Ecological Informatics最新文献

{"title":"Refining landsat-based annual NDVImax estimation using shape model fitting and phenological metrics","authors":"Lihao Zhang ,&nbsp;Miaogen Shen ,&nbsp;Licong Liu ,&nbsp;Xuehong Chen ,&nbsp;Ruyin Cao ,&nbsp;Qi Dong ,&nbsp;Yang Chen ,&nbsp;Jin Chen","doi":"10.1016/j.ecoinf.2025.103107","DOIUrl":"10.1016/j.ecoinf.2025.103107","url":null,"abstract":"<div><div>The annual maximum normalized difference vegetation index (NDVI_max) is widely used as a surrogate for annual aboveground net primary productivity (ANPP) of summer-green vegetation. Landsat data, with its 30-m spatial resolution and high temporal consistency, have revealed long-term changes in NDVI_max and ANPP. However, in cloudy regions with summer-green vegetation, such as the Tibetan Plateau, the scarcity of cloud-free Landsat NDVI observations complicates NDVI_max estimation, particularly due to interannual variations in phenology and NDVI_max. This study proposed a shape model fitting method that integrates interannual phenological similarity to estimate Landsat NDVI_max, using the Tibetan Plateau as an example. For a given target year, an annual NDVI shape model was constructed using all cloud-free Landsat NDVI observations from that year and phenologically similar years, identified using phenological metrics derived from MODIS and GIMMS NDVI datasets. The model was then fitted to the target year's cloud-free NDVI time series to correct seasonal biases in NDVI observations. Validations with simulated and real images indicated that the proposed method outperformed several commonly used approaches in estimating NDVI_max and detecting temporal trends across various conditions. The method more accurately captured the true annual NDVI trajectory and NDVI_max date for the target year. It enabled the retrieval of long-term high-resolution NDVI_max series for summer-green vegetation on the Tibetan Plateau and provided a reference for Landsat NDVI_max extraction in other summer-green vegetation regions. Additionally, by addressing the observational biases, the method corrected previous overestimates of greening on Tibetan Plateau, thereby improving global change studies on summer-green vegetation.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103107"},"PeriodicalIF":5.8,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680204","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":"The ‘everything is everywhere’ framework: Holistic network analysis as a marine spatial management tool","authors":"Molly K. James ,&nbsp;Gennadi Lessin ,&nbsp;Muchamad Al Azhar ,&nbsp;Michael Bedington ,&nbsp;Charlotte H. Clubley ,&nbsp;Paul Somerfield ,&nbsp;Antony M. Knights","doi":"10.1016/j.ecoinf.2025.103105","DOIUrl":"10.1016/j.ecoinf.2025.103105","url":null,"abstract":"<div><div>The North Sea hosts numerous man-made structures, some recently installed and others nearing end-of-life, with decisions about their decommissioning at the centre of current debate. Further there are plans for significant expansion of structures relating in particular to offshore wind energy. Here, using a combination of hydrodynamic modelling, particle tracking, and graph network analysis, we evaluate connectivity under two scenarios: existing structures – releasing particles from cells where structures are currently present – and “everything is everywhere” – releasing particles from every cell in the domain. Additionally, we introduce a Connectivity Importance Index (CII) to assess both current and potential future connectivity within the region. The CII under the ‘everything is everywhere’ scenario revealed cells with high potential connectivity that align with, but also extend beyond, those identified under the existing structures scenario, pointing to potentially valuable regions for future structure placement. The relocatable methodology described in this paper allows for the quantification of potential networks, applicable with or without existing habitat data, offering valuable insights for ecologically coherent marine spatial management strategies.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103105"},"PeriodicalIF":5.8,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680198","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":"Evaluation of a CNN model to map vegetation classification in a subalpine coniferous forest using UAV imagery","authors":"Weibo Shi ,&nbsp;Xiaohan Liao ,&nbsp;Shaoqiang Wang ,&nbsp;Huping Ye ,&nbsp;Dongliang Wang ,&nbsp;Huanyin Yue ,&nbsp;Jianli Liu","doi":"10.1016/j.ecoinf.2025.103111","DOIUrl":"10.1016/j.ecoinf.2025.103111","url":null,"abstract":"<div><div>Unmanned aerial vehicle (UAV) remote sensing based on deep learning has increasingly been applied for forest vegetation classification. However, existing studies have focused mainly on simple woodlands, and accurately mapping the vegetation distribution in complex natural forests remains challenging. To address this, we conducted a study in a natural alpine forest in Southwest China, leveraging high-resolution UAV imagery and deep learning for vegetation classification. We systematically assessed the effects of patch size, spatial resolution, and rotation angle on the model performance, considering their interactions. Our results demonstrate that UAVs combined with deep learning techniques achieve high classification accuracy in natural forests, with a mean F1-score of 0.91. Patch size has a significant influence on accuracy, although its impact diminishes as the spatial resolution decreases. As the patch size increased from 128 × 128 to 256 × 256, the model F1-score improved by 18 % at a 5 cm resolution, whereas it improved by only 3 % at a 10 cm resolution. A higher spatial resolution does not necessarily enhance model accuracy, and the effect of patch size also needs to be considered. The Rotation angle, as a data augmentation strategy, is crucial when training data are limited and can significantly increase model performance. These findings highlight the potential of combining deep learning and UAV remote sensing for natural forests. This approach facilitates more reliable access to forest information in forest areas where access is difficult. Overall, this study provides an efficient and cost-effective method for monitoring and protecting natural forests, serving as a reference for selecting appropriate parameters in UAV-based deep learning remote sensing.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103111"},"PeriodicalIF":5.8,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687622","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":"Continuous Real-Time Acoustic Monitoring of endangered bird species in Hawai‘i","authors":"Melissa Weidlich-Rau ,&nbsp;Amanda K. Navine ,&nbsp;Patrick T. Chaopricha ,&nbsp;Felix Günther ,&nbsp;Stefan Kahl ,&nbsp;Thomas Wilhelm-Stein ,&nbsp;Raymond C. Mack ,&nbsp;Hendrik Reers ,&nbsp;Aaron N. Rice ,&nbsp;Maximilian Eibl ,&nbsp;Patrick J. Hart ,&nbsp;Patrick Wolff ,&nbsp;Holger Klinck ,&nbsp;Lena D. Schnell ,&nbsp;Rogelio Doratt ,&nbsp;Michael Loquet ,&nbsp;Tiana Lackey","doi":"10.1016/j.ecoinf.2025.103102","DOIUrl":"10.1016/j.ecoinf.2025.103102","url":null,"abstract":"<div><div>The decline of endemic bird species in Hawai‘i requires innovative conservation measures enabled by passive acoustic monitoring (PAM). This paper describes a novel real-time PAM system used in the Pōhakuloa Training Area (PTA) to reduce wildlife collisions and minimize disruptions to military operations while ensuring the protection of endangered bird species such as the Nēnē and ‘Akē‘akē. The system is based on the BirdNET algorithm and was evaluated with over 16,000 soundscape recordings from Hawai‘i. The results show that the model version HI V2.0, based on BirdNET and specifically adapted to Hawaiian bird species, showed the clearest separation between true and false positive detections (average precision 49% to 52%), although this difference was not statistically significant. However, accuracy varied considerably between species and locations, emphasizing the need to adapt the models to the specific conditions of use. A novel web application allows immediate visualization of the predicted bird species, facilitating the implementation of conservation measures. The three acoustic monitoring units installed at the PTA in January 2023 demonstrate the system’s potential for continuous monitoring and protection of Hawaiian endangered bird species.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103102"},"PeriodicalIF":5.8,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680201","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":"A roadmap for advancing plant phenological studies through effective open research data management","authors":"Barbara Templ","doi":"10.1016/j.ecoinf.2025.103109","DOIUrl":"10.1016/j.ecoinf.2025.103109","url":null,"abstract":"<div><div>Phenological research, critical for understanding ecological dynamics in response to environmental changes, increasingly relies on Open Research Data Management (ORDM) to enhance scientific outcomes. Based on insights from a structured survey conducted among phenology experts, this paper explores how the adoption of FAIR principles - Findability, Accessibility, Interoperability, and Reusability - directly addresses the unique challenges of phenological data, such as inconsistent metadata, variability in data collection methods, and difficulties in data integration. This synthesis not only highlights the obstacles faced by phenologists but also proposes strategic solutions highlighting a clear call to action steering phenological research toward a more collaborative and open science future.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103109"},"PeriodicalIF":5.8,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680205","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":"Capturing constraints on boreal gross primary productivity using the remote sensing-based CAN-TG model.","authors":"Ramon Melser ,&nbsp;Nicholas C. Coops ,&nbsp;Michael A. Wulder ,&nbsp;Chris Derksen ,&nbsp;Sara H. Knox ,&nbsp;Tongli Wang","doi":"10.1016/j.ecoinf.2025.103108","DOIUrl":"10.1016/j.ecoinf.2025.103108","url":null,"abstract":"<div><div>In response to the limited number and distribution of in-situ carbon flux observations, remote sensing-based methods are increasingly relied upon for the estimation of Gross Primary Productivity (GPP) at regional to global scales. These remote sensing-informed estimates are commonly derived through process-based modelling frameworks which prescribe functional relationships between model inputs and target GPP. Across highly heterogeneous landscapes like the Canadian boreal, these parameters are difficult to constrain and often site-specific. Recent work has determined that parameterization alone may not improve model performance, instead requiring additional model inputs to capture the complex drivers of vegetation productivity across land cover types. In response to these challenges, we applied the remote sensing-based CAN-TG framework to estimate boreal GPP, leveraged through a random forest (RF) machine learning approach that does not assume linear or functional relationships between input variables and productivity. Stratified by land cover, fire disturbance history, and topography, models were assessed for their ability to capture reference GPP from NASA's complex, process-based Soil Moisture Active Passive (SMAP) GPP product. Across all boreal strata, model r² values ranged from 0.93 to 0.96, demonstrating that the variability in substantially more complex models can be successfully captured using a simple, interpretable remote sensing-based framework. Through the addition of remote sensing variables capturing freeze/thaw and soil moisture dynamics to surface temperature and greenness, the CAN-TG model demonstrated an improved ability to capture GPP compared to a benchmark GPP model. Seasonal RF models across key boreal land cover, fire disturbance history and topographic strata further demonstrated varying and complex non-linear relationships between model variables and GPP. Spring and fall models generally outperformed winter and summer models, reaffirming model strengths whilst also highlighting remaining uncertainty and areas for future model improvement.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103108"},"PeriodicalIF":5.8,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680199","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":"BEHAVE - facilitating behaviour coding from videos with AI-detected animals","authors":"Reinoud Elhorst ,&nbsp;Martyna Syposz ,&nbsp;Katarzyna Wojczulanis-Jakubas","doi":"10.1016/j.ecoinf.2025.103106","DOIUrl":"10.1016/j.ecoinf.2025.103106","url":null,"abstract":"<div><div>Applying video recording to investigate behaviour of wild animals reduces field workload, enhances data accuracy, and minimises disturbance to animals. However, extracting information from collected video data remains a cumbersome and time-consuming task if not, at least partly, automated. Recent advancements in artificial intelligence (AI) offer automatic detection of target animals in video streams, however integrating these detections with software to annotate behaviours is missing. In addition, programs that are able to do these AI detections are often not easy to install or require specialised hardware to run. To address this gap, we introduce BEHAVE, a user-friendly, open-source, free, zero-install tool for coding animal behaviour in video recordings. BEHAVE can use the results of AI detections to skip sections of the video, can extract timestamps from video data, and supports programmable ethograms. The results are saved in a .csv file for further processing. BEHAVE includes a component that allows doing AI detections, on non-specialised hardware, also in a zero-install, user-friendly way. Due to these advantages, the behaviour coding process can be significantly accelerated, resulting in well-organised and readily exportable/importable data.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103106"},"PeriodicalIF":5.8,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680202","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":"Predicting ground-level nitrogen dioxide concentrations using the BaYesian attention-based deep neural network","authors":"Angelo Casolaro,&nbsp;Vincenzo Capone,&nbsp;Francesco Camastra","doi":"10.1016/j.ecoinf.2025.103097","DOIUrl":"10.1016/j.ecoinf.2025.103097","url":null,"abstract":"<div><div>Nitrogen dioxide pollution is an ongoing and growing environmental issue that affects human health in developed Western countries. This study introduced a Bayesian attention-based deep neural network model for predicting ground-level nitrogen dioxide concentrations. The proposed model integrates the principles of the Bayesian neural network and the attention mechanism, enabling it to produce predicted values and their associated uncertainties, expressed as standard deviations. The proposed model was validated using 2020 data collected from 520 European Environmental Agency stations, located in Italy. The performance of the model was assessed using the mean absolute error.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103097"},"PeriodicalIF":5.8,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636787","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":"Canopy extraction of mango trees in hilly and plain orchards using UAV images: Performance of machine learning vs deep learning","authors":"Yuqi Yang ,&nbsp;Tiwei Zeng ,&nbsp;Long Li ,&nbsp;Jihua Fang ,&nbsp;Wei Fu ,&nbsp;Yang Gu","doi":"10.1016/j.ecoinf.2025.103101","DOIUrl":"10.1016/j.ecoinf.2025.103101","url":null,"abstract":"<div><div>Mango is an important fruit widely grown in tropical and subtropical regions. Intelligent and accurate pesticide spraying for mango orchard can significantly improve yield and quality of mango. To obtain the information of mango canopy accurately is the key to realize the precision pesticide spraying of mango orchard. However, it is still a challenge to use the remote sensing technology of unmanned aerial vehicle (UAV) to accurately extract canopy information in orchards with different landforms. The visible light images of mango orchards with different geomorphological characteristics were collected by a UAV, and the canopies were accurately extracted, and their canopy areas were accurately predicted based on deep learning method in this study. Firstly, visible light images collected by a UAV were used to segment and extract mango tree canopies using various machine learning (ML) and deep learning (DL) models. Based on their accuracy, the best-performing models, HR-Net from DL and Extra Trees Classification (ETC) from ML were selected. Subsequently, Mixed Dataset-HR-Net and ETC-CHM (Canopy height model) models were developed based on these optimal models, and their performance was evaluated for canopy segmentation and area extraction in four representative regions. Finally, the influences of different environmental factors, datasets, and Elevation features on the models were discussed. The results indicate that under the influence of factors such as terrain variation, shadows, weeds, and planting density, the Mixed Dataset-HR-Net outperformed the ETC-CHM model. Specifically, the ETC-CHM model was simultaneously affected by shadows, weeds, and planting density, achieving an average segmentation accuracy of 85.56 % and an average rRMSE of 14.53 % for canopy area extraction across the four regions. In contrast, the Mixed Dataset-HR-Net, trained on a diverse dataset, demonstrated strong generalization ability and superior canopy extraction performance. It was solely affected by planting density, achieving an average segmentation accuracy of 94.55 % and an average rRMSE of 8.50 % for canopy area extraction across the four regions. The results provide new perspectives for the accurate extraction of fruit tree canopies in different topographies, which can facilitate precision pesticide spraying in orchards.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103101"},"PeriodicalIF":5.8,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620642","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":"The ensemble learning combined with the pruning model reveals the spectral response mechanism of tidal flat mapping in China","authors":"Jiapeng Dong ,&nbsp;Kai Jia ,&nbsp;Chongyang Wang ,&nbsp;Guorong Yu ,&nbsp;Dan Li ,&nbsp;Shuisen Chen ,&nbsp;Xingda Chen ,&nbsp;Ni Wen ,&nbsp;Zitong Zhao","doi":"10.1016/j.ecoinf.2025.103104","DOIUrl":"10.1016/j.ecoinf.2025.103104","url":null,"abstract":"<div><div>Tidal flats play a crucial role in biogeochemical cycles, and the mapping of tidal flats is essential for coastal ecological protection. Remote sensing technology offers a powerful tool for large-scale mapping of tidal flats distribution. However, understanding the spectral response mechanism of tidal flats remains a challenge. This research utilized Rule Combination and Simplification (RuleCOSI+) to automatically prune Random Forest (RF) trees, enabling a more interpretable explanation of the black-box model and uncovering the spectral response mechanisms of tidal flats using Sentinel 1/2 imagery. By simplifying the RF, the number of rules was reduced by 99.7 %, from 11,587 to just 32, with only a 1 % decrease in overall accuracy (from 96.4 % to 95.4 %). Similarly, the identification of muddy and sandy tidal flats has also been simplified, with the number of rules reduced from 2018 to 18, a decrease of 99.1 %, while the accuracy increased by 1.2 % (from 97.4 % to 98.6 %). The simplified rules significantly reduce the complexity of understanding the spectral response mechanisms of tidal flats while enabling flexible and rapid mapping across different regions and periods. The soil moisture content was the dominant factor in tidal flat identification, with vegetation and built-up land indices providing supplementary information to distinguish other land types. Notably, the shortwave infrared response to moisture proved critical for distinguishing between muddy and sandy tidal flats. These findings offer valuable insights into the remote sensing mechanisms underlying tidal flat identification and can serve as a reference for interpreting other land use types or classification systems.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103104"},"PeriodicalIF":5.8,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610806","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}