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Intelligent Remote Sensing for Planning, Management, and Maintenance of Renewable Energy Infrastructures

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 22673

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Special Issue Editors

Dr. Qi Chen

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Guest Editor

School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: remote sensing; machine learning; building integrated photovoltaics
Special Issues, Collections and Topics in MDPI journals

Dr. Qinghua Xie

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Guest Editor

School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
Interests: vegetation mapping; SAR; LiDAR; satellite image processing
Special Issues, Collections and Topics in MDPI journals

Dr. Zhengjia Zhang

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Guest Editor

School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: InSAR/time-series; InSAR; infrastructure health monitoring
Special Issues, Collections and Topics in MDPI journals

Dr. Pengjie Tao

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Guest Editor

School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
Interests: photogrammetry; registration of optical images and LiDAR points; multi-view 3D reconstruction

Prof. Dr. Min Chen

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Guest Editor

Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China
Interests: geographic modeling and simulation; virtual geographic environments
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, fossil fuels are still the dominant sources of energy that support human lives and socio-economic activities. However, fossil energy is non-renewable and results in serious environmental pollution and global climate warming, thus threatening Earth’s ecosystem and human society. Developing renewable energy (RE) including hydro, wind, solar and hydrogen power is considered an essential approach to reducing carbon emission, diversifying energy supply and promoting sustainable development. For better utilization of RE, optimal planning, management and effective maintenance of its infrastructures (e.g., photovoltaic roofs, hydroelectric dams and wind turbines) are becoming increasingly important. Conducting a field investigation can generally obtain reliable data for RE infrastructures but usually suffers from high labor intensity, large time consumption and expensive costs. As a comparison, remote sensing (RS) technologies can provide practical, cost-effective and relatively objective solutions for observational studies of RE infrastructures such as urban 3D reconstruction, location optimization, spatial distribution estimation and structural health monitoring; additionally, the breakthrough of artificial intelligence in recent decades (i.e., the great achievement made by deep learning) further enhances RS data processing algorithms in terms of precision and generalization capability.

This Special Issue focuses on scientific research and technological development with respect to utilizing RS technologies (e.g., aerial/satellite photography, spectral imaging and radar interferometry techniques) for planning, management and the maintenance of renewable energy infrastructures. Studies focusing on a broader scope of developing RE with RS technologies are also welcome. Topics include but are not limited to the following:

Digital city modeling for developing renewable technologies;
Remote sensing for renewable energy potential estimation;
Remote sensing for renewable energy application assessment;
Detection/localization of renewable energy infrastructures;
Structural health monitoring of renewable energy infrastructures;
Deep learning-based remote sensing for renewable energy development.

Dr. Qi Chen
Dr. Qinghua Xie
Dr. Zhengjia Zhang
Dr. Pengjie Tao
Prof. Dr. Min Chen
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

renewable energy
sustainable urban development
digital city
urban 3D reconstruction
infrastructure site selection
infrastructure detection
infrastructure health monitoring
building integrated photovoltaic
wind turbines
dam safety
deep learning.

Published Papers (10 papers)

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23 pages, 5697 KiB

Open AccessArticle

Spatial-Temporal Semantic Perception Network for Remote Sensing Image Semantic Change Detection

by You He, Hanchao Zhang, Xiaogang Ning, Ruiqian Zhang, Dong Chang and Minghui Hao

Remote Sens. 2023, 15(16), 4095; https://doi.org/10.3390/rs15164095 - 20 Aug 2023

Cited by 4 | Viewed by 1570

Abstract

Semantic change detection (SCD) is a challenging task in remote sensing, which aims to locate and identify changes between the bi-temporal images, providing detailed “from-to” change information. This information is valuable for various remote sensing applications. Recent studies have shown that multi-task networks, with dual segmentation branches and single change branch, are effective in SCD tasks. However, these networks primarily focus on extracting contextual information and ignore spatial details, resulting in the missed or false detection of small targets and inaccurate boundaries. To address the limitations of the aforementioned methods, this paper proposed a spatial-temporal semantic perception network (STSP-Net) for SCD. It effectively utilizes spatial detail information through the detail-aware path (DAP) and generates spatial-temporal semantic-perception features through combining deep contextual features. Meanwhile, the network enhances the representation of semantic features in spatial and temporal dimensions by leveraging a spatial attention fusion module (SAFM) and a temporal refinement detection module (TRDM). This augmentation results in improved sensitivity to details and adaptive performance balancing between semantic segmentation (SS) and change detection (CD). In addition, by incorporating the invariant consistency loss function (ICLoss), the proposed method constrains the consistency of land cover (LC) categories in invariant regions, thereby improving the accuracy and robustness of SCD. The comparative experimental results on three SCD datasets demonstrate the superiority of the proposed method in SCD. It outperforms other methods in various evaluation metrics, achieving a significant improvement. The Sek improvements of 2.84%, 1.63%, and 0.78% have been observed, respectively. Full article

(This article belongs to the Special Issue Intelligent Remote Sensing for Planning, Management, and Maintenance of Renewable Energy Infrastructures)

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17 pages, 4909 KiB

Open AccessArticle

MASA-SegNet: A Semantic Segmentation Network for PolSAR Images

by Jun Sun, Shiqi Yang, Xuesong Gao, Dinghua Ou, Zhaonan Tian, Jing Wu and Mantao Wang

Remote Sens. 2023, 15(14), 3662; https://doi.org/10.3390/rs15143662 - 22 Jul 2023

Cited by 2 | Viewed by 1147

Abstract

Semantic segmentation of Polarimetric SAR (PolSAR) images is an important research topic in remote sensing. Many deep neural network-based semantic segmentation methods have been applied to PolSAR image segmentation tasks. However, a lack of effective means to deal with the similarity of object features and speckle noise in PolSAR images exists. Thisstudy aims to improve the discriminative capability of neural networks for various intensities of backscattering coefficients while reducing the effects of noise in PolSAR semantic segmentation tasks. Firstly, we propose pre-processing methods for PolSAR image data, which consist of the fusion of multi-source data and false color mapping. Then, we propose a Multi-axis Sequence Attention Segmentation Network (MASA-SegNet) for semantic segmentation of PolSAR data, which is an encoder–decoder framework. Specifically, within the encoder, a feature extractor is designed and implemented by stacking Multi-axis Sequence Attention blocks to efficiently extract PolSAR features at multiple scales while mitigating inter-class similarities and intra-class differences from speckle noise. Moreover, the process of serialized residual connection design enables the propagation of spatial information throughout the network, thereby improving the overall spatial awareness of MASA-SegNet. Within the decoder, it is used to accomplish the semantic segmentation task. The superiority of this algorithm for semantic segmentation will be explored through feature visualization. The experiments show that our proposed spatial sequence attention mechanism can effectively extract features and reduce noise interference and is thus able to obtain the best results on two large-scale public datasets (the AIR-POlSAR-Seg and FUSAR-Map datasets). Full article

(This article belongs to the Special Issue Intelligent Remote Sensing for Planning, Management, and Maintenance of Renewable Energy Infrastructures)

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19 pages, 27954 KiB

Open AccessArticle

Deformation Monitoring and Analysis of Reservoir Dams Based on SBAS-InSAR Technology—Banqiao Reservoir

by Zhiguo Pang, Qingguang Jin, Peng Fan, Wei Jiang, Juan Lv, Pengjie Zhang, Xiangrui Cui, Chun Zhao and Zhengjia Zhang

Remote Sens. 2023, 15(12), 3062; https://doi.org/10.3390/rs15123062 - 12 Jun 2023

Cited by 3 | Viewed by 1640

Abstract

Most dams in China have been operating for a long time and are products of the economic and technical limitations at the time of construction. Due to decades of aging engineering and ancillary problems, these reservoirs pose great threats to the safety of local people and the development of the surrounding economy. In this study, the surface deformation information for the Banqiao Reservoir is monitored with the small baseline subset–synthetic aperture radar interferometry (SBAS-InSAR) method using 80 Sentinel-1A images acquired from 3 January 2020 to 20 August 2022. Additionally, ground measurements from the BeiDou ground-based deformation monitoring stations were collected to validate the InSAR results. Based on the InSAR results, the spatiotemporal deformation features of the dam were analyzed in detail. The results show that the deformation in most areas, including the dam in the study area, is relatively stable, and the regional deformation velocity of the Banqiao Reservoir dam and other hydraulic engineering facilities varies between −1 mm/y and −4 mm/y. The Ru River area has a relatively obvious subsidence trend, and the maximum subsidence velocity reaches 30 mm/y. The InSAR monitoring results are consistent with the change trend in the BeiDou ground-based deformation measurement results. The monitoring results for the reservoir dam area provide a reference for local sustainable development and geological disaster prevention. Full article

(This article belongs to the Special Issue Intelligent Remote Sensing for Planning, Management, and Maintenance of Renewable Energy Infrastructures)

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25 pages, 8449 KiB

Open AccessArticle

Sustainability Evaluation of Chinese Capital Cities Based on Urban Geographic Environment Index

by Xiaogang Ning, Hanchao Zhang, Zhenfeng Shao, Xiao Huang, Hao Wang, Ruiqian Zhang and Minghui Hao

Remote Sens. 2023, 15(8), 1966; https://doi.org/10.3390/rs15081966 - 7 Apr 2023

Cited by 1 | Viewed by 1574

Abstract

Environmental assessments are important tasks for the long-term, sustainable development of cities. With the rapid urbanization in China, it is crucial to establish a City Sustainability Index (CSI) and evaluate the environmental conditions in major cities. However, most of the existing major sustainability indices/indicators are not able to assess cities at diverse levels of development using common axes of evaluation. In this work, we incorporate urban built-up areas extracted from high-resolution remote sensing images as indicators to measure the degree of urban development in city sustainability evaluation and propose a comprehensive index, i.e., the Urban Geographic Environment Index (UGEI). In order to eliminate the impact of urban development levels, UGEI mainly consists of area-averaged indices which are calculated from original indices and urban built-up areas. We adopt a comprehensive weighting method by using the analytic hierarchy process (AHP) method to weigh the high-level indicators and using the entropy weighting method to weigh low-level indicators. We evaluate the environmental conditions of 30 China’s provincial capitals from the aspects of pressure, state, response, and overall status. In addition, we analyze how diverse types of indicators affect the values of UGEIs. The major findings are as follows: (1) About half of the provincial capitals in China have poor sustainability in environmental conditions, and more environmental protection measures should be taken in developing cities; (2) the environmental conditions of the provincial capitals in China present a four-region distribution pattern, namely, the northeastern coastal region, northwest inland region, southwest region, and southeast region; (3) and indices based on urban built-up extents can be common axes of evaluation for cities at diverse levels of development. The proposed UGEI can serve as an effective and reliable index for sustainability evaluation in environmental conditions. Full article

(This article belongs to the Special Issue Intelligent Remote Sensing for Planning, Management, and Maintenance of Renewable Energy Infrastructures)

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20 pages, 9725 KiB

Open AccessArticle

Unsupervised Adversarial Domain Adaptation for Agricultural Land Extraction of Remote Sensing Images

by Junbo Zhang, Shifeng Xu, Jun Sun, Dinghua Ou, Xiaobo Wu and Mantao Wang

Remote Sens. 2022, 14(24), 6298; https://doi.org/10.3390/rs14246298 - 12 Dec 2022

Cited by 3 | Viewed by 1818

Abstract

Agricultural land extraction is an essential technical means to promote sustainable agricultural development and modernization research. Existing supervised algorithms rely on many finely annotated remote-sensing images, which is both time-consuming and expensive. One way to reduce the annotation cost approach is to migrate models trained on existing annotated data (source domain) to unannotated data (target domain). However, model generalization capability is often unsatisfactory due to the limit of the domain gap. In this work, we use an unsupervised adversarial domain adaptation method to train a neural network to close the gap between the source and target domains for unsupervised agricultural land extraction. The overall approach consists of two phases: inter-domain and intra-domain adaptation. In the inter-domain adaptation, we use a generative adversarial network (GAN) to reduce the inter-domain gap between the source domain (labeled dataset) and the target domain (unlabeled dataset). The transformer with robust long-range dependency modeling acts as the backbone of the generator. In addition, the multi-scale feature fusion (MSFF) module is designed in the generator to accommodate remote sensing datasets with different spatial resolutions. Further, we use an entropy-based approach to divide the target domain. The target domain is divided into two subdomains, easy split images and hard split images. By training against each other between the two subdomains, we reduce the intra-domain gap. Experiments results on the “DeepGlobe → LoveDA”, “GID → LoveDA” and “DeepGlobe → GID” unsupervised agricultural land extraction tasks demonstrate the effectiveness of our method and its superiority to other unsupervised domain adaptation techniques. Full article

(This article belongs to the Special Issue Intelligent Remote Sensing for Planning, Management, and Maintenance of Renewable Energy Infrastructures)

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23 pages, 8151 KiB

Open AccessArticle

Surface Deformation Mechanism Analysis in Shanghai Areas Based on TS-InSAR Technology

by Jiahao Li, Lv Zhou, Zilin Zhu, Jie Qin, Lingxiao Xian, Di Zhang and Ling Huang

Remote Sens. 2022, 14(17), 4368; https://doi.org/10.3390/rs14174368 - 2 Sep 2022

Cited by 9 | Viewed by 1829

Abstract

To address the problem that surface deformation causes in urban areas by affecting urban security and threatening human life and property, this study first measured the surface deformation in Shanghai from 2016 to 2020 using the time series InSAR method. Then, the spatial–temporal distribution and evolution characteristics of deformation were investigated in detail. The deformation mechanism is explained by factors including groundwater and rainfall. By introducing the seasonal changes of tides and sediment accumulation, the reason for the uplift in the Shanghai area is further explained. Finally, the surface deformation of the reclamation area is detected further. Meanwhile, the spatial–temporal variation characteristics of the surface in the reclamation area are explored. Through time series InSAR technology, the results of surface deformation in Shanghai demonstrate the following: (1) The deformation in the study area is uneven in time, and the subsidence is especially apparent during the 2016–2017 period. The maximum cumulative subsidence amounts to −131.1 mm, and the PS points with subsidence rates greater than −5 mm/y occupy 41.36% of all the subsidence points. In addition, PS points with uplift rates greater than 5 mm/y account for 39.55% of all the uplift points. The overall spatial distribution in the Shanghai area is characterized by the uplift in the north and subsidence in the south, whereas the cumulative subsidence in the time series presents a slowing trend; (2) Surface subsidence and groundwater, rainfall, and urban development in the Shanghai area are correlated. Seasonal changes in tides contribute to surface uplift in coastal areas. Coastal sediment accumulation and soil changes also make direct contributions to the occurrence of surface uplift; (3) The deformation of the reclamation area and the completion time are correlated, and the subsidence points of the reclamation area are mainly concentrated on the surrounding dikes from 2016 to 2020. The cumulative subsidence of the two years from 2016 to 2017 is up to −102.2 mm. The results of this study systematically explore the spatial–-temporal evolution and causes of surface deformation in Shanghai, providing scientific data which can support the development of Shanghai. Full article

(This article belongs to the Special Issue Intelligent Remote Sensing for Planning, Management, and Maintenance of Renewable Energy Infrastructures)

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22 pages, 4331 KiB

Open AccessArticle

RID—Roof Information Dataset for Computer Vision-Based Photovoltaic Potential Assessment

by Sebastian Krapf, Lukas Bogenrieder, Fabian Netzler, Georg Balke and Markus Lienkamp

Remote Sens. 2022, 14(10), 2299; https://doi.org/10.3390/rs14102299 - 10 May 2022

Cited by 9 | Viewed by 5483

Abstract

Computer vision has great potential to accelerate the global scale of photovoltaic potential analysis by extracting detailed roof information from high-resolution aerial images, but the lack of existing deep learning datasets is a major barrier. Therefore, we present the Roof Information Dataset for semantic segmentation of roof segments and roof superstructures. We assessed the label quality of initial roof superstructure annotations by conducting an annotation experiment and identified annotator agreements of 0.15–0.70 mean intersection over union, depending on the class. We discuss associated the implications on the training and evaluation of two convolutional neural networks and found that the quality of the prediction behaved similarly to the annotator agreement for most classes. The class photovoltaic module was predicted to be best with a class-specific mean intersection over union of 0.69. By providing the datasets in initial and reviewed versions, we promote a data-centric approach for the semantic segmentation of roof information. Finally, we conducted a photovoltaic potential analysis case study and demonstrated the high impact of roof superstructures as well as the viability of the computer vision approach to increase accuracy. While this paper’s primary use case was roof information extraction for photovoltaic potential analysis, its implications can be transferred to other computer vision applications in remote sensing and beyond. Full article

(This article belongs to the Special Issue Intelligent Remote Sensing for Planning, Management, and Maintenance of Renewable Energy Infrastructures)

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16 pages, 5015 KiB

Open AccessArticle

Analyzing the Error Pattern of InSAR-Based Mining Subsidence Estimation Caused by Neglecting Horizontal Movements

by Zelin Ma, Zefa Yang and Xuemin Xing

Remote Sens. 2022, 14(9), 1963; https://doi.org/10.3390/rs14091963 - 19 Apr 2022

Cited by 1 | Viewed by 1646

Abstract

It is common to estimate underground mining-induced subsidence from interferometric synthetic aperture radar (InSAR) displacement observations by Neglecting hOrizontal moVements (NOV). Such a strategy would cause large errors in the NOV-estimated subsidence. This issue was proven and the theoretical equation of the resulting errors has been deduced before. However, the systematic analysis of the error pattern (e.g., spatial distribution) and its relationship between some critical influence factors (e.g., lithology of overlying rock strata) is lacking to date. To circumvent this, a method was first presented to assess the errors of the NOV-estimated mining subsidence in this study. Then, the error pattern and the influence factors of the NOV-estimated mining subsidence were discussed. The results suggest that the errors of the NOV-estimated mining subsidence spatially follow a “peak-to-valley” shape, with an absolute “peak-to-valley angle” of 5–15°. In addition, for the same underground mining geometry, the error magnitudes of the NOV-estimated mining subsidence under hard lithology of overlying rock strata are smaller than those under soft lithology, and vice versa. These results would be beneficial to guide the scientific use of the NOV method for understanding the deformation mechanism and controlling the geohazards associated with underground mining and other similar anthropogenic activities. Full article

(This article belongs to the Special Issue Intelligent Remote Sensing for Planning, Management, and Maintenance of Renewable Energy Infrastructures)

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31 pages, 4577 KiB

Open AccessArticle

Unsupervised Building Extraction from Multimodal Aerial Data Based on Accurate Vegetation Removal and Image Feature Consistency Constraint

by Yan Meng, Shanxiong Chen, Yuxuan Liu, Li Li, Zemin Zhang, Tao Ke and Xiangyun Hu

Remote Sens. 2022, 14(8), 1912; https://doi.org/10.3390/rs14081912 - 15 Apr 2022

Cited by 8 | Viewed by 2261

Abstract

Accurate building extraction from remotely sensed data is difficult to perform automatically because of the complex environments and the complex shapes, colours and textures of buildings. Supervised deep-learning-based methods offer a possible solution to solve this problem. However, these methods generally require many high-quality, manually labelled samples to obtain satisfactory test results, and their production is time and labour intensive. For multimodal data with sufficient information, extracting buildings accurately in as unsupervised a manner as possible. Combining remote sensing images and LiDAR point clouds for unsupervised building extraction is not a new idea, but existing methods often experience two problems: (1) the accuracy of vegetation detection is often not high, which leads to limited building extraction accuracy, and (2) they lack a proper mechanism to further refine the building masks. We propose two methods to address these problems, combining aerial images and aerial LiDAR point clouds. First, we improve two recently developed vegetation detection methods to generate accurate initial building masks. We then refine the building masks based on the image feature consistency constraint, which can replace inaccurate LiDAR-derived boundaries with accurate image-based boundaries, remove the remaining vegetation points and recover some missing building points. Our methods do not require manual parameter tuning or manual data labelling, but still exhibit a competitive performance compared to 29 methods: our methods exhibit accuracies higher than or comparable to 19 state-of-the-art methods (including 8 deep-learning-based methods and 11 unsupervised methods, and 9 of them combine remote sensing images and 3D data), and outperform the top 10 methods (4 of them combine remote sensing images and LiDAR data) evaluated using all three test areas of the Vaihingen dataset on the official website of the ISPRS Test Project on Urban Classification and 3D Building Reconstruction in average area quality. These comparative results verify that our unsupervised methods combining multisource data are very effective. Full article

(This article belongs to the Special Issue Intelligent Remote Sensing for Planning, Management, and Maintenance of Renewable Energy Infrastructures)

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17 pages, 20508 KiB

Open AccessTechnical Note

Landslide Inventory in the Downstream of the Niulanjiang River with ALOS PALSAR and Sentinel-1 Datasets

by Ziyun Wang, Jinhu Xu, Xuguo Shi, Jianing Wang, Wei Zhang and Bao Zhang

Remote Sens. 2022, 14(12), 2873; https://doi.org/10.3390/rs14122873 - 15 Jun 2022

Cited by 6 | Viewed by 2188

Abstract

Landslide inventory and deformation monitoring is an essential task for human life and property security during the exploitation process of hydroelectric power resources. Synthetic Aperture Radar Interferometry (InSAR) is recognized as an effective tool for ground displacement monitoring with the advantages of wide coverage and high accuracy. In this study, we mapped the unstable slopes in the downstream of the Niulanjiang River with 22 ALOS PALSAR SAR images acquired from 2007 to 2011, and 90 Sentinel-1 SAR images from 2015 to 2019. A total of 94 active slopes are identified using a displacement map from the two datasets based on Small BAseline Subset (SBAS) InSAR analysis. By comparing the results from ALOS PALSAR and Sentinel-1 data stacks, we find that the number of active slopes increased dramatically. Several impact factors, e.g., earthquake, concentrated rainfall, and construction of hydropower stations, are discussed through time series analysis of typical landslides. Furthermore, nonlinear displacement of natural unstable slopes are found to be correlated with rainfall. A climate-driven model is used to qualify the relationship between rainfall and landslide displacement. Our results can provide valuable information for landslide detection and prevention. Full article

(This article belongs to the Special Issue Intelligent Remote Sensing for Planning, Management, and Maintenance of Renewable Energy Infrastructures)

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