Peng Liu
Hugo Kyo Lee
Marco Casazza- 1Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- 2NASA Jet Propulsion Laboratory (JPL), La Cañada Flintridge, CA, United States
- 3Department of Medicine, Surgery and Dentistry, University of Salerno Baronissi, Baronissi, Italy
Editorial on the Research Topic
Methods and applications in environmental informatics and remote sensing
We are living in an era where environmental science demand innovative approaches and cutting-edge technologies. Environmental informatics and remote sensing have emerged as powerful tools in this regard, revolutionizing our understanding of the environment and providing valuable insights for sustainable management. In this editorial, we are thrilled to introduce a topic dedicated to exploring the methods and applications in environmental informatics and remote sensing, shedding light on the latest advancements and showcasing their transformative potential.
The field of environmental informatics has witnessed significant advancements, fueled by rapid developments in information technology, data science, and computational modeling. Many studies related to environmental science, such as atmosphere (Zhang, et al., 2021), water (Zhang, et al., 2023), soil and forest, etc., are being deeply changed. This Research Topic aims to capture the latest methodologies and techniques that leverage these advancements to address complex environmental issues. From novel algorithms from data processing and fusion (Liu et al., 2022a) to advanced machine learning approaches, especially deep learning (Liu et al., 2022b), this issue delves into the state-of-the-art methods driving progress in environmental informatics. Simultaneously, remote sensing has experienced remarkable growth, propelled by the advent of high-resolution satellite imagery, unmanned aerial vehicles (UAVs), and other technological breakthroughs. These advancements have enabled scientists to monitor and analyze the Earth’s surface in unprecedented detail, uncovering crucial information about land cover changes, habitat degradation, climate patterns, and more. The Research Topic explores the diverse applications of remote sensing in environmental research, highlighting its role in driving informed decision-making and sustainable environmental management.
With this topic on environmental informatics and remote sensing, we try to introduce the latest theory and methods of applying remote sensing technology to environmental science. It contains eight papers that demonstrate the latest research to advance the science in research areas such as forest, surface water, air pollutants, land degradation, etc.
Forests remain perhaps one of the most relevant issues in the field of environmental information. Three of the papers are related to forests. Guo et al. present a forest cover map generation for the Qinghai-Tibet Plateau based on a multisource dataset and the random forest algorithm. The paper discusses the methodology and highlights the importance of accurate forest mapping for ecological studies and conservation planning. The study contributes to a better understanding of the forest dynamics in this region. Xiao et al. present a temporal-based forest disturbance monitoring analysis using a case study of nature reserves in Hainan Island, China, from 1987 to 2020. The paper discusses the impacts of various disturbances on the island’s forests and proposes effective monitoring strategies. The findings contribute to better understanding forest dynamics and conservation efforts. Cárdenas et al. propose a method for reconstructing tree branching structures using UAV-LiDAR data. The study focuses on developing a reliable and efficient approach to extract detailed tree information. The research has implications for ecological studies and forestry management, enabling accurate characterization of tree structures.
Surface water is also an important field of environment research based on remote sensing. Wang et al. compare the retrieval of phycocyanin concentrations in Chaohu Lake, China, using MODIS and OLCI images. The study focuses on assessing the accuracy and reliability of different remote sensing data for monitoring water quality. The research provides valuable information for effective environmental monitoring and management of freshwater bodies. Li et al. conducted a case study on the eco-environmental changes in typical coastal zones of southern China, specifically Guangdong coastal counties, from 1987 to 2020. The paper explores the transformations in the coastal areas and discusses their implications. The study provides valuable insights into the environmental dynamics of this region.
We also have two papers about air pollutants and land degradation, which are also questions at the heart of environmental information science. Wu explores the spatio-temporal heterogeneity and relationships of six criteria air pollutants in China using a tri-clustering-based approach and Li et al. investigate the extraction of rocky desertification information in karst areas using multispectral sensor data and multiple endmember spectral mixture analysis. There is other study on spatial pattern of scenic spots, such as Zhu et al. developed a simulation method combining optimal scale and deep learning to analyze the spatial pattern of scenic spots, which contributes to enhancing the planning and management of scenic areas.
The Research Topic on “Methods and Applications in Environmental Informatics and Remote Sensing” promises to be a platform for interdisciplinary research and collaboration, highlighting the latest advancements and breakthroughs in these fields. By bringing together scientists, technologists, and environmental practitioners, this topic advances our understanding of the environment. The researchers from around the world contributed their original research and shared their valuable insights, thereby shaping a better future for our planet through the power of environmental informatics and remote sensing.
Author contributions
PL wrote a first draft. All authors contributed to the article and approved the submitted version.
Funding
This work was supported by NSFC (No. 41971397, No. U2243222 and No. 42071413).
Acknowledgments
We are very grateful to all our colleagues who submitted, reviewed and edited manuscripts for this Research Topic. We also thank Professor Mengzhen Xu and Dr. Lajiao Chen for providing us many supports.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
Liu, P., Li, J., Wang, L., and He, G. (2022a). Remote sensing data fusion with generative adversarial networks: State-of-the-Art methods and future research directions. IEEE Geoscience Remote Sens. Mag. 10 (2), 295–328. doi:10.1109/mgrs.2022.3165967
Liu, P., Wang, L., Ranjan, R., He, G., and Zhao, L. (2022b). A survey on active deep learning: From model-driven to data-driven. ACM Comput. Surv. (CSUR) 54 (10s), 1–34. doi:10.1145/3510414
Zhang, L., Liu, P., Zhao, L., Wang, G., Zhang, W., and Liu, J. (2021). Air quality predictions with a semi-supervised bidirectional LSTM neural network. Atmos. Pollut. Res. 12 (1), 328–339. doi:10.1016/j.apr.2020.09.003
Keywords: remote sensing, artificial intelligence, deep learning, Earth observation, environmental informatics
Citation: Liu P, Lee HK and Casazza M (2023) Editorial: Methods and applications in environmental informatics and remote sensing. Front. Environ. Sci. 11:1255010. doi: 10.3389/fenvs.2023.1255010
Received: 08 July 2023; Accepted: 10 July 2023;
Published: 17 July 2023.
Edited and reviewed by:
Alexander Kokhanovsky, German Research Centre for Geosciences, GermanyCopyright © 2023 Liu, Lee and Casazza. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Peng Liu, bGl1cGVuZ0ByYWRpLmFjLmNu