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The following results are related to Rural Digital Europe. Are you interested to view more results? Visit OpenAIRE - Explore.

  • Rural Digital Europe
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  • Remote Sensing

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Sihui Zhang; Yong Nie; Huayu Zhang;

    Glacial lake outburst floods (GLOFs) are one of the most severe disasters in alpine regions, releasing a large amount of water and sediment that can cause fatalities and economic loss as well as substantial damage to downstream infrastructures. The risk of GLOFs in the Himalayas is exacerbated by glacier retreat caused by global warming. Critical economic corridors, such as the Rongxer Watershed, are threatened by GLOFs, but the lack of risk assessment specific to the watershed hinders hazard prevention. In this study, we propose a novel model to evaluate the risk of GLOF using a combination of remote sensing observations, GIS, and hydrological models and apply this model to the GLOF risk assessment in the Rongxer Watershed. The results show that (1) the area of glacial lakes in the Rongxer Watershed increased by 31.19% from 11.35 km2 in 1990 to 14.89 km2 in 2020, and (2) 18 lakes were identified as potentially dangerous glacial lakes (PDGLs) that need to be assessed for the GLOF risk, and two of them were categorized as very high risk (Niangzongmajue and Tsho Rolpa). The proposed model was robust in a GLOF risk evaluation by historical GLOFs in the Himalayas. The glacial lake data and GLOF risk assessment model of this study have the potential to be widely used in research on the relationships between glacial lakes and climate change, as well as in disaster mitigation of GLOFs.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Remote Sensingarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Remote Sensing
    Article . 2024
    Data sources: DOAJ
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Remote Sensing
    Article . 2024 . Peer-reviewed
    License: CC BY
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Remote Sensingarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Remote Sensing
      Article . 2024
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Remote Sensing
      Article . 2024 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Tianzhu Li; Caihong Ma; Yongze Lv; Ruilin Liao; +2 Authors

    The cement industry, as one of the primary contributors to global greenhouse gas emissions, accounts for 7% of the world’s carbon dioxide emissions. There is an urgent need to establish a rapid method for detecting cement plants to facilitate effective monitoring. In this study, a comprehensive method based on YOLOv5-IEG and the Thermal Signature Detection module using Google Earth optical imagery and SDGSAT-1 thermal infrared imagery was proposed to detect large-scale cement plant information, including geographic location and operational status. The improved algorithm demonstrated an increase of 4.8% in accuracy and a 7.7% improvement in MAP@.5:95. In a specific empirical investigation in China, we successfully detected 781 large-scale cement plants with an accuracy of 90.8%. Specifically, of the 55 cement plants in Shandong Province, we identified 46 as operational and nine as non-operational. The successful application of advanced models and remote sensing technology in efficiently and accurately tracking the operational status of cement plants provides crucial support for environmental protection and sustainable development.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Remote Sensingarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Remote Sensing
    Article . 2024 . Peer-reviewed
    License: CC BY
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Remote Sensing
    Article . 2024
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Remote Sensingarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Remote Sensing
      Article . 2024 . Peer-reviewed
      License: CC BY
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Remote Sensing
      Article . 2024
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Haoran Jiang; Luyan Ji; Kai Yu; Yongchao Zhao;

    In the process of urbanization, water bodies bear considerable anthropogenic pressure, resulting in a reduction of their surface area in most instances. Nevertheless, in contrast to many other regions, the Lixiahe region in Jiangsu Province, located in China’s eastern plain, has experienced a continuous expansion of water bodies over the past few decades amid rapid urbanization. Using Landsat images spanning from 1975 to 2023, this study analyzed changes in water resources and the growth of impervious surfaces during urbanization. The findings revealed that the area of impervious surfaces in the region increased from 227.1 km2 in 1975 to 1883.1 km2 in 2023. Natural wetland suffered significant losses, declining from 507.2 km2 in 1975 to near disappearance by the year 2000, with no significant recovery observed thereafter. Simultaneously, the water area expanded from 459.3 km2 in 1975 to 2373.1 km2 in 2023, primarily propelled by the significant contribution of aquaculture ponds, accounting for 2175.0 km2 or 91.7% of the total water area. Driver analysis revealed that these changes were found to be influenced by factors such as population, economy, demand, and policies. However, alongside the economic development brought by urbanization, negative impacts such as lake shrinkage, eutrophication, and increased flood risks have emerged. The Lixiahe region, as a relatively underdeveloped part of Jiangsu Province, faces the challenge of striking a balance between economic growth and environmental conservation.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Remote Sensingarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Remote Sensing
    Article . 2024 . Peer-reviewed
    License: CC BY
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Remote Sensing
    Article . 2024
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Remote Sensingarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Remote Sensing
      Article . 2024 . Peer-reviewed
      License: CC BY
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Remote Sensing
      Article . 2024
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Yuqi Tang; Xin Yang; Te Han; Fangyan Zhang; +2 Authors

    Heterogeneous change detection (CD) is widely applied in various fields such as urban planning, environmental monitoring, and disaster management. It enhances the accuracy and comprehensiveness of surface change monitoring by integrating multi-sensor remote sensing data. Scholars have proposed many graph-based methods to address the issue of incomparable heterogeneous images caused by imaging differences. However, these methods often overlook the influence of changes in vertex status on the graph structure, which limits their ability to represent image structural features. To tackle this problem, this paper presents an unsupervised heterogeneous CD method based on enhanced graph structure representation (EGSR). This method enhances the representation capacity of the graph structure for image structural features by measuring the unchanged probabilities of vertices, thereby making it easier to detect changes in heterogeneous images. Firstly, we construct the graph structure using image superpixels and measure the structural graph differences of heterogeneous images in the same image domain. Then, we calculate the unchanged probability of each vertex in the structural graph and reconstruct the graph structure using this probability. To accurately represent the graph structure, we adopt an iterative framework for enhancing the representation of the graph structure. Finally, at the end of the iteration, the final change map (CM) is obtained by binary segmentation of the graph vertices based on their unchanged probabilities. The effectiveness of this method is validated through experiments on four sets of heterogeneous image datasets and two sets of homogeneous image datasets.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Remote Sensingarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Remote Sensing
    Article . 2024
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Remote Sensing
    Article . 2024 . Peer-reviewed
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Remote Sensingarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Remote Sensing
      Article . 2024
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Remote Sensing
      Article . 2024 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Xiaodian Zhang; Kun Gao; Junwei Wang; Pengyu Wang; +4 Authors

    Hyperspectral target detection (HTD) is a crucial aspect of remote sensing applications, aiming to identify targets in hyperspectral images (HSIs) based on their known prior spectral signatures. However, the spectral variability resulting from various imaging conditions in multi-temporal hyperspectral images poses a challenge to both classical and deep learning (DL) methods. To overcome the limitations imposed by spectral variability, an implicit contrastive learning-based target detector (ICLTD) is proposed to exploit in-scene spectra in an unsupervised way. First, only prior spectra are utilized for explicit supervision, while an implicit contrastive learning module (ICLM) is designed to normalize the feature distributions of prior and in-scene spectra. This paper theoretically demonstrates that the ICLM can transfer the gradients from prior spectral features to those of in-scene spectra based on their feature similarities and differences. Because of transferred gradient signals, the ICLTD is regularized to extract similar representations for the prior and in-scene target spectra, while augmenting feature differences between the target and background spectra. Additionally, a local spectral similarity constraint (LSSC) is proposed to enhance the capability of scene adaptation by leveraging the spectral similarities among in-scene targets. To validate the performance of the ICLTD under spectral variability, multi-temporal HSIs captured under various imaging conditions are collected to generate prior spectra and in-scene spectra. Comparative evaluations against several DL detectors and classical methods reveal the superior performance of the ICLTD in achieving a balance between target detectability and background suppressibility under spectral variability.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Remote Sensingarrow_drop_down
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    Remote Sensing
    Article . 2024
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    Remote Sensing
    Article . 2024 . Peer-reviewed
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      Article . 2024
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      Article . 2024 . Peer-reviewed
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    Authors: Hui Ying Pak; Hieu Trung Kieu; Weisi Lin; Eugene Khoo; +1 Authors

    Uncrewed-Aerial Vehicles (UAVs) and hyperspectral sensors are emerging as effective alternatives for monitoring water quality on-demand. However, image mosaicking for largely featureless coastal water surfaces or open seas has shown to be challenging. Another pertinent issue observed is the systematic image misalignment between adjacent flight lines due to the time delay between the UAV-borne sensor and the GNSS system. To overcome these challenges, this study introduces a workflow that entails a GPS-based image mosaicking method for push-broom hyperspectral images, together with a correction method to address the aforementioned systematic image misalignment. An open-source toolkit, CoastalWQL, was developed to facilitate the workflow, which includes essential pre-processing procedures for improving the image mosaic’s quality, such as radiometric correction, de-striping, sun glint correction, and object masking classification. For validation, UAV-based push-broom hyperspectral imaging surveys were conducted to monitor coastal turbidity in Singapore, and the implementation of CoastalWQL’s pre-processing workflow was evaluated at each step via turbidity retrieval. Overall, the results confirm that the image mosaicking of the push-broom hyperspectral imagery over featureless water surface using CoastalWQL with time delay correction enabled better localisation of the turbidity plume. Radiometric correction and de-striping were also found to be the most important pre-processing procedures, which improved turbidity prediction by 46.5%.

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    Remote Sensing
    Article . 2024
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    Remote Sensing
    Article . 2024 . Peer-reviewed
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      Remote Sensing
      Article . 2024
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      Remote Sensing
      Article . 2024 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Patrick Killeen; Iluju Kiringa; Tet Yeap; Paula Branco;

    Food demand is expected to rise significantly by 2050 due to the increase in population; additionally, receding water levels, climate change, and a decrease in the amount of available arable land will threaten food production. To address these challenges and increase food security, input cost reductions and yield optimization can be accomplished using yield precision maps created by machine learning models; however, without considering the spatial structure of the data, the precision map’s accuracy evaluation assessment risks being over-optimistic, which may encourage poor decision making that can lead to negative economic impacts (e.g., lowered crop yields). In fact, most machine learning research involving spatial data, including the unmanned aerial vehicle (UAV) imagery-based yield prediction literature, ignore spatial structure and likely obtain over-optimistic results. The present work is a UAV imagery-based corn yield prediction study that analyzed the effects of image spatial and spectral resolution, image acquisition date, and model evaluation scheme on model performance. We used various spatial generalization evaluation methods, including spatial cross-validation (CV), to (a) identify over-optimistic models that overfit to the spatial structure found inside datasets and (b) estimate true model generalization performance. We compared and ranked the prediction power of 55 vegetation indices (VIs) and five spectral bands over a growing season. We gathered yield data and UAV-based multispectral (MS) and red-green-blue (RGB) imagery from a Canadian smart farm and trained random forest (RF) and linear regression (LR) models using 10-fold CV and spatial CV approaches. We found that imagery from the middle of the growing season produced the best results. RF and LR generally performed best with high and low spatial resolution data, respectively. MS imagery led to generally better performance than RGB imagery. Some of the best-performing VIs were simple ratio index(near-infrared and red-edge), normalized difference red-edge index, and normalized green index. We found that 10-fold CV coupled with spatial CV could be used to identify over-optimistic yield prediction models. When using high spatial resolution MS imagery, RF and LR obtained 0.81 and 0.56 correlation coefficient (CC), respectively, when using 10-fold CV, and obtained 0.39 and 0.41, respectively, when using a k-means-based spatial CV approach. Furthermore, when using only location features, RF and LR obtained an average CC of 1.00 and 0.49, respectively. This suggested that LR had better spatial generalizability than RF, and that RF was likely being over-optimistic and was overfitting to the spatial structure of the data.

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    Remote Sensing
    Article . 2024 . Peer-reviewed
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    Remote Sensing
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      Remote Sensing
      Article . 2024 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Wenhui Zhang; Zhinong Zhong; Hao Chen; Ning Jing;

    Cross-view geo-localization aims to locate street-view images by matching them with a collection of GPS-tagged remote sensing (RS) images. Due to the significant viewpoint and appearance differences between street-view images and RS images, this task is highly challenging. While deep learning-based methods have shown their dominance in the cross-view geo-localization task, existing models have difficulties in extracting comprehensive meaningful features from both domains of images. This limitation results in not establishing accurate and robust dependencies between street-view images and the corresponding RS images. To address the aforementioned issues, this paper proposes a novel and lightweight neural network for cross-view geo-localization. Firstly, in order to capture more diverse information, we propose a module for extracting multi-scale features from images. Secondly, we introduce contrastive learning and design a contrastive loss to further enhance the robustness in extracting and aligning meaningful multi-scale features. Finally, we conduct comprehensive experiments on two open benchmarks. The experimental results have demonstrated the superiority of the proposed method over the state-of-the-art methods.

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    Remote Sensing
    Article . 2024
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    Remote Sensing
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      Remote Sensing
      Article . 2024
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      Remote Sensing
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    Authors: Huifang Chen; Jingwei Wu; Chi Xu;

    Remote sensing (RS) technology can rapidly obtain spatial distribution information on soil salinization. However, (1) the scale effects resulting from the mismatch between ground-based “point” salinity data and remote sensing pixel-based “spatial” data often limit the accuracy of remote sensing monitoring of soil salinity, and (2) the same salinity RS monitoring model usually provides inconsistent or sometimes conflicting explanations for different data. Therefore, based on Landsat 8 imagery and synchronously collected ground-sampling data of two typical study regions (denoted as N and S, respectively) of the Yichang Irrigation Area in the Hetao Irrigation District for May 2013, this study used geostatistical methods to obtain “relative truth values” of salinity corresponding to the Landsat 8 pixel scale. Additionally, based on Landsat 8 multispectral data, 14 salinity indices were constructed. Subsequently, the Correlation-based Feature Selection (CFS) method was used to select sensitive features, and a strategy similar to the concept of ensemble learning (EL) was adopted to integrate the single-feature-sensitive Bayesian classification (BC) model in order to construct an RS monitoring model for soil salinization (Nonsaline, Slightly saline, Moderately saline, Strongly saline, and Solonchak). The research results indicated that (1) soil salinity exhibits moderate to strong variability within a 30 m scale, and the spatial heterogeneity of soil salinity needs to be considered when developing remote sensing models; (2) the theoretical models of salinity variance functions in the N and S regions conform to the exponential model and the spherical model, with R2 values of 0.817 and 0.967, respectively, indicating a good fit for the variance characteristics of salinity and suitability for Kriging interpolation; and (3) compared to a single-feature BC model, the soil salinization identification model constructed using the concept of EL demonstrated better potential for robustness and effectiveness.

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    Remote Sensing
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    Remote Sensing
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      Remote Sensing
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Lien Rodríguez-López; Denisse Alvarez; David Bustos Usta; Iongel Duran-Llacer; +5 Authors

    In this study, we employ in situ, meteorological, and remote sensing data to estimate chlorophyll-a concentration at different depths in a South American freshwater ecosystem, focusing specifically on a lake in southern Chile known as Lake Maihue. For our analysis, we explored four different scenarios using three deep learning and traditional statistical models. These scenarios involved using field data (Scenario 1), meteorological variables (Scenario 2), and satellite data (Scenarios 3.1 and 3.2) to predict chlorophyll-a levels in Lake Maihue at three different depths (0, 15, and 30 m). Our choice of models included SARIMAX, DGLM, and LSTM, all of which showed promising statistical performance in predicting chlorophyll-a concentrations in this lake. Validation metrics for these models indicated their effectiveness in predicting chlorophyll levels, which serve as valuable indicators of the presence of algae in the water body. The coefficient of determination values ranged from 0.30 to 0.98, with the DGLM model showing the most favorable statistics in all scenarios tested. It is worth noting that the LSTM model yielded comparatively lower metrics, mainly due to the limitations of the available training data. The models employed, which use traditional statistical and machine learning models and meteorological and remote sensing data, have great potential for application in lakes in Chile and the rest of the world with similar characteristics. In addition, these results constitute a fundamental resource for decision-makers involved in the protection and conservation of water resource quality.

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    Remote Sensing
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    Remote Sensing
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Sihui Zhang; Yong Nie; Huayu Zhang;

    Glacial lake outburst floods (GLOFs) are one of the most severe disasters in alpine regions, releasing a large amount of water and sediment that can cause fatalities and economic loss as well as substantial damage to downstream infrastructures. The risk of GLOFs in the Himalayas is exacerbated by glacier retreat caused by global warming. Critical economic corridors, such as the Rongxer Watershed, are threatened by GLOFs, but the lack of risk assessment specific to the watershed hinders hazard prevention. In this study, we propose a novel model to evaluate the risk of GLOF using a combination of remote sensing observations, GIS, and hydrological models and apply this model to the GLOF risk assessment in the Rongxer Watershed. The results show that (1) the area of glacial lakes in the Rongxer Watershed increased by 31.19% from 11.35 km2 in 1990 to 14.89 km2 in 2020, and (2) 18 lakes were identified as potentially dangerous glacial lakes (PDGLs) that need to be assessed for the GLOF risk, and two of them were categorized as very high risk (Niangzongmajue and Tsho Rolpa). The proposed model was robust in a GLOF risk evaluation by historical GLOFs in the Himalayas. The glacial lake data and GLOF risk assessment model of this study have the potential to be widely used in research on the relationships between glacial lakes and climate change, as well as in disaster mitigation of GLOFs.

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    Authors: Tianzhu Li; Caihong Ma; Yongze Lv; Ruilin Liao; +2 Authors

    The cement industry, as one of the primary contributors to global greenhouse gas emissions, accounts for 7% of the world’s carbon dioxide emissions. There is an urgent need to establish a rapid method for detecting cement plants to facilitate effective monitoring. In this study, a comprehensive method based on YOLOv5-IEG and the Thermal Signature Detection module using Google Earth optical imagery and SDGSAT-1 thermal infrared imagery was proposed to detect large-scale cement plant information, including geographic location and operational status. The improved algorithm demonstrated an increase of 4.8% in accuracy and a 7.7% improvement in MAP@.5:95. In a specific empirical investigation in China, we successfully detected 781 large-scale cement plants with an accuracy of 90.8%. Specifically, of the 55 cement plants in Shandong Province, we identified 46 as operational and nine as non-operational. The successful application of advanced models and remote sensing technology in efficiently and accurately tracking the operational status of cement plants provides crucial support for environmental protection and sustainable development.

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    Authors: Haoran Jiang; Luyan Ji; Kai Yu; Yongchao Zhao;

    In the process of urbanization, water bodies bear considerable anthropogenic pressure, resulting in a reduction of their surface area in most instances. Nevertheless, in contrast to many other regions, the Lixiahe region in Jiangsu Province, located in China’s eastern plain, has experienced a continuous expansion of water bodies over the past few decades amid rapid urbanization. Using Landsat images spanning from 1975 to 2023, this study analyzed changes in water resources and the growth of impervious surfaces during urbanization. The findings revealed that the area of impervious surfaces in the region increased from 227.1 km2 in 1975 to 1883.1 km2 in 2023. Natural wetland suffered significant losses, declining from 507.2 km2 in 1975 to near disappearance by the year 2000, with no significant recovery observed thereafter. Simultaneously, the water area expanded from 459.3 km2 in 1975 to 2373.1 km2 in 2023, primarily propelled by the significant contribution of aquaculture ponds, accounting for 2175.0 km2 or 91.7% of the total water area. Driver analysis revealed that these changes were found to be influenced by factors such as population, economy, demand, and policies. However, alongside the economic development brought by urbanization, negative impacts such as lake shrinkage, eutrophication, and increased flood risks have emerged. The Lixiahe region, as a relatively underdeveloped part of Jiangsu Province, faces the challenge of striking a balance between economic growth and environmental conservation.

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    Authors: Yuqi Tang; Xin Yang; Te Han; Fangyan Zhang; +2 Authors

    Heterogeneous change detection (CD) is widely applied in various fields such as urban planning, environmental monitoring, and disaster management. It enhances the accuracy and comprehensiveness of surface change monitoring by integrating multi-sensor remote sensing data. Scholars have proposed many graph-based methods to address the issue of incomparable heterogeneous images caused by imaging differences. However, these methods often overlook the influence of changes in vertex status on the graph structure, which limits their ability to represent image structural features. To tackle this problem, this paper presents an unsupervised heterogeneous CD method based on enhanced graph structure representation (EGSR). This method enhances the representation capacity of the graph structure for image structural features by measuring the unchanged probabilities of vertices, thereby making it easier to detect changes in heterogeneous images. Firstly, we construct the graph structure using image superpixels and measure the structural graph differences of heterogeneous images in the same image domain. Then, we calculate the unchanged probability of each vertex in the structural graph and reconstruct the graph structure using this probability. To accurately represent the graph structure, we adopt an iterative framework for enhancing the representation of the graph structure. Finally, at the end of the iteration, the final change map (CM) is obtained by binary segmentation of the graph vertices based on their unchanged probabilities. The effectiveness of this method is validated through experiments on four sets of heterogeneous image datasets and two sets of homogeneous image datasets.

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    Authors: Xiaodian Zhang; Kun Gao; Junwei Wang; Pengyu Wang; +4 Authors

    Hyperspectral target detection (HTD) is a crucial aspect of remote sensing applications, aiming to identify targets in hyperspectral images (HSIs) based on their known prior spectral signatures. However, the spectral variability resulting from various imaging conditions in multi-temporal hyperspectral images poses a challenge to both classical and deep learning (DL) methods. To overcome the limitations imposed by spectral variability, an implicit contrastive learning-based target detector (ICLTD) is proposed to exploit in-scene spectra in an unsupervised way. First, only prior spectra are utilized for explicit supervision, while an implicit contrastive learning module (ICLM) is designed to normalize the feature distributions of prior and in-scene spectra. This paper theoretically demonstrates that the ICLM can transfer the gradients from prior spectral features to those of in-scene spectra based on their feature similarities and differences. Because of transferred gradient signals, the ICLTD is regularized to extract similar representations for the prior and in-scene target spectra, while augmenting feature differences between the target and background spectra. Additionally, a local spectral similarity constraint (LSSC) is proposed to enhance the capability of scene adaptation by leveraging the spectral similarities among in-scene targets. To validate the performance of the ICLTD under spectral variability, multi-temporal HSIs captured under various imaging conditions are collected to generate prior spectra and in-scene spectra. Comparative evaluations against several DL detectors and classical methods reveal the superior performance of the ICLTD in achieving a balance between target detectability and background suppressibility under spectral variability.

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    Authors: Hui Ying Pak; Hieu Trung Kieu; Weisi Lin; Eugene Khoo; +1 Authors

    Uncrewed-Aerial Vehicles (UAVs) and hyperspectral sensors are emerging as effective alternatives for monitoring water quality on-demand. However, image mosaicking for largely featureless coastal water surfaces or open seas has shown to be challenging. Another pertinent issue observed is the systematic image misalignment between adjacent flight lines due to the time delay between the UAV-borne sensor and the GNSS system. To overcome these challenges, this study introduces a workflow that entails a GPS-based image mosaicking method for push-broom hyperspectral images, together with a correction method to address the aforementioned systematic image misalignment. An open-source toolkit, CoastalWQL, was developed to facilitate the workflow, which includes essential pre-processing procedures for improving the image mosaic’s quality, such as radiometric correction, de-striping, sun glint correction, and object masking classification. For validation, UAV-based push-broom hyperspectral imaging surveys were conducted to monitor coastal turbidity in Singapore, and the implementation of CoastalWQL’s pre-processing workflow was evaluated at each step via turbidity retrieval. Overall, the results confirm that the image mosaicking of the push-broom hyperspectral imagery over featureless water surface using CoastalWQL with time delay correction enabled better localisation of the turbidity plume. Radiometric correction and de-striping were also found to be the most important pre-processing procedures, which improved turbidity prediction by 46.5%.

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    Authors: Patrick Killeen; Iluju Kiringa; Tet Yeap; Paula Branco;

    Food demand is expected to rise significantly by 2050 due to the increase in population; additionally, receding water levels, climate change, and a decrease in the amount of available arable land will threaten food production. To address these challenges and increase food security, input cost reductions and yield optimization can be accomplished using yield precision maps created by machine learning models; however, without considering the spatial structure of the data, the precision map’s accuracy evaluation assessment risks being over-optimistic, which may encourage poor decision making that can lead to negative economic impacts (e.g., lowered crop yields). In fact, most machine learning research involving spatial data, including the unmanned aerial vehicle (UAV) imagery-based yield prediction literature, ignore spatial structure and likely obtain over-optimistic results. The present work is a UAV imagery-based corn yield prediction study that analyzed the effects of image spatial and spectral resolution, image acquisition date, and model evaluation scheme on model performance. We used various spatial generalization evaluation methods, including spatial cross-validation (CV), to (a) identify over-optimistic models that overfit to the spatial structure found inside datasets and (b) estimate true model generalization performance. We compared and ranked the prediction power of 55 vegetation indices (VIs) and five spectral bands over a growing season. We gathered yield data and UAV-based multispectral (MS) and red-green-blue (RGB) imagery from a Canadian smart farm and trained random forest (RF) and linear regression (LR) models using 10-fold CV and spatial CV approaches. We found that imagery from the middle of the growing season produced the best results. RF and LR generally performed best with high and low spatial resolution data, respectively. MS imagery led to generally better performance than RGB imagery. Some of the best-performing VIs were simple ratio index(near-infrared and red-edge), normalized difference red-edge index, and normalized green index. We found that 10-fold CV coupled with spatial CV could be used to identify over-optimistic yield prediction models. When using high spatial resolution MS imagery, RF and LR obtained 0.81 and 0.56 correlation coefficient (CC), respectively, when using 10-fold CV, and obtained 0.39 and 0.41, respectively, when using a k-means-based spatial CV approach. Furthermore, when using only location features, RF and LR obtained an average CC of 1.00 and 0.49, respectively. This suggested that LR had better spatial generalizability than RF, and that RF was likely being over-optimistic and was overfitting to the spatial structure of the data.

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    Authors: Wenhui Zhang; Zhinong Zhong; Hao Chen; Ning Jing;

    Cross-view geo-localization aims to locate street-view images by matching them with a collection of GPS-tagged remote sensing (RS) images. Due to the significant viewpoint and appearance differences between street-view images and RS images, this task is highly challenging. While deep learning-based methods have shown their dominance in the cross-view geo-localization task, existing models have difficulties in extracting comprehensive meaningful features from both domains of images. This limitation results in not establishing accurate and robust dependencies between street-view images and the corresponding RS images. To address the aforementioned issues, this paper proposes a novel and lightweight neural network for cross-view geo-localization. Firstly, in order to capture more diverse information, we propose a module for extracting multi-scale features from images. Secondly, we introduce contrastive learning and design a contrastive loss to further enhance the robustness in extracting and aligning meaningful multi-scale features. Finally, we conduct comprehensive experiments on two open benchmarks. The experimental results have demonstrated the superiority of the proposed method over the state-of-the-art methods.

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    Remote Sensing
    Article . 2024
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    Remote Sensing
    Article . 2024 . Peer-reviewed
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    Authors: Huifang Chen; Jingwei Wu; Chi Xu;

    Remote sensing (RS) technology can rapidly obtain spatial distribution information on soil salinization. However, (1) the scale effects resulting from the mismatch between ground-based “point” salinity data and remote sensing pixel-based “spatial” data often limit the accuracy of remote sensing monitoring of soil salinity, and (2) the same salinity RS monitoring model usually provides inconsistent or sometimes conflicting explanations for different data. Therefore, based on Landsat 8 imagery and synchronously collected ground-sampling data of two typical study regions (denoted as N and S, respectively) of the Yichang Irrigation Area in the Hetao Irrigation District for May 2013, this study used geostatistical methods to obtain “relative truth values” of salinity corresponding to the Landsat 8 pixel scale. Additionally, based on Landsat 8 multispectral data, 14 salinity indices were constructed. Subsequently, the Correlation-based Feature Selection (CFS) method was used to select sensitive features, and a strategy similar to the concept of ensemble learning (EL) was adopted to integrate the single-feature-sensitive Bayesian classification (BC) model in order to construct an RS monitoring model for soil salinization (Nonsaline, Slightly saline, Moderately saline, Strongly saline, and Solonchak). The research results indicated that (1) soil salinity exhibits moderate to strong variability within a 30 m scale, and the spatial heterogeneity of soil salinity needs to be considered when developing remote sensing models; (2) the theoretical models of salinity variance functions in the N and S regions conform to the exponential model and the spherical model, with R2 values of 0.817 and 0.967, respectively, indicating a good fit for the variance characteristics of salinity and suitability for Kriging interpolation; and (3) compared to a single-feature BC model, the soil salinization identification model constructed using the concept of EL demonstrated better potential for robustness and effectiveness.

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    Remote Sensing
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    Authors: Lien Rodríguez-López; Denisse Alvarez; David Bustos Usta; Iongel Duran-Llacer; +5 Authors

    In this study, we employ in situ, meteorological, and remote sensing data to estimate chlorophyll-a concentration at different depths in a South American freshwater ecosystem, focusing specifically on a lake in southern Chile known as Lake Maihue. For our analysis, we explored four different scenarios using three deep learning and traditional statistical models. These scenarios involved using field data (Scenario 1), meteorological variables (Scenario 2), and satellite data (Scenarios 3.1 and 3.2) to predict chlorophyll-a levels in Lake Maihue at three different depths (0, 15, and 30 m). Our choice of models included SARIMAX, DGLM, and LSTM, all of which showed promising statistical performance in predicting chlorophyll-a concentrations in this lake. Validation metrics for these models indicated their effectiveness in predicting chlorophyll levels, which serve as valuable indicators of the presence of algae in the water body. The coefficient of determination values ranged from 0.30 to 0.98, with the DGLM model showing the most favorable statistics in all scenarios tested. It is worth noting that the LSTM model yielded comparatively lower metrics, mainly due to the limitations of the available training data. The models employed, which use traditional statistical and machine learning models and meteorological and remote sensing data, have great potential for application in lakes in Chile and the rest of the world with similar characteristics. In addition, these results constitute a fundamental resource for decision-makers involved in the protection and conservation of water resource quality.

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