Advanced Remote Sensing Imaging for Environmental Sciences

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 2068

Special Issue Editors


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Guest Editor
1. Graduate School, Northern Arizona University, Flagstaff, AZ 86011, USA
2. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
Interests: neural networks; forecast modeling; deep learning
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Guest Editor
School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
Interests: time-series prediction; pattern recognition; deep learning; blockchain traceability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing imaging is an active source of spatial information which has been proven to be effective in measuring and monitoring the environment. Conversely, hyperspectral remote sensing imaging combines imaging technology with spectral detection technology, which is characterized by integrating image information of the sample with spectral information. Thus, remote sensing images contain information that can reflect external quality characteristics, such as the size, shape, and defects of the sample. However, different components have different spectral absorption capacities, and so the image will reflect defects at a certain wavelength more significantly, while the spectral information can fully reflect the sample. Differences in internal physical structure and chemical composition determine the unique advantages of hyperspectral image technology in environmental science applications and are widely used in environmental resource detection and environmental monitoring tasks. However, the ability of existing methods to identify features is significantly affected by the high data dimensionality and massive information redundancy of hyperspectral remote sensing data. To solve the above problems, we must develop a variety of advanced hyperspectral remote sensing imaging analysis techniques for use in environmental monitoring systems.

This Special Issue encourages scholars and experts to submit works that systematically explore various advanced remote sensing imaging methods for application to environmental science to provide new ideas and references for exploring and addressing pressing environmental science issues. We welcome both original research and review articles.

Dr. Weiwei Cai
Dr. Jianlei Kong
Guest Editors

Manuscript Submission Information

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Keywords

  • advanced remote sensing imaging
  • environmental resource detection
  • hyperspectral image classification
  • environmental monitoring
  • advanced machine learning methods
  • neural networks
  • feature selection of complex environmental data
  • smart monitoring system
  • prediction of environmental pollution
  • multi-sensor data fusion

Published Papers (2 papers)

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Research

22 pages, 32270 KiB  
Article
A Cloud Coverage Image Reconstruction Approach for Remote Sensing of Temperature and Vegetation in Amazon Rainforest
by Emili Bezerra, Salomão Mafalda, Ana Beatriz Alvarez, Diego Armando Uman-Flores, William Isaac Perez-Torres and Facundo Palomino-Quispe
Appl. Sci. 2023, 13(23), 12900; https://doi.org/10.3390/app132312900 - 01 Dec 2023
Viewed by 1022
Abstract
Remote sensing involves actions to obtain information about an area located on Earth. In the Amazon region, the presence of clouds is a common occurrence, and the visualization of important terrestrial information in the image, like vegetation and temperature, can be difficult. In [...] Read more.
Remote sensing involves actions to obtain information about an area located on Earth. In the Amazon region, the presence of clouds is a common occurrence, and the visualization of important terrestrial information in the image, like vegetation and temperature, can be difficult. In order to estimate land surface temperature (LST) and the normalized difference vegetation index (NDVI) from satellite images with cloud coverage, the inpainting approach will be applied to remove clouds and restore the image of the removed region. This paper proposes the use of the neural network LaMa (large mask inpainting) and the scalable model named Big LaMa for the automatic reconstruction process in satellite images. Experiments are conducted on Landsat-8 satellite images of the Amazon rainforest in the state of Acre, Brazil. To evaluate the architecture’s accuracy, the RMSE (root mean squared error), SSIM (structural similarity index) and PSNR (peak signal-to-noise ratio) metrics were used. The LST and NDVI of the reconstructed image were calculated and compared qualitatively and quantitatively, using scatter plots and the chosen metrics, respectively. The experimental results show that the Big LaMa architecture performs more effectively and robustly in restoring images in terms of visual quality. And the LaMa network shows minimal superiority for the measured metrics when addressing medium marked areas. When comparing the results achieved in NDVI and LST of the reconstructed images with real cloud coverage, great visual results were obtained with Big LaMa. Full article
(This article belongs to the Special Issue Advanced Remote Sensing Imaging for Environmental Sciences)
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13 pages, 1855 KiB  
Communication
Sensitivity Analysis of Regression-Based Trend Estimates to Input Errors in Spatial Downscaling of Coarse Resolution Remote Sensing Data
by Geun-Ho Kwak, Sungwook Hong and No-Wook Park
Appl. Sci. 2023, 13(18), 10233; https://doi.org/10.3390/app131810233 - 12 Sep 2023
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Abstract
This paper compared the predictive performance of different regression models for trend component estimation in the spatial downscaling of coarse resolution satellite data using area-to-point regression kriging in the context of the sensitivity to input data errors. Three regression models, linear regression, random [...] Read more.
This paper compared the predictive performance of different regression models for trend component estimation in the spatial downscaling of coarse resolution satellite data using area-to-point regression kriging in the context of the sensitivity to input data errors. Three regression models, linear regression, random forest, and support vector regression, were applied to trend component estimation. An experiment on downscaling synthetic Landsat data with different noise levels demonstrated that a regression model with higher explanatory power and residual correction led to the highest predictive performance only when the input coarse resolution data were assumed to be error-free. Through an experiment on spatial downscaling of coarse resolution monthly Advanced Microwave Scanning Radiometer-2 soil moisture products with significant errors, we found that the higher explanatory power of regression models did not always lead to better predictive performance. The residual correction and normalization of trend components also degraded the predictive performance. Using trend components as a final downscaling result showed the best performance in both experiments as the input errors increased. As the predictive performance of spatial downscaling results is susceptible to input errors, the findings of this study should be considered to evaluate downscaling results and develop advanced spatial downscaling methods. Full article
(This article belongs to the Special Issue Advanced Remote Sensing Imaging for Environmental Sciences)
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