Ecological Informatics最新文献

{"title":"Framework for deep learning diagnosis of plant disorders in horticultural crops: From data collection tools to user-friendly web and mobile apps","authors":"Ma. Luisa Buchaillot ,&nbsp;Jose A. Fernandez-Gallego ,&nbsp;Henda Mahmoudi ,&nbsp;Sumitha Thushar ,&nbsp;Amna Abdulnoor Aljanaahi ,&nbsp;Sherzod Kosimov ,&nbsp;Zied Hammami ,&nbsp;Ghazi Al Jabri ,&nbsp;Alexandra La Cruz Puente ,&nbsp;Alexi Akl ,&nbsp;M. Isabel Trillas ,&nbsp;Jose Luis Araus ,&nbsp;Shawn C. Kefauver","doi":"10.1016/j.ecoinf.2024.102900","DOIUrl":"10.1016/j.ecoinf.2024.102900","url":null,"abstract":"<div><div>Food security is a pressing global concern, particularly highlighted by the United Nations Sustainable Development Goal 2 (SDG 2), which focuses on enhancing the productivity and incomes of smallholder farmers. In the Middle East and North Africa (MENA) region, horticultural crops are increasingly threatened by pests and diseases, exacerbated by climate change. Local farmers often lack the necessary expertise to effectively manage these issues, resulting in significant reductions in both yield and quality of their crops. This study seeks to develop an accessible mobile crop diagnosis application. By utilizing machine learning and deep learning technologies, the app is designed to help MENA farmers quickly and accurately identify and treat crop disorders. We used Open Data Kit (ODK) to gather a large dataset of crop images required to train deep learning models. These models, built on open-source deep learning architectures, were designed to classify 21 different leaf disorders, including diseases, pests, and nutritional deficiencies. The system was implemented in both a web app and an Android mobile app. Our deep learning models demonstrated an overall accuracy of 94 % in diagnosing plant disorders. The app, Doctor Nabat, includes a decision support system that offers treatment options in the three primary languages spoken in the MENA region. Doctor Nabat is an effective and scalable tool for enhancing crop management in the MENA region, promoting food security by minimizing crop losses through improved pest and disease diagnosis and treatment strategies.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102900"},"PeriodicalIF":5.8,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated classification of tree species using graph structure data and neural networks","authors":"Hadi Yazdi ,&nbsp;Kai Zhe Boey ,&nbsp;Thomas Rötzer ,&nbsp;Frank Petzold ,&nbsp;Qiguan Shu ,&nbsp;Ferdinand Ludwig","doi":"10.1016/j.ecoinf.2024.102874","DOIUrl":"10.1016/j.ecoinf.2024.102874","url":null,"abstract":"<div><div>The classification of tree species in urban contexts is pivotal in assessing ecosystem services and fostering sustainable urban development. This paper explores using graph neural networks (GNNs) on graph structure data derived from quantitative structure models (QSMs) and tree structural measurement for appropriate species classification. The study addresses gaps in existing methods by integrating relationships between tree components, such as branches and cylinders, and considering the entire tree structure in a novel graph data format. The results demonstrate the efficacy of GNNs, particularly the Gated Graph Convolutional Network (GatedGCN), in appropriately classifying urban tree species. It gained an overall classification accuracy and weighted F1 score of 0.84. An analysis of confusion matrices revealed similarities in visual characteristics among several species, including <em>A. platanoides</em> and <em>T. cordata</em>, which pose significant challenges in accurately distinguishing between them. However, certain species, such as <em>A. hippocastanum</em> and <em>P. nigra var. italica</em>, have proved easier to classify than others. Furthermore, the results highlight the importance of relationships between different tree components in species recognition, such as the ratio between branch radius and parent branch radius, the factors often overlooked by previous methods. This underscores the novelty and effectiveness of the proposed approach in this study. Future research could explore integrating additional data sources, such as Leaf Area Density (LAD) calculated from LiDAR and hyperspectral data, to enhance classification accuracy. In conclusion, the evaluation results of the GatedGCN model demonstrated its effectiveness in classifying tree species using a novel data structure format derived from QSM tree characteristics. Advancing urban tree species classification through such methods can enhance future urban tree management using automated AI and robotics solutions.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102874"},"PeriodicalIF":5.8,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Operationalising weather surveillance radar data for use in ecological research","authors":"Maryna Lukach ,&nbsp;Thomas Dally ,&nbsp;William Evans ,&nbsp;Elizabeth J. Duncan ,&nbsp;Lindsay Bennett ,&nbsp;Freya I. Addison ,&nbsp;William E. Kunin ,&nbsp;Jason W. Chapman ,&nbsp;Ryan R. Neely III ,&nbsp;Christopher Hassall","doi":"10.1016/j.ecoinf.2024.102901","DOIUrl":"10.1016/j.ecoinf.2024.102901","url":null,"abstract":"<div><div>Global biodiversity declines require a step change in monitoring frameworks to properly track and diagnose population trends. National weather surveillance radar (WSR) networks offer high spatial (ca. 1-10 km) and temporal (5–10 min) resolution data collected over regional and decadal scales, with well-supported infrastructure that holds great promise for the study of biodiversity. However, WSR datasets pose new challenges for ecologists due to their format, volume, and three-dimensional spatial structure. Here, we define a novel approach to the processing of WSR data to produce a product that can be used to interrogate trends in aerial biodiversity (abundance or diversity) at and across individual ground-level sites. From the full volume of WSR data collected approximately every six minutes we extract vertical columns of WSR observations above sites to compare against standardised nocturnal macro-moth monitoring data at ground level. The results show that there is strong agreement between the WSR-derived proxy of biodiversity in the air column and ground-level measurements of abundance and diversity in nocturnal moth communities. The columnar product operates on a biologically relevant scale with a diameter of 5 km, although column dimensions can easily be customised, and can be deployed at any site within a WSR's observable range. These findings have the potential to unlock past and present WSR observations for widespread application to existing and novel ecological questions and can be applied to weather radar networks around the world.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102901"},"PeriodicalIF":5.8,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PlantView: Integrating deep learning with 3D modeling for indoor plant augmentation","authors":"Sitara Afzal,&nbsp;Haseeb Ali Khan,&nbsp;Jong Weon Lee","doi":"10.1016/j.ecoinf.2024.102899","DOIUrl":"10.1016/j.ecoinf.2024.102899","url":null,"abstract":"<div><div>Indoor plant recognition poses significant challenges due to the variability in lighting conditions, plant species, and growth stages. Despite the growing interest in applying deep learning techniques to plant data, there still needs to be more research focused on the automatic recognition of indoor plant species, highlighting the need for real-time, automated solutions. To address this gap, this study introduces a novel approach for real-time identification and visualization of indoor plants using a Convolutional Neural Network (CNN)-based model called PlantView, integrated with Augmented Reality (AR) for enhanced user interaction. The proposed PlantView model not only accurately classifies the plant species but also visualizes them in a 3D AR environment, allowing users to interact with virtual plant models seamlessly integrated into their real-world surroundings. We developed a custom dataset comprising over 28,000 images of 48 different plant species at various growth stages, captured under diverse lighting conditions and camera settings. Our proposed approach achieves an impressive accuracy of 98.20 %. To validate the effectiveness of PlantView model, we conduct extensive experiments and compared its performance against state-of-the-art methods, demonstrating its superior accuracy and processing speed. The results indicate that our method is not only highly effective for real-time indoor plant recognition but also offers practical applications for enhancing indoor plant care and visualization. This research offers a comprehensive solution for indoor plant enthusiasts and professionals, combining advanced computer vision techniques with immersive AR visualization to revolutionize the way indoor plants are identified, visualized, and integrated into living spaces.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102899"},"PeriodicalIF":5.8,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient dual-stream neural networks: A modeling approach for inferring wild mammal behavior from video data","authors":"Ao Xu ,&nbsp;Zhenjie Hou ,&nbsp;Jiuzhen Liang ,&nbsp;Xing Li ,&nbsp;Xinwen Zhou ,&nbsp;Hongbo Geng","doi":"10.1016/j.ecoinf.2024.102902","DOIUrl":"10.1016/j.ecoinf.2024.102902","url":null,"abstract":"<div><div>Monitoring animal behavior is crucial for protecting ecosystems, maintaining ecological balance, and improving animal welfare. By utilizing various monitoring devices, a wealth of behavioral data can be collected, which machine learning techniques can then analyze to identify specific behaviors. Artificial neural networks are particularly important in movement ecology. However, current research on animal behavior recognition faces several limitations. Many existing datasets are limited by homogeneous species categories, simplistic environmental conditions, restricted video perspectives, and a lack of alignment with the complexity of real-world environments. Consequently, there is significant room for improvement in the robustness and generalization of automatic animal behavior recognition models. To address these challenges, this paper introduces a diverse dataset of wild mammal behaviors. This dataset includes a wide variety of typical wild mammal species, providing a foundation for enhancing the generality and robustness of recognition models. The videos in this dataset capture different environmental contexts where wild mammals reside, across various times of day. Based on this dataset, a novel and highly efficient wild mammal behavior recognition model, EDNN, is proposed. The EDNN model integrates both temporal and spatial scales and achieves an average recognition accuracy of 79.17 % for basic locomotion behaviors, with a Top-1 accuracy of 81.37 % and a Top-5 accuracy of 98.04 %. These results demonstrate the feasibility of automating animal behavior recognition using large datasets collected from modern monitoring devices. The EDNN model is highly effective for behavior recognition and can be readily applied across diverse species and scenarios. It efficiently processes various video data and contributes to a deeper understanding of the movement ecology of species that are challenging to observe.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102902"},"PeriodicalIF":5.8,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lake surface water temperature in China from 2001 to 2021 based on GEE and HANTS","authors":"Song Song ,&nbsp;Jinxin Yang ,&nbsp;Linjie Liu ,&nbsp;Gale Bai ,&nbsp;Jie Zhou ,&nbsp;Deirdre McKay","doi":"10.1016/j.ecoinf.2024.102903","DOIUrl":"10.1016/j.ecoinf.2024.102903","url":null,"abstract":"<div><div>Warming of lakes' surface water leads to accelerated loss of biodiversity and eco-environmental collapse of aquatic systems. Changes in lack surface water temperature (LSWT) are a crucial indicator of lake warming. LSWT growth potentially leads to a higher greenhouse gas emissions and deterioration of the ecological environment within lake systems. However, the magnitude of these changes remains uncertain due to data limitations, particularly for small lakes (1–5 km^{<strong>2</strong>}). Small lakes will experience increasing perturbation with accelerating climate change and our methods demonstrate how the impacts of changes in lakes can be accurately measured and monitored. Our study assessed the spatial and temporal patterns of LSWT in China from 2001 to 2021. We utilized Google Earth Engine (GEE) and the Harmonic Analysis of Time Series (HANTS) algorithm to reconstruct LSWT series and detect spatiotemporal dynamics. The innovative connection of GEE and HANTS provides powerful tool for LSWT analysis. Our results show LSWT increased at a rate of 0.24 °C per decade, albeit with notable spatial and temporal variations. The nighttime rate of increase was greater than the daytime rate of increase. However, there was an abrupt change in daytime LSWT in approximately 2010 and this occurred earlier than an abrupt change in nighttime LSWT. Geographically, the lakes in the Eastern Plain zone exhibited the most significant LSWT warming trend. The majority of lakes warmed more rapidly between 2011 and 2021 as compared to 2001 to 2010. We found a concurrent and pronounced increase in the frequency of algal bloom occurrences after 2010. Our results demonstrate how GEE and HANTS can deliver the continued monitoring and assessment of LSWT trends needed to inform management strategies aimed at mitigating potential negative impacts of climate change on lake ecosystems, both locally and globally. Building on this method, future research should explore the underlying mechanisms driving LSWT trends and their long-term impacts on lake health.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102903"},"PeriodicalIF":5.8,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of global and zonal modeling strategies - A case study of soil organic matter and C:N ratio mapping in Altay, Xinjiang, China","authors":"Hongwu Liang ,&nbsp;Guli Japaer ,&nbsp;Tao Yu ,&nbsp;Liancheng Zhang ,&nbsp;Bojian Chen ,&nbsp;Kaixiong Lin ,&nbsp;Tongwei Ju ,&nbsp;Yongyu Zhao ,&nbsp;Ting Pei ,&nbsp;Yimuranzi Aizizi","doi":"10.1016/j.ecoinf.2024.102882","DOIUrl":"10.1016/j.ecoinf.2024.102882","url":null,"abstract":"<div><div>Digital soil mapping (DSM) based on remote sensing is the dominant method for soil monitoring. Currently, the global modeling strategy (GMS) is used in most soil mapping studies. In the GMS, it is assumed that the relationship between soil and the landscape is the same throughout a region. However, the soil–landscape relationship varies in different geographic zones, such as among different land cover types. In the zonal modeling strategy (ZMS), a region is divided into multiple geographic zones on the basis of zoning rules, and each geographic zone is modeled individually, to fully capture the soil–landscape relationships within different zones. This study was conducted in Altay, Xinjiang, China. The soil organic matter (SOM) content and C:N ratio were mapped on the basis of the GMS and the ZMS to compare the performance differences between the two strategies. The ZMS mapping results exhibited better spatial heterogeneity across different land cover types. Moreover, the ZMS mapping results displayed lower uncertainty and were closer to the observed values than were the GMS results, which included more outliers. Overall, we recommend the ZMS. The accuracy validation results indicated that the accuracy of the ZMS is not necessarily higher than that of the GMS in some zones, but the overall accuracy is similar. Combining similar zones for modeling can improve the accuracy of the ZMS, surpassing that of the GMS. Moreover, the importance of synthetic aperture radar (SAR) data was analyzed. The results revealed that SAR data are highly important for mapping the SOM of bare land and cropland and the C:N ratio of bare land and forest. SAR data may provide soil nutrient information indirectly from moisture levels; therefore, we believe that SAR data have great potential for soil nutrient mapping.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102882"},"PeriodicalIF":5.8,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamical systems-inspired machine learning methods for drought prediction","authors":"Andrew Watford ,&nbsp;Chris T. Bauch ,&nbsp;Madhur Anand","doi":"10.1016/j.ecoinf.2024.102889","DOIUrl":"10.1016/j.ecoinf.2024.102889","url":null,"abstract":"<div><div>Drought is a naturally occurring phenomenon that affects millions of people and results in billions of dollars in damages each year, with impacts expected to worsen due to climate change. At the same time, definitions of drought are nebulous, and extant quantitative drought indicators suffer from short prediction horizons. One such indicator is the Normalized Vegetation Difference Index (NDVI), which measures photosynthetic activity, making it a strong proxy for vegetation density. Recent studies have identified chaotic attractors in satellite-derived NDVI time-series, suggesting a dynamical systems framework may be helpful for time-series prediction of NDVI. In this study, we compare the performance of a mechanistic model and two physics-informed machine learning methods (Sparse Identification of Nonlinear Dynamics [SINDy] and reservoir computing) on the prediction of NDVI time-series data in drought-prone regions of Kenya. We find that SINDy, a sparse polynomial modelling architecture, narrowly outperforms the other two methods with the use of precipitation data, while also retaining some of the interpretability of the mechanistic model. We also find that none of the methods perform as well in the regions in which the chaotic NDVI attractors were originally identified. We conclude by proposing more sophisticated extensions to the methods presented here, both with and without the availability of precipitation data, that draw on the existing dynamical systems and machine learning literature to enable better quantitative predictions of key drought indicators.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102889"},"PeriodicalIF":5.8,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DEBEcoMod: A dynamic energy budget R tool to predict life-history traits of marine organisms across time and space","authors":"A. Giacoletti ,&nbsp;M. Bosch-Belmar ,&nbsp;G. Di Bona ,&nbsp;M.C. Mangano ,&nbsp;B. Stechele ,&nbsp;G. Sarà","doi":"10.1016/j.ecoinf.2024.102897","DOIUrl":"10.1016/j.ecoinf.2024.102897","url":null,"abstract":"<div><div>DEBEcoMod is an open-source R script designed to apply Dynamic Energy Budget (DEB) theory to predict life-history traits of marine organisms under various environmental and anthropogenic stressors. It presents a novel approach to overcoming the computational and scale limitations of previous DEB applications, enabling the generation of spatially explicit outputs. DEBEcoMod is intended to predict traits such as maximum size, reproductive output, and life-history traits across different temporal and spatial scales. It utilises parameters from the AddMyPet database for various species and environmental time series to simulate the past, present, and future performance of organisms. The tool also includes a module for spatio-temporal representation, producing clear and accessible maps for stakeholders. The document highlights DEBEcoMod's application in invasion biology, marine spatial planning, integrated multi-trophic aquaculture, and marine ecology, drawing on published examples of spatial applications to demonstrate its versatility and potential in ecological research and adaptive management. Furthermore, the code has been cross-validated with the official DEBtool to ensure its accuracy and reliability. DEBEcoMod is available for download on GitHub, enhancing its accessibility and utility for a wide range of ecological and conservation applications.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102897"},"PeriodicalIF":5.8,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatio-temporal evaluation of MODIS temperature vegetation dryness index in the Middle East","authors":"Younes Khosravi ,&nbsp;Saeid Homayouni ,&nbsp;Taha B.M.J. Ouarda","doi":"10.1016/j.ecoinf.2024.102894","DOIUrl":"10.1016/j.ecoinf.2024.102894","url":null,"abstract":"<div><div>Drought, a recurring meteorological event, can potentially cause devastating consequences for human populations, and its attributes vary significantly across diverse geographic areas. Therefore, recognizing drought events is paramount for strategically planning and managing water resource systems. In this study, the Temperature Vegetation Dryness Index (TVDI), derived using Moderate-Resolution Imaging Spectroradiometer (MODIS) data spanning from 2003 to 2022 in the Middle East, was used as the foundation for both trend and spectral analyses. To assess TVDI trends, the Mann-Kendall test and Sen's slope estimator were utilized, and harmonic analysis was conducted for spectral analyses. These methods were applied to a dataset comprising 258,087 pixels within the specified region, covering various time scales, including monthly and seasonal analyses. The monthly analyses indicated significant growth in March and April, with September showing the least significant increase, suggesting stability or decline. Geographically, upward trends were predominant in the northern Middle East, including Turkey, Syria, Iraq, western Iran, and eastern Jordan. Significant downward trends were observed in the southern Middle East during the warmer months. Seasonal assessments showed no significant TVDI trends in winter, but upward trends in the south, west, and northwest were identified during spring. The annual trend map indicates a long-term declining trend in TVDI for most regions within specific latitudes, particularly those below 34 degrees. The results of harmonic analysis revealed the presence of multiple cycles at a 95 % confidence level. Notably, there was a heightened prevalence of significant sinusoidal cycles, especially the 2–3-year cycles. This cycle was widespread in countries such as Iran, Oman, Yemen, and Turkey, as well as in the southern regions of Saudi Arabia and Egypt.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102894"},"PeriodicalIF":5.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}