Egyptian Journal of Remote Sensing and Space Sciences最新文献

{"title":"Remote sensing for UN SDGs: A global analysis of research and collaborations","authors":"Omer Ekmen ,&nbsp;Sultan Kocaman","doi":"10.1016/j.ejrs.2024.04.002","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.04.002","url":null,"abstract":"<div><p>The Sustainable Development Goals (SDGs) provide a policy-making baseline for countries to overcome shortcomings and barriers for people and the planet Earth by 2030. Remote sensing (RS) enables evidence-based policy making and can contribute to realization of the SDGs by monitoring the indicators and evaluating the targets related to human and physical geography. This study exploited the RS research concerning the SDGs based on a Web of Science Core Collection database query [TS=((“remote sensing” OR “Earth observation*”) AND (“Sustainable Development Goal*”))] between 2016 and 2022 and by utilizing an artificial intelligence tool developed for SDG classification. We retrieved and analyzed articles (n = 308) using science mapping techniques. Remote Sensing is the most relevant journal publishing articles related to this theme. While the dominance of Chinese institutes in terms of authors' affiliation is clear, the highest collaboration network is between the USA and China. Our findings revealed that subjects related to carbon storage, ecological quality and impervious surface draw attention of researchers increasingly and becoming trend topics. From the SDG classification results, SDG 15 and SDG 11 emerged as the most prevalent subjects related to the RS research. Given the exponential increase in the number of studies, we recommend to employ bibliometric analysis and science mapping tools to systematically identify research patterns and gaps in both fields, as manual efforts may progressively become challenging.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 2","pages":"Pages 329-341"},"PeriodicalIF":6.4,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000309/pdfft?md5=bbe2d14bc150b59af9741425e1d18767&pid=1-s2.0-S1110982324000309-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140540775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing high temporal-resolution of GNSS-based ionospheric VTEC over Nigeria","authors":"Solomon O. Faruna ,&nbsp;Dudy D. Wijaya ,&nbsp;Bambang Setyadji ,&nbsp;Irwan Meilano ,&nbsp;Aditya K. Utama ,&nbsp;Daniel Okoh","doi":"10.1016/j.ejrs.2024.03.006","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.03.006","url":null,"abstract":"<div><p>This study focuses on high-temporal-resolution Vertical Total Electron Content (VTEC) estimation over Nigeria, which is crucial for enhancing satellite-based applications. Utilizing RINEX, IONEX, and SP3 data from 2011 across 10 stations, the research integrates a novel VTEC model (LIMS) based on orthogonal transformation, achieving an unprecedented 10-minute temporal resolution sampling. The model incorporates multi-Global Navigation Satellite Systems (GNSS) constellations. Geomagnetic and solar activity impact assessments involve the Ap index, sunspot number, and DSt index. Specifically, the DSt index for March 16–18, 2015, analyzes the geomagnetic storm of St Patrick’s Day. Validation compares LIMS with International GNSS Service (IGS), Center for Orbit Determination in Europe (CODE), and International Reference Ionosphere (IRI-2020) estimates, showing strong correlations during various conditions. Daily VTEC patterns reveal the lowest values in the early morning, a midday peak, occasional double peaks, secondary maximum, and post-sunset enhancements, especially during equinoxes. Seasonal analysis highlights the highest mean VTEC in September Equinox and December Solstice, and the lowest during June Solstice. Spectral analysis identifies prominent diurnal, semi-diurnal, and sub-diurnal frequency components. This research significantly advances the understanding of VTEC in Nigeria, offering a valuable tool for precise positioning, satellite communication, and space weather forecasting. Notably, 9 stations processed 2011 data, while one station from this group and an additional station were used for a 3-day storm analysis in 2015 due to data availability.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 2","pages":"Pages 317-328"},"PeriodicalIF":6.4,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000267/pdfft?md5=7f698e636b390b9593093b0bbe47fd0a&pid=1-s2.0-S1110982324000267-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140536385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Environmental studies of the Khorramrood River in Iran, based on transformed high-resolution remotely sensed spectroscopic data","authors":"Paria Darvishi ,&nbsp;Danya Karimi","doi":"10.1016/j.ejrs.2024.03.008","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.03.008","url":null,"abstract":"<div><p>An investigation was conducted on the Khorramrood River in Iran to evaluate pollution levels resulting from human activities. Water samples were collected from eleven stations and analyzed for four parameters: pH, temperature, dissolved oxygen (DO), and nitrate (NO3). Additionally, a biodiversity assessment of macroinvertebrates was conducted to evaluate water quality. Eleven invertebrate families from seven classes were identified, with Chironomidae and Baetidae as the predominant groups, suggesting a significant deterioration in water quality. As the first objective, water quality assessment using macroinvertebrates was done using two diversity indices (Shannon-Wiener and Simpson) and four biotic indices (ASPT, FBI, EPT, and BMWP). The results consistently indicated poor water quality in the river. These findings are consistent with the conclusions drawn from the analysis of physicochemical parameters, which is the second objective, and both confirm inadequate water quality. As a part of the last objective, to map the physicochemical parameters, three scenarios were used. They involved utilizing a transformed high-resolution PRISMA image, a traditional method with Landsat 9 images, and a fusion of Landsat 9 and PRISMA images. The first scenario produced the most accurate results (RMSE = 0.624, 0.942, 0.167, and 0.98 for DO, NO3, pH, and temperature. respectively). Mapping biodiversity indices, another part of the last objective, using the transformed pan-sharpened PRISMA image proved highly reliable. A strong correlation was observed between most indices and the DO (CR = 0.972, −0.496, −0.973, and −0.978 for Simpson, EPT, BMWP, and ASPT, respectively), indicating the significant influence of DO on the river's biological state.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 2","pages":"Pages 298-316"},"PeriodicalIF":6.4,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000280/pdfft?md5=ac8f50f72e3ec6a21f509719f037b801&pid=1-s2.0-S1110982324000280-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140348330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Segment-driven anomaly detection in hyperspectral data using watershed technique","authors":"Mohamad Ebrahim Aghili,&nbsp;Maryam Imani,&nbsp;Hassan Ghassemian","doi":"10.1016/j.ejrs.2024.03.007","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.03.007","url":null,"abstract":"<div><p>A significant portion of hyperspectral image (HSI) analysis involves detecting anomalous pixels, which are indicative of interesting phenomena or objects. One of the main challenges is the presence of outlier and noisy pixels in background data due to the variety of spectral signatures in heterogeneous HSIs. This article presents an effective approach using both spectral and spatial features for anomaly detection. The median filter with an appropriate size driven by using the principal component information is used for cleaning the background. Then, the image is segmented using the watershed approach. The anomaly detection occurs based on the spatial resolution by calculating each pixel's distance from its segment via spectral angle or Euclidean distance. The proposed Watershed Anomaly Detector (WAD), employs spatial features to segment the HSI properly. It also uses spectral features within each segment to detect anomalous pixels. The WAD outperforms other methods due to its simplicity and conceptual clarity. Notably, its underlying equation offers broader applicability for HSI segmentation tasks. Experiments on three benchmark datasets show WAD achieves higher accuracy and faster execution versus state-of-the-art techniques. On average across the datasets and methods, WAD attained a 6.45% higher area under the receiver operating characteristic (ROC) curve and ran 26.95 s faster than other detectors. The WAD effectively detects anomalies in varied spectral and spatial resolutions. The results highlight the stability, robustness and computational efficiency of the proposed approach across diverse data. The simultaneous effectiveness and efficiency make WAD well-suited for near real-time anomaly detection applications.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 2","pages":"Pages 288-297"},"PeriodicalIF":6.4,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000279/pdfft?md5=79773c2986296e1d40eb1c01293a8ab8&pid=1-s2.0-S1110982324000279-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140347527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling the effect of LULC change on water quantity and quality in Big Creek Lake Watershed, South Alabama USA","authors":"Eshita A. Eva ,&nbsp;Luke J. Marzen ,&nbsp;Jasmeet Singh Lamba","doi":"10.1016/j.ejrs.2024.03.005","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.03.005","url":null,"abstract":"<div><p>The land use and land cover (LULC) of a watershed play an important role in controlling its hydrological processes. With the help of applying the Soil and Water Assessment Tool (SWAT), this study aims to quantify the impact of changing LULC on hydrological responses and water quality in the Big Creek Lake watershed in Mobile County, South Alabama. A number of data sources were input into the SWAT model as part of its calibration and validation, including land use and land cover (LULC), weather variables, digital elevation models (DEMs), soil properties, and measured streamflows. The total monthly streamflow increased by about 62 m³/s and the average nitrogen and phosphorus are estimated to have increased by about 3,172 kg/Ha and 892 kg/Ha per year respectively over the thirty years because of the increasing agricultural land (11,406 acres), urban development (3,350 acres), and decreasing forested areas (11,482 acres). This research could be helpful for water resource managers and planners by incorporating the results in the monitoring and planning for the future.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 2","pages":"Pages 277-287"},"PeriodicalIF":6.4,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000255/pdfft?md5=402b1876de0797104188ff5ee792b58a&pid=1-s2.0-S1110982324000255-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140341143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MFFNet: A lightweight multi-feature fusion network for UAV infrared object detection","authors":"Yunlei Chen ,&nbsp;Ziyan Liu ,&nbsp;Lihui Zhang ,&nbsp;Yingyu Wu ,&nbsp;Qian Zhang ,&nbsp;Xuhui Zheng","doi":"10.1016/j.ejrs.2024.03.001","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.03.001","url":null,"abstract":"<div><p>In light of issues such as unnoticeable texture features and limited resolution of infrared image objects, a lightweight multi-scale feature fusion method for UAV infrared object recognition is presented to enhance the performance of UAVs carrying intelligent devices to detect infrared objects. By changing the anchorless frame strategy of the YOLOX method, a lightweight Multi-Feature Fusion Network (MFFNet) for UAV IR image object recognition is proposed. First, a lightweight backbone network is built using ShuffleNetv2_block, spatial pyramid pooling, and other modules to reduce the network's number of parameters and inference time while maintaining its capacity to extract features. Second, we develop a multi-feature fusion module to improve the detection capabilities of the model for IR objects by fusing the local features and the overall characteristics of IR objects since the texture features of IR objects are challenging to employ, but the boundary information is evident. The boundary frame regression loss is then optimized using SIoU by comparing the predicted frame to the actual frame in terms of angle, distance, shape, and IoU (Intersection over Union), which forces the model to reach the optimum predicted box more quickly.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 2","pages":"Pages 268-276"},"PeriodicalIF":6.4,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000218/pdfft?md5=85d30684c98bfb92e8845e2acca9c06c&pid=1-s2.0-S1110982324000218-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140327714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Land subsidence susceptibility mapping based on InSAR and a hybrid machine learning approach","authors":"Ali Asghar Alesheikh ,&nbsp;Zahra Chatrsimab ,&nbsp;Fatemeh Rezaie ,&nbsp;Saro Lee ,&nbsp;Ali Jafari ,&nbsp;Mahdi Panahi","doi":"10.1016/j.ejrs.2024.03.004","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.03.004","url":null,"abstract":"<div><p>Land subsidence (LS) due to natural processes or human activity can irreparably damage the environment. This study employed the quasi-permanent scatterer method to detect areas with and without subsidence in the period from 2018 to 2020. In addition, 12 factors affecting subsidence were selected to detect LS-prone areas. Information gain ratio (IGR) and frequency ratio methods were used to determine the importance and weighting of various factors and sub-factors affecting subsidence. Novel approaches, including the standard adaptive-network-based fuzzy inference system (ANFIS) algorithm and its integration with the particle swarm optimization (PSO) algorithm, yielded LS maps. The models’ predictive performance was assessed using statistical indexes such as the root mean square error (RMSE), area under the receiver operating characteristic curve (AUROC) and confusion matrix criteria (e.g., sensitivity, specificity, precision, accuracy, and recall). Finally, Shapley additive explanations approach was used to explore the importance of features in modeling. The findings showed that the subsidence pattern was V-shaped, averaging 321 mm/year. Ground-leveling and interferometric synthetic aperture radar measurements revealed a 0.93 correlation coefficient with a σ = 1.43 mm/year deformation rate. Based on IGR analysis, aquifer media, the decline of the water table, and aquifer thickness played pivotal roles in LS occurrences. In addition, the ANFIS-PSO model classified approximately 50.26 % of the study area as high and very high susceptibility. The AUROC values of ANFIS-PSO and ANFIS models for the training dataset were 0.912 and 0.807, respectively. For the testing dataset, the ANFIS-PSO model produced a higher AUROC value of 0.863, while the ANFIS model had a value of 0.771. In addition, the RMSE values for the ANFIS-PSO model were lower. Given its remarkable accuracy, the ANFIS-PSO model was deemed suitable for evaluating subsidence susceptibility in the study area.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 2","pages":"Pages 255-267"},"PeriodicalIF":6.4,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000243/pdfft?md5=716d865dbcbf1efa7542c8800ffe7a5d&pid=1-s2.0-S1110982324000243-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140290314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nonreference object-based pansharpening quality assessment","authors":"Shiva Aghapour Maleki,&nbsp;Hassan Ghassemian,&nbsp;Maryam Imani","doi":"10.1016/j.ejrs.2024.03.002","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.03.002","url":null,"abstract":"<div><p>Pansharpening involves the fusion of panchromatic (PAN) and multispectral (MS) images to obtain a high-resolution image with enhanced spectral and spatial information. Assessing the quality of the resulting fused image poses a challenge due to the absence of a high-resolution reference image. Numerous methods have been proposed to address this, from assessing quality at reduced resolution to full-resolution evaluations. Many existing approaches are pixel-based, where quality metrics are applied and averaged on individual pixels. In this article, we introduce a novel object-based method for assessing the quality of pansharpened images at full resolution. In object-based quality assessment methods, the reaction of different areas of the fused image to the fusion process is reflected. Our approach revolves around extracting objects from the given image and evaluating extracted objects. By doing so, the distinct responses of different objects within the fused image to the fusion process are captured. The proposed method leverages a unique object extraction technique known as segmentation by nearest neighbor (SNN) to extract objects of the MS image. This method extracts the objects based on the image’s characteristics without any requirement for parameter tuning. These extracted objects are then mapped onto both PAN and fused images. The proposed spectral index measures the spectral homogeneity of the fused image’s objects and the proposed spatial index measures the injected spatial content from the PAN image to the fused image’s objects. Experimental results underscore the robustness and reliability of the proposed method. Additionally, by visualizing distortion values on object-maps, we gain insights into fusion quality across diverse areas within the scene.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 2","pages":"Pages 227-241"},"PeriodicalIF":6.4,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S111098232400022X/pdfft?md5=7dc512ed1d8a885a84a80f360ca1e4a9&pid=1-s2.0-S111098232400022X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140180576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Morphological characterization of Maize (Zea mays.) utilising the stage-wise structural and architectural perspective from temporal fully-polarimetric SAR","authors":"Dipanwita Haldar ,&nbsp;E. Suriya ,&nbsp;Abhishek Danodia ,&nbsp;R.P. Singh","doi":"10.1016/j.ejrs.2024.02.007","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.02.007","url":null,"abstract":"<div><p>The morphological shape and structure of the crop vary with phenological stages. Model and eigen based decomposition model parameters extracted from the Radarsat-2 data and the trend with respect to ground truth crop phenology were analysed. Sensitive parameters were devised through stepwise approach under 7 combinations of polarimetric variables of increasing complexity were assessed. Compared under the three machine learning algorithms (ANN, RF and SVM) where ANN rendered the maximum correlation with 0.92 with a MAE of 4 days which was implemented on a large parcel of maize mask in the study area. SVM performed poorly with highly overlapping parameters such as backscatter but performed well (r = 0.85). For assessing the crop biophysical parameters, the three algorithms were evaluated and sensitivity analysis for statistically significant polarimetric variables for biophysical parameters was performed. The assessment was performed on Multi-Layer Perception (MLP) neural network. The networks were trained with algorithms and hidden layer nodes until the MAE achieved permissible limits. Plant height could be estimated more profoundly with an r = 0.8 with a considerably good MAE of 24.9 cm but other parameters (WB, DB and LAI) were estimated in moderate correlation of 0.6–0.65 where the MAE of WB, DB and LAI were found to be 1317gm⁻², 553 gm⁻² and 0.78 respectively. This is the first step towards understanding the complex scattering mechanisms in Indian maize scenario assessing the growth parameters from polarimetric data. Thus, the analytical findings brought out possess the potential to serve as the reference for the future research initiatives.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 2","pages":"Pages 242-254"},"PeriodicalIF":6.4,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000139/pdfft?md5=ab45c7b042e521d22619b44a72ce9fd4&pid=1-s2.0-S1110982324000139-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140180577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance assessment of machine learning algorithms for mapping of land use/land cover using remote sensing data","authors":"Zeeshan Zafar ,&nbsp;Muhammad Zubair ,&nbsp;Yuanyuan Zha ,&nbsp;Shah Fahd ,&nbsp;Adeel Ahmad Nadeem","doi":"10.1016/j.ejrs.2024.03.003","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.03.003","url":null,"abstract":"<div><p>The rapid increase in population accelerates the rate of change of Land use/Land cover (LULC) in various parts of the world. This phenomenon caused a huge strain for natural resources. Hence, continues monitoring of LULC changes gained a significant importance for management of natural resources and assessing the climate change impacts. Recently, application of machine learning algorithms on RS (remote sensing) data for rapid and accurate mapping of LULC gained significant importance due to growing need of LULC estimation for ecosystem services, natural resource management and environmental management. Hence, it is crucial to access and compare the performance of different machine learning classifiers for accurate mapping of LULC. The primary objective of this study was to compare the performance of CART (Classification and Regression Tree), RF (Random Forest) and SVM (Support Vector Machine) for LULC estimation by processing RS data on Google Earth Engine (GEE). In total four classes of LULC (Water Bodies, Vegetation Cover, Urban Land and Barren Land) for city of Lahore were extracted using satellite images from Landsat-7, Landsat-8 and Landsat-9 for years 2008, 2015 and 2022, respectively. According to results, RF is the best performing classifier with maximum overall accuracy of 95.2% and highest Kappa coefficient value of 0.87, SVM achieved maximum accuracy of 89.8% with highest Kappa of 0.84 and CART showed maximum overall accuracy of 89.7% with Kappa value of 0.79. Results from this study can give assistance for decision makers, planners and RS experts to choose a suitable machine learning algorithm for LULC classification in an unplanned urbanized city like Lahore.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 2","pages":"Pages 216-226"},"PeriodicalIF":6.4,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000231/pdfft?md5=248a24bc9935c1a4646bb7ace2188f1d&pid=1-s2.0-S1110982324000231-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140103632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}