp; GNDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSentinel-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB12, B11, B8A, B8, B7, B6, B5, B4, B3 \u0026amp; B2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSentinel-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePSSR, NDVI, NDWI, NDBI \u0026amp; Ui\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSentinel-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVVA \u0026amp; VHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSentinel-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVVD \u0026amp; VHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eTotal bands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData processing\u003c/h2\u003e \u003cp\u003eAfter preparing dataset, the process of producing LULC map via Support Vector Machines Library (LibSVM) (Chang and Lin, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) was fulfilled. For Ankara, classes Build-up 1, Build-up 2, Vegetation-1, Vegetation-2, Road, Bare land and water; and for Eskişehir classes Build-up 1, Build-up 2, Vegetation-1, Road, Bare land and water were considered. Vegetation-1 pertained to trees and vegetation-2 pertained to bushes. Build-up 1 was related to buildings with Roof tiles and Build-up 2 was related to buildings with non-Roof tile. After selecting classes, 400 points were chosen for each class using GEE. Then, the selected points were checked based on RGB picture of PlanetScope, Sentinel-2, and indexes of NDBI, NDWI, AND NDVI. 70 percent of the points was considered for modelling (training points) and 30 percent (test points) for examining the accuracy of the model (overall Kappa coefficient \u0026amp; accuracy).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe result of classification is production of urban LULC map. This map was estimated with 3-meter resolution with Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e dataset that included images of the three above-mentioned satellite with different indexes from PlanetScope and Sentinel-2 images. It was done in GEE platform using LibSVM method. The accuracy and efficiency of the classification can be calculated through overall accuracy and Kappa coefficient. It turned out to be 98.94 and 98.34 for Ankara and 93.97 and 93.1 for Eskişehir, which are high accuracy.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of classification by using LibSVM method\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAnkara\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eEskişehir\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKappa coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKappa coefficient\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e98.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e is Ankara\u0026rsquo;s LULC. In this figure, two areas (a and b) have been highlighted. As the figure shows, the two classes are clearly distinct from each other. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e also displays the results of extraction of each of the classes of urban green space, roads, and buildings from LULC map. As the Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e show, in some streets of Ankara, tall trees have caused the roads class not to be clearly visible.\u003c/p\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e also uncover the result of classification and production of LULC map for Eskişehir. They indicate that each of the classes are clearly distinct from others. The dataset of this city was based on the month when NDVI was minimum (January). The green space is the least in this month but the accuracy rate of ways (roads and streets) was high. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e clearly indicates the effect of using this method on accuracy of classification. Additionally, the urban green space in this figure is the least. Thus, the farmlands around the city are also visible in this figure.\u003c/p\u003e "},{"header":"Discussion and conclusion","content":"\u003cp\u003eUrban planning typically refers to activities that provide development plans for monitoring the use of urban spaces with the purpose of living conditions development and welfare of dwellers in urban areas. Urban planning plays a key role in urban dwelling and economic development of cities. It is taken as an important tool in decision making by local governments. Urban planning needs to be supported by different sciences. One of them is related to data extraction science. In order to pinpoint the current problems and needs and have a comprehensive understanding of the interactive components and their impacts, urban planners must take a wide range of information to analyze with some techniques and tools. They often obtain this information by field operations, document analysis, and typographic maps, which are costly, cumbersome, and time consuming.\u003c/p\u003e \u003cp\u003eIn recent years, remote sensing technology has considerably been used to obtain information on urban areas. Fundamental urban planning needs to include urban plan strategies, city development and expansion purposes, standards and criteria for general urban building and land uses, comprehensive transportation system, and green space system. Comprehensive urban planning has been a complicated periodic program that needs a wide range of information that is prepared partly by GIS and remote sensing. Some merits of remote sensing and GIS systems include wide cover of satellite images, multi-time and multi-range imaging, image receive at different weather conditions, being economical, and easy access. Data obtained from GIS and remote sensing can be used to examine and analyze cities natural conditions, resource distribution, roads network, and land use changes.\u003c/p\u003e \u003cp\u003eGreen space, roads, and buildings are among important elements of cities. Recent progress in remote sensing and GIS has made it possible to monitor them. New processing systems such as GEE and remote sensing satellite images have made it possible to analyze them freely and quickly. There are, however, some limitations such as technology-related constraints and weather condition-related restrictions.\u003c/p\u003e \u003cp\u003eIn urban space, we need images that have high resolution, because spatial resolution is a determinant in mapping of green space, roads, and buildings. For example, in a image with 3-meter resolution, the green space that has 9 square meter area can form a complete one pixel in satellite image and this one pixel can be a tree or a small green space in a yard, school, or street. Thus, the images of PlanetScope satellite were used in the present study as they have 3-meter resolution with four bands. Although spatial resolution is high, spectral resolution is low and it can be stated that four bands of Green, Blue, Red, and NIR are not enough for classification. Therefore, four indexes of NDVI, NDWI, PSSR, and GNDVI were also estimated and added to the dataset. Increasing dataset leads to increasing machine learning algorithms efficiency that had remarkable classification accuracy by adding bands of Sentinel-2 along with PSSR, NDVI, NDWI, NDBI, and Ui indexes and VH and VI bands of Sentinel-1 images (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Adding Sentinel-1 bands amended errors such as tree shadows and improved classification accuracy in vegetation classes (trees and lawn). Another positive impact of Sentinel-1 was related to extraction of data from roads. Using the ascending and descending bands helped to increase their related classification (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the errors that generally happen in classification is erroneous categorization of phenomena. For example, a point that has been identified as building is categorized in roads classification or a point selected as road may be placed in bare land classification. This error is the least in Ankara classification because the scope of urban green space, roads, and buildings is considerable which, in turn, increases the accuracy of classification and mapping. In small cities such as Eskişehir with smaller green space, roads, and buildings, it is more likely to see errors. Combining other data can minimize these errors. In fact, combing data and putting SAR data and different indexes can increase the features of each pixel of image. GEE cloud platform is a revolution in remote sensing and GIS system. Almost all remote sensing data are present in it in pre-processed form. Additionally, if specific data does not exist in it, it can be uploaded from other sources. Another advantage of GEE is the possibility of parallel analysis. This causes the calling of data, calculation of indexes, classification, and accuracy assessment to be fulfilled in less than 60 seconds. In this study, supervised classification offered by GEE was used. They are free to access. In addition, they can be revised as well. The other merit of this platform is the existence of aerial photos in base map and possibility of simultaneous show of varying satellites and indexes that eases the selection of training points for supervised classification in the shortest possible time.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest disclosure\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interests.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThis research is related to the doctoral thesis of Majid Aghlmand, who was the second and third author of the supervisors.\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAl-Shaibah, B. \u003cem\u003eet al.\u003c/em\u003e (2021) \u0026lsquo;Modeling water quality parameters using landsat multispectral images: a case study of Erlong Lake, Northeast China\u0026rsquo;, Remote Sensing, 13(9), p. 1603. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs13091603\u003c/span\u003e\u003cspan address=\"10.3390/rs13091603\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAwty-Carroll, K. \u003cem\u003eet al.\u003c/em\u003e (2019) \u0026lsquo;Using continuous change detection and classification of landsat data to investigate long-term mangrove dynamics in the Sundarbans region\u0026rsquo;, Remote Sensing, 11(23), p. 2833. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs11232833\u003c/span\u003e\u003cspan address=\"10.3390/rs11232833\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalew, A. and Korme, T. 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(2021) \u0026lsquo;Scenario simulation studies of urban development using remote sensing and GIS\u0026rsquo;, Remote Sensing Applications: Society and Environment, 22, p. 100474.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"remote sensing in urban studies, google earth engine, machine learning algorithms, land use/land cover, urban form","lastPublishedDoi":"10.21203/rs.3.rs-4850131/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4850131/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRecent progress in remote sensing and Geographical Information System (GIS) has revolutionized the research studies on urban space. Satellites that image on daily basis and cloud platforms that increase mathematical modelling precision and speed have given rise to extraction of high-quality data. In this study, we propose a novel approach in extracting information on green space, buildings, and roads in Ankara and Eskişehir cities in T\u0026uuml;rkiye. In this approach, optical and SAR images are utilized. Modelling is also fulfilled in google earth engine cloud platform using machine learning algorithm. We show how optical and SAR images with varying indexes may lead to a Land use/Land cover map with the highest overall accuracy (98.94 for Ankara and 93.97 for Eskişehir). Additionally, techniques offered in this study can help to extract other classes other than the present study ones. Land use/Land cover map is the basis of many studies and can benefit urban management, planning, urban policy making, protection and renovation, and environmental sustainment.\u003c/p\u003e","manuscriptTitle":"Mapping Green space, Roads \u0026amp; Built-up Areas with Optical Remote Sensing and Polarimetric SAR (A Novel Approach)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-16 16:46:34","doi":"10.21203/rs.3.rs-4850131/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4d8c9385-a230-4372-b1c8-16ddb9b5cef8","owner":[],"postedDate":"September 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-11T13:23:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-16 16:46:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4850131","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4850131","identity":"rs-4850131","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}