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Recent Progress of Change Detection Based on Remote Sensing

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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 (31 July 2023) | Viewed by 23624

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Special Issue Editors

Prof. Dr. Lizhe Wang

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Guest Editor

School of Computer Science, China University of Geosciences, Wuhan 430078, China
Interests: digital earth; remote sensing image processing; high performance geocomputing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Xiaodong Zhang

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Guest Editor

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China
Interests: photogrammetry and remote sensing; GIS applications

Dr. Jining Yan

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Guest Editor

School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430074, China
Interests: remote sensing time series change detection and classification

Dr. Guanzhou Chen

E-Mail Website
Guest Editor

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China
Interests: photogrammetry and remote sensing

Special Issue Information

Dear Colleagues,

Change detection aims to identify differences in multi-temporal images of the same area, as well as the specific time at which the change occurred. The advent of high-resolution remote sensing images has greatly improved our ability to monitor land surface changes from space. Monitoring differences in bi-temporal and multi-temporal remotely sensed images and clarifying the change process is crucial for understanding the relationship between human society and nature, and is of great significance to environmental protection and land use planning.

Researchers have conducted extensive research on data sources, theoretical models, and applications related to change detection in remote sensing images, and have made great progress. In recent years, the hotspot of related research has shifted from classical methods such as pixel- and object-based methods to deep-learning-based, data-driven methods. The data source of change detection has expanded from single-sensor data to multi-source and multi-modal data, and the detection paradigm has increased from binary change to multiple classes of semantic changes.

Nevertheless, the state-of-the-art change detection methods still face problems such as lack of large-scale, high-quality open datasets, detection accuracy still struggles to reach the human level, and the generalization ability of algorithm models is insufficient.

Fortunately, continuous and uninterrupted remote sensing observations provide ample data sources for land cover/use change detection.

Therefore, the Special Issue “Recent Progress of Change Detection based on Remote Sensing” was proposed to summarize and share new progress, methods, and ideas about remote sensing change detection.

Articles and reviews on land cover/use change detection using remote sensing data are welcome. The specific topics to be addressed include, but are not limited to:

Change detection using bi-temporal remote sensing images;
Continuous change detection using dense remote sensing time series;
Pixel-level, object-based and scene-level change detection;
Machine learning- and deep-learning-based change detection;
Benchmark dataset for change detection;
Semantic change detection;
Unsupervised/self-supervised change detection;
Change detection-based remote sensing application.

Prof. Dr. Lizhe Wang
Prof. Dr. Xiaodong Zhang
Dr. Jining Yan
Dr. Guanzhou 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

remote sensing
binary change detection
semantic change detection
land use and land cover change
bi-temporal remote sensing images
continuous change detection
remote sensing time series

Published Papers (13 papers)

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30 pages, 13044 KiB

Open AccessArticle

An Experimental Study of the Accuracy and Change Detection Potential of Blending Time Series Remote Sensing Images with Spatiotemporal Fusion

by Jingbo Wei, Lei Chen, Zhou Chen and Yukun Huang

Remote Sens. 2023, 15(15), 3763; https://doi.org/10.3390/rs15153763 - 28 Jul 2023

Cited by 2 | Viewed by 898

Abstract

Over one hundred spatiotemporal fusion algorithms have been proposed, but convolutional neural networks trained with large amounts of data for spatiotemporal fusion have not shown significant advantages. In addition, no attention has been paid to whether fused images can be used for change detection. These two issues are addressed in this work. A new dataset consisting of nine pairs of images is designed to benchmark the accuracy of neural networks using one-pair spatiotemporal fusion with neural-network-based models. Notably, the size of each image is significantly larger compared to other datasets used to train neural networks. A comprehensive comparison of the radiometric, spectral, and structural losses is made using fourteen fusion algorithms and five datasets to illustrate the differences in the performance of spatiotemporal fusion algorithms with regard to various sensors and image sizes. A change detection experiment is conducted to test if it is feasible to detect changes in specific land covers using the fusion results. The experiment shows that convolutional neural networks can be used for one-pair spatiotemporal fusion if the sizes of individual images are adequately large. It also confirms that the spatiotemporally fused images can be used for change detection in certain scenes. Full article

(This article belongs to the Special Issue Recent Progress of Change Detection Based on Remote Sensing)

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25 pages, 4129 KiB

Open AccessArticle

High-Resolution Remote Sensing Image Change Detection Method Based on Improved Siamese U-Net

by Qing Wang, Mengqi Li, Gongquan Li, Jiling Zhang, Shuoyue Yan, Zhuoran Chen, Xiaodong Zhang and Guanzhou Chen

Remote Sens. 2023, 15(14), 3517; https://doi.org/10.3390/rs15143517 - 12 Jul 2023

Cited by 1 | Viewed by 1362

Abstract

Focusing on problems of blurred detection boundary, small target miss detection, and more pseudo changes in high-resolution remote sensing image change detection, a change detection algorithm based on Siamese neural networks is proposed. Siam-FAUnet can implement end-to-end change detection tasks. Firstly, the improved VGG16 is utilized as an encoder to extract the image features. Secondly, the atrous spatial pyramid pooling module is used to increase the receptive field of the model to make full use of the global information of the image and obtain the multi-scale contextual information of the image. The flow alignment module is used to fuse the low-level features in the encoder to the decoder and solve the problem of semantic misalignment caused by the direct concatenation of features when the features are fused, so as to obtain the change region of the image. The experiments are trained and tested using publicly available CDD and SZTAKI datasets. The results show that the evaluation metrics of the Siam-FAUnet model are improved compared to the baseline model, in which the F1-score is improved by 4.00% on the CDD and by 7.32% and 2.62% on the sub-datasets of SZTAKI (SZADA and TISZADOB), respectively; compared to other state-of-the-art methods, the Siam-FAUnet model has improved in both evaluation metrics, indicating that the model has a good detection performance. Full article

(This article belongs to the Special Issue Recent Progress of Change Detection Based on Remote Sensing)

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29 pages, 4838 KiB

Open AccessArticle

User-Relevant Land Cover Products for Informed Decision-Making in the Complex Terrain of the Peruvian Andes

by Vasco Mantas and Claudia Caro

Remote Sens. 2023, 15(13), 3303; https://doi.org/10.3390/rs15133303 - 27 Jun 2023

Cited by 2 | Viewed by 1462

Abstract

Land cover in mountainous regions is shaped by a complex web of stressors arising from natural and anthropogenic processes. The co-design process implemented with regional stakeholders in this study highlighted persistent data gaps and the need for locally relevant (thematic, spatial, and temporal) data products, which global alternatives still fail to deliver. This study describes the development of a land cover database designed for the Junín National Reserve (JNR) in Peru as a precursor of a broader effort designed to serve Andean wetland ecosystems. The products were created using Random Forest models leveraging Sentinel-1 and Sentinel-2 data and trained using a large database of in situ data enhanced by the use of high-resolution commercial imagery (Planet). The land cover basemap includes eight classes (two of vegetation) with an overall accuracy of 0.9 and Cohen’s Kappa of 0.93. A second product further subdivided vegetation into locally meaningful vegetation classes, for a total of four types (overall accuracy of 0.85). Finally, a surface water product (snapshot and frequency) delivered a representation of the highly variable water extent around Lake Junín. It was the result of a model incorporating 150 Sentinel-1 images from 2016 to 2021 (an overall accuracy of 0.91). The products were successfully employed in identifying 133 ecosystem services provided by the different land cover classes existing in the JNR. The study highlights the value of participatory monitoring and open-data sharing for enhanced stewardship of social-ecological systems. Full article

(This article belongs to the Special Issue Recent Progress of Change Detection Based on Remote Sensing)

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25 pages, 4804 KiB

Open AccessFeature PaperArticle

The Changes in Dominant Driving Factors in the Evolution Process of Wetland in the Yellow River Delta during 2015–2022

by Cuixia Wei, Bing Guo, Miao Lu, Wenqian Zang, Fei Yang, Chuan Liu, Baoyu Wang, Xiangzhi Huang, Yifeng Liu, Yang Yu, Jialin Li and Mei Xu

Remote Sens. 2023, 15(11), 2858; https://doi.org/10.3390/rs15112858 - 31 May 2023

Cited by 1 | Viewed by 1276

Abstract

Most of the previous studies exploring the changing patterns of wetland in the Yellow River Delta (YRD) were conducted based on sparse time-series images, which ignored its severe environmental gradient and rapid evolution process of the wetland. The changes in the dominant factors in the evolution of the wetland in the YRD are not clear. This study used the dense time-series Sentinel-2 images to establish a wetland database of the YRD, and then analyzed the spatial distribution characteristics of, and temporal changes in, the wetland during 2015–2022. Finally, the dominant factors of the spatio-temporal evolutions of the wetland were explored and revealed. The results showed the following. (1) During 2015–2022, the wetland in the YRD was dominated by artificial wetland, accounting for 54.02% of the total wetland area in the study area. In 2015–2022, the total wetland area increased by 309.90 km², including an increase of 222.63 km² in natural wetlands and 87.27 km² in artificial wetlands. In the conversion between wetland types, 218.73 km² of artificial wetlands were converted into natural wetlands, and 75.18 km² of natural wetlands were converted into artificial wetlands. The patch density of rivers, swamps, and salt pans increased, showing a trend of fragmentation. However, the overall degree of landscape fragmentation in wetlands weakened. The trend of changes in the number of patches and landscape shape index was the same, while the trend of changes in Shannon’s diversity index and Contagion index was completely opposite. (2) Natural factors, such as precipitation (0.51, 2015; 0.65, 2016), DEM (0.57, 2017; 0.47, 2018; 0.49, 2020; 0.46, 2021), vegetation coverage (0.59, 2019), and temperature (0.48, 2022), were the dominant influencing factors of wetland changes in the YRD. The dominant single factor causing the changes in artificial wetlands was vegetation coverage, while socio-economic factors had lower explanatory power, with the average q value of 0.18. (3) During 2015–2022, the interactions between the natural and artificial factors of the wetland changes were mostly nonlinear and showed double-factor enhancement. The interactions between temperature and sunshine hours had the largest explanatory power for natural wetland change, while interactions between precipitation and vegetation coverage, and between temperature and vegetation coverage, had large contribution rates for artificial wetland change. The interactions among natural factors had the greatest impacts on wetland change, followed by interactions between natural factors and socio-economic factors, while interactions among socio-economic factors had more slight impacts on wetland change. The results can provide a scientific basis for regional wetland protection and management. Full article

(This article belongs to the Special Issue Recent Progress of Change Detection Based on Remote Sensing)

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20 pages, 6276 KiB

Open AccessArticle

The Responses of Vegetation NPP Dynamics to the Influences of Climate–Human Factors on Qinghai–Tibet Plateau from 2000 to 2020

by Xingming Yuan, Bing Guo and Miao Lu

Remote Sens. 2023, 15(9), 2419; https://doi.org/10.3390/rs15092419 - 05 May 2023

Cited by 2 | Viewed by 1417

Abstract

The dominant influencing factors of changes in vegetation NPP and the relative roles of climate–human factors on the Qinghai–Tibet Plateau (QTP) differ between historical periods and are unclear. Therefore, there is an urgent need to systematically and quantitatively analyze the evolution process of the QTP’s ecosystem pattern and the driving factors of this process. Based on MOD17A3H and meteorological data, the Miami model, correlation analysis, and the residual coefficient method were used to investigate the spatiotemporal patterns of changes in vegetation NPP on the QTP from 2000 to 2020. We then quantitatively distinguished the relative roles of climate change and human activity in the process of vegetation NPP change during different historical periods. The results show the following: (1) From 2000 to 2020, zones with increasing vegetation NPP (10–30%) were the most widely distributed, and were mainly located in the Three-Rivers Headwater Region and the northern part of the Hengduan Mountains. (2) From 2000 to 2020, zones with a significant positive correlation between vegetation NPP and annual precipitation were mostly distributed in the northeastern QTP and the Three-Rivers Headwater Region, while zones with a positive correlation between vegetation NPP and annual average temperature were mostly located in southern Tibet. Zones with a significant positive correlation between NPP and annual sunshine hours were mainly distributed in the southeastern part of the QTP and the southern part of the Tanggula Mountains. In contrast, zones with a significant positive correlation between NPP and accumulated temperature (>10 °C) were mainly concentrated in the northern and eastern parts of the QTP. (3) During different historical periods, the relative roles of climate–human factors in the process of vegetation NPP change on the QTP had obvious spatiotemporal differences. These results could provide scientific support for the protection and restoration of regional ecosystems on the QTP. Full article

(This article belongs to the Special Issue Recent Progress of Change Detection Based on Remote Sensing)

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18 pages, 6910 KiB

Open AccessArticle

Impact Eichhornia crassipes Cultivation on Water Quality in the Caohai Region of Dianchi Lake Using Multi-Temporal Sentinel-2 Images

by Jinxiang Shen, Ping He, Xiaoli Sun, Zhanfeng Shen and Rong Xu

Remote Sens. 2023, 15(9), 2260; https://doi.org/10.3390/rs15092260 - 25 Apr 2023

Cited by 1 | Viewed by 1619

Abstract

In order to comprehensively and accurately evaluate the adsorption effect of Eichhornia crassipes planting on algae in water, this study used 131 cloudless Sentinel-2 images to monitor the scale and growth dynamics of Eichhornia crassipes planting in Caohai and its surrounding waters. Based on the single and multi-peak histogram characteristics of the NDWI image in the study area, the empirical threshold and the optimized histogram slope adaptive threshold (OHSAT) segmentation methods were respectively used and manual inspection was added to accurately segment the water bodies and Eichhornia crassipes. The results of the analysis of Eichhornia crassipes area, growth status (NDVI), water body algae density (NDVI), and their spatial–temporal co-variation and trends show that the Eichhornia crassipes has a significant enrichment effect on algae, showing a significant decreasing trend of water bloom centered on it. The size and growth status of Eichhornia crassipes are inversely proportional to the water body NDVI, and reasonable harvesting plans based on these findings are expected to achieve optimized pollution control effects. Full article

(This article belongs to the Special Issue Recent Progress of Change Detection Based on Remote Sensing)

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19 pages, 6868 KiB

Open AccessArticle

SMNet: Symmetric Multi-Task Network for Semantic Change Detection in Remote Sensing Images Based on CNN and Transformer

by Yiting Niu, Haitao Guo, Jun Lu, Lei Ding and Donghang Yu

Remote Sens. 2023, 15(4), 949; https://doi.org/10.3390/rs15040949 - 09 Feb 2023

Cited by 11 | Viewed by 2627 | Correction

Abstract

Deep learning has achieved great success in remote sensing image change detection (CD). However, most methods focus only on the changed regions of images and cannot accurately identify their detailed semantic categories. In addition, most CD methods using convolutional neural networks (CNN) have difficulty capturing sufficient global information from images. To address the above issues, we propose a novel symmetric multi-task network (SMNet) that integrates global and local information for semantic change detection (SCD) in this paper. Specifically, we employ a hybrid unit consisting of pre-activated residual blocks (PR) and transformation blocks (TB) to construct the (PRTB) backbone, which obtains more abundant semantic features with local and global information from bi-temporal images. To accurately capture fine-grained changes, the multi-content fusion module (MCFM) is introduced, which effectively enhances change features by distinguishing foreground and background information in complex scenes. In the meantime, the multi-task prediction branches are adopted, and the multi-task loss function is used to jointly supervise model training to improve the performance of the network. Extensive experimental results on the challenging SECOND and Landsat-SCD datasets, demonstrate that our SMNet obtains 71.95% and 85.65% at mean Intersection over Union (mIoU), respectively. In addition, the proposed SMNet achieves 20.29% and 51.14% at Separated Kappa coefficient (Sek) on the SECOND and Landsat-SCD datasets, respectively. All of the above proves the effectiveness and superiority of the proposed method. Full article

(This article belongs to the Special Issue Recent Progress of Change Detection Based on Remote Sensing)

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22 pages, 1900 KiB

Open AccessArticle

A Deeply Supervised Attentive High-Resolution Network for Change Detection in Remote Sensing Images

by Jinming Wu, Chunhui Xie, Zuxi Zhang and Yongxin Zhu

Remote Sens. 2023, 15(1), 45; https://doi.org/10.3390/rs15010045 - 22 Dec 2022

Cited by 6 | Viewed by 2195

Abstract

Change detection (CD) is a crucial task in remote sensing (RS) to distinguish surface changes from bitemporal images. Recently, deep learning (DL) based methods have achieved remarkable success for CD. However, the existing methods lack robustness to various kinds of changes in RS images, which suffered from problems of feature misalignment and inefficient supervision. In this paper, a deeply supervised attentive high-resolution network (DSAHRNet) is proposed for remote sensing image change detection. First, we design a spatial-channel attention module to decode change information from bitemporal features. The attention module is able to model spatial-wise and channel-wise contexts. Second, to reduce feature misalignment, the extracted features are refined by stacked convolutional blocks in parallel. Finally, a novel deeply supervised module is introduced to generate more discriminative features. Extensive experimental results on three challenging benchmark datasets demonstrate that the proposed DSAHRNet outperforms other state-of-the-art methods, and achieves a great trade-off between performance and complexity. Full article

(This article belongs to the Special Issue Recent Progress of Change Detection Based on Remote Sensing)

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15 pages, 4465 KiB

Open AccessArticle

Building Change Detection in Remote Sensing Images Based on Dual Multi-Scale Attention

by Jian Zhang, Bin Pan, Yu Zhang, Zhangle Liu and Xin Zheng

Remote Sens. 2022, 14(21), 5405; https://doi.org/10.3390/rs14215405 - 28 Oct 2022

Cited by 10 | Viewed by 2376

Abstract

Accurate change detection continues to pose challenges due to the continuous renovation of old urban areas and the emergence of cloud cover in coastal areas. There have been numerous methods proposed to detect land-cover changes from optical images. However, there are still many flaws in many existing deep learning methods. In response to the problems of unpredictable change details and the lack of global semantic information in deep learning-based change detection models, a change detection model based on multi-scale and attention is proposed. Firstly, a multi-scale attention module is proposed to effectively obtain multi-scale semantic information to build an end-to-end dual multi-scale attention building change detection model. Secondly, an efficient double-threshold automatic data equalization rule is proposed to address the imbalance of data categories existing in the building change detection dataset, which effectively alleviates the severely skewed data distribution and facilitates the training and convergence of the model. The validation experiments are conducted on three open-source high-resolution building change detection datasets. The experimental results show that the proposed method in this paper can detect the location and area of the actual building changes more accurately and has better results in the detail detection part. This verifies the effectiveness and accuracy of the proposed method. Full article

(This article belongs to the Special Issue Recent Progress of Change Detection Based on Remote Sensing)

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24 pages, 10787 KiB

Open AccessArticle

The Change Pattern and Its Dominant Driving Factors of Wetlands in the Yellow River Delta Based on Sentinel-2 Images

by Cuixia Wei, Bing Guo, Yewen Fan, Wenqian Zang and Jianwan Ji

Remote Sens. 2022, 14(17), 4388; https://doi.org/10.3390/rs14174388 - 03 Sep 2022

Cited by 15 | Viewed by 1938

Abstract

There were significant differences in the dominant driving factors of the change process of different types of wetlands in the Yellow River delta. In addition, to our knowledge, the optimal classification feature sets with the Random Forest algorithm for wetlands in the Yellow River delta were least explored. In this paper, the wetland information in the study area was extracted based on a Random Forest algorithm with de-feature variable redundancy, and then the change process of wetland and its dominant factors from 2015 to 2021 was monitored and analyzed using the Geodetector and gravity center model. The results showed that (1) the optimal variable sets composed of red edge indexes based on the Random Forest algorithm had the highest classification accuracy, with the overall accuracy and Kappa coefficient of 95.75% and 0.93. (2) During 2015–2021, a large area of natural wetland in the Yellow River delta was transformed into an artificial wetland. The wetlands showed an overall development direction of “northwest–southeast” along the Yellow River. (3) The interaction between vegetation coverage and accumulated temperature had the largest explanatory power of the change in the natural wetland area. The interaction between solar radiation and DEM had the largest explanatory power for the change in the artificial wetland area. The research results could better provide decisions for wetland protection and restoration in the Yellow River delta. Full article

(This article belongs to the Special Issue Recent Progress of Change Detection Based on Remote Sensing)

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17 pages, 5959 KiB

Open AccessArticle

TDA-Net: A Novel Transfer Deep Attention Network for Rapid Response to Building Damage Discovery

by Haiming Zhang, Mingchang Wang, Yongxian Zhang and Guorui Ma

Remote Sens. 2022, 14(15), 3687; https://doi.org/10.3390/rs14153687 - 01 Aug 2022

Cited by 2 | Viewed by 1597

Abstract

The rapid and accurate discovery of damage information of the affected buildings is of great significance for postdisaster emergency rescue. In some related studies, the models involved can detect damaged buildings relatively accurately, but their time cost is high. Models that can guarantee both detection accuracy and high efficiency are urgently needed. In this paper, we propose a new transfer-learning deep attention network (TDA-Net). It can achieve a balance of accuracy and efficiency. The benchmarking network for TDA-Net uses a pair of deep residual networks and is pretrained on a large-scale dataset of disaster-damaged buildings. The pretrained deep residual networks have strong sensing properties on the damage information, which ensures the effectiveness of the network in prefeature grasping. In order to make the network have a more robust perception of changing features, a set of deep attention bidirectional encoding and decoding modules is connected after the TDA-Net benchmark network. When performing a new task, only a small number of samples are needed to train the network, and the damage information of buildings in the whole area can be extracted. The bidirectional encoding and decoding structure of the network allows two images to be input into the model independently, which can effectively capture the features of a single image, thereby improving the detection accuracy. Our experiments on the xView2 dataset and three datasets of disaster regions achieve high detection accuracy, which demonstrates the feasibility of our method. Full article

(This article belongs to the Special Issue Recent Progress of Change Detection Based on Remote Sensing)

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24 pages, 8012 KiB

Open AccessArticle

The Changes of Spatiotemporal Pattern of Rocky Desertification and Its Dominant Driving Factors in Typical Karst Mountainous Areas under the Background of Global Change

by Bing Guo, Fei Yang, Junfu Fan and Yuefeng Lu

Remote Sens. 2022, 14(10), 2351; https://doi.org/10.3390/rs14102351 - 12 May 2022

Cited by 34 | Viewed by 2587

Abstract

There are significant differences in the dominant driving factors of rocky desertification evolution in different historical periods in southwest karst mountainous areas. However, previous studies were mostly conducted in specific periods. In this study, taking Bijie City as an example, the spatial and temporal evolution pattern of rocky desertification in Bijie City in the recent 35 years was analyzed by introducing the feature space model and the gravity center model, and then the dominant driving factors of rocky desertification in the study area in different historical periods were clarified based on GeoDetector. The results were as follows: (1) The point-to-point B (bare land index)-DI (dryness index) feature space model has high applicability for rocky desertification monitoring, and its inversion accuracy was 91.3%. (2) During the past 35 years, the rocky desertification in Bijie belonged to the moderate rocky desertification on the whole, and zones of intensive and severe rocky desertification were mainly distributed in the Weining Yi, Hui, and Miao Autonomous Region. (3) During 1985–2020, the rocky desertification in Bijie City showed an overall weakening trend (‘weakening–aggravating–weakening’). (4) From 1985 to 2020, the gravity center of rocky desertification in Bijie City moved westward, indicating that the aggravating degree of rocky desertification in the western region of the study area was higher than that in the eastern region. (5) The dominant factors affecting the evolution of rocky desertification in the past 35 years shifted from natural factor (vegetation coverage) to human activity factor (population density). The research results could provide decision supports for the prevention and control of rocky desertification in Bijie City and even the southwest karst mountainous area. Full article

(This article belongs to the Special Issue Recent Progress of Change Detection Based on Remote Sensing)

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