Pub Date : 2023-01-19DOI: 10.1080/22797254.2022.2163707
Ryan P. Smith, F. Dias, G. Facciolo, T. B. Murphy
{"title":"Pre-computation of image features for the classification of dynamic properties in breaking waves","authors":"Ryan P. Smith, F. Dias, G. Facciolo, T. B. Murphy","doi":"10.1080/22797254.2022.2163707","DOIUrl":"https://doi.org/10.1080/22797254.2022.2163707","url":null,"abstract":"","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44519089","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}
Pub Date : 2023-01-19DOI: 10.1080/22797254.2022.2155582
Hong Wang, Hua Deng, Weiyu Ding, Jinfang Yin
{"title":"Polarimetric radar observation of the melting layer during the pre-summer rainy season over South China","authors":"Hong Wang, Hua Deng, Weiyu Ding, Jinfang Yin","doi":"10.1080/22797254.2022.2155582","DOIUrl":"https://doi.org/10.1080/22797254.2022.2155582","url":null,"abstract":"","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43100367","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}
Pub Date : 2023-01-19DOI: 10.1080/22797254.2022.2162441
Irene Capecchi, Tommaso Borghini, I. Bernetti
{"title":"Automated urban tree survey using remote sensing data, Google street view images, and plant species recognition apps","authors":"Irene Capecchi, Tommaso Borghini, I. Bernetti","doi":"10.1080/22797254.2022.2162441","DOIUrl":"https://doi.org/10.1080/22797254.2022.2162441","url":null,"abstract":"","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49271442","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}
Pub Date : 2023-01-06DOI: 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
{"title":"Remote sensing techniques for water management and climate change monitoring in drought areas: case studies in Egypt and Tunisia","authors":"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","doi":"10.1080/22797254.2022.2157335","DOIUrl":"https://doi.org/10.1080/22797254.2022.2157335","url":null,"abstract":"","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42959854","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}
Pub Date : 2023-01-05DOI: 10.1080/22797254.2022.2161070
Edvinas Tiškus, D. Vaičiūtė, M. Bučas, Jonas Gintauskas
{"title":"Evaluation of common reed (Phragmites australis) bed changes in the context of management using earth observation and automatic threshold","authors":"Edvinas Tiškus, D. Vaičiūtė, M. Bučas, Jonas Gintauskas","doi":"10.1080/22797254.2022.2161070","DOIUrl":"https://doi.org/10.1080/22797254.2022.2161070","url":null,"abstract":"","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47625669","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}
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.
{"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}
Pub Date : 2022-12-31DOI: 10.1080/22797254.2022.2079565
Yongliu Li, Zhongfa Zhou, Jie Kong, Chaocheng Wen, Shaohui Li, Yongrong Zhang, Jiangting Xie, Cui Wang
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.
{"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}
Pub Date : 2022-12-31DOI: 10.1080/22797254.2022.2075794
Gladima Nisia T, R. S
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.
特征提取是高分辨率遥感图像分类的重要方法之一。一个好的特征集可以产生一个高效的分类过程。最近的趋势是在没有人为干预的情况下使用神经网络从图像中提取特征。我们的方法使用深度卷积神经网络来提取深度特征。为了提高提取特征的效率，该系统将深度特征与Gabor特征和新的改进的局部二值模式特征相结合。这些特征被组合并发送给分类。然后，对图像进行分类处理。该系统在特征提取实现中引入了两个新颖的思路，即(1)CNN滤波器值的初始化和(2)局部二值模式特征提取过程的改变。实验结果用LISS IV Madurai图像进行了验证，并对结果进行了评价。结果表明，与现有的方法相比，该系统取得了较好的效果。
{"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}
Pub Date : 2022-12-28DOI: 10.1080/22797254.2022.2161420
Jussi Juola, A. Hovi, M. Rautiainen
{"title":"Classification of tree species based on hyperspectral reflectance images of stem bark","authors":"Jussi Juola, A. Hovi, M. Rautiainen","doi":"10.1080/22797254.2022.2161420","DOIUrl":"https://doi.org/10.1080/22797254.2022.2161420","url":null,"abstract":"","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44892893","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}
Pub Date : 2022-12-28DOI: 10.1080/22797254.2022.2161418
Valentino Demurtas, Paolo Emanuele Orrù, G. Deiana
{"title":"Active lateral spreads monitoring system in East-Central Sardinia","authors":"Valentino Demurtas, Paolo Emanuele Orrù, G. Deiana","doi":"10.1080/22797254.2022.2161418","DOIUrl":"https://doi.org/10.1080/22797254.2022.2161418","url":null,"abstract":"","PeriodicalId":49077,"journal":{"name":"European Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49492573","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}
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