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Applied Sciences
Special Issues
Application of Multitemporal Remote Sensing in Land-Cover/Land-Use Change
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Application of Multitemporal Remote Sensing in Land-Cover/Land-Use Change

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 3293

Special Issue Editors

Dr. Qiqi Zhu

E-Mail Website
Guest Editor
School of Geography and Information Engineering, Future City Campus, China University of Geosciences, Wuhan 430079, China
Interests: high-resolution remote sensing; deep learning; land-use/land-cover classification; change detection
Special Issues, Collections and Topics in MDPI journals
Dr. Nan Wang

E-Mail Website
Guest Editor
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: time series change detection; hyperspectral image classification
Dr. Sicong Liu

E-Mail Website
Guest Editor
College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
Interests: multitemporal data analysis and processing; change detection; spectral signal processing; information fusion; multispectral/hyperspectral images processing; remote sensing applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the change in the natural environment and the effect of human activities, the Earth's surface changes all the time. Land use and land cover changes are the leading cause of environmental changes worldwide. In order to ensure the sustainable development of the environment and humans, monitoring changes with massive and multi-source remote sensing data has become an important application in the remote sensing field. Earth observation satellites collect hundreds of terabytes of remote sensing data each year. How to effectively process and analyze these data for the purpose of monitoring land changes is the issue that we want to address.

To this end, this Special Issue seeks papers presenting novel ideas, techniques and tools to improve remote sensing change detection. Potential topics for this Special Issue may include, but are not limited to the following:

  • Multitemporal remote sensing image analysis;
  • Land-cover/ Land-use change analysis;
  • Building change identification;
  • Scene-level classification and change analysis;
  • Multi-source remote sensing data fusion for change detection;
  • Urban change monitoring;
  • Semantic change analysis;
  • Geospatial databases for sustainable smart cities;
  • Advanced algorithms for change detection based on machine learning/deep learning;
  • Applications of remote sensing image change detection (vegetation change/coastline change/etc.) ;

Dr. Qiqi Zhu
Dr. Nan Wang
Dr. Sicong Liu
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 submissions that pass pre-check are 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. Applied Sciences 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.

Keywords

  • multitemporal analysis
  • land-cover/land-use change analysis
  • remote sensing data fusion
  • urban change monitoring
  • machine learning
  • change detection applications

Published Papers (2 papers)

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Research

14 pages, 4690 KiB  
Article
Time-Series Characterization of Grassland Biomass Intensity to Examine Management Change at Two Sites in Germany Using 25 Years of Remote Sensing Imagery
by Christopher M. Holmes, Joshua Pritsolas, Randall Pearson, Carolyn Butts-Wilmsmeyer and Thorsten Schad
Appl. Sci. 2023, 13(22), 12467; https://doi.org/10.3390/app132212467 - 17 Nov 2023
Viewed by 625
Abstract
In cultivated landscapes, grasslands are an important land use type for insect life. Grassland management practices can have a significant impact on insect ecology. For example, intense fertilization and frequent cutting can reduce the diversity and abundance of insects by destroying their habitat [...] Read more.
In cultivated landscapes, grasslands are an important land use type for insect life. Grassland management practices can have a significant impact on insect ecology. For example, intense fertilization and frequent cutting can reduce the diversity and abundance of insects by destroying their habitat and food sources. Thus, the quality of grassland habitat for insect development depends on its management intensity. The intensification of grassland production is discussed as one factor contributing to the decline in insect biomass over recent decades. Characterizing grassland changes over time provides one piece to the larger puzzle of insect decline. We analyzed landscape-level trends in grassland biomass near Orbroich and Wahnbachtal in North Rhine-Westphalia, Germany, over a 25-year period. In both areas, pronounced insect biomass decline had been observed. More than 430 Landsat images were used. An image normalization process was developed and employed to ensure that observed changes over time were attributed to grassland changes and not systemic changes inherent within image time series. Distinct clusters of grassland parcels were identified based on intensity and temporal changes in biomass using Normalized Difference Vegetation Index (NDVI) as an indicator. Cluster separability was confirmed using the Transform Divergence method. The results showed clusters having periods of distinct trends in vegetation biomass, indicating changes in grassland agronomic and/or management practices over time (e.g., fertilization, increased silage production). Changes in management practices coincided with regional trends in cultivation as documented by official statistics. We demonstrated the feasibility of using 100+ images over multiple decades to perform a long-term remote sensing analysis examining grassland change. These temporally expansive and spatially detailed trends of grassland change can be included as factors in the multi-variate analysis of insect decline. The methodology can be applied to other geographic areas. Such improved insights can support informed landscape design and cultivation patterns in relation to insect ecology and the broader context of biodiversity enhancement. Full article
(This article belongs to the Special Issue Application of Multitemporal Remote Sensing in Land-Cover/Land-Use Change)
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15 pages, 36389 KiB  
Article
Multi-Task Learning for Building Extraction and Change Detection from Remote Sensing Images
by Danyang Hong, Chunping Qiu, Anzhu Yu, Yujun Quan, Bing Liu and Xin Chen
Appl. Sci. 2023, 13(2), 1037; https://doi.org/10.3390/app13021037 - 12 Jan 2023
Cited by 1 | Viewed by 1953
Abstract
Building extraction (BE) and change detection (CD) from remote sensing (RS) imagery are significant yet highly challenging tasks with substantial application potential in urban management. Learning representative multi-scale features from RS images is a crucial step toward practical BE and CD solutions, as [...] Read more.
Building extraction (BE) and change detection (CD) from remote sensing (RS) imagery are significant yet highly challenging tasks with substantial application potential in urban management. Learning representative multi-scale features from RS images is a crucial step toward practical BE and CD solutions, as in other DL-based applications. To better exploit the available labeled training data for representation learning, we propose a multi-task learning (MTL) network for simultaneous BE and CD, comprising the state-of-the-art (SOTA) powerful Swin transformer as a shared backbone network and multiple heads for predicting building labels and changes. Using the popular CD dataset the Wuhan University building change detection dataset (WHU-CD), we benchmarked detailed designs of the MTL network, including backbone and pre-training choices. With a selected optimal setting, the intersection over union (IoU) score was improved from 70 to 81 for the WHU-CD. The experimental results of different settings demonstrated the effectiveness of the proposed MTL method. In particular, we achieved top scores in BE and CD from optical images in the 2021 Gaofen Challenge. Our method also shows transferable performance on an unseen CD dataset, indicating high label efficiency. Full article
(This article belongs to the Special Issue Application of Multitemporal Remote Sensing in Land-Cover/Land-Use Change)
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