International Journal of Applied Earth Observation and Geoinformation最新文献

{"title":"Generation of 1 km high resolution Standardized precipitation evapotranspiration Index for drought monitoring over China using Google Earth Engine","authors":"Yile He, Youping Xie, Junchen Liu, Zengyun Hu, Jun Liu, Yuhua Cheng, Lei Zhang, Zhihui Wang, Man Li","doi":"10.1016/j.jag.2024.104296","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104296","url":null,"abstract":"Under the background of climate change and global warming, extreme drought events in China are becoming increasingly frequent. Drought is one of the primary natural causes of damage to China’s agriculture, economy, and environment, making timely, accurate, and high-resolution drought monitoring particularly crucial. The global standardized precipitation − evapotranspiration index database (SPEIbase) is a widely accepted and used global-scale drought monitoring product. However, limited by its spatial resolution of 0.5 degrees, it is difficult to describe the local spatio-temporal structure of drought. How to improve its spatial resolution while maintaining spatio-temporal consistency is one of the current research hotspots. Based on the response of vegetation growth status to drought, this paper proposes a simple and feasible SPEI prediction method, which improves the resolution of SPEIbase from 0.5 degrees to 1 km. Sixteen remote sensing inversion indices, reflectance and elevation data related to drought were selected from Google Earth Engine (GEE) as features. After preprocessing such as gridding and sample balancing, a random forest regression model was constructed to achieve high spatial resolution prediction of SPEI. SPEI with time scales of 1, 3, 6, 9, 12 and 24 months in July 2020, August 2019 and August 2018 in China was selected for experiments. The accuracy of 1 km resolution SPEI was evaluated through metrics such as root mean square error (RMSE), Pearson correlation coefficient (PCC) and determination coefficient (R<ce:sup loc=\"post\">2</ce:sup>). At the same time, it was compared with the existing 1 km resolution SPEI dataset and the site-scale SPEI values. The results show that the method in this paper can obtain accurate prediction results more stably. The PCC and R<ce:sup loc=\"post\">2</ce:sup> of different months and multiple time scales are all higher than 0.9 and 0.8, and the RMSE is lower than 0.4, showing a good application prospect. Despite the good consistency between the Proposed SPEI and SPEIbase with the site-scale SPEI values, there is still significant room for improvement.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"10 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142793735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detailed hazard identification of urban subsidence in Guangzhou and Foshan by combining InSAR and optical imagery","authors":"Yufang He, Mahdi Motagh, Xiaohang Wang, Xiaojie Liu, Hermann Kaufmann, Guochang Xu, Bo Chen","doi":"10.1016/j.jag.2024.104291","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104291","url":null,"abstract":"Recently Guangzhou and Foshan in China are experiencing significant urbanization and economic development. However, the accelerated urbanization process has contributed significantly to urban land subsidence, causing huge economic losses and endangering safety of infrastructure. This intricate activities on urban surfaces can also lead to pseudo danger in interpreting InSAR-based urban surface deformation, resulting in hazard misidentification in two cities. In order to more accurately identify the hazard of urban surface deformation, we innovatively present a combination of InSAR technology with multi-temporal optical remote sensing data. It can also analyze the specific causes of urban deformation at SAR pixel level in two cities. The SBAS-InSAR method was adopted to obtain an urban subsidence map from 2017 to 2020 based on 110 Sentinel-1 SAR image scenes. To obtain an urban surface change map with a high accuracy, an improved SwiT-UNet++ model was applied based on multi optical Google Earth imagery. By a combined analysis of SAR and optical images, we discovered multiple irregular funnels with subsidence at different scales in both cities, that are mostly relatable to urban surface constructions such as foundation compression, building demolition, and the construction of public facilities. Furthermore, to identify detailed hazard around surface changes, the buffer analysis based on InSAR surface deformation and urban surface change maps was conducted. It revealed the surface deformation signals around certain urban surface change areas are more obvious and pose certain hazard. Finally additional high-risk areas are found in the two cities. By subtracting the optical surface change detection map from the InSAR-based urban subsidence map, the “pseudo danger” caused by urban activities in the interpretation of InSAR-based urban surface deformation is eliminated, enabling precise identification of actual land subsidence hazards. It is realized through a risk assessment experiment in the research area by adding factors of urbanization processes. By combining multiple sources of data and using advanced analytical techniques, we could identify the determining factors contributing to urban subsidence and the detailed hazards and thus, provide valuable information for future urban developments.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"9 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142793565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global vegetation productivity has become less sensitive to drought in the first two decades of the 21st century","authors":"Meng Luo, Shengwei Zhang, Ruishen Li, Xi Lin, Shuai Wang, Lin Yang, Kedi Fang","doi":"10.1016/j.jag.2024.104297","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104297","url":null,"abstract":"Vegetation carbon sequestration is a fundamental process that supports ecosystem biodiversity and ecological services. It is a key factor in shaping ecosystem state and energy flow. Global climate change has intensified in recent years. Frequent drought events affect the stabilization of carbon cycle. In this study, we used correlation analysis method to explore the relationship between standardized precipitation evapotranspiration index (SPEI) and gross primary productivity (GPP). Our study found that the global drought degree is decreasing, and drought sensitivity of global surface vegetation decreased. The drought index value increased 91.3% and the sensitivity decreased 35.71% during the 2010–2020 period (P2) compared to the 2000–2010 period (P1). Our study also found that the global area of drought decreased by 4.03% in P2, but the global area with high drought frequency increased by 0.21%. The drought response time scale shortened by 5.19%. GPP showed an increasing trend, with the largest increase in agricultural land. By studying the interaction between drought and different vegetation types, we can better understand the mechanisms by which vegetation responds, adapts and regulates to climate change. It is necessary for understanding the sustainable development of global ecosystems and climate change response.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"28 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142793570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal simulation and projection of soil erosion as affected by climate change in Northeast China","authors":"Ziwei Liu, Mingchang Wang, Xingnan Liu, Xiaoyue Lyu, Minshui Wang, Fengyan Wang, Xue Ji, Xiaoyan Li","doi":"10.1016/j.jag.2024.104305","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104305","url":null,"abstract":"Long-term climate change significantly affects the spatiotemporal dynamics of soil erosion. To explore this, remote sensing technology, future climate scenarios, and deep learning are combined to model the historical and future variations in soil erosion, investigating its spatiotemporal dynamics influenced by climate change. This paper uses the Revised Universal Soil Loss Equation (RUSLE) to assess the historical changes in erosion in northeast China from 1980 to 2020. A soil erosion simulation (SES) model was developed, incorporating deep learning models, to forecast future trends in soil erosion under various climate scenarios. The SES model achieves an R-squared (R<ce:sup loc=\"post\">2</ce:sup>) value of 0.7513. The SES model can simulate the Spatiotemporal dynamics of soil erosion influenced by long-term climate change. Soil erosion from 2001 to 2020 is lower than that from 1980 to 2000, indicating a decrease in soil erosion under natural variability conditions. Unlike historical trends, future soil erosion demonstrates significant variation across three scenarios: SSP1-RCP1.9 (SSP119), SSP2-RCP4.5 (SSP245), and SSP5-RCP8.5 (SSP585). The simulation results show that the SSP119 climate scenario has a minor impact on soil erosion, whereas the SSP245 scenario leads to a gradual increase in soil erosion. The SSP585 scenario, characterized by high social vulnerability and substantial radiative forcing, exacerbates the risk of soil erosion. The study provides valuable references for maintaining soil stability and managing surface runoff.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"69 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142793566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unravelling long-term spatiotemporal deformation and hydrological triggers of slow-moving reservoir landslides with multi-platform SAR data","authors":"Fengnian Chang, Shaochun Dong, Hongwei Yin, Xiao Ye, Zhenyun Wu, Wei Zhang, Honghu Zhu","doi":"10.1016/j.jag.2024.104301","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104301","url":null,"abstract":"Active landslides pose significant global risks, underscoring precise displacement monitoring for effective geohazard management and early warning. The Three Gorges Reservoir Area (TGRA) in China, a pivotal section of the world’s largest water conservancy project, has developed thousands of landslides due to unique hydrogeological conditions and reservoir operations. Many of these landslides are oriented north–south and covered by seasonal vegetation, which complicates the conventional remote sensing-based displacement monitoring, particularly in estimating the three-dimensional (3D) deformation and long-term time series displacement. To address these challenges, we propose an approach that integrates interferometric synthetic aperture radar (InSAR), pixel offset tracking (POT), stacking, and priori kinematic models to fully utilize the phase and amplitude information of multi-platform, multi-band SAR images (i.e., L-band ALOS-1, C-band Sentinel-1, and X-band TerraSAR-X). This approach is employed to scrutinize the long-term spatiotemporal deformation and evolution mechanism of two slow-moving, north-facing reservoir landslides in the TGRA. The results reveal for the first time the 15-year-long displacement evolution of these landslides before and after reservoir impoundment, highlighting the spatiotemporal heterogeneity of landslide deformation induced by hydrologic triggers. The impoundment in September 2008 induced transient acceleration in both landslides, followed by a relatively stable, step-like deformation pattern subject to rainfall and reservoir water level (RWL) fluctuations. Rainfall, with a lag of approximately 20 days, predominantly affects both landslides, while RWL fluctuations mainly influence the deformation at landslide toes. Notably, as the distance from the reservoir increases, the influence of RWL diminishes, with lag times increasing from 8 to about 40 days. This quantitative characterization of landslide responses to triggers represents a crucial step towards improved hazard mitigation capabilities.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"210 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142793568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quasi-HSL color space and its application: Sunlit and shaded component fractional cover estimation in vegetated ecosystem","authors":"Jia Tian, Qingjiu Tian, Suju Li, Qianjing Li, Sen Zhang, Shuang He","doi":"10.1016/j.jag.2024.104298","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104298","url":null,"abstract":"Sunlit and shaded components are commonly present in both airborne and satellite remote sensing images. In vegetated ecosystems, shaded component often result from sunlight being obstructed by topographic relief or canopy structures, and shaded component may impact plant growth, leaf photosynthesis, and ultimately carbon sequestration. To accurately estimate the fractional cover of the shaded and sunlit components, including both green and non-green vegetation within vegetated ecosystems, a novel method called the quasi-Hue-Saturation-Lightness (quasi-HSL) method is proposed in this study. Inspired by the RGB to HSL conversion, this method utilizes near-infrared, green, and red bands to compute hue (and normalized hue), saturation, and lightness. Subsequently, two indices, namely Hue-Lightness Index (HLI) and Saturation-Lightness Index (SLI), are introduced to construct a triangular space for estimating the fractional cover of the three components. Through unmanned aerial vehicle field experiments conducted in two forested areas, the accuracy of fractional cover estimation for three components reaches an R<ce:sup loc=\"post\">2</ce:sup> value of 0.50–0.67. Furthermore, this fractional cover estimation approach can be extended to a four-component estimation, including sunlit green vegetation, sunlit non-green vegetation, shaded green vegetation, and shaded non-green vegetation. With this detailed fractional cover estimation in vegetated area, the fractional vegetation coverage can be retrieved. Cross-validated with the fractional vegetation coverage retrieved by NDVI, the accuracy reaches R<ce:sup loc=\"post\">2</ce:sup> = 0.92. The advantages of the proposed method are (1) estimating fractional cover of shaded component without blue band, which is easily impacted by atmospheric conditions and sensor performance, and (2) differentiating the sunlit green and non-green vegetation components in the vegetated ecosystem.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"92 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142793574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal variation of spring phenology and the corresponding scale effects and uncertainties: A case study in southwestern China","authors":"Chongjing Zhu, Xiaojun She, Xiaojie Gao, Yajun Huang, Yelu Zeng, Chao Ding, Dongjie Fu, Jing Shao, Yao Li","doi":"10.1016/j.jag.2024.104294","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104294","url":null,"abstract":"Understanding terrestrial vegetation phenology—the timing of life-cycle events—is crucial for insights into ecosystem energy and material cycles. Land surface phenology (LSP) derived from satellite observations has become a critical tool for tracking vegetation phenology across large spatial scales. However, LSP data from coarse spatial resolutions often mix phenological signals from multiple land cover types, a limitation that fine-resolution satellite data can help overcome. Recent studies indicate that spring phenology derived from the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) data tends to be biased earlier than that from the 30-m Landsat data due to scale effects. The extent of this bias across other satellite sensors and its impact on long-term phenological trends remains unclear. Additionally, few studies have used medium- to high-resolution LSP data to investigate southwestern China, partly due to limited data availability, which may exacerbate uncertainties related to scale effects in LSP observations. To address these gaps, we selected Jinfo Mountain in southwestern China—a region with high spatial heterogeneity—to analyze the spatiotemporal patterns of spring phenology and examine associated scale effects and uncertainties. We applied two phenology retrieval methods to multi-resolution LSP data from various sensors: 30-m Landsat (1984–2023), 250-m MODIS (2002–2021), 500-m MODIS (2000–2023), 1-km SPOT (1999–2019), and 8-km AVHRR (1982–2022). Our findings revealed that all sensors consistently captured the spatial patterns of spring phenology, indicating an advancing trend of 6–8 days per decade, though the trend’s magnitude varied notably across sensors. Data quality, rather than retrieval methods, emerged as the primary source of uncertainty in characterizing phenological dynamics, with elevation contributing significantly to bias due to its negative correlation with the number of available clear observations. Moreover, we found that the MODIS-Landsat bias in spring phenology may not generalize across other coarse-to-fine LSP comparisons. This study provides valuable insights into phenology in the understudied region of southwestern China, highlighting the importance of spatial resolution and sensor characteristics for accurate plant phenology mapping and monitoring.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"15 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142793572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MB-Net: A network for accurately identifying creeping landslides from wrapped interferograms","authors":"Ruixuan Zhang, Wu Zhu, Baodi Fan, Qian He, Jiewei Zhan, Chisheng Wang, Bochen Zhang","doi":"10.1016/j.jag.2024.104300","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104300","url":null,"abstract":"The efficient and automated identification of landslide hazards is essential for socio-economic development and human safety. Integrating the feature extraction capabilities of deep learning with the millimeter-level precision of Interferometric Synthetic Aperture Radar (InSAR) technology establishes a foundation for this task. However, current methods require unwrapping interferograms, and even converting them into deformation products before identifying landslide hazards. This process is susceptible to unwrapping errors, resulting in inefficient data utilization, and demands considerable time and labor. To overcome these challenges, wrapped interferograms are directly utilized for identifying creeping landslides. In this study, trigonometric functions are applied to improve the representation of interferograms and to further enhance the data through rendering. Secondly, a multi-branch semantic segmentation network (MB-Net) was designed, with parallel branch encoding and progressive feature fusion to optimize the model’s ability to learn interferometric phases. Experimental results indicate a good performance, with the F1-score of 80.91 %, the Intersection over Union (IoU) of 67.94 %, and the Matthews correlation coefficient (MCC) of 80.16 % on the ISSLIDE dataset. To further validate the generalization capability of MB-Net, the public COMET-LiCS Sentinel-1 InSAR portal data was utilized, focusing on the middle reaches of the Jinsha River in China. The results highlight MB-Net’s efficacy in spatial transferability analysis. These findings emphasize the potential of our approach for large-scale landslide hazard identification, providing a crucial foundation for the utilization of interferograms in creeping landslide detection.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"48 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142793573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised hyperspectral noise estimation and restoration via interband-invariant representation learning","authors":"Zhaozhi Luo, Janne Heiskanen, Xinyu Wang, Yanfei Zhong, Petri Pellikka","doi":"10.1016/j.jag.2024.104295","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104295","url":null,"abstract":"Hyperspectral images (HSIs) acquired from different imaging platforms are inevitably contaminated by multiple types of noise. However, the existing supervised learning based denoising methods often show poor generalizability on data with complex degradation, due to the discrepancy between synthetic training data and real data. Although some unsupervised denoisers have been developed to learn priors on real data, the noise assumptions or image priors in these methods limit their performances. In this paper, an unsupervised noise estimation and restoration (UNER) framework is proposed based on disentangled representation learning, to create an interband representation space that is resistant to noise within a single HSI, i.e., an interband-invariant representation space. Real HSIs are categorized internally into noisy and clean bands by noise intensity estimation. Intraband and interband disentangled reconstruction are designed to train two encoder-decoder modules to learn the interband-invariant representation. Noise patterns are separated from HSIs by transforming noisy bands into the representation space without introducing hand-crafted priors. The estimated noise and clean bands are then combined to train the self-supervised interband information restoration module, thereby exploiting spectral correlation and restoring the latent noise-free data with high information fidelity. In this way, the UNER framework can learn specific priors from real HSIs without clean data and be applied to various scenarios with complicated noise patterns. Noise removal experiments conducted on three airborne hyperspectral datasets representing urban, agricultural, and forestry areas respectively demonstrate the superiority of the proposed UNER framework over the state-of-the-art hyperspectral denoising methods.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"79 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142793388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel surface deformation prediction method based on AWC-LSTM model","authors":"Yu Chen, Xinlong Chen, Shanchuan Guo, Huaizhan Li, Peijun Du","doi":"10.1016/j.jag.2024.104292","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104292","url":null,"abstract":"Severe surface deformation can damage the ecological environment, trigger geological disasters, and threaten human life and property. Reliable surface deformation prediction is conducive to reducing potential risks and mitigating disaster losses. Currently, machine learning-based surface deformation prediction models have shown significant improvements in prediction performance. However, most prediction models do not sufficiently consider the characteristics of surface deformation, exhibit subjectivity in parameter settings, and inadequately capture local features in time series data. We introduce the AWC-LSTM model to predict surface deformation. Initially, leveraging the strengths of the autoregressive integrated moving average (ARIMA) model in handling linear signals, the obtained surface deformation information is decomposed to linear and nonlinear parts, and the linear part is predicted. Secondly, by incorporating convolutional neural network (CNN) layers into the long short term memory (LSTM) model, the ability to learn local features is enhanced and the whale optimization algorithm (WOA) is introduced to determine the optimal hyperparameters of the model, thereby predicting nonlinear deformation. The proposed AWC-LSTM model was validated using the Shilawusu coal mine and Beijing as case studies. The outcomes indicate that the deformation predictions for the Shilawusu coal mine and Beijing exhibit a high degree of consistency with the monitored data, with root mean square errors (RMSE) not exceeding 3 mm. This underscores the model’s reliability and applicability across different areas. Comparisons with existing prediction models indicate that the AWC-LSTM model achieves higher predictive accuracy, with an average improvement in accuracy ranging from 28.38 % to 80.59 % over other models.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"18 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142793389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}