Article
Geochemistry & Geophysics
Ailong Ma, Wen Zhou, Mi Song, Yanfei Zhong
Summary: This article proposes a multiobjective memetic spatiotemporal SPM (MOMSPM) framework that combines the abundance, spatial, and temporal information through a multiobjective optimization approach. It utilizes a global multiobjective search method and two single-objective local search operators to optimize the three objective functions simultaneously. Experimental results on synthetic and real datasets demonstrate the superiority of the proposed method over state-of-the-art spatiotemporal SPM methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Mi Song, Yanfei Zhong, Ailong Ma, Xiong Xu, Liangpei Zhang
Summary: This article proposes a novel joint subpixel mapping and spectral unmixing framework based on multiobjective optimization, which can perform unmixing and mapping simultaneously and improve the interpretation accuracy of hyperspectral remote-sensing imagery.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Yuan Fang, Yuxian Wang, Linlin Xu, Yujia Chen, Alexander Wong, David A. Clausi
Summary: This article proposes a Bayesian neural network for unsupervised subpixel mapping, which integrates different prior information and model constraints to achieve more accurate and visual results compared to other state-of-the-art methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Da He, Qian Shi, Xiaoping Liu, Yanfei Zhong, Xinchang Zhang
Summary: The mixed pixel problem is common in urban land use interpretation in remote sensing images due to hardware limitations. Subpixel mapping is a common approach to solve this problem, while deep learning-based subpixel mapping network has been recently proposed for finer mapping. The article introduces a semantic information modulated (SIM) deep subpixel mapping network (SIMNet), which uses low-resolution semantic images as prior to enhance spatial context features representation.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Geochemistry & Geophysics
Chengyuan Zhang, Qunming Wang, Ping Lu, Yong Ge, Peter M. Atkinson
Summary: This article proposes a fast and slow changes constrained spatio-temporal subpixel mapping (FSSTSPM) method to enhance subpixel mapping (SPM) by utilizing temporal information from time-series images. The FSSTSPM method is validated using synthetic datasets and real datasets, and the results demonstrate its superiority, especially in the presence of proportion errors.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Meiping Song, Lan Li, Chunyun Zhang, Pengliang Shi, Liaoying Zhao, Bai Xue
Summary: This paper proposes a subpixel mapping (SPM) method based on multiscale and multifeature (MSMF) to address the accuracy issue caused by mixed pixels in hyperspectral images. By utilizing the maximum linearization index method and different spatial feature processing methods, the proposed method can effectively improve the accuracy of subpixel mapping.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Environmental Sciences
Chengyuan Zhang, Qunming Wang, Huan Xie, Yong Ge, Peter M. Atkinson
Summary: This research proposes a cloud-independent spatio-temporal subpixel mapping (C-STSPM) method to reconstruct fine spatial resolution land cover maps using cloudy images directly. The advantage of C-STSPM is more evident when the clouds are distributed sparsely. By utilizing land cover information of clear pixels in cloudy images, more accurate prediction can be achieved compared to discarding cloudy images.
SCIENCE OF REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Peng Wang, Zhongchen He, Cai Li, Kang Ni
Summary: This letter proposes a subpixel flood inundation mapping method based on spatial-spectral information in irregular regions (SSIIIR). By calculating the spatial correlation and spectral information in irregular regions, the mapping results are improved compared to traditional methods.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Peng Wang, Xun Shen, Gong Zhang
Summary: This study proposed a general SSM model based on FCSTD, considering PSF effect, to improve mapping results using auxiliary information from PFSI in the same region.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Biodiversity Conservation
M. Arasumani, Fabian Thiel, Vu-Dong Pham, Christina Hellmann, Moritz Kaiser, Sebastian van der Linden
Summary: Peatlands are important for carbon sequestration, but drained peatlands contribute to carbon emissions. Rewetting drained peatlands can reduce emissions and create new carbon sinks, but alternative agriculture schemes are needed. Remote sensing, especially with hyperspectral images, can be used to monitor the vegetation composition of rewetted peatland areas.
ECOLOGICAL INDICATORS
(2023)
Article
Environmental Sciences
Zhenxuan Li, Wenzhong Shi, Yongchao Zhu, Hua Zhang, Ming Hao, Liping Cai
Summary: A new subpixel change detection method based on radial basis function (RBF) for remote sensing images is proposed in this paper. By utilizing the abundance image difference measure to borrow the fine spatial distribution, the method aims to decrease the influence of spectral unmixing error and improve the subpixel change detection results. Experimental results demonstrate the effectiveness of the proposed method.
Article
Engineering, Electrical & Electronic
Minghui Chang, Xiangchao Meng, Weiwei Sun, Gang Yang, Jiangtao Peng
Summary: In this article, a novel method for change detection in coastal wetlands using collaborative unmixing technology is proposed. By integrating spatial and spectral information, the method achieves more accurate detection results through endmember extraction and abundance estimation.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Engineering, Electrical & Electronic
Yujia Chen, Cheng Huang, Cheng Yang, Junhuan Peng, Jun Zhang, Yuxian Wang, Zhengxue Yao, Guang Chen, Wenhua Yu, Qinghao Liu
Summary: In this study, a new subpixel mapping (SPM) method based on the spatial adaptive attraction model (SAAM) and conditional random fields (CRFs) is proposed. It simultaneously improves the operational efficiency of the algorithm and mitigates the effect of abundance errors. The SAAM obtains the spatial adaptive attraction value by adaptively adjusting the spatial attraction value obtained using the traditional model, and the CRFs model the implicitly represented abundance constraints and the local spatial smoothing prior. This method outperforms existing SPM methods in terms of accuracy and time consumption, providing a new solution for SPM in remote sensing images.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Engineering, Electrical & Electronic
Peng Wang, Mingxuan Huang, Liguo Wang, Gong Zhang, Henry Leung, Chunlei Zhao
Summary: In this paper, a spatiotemporal subpixel mapping (SSPM) method based on priori remote sensing image with variation differences (CVDBI) is proposed to improve the mapping accuracy of fine land-cover class. The experimental results show that the proposed CVDBI outperforms traditional SPM methods in terms of accuracy.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Yi-Hang Tung, Myung-Ho In, Sinyeob Ahn, Oliver Speck
Summary: VAT-PSF-EPI is a novel spin-echo EPI-based sequence that enables fast high-resolution diffusion imaging at ultrahigh field. It effectively suppresses distortion and corrects the introduced image blurring through PSF encoding. Up to fourfold acceleration can be achieved compared to standard PSF-EPI.
MAGNETIC RESONANCE IN MEDICINE
(2022)
Article
Geochemistry & Geophysics
Qunming Wang, Xinyu Ding, Xiaohua Tong, Peter M. Atkinson
Summary: The article proposes a real-time STSU method (RSTSU) for monitoring land cover changes in real-time, which only requires a single coarse-to-fine spatial resolution image pair for training the learning model, suitable for real-time analysis, and its effectiveness is validated through experiments.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Kaidi Peng, Qunming Wang, Yijie Tang, Xiaohua Tong, Peter M. Atkinson
Summary: The article introduces a geographically weighted spatiotemporal fusion method (SU-GW) to address spatial variation in land cover and increase the accuracy of spatiotemporal fusion. Experimental results comparing 24 versions indicated that SU-GW was effective in increasing prediction accuracy, providing a general solution for enhancing spatiotemporal fusion and potentially updating existing methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Lifeng Wang, Liguo Wang, Qunming Wang, Peter M. Atkinson
Summary: This study proposes an end-to-end Siamese CNN with a spectral-spatial-wise attention mechanism for change detection in hyperspectral images. The method can adaptively emphasize informative spectral channels and spatial locations to improve the accuracy of change detection.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Rui Li, Shunyi Zheng, Ce Zhang, Chenxi Duan, Jianlin Su, Libo Wang, Peter M. Atkinson
Summary: Semantic segmentation of remote sensing images is vital for various applications such as land resource management and urban planning. Despite the improvement in accuracy with deep convolutional neural networks, standard models have limitations like underuse of information and insufficient exploration of long-range dependencies. This article introduces a multiattention network (MANet) with efficient attention modules to address these issues and demonstrates superior performance on large-scale remote sensing datasets.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Artificial Intelligence
Xiaowei Gu, Ce Zhang, Qiang Shen, Jungong Han, Plamen P. Angelov, Peter M. Atkinson
Summary: A novel semi-supervised ensemble framework was proposed for remote sensing scene classification, utilizing a self-training hierarchical prototype-based classifier to address the challenges of labelled data scarcity and scene complexity. Experimental results demonstrated significant improvements in classification accuracy on popular benchmark datasets with limited labelled images available.
INFORMATION FUSION
(2022)
Article
Ecology
Juan M. Escamilla Molgora, Luigi Sedda, Peter Diggle, Peter M. Atkinson
Summary: This study proposes a Bayesian framework for presence-only (PO) species distribution models (SDM) that explicitly models the sampling effect. It provides three modeling alternatives to account for a spatial autocorrelation structure and achieves higher predictive accuracy than MaxEnt in two case studies. The framework is aided by a sampling effort process informed by accumulated observations of independent and heterogeneous surveys.
JOURNAL OF BIOGEOGRAPHY
(2022)
Article
Environmental Sciences
Shawky Mansour, Mohammed Alahmadi, Peter M. Atkinson, Ashraf Dewan
Summary: In recent years, socioeconomic transformation and social modernisation have led to the expansion of urban settlements in the Gulf Cooperation Council states, including desert cities. However, the prediction and research of desert urban patterns have been lacking, and this study focuses on the land use-land cover changes in the desert city of Ibri in Oman. The results show that the observed changes were rapid, with desert, bare land, and vegetation transforming into built-up areas. The forecast predicts a significant increase in land conversion from desert to urban in the next two decades.
Article
Geography
Shawky Mansour, Ammar Abulibdeh, Mohammed Alahmadi, Adham Al-Said, Alkhattab Al-Said, Gary Watmough, Peter M. Atkinson
Summary: This research used spatial modeling to investigate the spatial associations between COVID-19 incidence rates and migrant workers, revealing significant differences in disease occurrence among different work sectors. The study found that workers in the health sector are at higher risk of COVID-19 infection, serving as potential hotspots for transmission. These findings are valuable for decision makers to develop policies and plans to control virus outbreaks in various societies.
ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS
(2022)
Article
Engineering, Electrical & Electronic
Yijie Tang, Qunming Wang, Peter M. M. Atkinson
Summary: This article proposes a filling then spatio-temporal fusion (FSTF) method to address the challenge of large gaps in MODIS LST data. By utilizing the CLDAS LST product, the FSTF method can more accurately reconstruct the MODIS LST images. The results of the study demonstrate the potential of FSTF for updating the current MODIS LST product globally.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Remote Sensing
Idham Khalil, Aidy M. Muslim, Mohammad Shawkat Hossain, Peter M. Atkinson
Summary: This research explores the potential consequences of Sea Surface Temperature (SST) warming on the ecosystems of the Indo-Pacific (IP) region, specifically on coral bleaching. The findings predict widespread coral bleaching in many places in the IP region over the next 30 years, posing a significant threat to the marine environment.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2023)
Review
Environmental Studies
Seyed Kazem Alavipanah, Mohammad Karimi Firozjaei, Amir Sedighi, Solmaz Fathololoumi, Saeid Zare Naghadehi, Samiraalsadat Saleh, Maryam Naghdizadegan, Zinat Gomeh, Jamal Jokar Arsanjani, Mohsen Makki, Salman Qureshi, Qihao Weng, Dagmar Haase, Biswajeet Pradhan, Asim Biswas, Peter M. Atkinson
Summary: In remote sensing, shadows significantly influence the quality of data and play a crucial role in environmental studies. Different types of shadows have been identified, affecting various properties and outputs in remote sensing processes. Modeling and mitigating the shadow effect pose challenges, but valuable information can still be extracted from shadows.
Article
Multidisciplinary Sciences
Javier Houspanossian, Raul Gimenez, Juan I. Whitworth-Hulse, Marcelo D. Nosetto, Wlodek Tych, Peter M. Atkinson, Mariana C. Rufino, Esteban G. Jobbagy
Summary: This study demonstrates the impacts of rainfed agriculture on hydrology through remote sensing analysis, field studies, and simulation experiments. The findings reveal that the expansion of farming in the South American plains has led to increased flood coverage and shallower groundwater levels, which are attributed to the reduced rooting depths and evapotranspiration in croplands. These results highlight the escalating flood risks associated with rainfed agriculture expansion.
Article
Geochemistry & Geophysics
Yonghao Xu, Tao Bai, Weikang Yu, Shizhen Chang, Peter M. Atkinson, Pedram Ghamisi
Summary: Recent advances in AI have led to extensive application of AI algorithms, especially deep learning, in geoscience and remote sensing. Although AI enables more accurate observation and understanding of the earth, the vulnerability and uncertainty of AI models deserve further attention, especially for safety critical tasks. This article reviews the development of AI security in the geoscience and RS field, covering adversarial attack, backdoor attack, federated learning, uncertainty, and explainability. It also discusses potential opportunities and trends for future research.
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
(2023)
Article
Geochemistry & Geophysics
Chengyuan Zhang, Qunming Wang, Ping Lu, Yong Ge, Peter M. Atkinson
Summary: This article proposes a fast and slow changes constrained spatio-temporal subpixel mapping (FSSTSPM) method to enhance subpixel mapping (SPM) by utilizing temporal information from time-series images. The FSSTSPM method is validated using synthetic datasets and real datasets, and the results demonstrate its superiority, especially in the presence of proportion errors.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Ruonan Chen, Liangyun Liu, Xinjie Liu, Zhunqiao Liu, Lianhong Gu, Uwe Rascher
Summary: This study presents methods to accurately estimate sub-daily GPP from SIF in evergreen needleleaf forests and demonstrates that the interactions among light, canopy structure, and leaf physiology regulate the SIF-GPP relationship at the canopy scale.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Daniel L. Goldberg, Madankui Tao, Gaige Hunter Kerr, Siqi Ma, Daniel Q. Tong, Arlene M. Fiore, Angela F. Dickens, Zachariah E. Adelman, Susan C. Anenberg
Summary: A novel method is applied in this study to directly use satellite data to evaluate the spatial patterns of urban NOx emissions inventories. The results show that the 108 spatial surrogates used by NEMO are generally appropriate, but there may be underestimation in areas with dense intermodal facilities and overestimation in wealthy communities.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Zhuoyue Hu, Xiaoyan Li, Liyuan Li, Xiaofeng Su, Lin Yang, Yong Zhang, Xingjian Hu, Chun Lin, Yujun Tang, Jian Hao, Xiaojin Sun, Fansheng Chen
Summary: This paper proposes a whisk-broom imaging method using a long-linear-array detector and high-precision scanning mirror to achieve high-resolution and wide-swath thermal infrared data. The method has been implemented in the SDGs satellite and has shown promising test results.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Dandan Wang, Leiqiu Hu, James A. Voogt, Yunhao Chen, Ji Zhou, Gaijing Chang, Jinling Quan, Wenfeng Zhan, Zhizhong Kang
Summary: This study evaluates different schemes for determining model coefficients to quantify and correct the anisotropic impact from remote sensing LST for urban applications. The schemes have consistent results and accurately estimate parameter values, facilitating the broadening of parametric models.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Jamie Tolan, Hung - Yang, Benjamin Nosarzewski, Guillaume Couairon, Huy V. Vo, John Brandt, Justine Spore, Sayantan Majumdar, Daniel Haziza, Janaki Vamaraju, Theo Moutakanni, Piotr Bojanowski, Tracy Johns, Brian White, Tobias Tiecke, Camille Couprie
Summary: Vegetation structure mapping is crucial for understanding the global carbon cycle and monitoring nature-based approaches to climate adaptation and mitigation. This study presents the first high-resolution canopy height maps for California and Sao Paulo, achieved through the use of very high resolution satellite imagery and aerial lidar data. The maps provide valuable tools for forest structure assessment and land use monitoring.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Regina Eckert, Steffen Mauceri, David R. Thompson, Jay E. Fahlen, Philip G. Brodrick
Summary: In this paper, a mathematical framework is proposed to improve the retrieval of surface reflectance and atmospheric parameters by leveraging the expected spatial smoothness of the atmosphere. Experimental results show that this framework can reduce the surface reflectance retrieval error and surface-related biases.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Chongya Jiang, Kaiyu Guan, Yizhi Huang, Maxwell Jong
Summary: This study presents the Field Rover method, which uses vehicle-mounted cameras to collect ground truth data on crop harvesting status. The machine learning approach and remote sensing technology are employed to upscale the results to a regional scale. The accuracy of the remote sensing method in predicting crop harvesting dates is validated through comparison with satellite data.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Oksana V. Lunina, Anton A. Gladkov, Alexey V. Bochalgin
Summary: In this study, an unmanned aerial vehicle (UAV) was used to detect and map surface discontinuities with displacements of a few centimeters, indicating the presence of initial geological deformations. The study found that sediments of alluvial fans are susceptible to various tectonic and exogenous deformational processes, and the interpretation of ultra-high resolution UAV images can help recognize low-amplitude brittle deformations at an early stage. UAV surveys are critical for discerning neotectonic activity and its related hazards over short observation periods.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Feng Zhao, Weiwei Ma, Jun Zhao, Yiqing Guo, Mateen Tariq, Juan Li
Summary: This study presents a data-driven approach to reconstruct the terrestrial SIF spectrum using measurements from the TROPOMI instrument on Sentinel-5 precursor mission. The reconstructed SIF spectrum shows improved spatiotemporal distributions and demonstrates consistency with other datasets, indicating its potential for better understanding of the ecosystem function.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Stephen Stehman, John E. Wagner
Summary: This article investigates optimal sample allocation in stratified random sampling for estimation of accuracy and proportion of area in applications where the target class is rare. The study finds that precision of estimated accuracy has a stronger impact on sample allocation than estimation of proportion of area, and the trade-offs among these estimates become more pronounced as the target class becomes rarer. The results provide quantitative evidence to guide sample allocation decisions in specific applications.
REMOTE SENSING OF ENVIRONMENT
(2024)
Article
Environmental Sciences
Jingyao Zheng, Tianjie Zhao, Haishen Lu, Defu Zou, Nemesio Rodriguez-Fernandez, Arnaud Mialon, Philippe Richaume, Jianshe Xiao, Jun Ma, Lei Fan, Peilin Song, Yonghua Zhu, Rui Li, Panpan Yao, Qingqing Yang, Shaojie Du, Zhen Wang, Zhiqing Peng, Yuyang Xiong, Zanpin Xing, Lin Zhao, Yann Kerr, Jiancheng Shi
Summary: Soil moisture and freeze/thaw (F/T) play a crucial role in water and heat exchanges at the land-atmosphere interface. This study reports the establishment of a wireless sensor network for soil moisture and temperature over the permafrost region of Tibetan Plateau. Satellite-based surface soil moisture (SSM) and F/T products were evaluated using ground-based measurements. The results show the reliability of L-band passive microwave SSM and F/T products, while existing F/T products display earlier freezing and later thawing, leading to unsatisfactory accuracy.
REMOTE SENSING OF ENVIRONMENT
(2024)
