International journal of applied earth observation and geoinformation : ITC journal最新文献

{"title":"A rangeland management-oriented approach to map dry savanna − Woodland mosaics","authors":"Vera De Cauwer ,&nbsp;Marie-Pascale Colace ,&nbsp;John Mendelsohn ,&nbsp;Telmo Antonio ,&nbsp;Cornelis Van Der Waal","doi":"10.1016/j.jag.2024.104193","DOIUrl":"10.1016/j.jag.2024.104193","url":null,"abstract":"<div><div>Tropical savannas have a patchy vegetation structure and heterogeneous composition that complicates their mapping and management. Land managers need detailed vegetation information, especially as tropical savannas often support extensive ranching systems or wildlife-based tourism and face specific challenges such as bush thickening, drought, bushfires and, in Africa, browsing by large game. Since existing methods to map savanna vegetation mosaics rarely provide the resolution or speed required, this study aimed to characterise savanna vegetation with sufficient detail for management purposes and sufficient generalisation for the assessment of processes at a landscape level, using an easy, quick, and cost-efficient system. The study area is a semi-arid savanna in a small game reserve south of Etosha National Park in Namibia. A rapid field assessment focused on the woody vegetation and used the Bitterlich method. Indicator species analysis and MRPP tests resulted in five mixed woody vegetation classes. Random Forest was used to model vegetation composition, structure and woody cover. The highest accuracy was obtained for vegetation composition (77 %) and the lowest for vegetation cover (71 %) with similar accuracies at a resolution of 10 m compared to 30 m. The most important predictors were a radar mosaic (ALOS PALSAR HV) and Sentinel-2 data representing days in wet and dry seasons, with MSAVI₂ a more suitable vegetation index than NDVI. Other predictors such as land surface temperature during winter nights, geology, and distance to water points contributed to the models. The final vegetation map contains 10 classes based on woody vegetation composition and structure. The most dominant classes were <em>Colophospermum mopane – Terminalia prunioides</em> woodland (33 %) and bushland (18 %) with grassland only covering 2.5 %. The method described here was driven by management requirements and can be used for bush control monitoring, quantifying the carbon pool and carrying capacity. It combines an old field survey method with free state-of-the-art datasets and algorithms. The focus on woody vegetation minimises the dependence on the intermittent presence of grasses and herbs in semi-arid savannas.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"134 ","pages":"Article 104193"},"PeriodicalIF":7.6,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How can geostatistics help us understand deep learning? An exploratory study in SAR-based aircraft detection","authors":"Lifu Chen ,&nbsp;Zhenhuan Fang ,&nbsp;Jin Xing ,&nbsp;Xingmin Cai","doi":"10.1016/j.jag.2024.104185","DOIUrl":"10.1016/j.jag.2024.104185","url":null,"abstract":"<div><div>Deep Neural Networks (DNNs) have garnered significant attention across various research domains due to their impressive performance, particularly Convolutional Neural Networks (CNNs), known for their exceptional accuracy in image processing tasks. However, the opaque nature of DNNs has raised concerns about their trustworthiness, as users often cannot understand how the model arrives at its predictions or decisions. This lack of transparency is particularly problematic in critical fields such as healthcare, finance, and law, where the stakes are high. Consequently, there has been a surge in the development of explanation methods for DNNs. Typically, the effectiveness of these methods is assessed subjectively via human observation on the heatmaps or attribution maps generated by eXplanation AI (XAI) methods. In this paper, a novel GeoStatistics Explainable Artificial Intelligence (GSEAI) framework is proposed, which integrates spatial pattern analysis from Geostatistics with XAI algorithms to assess and compare XAI understandability. Global and local Moran’s I indices, commonly used to assess the spatial autocorrelation of geographic data, assist in comprehending the spatial distribution patterns of attribution maps produced by the XAI method, through measuring the levels of aggregation or dispersion. Interpreting and analyzing attribution maps by Moran’s I scattergram and LISA clustering maps provide an accurate global objective quantitative assessment of the spatial distribution of feature attribution and achieves a more understandable local interpretation. In this paper, we conduct experiments on aircraft detection in SAR images based on the widely used YOLOv5 network, and evaluate four mainstream XAI methods quantitatively and qualitatively. By using GSEAI to perform explanation analysis of the given DNN, we could gain more insights about the behavior of the network, to enhance the trustworthiness of DNN applications. To the best of our knowledge, this is the first time XAI has been integrated with geostatistical algorithms in SAR domain knowledge, which expands the analytical approaches of XAI and also promotes the development of XAI within SAR image analytics.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"134 ","pages":"Article 104185"},"PeriodicalIF":7.6,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving the observations of suspended sediment concentrations in rivers from Landsat to Sentinel-2 imagery","authors":"Zhiqiang Qiu ,&nbsp;Dong Liu ,&nbsp;Nuoxiao Yan ,&nbsp;Chen Yang ,&nbsp;Panpan Chen ,&nbsp;Chenxue Zhang ,&nbsp;Hongtao Duan","doi":"10.1016/j.jag.2024.104209","DOIUrl":"10.1016/j.jag.2024.104209","url":null,"abstract":"<div><div>Yellow River is famous for its exceptionally higher suspended sediment concentrations (SSC), displaying significant spatiotemporal heterogeneity across diverse sections. Although SSC monitoring of the Yellow River and some of its tributaries has been achieved using Landsat data, it remains unclear whether the inclusion of higher spatial resolution satellites can expand the spatiotemporal monitoring capabilities for the Yellow River and most of its tributaries. In this study, we employed Sentinel-2 imagery, offering superior spatiotemporal resolution, to develop a higher-accurate SSC model and quantitatively evaluated its potential to improve the spatiotemporal coverage of SSC monitoring compared to Landsat satellites. For the Yellow River in the Loess Plateau, the optimized Sentinel-2 model exhibited superior accuracy, achieving <em>R²</em> = 0.91, root mean square error of 728.76 mg/L, and unbiased percentage difference of 16.75%. Notably, distinct SSC distribution differences were observed across different rivers, indicating significant spatial heterogeneity (SSC: 0.58 – 3.01 × 10⁵ mg/L). Moreover, Sentinel-2 showed a significant increase in observation frequency and spatial coverage (204.08% and 107.15%, respectively) compared to Landsat. An additional 35.29% increase in observation frequency was achieved through the combined satellite observation method. Furthermore, based on river width statistics, we found that upgrading the spatial resolution from 10 m to 1 m enhanced the coverage of observable river segments in the Loess Plateau by approximately 47.96%, and by about 50.56% globally. This study established a crucial scientific foundation for integrating Sentinel-2 and Landsat, enabling finer-scale monitoring and management of river sediment.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"134 ","pages":"Article 104209"},"PeriodicalIF":7.6,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UAV and field hyperspectral imaging for Sphagnum discrimination and vegetation modelling in Finnish aapa mires","authors":"Franziska Wolff ,&nbsp;Sandra Lorenz ,&nbsp;Pasi Korpelainen ,&nbsp;Anette Eltner ,&nbsp;Timo Kumpula","doi":"10.1016/j.jag.2024.104201","DOIUrl":"10.1016/j.jag.2024.104201","url":null,"abstract":"<div><div>Detailed knowledge of vegetation patterns allows to evaluate mire ecosystems and their dynamics. The use of hyperspectral information has the benefits of exploring spectral characteristics of species and vegetation modelling. Our study employed multi-scale and multi-source hyperspectral imaging with a handheld camera in the field and an UAV (Unoccupied Aerial Vehicle) sensor covering the wavelengths of 400 – 1000 nm. Plot-level spectra acquired with a UAV and field spectra collected at 1 m height were combined to develop a spectral library for <em>Sphagnum</em> moss species. This library was then used to map dominant <em>Sphagnum</em> species in a Finnish Aapa mire complex using the Spectral Angle Mapper (SAM) classifier. Classification performance assessment was supported by calculating a water index from the UAV-information. Additionally, we examined the transferability of site-specific spectral libraries to an aapa mire with similar vegetation. The results showed little spectral variation in the plot spectrum between the sensors. A fusion of species- and plot-level libraries yielded the highest accuracy of 62 %. For both mires, there was a great variation among the class accuracies. Floating mosses had an accuracy of 86 %, followed by lawn-forming <em>Sphagnum balticum</em> with 77 %. For the test site, the latter species was mapped with an accuracy of 59 %. Red moss species achieved low accuracies of 45 % and 38 %, likely due to effects from sub-pixel and mixed-pixel effects of neighbouring graminoid species and the presence of litter. This might have also enhanced the contrast of adjacent pixels contributing to spectral alterations. Water table depth measurements and the water index revealed a hydrological preference for most species, with classification performance notably improving with higher water index values. We recommend collecting on-site hyperspectral information at varying hydrological circumstances to build a comprehensive spectral library for mire vegetation and modelling.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"134 ","pages":"Article 104201"},"PeriodicalIF":7.6,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Individual mapping of large polymorphic shrubs in high mountains using satellite images and deep learning","authors":"Rohaifa Khaldi ,&nbsp;Siham Tabik ,&nbsp;Sergio Puertas-Ruiz ,&nbsp;Julio Peñas de Giles ,&nbsp;José Antonio Hódar Correa ,&nbsp;Regino Zamora ,&nbsp;Domingo Alcaraz Segura","doi":"10.1016/j.jag.2024.104191","DOIUrl":"10.1016/j.jag.2024.104191","url":null,"abstract":"<div><div>Monitoring the distribution and size of long-living large shrubs, such as junipers, is crucial for assessing the long-term impacts of global change on high-mountain ecosystems. While deep learning models have shown remarkable success in object segmentation, adapting these models to detect shrub species with polymorphic nature remains challenging. In this research, we release a large dataset of individual shrub delineations on freely available satellite imagery and use an instance segmentation model to map all junipers over the treeline for an entire biosphere reserve (Sierra Nevada, Spain). To optimize performance, we introduced a novel dual data construction approach: using photo-interpreted (PI) data for model development and fieldwork (FW) data for validation. To account for the polymorphic nature of junipers during model evaluation, we developed a soft version of the Intersection over Union metric. Finally, we assessed the uncertainty of the resulting map in terms of canopy cover and density of shrubs per size class. Our model achieved an F1-score in shrub delineation of 87.87% on the PI data and 76.86% on the FW data. The R2 and RMSE of the observed versus predicted relationship were 0.63 and 6.67% for canopy cover, and 0.90 and 20.62 for shrub density. The greater density of larger shrubs in lower altitudes and smaller shrubs in higher altitudes observed in the model outputs was also present in the PI and FW data, suggesting an altitudinal uplift in the optimal performance of the species. This study demonstrates that deep learning applied on freely available high-resolution satellite imagery is useful to detect medium to large shrubs of high ecological value at the regional scale, which could be expanded to other high-mountains worldwide and to historical and fothcoming imagery.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"134 ","pages":"Article 104191"},"PeriodicalIF":7.6,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Solid-state LiDAR and IMU coupled urban road non-revisiting mapping","authors":"Xiaolong Ma ,&nbsp;Chun Liu ,&nbsp;Akram Akbar ,&nbsp;Yuanfan Qi ,&nbsp;Xiaohang Shao ,&nbsp;Yihong Qiao ,&nbsp;Xuefei Shao","doi":"10.1016/j.jag.2024.104207","DOIUrl":"10.1016/j.jag.2024.104207","url":null,"abstract":"<div><div>3D mapping provides highly accurate environmental data, which is essential for critical applications such as autonomous driving and urban emergency response. Light detection and ranging (LiDAR) sensors, particularly solid-state ones, play a pivotal role in spatial–temporal mapping by providing precise three-dimensional data of the environment, significantly enhancing remote sensing capabilities and adaptability to challenging environments compared to mechanical LiDAR systems. However, the limited field of view results in a sparse point cloud frame with few features, which poses challenges to feature matching, causes pose offset, and hinders spatial–temporal continuity, and further significant obstacle for existing vehicle-mounted mobile mapping methods. To address the above issues, we proposed a novel approach that integrating inertial measurement unit (IMU) with solid-state LiDAR. Specifically, it comprises two key modules: an initial localization mapping module, mitigating the limitations of solid-state LiDAR in positioning and mapping accuracy, and an attitude optimization mapping module utilizing real-time high-frequency IMU data to identify key frames for correcting initial attitudes and generating accurate 3D maps. The effectiveness of the method is validated through extensive experiments in complex community and high-speed urban road scenarios. Furthermore, our approach outperforms than the state-of-the-art techniques in test scenarios, achieving a significant 35% reduction in average absolute pose error and enhancing the robustness of vehicle-mounted mapping.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"134 ","pages":"Article 104207"},"PeriodicalIF":7.6,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new attention-based deep metric model for crop type mapping in complex agricultural landscapes using multisource remote sensing data","authors":"Yizhen Zheng ,&nbsp;Wen Dong ,&nbsp;ZhipingYang ,&nbsp;Yihang Lu ,&nbsp;Xin Zhang ,&nbsp;Yanni Dong ,&nbsp;Fengqing Sun","doi":"10.1016/j.jag.2024.104204","DOIUrl":"10.1016/j.jag.2024.104204","url":null,"abstract":"<div><div>Accurate crop mapping is critical for agricultural decisions and food security. Despite the widespread use of machine learning and deep learning in remote sensing for crop classification, mapping crops in mountainous smallholder farming systems remains challenging. In particular, cloudy and rainy weather limits high-quality satellite imagery, potentially limiting the availability of reliable data for classification. Additionally, the substantial intraclass variability among multiple crops further impedes classification accuracy. In this context, this study sought to resolve these two issues by applying a hybrid approach that combines multisource remote sensing data and deep metric learning. For the first challenge, multisource remote sensing data, including Landsat-8, Sentinel-2 and Sentinel-1 data from the Google Earth Engine, were integrated to provide more comprehensive information on crop growth and differences. To address the second challenge, we proposed a 2D-CNN network enhanced by CBAM attention and an online hard example mining strategy. The network focuses on the channel-spatial information of crop samples and their surrounding pixels while promoting the convergence of similar crop samples within the latent feature space and enhancing the separation among different samples. This process is exemplified through a case study of crop mapping in Jiangjin District, Chongqing city, an area representing the typical mountain smallholder farming systems in Southwest China. Compared to six state-of-the-art methods, RF, SVM, XGBoost, ResNet18, and DMLOHM, our approach achieves the highest performance, with 93.99% overall accuracy, a kappa coefficient of 0.9253, and excellent F1 scores across numerous crop categories. The results of this study provide an effective solution for crop classification in complex mountainous regions and have promising potential for mapping under challenging natural conditions.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"134 ","pages":"Article 104204"},"PeriodicalIF":7.6,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A TDFC-RNNs framework integrated temporal convolutional attention mechanism for InSAR surface deformation prediction: A case study in Beijing Plain","authors":"Sheng Yao ,&nbsp;Changfeng Jing ,&nbsp;Xu He ,&nbsp;Yi He ,&nbsp;Lifeng Zhang","doi":"10.1016/j.jag.2024.104199","DOIUrl":"10.1016/j.jag.2024.104199","url":null,"abstract":"<div><div>The precise time series prediction method is the key technology for the monitoring and management of ground deformation. Current prediction methods mostly rely on independent sampling points for prediction, limiting the effective utilization of spatial features by the model, thereby affecting the overall spatial prediction accuracy, and it also restricts the prediction efficiency of the model. In response to the above-mentioned issues in previous research, this study proposes a Time Distributed Fully Connected (TDFC) Recurrent Neural Networks (RNNs) framework that integrates Temporal Convolutional Attention Mechanism (TCAM) for joint prediction of sampling points in time series Interferometric Synthetic Aperture Radar (InSAR) surface deformation data. Firstly, based on Sentinel-1A imagery over the Beijing Plain, the time series surface deformation data from May 2017 to April 2020 are obtained utilizing the Small Baseline Subset InSAR (SBAS-InSAR) technology. After data processing and production into a dataset, based on the TDFC-RNNs framework integrated with TCAM, five different RNN structures were used as prediction modules to construct time series prediction models for InSAR surface deformation. To investigate the effectiveness of the TCAM module on prediction performance, ablation experiments were conducted specifically targeting it. Furthermore, to explore the relative optimality choice of prediction modules under the current dataset and the compatibility of this framework with non-RNN structures, various other sequence models were selected as prediction modules. The predictive performance of the models constructed by this framework was compared in two aspects with benchmark methods, ablation models, and other exploratory models. This included evaluating the predictive results of the test set using various metrics and analyzing the trends in numerical characteristics of the predicted results for the next 60 time steps (720 days). The comprehensive comparison results indicate that the model constructed by this framework outperforms other methods or models in terms of overall performance across various evaluation metrics. At the same time, the future predicted results exhibit more reliable numerical characteristics, aligning well with the developmental trends of surface deformation. This suggests that the above-mentioned models demonstrate favorable predictive capabilities for time series InSAR surface deformation. Such results can be instrumental in intuitively assessing the overall situation of surface deformation in the study area, promptly identifying risks, and swiftly implementing measures to address potential hazards.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"134 ","pages":"Article 104199"},"PeriodicalIF":7.6,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Immediate assessment of forest fire using a novel vegetation index and machine learning based on multi-platform, high temporal resolution remote sensing images","authors":"Hanqiu Xu ,&nbsp;Jiahui Chen ,&nbsp;Guojin He ,&nbsp;Zhongli Lin ,&nbsp;Yafen Bai ,&nbsp;Mengjie Ren ,&nbsp;Hao Zhang ,&nbsp;Huimin Yin ,&nbsp;Fenfen Liu","doi":"10.1016/j.jag.2024.104210","DOIUrl":"10.1016/j.jag.2024.104210","url":null,"abstract":"<div><div>Forest fires pose a significant threat to ecosystems, biodiversity, and human settlements, necessitating accurate and timely detection of burned areas for post-fire management. This study focused on the immediate assessment of a recent major forest fire that occurred on March 15, 2024, in southwestern China. We comprehensively utilized high temporal resolution MODIS and Black Marble nighttime light images to monitor the fire’s development and introduced a novel method for detecting burned forest areas using a new Shadow-Enhanced Vegetation Index (SEVI) coupling with a machine learning technique. The SEVI effectively enhances the vegetation index (VI) values on shaded slopes and hence reduces the VI disparity between shaded and sunlit areas, which is critical for accurately extracting fire scars in such terrain. While SEVI primarily identifies burned forest areas, the Random Forest (RF) technique detects all burned areas, including both forested and non-forested regions. Consequently, the total burned area of the Yajiang forest fire was estimated at 23,588 ha, with the burned forest area covering 19,266 ha. The combination of SEVI and RF algorithms provided a comprehensive and efficient tool for identifying burned areas. Additionally, our study employed the Remote Sensing-based Ecological Index (RSEI) to assess the ecological impact of the fire on the region, uncovering an immediate 15 % decline in regional ecological conditions following the fire. The usage of RSEI has the potential to quantitatively understand ecological responses to the fire. The findings achieved in this study underscore the significance of precise fire-burned area extraction techniques for enhancing forest fire management and ecosystem recovery strategies, while also highlighting the broader ecological implications of such events.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"134 ","pages":"Article 104210"},"PeriodicalIF":7.6,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantification of the spatiotemporal dynamics of diurnal fog and low stratus occurrence in subtropical montane cloud forests using Himawari-8 imagery and topographic attributes","authors":"Jie-Yun Chong ,&nbsp;Min-Hui Lo ,&nbsp;Cho-ying Huang","doi":"10.1016/j.jag.2024.104212","DOIUrl":"10.1016/j.jag.2024.104212","url":null,"abstract":"<div><div>Montane cloud forests (MCFs) feature frequent, wind-driven cloud bands (fog and low stratus [FLS]), providing crucial moisture to the ecosystems. Elevated temperatures may displace FLS, impacting MCFs significantly. To evaluate the consequences, quantifying FLS occurrences is vital. In this study, we employed “RANdom forest GEneRator” (Ranger), an advanced machine learning algorithm, to detect diurnal (07:00–17:00) FLS (dFLS) occurrence from 2018 to 2021 in MCFs in northeast Taiwan using 31 variables, including the visible and infrared bands of the Advanced Himawari Imager onboard Himawari-8, pixel solar azimuth and zenith angles, band differences, the Normalized Difference Vegetation Index (NDVI) and topographic attributes. We applied simple (lumping all data) and three-mode (sunrise/sunset, cloudy and clear sky) models to predict dFLS occurrence. We randomly selected 80 % of the data for model development and the rest for validation by referring to four ground dFLS observation stations across an elevation range of 1151–1811 m a.s.l with 53,358 diurnal time-lapse photographs. We found that it was possible to detect dFLS occurrence in MCFs using both simple and three-mode models regardless of the weather conditions (F1 ≥ 0.864, accuracy ≥ 0.905 and the Matthews correlation coefficient ≥ 0.786); the performance of the simple model was slightly better. The NDVI was more important than other variables in both models. This study demonstrates that Ranger may be able to detect dFLS in MCFs solely using a comprehensive array of satellite features insensitive to varying atmospheric conditions and terrain effects, permitting systematic monitoring of dFLS over vast regions.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"134 ","pages":"Article 104212"},"PeriodicalIF":7.6,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}