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Deep Learning and IoT Applications for Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 5211

Special Issue Editor


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Guest Editor
Associate Professor, Faculty and Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8572, Japan
Interests: remote sensing; precision agriculture; big data; GIS; decision support systems; agricultural machinery sensing systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I am happy to inform you that Remote Sensing has taken on the interesting project of a Special Issue on Deep Learning and IoT-based Remote Sensing Applications in Agriculture, Forestry, and Urban Planning to Big Data Scheme. Rapid evolution in almost all fields is becoming very common due to emerging trends of collaborative and communication technologies. The rapid advancement of technology, deployment, and integration of IoT, cloud computation, artificial intelligence, big data analytics, and future communication networks have been accepted as key enablers for different smart applications in agricultural land management, forestry, and urban applications. Agricultural remote sensing has proven higher spatial accuracy from commercial satellites, and USGS and European Agency-based satellites. Temporal resolutions of satellite imageries also increased as well, along with cloud computation performances through Google Earth Engines.

At present, land management for sustainable intensification is a challenging issue which requires detailed land suitability and vulnerability analysis. How can we construct an appropriate land use management system and forest productivity to adapt to the microclimatic environment regionally? In the recent decades, geographic information systems (GIS) and satellite remote sensing (RS) have become very effective and attracted attention in different fields, such as sustainable agriculture, land use planning, urban planning, and forestry for their spatial coverage. Continuous growth in hardware, software, and internet technology has enabled the growth of internet-based sensor tools that provide physical world observations and data measurement. A good number of smart applications have been made by Internet of Things (IoT) that communicate, extending the boundaries of physical and virtual entities of the world further to link with Big Data Analytics for decision analysis. The deep learning approaches have been used for training and testing of imageries in a variety of projects for decision making involving IoT with encouraging early results. With its data-driven, anomaly-based methodology and capacity to detect developing deep learning may deliver cutting-edge solutions for agricultural remote sensing challenges.

Therefore, the aim of this Special Issue of Remote Sensing is to collect articles (original research papers, review articles, and case studies) to provide insight into the application of deep leaning approach and IoT in satellite remote sensing and GIS datasets to handle big data for generating more faster and accurate solutions in site-specific management of lands which involves monitoring, change detection forest vegetation mapping, and modelling for selecting suitable sites (e.g., flooding and drought) at various spatial and temporal changes.

Deep learning and IoT applications in Remote Sensing is an open Special Issue, welcoming a variety of novel scientific articles including innovative and cutting-edge research using remote sensing techniques and data using different deep learning approaches and IoT from remote sensing platforms (ground truth data, satellite, aircraft, radar, drones, etc.) to the study-related issues in agriculture, forestry, urban planning, and management. The editor invites contributions on social, economic, and legal aspects of agriculture, urban planning, and forest management.

Dr. Ahamed Tofael
Guest Editor

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. 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 2700 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

  • Artificial Intelligence (AI)
  • deep learning
  • IoT
  • cloud computation
  • big data
  • remote sensing
  • agriculture, forestry, urban planning and management

Published Papers (2 papers)

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Research

27 pages, 14163 KiB  
Article
Evaluation of InSAR Tropospheric Delay Correction Methods in a Low-Latitude Alpine Canyon Region
by Yanxi Zhao, Xiaoqing Zuo, Yongfa Li, Shipeng Guo, Jinwei Bu and Qihang Yang
Remote Sens. 2023, 15(4), 990; https://doi.org/10.3390/rs15040990 - 10 Feb 2023
Cited by 3 | Viewed by 1609
Abstract
Tropospheric delay error must be reduced during interferometric synthetic aperture radar (InSAR) measurement. Depending on different geographical environments, an appropriate correction method should be selected to improve the accuracy of InSAR deformation monitoring. In this study, surface deformation monitoring was conducted in a [...] Read more.
Tropospheric delay error must be reduced during interferometric synthetic aperture radar (InSAR) measurement. Depending on different geographical environments, an appropriate correction method should be selected to improve the accuracy of InSAR deformation monitoring. In this study, surface deformation monitoring was conducted in a high mountain gorge region in Yunnan Province, China, using Sentinel-1A images of ascending and descending tracks. The tropospheric delay in the InSAR interferogram was corrected using the Linear, Generic Atmospheric Correction Online Service for InSAR (GACOS) and ERA-5 meteorological reanalysis data (ERA5) methods. The correction effect was evaluated by combining phase standard deviation, semi-variance function, elevation correlation, and global navigation satellite system (GNSS) deformation monitoring results. The mean value of the phase standard deviation (Aver) of the linear correction interferogram and the threshold value (sill) of the semi-variogram were reduced by –20.98% and –41%, respectively, while the accuracy of the InSAR deformation points near the GNSS site was increased by 58%. The results showed that the three methods reduced the tropospheric delay error of InSAR deformation monitoring by different degrees in low-latitude mountains and valleys. Linear correction was the best at alleviating the tropospheric delay, followed by GACOS, while ERA5 had poor correction stability. Full article
(This article belongs to the Special Issue Deep Learning and IoT Applications for Remote Sensing)
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25 pages, 8216 KiB  
Article
Shoreline Change Assessment in the Coastal Region of Bangladesh Delta Using Tasseled Cap Transformation from Satellite Remote Sensing Dataset
by Md Shamsuzzoha and Tofael Ahamed
Remote Sens. 2023, 15(2), 295; https://doi.org/10.3390/rs15020295 - 4 Jan 2023
Cited by 8 | Viewed by 2969
Abstract
Bangladesh is a global south hotspot due to climate change and sea level rise concerns. It is a highly disaster-prone country in the world with active deltaic shorelines. The shorelines are quickly changing to coastal accretion and erosion. Erosion is one of the [...] Read more.
Bangladesh is a global south hotspot due to climate change and sea level rise concerns. It is a highly disaster-prone country in the world with active deltaic shorelines. The shorelines are quickly changing to coastal accretion and erosion. Erosion is one of the water hazards to landmass sinking, and accretion relates to land level rises due to sediment load deposition on the Bay of Bengal continental shelf. Therefore, this study aimed to explore shoreline status with change assessment for the three study years 1991, 2006, and 2021 using satellite remote sensing and geographical information system (GIS) approaches. Landsat 5, 7 ETM+, and 8 OLI satellite imageries were employed for onshore tasseled cap transformation (TCT) and land and sea classification calculations to create shore boundaries, baseline assessment, land accretion, erosion, point distance, and near feature analysis. We converted 16,550 baseline vertices to points as the study ground reference points (GRPs) and validated those points using the country datasheet collected from the Survey of Bangladesh (SoB). We observed that the delta’s shorelines were changed, and the overall lands were accredited for the land-increasing characteristics analysis. The total accredited lands in the coastal areas observed during the time periods from 1991 to 2006 were 825.15 km2, from 2006 to 2021 was 756.69 km2, and from 1991 to 2021 was 1223.94 km2 for the 30-year period. Similarly, coastal erosion assessment analysis indicated that the results gained for the period 1991 to 2006 and 2006 to 2021 were 475.87 km2 and 682.75 km2, respectively. Therefore, the total coastal erosion was 800.72 km2 from 1991 to 2021. Neat accretion was 73.94 km2 for the 30-year period from 1991 to 2021. This research indicates the changes in shorelines, referring to the evidence for the delta’s active formation through accretion and erosion processes of ‘climate change’ and ‘sea level rise’. This research projects the erosion process and threatens land use changes toward agriculture and settlements in the coastal regions of Bangladesh. Full article
(This article belongs to the Special Issue Deep Learning and IoT Applications for Remote Sensing)
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