Egyptian Journal of Remote Sensing and Space Sciences最新文献_第8页

Evaluation of SMOS Sea Surface Salinity with Argo data along the Exclusive Economic Zone (EEZ) of Pakistan 利用 Argo 数据对巴基斯坦专属经济区（EEZ）沿岸的 SMOS 海洋表面盐度进行评估

IF 6.4 3区地球科学

Egyptian Journal of Remote Sensing and Space Sciences Pub Date : 2024-01-28 DOI: 10.1016/j.ejrs.2024.01.006

Muhammad Shafiq, Muhammad Naveed Javed, Adnan Aziz, Mudassar Umar

{"title":"Evaluation of SMOS Sea Surface Salinity with Argo data along the Exclusive Economic Zone (EEZ) of Pakistan","authors":"Muhammad Shafiq, Muhammad Naveed Javed, Adnan Aziz, Mudassar Umar","doi":"10.1016/j.ejrs.2024.01.006","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.01.006","url":null,"abstract":"<div><p>Ocean-Atmosphere interactions have been gradually recognized to play a significant role in hydrological cycle and climate change. It is essential to understand ocean-circulation behaviour, including the Sea Surface Salinity (SSS) which is a root cause of variations in sea water density in both coastal system and open ocean. The study has evaluated the performance of SSS obtained from the Soil Moisture and Ocean Salinity (SMOS) satellite data. Daily Barcelona Expert Center (BEC), SMOS, SSS data from 2012 to 2016 are compared with the salinity observations from Argo floats within the Exclusive Economic Zone (EEZ) of Pakistan. Statistics between a daily reporting Argo float and daily SMOS SSS resulted in a spatial correlation, a bias, a standard deviation, and a variance has been examined to determine the monthly, annual and seasonal variations of SSS. Bias analysis showed the underestimation between −0.52 and −0.008 psu while variance has been observed to be between 0.02 and 0.19 psu. The monthly, seasonal and yearly comparison suggests both SMOS and Argo are are found to be in concurrence. Finally, it has been revealed that SSS retrieval algorithm by BEC SMOS provides good estimation along the EEZ of Pakistan.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 1","pages":"Pages 69-81"},"PeriodicalIF":6.4,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000061/pdfft?md5=31150361d7a22975251002472217fcd5&pid=1-s2.0-S1110982324000061-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139653002","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}

引用次数: 0

A comprehensive framework for landslide risk assessment of archaeological sites in Gujarat, India 印度古吉拉特考古遗址滑坡风险评估综合框架

IF 6.4 3区地球科学

Egyptian Journal of Remote Sensing and Space Sciences Pub Date : 2024-01-25 DOI: 10.1016/j.ejrs.2024.01.002

Haritha Kadapa

{"title":"A comprehensive framework for landslide risk assessment of archaeological sites in Gujarat, India","authors":"Haritha Kadapa","doi":"10.1016/j.ejrs.2024.01.002","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.01.002","url":null,"abstract":"<div><p>Landslides, even shallow ones, can displace and destroy the fragile archaeological record. Therefore, it is essential to develop a comprehensive risk assessment and predict the sites at risk before a disaster, which this study aims to provide for 508 archaeological sites associated with Indus civilization and regional Chalcolithic cultures in Gujarat, India. As a hazard inventory for the study area is not available, this study integrates multi-criteria decision-making (MCDM), satellite remote sensing, and Geographic Information Systems (GIS) first to generate a landslide susceptibility map and then to use it for assessing the landslide risk of archaeological sites. Fifteen parameters, viz., elevation, slope, aspect, curvature, average rainfall, drainage density, Topographic Wetness Index (TWI), Stream Power Index (SPI), lithology, soil type, geomorphology, distance from lineaments, Normalized Difference Vegetation Index (NDVI), Land Use Land Cover (LULC), and distance from roads were selected to determine susceptibility. The weights of each parameter were derived using the Analytical Hierarchy Process (AHP). The novelty of this study lies in the spatial overlay of the area of the sites and landslide susceptibility to measure the value loss of the archaeological sites. The results revealed that three of the 508 sites studied are at high risk, and 214 are at medium risk of landslides. With this proposed methodology, this study generates a new dataset on landslide susceptibility for the study area. In addition, it attempts to provide an integrated risk assessment framework for the archaeological sites in India that aids in identifying and mitigating risks.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 1","pages":"Pages 41-51"},"PeriodicalIF":6.4,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000024/pdfft?md5=0c4950b768128efca5e9042488a6e09d&pid=1-s2.0-S1110982324000024-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139652991","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}

引用次数: 0

Land use/land cover (LULC) classification using deep-LSTM for hyperspectral images 利用深度 LSTM 对高光谱图像进行土地利用/土地覆被 (LULC) 分类

IF 6.4 3区地球科学

Egyptian Journal of Remote Sensing and Space Sciences Pub Date : 2024-01-25 DOI: 10.1016/j.ejrs.2024.01.004

Ganji Tejasree, L. Agilandeeswari

{"title":"Land use/land cover (LULC) classification using deep-LSTM for hyperspectral images","authors":"Ganji Tejasree, L. Agilandeeswari","doi":"10.1016/j.ejrs.2024.01.004","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.01.004","url":null,"abstract":"<div><p>Land Use/Land Cover (LULC) classification using hyperspectral images in remote sensing is a leading technology. However, LULC classification using hyperspectral images is a difficult task and time-consuming process because it has fewer training samples. To overcome these issues, we proposed a deep-Long Short-Term Memory (deep-LSTM) to classify the LULC. Before classifying the LULC, extracting valuable features from an image is needed, and after extracting the features, selecting the bands which are helpful for classification should be done. In this work, we have proposed an auto-encoder model for feature extraction, a ranking-based band selection model to select the bands, and deep-LSTM for classification. We have used three publicly available benchmark datasets; they are Pavia University (PU), Kennedy Space Centre (KSC), and Indian Pines (IP). Average Accuracy (AA), Overall Accuracy (OA), and Kappa Coefficient (KC) are used to measure the classification accuracy. The suggested technique has provided the top outcomes compared to the other state-of-the-art methods.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 1","pages":"Pages 52-68"},"PeriodicalIF":6.4,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000048/pdfft?md5=b376a16344b9c0c5982a335de34305d3&pid=1-s2.0-S1110982324000048-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139652992","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}

引用次数: 0

Analytical simulation and experimental validation of viscoplastic bending response of textile-reinforced composites for CubeSats 用于立方体卫星的织物增强复合材料粘塑性弯曲响应的分析模拟和实验验证

IF 6.4 3区地球科学

Egyptian Journal of Remote Sensing and Space Sciences Pub Date : 2024-01-16 DOI: 10.1016/j.ejrs.2023.12.005

Ehsan Shafiei , Gasser Abdelal

引用次数: 0

Feasibility study on Multiphysics H2-O2 combustion model for space debris removal system – NIRCSAT-X 空间碎片清除系统多物理场 H2-O2 燃烧模型可行性研究 - NIRCSAT-X

IF 6.4 3区地球科学

Egyptian Journal of Remote Sensing and Space Sciences Pub Date : 2024-01-12 DOI: 10.1016/j.ejrs.2023.12.004

Gasser Abdelal , Lorenzo Stella , Yasser Mahmoudi , Michael Murphy , Wasif Naeem

{"title":"Feasibility study on Multiphysics H2-O2 combustion model for space debris removal system – NIRCSAT-X","authors":"Gasser Abdelal , Lorenzo Stella , Yasser Mahmoudi , Michael Murphy , Wasif Naeem","doi":"10.1016/j.ejrs.2023.12.004","DOIUrl":"https://doi.org/10.1016/j.ejrs.2023.12.004","url":null,"abstract":"<div><p>Space debris is a growing problem for low earth orbit (LEO) and geosynchronous orbit (GEO). The risk of space debris currently affects human activities in Space and is controlled by the collision avoidance alert. However, the risk is growing, which increases future space mission costs to avoid or shield against space debris impact.</p><p>The project has evolved over four years, culminating in Meng/BEng graduation projects. At the heart of our innovation is utilising the naturally high temperatures in the exosphere and stratosphere, which can soar to 1200 °C. This environment is ideal for initiating a chemical reaction within a pressurised chamber containing a mix of H2-O2 gases, generating heat sufficient to ablate common space debris materials such as titanium, aluminium, and composites. We have crafted an initial satellite design and performed Multiphysics simulations using COMSOL to validate our concept. The project now seeks investment to enhance four critical areas: the satellite's mechanical design to ensure safe operation within a debris field, the development of a dynamic control system for debris collection and satellite navigation, the management of H2 and O2 tank refilling, and the creation of a mechanism for the safe release of ablated materials back into Space.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 1","pages":"Pages 18-29"},"PeriodicalIF":6.4,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982323001102/pdfft?md5=b64f2849ce2a62cc86a6af36604912d1&pid=1-s2.0-S1110982323001102-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139433768","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}

引用次数: 0

Multi-branch reverse attention semantic segmentation network for building extraction 用于建筑物提取的多分支反向关注语义分割网络

IF 6.4 3区地球科学

Egyptian Journal of Remote Sensing and Space Sciences Pub Date : 2023-12-16 DOI: 10.1016/j.ejrs.2023.12.003

Wenxiang Jiang , Yan Chen , Xiaofeng Wang , Menglei Kang , Mengyuan Wang , Xuejun Zhang , Lixiang Xu , Cheng Zhang

{"title":"Multi-branch reverse attention semantic segmentation network for building extraction","authors":"Wenxiang Jiang , Yan Chen , Xiaofeng Wang , Menglei Kang , Mengyuan Wang , Xuejun Zhang , Lixiang Xu , Cheng Zhang","doi":"10.1016/j.ejrs.2023.12.003","DOIUrl":"https://doi.org/10.1016/j.ejrs.2023.12.003","url":null,"abstract":"<div><p>Extraction of color and texture features of buildings from high-resolution remote sensing images often encounters the problems of interference of background information and varying target scales. In addition, most of the current attention mechanisms focus on building key feature selection for building extraction optimization, but ignore the influence of the complex background. Hence, we propose incorporating a novel reverse attention module into the network. The innovative module enables the model to selectively extract crucial building features while suppressing the impact of intricate background noise. It mitigates the influence of uniform spectral and structurally similar heterogeneous background targets on building segmentation and extraction. As a result, the overall generalizability of the model is improved. The reverse attention can also emphasize and amplify the specific details pertaining to the boundaries of the target. Furthermore, we couple a new multi-branch convolution block into the encoder, integrating dilated convolutions with multiple dilation rates. Compared to other methods that use only one multi-scale module to extract multi-scale information from high-level features, we use different receptive field convolutions to simultaneously capture multi-scale targets from multi-level features, effectively improving the ability of the model to extract multi-scale building features. The experimental findings demonstrate that our proposed multi-branch reverse attention semantic segmentation network achieves IoU of 90.59% and 81.79% on the well-known WHU building and Inria aerial image datasets, respectively.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 1","pages":"Pages 10-17"},"PeriodicalIF":6.4,"publicationDate":"2023-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982323001035/pdfft?md5=0f9a312c78c3551ba2cf17857997a7db&pid=1-s2.0-S1110982323001035-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138713208","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}

引用次数: 0

A machine learning-based method for multi-satellite SAR data integration 基于机器学习的多卫星合成孔径雷达数据集成方法

IF 6.4 3区地球科学

Egyptian Journal of Remote Sensing and Space Sciences Pub Date : 2023-12-14 DOI: 10.1016/j.ejrs.2023.12.001

Doha Amr , Xiao-li Ding , Reda Fekry

{"title":"A machine learning-based method for multi-satellite SAR data integration","authors":"Doha Amr , Xiao-li Ding , Reda Fekry","doi":"10.1016/j.ejrs.2023.12.001","DOIUrl":"https://doi.org/10.1016/j.ejrs.2023.12.001","url":null,"abstract":"<div><p>Large- and small-scale subsidence coexist in the world's coastal cities due to extensive land reclamation and fast urbanization. Synthetic aperture radar (SAR) images are typically limited by either low resolution or small coverage, making them ineffective for fully monitoring displacement in coastal areas. In this research, a machine learning-based method is developed to investigate the reclaimed land subsidence based on multi-satellite SAR data integration. The proposed method requires at least a pair of SAR images from complementary tracks. First, the line-of-sight (LOS) displacements are recovered in connection to a series of extremely coherent points based on the differential interferometry synthetic aperture radar (DInSAR). These LOS displacements are then converted into their vertical component, geocoded to a common grid, and simultaneously integrated (i.e., pixel-by-pixel) based on Support Vector Regression (SVR). The proposed methodology does not necessitate the simultaneous processing of huge DInSAR interferogram sequences. The experiments include high-resolution COSMO-SkyMed (CSK) and TerraSAR-X (TSX) images, as well as a small monitoring cycle Sentinel-1 (S1) images of reclaimed territories near Hong Kong Kowloon City. The overall average annual displacement (AAD) ranges from -12.86 to 11.63 mm/year derived from 2008 to 2019. The evaluation metrics including RMSE, MAE, correlation coefficient, and R-squared are used to investigate the impact of SVR in the integration of SAR datasets. Based on these evaluation metrics, SVR is superior in terms of integration performance, accuracy, and generalization ability. Thus, the proposed method has potentially performed multi-satellite SAR data integration.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 1","pages":"Pages 1-9"},"PeriodicalIF":6.4,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982323001011/pdfft?md5=df57f2174f9a6da36d52abc7f7eda7a6&pid=1-s2.0-S1110982323001011-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138713207","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}

引用次数: 0

Spatiotemporal patterns of land surface temperature and their response to land cover change: A case study in Sichuan Basin 地表温度的时空模式及其对土地覆被变化的响应：四川盆地案例研究

IF 6.4 3区地球科学

Egyptian Journal of Remote Sensing and Space Sciences Pub Date : 2023-12-01 DOI: 10.1016/j.ejrs.2023.12.002

Dongming Yan , Huan Yu , Qing Xiang , Xiaoyu Xu

{"title":"Spatiotemporal patterns of land surface temperature and their response to land cover change: A case study in Sichuan Basin","authors":"Dongming Yan , Huan Yu , Qing Xiang , Xiaoyu Xu","doi":"10.1016/j.ejrs.2023.12.002","DOIUrl":"https://doi.org/10.1016/j.ejrs.2023.12.002","url":null,"abstract":"<div><p>Land surface temperature (LST) is a critical geo-parameter in terrestrial environmental interaction processes, directly related to land cover change (LCC) which modifies surface energy balance. In this study, LST data from 2003 to 2019 were reconstructed in the Sichuan Basin with average R<sup>2</sup> of 0.85 (daytime) and 0.91 (nighttime), effectively filling in the missing pixels and reducing the noise components. Emerging hot spot analysis (EHSA) and land cover transfer matrix were utilized to analyze the multi-patterns of LST spatiotemporal evolution and responses to LCC. Results indicate that LST hot spots are concentrated in low-altitude basin floor and are dominated by sporadic hot spots. Cold spots are mainly in marginal high-elevation mountains, but the dominant pattern varies with time scale. The largest proportions of hot and cold spots are found in summer (>46 %) and autumn (>29 %), respectively. Moreover, the significant upward and downward trends of LST cold and hot spots are most prominent in western plain and marginal mountains, respectively, and have the largest coverage in summer and autumn, respectively. In total LCC area, cropland-to-forest (CF), cropland-to-impervious (CI), and forest-to-cropland (FC) account for 93.55 %. Among them, CI significantly promotes the aggregation and upward trend of daytime LST hot spots. CF and FC have the strongest effect of aggregating LST cold spots and cooling LST in daytime, with CF being more effective. The information can serve as a reference for regional planning and climate change mitigation measures.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"26 4","pages":"Pages 1080-1089"},"PeriodicalIF":6.4,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982323001023/pdfft?md5=29ba3b2ab5f58d021ab1954711fd78db&pid=1-s2.0-S1110982323001023-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138558759","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}

引用次数: 0

Estimation of national sources and sinks of greenhouse gases based on satellite observations 基于卫星观测的国家温室气体源汇估算

IF 6.4 3区地球科学

Egyptian Journal of Remote Sensing and Space Sciences Pub Date : 2023-12-01 DOI: 10.1016/j.ejrs.2023.11.012

Naglaa Zanaty, Elham M. Ali, Islam Abou El-Magd

{"title":"Estimation of national sources and sinks of greenhouse gases based on satellite observations","authors":"Naglaa Zanaty, Elham M. Ali, Islam Abou El-Magd","doi":"10.1016/j.ejrs.2023.11.012","DOIUrl":"https://doi.org/10.1016/j.ejrs.2023.11.012","url":null,"abstract":"<div><p>Human-driven Greenhouse gases (GHGs) are the most significant contributor to climate change. World countries and Egypt are moving towards achieving sustainable development goals (SDGs) 2030, and 2050, to reach Net-Zero emissions. Based on satellite observations, this research assesses and monitors the GHG emissions induced by human activities in Egypt. Different satellite sensors were utilized in this study to obtain Methane (CH<sub>4</sub>), Carbon Dioxide (CO<sub>2</sub>) amounts during 2015–2022. To get a deeper insight into the effects of anthropogenic activities on CO<sub>2</sub> and CH<sub>4</sub> amounts, they were correlated with land use and land cover, fire incidents, and industrial activities in Egypt. Results revealed a noticeable increase in CH<sub>4</sub> and CO<sub>2</sub> emissions over the country with a maximum level in 2022. CO<sub>2</sub> has a seasonal variation mode, with the highest amounts in spring reaching 0.000409 CO<sub>2</sub>/mol dry-air. As well, the high CH<sub>4</sub> concentration fluctuates all the year-round, with a peak around 1890 ppbv in August. The high levels of GHGs mostly concentrated in the Nile Delta and Nile Valley, where most of the anthropogenic activities are existing. Fire incidents, industries, and land cover change maps showed a spatial matching with the high emission zones. However, the emissions are increasing in Egypt it does not exceed the global average. In conclusion, unmanaged human activities in Egypt increased GHGs release and affected environmental sustainability. This study attempts to better understand the ambient environment in Egypt and support the decision-makers with full insight into the GHG emission hotspots in the country to mitigate their release into the atmosphere and achieve Net-Zero emissions.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"26 4","pages":"Pages 1071-1079"},"PeriodicalIF":6.4,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S111098232300100X/pdfft?md5=3869505426376beb4a5bd736cd2a5b97&pid=1-s2.0-S111098232300100X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138472079","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}

引用次数: 0

Quantifying urban expansion and its driving forces in Chengdu, western China 成都城市扩张量化及其驱动力分析

IF 6.4 3区地球科学

Egyptian Journal of Remote Sensing and Space Sciences Pub Date : 2023-11-28 DOI: 10.1016/j.ejrs.2023.11.010

Guangjie Wang , Wenfu Peng , Lindan Zhang , Jiayao Xiang , Jingwen Shi , Lu Wang

{"title":"Quantifying urban expansion and its driving forces in Chengdu, western China","authors":"Guangjie Wang , Wenfu Peng , Lindan Zhang , Jiayao Xiang , Jingwen Shi , Lu Wang","doi":"10.1016/j.ejrs.2023.11.010","DOIUrl":"https://doi.org/10.1016/j.ejrs.2023.11.010","url":null,"abstract":"<div><p>Understanding urban sprawl and its drivers is crucial for sustainable urban development. Most studies on Chinese urbanization have focused on coastal areas, paying little attention to urban centers in western China. This study examines urban expansion based on the Google Earth Engine (GEE), remotely sensed image, urban expansion model, and analysis of buffer and quadrant location in the Geographic Information System (GIS). Additionally, driving forces of urban expansion are examined based on the principle component analysis (PCA). Results indicate that urban land area increased more than 5.60 times, reaching 124,723 ha, an increase of over 400 % during 1990–2020. The urban expansion rate and intensity significantly increased and exhibited spatio-temporal heterogeneity. We identified that urban spatial expansion patterns changed from patch filling to patch border expansion, and urban expansion direction was mainly in the southern, northeastern, southwestern, and northwestern regions, extending along the traffic corridor, ring road, and adjacent cities. We suggest that economic development, population, and urbanization have become the driving factors of urban expansion. The GEE provides a new geographic processing algorithm based on massive image datasets, facilitating remote sensing processing. The results revealed that Chengdu is following trends witnessed in coastal cities of China; however, the significance of various drivers of urban expansion in these cities differs from that of the eastern cities. This study will help formulate policies for better urban land management and sustainable land development.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"26 4","pages":"Pages 1057-1070"},"PeriodicalIF":6.4,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982323000984/pdfft?md5=aeefb71f74cf464559532fb776c1ce61&pid=1-s2.0-S1110982323000984-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138453979","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}

引用次数: 0