Remote Sensing 专刊"基于多源数据集和云计算的环境监测土地覆盖制图方法及应用"
Remote Sensing 专刊"基于多源数据集和云计算的环境监测土地覆盖制图方法及应用"
Remote Sensing 特刊"基于多源数据集和云计算的环境监测土地覆盖制图方法及应用"
Remote Sensing- Special Issue
Special Issue "Methods and Applications in Land Cover Mapping for Environmental Monitoring Using Multi-Source Datasets and Cloud Computing"
Deadline
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 6 August 2021.
Special Issue Editors
Dr. Kristofer Lasko Website Guest Editor
Geospatial Research Laboratory, Engineer Research and Development Center, United States Interests: biomass burning; agricultural fires; synthetic aperture radar; data fusion; land cover mapping
Dr. Elena Sava Guest Editor
Geospatial Research Laboratory, Engineer Research and Development Center Interests: natural hazards; machine learning; data fusion; HPC
Special Issue Information
Dear Colleagues,
In an era of persistent land surface changes and rapidly increasing environmental hazards, advancements in environmental monitoring methods and applications are critical. In order to effectively characterize spatiotemporal patterns at various scales, studies must leverage numerous high and moderate spatial resolution remote sensing instruments that provide dense time series of data. Thus, advances in data, algorithms, and computing platforms are needed in order to efficiently characterize land cover changes. This is especially true of time-sensitive situations that require accurate, timely, and up-to date information for decision-making, such as in times of wildland fires, deforestation, agricultural monitoring, flood events, and land cover/land use change associated with land tenure or governmental policies.
In order to continuously monitor these and similar events, it is often necessary to combine disparate satellite data sources with ancillary data. After combining multiple data sources, geo-temporal gaps may persist as a result of satellite revisit time, atmospheric opacity, or other obstructions. Subsequently, additional novel data sources such as social media content, or other crowd-sourced data enable observations of obscured locations, especially in the aftermath of natural hazards. The combination of multiple datasets enables higher temporal resolution for timely observations, which in turn requires new techniques or platforms to optimize data handling and analysis.
These datasets often demonstrate significant computing demands that exceed the capabilities of the typical desktop computer. The availability of cloud computing platforms and resources such as Google Earth Engine (GEE), NASA Earth Exchange (NEX), Amazon AWS, and the Descartes Labs Platform enable analysis and monitoring at scales and depths that were previously difficult or impossible to attain.
Examples of appropriate topics for this Special Issue include but are not limited to:
- environmental monitoring or impacts using multi-source time series;
- cloud computing applications;
- advancements in agricultural mapping and monitoring;
- forest and vegetation dynamics;
- advancements in data fusion methodologies.
Dr. Kristofer Lasko Dr. Elena Sava Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Key Words
1 Machine learning
2 Land Cover
3 Natural Disasters
4 Google Earth Engine
5 Data Fusion
6 Time Series
7 Computer Vision
8 Cloud Computing
9 Crop Monitoring
10 Environmental Mapping
Published Papers (1 papers)
Koskikala, Joni; Kukkonen, Markus; Käyhkö, Niina (2020). Mapping Natural Forest Remnants with Multi-Source and Multi-Temporal Remote Sensing Data for More Informed Management of Global Biodiversity Hotspots. Remote Sensing, 12(9), 1429–. doi:10.3390/rs12091429
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