Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Browser

Remote Sensing Interpretation Systematic Engineering for Natural Resources Monitoring and Management

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 14358

Share This Special Issue

Special Issue Editors

Prof. Dr. Dongping Ming

E-Mail Website
Guest Editor

School of Information Engineering, China University of Geosciences (Beijing), Beijing 10083, China
Interests: geoinformatics; remote sensing; resources and environment monitoring; geological hazard monitoring

Prof. Dr. Kasturi Devi Kanniah

E-Mail Website
Guest Editor

Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, UTM, Johor Bahru 81310, Malaysia
Interests: remote sensing; land use land cover change mapping; urban land use changes, vegetation processes and atmospheric aerosols
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jagannath Aryal

E-Mail Website
Guest Editor

Faculty of Engineering and Information Technology (FEIT), The University of Melbourne, Melbourne, VIC 3010, Australia
Interests: geomatics, remote sensing; Geo-AI; disaster risk management; sustainability and resilience
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing technology is indispensable for high level land use, land management, and sustainable infrastructure design. In recent years, multi-source and multi-temporal remote sensing big data, from optical to microwave, from low to very high spatial resolution, from multispectral to hyperspectral, and LiDAR data are available and can be applied for broader land management. Meanwhile, with the development of artificial intelligence, big data, and cloud computing techniques, efficient and intelligent remote sensing image interpretation in high level land use, land management, and sustainable infrastructure design has been a systematically engineered and these issues are currently faced with various challenges in terms of data, information, knowledge, modeling, and computing power. Such challenges can be tackled with innovations in image interpretation, information/feature extraction, modeling techniques, and applications to problems towards understanding our environment.

Under such circumstances, this Special Issue aims at providing knowledge, methodologies, and approaches for scientific research and decision support systems related to intelligent remote sensing image interpretation in land use, land management, and sustainable infrastructure design.

We invite contributions from colleagues who wish to innovate the discipline of remote sensing for land use, land management, and sustainable infrastructure design.

Prof. Dr. Dongping Ming
Prof. Dr. Kasturi Devi Kanniah
Dr. Jagannath Aryal
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

multi-source data fusion
sample database construction
object based image analysis
GeoAI
region/parcel partition on different scales
land cover/use classification
land cover/use change monitoring/projection/modelling
thematic information extraction
advanced image processing techniques (machine learning and deep learning)
authenticity verification
cloud based intelligent remote sensing application
GIS for land use/cover management
impact of land use/cover on environment

Published Papers (7 papers)

Download All Papers

Research

21 pages, 5156 KiB

Open AccessArticle

Tree Species Classification Based on ASDER and MALSTM-FCN

by Hongjian Luo, Dongping Ming, Lu Xu and Xiao Ling

Remote Sens. 2023, 15(7), 1723; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15071723 - 23 Mar 2023

Viewed by 1237

Abstract

Tree species classification based on multi-source remote sensing data is essential for ecological evaluation, environmental monitoring, and forest management. The optimization of classification features and the performance of classification methods are crucial to tree species classification. This paper proposes Angle-weighted Standard Deviation Elliptic Cross-merge Rate (ASDER) as a separability metric for feature optimization. ASDER uses mutual information to represent the separability metric and avoids the difficulty of differentiation caused by multiple ellipse centers and coordinate origins forming straight lines by angle weighting. In classification method, Multi-head Self-attention Long Short-Term Memory—Full Convolution Network (MALSTM-FCN) is constructed in this paper. MALSTM-FCN enhances the global correlation in time series and improves classification accuracy through a multi-head self-attention mechanism. This paper takes Beijing Olympic Forest Park (after this, referred to as Aosen) as the research area, constructs a tree species classification dataset based on an actual ground survey, and obtains a classification accuracy of 95.20% using the above method. This paper demonstrates the effectiveness of ASDER and MALSTM-FCN by comparing temporal entropy and LSTM-FCN and shows that the method has some practicality for tree species classification. Full article

(This article belongs to the Special Issue Remote Sensing Interpretation Systematic Engineering for Natural Resources Monitoring and Management)

► Show Figures

Figure 1

22 pages, 9811 KiB

Open AccessArticle

Semantic Segmentation Model for Wide-Area Coseismic Landslide Extraction Based on Embedded Multichannel Spectral–Topographic Feature Fusion: A Case Study of the Jiuzhaigou Ms7.0 Earthquake in Sichuan, China

by Xiangxiang Zheng, Lingyi Han, Guojin He, Ning Wang, Guizhou Wang and Lei Feng

Remote Sens. 2023, 15(4), 1084; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15041084 - 16 Feb 2023

Cited by 1 | Viewed by 1490

Abstract

The rapid and accurate extraction of wide-area coseismic landslide locations is critical in earthquake emergencies. At present, the extraction of coseismic landslides is mainly based on post-earthquake site investigation or the interpretation of human–computer interactions based on remote sensing images. However, the identification efficiency is low, which seriously delays the earthquake emergency response. On the basis of the available multisource and multiscale remote sensing data, numerous studies have been carried out on the methods of coseismic landslide extraction, such as pixel analysis, object-oriented analysis, change detection, and machine learning. However, the effectiveness of coseismic landslide extraction was low in wide areas with complex topographic and geomorphic backgrounds. Therefore, this paper offers a comprehensive study of the factors influencing coseismic landslides and researches rapid and accurate wide-area coseismic landslide extraction methods with multisource remote sensing and geoscience technology. These techniques include digital elevation modeling (DEM) and its derived slopes and aspects. An embedded multichannel spectral–topographic feature fusion model for coseismic landslide extraction based on DeepLab V3+ is proposed, and a knowledge-enhanced deep learning information extraction method integrating geological knowledge is formed. Using the Jiuzhaigou Ms7.0 earthquake (seismic intensity VIII) in Sichuan Province, China, a comparison of landslide extraction models and strategies is carried out. The results show that the model proposed in this paper achieves the best balance in the accuracy and efficiency of wide-area extractions. Using multiple feature data of coseismic landslides, the problem of mixed pixels is solved. The rate of the misidentification of landslides as clouds, snow, buildings, and roads is significantly lower than in other methods. The identified landslide boundaries are smoother and more accurate, and the connectivity is better. Compared with other methods, ours can more accurately eliminate landslides not triggered by the Jiuzhaigou earthquake. While using the image block strategy to ensure extraction efficiency, it also improves the extraction accuracy of wide-area coseismic landslides in complex backgrounds. Full article

(This article belongs to the Special Issue Remote Sensing Interpretation Systematic Engineering for Natural Resources Monitoring and Management)

► Show Figures

Figure 1

18 pages, 5054 KiB

Open AccessArticle

An Attention-Based 3D Convolutional Autoencoder for Few-Shot Hyperspectral Unmixing and Classification

by Chunyu Li, Rong Cai and Junchuan Yu

Remote Sens. 2023, 15(2), 451; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15020451 - 12 Jan 2023

Cited by 7 | Viewed by 3604

Abstract

Few-shot hyperspectral classification is a challenging problem that involves obtaining effective spatial–spectral features in an unsupervised or semi-supervised manner. In recent years, as a result of the development of computer vision, deep learning techniques have demonstrated their superiority in tackling the problems of hyperspectral unmixing (HU) and classification. In this paper, we present a new semi-supervised pipeline for few-shot hyperspectral classification, where endmember abundance maps obtained by HU are treated as latent features for classification. A cube-based attention 3D convolutional autoencoder network (CACAE), is applied to extract spectral–spatial features. In addition, an attention approach is used to improve the accuracy of abundance estimation by extracting the diagnostic spectral features associated with the given endmember more effectively. The endmember abundance estimated by the proposed model outperforms other convolutional neural networks (CNNs) with respect to the root mean square error (RMSE) and abundance spectral angle distance (ASAD). Classification experiments are performed on real hyperspectral datasets and compared to several supervised and semi-supervised models. The experimental findings demonstrate that the proposed approach has promising potential for hyperspectral feature extraction and has better performance relative to CNN-based supervised classification under small-sample conditions. Full article

(This article belongs to the Special Issue Remote Sensing Interpretation Systematic Engineering for Natural Resources Monitoring and Management)

► Show Figures

Figure 1

27 pages, 8437 KiB

Open AccessArticle

Feature Engineering of Geohazard Susceptibility Analysis Based on the Random Forest Algorithm: Taking Tianshui City, Gansu Province, as an Example

by Xiao Ling, Yueqin Zhu, Dongping Ming, Yangyang Chen, Liang Zhang and Tongyao Du

Remote Sens. 2022, 14(22), 5658; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14225658 - 09 Nov 2022

Cited by 3 | Viewed by 1365

Abstract

In this paper, Feature Engineering (FE) was applied to Landslide Susceptibility Mapping (LSM), while the most suitable conditioning feature dataset and analysis method were tested and analyzed. Tianshui city was taken as the study area, three types of geohazard (collapse, landslide, and unstable slopes) were used, while a total of twenty-three conditioning features were generated; two dimensionless methods (normalization and standardization) were tested afterward. Four Random-Forest-based (RF-based) feature selection methods using different indicators (Gini Impurity, GI; Out of Bag Accuracy, OOBA) were proposed and tested separately. The LSMs of four models were carried out under the guidance results of FE, namely Classification and Regression Tree (CART), Random Forest (RF), Logistic Regression (LR), and Support Vector Machine for Classification (SVC). For feature enhancement, standardization had significant advantages over normalization. All RF-based methods were proven effective, lifting the AUC by 0.01~0.02. The RF model achieved the highest LSM accuracies, respectively, 0.949 (landslide), 0.957, and 0.949 (unstable slopes), improved by 0.008 (landslide), 0.005 (collapse), and 0.013 (unstable slopes). This proved that the FE helped to improve LSM and can help to decide the dominant conditioning factors for regional geohazards. Full article

(This article belongs to the Special Issue Remote Sensing Interpretation Systematic Engineering for Natural Resources Monitoring and Management)

► Show Figures

Graphical abstract

18 pages, 11641 KiB

Open AccessArticle

Improving Building Extraction by Using Knowledge Distillation to Reduce the Impact of Label Noise

by Gang Xu, Min Deng, Geng Sun, Ya Guo and Jie Chen

Remote Sens. 2022, 14(22), 5645; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14225645 - 08 Nov 2022

Cited by 2 | Viewed by 1453

Abstract

Building extraction using deep learning techniques has advantages but relies on a large number of clean labeled samples to train the model. Complex appearance and tilt shots often cause many offsets between building labels and true locations, and these noises have a considerable impact on building extraction. This paper proposes a new knowledge distillation-based building extraction method to reduce the impact of noise on the model and maintain the generalization of the model. The method can maximize the generalizable knowledge of large-scale noisy samples and the accurate supervision of small-scale clean samples. The proposed method comprises two similar teacher and student networks, where the teacher network is trained by large-scale noisy samples and the student network is trained by small-scale clean samples and guided by the knowledge of the teacher network. Experimental results show that the student network can not only alleviate the influence of noise labels but also obtain the capability of building extraction without incorrect labels in the teacher network and improve the performance of building extraction. Full article

(This article belongs to the Special Issue Remote Sensing Interpretation Systematic Engineering for Natural Resources Monitoring and Management)

► Show Figures

Figure 1

17 pages, 7344 KiB

Open AccessArticle

Spatial Quantitative Model of Human Activity Disturbance Intensity and Land Use Intensity Based on GF-6 Image, Empirical Study in Southwest Mountainous County, China

by Xuedong Zhang, Xuedi Wang, Zexu Zhou, Mengwei Li and Changfeng Jing

Remote Sens. 2022, 14(18), 4574; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14184574 - 13 Sep 2022

Cited by 7 | Viewed by 1489

Abstract

Vigorous human activities have strengthened the development and utilization of land, causing huge damage to the earth’s surface, while mining the disturbance pattern of human activities can capture the influence process and spatial interaction between human activities and land use. Therefore, in order to explore the inherent relationship between human activities and land use in mountainous counties, a spatial quantitative model of human activity disturbance intensity and land use intensity was proposed based on GF-6 image, traffic data, and socioeconomic data. The model can quantitatively evaluate the disturbance intensity of human activity and land use intensity from “production-living-ecological space”, and unfold the correlation between human activity disturbance intensity and land use intensity with Pearson correlation coefficient and bivariate spatial autocorrelation method. Our study presents several key findings: (1) the spatial difference of human activity disturbance is significant in Mianzhu City, and it has steady aggregation (Moran’s I index is 0.929), showing a decreasing trend from the southeast to the northwest area; (2) there is a strong positive correlation between the disturbance intensity of human activity and the intensity of land use with Pearson value 0.949; (3) among the eight selected factors, the proportion of construction land area plays a leading role in the disturbance intensity of human activity in Mianzhu City, while the township final account data have the least impact. The study results can provide an important reference for the quantitative identification and evaluation of human disturbances in similar cities and the coordinated development of the human–land relationship. Full article

(This article belongs to the Special Issue Remote Sensing Interpretation Systematic Engineering for Natural Resources Monitoring and Management)

► Show Figures

Figure 1

23 pages, 7370 KiB

Open AccessArticle

Remote Sensing Mapping of Build-Up Land with Noisy Label via Fault-Tolerant Learning

by Gang Xu, Yongjun Fang, Min Deng, Geng Sun and Jie Chen

Remote Sens. 2022, 14(9), 2263; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092263 - 08 May 2022

Cited by 2 | Viewed by 2233

Abstract

China’s urbanization has dramatically accelerated in recent decades. Land for urban build-up has changed not only in large cities but also in small counties. Land cover mapping is one of the fundamental tasks in the field of remote sensing and has received great attention. However, most current mapping requires a significant manual effort for labeling or classification. It is of great practical value to use the existing low-resolution label data for the classification of higher resolution images. In this regard, this work proposes a method based on noise-label learning for fine-grained mapping of urban build-up land in a county in central China. Specifically, this work produces a build-up land map with a resolution of 10 m based on a land cover map with a resolution of 30 m. Experimental results show that the accuracy of the results is improved by 5.5% compared with that of the baseline method. This notion indicates that the time required to produce a fine land cover map can be significantly reduced using existing coarse-grained data. Full article

(This article belongs to the Special Issue Remote Sensing Interpretation Systematic Engineering for Natural Resources Monitoring and Management)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Remote Sensing Interpretation Systematic Engineering for Natural Resources Monitoring and Management

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (7 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI