European Journal of Remote Sensing最新文献_第9页

Pre-computation of image features for the classification of dynamic properties in breaking waves 用于破碎波动态特性分类的图像特征预计算

IF 4 4区地球科学

European Journal of Remote Sensing Pub Date : 2023-01-19 DOI: 10.1080/22797254.2022.2163707

Ryan P. Smith, F. Dias, G. Facciolo, T. B. Murphy

引用次数: 0

Polarimetric radar observation of the melting layer during the pre-summer rainy season over South China 夏季前雨季华南上空融化层的极化雷达观测

IF 4 4区地球科学

European Journal of Remote Sensing Pub Date : 2023-01-19 DOI: 10.1080/22797254.2022.2155582

Hong Wang, Hua Deng, Weiyu Ding, Jinfang Yin

引用次数: 1

Automated urban tree survey using remote sensing data, Google street view images, and plant species recognition apps 使用遥感数据、谷歌街景图像和植物物种识别应用程序的自动城市树木调查

IF 4 4区地球科学

European Journal of Remote Sensing Pub Date : 2023-01-19 DOI: 10.1080/22797254.2022.2162441

Irene Capecchi, Tommaso Borghini, I. Bernetti

引用次数: 0

Remote sensing techniques for water management and climate change monitoring in drought areas: case studies in Egypt and Tunisia 干旱地区水资源管理和气候变化监测的遥感技术：埃及和突尼斯的案例研究

IF 4 4区地球科学

European Journal of Remote Sensing Pub Date : 2023-01-06 DOI: 10.1080/22797254.2022.2157335

G. Ramat, E. Santi, S. Paloscia, G. Fontanelli, S. Pettinato, L. Santurri, Najet Souissi, E. Da Ponte, M. Wahab, A. Khalil, Y. H. Essa, M. Ouessar, H. Dhaou, A. Sghaier, Amal Hachani, Z. Kassouk, Z. Lili Chabaane

引用次数: 2

Evaluation of common reed (Phragmites australis) bed changes in the context of management using earth observation and automatic threshold 利用地球观测和自动阈值评估管理背景下普通芦苇（芦苇）床的变化

IF 4 4区地球科学

European Journal of Remote Sensing Pub Date : 2023-01-05 DOI: 10.1080/22797254.2022.2161070

Edvinas Tiškus, D. Vaičiūtė, M. Bučas, Jonas Gintauskas

引用次数: 2

Phenological piecewise modelling is more conducive than whole-season modelling to winter wheat yield estimation based on remote sensing data 物候分段模型比全季模型更有利于基于遥感数据估算冬小麦产量

IF 4 4区地球科学

European Journal of Remote Sensing Pub Date : 2022-12-31 DOI: 10.1080/22797254.2022.2073916

Xin Huang, Wenquan Zhu, Cenliang Zhao, Zhiying Xie, Hui Zhang

{"title":"Phenological piecewise modelling is more conducive than whole-season modelling to winter wheat yield estimation based on remote sensing data","authors":"Xin Huang, Wenquan Zhu, Cenliang Zhao, Zhiying Xie, Hui Zhang","doi":"10.1080/22797254.2022.2073916","DOIUrl":"https://doi.org/10.1080/22797254.2022.2073916","url":null,"abstract":"ABSTRACT Most of the existing remote sensing-based yield estimation methods adopt the mean or cumulative value of meteorological factors within the whole growing season, which may ignore the impact of adverse meteorological conditions on the growth of winter wheat in a certain phenological period. In this study, we distinguished the developmental progression of winter wheat as three phenological periods. In each phenological period, the vegetation indices and meteorological factors were optimized. Then the accuracy and spatiotemporal transferability of the phenological piecewise modelling was compared with that of the whole-season modelling based on four regression methods (i.e. multiple linear regression, artificial neural network, support vector regression and random forest). The results showed that the optimal combinations of variables for the whole-season modelling and the phenological piecewise modelling were different. Compared with the whole-season models, the R2 for the phenological piecewise models improved by 1.4% to 7.6%, the root mean square error (RMSE) decreased by 1.1% to 8.2% among four regression methods . In addition, compared with the whole-season models, the spatiotemporal transferability for the phenological piecewise models was generally better. The accuracies after spatiotemporal transfer for the phenological piecewise models were still higher than that for the whole-season models.","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":"55 1","pages":"338 - 352"},"PeriodicalIF":4.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48300964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Monitoring Chlorophyll-a concentration in karst plateau lakes using Sentinel 2 imagery from a case study of pingzhai reservoir in Guizhou, China 基于Sentinel 2影像的喀斯特高原湖泊叶绿素-a浓度监测——以贵州平寨水库为例

IF 4 4区地球科学

European Journal of Remote Sensing Pub Date : 2022-12-31 DOI: 10.1080/22797254.2022.2079565

Yongliu Li, Zhongfa Zhou, Jie Kong, Chaocheng Wen, Shaohui Li, Yongrong Zhang, Jiangting Xie, Cui Wang

{"title":"Monitoring Chlorophyll-a concentration in karst plateau lakes using Sentinel 2 imagery from a case study of pingzhai reservoir in Guizhou, China","authors":"Yongliu Li, Zhongfa Zhou, Jie Kong, Chaocheng Wen, Shaohui Li, Yongrong Zhang, Jiangting Xie, Cui Wang","doi":"10.1080/22797254.2022.2079565","DOIUrl":"https://doi.org/10.1080/22797254.2022.2079565","url":null,"abstract":"ABSTRACT Chlorophyll-a concentration (Chla) is an important index for water eutrophication. In this study, retrieval models of Chla were established based on the measured water spectra, spectral response function, measured Chla and the corresponding Sentinel-2 imagery of the Pingzhai Reservoir, the first large-scale trans-regional, trans-basin, and long-distance source reservoir in Guizhou. The retrieved results from 11 Sentinel-2 from 2018 to 2021 were used to analyze the spatiotemporal variations in Chla and the influence of different environmental factors on their spatial differentiation, providing a powerful approach for monitoring Chla in the Pingzhai Reservoir. Our binomial function model based on B8*(B7-B5) of Sentinel-2 yielded acceptable to high fitting accuracies, accounting for 89% of the variation in Chla. Overall, the Chla was relatively low, with a mean value of 10.24 μg/L. Higher Chla were distributed in the catchment area, such as the Nayong River and the dam. Moreover, significant seasonal fluctuations and intra-year changes were observed . Spatio-temporal variations in Chla were influenced by human activities and environmental factors such as Dissolved Oxygen (DO), Total Nitrogen (TN), and Ammoniacal Nitrogen (NH4 +-N). Our work provided compelling evidence that Sentinel-2 could be used for quantitative inversion of Chla in Pingzhai Reservoir.","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":"55 1","pages":"1 - 19"},"PeriodicalIF":4.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43482628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Ensemble of features for efficient classification of high-resolution remote sensing image 基于特征集合的高分辨率遥感图像有效分类

IF 4 4区地球科学

European Journal of Remote Sensing Pub Date : 2022-12-31 DOI: 10.1080/22797254.2022.2075794

Gladima Nisia T, R. S

{"title":"Ensemble of features for efficient classification of high-resolution remote sensing image","authors":"Gladima Nisia T, R. S","doi":"10.1080/22797254.2022.2075794","DOIUrl":"https://doi.org/10.1080/22797254.2022.2075794","url":null,"abstract":"ABSTRACT Extracting feature is one of the important methods in classification of high-resolution remote sensing image. A good feature set can result in an efficient classification process. Recent trend moves in extracting the features from the image using neural networks with no human intervention. Our approach uses the deep convolutional neural network for extracting deep features. To still rise the efficiency of the extracted features, the proposed system combines the deep features with other features like Gabor features and novel reformed local binary pattern features. The features are combined and sent for classification. Then, the classification process is done to classify the images. The proposed system introduces two novel ideas, in its feature extraction implementation, namely (1) initialisation of filter values for the CNN and (2) change in local binary pattern feature extraction process. The experimental results are carried out with LISS IV Madurai image, and evaluation is done for the verification of the results. It is found that the system proposed produces good results when compared with other existing methods.","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":"55 1","pages":"326 - 337"},"PeriodicalIF":4.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45983024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Classification of tree species based on hyperspectral reflectance images of stem bark 基于树干树皮高光谱反射图像的树种分类

IF 4 4区地球科学

European Journal of Remote Sensing Pub Date : 2022-12-28 DOI: 10.1080/22797254.2022.2161420

Jussi Juola, A. Hovi, M. Rautiainen

引用次数: 2

Active lateral spreads monitoring system in East-Central Sardinia 撒丁岛中东部的主动横向扩散监测系统

IF 4 4区地球科学

European Journal of Remote Sensing Pub Date : 2022-12-28 DOI: 10.1080/22797254.2022.2161418

Valentino Demurtas, Paolo Emanuele Orrù, G. Deiana

引用次数: 2