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Advanced Artificial Intelligence for Environmental Remote Sensing

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

Deadline for manuscript submissions: closed (28 February 2024) | Viewed by 17797

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Dr. Ce Zhang

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Guest Editor

School of Geographical Sciences, University of Bristol, Bristol BS8 1QU, UK
Interests: machine learning; deep learning; geospatial data science; remote sensing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Peter M. Atkinson

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Faculty of Science and Technology, Lancaster University, Lancaster LA1 4YR, UK
Interests: geostatistics; space-time statistics; geospatial data science; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing and Earth observation (EO) from diverse sources, including satellite, airborne, and in situ platforms and citizen observatories, offer great opportunities to identify changes to the Earth’s surface across different scales. For environmental systems, such as climate and weather, different types of EO sensors are adopted, with distinct spatial, spectral, and temporal resolutions. This generates substantial quantities of data, and environmental remote sensing is now considered an area of “massive big data”. Traditional remote sensing techniques are inadequate for extracting the most meaningful information from these data. This calls for novel technologies to mine the potential information in a robust, accurate, and automatic fashion.

The latest developments in artificial intelligence (AI), in particular deep learning (DL), have gained tremendous interest in the field of remote sensing. DL is considered a state-of-the-art breakthrough in AI that automates the process of feature learning and feature representation and is capable of hierarchically extracting valuable end-to-end information about natural phenomena. Advanced AI techniques such as attention mechanisms, transformers, graph neural networks, and explainable AI further boost the precision of many practical applications with ground-breaking performance. The combination of remote sensing and advanced artificial intelligence can help improve experts’ understanding of land, ocean, and atmosphere systems. This can lead to many benefits, including more accurate predictions about the behavior of such environmental systems, the automation of data analysis, improved management of resources, and the discovery of new insights from complex datasets.

For this Special Issue, we encourage the submission of articles that focus on advanced artificial intelligence, with primary environmental applications using remotely sensed data across different sensors and platforms. Results can be derived from existing or planned instruments, including acquired data or modeled outcomes. Applications can be related to classification and prediction tasks in agricultural and urban space monitoring, forest inventory, natural and land resource management, weather forecasting, environmental hazards and disasters, etc. Deep learning algorithms can include tasks related to semantic segmentation, object detection, scene recognition, and parameter estimation.

Dr. Ce Zhang
Prof. Dr. Peter M. Atkinson
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. 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

environmental remote sensing
artificial intelligence
deep learning
mapping
monitoring
deep networks

Published Papers (11 papers)

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31 pages, 8833 KiB

Open AccessArticle

Object-Based Semi-Supervised Spatial Attention Residual UNet for Urban High-Resolution Remote Sensing Image Classification

by Yuanbing Lu, Huapeng Li, Ce Zhang and Shuqing Zhang

Remote Sens. 2024, 16(8), 1444; https://doi.org/10.3390/rs16081444 - 18 Apr 2024

Viewed by 403

Abstract

Accurate urban land cover information is crucial for effective urban planning and management. While convolutional neural networks (CNNs) demonstrate superior feature learning and prediction capabilities using image-level annotations, the inherent mixed-category nature of input image patches leads to classification errors along object boundaries. Fully convolutional neural networks (FCNs) excel at pixel-wise fine segmentation, making them less susceptible to heterogeneous content, but they require fully annotated dense image patches, which may not be readily available in real-world scenarios. This paper proposes an object-based semi-supervised spatial attention residual UNet (OS-ARU) model. First, multiscale segmentation is performed to obtain segments from a remote sensing image, and segments containing sample points are assigned the categories of the corresponding points, which are used to train the model. Then, the trained model predicts class probabilities for all segments. Each unlabeled segment’s probability distribution is compared against those of labeled segments for similarity matching under a threshold constraint. Through label propagation, pseudo-labels are assigned to unlabeled segments exhibiting high similarity to labeled ones. Finally, the model is retrained using the augmented training set incorporating the pseudo-labeled segments. Comprehensive experiments on aerial image benchmarks for Vaihingen and Potsdam demonstrate that the proposed OS-ARU achieves higher classification accuracy than state-of-the-art models, including OCNN, 2OCNN, and standard OS-U, reaching an overall accuracy (OA) of 87.83% and 86.71%, respectively. The performance improvements over the baseline methods are statistically significant according to the Wilcoxon Signed-Rank Test. Despite using significantly fewer sparse annotations, this semi-supervised approach still achieves comparable accuracy to the same model under full supervision. The proposed method thus makes a step forward in substantially alleviating the heavy sampling burden of FCNs (densely sampled deep learning models) to effectively handle the complex issue of land cover information identification and classification. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Environmental Remote Sensing)

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20 pages, 5576 KiB

Open AccessArticle

Assessment and Prediction of Sea Level and Coastal Wetland Changes in Small Islands Using Remote Sensing and Artificial Intelligence

by Nawin Raj and Sarah Pasfield-Neofitou

Remote Sens. 2024, 16(3), 551; https://doi.org/10.3390/rs16030551 - 31 Jan 2024

Viewed by 769

Abstract

Pacific Island countries are vulnerable to the impacts of climate change, which include the risks of increased ocean temperatures, sea level rise and coastal wetland loss. The destruction of wetlands leads not only to a loss of carbon sequestration but also triggers the release of already sequestered carbon, in turn exacerbating global warming. These climate change effects are interrelated, and small island nations continuously need to develop adaptive and mitigative strategies to deal with them. However, accurate and reliable research is needed to know the extent of the climate change effects with future predictions. Hence, this study develops a new hybrid Convolutional Neural Network (CNN) Multi-Layer Bidirectional Long Short-Term Memory (BiLSTM) deep learning model with Multivariate Variational Mode Decomposition (MVMD) to predict the sea level for study sites in the Solomon Islands and Federated States of Micronesia (FSM). Three other artificial intelligence (AI) models (Random Forest (FR), multilinear regression (MLR) and multi-layer perceptron (MLP) are used to benchmark the CNN-BiLSTM model. In addition to this, remotely sensed satellite Landsat imagery data are also used to assess and predict coastal wetland changes using a Random Forest (RF) classification model in the two small Pacific Island states. The CNN-BiLSTM model was found to provide the most accurate predictions (with a correlation coefficient of >0.99), and similarly a high level of accuracy (>0.98) was achieved using a Random Forest (RF) model to detect wetlands in both study sites. The mean sea levels were found to have risen 6.0 ± 2.1 mm/year in the Solomon Islands and 7.2 ± 2.2 mm/year in the FSM over the past two decades. Coastal wetlands in general were found to have decreased in total area for both study sites. The Solomon Islands recorded a greater decline in coastal wetland between 2009 and 2022. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Environmental Remote Sensing)

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17 pages, 47978 KiB

Open AccessArticle

A Cross-Domain Change Detection Network Based on Instance Normalization

by Yabin Song, Jun Xiang, Jiawei Jiang, Enping Yan, Wei Wei and Dengkui Mo

Remote Sens. 2023, 15(24), 5785; https://doi.org/10.3390/rs15245785 - 18 Dec 2023

Viewed by 836

Abstract

Change detection is a crucial task in remote sensing that finds broad application in land resource planning, forest resource monitoring, natural disaster monitoring, and evaluation. In this paper, we propose a change detection model for cross-domain recognition, which we call CrossCDNet. Our model significantly improves the modeling ability of the change detection on one dataset and demonstrates good generalization on another dataset without any additional operations. To achieve this, we employ a Siamese neural network for change detection and design an IBNM (Instance Normalization and Batch Normalization Module) that utilizes instance normalization and batch normalization in order to serve as the encoder backbone in the Siamese neural network. The IBNM extracts feature maps for each layer, and the Siamese neural network fuses the feature maps of the two branches using a unique operation. Finally, a simple MLP decoder is used for end-to-end change detection. We train our model on the LEVIR-CD dataset and achieve competitive performance on the test set. In cross-domain dataset testing, CrossCDNet outperforms all the other compared models. Specifically, our model achieves an F1-score of 91.69% on the LEVIR-CD dataset and an F1-score of 77.09% on the WHU-CD dataset, where the training set was LEVIR-CD. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Environmental Remote Sensing)

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20 pages, 5209 KiB

Open AccessArticle

Using Remote and Proximal Sensing Data and Vine Vigor Parameters for Non-Destructive and Rapid Prediction of Grape Quality

by Hongyi Lyu, Miles Grafton, Thiagarajah Ramilan, Matthew Irwin, Hsiang-En Wei and Eduardo Sandoval

Remote Sens. 2023, 15(22), 5412; https://doi.org/10.3390/rs15225412 - 19 Nov 2023

Cited by 2 | Viewed by 927

Abstract

The traditional method for determining wine grape total soluble solid (TSS) is destructive laboratory analysis, which is time consuming and expensive. In this study, we explore the potential of using different predictor variables from various advanced techniques to predict the grape TSS in a non-destructive and rapid way. Calculating Pearson’s correlation coefficient between the vegetation indices (VIs) obtained from UAV multispectral imagery and grape TSS resulted in a strong correlation between OSAVI and grape TSS with a coefficient of 0.64. Additionally, seven machine learning models including ridge regression and lasso regression, k-Nearest neighbor (KNN), support vector regression (SVR), random forest regression (RFR), extreme gradient boosting (XGBoost), and artificial neural network (ANN) are used to build the prediction models. The predictor variables include the unmanned aerial vehicles (UAV) derived VIs, and other ancillary variables including normalized difference vegetation index (NDVI_proximal) and soil electrical conductivity (EC_a) measured by proximal sensors, elevation, slope, trunk circumference, and day of the year for each sampling date. When using 23 VIs and other ancillary variables as input variables, the results show that ensemble learning models (RFR, and XGBoost) outperform other regression models when predicting grape TSS, with the average of root mean square error (RMSE) of 1.19 and 1.2 °Brix, and coefficient of determination (R²) of 0.52 and 0.52, respectively, during the 20 times testing process. In addition, this study examines the prediction performance of using optimized soil adjusted vegetation index (OSAVI) or normalized green-blue difference index (NGBDI) as the main input for different machine learning models with other ancillary variables. When using OSAVI-based models, the best prediction model is RFR with an average R² of 0.51 and RMSE of 1.19 °Brix, respectively. For NGBDI-based model, the RFR model showed the best average result of predicting TSS were a R² of 0.54 and a RMSE of 1.16 °Brix, respectively. The approach proposed in this study provides an opportunity to grape growers to estimate the whole vineyard grape TSS in a non-destructive way. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Environmental Remote Sensing)

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22 pages, 6930 KiB

Open AccessArticle

Forest-PointNet: A Deep Learning Model for Vertical Structure Segmentation in Complex Forest Scenes

by Zhibin Ma, Yanqi Dong, Jiali Zi, Fu Xu and Feixiang Chen

Remote Sens. 2023, 15(19), 4793; https://doi.org/10.3390/rs15194793 - 30 Sep 2023

Cited by 1 | Viewed by 1274

Abstract

The vertical structure of forest ecosystems influences and reflects ecosystem functioning. Terrestrial laser scanning (TLS) enables the rapid acquisition of 3D forest information and subsequent reconstruction of the vertical structure, which provides new support for acquiring forest vertical structure information. We focused on artificial forest sample plots in the north-central of Nanning, Guangxi, China as the research area. Forest sample point cloud data were obtained through TLS. By accurately capturing the gradient information of the forest vertical structure, a classification boundary was delineated. A complex forest vertical structure segmentation method was proposed based on the Forest-PointNet model. This method comprehensively utilized the spatial and shape features of the point cloud. The study accurately segmented four types of vertical structure features in the forest sample location cloud data: ground, bushes, trunks, and leaves. With optimal training, the average classification accuracy reaches 90.98%. The results indicated that segmentation errors are mainly concentrated at the branch intersections of the canopy. Our model demonstrates significant advantages, including effective segmentation of vertical structures, strong generalization ability, and feature extraction capability. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Environmental Remote Sensing)

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15 pages, 2447 KiB

Open AccessArticle

Detection of Multitemporal Changes with Artificial Neural Network-Based Change Detection Algorithm Using Hyperspectral Dataset

by Neelam Dahiya, Sartajvir Singh, Sheifali Gupta, Adel Rajab, Mohammed Hamdi, M. A. Elmagzoub, Adel Sulaiman and Asadullah Shaikh

Remote Sens. 2023, 15(5), 1326; https://doi.org/10.3390/rs15051326 - 27 Feb 2023

Cited by 9 | Viewed by 2221

Abstract

Monitoring the Earth’s surface and objects is important for many applications, such as managing natural resources, crop yield predictions, and natural hazard analysis. Remote sensing is one of the most efficient and cost-effective solutions for analyzing land-use and land-cover (LULC) changes over the Earth’s surface through advanced computer algorithms, such as classification and change detection. In the past literature, various developments were made to change detection algorithms to detect LULC multitemporal changes using optical or microwave imagery. The optical-based hyperspectral highlights the critical information, but sometimes it is difficult to analyze the dataset due to the presence of atmospheric distortion, radiometric errors, and misregistration. In this work, an artificial neural network-based post-classification comparison (ANPC) as change detection has been utilized to detect the muti-temporal LULC changes over a part of Uttar Pradesh, India, using the Hyperion EO-1 dataset. The experimental outcomes confirmed the effectiveness of ANPC (92.6%) as compared to the existing models, such as a spectral angle mapper (SAM) based post-classification comparison (SAMPC) (89.7%) and k-nearest neighbor (KNN) based post-classification comparison (KNNPC) (91.2%). The study will be beneficial in extracting critical information about the Earth’s surface, analysis of crop diseases, crop diversity, agriculture, weather forecasting, and forest monitoring. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Environmental Remote Sensing)

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21 pages, 20713 KiB

Open AccessArticle

STCD-EffV2T Unet: Semi Transfer Learning EfficientNetV2 T-Unet Network for Urban/Land Cover Change Detection Using Sentinel-2 Satellite Images

by Masoomeh Gomroki, Mahdi Hasanlou and Peter Reinartz

Remote Sens. 2023, 15(5), 1232; https://doi.org/10.3390/rs15051232 - 23 Feb 2023

Cited by 9 | Viewed by 2976

Abstract

Change detection in urban areas can be helpful for urban resource management and smart city planning. The effects of human activities on the environment and ground have gained momentum over the past decades, causing remote sensing data sources analysis (such as satellite images) to become an option for swift change detection in the environment and urban areas. We proposed a semi-transfer learning method of EfficientNetV2 T-Unet (EffV2 T-Unet) that combines the effectiveness of composite scaled EfficientNetV2 T as the first path or encoder for feature extraction and convolutional layers of Unet as the second path or decoder for reconstructing the binary change map. In the encoder path, we use EfficientNetV2 T, which was trained by the ImageNet dataset. In this research, we employ two datasets to evaluate the performance of our proposed method for binary change detection. The first dataset is Sentinel-2 satellite images which were captured in 2017 and 2021 in urban areas of northern Iran. The second one is the Onera Satellite Change Detection dataset (OSCD). The performance of the proposed method is compared with YoloX-Unet families, ResNest-Unet families, and other well-known methods. The results demonstrated our proposed method’s effectiveness compared to other methods. The final change map reached an overall accuracy of 97.66%. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Environmental Remote Sensing)

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16 pages, 22007 KiB

Open AccessArticle

An Unsupervised Saliency-Guided Deep Convolutional Neural Network for Accurate Burn Mapping from Sentinel-1 SAR Data

by Ali Radman, Reza Shah-Hosseini and Saeid Homayouni

Remote Sens. 2023, 15(5), 1184; https://doi.org/10.3390/rs15051184 - 21 Feb 2023

Cited by 5 | Viewed by 1661

Abstract

SAR data provide sufficient information for burned area detection in any weather condition, making it superior to optical data. In this study, we assess the potential of Sentinel-1 SAR images for precise forest-burned area mapping using deep convolutional neural networks (DCNN). Accurate mapping with DCNN techniques requires high quantity and quality training data. However, labeled ground truth might not be available in many cases or requires professional expertise to generate them via visual interpretation of aerial photography or field visits. To overcome this problem, we proposed an unsupervised method that derives DCNN training data from fuzzy c-means (FCM) clusters with the highest and lowest probability of being burned. Furthermore, a saliency-guided (SG) approach was deployed to reduce false detections and SAR image speckles. This method defines salient regions with a high probability of being burned. These regions are not affected by noise and can improve the model performance. The developed approach based on the SG-FCM-DCNN model was investigated to map the burned area of Rossomanno-Grottascura-Bellia, Italy. This method significantly improved the burn detection ability of non-saliency-guided models. Moreover, the proposed model achieved superior accuracy of 87.67% (i.e., more than 2% improvement) compared to other saliency-guided techniques, including SVM and DNN. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Environmental Remote Sensing)

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17 pages, 7591 KiB

Open AccessArticle

Intelligent Estimating the Tree Height in Urban Forests Based on Deep Learning Combined with a Smartphone and a Comparison with UAV-LiDAR

by Jie Xuan, Xuejian Li, Huaqiang Du, Guomo Zhou, Fangjie Mao, Jingyi Wang, Bo Zhang, Yulin Gong, Di’en Zhu, Lv Zhou, Zihao Huang, Cenheng Xu, Jinjin Chen, Yongxia Zhou, Chao Chen, Cheng Tan and Jiaqian Sun

Remote Sens. 2023, 15(1), 97; https://doi.org/10.3390/rs15010097 - 24 Dec 2022

Cited by 4 | Viewed by 2050

Abstract

In this paper, a method for extracting the height of urban forest trees based on a smartphone was proposed to efficiently and accurately determine tree heights. First, a smartphone was used to obtain person–tree images, LabelImg was used to label the images, and a dataset was constructed. Secondly, based on a deep learning method called You Only Look Once v5 (YOLOv5) and the small-hole imaging and scale principles, a person–tree scale height measurement model was constructed. This approach supports recognition and mark functions based on the characteristics of a person and a tree in a single image. Finally, tree height measurements were obtained. By using this method, the heights of three species in the validation set were extracted; the range of the absolute error was 0.02 m–0.98 m, and the range of the relative error was 0.20–10.33%, with the RMSE below 0.43 m, the rRMSE below 4.96%, and the

R^{2}

above 0.93. The person–tree scale height measurement model proposed in this paper greatly improves the efficiency of tree height measurement while ensuring sufficient accuracy and provides a new method for the dynamic monitoring and investigation of urban forest resources. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Environmental Remote Sensing)

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Review

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21 pages, 14320 KiB

Open AccessReview

Artificial Intelligence Methods in Safe Ship Control Based on Marine Environment Remote Sensing

by Józef Lisowski

Remote Sens. 2023, 15(1), 203; https://doi.org/10.3390/rs15010203 - 30 Dec 2022

Cited by 3 | Viewed by 2035

Abstract

This article presents a combination of remote sensing, an artificial neural network, and game theory to synthesize a system for safe ship traffic management at sea. Serial data transmission from the ARPA anti-collision radar system are used to enable computer support of the navigator’s maneuvering decisions in situations where a large number of ships must be passed. The following methods were used to determine the safe and optimal trajectory of one’s own ship: static optimization, dynamic programming with neural constraints on the state of the control process in the form of domains of encountered ships generated by a three-layer artificial neural network, and positional and matrix games. Then, computer calculations for the safe trajectory of one’s own ship were carried out using the presented algorithms. The calculations were carried out for an actual navigational situation recorded on a r/v HORYZONT II research/training vessel radar screen under a real navigational situation in the Skagerrak–Kattegat Straits. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Environmental Remote Sensing)

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10 pages, 1840 KiB

Open AccessTechnical Note

Effective Video Scene Analysis for a Nanosatellite Based on an Onboard Deep Learning Method

by Natnael Alemayehu Tamire and Hae-Dong Kim

Remote Sens. 2023, 15(8), 2143; https://doi.org/10.3390/rs15082143 - 19 Apr 2023

Cited by 2 | Viewed by 1119

Abstract

The latest advancements in satellite technology have allowed us to obtain video imagery from satellites. Nanosatellites are becoming widely used for earth-observing missions as they require a low budget and short development time. Thus, there is a real interest in using nanosatellites with a video payload camera, especially for disaster monitoring and fleet tracking. However, as video data requires much storage and high communication costs, it is challenging to use nanosatellites for such missions. This paper proposes an effective onboard deep-learning-based video scene analysis method to reduce the high communication cost. The proposed method will train a CNN+LSTM-based model to identify mission-related sceneries such as flood-disaster-related scenery from satellite videos on the ground and then load the model onboard the nanosatellite to perform the scene analysis before sending the video data to the ground. We experimented with the proposed method using Nvidia Jetson TX2 as OBC and achieved an 89% test accuracy. Additionally, by implementing our approach, we can minimize the nanosatellite video data download cost by 30% which allows us to send the important mission video payload data to the ground using S-band communication. Therefore, we believe that our new approach can be effectively applied to obtain large video data from a nanosatellite. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Environmental Remote Sensing)

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