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Atmosphere
Special Issues
Application of Remote Sensing Cloud Computing in Land Surface Change
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Application of Remote Sensing Cloud Computing in Land Surface Change

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (10 September 2021) | Viewed by 10032

Special Issue Editors

Dr. Yaoping Cui

E-Mail Website
Guest Editor
College of Environment and Planning, Henan University, Kaifeng 475004, China
Interests: land use and land cover; climate change; ecosystem service; remote sensing
Dr. Abhnil Prasad

E-Mail Website
Guest Editor
School of Photovoltaics and Renewable Energy Engineering, Faculty of Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Interests: atmospheric modelling; numerical weather prediction; climate change and predictability; tropical cyclones; weather and climate extremes;turbulence; atmospheric convection;atmospheric radiation; meteorology;solar and wind energy assessment and prediction;cloud physics;large-scale climate dynamics and satellite remote sensing of clouds and aerosols

Special Issue Information

Dear Colleagues,

Human behavior leads to changes in the earth’s land surface, which has become the primary problem hindering global sustainable development. In nearly half a century, remote sensing technology, especially cloud computing technology in remote sensing, has provided an advanced detection and research approach for investigating land surface change, monitoring regional environmental change, and even global warming. Remote sensing imagery, as one of the sources for big data, is generating earth-observation data and analysis results daily from satellites, manned/unmanned aircraft, and cloud computing platform, which can directly be applied to ecology, geography, and sociology.

Remote sensing data is increasingly rich, but limited by software and hardware, the ability of data operation makes the analysis of multi-source remote sensing data very difficult and time-consuming. The remote sensing cloud computing platforms such as Google Earth Engine (GEE) provided a feasible way to deal with the outlined difficulties. This Special Issue invites contributors to discuss their latest findings using remote sensing data and cloud computing to explore related topics on exploring land surface change.

Topics include the application of remote sensing in quantifying spatio-temporal changes of land cover, land-use mapping, urban expansion, flood and drought, and ecosystem services and agriculture. We encourage a synthesis of the emerging cloud computing methods, which should strengthen the role of remote sensing in providing operational, efficient, and long-term services for ecology, agriculture, geography, climate change, and sociology applications.

Dr. Yaoping Cui
Dr. Abhnil Prasad
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. Atmosphere is an international peer-reviewed open access monthly 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

  • remote sensing
  • data fusion
  • spatio-temporal change
  • land use mapping
  • urban expansion
  • cloud computing

Published Papers (3 papers)

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Research

21 pages, 1740 KiB  
Article
Blockchain-Aware Distributed Dynamic Monitoring: A Smart Contract for Fog-Based Drone Management in Land Surface Changes
by Abdullah Ayub Khan, Zaffar Ahmed Shaikh, Asif Ali Laghari, Sami Bourouis, Asif Ali Wagan and Ghulam Ali Alias Atif Ali
Atmosphere 2021, 12(11), 1525; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12111525 - 19 Nov 2021
Cited by 25 | Viewed by 2694
Abstract
In this paper, we propose a secure blockchain-aware framework for distributed data management and monitoring. Indeed, images-based data are captured through drones and transmitted to the fog nodes. The main objective here is to enable process and schedule, to investigate individual captured entity [...] Read more.
In this paper, we propose a secure blockchain-aware framework for distributed data management and monitoring. Indeed, images-based data are captured through drones and transmitted to the fog nodes. The main objective here is to enable process and schedule, to investigate individual captured entity (records) and to analyze changes in the blockchain storage with a secure hash-encrypted (SH-256) consortium peer-to-peer (P2P) network. The proposed blockchain mechanism is also investigated for analyzing the fog-cloud-based stored information, which is referred to as smart contracts. These contracts are designed and deployed to automate the overall distributed monitoring system. They include the registration of UAVs (drones), the day-to-day dynamic captured drone-based images, and the update transactions in the immutable storage for future investigations. The simulation results show the merit of our framework. Indeed, through extensive experiments, the developed system provides good performances regarding monitoring and management tasks. Full article
(This article belongs to the Special Issue Application of Remote Sensing Cloud Computing in Land Surface Change)
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19 pages, 7104 KiB  
Article
Determine the Land-Use Land-Cover Changes, Urban Expansion and Their Driving Factors for Sustainable Development in Gazipur Bangladesh
by Hossain Mohammad Arifeen, Khamphe Phoungthong, Ali Mostafaeipour, Nuttaya Yuangyai, Chumpol Yuangyai, Kuaanan Techato and Warangkana Jutidamrongphan
Atmosphere 2021, 12(10), 1353; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12101353 - 16 Oct 2021
Cited by 21 | Viewed by 4370
Abstract
At present, urbanization is a very common phenomenon around the world, especially in developing countries, and has a significant impact on the land-use/land-cover of specific areas, producing some unwanted effects. Bangladesh is a tightly inhabited country whose urban population is increasing every day [...] Read more.
At present, urbanization is a very common phenomenon around the world, especially in developing countries, and has a significant impact on the land-use/land-cover of specific areas, producing some unwanted effects. Bangladesh is a tightly inhabited country whose urban population is increasing every day due to the expansion of infrastructure and industry. This study explores the land-use/land-cover change detection and urban dynamics of Gazipur district, Bangladesh, a newly developed industrial hub and city corporation, by using satellite imagery covering every 10-year interval over the period from 1990 to 2020. Supervised classification with a maximum likelihood classifier was used to gather spatial and temporal information from Landsat 5 (TM), 7 (ETM+) and 8 (OLI/TIRS) images. The Geographical Information System (GIS) methodology was also employed to detect changes over time. The kappa coefficient ranged between 0.75 and 0.90. The agricultural land was observed to be shrinking very rapidly, with an area of 716 km2 in 2020. Urbanization increased rapidly in this area, and the urban area grew by more than 500% during the study period. The urbanized area expanded along major roads such as the Dhaka–Mymensingh Highway and Dhaka bypass road. The urbanized area was, moreover, concentrated near the boundary line of Dhaka, the capital city of Bangladesh. Urban expansion was found to be influenced by demographic-, economic-, location- and accessibility-related factors. Therefore, similarly to many countries, concrete urban and development policies should be formulated to preserve the environment and, thereby, achieve sustainable development goal (SDG) 11 (sustainable cities and communities). Full article
(This article belongs to the Special Issue Application of Remote Sensing Cloud Computing in Land Surface Change)
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16 pages, 3979 KiB  
Article
Comparing Four Types Methods for Karst NDVI Prediction Based on Machine Learning
by Yuju Ma, Liyuan Zuo, Jiangbo Gao, Qiang Liu and Lulu Liu
Atmosphere 2021, 12(10), 1341; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12101341 - 13 Oct 2021
Cited by 2 | Viewed by 2021
Abstract
As a link for energy transfer between the land and atmosphere in the terrestrial ecosystem, karst vegetation plays an important role. Karst vegetation is not only affected by environmental factors but also by intense human activities. The nonlinear characteristics of vegetation growth are [...] Read more.
As a link for energy transfer between the land and atmosphere in the terrestrial ecosystem, karst vegetation plays an important role. Karst vegetation is not only affected by environmental factors but also by intense human activities. The nonlinear characteristics of vegetation growth are induced by the interaction mechanism of these factors. Previous studies of this relationship were not comprehensive, and it is necessary to further explore it using a suitable method. In this study, we selected climate, human activities, topography, and soil texture as the response factors; a nonlinear relationship model between the karst normalized difference vegetation index (NDVI) and these factors was established by applying a back propagation neural network (BPNN), a radial basis function neural network (RBFNN), the random forest (RF) algorithm, and support vector regression (SVR); and then, the karst NDVI was predicted. The coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the obtained results were calculated, and the mean R2 values of the BPNN, RBFNN, RF, and SVR models were determined to be 0.77, 0.86, 0.89, and 0.91, respectively. Compared with the BPNN, RBFNN, and RF models, the SVR model had the lowest errors, with mean MSE, RMSE, and MAPE values of 0.001, 0.02, and 2.77, respectively. The results show that the BPNN, RBFNN, RF, and SVR models are within acceptable ranges for karst NDVI prediction, but the overall performance of the SVR model is the best, and it is more suitable for karst vegetation prediction. Full article
(This article belongs to the Special Issue Application of Remote Sensing Cloud Computing in Land Surface Change)
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