Pub Date : 2024-12-04DOI: 10.1016/j.jag.2024.104294
Chongjing Zhu, Xiaojun She, Xiaojie Gao, Yajun Huang, Yelu Zeng, Chao Ding, Dongjie Fu, Jing Shao, Yao Li
Understanding terrestrial vegetation phenology—the timing of life-cycle events—is crucial for insights into ecosystem energy and material cycles. Land surface phenology (LSP) derived from satellite observations has become a critical tool for tracking vegetation phenology across large spatial scales. However, LSP data from coarse spatial resolutions often mix phenological signals from multiple land cover types, a limitation that fine-resolution satellite data can help overcome. Recent studies indicate that spring phenology derived from the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) data tends to be biased earlier than that from the 30-m Landsat data due to scale effects. The extent of this bias across other satellite sensors and its impact on long-term phenological trends remains unclear. Additionally, few studies have used medium- to high-resolution LSP data to investigate southwestern China, partly due to limited data availability, which may exacerbate uncertainties related to scale effects in LSP observations. To address these gaps, we selected Jinfo Mountain in southwestern China—a region with high spatial heterogeneity—to analyze the spatiotemporal patterns of spring phenology and examine associated scale effects and uncertainties. We applied two phenology retrieval methods to multi-resolution LSP data from various sensors: 30-m Landsat (1984–2023), 250-m MODIS (2002–2021), 500-m MODIS (2000–2023), 1-km SPOT (1999–2019), and 8-km AVHRR (1982–2022). Our findings revealed that all sensors consistently captured the spatial patterns of spring phenology, indicating an advancing trend of 6–8 days per decade, though the trend’s magnitude varied notably across sensors. Data quality, rather than retrieval methods, emerged as the primary source of uncertainty in characterizing phenological dynamics, with elevation contributing significantly to bias due to its negative correlation with the number of available clear observations. Moreover, we found that the MODIS-Landsat bias in spring phenology may not generalize across other coarse-to-fine LSP comparisons. This study provides valuable insights into phenology in the understudied region of southwestern China, highlighting the importance of spatial resolution and sensor characteristics for accurate plant phenology mapping and monitoring.
了解陆地植被物候——生命周期事件的时间——对于了解生态系统能量和物质循环至关重要。基于卫星观测的地表物候已经成为跟踪大空间尺度植被物候的重要工具。然而，来自粗空间分辨率的LSP数据经常混合来自多种土地覆盖类型的物候信号，这是精细分辨率卫星数据可以帮助克服的一个限制。最近的研究表明，由于尺度效应，500米MODIS数据的春季物候比30米Landsat数据的春季物候更早出现偏倚。其他卫星传感器的这种偏差程度及其对长期物候趋势的影响尚不清楚。此外，很少有研究使用中高分辨率的LSP数据来研究中国西南地区，部分原因是数据可用性有限，这可能会加剧LSP观测中规模效应的不确定性。为了解决这些空白，我们选择了中国西南部空间异质性较高的金佛山地区，分析了春季物候的时空格局，并研究了相关的尺度效应和不确定性。我们采用两种物候检索方法对来自不同传感器的多分辨率LSP数据进行检索：30 m Landsat（1984-2023）、250 m MODIS（2002-2021）、500 m MODIS（2000-2023）、1 km SPOT（1999-2019）和8 km AVHRR（1982-2022）。结果表明，各传感器对春季物候空间格局的捕捉一致，每10年增加6 ~ 8天，但各传感器的变化幅度差异较大。数据质量，而不是检索方法，成为物候动态特征不确定性的主要来源，海拔高度与可获得的清晰观测值的数量负相关，对偏差有显著影响。此外，我们发现春季物候的MODIS-Landsat偏差可能无法推广到其他粗-细LSP比较中。该研究为中国西南地区物候研究提供了有价值的见解，强调了空间分辨率和传感器特性对准确的植物物候制图和监测的重要性。
{"title":"Spatiotemporal variation of spring phenology and the corresponding scale effects and uncertainties: A case study in southwestern China","authors":"Chongjing Zhu, Xiaojun She, Xiaojie Gao, Yajun Huang, Yelu Zeng, Chao Ding, Dongjie Fu, Jing Shao, Yao Li","doi":"10.1016/j.jag.2024.104294","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104294","url":null,"abstract":"Understanding terrestrial vegetation phenology—the timing of life-cycle events—is crucial for insights into ecosystem energy and material cycles. Land surface phenology (LSP) derived from satellite observations has become a critical tool for tracking vegetation phenology across large spatial scales. However, LSP data from coarse spatial resolutions often mix phenological signals from multiple land cover types, a limitation that fine-resolution satellite data can help overcome. Recent studies indicate that spring phenology derived from the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) data tends to be biased earlier than that from the 30-m Landsat data due to scale effects. The extent of this bias across other satellite sensors and its impact on long-term phenological trends remains unclear. Additionally, few studies have used medium- to high-resolution LSP data to investigate southwestern China, partly due to limited data availability, which may exacerbate uncertainties related to scale effects in LSP observations. To address these gaps, we selected Jinfo Mountain in southwestern China—a region with high spatial heterogeneity—to analyze the spatiotemporal patterns of spring phenology and examine associated scale effects and uncertainties. We applied two phenology retrieval methods to multi-resolution LSP data from various sensors: 30-m Landsat (1984–2023), 250-m MODIS (2002–2021), 500-m MODIS (2000–2023), 1-km SPOT (1999–2019), and 8-km AVHRR (1982–2022). Our findings revealed that all sensors consistently captured the spatial patterns of spring phenology, indicating an advancing trend of 6–8 days per decade, though the trend’s magnitude varied notably across sensors. Data quality, rather than retrieval methods, emerged as the primary source of uncertainty in characterizing phenological dynamics, with elevation contributing significantly to bias due to its negative correlation with the number of available clear observations. Moreover, we found that the MODIS-Landsat bias in spring phenology may not generalize across other coarse-to-fine LSP comparisons. This study provides valuable insights into phenology in the understudied region of southwestern China, highlighting the importance of spatial resolution and sensor characteristics for accurate plant phenology mapping and monitoring.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"15 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142793572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The efficient and automated identification of landslide hazards is essential for socio-economic development and human safety. Integrating the feature extraction capabilities of deep learning with the millimeter-level precision of Interferometric Synthetic Aperture Radar (InSAR) technology establishes a foundation for this task. However, current methods require unwrapping interferograms, and even converting them into deformation products before identifying landslide hazards. This process is susceptible to unwrapping errors, resulting in inefficient data utilization, and demands considerable time and labor. To overcome these challenges, wrapped interferograms are directly utilized for identifying creeping landslides. In this study, trigonometric functions are applied to improve the representation of interferograms and to further enhance the data through rendering. Secondly, a multi-branch semantic segmentation network (MB-Net) was designed, with parallel branch encoding and progressive feature fusion to optimize the model’s ability to learn interferometric phases. Experimental results indicate a good performance, with the F1-score of 80.91 %, the Intersection over Union (IoU) of 67.94 %, and the Matthews correlation coefficient (MCC) of 80.16 % on the ISSLIDE dataset. To further validate the generalization capability of MB-Net, the public COMET-LiCS Sentinel-1 InSAR portal data was utilized, focusing on the middle reaches of the Jinsha River in China. The results highlight MB-Net’s efficacy in spatial transferability analysis. These findings emphasize the potential of our approach for large-scale landslide hazard identification, providing a crucial foundation for the utilization of interferograms in creeping landslide detection.
有效、自动识别滑坡灾害对社会经济发展和人类安全至关重要。将深度学习的特征提取能力与干涉合成孔径雷达（InSAR）技术的毫米级精度相结合，为该任务奠定了基础。然而，目前的方法需要解开干涉图，甚至在确定滑坡危害之前将其转换为变形产物。此过程容易出现展开错误，导致数据利用效率低下，并且需要大量的时间和人力。为了克服这些挑战，直接利用包裹干涉图来识别蠕变滑坡。在本研究中，利用三角函数来改进干涉图的表示，并通过渲染进一步增强数据。其次，设计多分支语义分割网络（MB-Net），采用并行分支编码和渐进式特征融合，优化模型对干涉相位的学习能力；实验结果表明，该方法在ISSLIDE数据集上的f1得分为80.91%,Intersection over Union （IoU）为67.94%，Matthews相关系数（MCC）为80.16%。为了进一步验证MB-Net的泛化能力，以中国金沙江中游为重点，利用comet - lic Sentinel-1 InSAR门户网站的公开数据。研究结果突出了MB-Net在空间可转移性分析中的有效性。这些发现强调了我们的方法在大规模滑坡危险识别方面的潜力，为在蠕变滑坡检测中使用干涉图提供了重要的基础。
{"title":"MB-Net: A network for accurately identifying creeping landslides from wrapped interferograms","authors":"Ruixuan Zhang, Wu Zhu, Baodi Fan, Qian He, Jiewei Zhan, Chisheng Wang, Bochen Zhang","doi":"10.1016/j.jag.2024.104300","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104300","url":null,"abstract":"The efficient and automated identification of landslide hazards is essential for socio-economic development and human safety. Integrating the feature extraction capabilities of deep learning with the millimeter-level precision of Interferometric Synthetic Aperture Radar (InSAR) technology establishes a foundation for this task. However, current methods require unwrapping interferograms, and even converting them into deformation products before identifying landslide hazards. This process is susceptible to unwrapping errors, resulting in inefficient data utilization, and demands considerable time and labor. To overcome these challenges, wrapped interferograms are directly utilized for identifying creeping landslides. In this study, trigonometric functions are applied to improve the representation of interferograms and to further enhance the data through rendering. Secondly, a multi-branch semantic segmentation network (MB-Net) was designed, with parallel branch encoding and progressive feature fusion to optimize the model’s ability to learn interferometric phases. Experimental results indicate a good performance, with the F1-score of 80.91 %, the Intersection over Union (IoU) of 67.94 %, and the Matthews correlation coefficient (MCC) of 80.16 % on the ISSLIDE dataset. To further validate the generalization capability of MB-Net, the public COMET-LiCS Sentinel-1 InSAR portal data was utilized, focusing on the middle reaches of the Jinsha River in China. The results highlight MB-Net’s efficacy in spatial transferability analysis. These findings emphasize the potential of our approach for large-scale landslide hazard identification, providing a crucial foundation for the utilization of interferograms in creeping landslide detection.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"48 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142793573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-12-02DOI: 10.1016/j.jag.2024.104295
Zhaozhi Luo, Janne Heiskanen, Xinyu Wang, Yanfei Zhong, Petri Pellikka
Hyperspectral images (HSIs) acquired from different imaging platforms are inevitably contaminated by multiple types of noise. However, the existing supervised learning based denoising methods often show poor generalizability on data with complex degradation, due to the discrepancy between synthetic training data and real data. Although some unsupervised denoisers have been developed to learn priors on real data, the noise assumptions or image priors in these methods limit their performances. In this paper, an unsupervised noise estimation and restoration (UNER) framework is proposed based on disentangled representation learning, to create an interband representation space that is resistant to noise within a single HSI, i.e., an interband-invariant representation space. Real HSIs are categorized internally into noisy and clean bands by noise intensity estimation. Intraband and interband disentangled reconstruction are designed to train two encoder-decoder modules to learn the interband-invariant representation. Noise patterns are separated from HSIs by transforming noisy bands into the representation space without introducing hand-crafted priors. The estimated noise and clean bands are then combined to train the self-supervised interband information restoration module, thereby exploiting spectral correlation and restoring the latent noise-free data with high information fidelity. In this way, the UNER framework can learn specific priors from real HSIs without clean data and be applied to various scenarios with complicated noise patterns. Noise removal experiments conducted on three airborne hyperspectral datasets representing urban, agricultural, and forestry areas respectively demonstrate the superiority of the proposed UNER framework over the state-of-the-art hyperspectral denoising methods.
{"title":"Unsupervised hyperspectral noise estimation and restoration via interband-invariant representation learning","authors":"Zhaozhi Luo, Janne Heiskanen, Xinyu Wang, Yanfei Zhong, Petri Pellikka","doi":"10.1016/j.jag.2024.104295","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104295","url":null,"abstract":"Hyperspectral images (HSIs) acquired from different imaging platforms are inevitably contaminated by multiple types of noise. However, the existing supervised learning based denoising methods often show poor generalizability on data with complex degradation, due to the discrepancy between synthetic training data and real data. Although some unsupervised denoisers have been developed to learn priors on real data, the noise assumptions or image priors in these methods limit their performances. In this paper, an unsupervised noise estimation and restoration (UNER) framework is proposed based on disentangled representation learning, to create an interband representation space that is resistant to noise within a single HSI, i.e., an interband-invariant representation space. Real HSIs are categorized internally into noisy and clean bands by noise intensity estimation. Intraband and interband disentangled reconstruction are designed to train two encoder-decoder modules to learn the interband-invariant representation. Noise patterns are separated from HSIs by transforming noisy bands into the representation space without introducing hand-crafted priors. The estimated noise and clean bands are then combined to train the self-supervised interband information restoration module, thereby exploiting spectral correlation and restoring the latent noise-free data with high information fidelity. In this way, the UNER framework can learn specific priors from real HSIs without clean data and be applied to various scenarios with complicated noise patterns. Noise removal experiments conducted on three airborne hyperspectral datasets representing urban, agricultural, and forestry areas respectively demonstrate the superiority of the proposed UNER framework over the state-of-the-art hyperspectral denoising methods.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"79 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142793388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2024-12-02DOI: 10.1016/j.jag.2024.104292
Yu Chen, Xinlong Chen, Shanchuan Guo, Huaizhan Li, Peijun Du
Severe surface deformation can damage the ecological environment, trigger geological disasters, and threaten human life and property. Reliable surface deformation prediction is conducive to reducing potential risks and mitigating disaster losses. Currently, machine learning-based surface deformation prediction models have shown significant improvements in prediction performance. However, most prediction models do not sufficiently consider the characteristics of surface deformation, exhibit subjectivity in parameter settings, and inadequately capture local features in time series data. We introduce the AWC-LSTM model to predict surface deformation. Initially, leveraging the strengths of the autoregressive integrated moving average (ARIMA) model in handling linear signals, the obtained surface deformation information is decomposed to linear and nonlinear parts, and the linear part is predicted. Secondly, by incorporating convolutional neural network (CNN) layers into the long short term memory (LSTM) model, the ability to learn local features is enhanced and the whale optimization algorithm (WOA) is introduced to determine the optimal hyperparameters of the model, thereby predicting nonlinear deformation. The proposed AWC-LSTM model was validated using the Shilawusu coal mine and Beijing as case studies. The outcomes indicate that the deformation predictions for the Shilawusu coal mine and Beijing exhibit a high degree of consistency with the monitored data, with root mean square errors (RMSE) not exceeding 3 mm. This underscores the model’s reliability and applicability across different areas. Comparisons with existing prediction models indicate that the AWC-LSTM model achieves higher predictive accuracy, with an average improvement in accuracy ranging from 28.38 % to 80.59 % over other models.
{"title":"A novel surface deformation prediction method based on AWC-LSTM model","authors":"Yu Chen, Xinlong Chen, Shanchuan Guo, Huaizhan Li, Peijun Du","doi":"10.1016/j.jag.2024.104292","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104292","url":null,"abstract":"Severe surface deformation can damage the ecological environment, trigger geological disasters, and threaten human life and property. Reliable surface deformation prediction is conducive to reducing potential risks and mitigating disaster losses. Currently, machine learning-based surface deformation prediction models have shown significant improvements in prediction performance. However, most prediction models do not sufficiently consider the characteristics of surface deformation, exhibit subjectivity in parameter settings, and inadequately capture local features in time series data. We introduce the AWC-LSTM model to predict surface deformation. Initially, leveraging the strengths of the autoregressive integrated moving average (ARIMA) model in handling linear signals, the obtained surface deformation information is decomposed to linear and nonlinear parts, and the linear part is predicted. Secondly, by incorporating convolutional neural network (CNN) layers into the long short term memory (LSTM) model, the ability to learn local features is enhanced and the whale optimization algorithm (WOA) is introduced to determine the optimal hyperparameters of the model, thereby predicting nonlinear deformation. The proposed AWC-LSTM model was validated using the Shilawusu coal mine and Beijing as case studies. The outcomes indicate that the deformation predictions for the Shilawusu coal mine and Beijing exhibit a high degree of consistency with the monitored data, with root mean square errors (RMSE) not exceeding 3 mm. This underscores the model’s reliability and applicability across different areas. Comparisons with existing prediction models indicate that the AWC-LSTM model achieves higher predictive accuracy, with an average improvement in accuracy ranging from 28.38 % to 80.59 % over other models.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"18 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142793389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2023-09-01DOI: 10.1016/j.jag.2023.103445
S. Atzori, Fernando Monterroso, A. Antonioli, C. Luca, N. Svigkas, F. Casu, M. Manunta, M. Quintiliani, R. Lanari
{"title":"Automatic seismic source modeling of InSAR displacements","authors":"S. Atzori, Fernando Monterroso, A. Antonioli, C. Luca, N. Svigkas, F. Casu, M. Manunta, M. Quintiliani, R. Lanari","doi":"10.1016/j.jag.2023.103445","DOIUrl":"https://doi.org/10.1016/j.jag.2023.103445","url":null,"abstract":"","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"123 1","pages":"103445"},"PeriodicalIF":7.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54752467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2023-09-01Epub Date: 2023-08-28DOI: 10.1016/j.jag.2023.103469
Johannes H Uhl, Stefan Leyk
Geospatial datasets derived from remote sensing data by means of machine learning methods are often based on probabilistic outputs of abstract nature, which are difficult to translate into interpretable measures. For example, the Global Human Settlement Layer GHS-BUILT-S2 product reports the probability of the presence of built-up areas in 2018 in a global 10 m × 10 m grid. However, practitioners typically require interpretable measures such as binary surfaces indicating the presence or absence of built-up areas or estimates of sub-pixel built-up surface fractions. Herein, we assess the relationship between the built-up probability in GHS-BUILT-S2 and reference built-up surface fractions derived from a highly reliable reference database for several regions in the United States. Furthermore, we identify a binarization threshold using an agreement maximization method that creates binary built-up land data from these built-up probabilities. These binary surfaces are input to a spatially explicit, scale-sensitive accuracy assessment which includes the use of a novel, visual-analytical tool which we call focal precision-recall signature plots. Our analysis reveals that a threshold of 0.5 applied to GHS-BUILT-S2 maximizes the agreement with binarized built-up land data derived from the reference built-up area fraction. We find high levels of accuracy (i.e., county-level F-1 scores of almost 0.8 on average) in the derived built-up areas, and consistently high accuracy along the rural-urban gradient in our study area. These results reveal considerable accuracy improvements in human settlement models based on Sentinel-2 data and deep learning, as compared to earlier, Landsat-based versions of the Global Human Settlement Layer.
{"title":"Spatially explicit accuracy assessment of deep learning-based, fine-resolution built-up land data in the United States.","authors":"Johannes H Uhl, Stefan Leyk","doi":"10.1016/j.jag.2023.103469","DOIUrl":"10.1016/j.jag.2023.103469","url":null,"abstract":"<p><p>Geospatial datasets derived from remote sensing data by means of machine learning methods are often based on probabilistic outputs of abstract nature, which are difficult to translate into interpretable measures. For example, the Global Human Settlement Layer GHS-BUILT-S2 product reports the probability of the presence of built-up areas in 2018 in a global 10 m × 10 m grid. However, practitioners typically require interpretable measures such as binary surfaces indicating the presence or absence of built-up areas or estimates of sub-pixel built-up surface fractions. Herein, we assess the relationship between the built-up probability in GHS-BUILT-S2 and reference built-up surface fractions derived from a highly reliable reference database for several regions in the United States. Furthermore, we identify a binarization threshold using an agreement maximization method that creates binary built-up land data from these built-up probabilities. These binary surfaces are input to a spatially explicit, scale-sensitive accuracy assessment which includes the use of a novel, visual-analytical tool which we call focal precision-recall signature plots. Our analysis reveals that a threshold of 0.5 applied to GHS-BUILT-S2 maximizes the agreement with binarized built-up land data derived from the reference built-up area fraction. We find high levels of accuracy (i.e., county-level F-1 scores of almost 0.8 on average) in the derived built-up areas, and consistently high accuracy along the rural-urban gradient in our study area. These results reveal considerable accuracy improvements in human settlement models based on Sentinel-2 data and deep learning, as compared to earlier, Landsat-based versions of the Global Human Settlement Layer.</p>","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"123 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10653213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54752685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.1016/j.jag.2023.103470
Y. Dvornikov, V. Grigorieva, M. Varentsov, V. Vasenev
{"title":"Optimal spectral index and threshold applied to Sentinel-2 data for extracting impervious surface: Verification across latitudes, growing seasons, approaches, and comparison to global datasets","authors":"Y. Dvornikov, V. Grigorieva, M. Varentsov, V. Vasenev","doi":"10.1016/j.jag.2023.103470","DOIUrl":"https://doi.org/10.1016/j.jag.2023.103470","url":null,"abstract":"","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"123 1","pages":"103470"},"PeriodicalIF":7.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54752739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2023-09-01DOI: 10.1016/j.jag.2023.103474
A. Tomczyk, M. Ewertowski, Noah Creany, F. Ancin‐Murguzur, Christopher Monz
{"title":"The application of unmanned aerial vehicle (UAV) surveys and GIS to the analysis and monitoring of recreational trail conditions","authors":"A. Tomczyk, M. Ewertowski, Noah Creany, F. Ancin‐Murguzur, Christopher Monz","doi":"10.1016/j.jag.2023.103474","DOIUrl":"https://doi.org/10.1016/j.jag.2023.103474","url":null,"abstract":"","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"123 1","pages":"103474"},"PeriodicalIF":7.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54752910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2023-09-01DOI: 10.1016/j.jag.2023.103468
Qihao Chen, Mengqing Pang, Xiuguo Liu, Zeyu Zhang
{"title":"A polarization-spectrum fusion framework based on multiscale transform and generative adversarial network for improving water and different vegetation distinguishability","authors":"Qihao Chen, Mengqing Pang, Xiuguo Liu, Zeyu Zhang","doi":"10.1016/j.jag.2023.103468","DOIUrl":"https://doi.org/10.1016/j.jag.2023.103468","url":null,"abstract":"","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"20 1","pages":"103468"},"PeriodicalIF":7.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54752653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2023-09-01DOI: 10.1016/j.jag.2023.103447
Junjie Zhu, Ke Yang, Naiyang Guan, Xiaodong Yi, C. Qiu
{"title":"HCPNet: Learning discriminative prototypes for few-shot remote sensing image scene classification","authors":"Junjie Zhu, Ke Yang, Naiyang Guan, Xiaodong Yi, C. Qiu","doi":"10.1016/j.jag.2023.103447","DOIUrl":"https://doi.org/10.1016/j.jag.2023.103447","url":null,"abstract":"","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"123 1","pages":"103447"},"PeriodicalIF":7.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54752584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：