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AI-Driven Satellite Data for Global Environment Monitoring
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AI-Driven Satellite Data for Global Environment Monitoring

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

Deadline for manuscript submissions: closed (26 May 2024) | Viewed by 23385

Special Issue Editor

Prof. Dr. Yang-Won Lee

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Guest Editor
Department of Spatial Information Engineering, Pukyong National University, Busan 48513, Republic of Korea
Interests: artificial intelligence; semantic segmentation; remote sensing of disaster; applications in agriculture, forest, hydrology, and meteorology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The acceleration of environmental changes on Earth may significantly affect the global atmosphere, oceans, agriculture, forests, and water. Indeed, the Earth belongs to our descendants, not us, so we must deliver a safe and clean Earth to them. Satellite remote-sensing data is the essential material for spatially and temporally continuous observation of the Earth. Moreover, recent technological developments ensure higher resolution and broader coverage to monitor disasters, meteorology, air quality, vegetation, hydrology, and polar regions. AI is a powerful tool for creating high-quality satellite images and for observation of the Earth’s environmental phenomena using advanced computing power. In addition to the classical algorithms, various state-of-the-art models can help improve AI-driven satellite data for global environmental monitoring. We invite colleagues' insights and contributions to various research areas involving remote sensing combined with an AI approach. Papers can be focused on, but are not limited to, the following:

  • Deep-learning-based object detection from satellite images for environmental monitoring of Earth;
  • Semantic segmentation of satellite images for environmental monitoring of Earth;
  • Super-resolution techniques for environmental monitoring of Earth;
  • AI-based spatiotemporal image fusion for environmental monitoring of Earth;
  • AI-based change detection for environmental monitoring of Earth;
  • Satellite-based disaster management using AI models;
  • AI-based retrieval algorithm for the satellite products in atmosphere, meteorology, ocean, and air quality;
  • AI-based retrieval algorithm for the satellite products in agriculture, forests, hydrology, and ecology;
  • AI-driven novel methods for Earth’s environmental monitoring with satellite images.

Prof. Dr. Yang-Won Lee
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.

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Related Special Issue

Published Papers (8 papers)

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23 pages, 29625 KiB  
Article
HA-Net for Bare Soil Extraction Using Optical Remote Sensing Images
by Junqi Zhao, Dongsheng Du, Lifu Chen, Xiujuan Liang, Haoda Chen and Yuchen Jin
Remote Sens. 2024, 16(16), 3088; https://doi.org/10.3390/rs16163088 - 21 Aug 2024
Viewed by 780
Abstract
Bare soil will cause soil erosion and contribute to air pollution through the generation of dust, making the timely and effective monitoring of bare soil an urgent requirement for environmental management. Although there have been some researches on bare soil extraction using high-resolution [...] Read more.
Bare soil will cause soil erosion and contribute to air pollution through the generation of dust, making the timely and effective monitoring of bare soil an urgent requirement for environmental management. Although there have been some researches on bare soil extraction using high-resolution remote sensing images, great challenges still need to be solved, such as complex background interference and small-scale problems. In this regard, the Hybrid Attention Network (HA-Net) is proposed for automatic extraction of bare soil from high-resolution remote sensing images, which includes the encoder and the decoder. In the encoder, HA-Net initially utilizes BoTNet for primary feature extraction, producing four-level features. The extracted highest-level features are then input into the constructed Spatial Information Perception Module (SIPM) and the Channel Information Enhancement Module (CIEM) to emphasize the spatial and channel dimensions of bare soil information adequately. To improve the detection rate of small-scale bare soil areas, during the decoding stage, the Semantic Restructuring-based Upsampling Module (SRUM) is proposed, which utilizes the semantic information from input features and compensate for the loss of detailed information during downsampling in the encoder. An experiment is performed based on high-resolution remote sensing images from the China–Brazil Resources Satellite 04A. The results show that HA-Net obviously outperforms several excellent semantic segmentation networks in bare soil extraction. The average precision and IoU of HA-Net in two scenes can reach 90.9% and 80.9%, respectively, which demonstrates the excellent performance of HA-Net. It embodies the powerful ability of HA-Net for suppressing the interference from complex backgrounds and solving multiscale issues. Furthermore, it may also be used to perform excellent segmentation tasks for other targets from remote sensing images. Full article
(This article belongs to the Special Issue AI-Driven Satellite Data for Global Environment Monitoring)
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19 pages, 5744 KiB  
Article
Single-Temporal Sentinel-2 for Analyzing Burned Area Detection Methods: A Study of 14 Cases in Republic of Korea Considering Land Cover
by Doi Lee, Sanghun Son, Jaegu Bae, Soryeon Park, Jeongmin Seo, Dongju Seo, Yangwon Lee and Jinsoo Kim
Remote Sens. 2024, 16(5), 884; https://doi.org/10.3390/rs16050884 - 2 Mar 2024
Cited by 5 | Viewed by 1946
Abstract
Forest fires are caused by various climatic and anthropogenic factors. In Republic of Korea, forest fires occur frequently during spring when the humidity is low. During the past decade, the number of forest fire incidents and the extent of the damaged area have [...] Read more.
Forest fires are caused by various climatic and anthropogenic factors. In Republic of Korea, forest fires occur frequently during spring when the humidity is low. During the past decade, the number of forest fire incidents and the extent of the damaged area have increased. Satellite imagery can be applied to assess damage from these unpredictable forest fires. Despite the increasing threat, there is a lack of comprehensive analysis and effective strategies for addressing these forest fires, particularly considering the diverse topography of Republic of Korea. Herein, we present an approach for the automated detection of forest fire damage using Sentinel-2 images of 14 areas affected by forest fires in Republic of Korea during 2019–2023. The detection performance of deep learning (DL), machine learning, and spectral index methods was analyzed, and the optimal model for detecting forest fire damage was derived. To evaluate the independent performance of the models, two different burned areas exhibiting distinct characteristics were selected as test subjects. To increase the classification accuracy, tests were conducted on various combinations of input channels in DL. The combination of false-color RNG (B4, B8, and B3) images was optimal for detecting forest fire damage. Consequently, among the DL models, the HRNet model achieved excellent results for both test regions with intersection over union scores of 89.40 and 82.49, confirming that the proposed method is applicable for detecting forest fires in diverse Korean landscapes. Thus, suitable mitigation measures can be promptly designed based on the rapid analysis of damaged areas. Full article
(This article belongs to the Special Issue AI-Driven Satellite Data for Global Environment Monitoring)
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21 pages, 16048 KiB  
Article
Tree-Structured Parzan Estimator–Machine Learning–Ordinary Kriging: An Integration Method for Soil Ammonia Spatial Prediction in the Typical Cropland of Chinese Yellow River Delta with Sentinel-2 Remote Sensing Image and Air Quality Data
by Yingqiang Song, Mingzhu Ye, Zhao Zheng, Dexi Zhan, Wenxu Duan, Miao Lu, Zhenqi Song, Dengkuo Sun, Kaizhong Yao and Ziqi Ding
Remote Sens. 2023, 15(17), 4268; https://doi.org/10.3390/rs15174268 - 30 Aug 2023
Cited by 2 | Viewed by 1453
Abstract
Spatial prediction of soil ammonia (NH3) plays an important role in monitoring climate warming and soil ecological health. However, traditional machine learning (ML) models do not consider optimal parameter selection and spatial autocorrelation. Here, we present an integration method (tree-structured Parzen [...] Read more.
Spatial prediction of soil ammonia (NH3) plays an important role in monitoring climate warming and soil ecological health. However, traditional machine learning (ML) models do not consider optimal parameter selection and spatial autocorrelation. Here, we present an integration method (tree-structured Parzen estimator–machine learning–ordinary kriging (TPE–ML–OK)) to predict spatial variability of soil NH3 from Sentinel-2 remote sensing image and air quality data. In TPE–ML–OK, we designed the TPE search algorithm, which encourages gradient boosting decision tree (GBDT), random forest (RF), and extreme gradient boosting (XGB) models to pay more attention to the optimal hyperparameters’ high-possibility range, and then the residual ordinary kriging model is used to further improve the prediction accuracy of soil NH3 flux. We found a weak linear correlation between soil NH3 flux and environmental variables using scatter matrix correlation analysis. The optimal hyperparameters from the TPE search algorithm existed in the densest iteration region, and the TPE–XGB–OK method exhibited the highest predicted accuracy (R2 = 85.97%) for soil NH3 flux in comparison with other models. The spatial mapping results based on TPE–ML–OK methods showed that the high fluxes of soil NH3 were concentrated in the central and northeast areas, which may be influenced by rivers or soil water. The analysis result of the SHapley additive explanation (SHAP) algorithm found that the variables with the highest contribution to soil NH3 were O3, SO2, PM10, CO, and NDWI. The above results demonstrate the powerful linear–nonlinear interpretation ability between soil NH3 and environmental variables using the integration method, which can reduce the impact on agricultural nitrogen deposition and regional air quality. Full article
(This article belongs to the Special Issue AI-Driven Satellite Data for Global Environment Monitoring)
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21 pages, 4130 KiB  
Article
Image Texture Analysis Enhances Classification of Fire Extent and Severity Using Sentinel 1 and 2 Satellite Imagery
by Rebecca Kate Gibson, Anthea Mitchell and Hsing-Chung Chang
Remote Sens. 2023, 15(14), 3512; https://doi.org/10.3390/rs15143512 - 12 Jul 2023
Cited by 6 | Viewed by 2314
Abstract
Accurate and reliable mapping of fire extent and severity is critical for assessing the impact of fire on vegetation and informing post-fire recovery trajectories. Classification approaches that combine pixel-wise and neighbourhood statistics including image texture derived from high-resolution satellite data may improve on [...] Read more.
Accurate and reliable mapping of fire extent and severity is critical for assessing the impact of fire on vegetation and informing post-fire recovery trajectories. Classification approaches that combine pixel-wise and neighbourhood statistics including image texture derived from high-resolution satellite data may improve on current methods of fire severity mapping. Texture is an innate property of all land cover surfaces that is known to vary between fire severity classes, becoming increasingly more homogenous as fire severity increases. In this study, we compared candidate backscatter and reflectance indices derived from Sentinel 1 and Sentinel 2, respectively, together with grey-level-co-occurrence-matrix (GLCM)-derived texture indices using a random forest supervised classification framework. Cross-validation (for which the target fire was excluded in training) and target-trained (for which the target fire was included in training) models were compared to evaluate performance between the models with and without texture indices. The results indicated that the addition of texture indices increased the classification accuracies of severity for both sensor types, with the greatest improvements in the high severity class (23.3%) for the Sentinel 1 and the moderate severity class (17.4%) for the Sentinel 2 target-trained models. The target-trained models consistently outperformed the cross-validation models, especially with regard to Sentinel 1, emphasising the importance of local training data in capturing post-fire variation in different forest types and severity classes. The Sentinel 2 models more accurately estimated fire extent and were improved with the addition of texture indices (3.2%). Optical sensor data yielded better results than C-band synthetic aperture radar (SAR) data with respect to distinguishing fire severity and extent. Successful detection using C-band data was linked to significant structural change in the canopy (i.e., partial-complete canopy consumption) and is more successful over sparse, low-biomass forest. Future research will investigate the sensitivity of longer-wavelength (L-band) SAR regarding fire severity estimation and the potential for an integrated fire-mapping system that incorporates both active and passive remote sensing to detect and monitor changes in vegetation cover and structure. Full article
(This article belongs to the Special Issue AI-Driven Satellite Data for Global Environment Monitoring)
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19 pages, 7409 KiB  
Article
Deep Learning-Based Framework for Soil Moisture Content Retrieval of Bare Soil from Satellite Data
by Mohammed Dabboor, Ghada Atteia, Souham Meshoul and Walaa Alayed
Remote Sens. 2023, 15(7), 1916; https://doi.org/10.3390/rs15071916 - 3 Apr 2023
Cited by 9 | Viewed by 3235
Abstract
Machine learning (ML) is a branch of artificial intelligence (AI) that has been successfully applied in a variety of remote sensing applications, including geophysical information retrieval such as soil moisture content (SMC). Deep learning (DL) is a subfield of ML that uses models [...] Read more.
Machine learning (ML) is a branch of artificial intelligence (AI) that has been successfully applied in a variety of remote sensing applications, including geophysical information retrieval such as soil moisture content (SMC). Deep learning (DL) is a subfield of ML that uses models with complex structures to solve prediction problems with higher performance than traditional ML. In this study, a framework based on DL was developed for SMC retrieval. For this purpose, a sample dataset was built, which included synthetic aperture radar (SAR) backscattering, radar incidence angle, and ground truth data. Herein, the performance of five optimized ML prediction models was evaluated in terms of soil moisture prediction. However, to boost the prediction performance of these models, a DL-based data augmentation technique was implemented to create a reconstructed version of the available dataset. This includes building a sparse autoencoder DL network for data reconstruction. The Bayesian optimization strategy was employed for fine-tuning the hyperparameters of the ML models in order to improve their prediction performance. The results of our study highlighted the improved performance of the five ML prediction models with augmented data. The Gaussian process regression (GPR) showed the best prediction performance with 4.05% RMSE and 0.81 R2 on a 10% independent test subset. Full article
(This article belongs to the Special Issue AI-Driven Satellite Data for Global Environment Monitoring)
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23 pages, 3374 KiB  
Article
Creation of a Walloon Pasture Monitoring Platform Based on Machine Learning Models and Remote Sensing
by Charles Nickmilder, Anthony Tedde, Isabelle Dufrasne, Françoise Lessire, Noémie Glesner, Bernard Tychon, Jérome Bindelle and Hélène Soyeurt
Remote Sens. 2023, 15(7), 1890; https://doi.org/10.3390/rs15071890 - 31 Mar 2023
Cited by 1 | Viewed by 1718
Abstract
The use of remote sensing data and the implementation of machine learning (ML) algorithms is growing in pasture management. In this study, ML models predicting the available compressed sward height (CSH) in Walloon pastures based on Sentinel-1, Sentinel-2, and meteorological data were developed [...] Read more.
The use of remote sensing data and the implementation of machine learning (ML) algorithms is growing in pasture management. In this study, ML models predicting the available compressed sward height (CSH) in Walloon pastures based on Sentinel-1, Sentinel-2, and meteorological data were developed to be integrated into a decision support system (DSS). Given the area covered (>4000 km2 of pastures of 100 m2 pixels), the consequent challenge of computation time and power requirements was overcome by the development of a platform predicting CSH throughout Wallonia. Four grazing seasons were covered in the current study (between April and October from 2018 to 2021, the mean predicted CSH per parcel per date ranged from 48.6 to 67.2 mm, and the coefficient of variation from 0 to 312%, suggesting a strong heterogeneity of variability of CSH between parcels. Further exploration included the number of predictions expected per grazing season and the search for temporal and spatial patterns and consistency. The second challenge tackled is the poor data availability for concurrent acquisition, which was overcome through the inclusion of up to 4-day-old data to fill data gaps up to the present time point. For this gap filling methodology, relevancy decreased as the time window width increased, although data with 4-day time lag values represented less than 4% of the total data. Overall, two models stood out, and further studies should either be based on the random forest model if they need prediction quality or on the cubist model if they need continuity. Further studies should focus on developing the DSS and on the conversion of CSH to actual forage allowance. Full article
(This article belongs to the Special Issue AI-Driven Satellite Data for Global Environment Monitoring)
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15 pages, 8585 KiB  
Article
A Novel Framework for Correcting Satellite-Based Precipitation Products for Watersheds with Discontinuous Observed Data, Case Study in Mekong River Basin
by Giha Lee, Duc Hai Nguyen and Xuan-Hien Le
Remote Sens. 2023, 15(3), 630; https://doi.org/10.3390/rs15030630 - 20 Jan 2023
Cited by 4 | Viewed by 1937
Abstract
Satellite-based precipitation (SP) data are gaining scientific interest due to their advantage in producing high-resolution products with quasi-global coverage. However, since the major reliance of precipitation data is on the distinctive geographical features of each location, they remain at a considerable distance from [...] Read more.
Satellite-based precipitation (SP) data are gaining scientific interest due to their advantage in producing high-resolution products with quasi-global coverage. However, since the major reliance of precipitation data is on the distinctive geographical features of each location, they remain at a considerable distance from station-based data. This paper examines the effectiveness of a convolutional autoencoder (CAE) architecture in pixel-by-pixel bias correction of SP products for the Mekong River Basin (MRB). Two satellite-based products (TRMM and PERSIANN-CDR) and a gauge-based product (APHRODITE) are gridded rainfall products mined in this experiment. According to the estimated statistical criteria, the CAE model was effective in reducing the gap between SP products and benchmark data both in terms of spatial and temporal correlations. The two corrected SP products (CAE_TRMM and CAE_CDR) performed competitively, with CAE TRMM appearing to have a slight advantage over CAE CDR, however, the difference was minor. This study’s findings proved the effectiveness of deep learning-based models (here CAE) for bias correction of SP products. We believe that this technique will be a feasible alternative for delivering an up-to-current and reliable dataset for MRB studies, given that the sole available gauge-based dataset for this area has been out of date for a long time. Full article
(This article belongs to the Special Issue AI-Driven Satellite Data for Global Environment Monitoring)
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18 pages, 8441 KiB  
Technical Note
Mapping Remote Roads Using Artificial Intelligence and Satellite Imagery
by Sean Sloan, Raiyan R. Talkhani, Tao Huang, Jayden Engert and William F. Laurance
Remote Sens. 2024, 16(5), 839; https://doi.org/10.3390/rs16050839 - 28 Feb 2024
Cited by 5 | Viewed by 8099
Abstract
Road building has long been under-mapped globally, arguably more than any other human activity threatening environmental integrity. Millions of kilometers of unmapped roads have challenged environmental governance and conservation in remote frontiers. Prior attempts to map roads at large scales have proven inefficient, [...] Read more.
Road building has long been under-mapped globally, arguably more than any other human activity threatening environmental integrity. Millions of kilometers of unmapped roads have challenged environmental governance and conservation in remote frontiers. Prior attempts to map roads at large scales have proven inefficient, incomplete, and unamenable to continuous road monitoring. Recent developments in automated road detection using artificial intelligence have been promising but have neglected the relatively irregular, sparse, rustic roadways characteristic of remote semi-natural areas. In response, we tested the accuracy of automated approaches to large-scale road mapping across remote rural and semi-forested areas of equatorial Asia-Pacific. Three machine learning models based on convolutional neural networks (UNet and two ResNet variants) were trained on road data derived from visual interpretations of freely available high-resolution satellite imagery. The models mapped roads with appreciable accuracies, with F1 scores of 72–81% and intersection over union scores of 43–58%. These results, as well as the purposeful simplicity and availability of our input data, support the possibility of concerted program of exhaustive, automated road mapping and monitoring across large, remote, tropical areas threatened by human encroachment. Full article
(This article belongs to the Special Issue AI-Driven Satellite Data for Global Environment Monitoring)
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