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The World Cup reshaped the urban green space pattern of Qatar Ecol. Inform. (IF 5.1) Pub Date : 2024-03-07 Liang Zhou, Xi Wang, David López-Carr, Zhenbo Wang, Bao Wang, Feng Gao, Wei Wei
The World Cup stands as the most momentous global sporting event, and significantly impacts the urban green space (UGS) of host cities. However, the impacts, processes, and pattern characteristic of the World Cup on UGS have not yet been fully understood. To fill this gap, we employ time-series satellite imagery and compute the normalized difference vegetation index (NDVI) across detailed maps of UGS
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Using mixed-method analytical historical ecology to map land use and land cover change for ecocultural restoration in the Klamath River Basin (Northern California) Ecol. Inform. (IF 5.1) Pub Date : 2024-03-05 M.V. Eitzel, Daniel Sarna-Wojcicki, Sean Hogan, Jennifer Sowerwine, Megan Mucioki, Kathy McCovey, Shawn Bourque, Leaf Hillman, Lisa Morehead-Hillman, Frank Lake, Vikki Preston, Chook-Chook Hillman, Andy Lyons, Bill Tripp
Ecocultural restoration involves the reciprocal repair of ecosystems and revitalization of cultural practices to enhance their mutual resilience to natural and anthropogenic disturbances and climate change stressors. Resilient ecocultural systems are adapted to retain structure and function in the face of disturbances that remain within historical ranges of severity. To assist in ecocultural restoration
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Accuracy of two LiDAR-based augmented reality apps in breast height diameter measurement Ecol. Inform. (IF 5.1) Pub Date : 2024-03-02 Stelian Alexandru Borz, Jenny Magali Morocho Toaza, Andrea Rosario Proto
Accurate measurement of the diameter at the breast height (DBH) is essential in forestry-related science and practice, but its measurement is currently done by labor-intensive tools such as calipers or devices designed to measure the girth. With the development in light detection and ranging (LiDAR) and augmented reality (AR) technologies, and their integration in low-cost mobile platforms, affordable
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MobileNet-GRU fusion for optimizing diagnosis of yellow vein mosaic virus Ecol. Inform. (IF 5.1) Pub Date : 2024-03-02 Tisha Chawla, Shubh Mittal, Hiteshwar Kumar Azad
Yellow vein mosaic virus (YVMV) is a destructive plant virus that commonly affects crops, particularly okra, in India. The virus is transmitted by whiteflies and poses significant challenges to agricultural productivity. Infection with YVMV leads to distinct yellow vein patterns on leaves, stunted growth, reduced yield, and ultimately economic losses for farmers. Timely and accurate detection of YVMV
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A shiny R app for spatial analysis of species distribution models Ecol. Inform. (IF 5.1) Pub Date : 2024-03-01 Mario Figueira, David Conesa, Antonio López-Quílez
In ecology, Species Distribution Models (SDMs) are a statistical tool whose use has expanded considerably over the last two decades. As their use has grown, so has the complexity of the data analysed and the structures of the models used.
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Weed database development: An updated survey of public weed datasets and cross-season weed detection adaptation Ecol. Inform. (IF 5.1) Pub Date : 2024-02-29 Boyang Deng, Yuzhen Lu, Jiajun Xu
Weeds are a major threat to crop production. Automated innovations for reducing herbicides and labor needed for weeding have become a high priority for sustainable weed management. The current state-of-the-art weeding systems still cannot reliably recognize weeds in changing field conditions for precision weed control. Enhancing weed recognition accentuates the critical need to develop dedicated, labeled
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Classification of inland lake water quality levels based on Sentinel-2 images using convolutional neural networks and spatiotemporal variation and driving factors of algal bloom Ecol. Inform. (IF 5.1) Pub Date : 2024-02-29 Haobin Meng, Jing Zhang, Zhen Zheng, Yongyu Song, Yuequn Lai
Water quality monitoring in inland lakes is crucial to ensuring the health and stability of aquatic ecosystems. For regional water environment agencies and researchers, remote sensing offers a cost-effective alternative to traditional in-situ water sampling methods. In this study, we designed a convolutional neural network (CNN) based on AlexNet to represent the relationship between Sentinel-2 images
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A lightweight and enhanced model for detecting the Neotropical brown stink bug, Euschistus heros (Hemiptera: Pentatomidae) based on YOLOv8 for soybean fields Ecol. Inform. (IF 5.1) Pub Date : 2024-02-27 Bruno Pinheiro de Melo Lima, Lurdineide de Araújo Barbosa Borges, Edson Hirose, Díbio Leandro Borges
Insect pest detection and monitoring are vital in an agricultural crop to help prevent losses and be more precise and sustainable regarding the consequent actions to be taken. Deep learning (DL) approaches have attracted attention, showing triumphant performance in many image-based applications. In the adult stage, this research considers detecting a vital insect pest in soybean crops, the Neotropical
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Detecting and counting sorghum aphid alates using smart computer vision models Ecol. Inform. (IF 5.1) Pub Date : 2024-02-24 Ivan Grijalva, H. Braden Adams, Nicholas Clark, Brian McCornack
Sorghum aphid [ (Theobald)] is considered an economic pest causing significant yield losses in susceptible sorghum in the southern U.S. Infestations start with the migration of alates (i.e., winged adults) to sorghum and establishing aphid colonies. In favorable conditions, sorghum aphid can exponentially reproduce via asexual reproduction. A suggested strategy is to monitor alates to determine initial
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Association mining of coastline change and land use patterns to enhance conservation Ecol. Inform. (IF 5.1) Pub Date : 2024-02-24 Jinfeng Yan, Congcong Miao, Fenzhen Su, Yongzhu Zhao
Coastal zones, as interfaces between marine and terrestrial ecosystems, possess great ecological and economic value but are environmentally fragile. This research provides a new perspective for studying development and change patterns in coastal zones, offering insights for land planning and contributing to the ecological preservation and sustainable development of coastal areas. We utilized Landsat
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Foundation models in shaping the future of ecology Ecol. Inform. (IF 5.1) Pub Date : 2024-02-24 Albert Morera
In the field of ecology, we are facing urgent challenges related to biodiversity loss, global change and ecosystem sustainability. In this context, the application of Foundation Models emerges as a powerful tool. These models have the potential to reshape our understanding of natural systems by the incorporation of large volumes of data from different sources the generation of results with a more holistic
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Expert knowledge-based modelling approach for mapping beekeeping suitability area Ecol. Inform. (IF 5.1) Pub Date : 2024-02-23 Guy A. Fotso Kamga, Yacine Bouroubi, Mickaël Germain, A. Mengue Mbom, Madeleine Chagnon
It is becoming increasingly accepted that beekeeping is declining due to the damaging effect of global changes such as climate and land-use change that directly and indirectly impact . Despite numerous investigations, a comprehensive study that incorporates both global and local knowledge has yet to be conducted. For a long time, researchers have suggested that expert knowledge should be taken into
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WildARe-YOLO: A lightweight and efficient wild animal recognition model Ecol. Inform. (IF 5.1) Pub Date : 2024-02-23 Sibusiso R. Bakana, Yongfei Zhang, Bhekisipho Twala
For the protection of endangered species and successful wildlife population monitoring, wild animal recognition is essential. While deep learning models like YOLOv5 have shown promise in real-time object recognition, their practical applicability may be constrained by their high processing requirements. In this paper, we suggest a faster and lighter version of YOLOv5s for wild animal recognition. To
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Application of machine learning in automatic image identification of insects - a review Ecol. Inform. (IF 5.1) Pub Date : 2024-02-23 Yuanyi Gao, Xiaobao Xue, Guoqing Qin, Kai Li, Jiahao Liu, Yulong Zhang, Xinjiang Li
Fast and reliable identification of insect species is crucial for pest management, animal quarantine, and effective utilization of insect resources. Traditional morphological classification is time-consuming and laborious, while automatic image identification techniques based on machine learning (ML) can greatly improve efficiency. ML is a promising approach for the automatic image identification,
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Exploring spatiotemporal dynamics of NDVI and climate-driven responses in ecosystems: Insights for sustainable management and climate resilience Ecol. Inform. (IF 5.1) Pub Date : 2024-02-22 Kaleem Mehmood, Shoaib Ahmad Anees, Akhtar Rehman, Shao'’an Pan, Aqil Tariq, Muhammad Zubair, Qijing Liu, Fazli Rabbi, Khalid Ali Khan, Mi Luo
Understanding the intricate relationship between climate variables and the Normalized Difference Vegetation Index (NDVI) is essential for effective ecosystem management. This study focuses on the spatiotemporal dynamics of NDVI and its interaction with climate variables in the ecologically diverse Khyber Pakhtunkhwa (KPK) Province, Pakistan, from 2000 to 2022. The research methodology involves analyzing
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Standardizing the fish freshness class during ice storage using clustering approach Ecol. Inform. (IF 5.1) Pub Date : 2024-02-22 Eko Prasetyo, Nanik Suciati, Chastine Fatichah, Eric Pardede
The freshness of fish before consumption affects the taste of the food and human health, so many parties should consider it carefully. Fish is usually stored on ice to maintain its freshness. However, there has yet to be a standard for the fish freshness class during ice storage. On the other hand, each fish species has a different vigor in retaining freshness. In this research, we propose (1) a dataset
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Convolutional neural networks for hydrothermal vents substratum classification: An introspective study Ecol. Inform. (IF 5.1) Pub Date : 2024-02-22 Pedro Juan Soto Vega, Panagiotis Papadakis, Marjolaine Matabos, Loïc Van Audenhaege, Annah Ramiere, Jozée Sarrazin, Gilson Alexandre Ostwald Pedro da Costa
The increasing availability of seabed images has created new opportunities and challenges for monitoring and better understanding the spatial distribution of fauna and substrata. To date, however, deep-sea substratum classification relies mostly on visual interpretation, which is costly, time-consuming, and prone to human bias or error. Motivated by the success of convolutional neural networks in learning
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Hierarchical-taxonomy-aware and attentional convolutional neural networks for acoustic identification of bird species: A phylogenetic perspective Ecol. Inform. (IF 5.1) Pub Date : 2024-02-22 Qingyu Wang, Yanzhi Song, Yeqian Du, Zhouwang Yang, Peng Cui, Binnan Luo
The study of bird populations is crucial for biodiversity research and conservation. Deep artificial neural networks have revolutionized bird acoustic recognition; however, most methods overlook inherent relationships among bird populations, resulting in the loss of biological information. To address this limitation, we propose the Phylogenetic Perspective Neural Network (PPNN), which incorporates
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Partitioning climate uncertainty in ecological projections: Pacific oysters in a hotter Europe Ecol. Inform. (IF 5.1) Pub Date : 2024-02-21 Robert J. Wilson, Susan Kay, Stefano Ciavatta
Projections of the range expansions of marine species are critical if we are to anticipate and mitigate the impacts of climate change on marine ecosystems. However, most projections do not assess the level of uncertainty of future changes, which brings their usefulness for scenario planning and ecosystem management into question. For the overall climate system, these uncertainties take three forms:
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Automatic bioacoustics noise reduction method based on a deep feature loss network Ecol. Inform. (IF 5.1) Pub Date : 2024-02-21 Chengyun Zhang, Kaiying He, Xinghui Gao, Yingying Guo
Acoustic sensors that collect acoustic data over extended periods and broad ranges are widely used in bioacoustics monitoring. However, in open environments, acoustic data collected using acoustic sensors can be subject to interference from various real-world noises, thereby influencing the subsequent analysis and processing of bioacoustic data. Existing bioacoustic noise reduction methods are limited
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A spatio-temporal modelling approach to understand the effect of urban fruit fly outbreaks on peri-urban orchards Ecol. Inform. (IF 5.1) Pub Date : 2024-02-21 Andrew Broadley, Rieks D. van Klinken, Dean R. Paini, Matt Hill, Elliot Howse
Urban areas are well-known sources of fruit fly introduction and unmanaged populations pose a high risk of dispersal to surrounding agricultural areas. Although management of urban fruit fly populations is recognised as being important for area-wide control programmes, its relative importance is rarely quantified. Identifying possible sources of fruit fly populations is important to the success of
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Analysis of forest fire patterns and their relationship with climate variables in Alberta's natural subregions Ecol. Inform. (IF 5.1) Pub Date : 2024-02-19 Hatef Dastour, M. Razu Ahmed, Quazi K. Hassan
Forest fires are significant ecological and environmental phenomena that can be influenced by various climatic factors. This study used fire point records from the Canadian National Fire Database (CNFDB) and interpolated climate data, which include the minimum and maximum air temperature, the average relative humidity, and the precipitation for each subregion of Alberta, Canada, to analyze the patterns
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Vulnerability of lowland and upland orchids in their spatially response to climate change and land cover change Ecol. Inform. (IF 5.1) Pub Date : 2024-02-18 Angga Yudaputra, Esti Munawaroh, Didi Usmadi, Danang Wahyu Purnomo, Inggit Puji Astuti, Dwi Murti Puspitaningtyas, Tri Handayani, R. Vitri Garvita, Popi Aprilianti, Hary Wawangningrum, Elga Renjana, Elizabeth Handini, Melisnawati H. Angio, Elok Rifqi Firdiana, Joko Ridho Witono, Lina Susanti Juswara, Izu Andry Fijridiyanto, Siti Roosita Ariati, Sudarmono Sudarmono, Irvan Fadli Wanda, Aninda Retno Utami
Climate change and land cover change often interactively affect plant species distributions. This study addresses the vulnerability of lowland and upland orchids to climate change and land cover change. Endemic orchids of New Guinea were grouped into four classes (lowland epiphyte, lowland terrestrial, upland epiphyte, upland terrestrial) based on their life form and elevation range. Forty occurrence
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Ecological informatics: Metamorphosing ecology to a translational discipline Ecol. Inform. (IF 5.1) Pub Date : 2024-02-18 Jaishanker R, Athira Kakkara
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The bioacoustic soundscape of a pandemic: Continuous annual monitoring using a deep learning system in Agmon Hula Lake Park Ecol. Inform. (IF 5.1) Pub Date : 2024-02-17 Yizhar Lavner, Ronen Melamed, Moshe Bashan, Yoni Vortman
Continuous bioacoustic monitoring is an emerging opportunity as well as a challenge, allowing detection of cryptic species' activity while producing high computational demands. In this paper, we present an automated framework that allows the monitoring of a large number of bird species by their vocalizations over extended periods. The framework relies on the BirdNET-Analyzer deep learning model. We
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A systematic study on transfer learning: Automatically identifying empty camera trap images using deep convolutional neural networks Ecol. Inform. (IF 5.1) Pub Date : 2024-02-17 Deng-Qi Yang, De-Yao Meng, Hao-Xuan Li, Meng-Tao Li, Han-Lin Jiang, Kun Tan, Zhi-Pang Huang, Na Li, Rong-Hai Wu, Xiao-Wei Li, Ben-Hui Chen, Mei Zhang, Guo-Peng Ren, Wen Xiao
Transfer learning is extensively utilized for automatically recognizing and filtering out empty camera trap images that lack animal presence. Current research that uses transfer learning for identifying empty images typically solely updates the fully connected layer of models, and they usually select a pre-trained source model only based on its relevance to the target task. However, they do not consider
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Agroecosystem transformation and its driving factors in karst mountainous areas of Southwest China: The case of Puding County, Guizhou Province Ecol. Inform. (IF 5.1) Pub Date : 2024-02-16 Limin Yu, Yangbing Li, Mei Chen, Linyu Yang, Fang Tang, Yiyi Zhang
With the rapid socioeconomic development and urbanization, global agroecosystems (AESs) have undergone varying degrees of transformation, and conducting an in-depth study on how AESs are transforming in karst mountainous areas (KMAs) is essential. To further reveal the transformation process of AESs in KMAs, we proposed a theoretical framework for the transformation of AESs in KMAs. Following a “theory
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Defining the target population to make marine image-based biological data FAIR Ecol. Inform. (IF 5.1) Pub Date : 2024-02-16 Jennifer M. Durden, Timm Schoening, Emma J. Curtis, Anna Downie, Andrew R. Gates, Daniel O.B. Jones, Alexandra Kokkinaki, Erik Simon-Lledó, Danielle Wright, Brian J. Bett
Marine imaging studies have unique constraints on the data collected requiring a tool for defining the biological scope to facilitate data discovery, quality evaluation, sharing and reuse. Defining the ‘target population’ is way of scoping biological sampling or observations by setting the pool of organisms to be observed or sampled. It is used in survey design and planning, to determine statistical
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Characterizing the dynamic linkages between environmental changes and wheat Fusarium head blight epidemics Ecol. Inform. (IF 5.1) Pub Date : 2024-02-15 Yan Zhu, Jinfeng Xi, Yuanyuan Yao, Hongwei Xu, Caiguo Tang, Lifang Wu
Fusarium head blight (FHB) is a prevalent wheat disease, mainly influenced by environmental factors, such as irradiance, temperature, precipitation, and levels. Elucidating the relationship between FHB outbreaks and environmental changes is crucial for predictive models and control measures. This study employs historical data to map the dynamic associations between environmental changes and FHB epidemics
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Optimizing the ecological source area identification method and building ecological corridor using a genetic algorithm: A case study in Weihe River Basin, NW China Ecol. Inform. (IF 5.1) Pub Date : 2024-02-15 Xueting Wu, Jinghu Pan, Xiuwei Zhu
The extraction of ecological corridors is influenced by the accuracy of ecological source area identification, which is a crucial component of ecological security construction. The ecological source areas of the Weihe River Basin (WRB) were comprehensively identified by analyzing the supply, demand, and ecological sensitivity of ecosystem services. Different initial populations were set using a genetic
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Research on regional terrestrial carbon storage based on the pattern-process-function Ecol. Inform. (IF 5.1) Pub Date : 2024-02-14 Yuepeng Zhai, Guoqing Zhai, Yanmei Chen, Jingze Liu
The evolution of ecological pattern caused by human activities has altered the terrestrial carbon cycle process, and it is important to explore systematic approaches to study the relationship between ecological pattern and carbon sequestration function within regions. From the new perspective of the pattern-process-function in landscape ecology, this study couples NPP-InVEST, GeoDa, MOP-PLUS, and GeoDetector
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Assessing water quality of an ecologically critical urban canal incorporating machine learning approaches Ecol. Inform. (IF 5.1) Pub Date : 2024-02-13 Abdul Majed Sajib, Mir Talas Mahammad Diganta, Md. Moniruzzaman, Azizur Rahman, Tomasz Dabrowski, Md Galal Uddin, Agnieszka I. Olbert
This study assessed water quality (WQ) in Tongi Canal, an ecologically critical and economically important urban canal in Bangladesh. The researchers employed the Root Mean Square Water Quality Index (RMS-WQI) model, utilizing seven WQ indicators, including temperature, dissolve oxygen, electrical conductivity, lead, cadmium, and iron to calculate the water quality index (WQI) score. The results showed
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Modeling human transmissibility via nighttime light remote sensing for Hyphantria cunea propagation pattern prediction Ecol. Inform. (IF 5.1) Pub Date : 2024-02-13 Jiangxia Ye, Wenbin Quan, Ruliang Zhou, Ting Du, Lei Shi, Xiaoyan Wei
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STARdbi: A pipeline and database for insect monitoring based on automated image analysis Ecol. Inform. (IF 5.1) Pub Date : 2024-02-12 Tamar Keasar, Michael Yair, Daphna Gottlieb, Liraz Cabra-Leykin, Chen Keasar
Insects are highly abundant and diverse, and play major roles in ecosystem functions. Monitoring of insect populations is key to their sustainable management. However, the labor and expertise needed to identify insects, and the challenges of archiving the wealth of data collected in monitoring programs, often limit these efforts. We describe a pipeline to reduce the barriers associated with curating
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Mapping the potential distribution of Asian elephants: Implications for conservation and human–elephant conflict mitigation in South and Southeast Asia Ecol. Inform. (IF 5.1) Pub Date : 2024-02-12 Haixia Xu, Luguang Jiang, Ye Liu
Asian elephants play a pivotal role in their ecosystem. Understanding the potential distribution area of this species is vital for effective conservation efforts and mitigation of human-elephant conflicts. In this study, we used the maximum entropy to simulate the potential distribution area of Asian elephants across South and Southeast Asia, leveraging Maximum Entropy (MaxEnt) and presence data sourced
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Geostatistical modeling approach for studying total soil nitrogen and phosphorus under various land uses of North-Western Himalayas Ecol. Inform. (IF 5.1) Pub Date : 2024-02-12 Owais Bashir, Shabir Ahmad Bangroo, Shahid Shuja Shafai, Nicola Senesi, Shuraik Kader, Saud Alamri
The distribution of total soil nitrogen (TSN) and total soil phosphorus (TSP) plays a pivotal role in shaping soil quality, fertility, agricultural practices, and environmental balance, especially in ecologically sensitive regions like the North-Western Himalayas (NWH). The primary objectives of this study were to contribute to clarify the impact and the rationale of various land uses on the spatial
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A model for forest type identification and forest regeneration monitoring based on deep learning and hyperspectral imagery Ecol. Inform. (IF 5.1) Pub Date : 2024-02-10 Feng-Cheng Lin, Yi-Shiang Shiu, Pei-Jung Wang, Uen-Hao Wang, Jhe-Syuan Lai, Yung-Chung Chuang
Traditional ground-based forest survey methods involve high labor costs, and their inefficiency makes comprehensive forest resource surveys challenging. With the development of new sensors and vehicles in recent years, more diverse and novel remote sensing detection and survey techniques have emerged. This study aims to use hyperspectral imagery to classify forest types containing representative tree
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Determining the community composition of herbaceous species from images using convolutional neural networks Ecol. Inform. (IF 5.1) Pub Date : 2024-02-10 Matthias Körschens, Solveig Franziska Bucher, Paul Bodesheim, Josephine Ulrich, Joachim Denzler, Christine Römermann
Global change has a detrimental impact on the environment and changes biodiversity patterns, which can be observed, among others, via analyzing changes in the composition of plant communities. Typically, vegetation relevées are done manually, which is time-consuming, laborious, and subjective. Applying an automatic system for such an analysis that can also identify co-occurring species would be beneficial
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Surveillance of coastal biodiversity through social network monitoring Ecol. Inform. (IF 5.1) Pub Date : 2024-02-08 P. Otero, E. Velasco, J. Valeiras
Knowledge of marine biodiversity is vital for developing appropriate conservation policies. In the current Information Age, data shared by citizens in social networks are a cost-effective alternative to complement on-going marine biodiversity monitoring programs, as well as to understand human interactions with the natural environment from a current perspective. This information can be obtained in
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Geo-spatial analysis of urbanization and environmental changes with deep neural networks: Insights from a three-decade study in Kerch peninsula Ecol. Inform. (IF 5.1) Pub Date : 2024-02-07 Denis Krivoguz
This study presents a comprehensive analysis of land use and land cover (LULC) changes on the Kerch Peninsula over the last thirty years, utilizing advanced satellite data and spatial modeling techniques. The research used Landsat 5, 7 and 8 satellite images to capture the intricate dynamics of LULC changes from 1990 to 2020. A quantitative approach was adopted, involving the use of convolutional neural
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Open-source machine learning BANTER acoustic classification of beaked whale echolocation pulses Ecol. Inform. (IF 5.1) Pub Date : 2024-02-07 Shannon Rankin, Taiki Sakai, Frederick I. Archer, Jay Barlow, Danielle Cholewiak, Annamaria I. DeAngelis, Jennifer L.K. McCullough, Erin M. Oleson, Anne E. Simonis, Melissa S. Soldevilla, Jennifer S. Trickey
Passive acoustic monitoring is increasingly used for assessing populations of marine mammals; however, analysis of large datasets is limited by our ability to easily classify sounds detected. Classification of beaked whale acoustic events, in particular, requires evaluation of multiple lines of evidence by expert analysts. Here we present a highly automated approach to acoustic detection and classification
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Development of a spatially explicit model of blue carbon storages in tropical mudflat environment through integrated radar-optical approach and ground-based measurements Ecol. Inform. (IF 5.1) Pub Date : 2024-02-04 Debajit Datta, Mansa Dey, Proshanta Kumar Ghosh, Argha Pratim Pal
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Propagation characteristics of meteorological drought to hydrological drought considering nonlinear correlations - A case study of the Hanjiang River Basin, China Ecol. Inform. (IF 5.1) Pub Date : 2024-02-04 Hengli Wang, Zongzhi Wang, Ying Bai, Wensheng Wang
Meteorological drought serves as the precursor to hydrological drought. Clarifying the propagation characteristics from meteorological drought to hydrological drought is crucial for predicting and mitigating this severe natural disaster. In this study, we proposed an assessment framework that integrates both linear and nonlinear methods to characterize the transition from meteorological to hydrological
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Bird species recognition using transfer learning with a hybrid hyperparameter optimization scheme (HHOS) Ecol. Inform. (IF 5.1) Pub Date : 2024-02-04 Samparthi V.S. Kumar, Hari Kishan Kondaveeti
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An ensembled method for predicting dissolved oxygen level in aquaculture environment Ecol. Inform. (IF 5.1) Pub Date : 2024-01-29 Dachun Feng, Qianyu Han, Longqin Xu, Ferdous Sohel, Shahbaz Gul Hassan, Shuangyin Liu
Dissolved oxygen (DO) level is an important indicator aquaculture quality. This study proposes an ensembled method, WTD-GWO-SVR, combining wavelet threshold denoising (WTD), grey wolf optimization (GWO), and support vector regression (SVR) for accurately predicting DO levels. Addressing challenges such as high noise, poor data quality, and non-linearity and non-stationary properties of time series
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Effects of precipitation changes on fractional vegetation cover in the Jinghe River basin from 1998 to 2019 Ecol. Inform. (IF 5.1) Pub Date : 2024-01-30 Yu Liu, Tingting Huang, Zhiyuan Qiu, Zilong Guan, Xiaoyi Ma
Studying the spatiotemporal evolutionary characteristics of vegetation and the effect of precipitation changes is necessary for understanding the regional ecological environment. We used trend analysis, partial correlation analysis, significance tests, and residual trend analysis to analyze the spatiotemporal evolution characteristics and driving factors of fractional vegetation cover (FVC) in the
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Bibliometric network analysis of scientific research on early warning signals for cyanobacterial blooms in lakes and rivers Ecol. Inform. (IF 5.1) Pub Date : 2024-01-28 Hyo Gyeom Kim, Kyung Hwa Cho, Friedrich Recknagel
Harmful cyanobacterial blooms (HCBs) present a major risk to inland waters; therefore, various monitoring and management frameworks have been implemented to protect water quality, aquatic organisms, and humans from their negative impacts. Enabling proactive rather than reactive management, early warning systems within the lead time of HCBs at timescales ranging from hours to days is necessary to provide
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Balancing urban expansion with ecological integrity: An ESP framework for rapidly urbanizing small and medium-sized cities, with insights from Suizhou, China Ecol. Inform. (IF 5.1) Pub Date : 2024-02-02 Chen Jiayu, Xue Jiefu, Gu Kang, Wang Yiwu
In China, the Huang-Huai Plain and the middle reaches of the Yangtze River, which are populated with numerous small and medium-sized cities, have emerged as critical regions for future urbanization. As a result, these regions face significant ecological risks due to this rapid urbanization. This study employs the concept of the ecological security pattern (ESP), which synergizes landscape patterns
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A stacking ANN ensemble model of ML models for stream water quality prediction of Godavari River Basin, India Ecol. Inform. (IF 5.1) Pub Date : 2024-01-28 Nagalapalli Satish, Jagadeesh Anmala, K. Rajitha, Murari R.R. Varma
The importance of water quality models has increased as their inputs are critical to the development of risk assessment framework for environmental management and monitoring of rivers. However, with the advent of a plethora of recent advances in ML algorithms better predictions are possible. This study proposes a causal and effect model by considering climatological such as temperature and precipitation
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Multiscale geographically weighted regression-based analysis of vegetation driving factors and mining-induced quantification in the Fengfeng District, China Ecol. Inform. (IF 5.1) Pub Date : 2024-02-01 Wanqiu Zhang, Linda Dai, Yueguan Yan, Xiaoqing Han, Yongjia Teng, Ming Li, Yuanhao Zhu, Yanjun Zhang
Mining cities are special ecosystems that distinguish from general mining areas. They combine the influence of mining activities, natural factors, and other anthropogenic factors. In this study, we proposed a method called MMD to extract the mining disturbance on vegetation, and explored the driving mechanism between NDVI and natural, socio-economic and accessibility factors. The main results were
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Air pollution in industrial clusters: A comprehensive analysis and prediction using multi-source data Ecol. Inform. (IF 5.1) Pub Date : 2024-01-29 A. Nakhjiri, A.A. Kakroodi
Air pollution is a pressing concern, especially in developing countries, and its impact on the climate, physical health, and overall quality of life cannot be overstated. This study focuses on the Tehran province, Iran, aiming to clarify the role of different industrial activities in emitting air pollution. To achieve this objective, zonal areas spanning 3.2 km were designated for each industrial establishment
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Population dynamic life history models of the birds and mammals of the world Ecol. Inform. (IF 5.1) Pub Date : 2024-01-29 Lars Witting
With life history traits determining the natural selection fitnesses of individuals and growth of populations, estimates of their variation are essential to advance evolutionary understanding and ecological management during times of global change. As life history data are incomplete or missing for most species, I combine data and natural selection theory to construct a meta natural selection model
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Impacts of socio-environmental policy mix on mitigating agricultural abandonment: An empirical agent-based modeling Ecol. Inform. (IF 5.1) Pub Date : 2024-01-26 Ian Estacio, Corinthias P.M. Sianipar, Kenichiro Onitsuka, Satoshi Hoshino
The complexity of socio-ecological systems in agricultural landscapes has been a subject of previous agent-based modeling studies in order to understand the impacts of agricultural policies. However, there is still a lack of models that incorporate the processes of farm succession within farm households and how these lead to agricultural abandonment. This study aims to simulate the effects of socio-environmental
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GEE_xtract: High-quality remote sensing data preparation and extraction for multiple spatio-temporal ecological scaling Ecol. Inform. (IF 5.1) Pub Date : 2024-01-28 Francesco Valerio, Sérgio Godinho, Ana T. Marques, Tiago Crispim-Mendes, Ricardo Pita, João Paulo Silva
Environmental sensing via Earth Observation Satellites (EOS) is critically important for understanding Earth’ biosphere. The last decade witnessed a “Klondike Gold Rush” era for ecological research given a growing multidisciplinary interest in EOS. Presently, the combination of repositories of remotely sensed big data, with cloud infrastructures granting exceptional analytical power, may now mark the
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Toward an artificial intelligence-assisted counting of sharks on baited video Ecol. Inform. (IF 5.1) Pub Date : 2024-01-28 Sébastien Villon, Corina Iovan, Morgan Mangeas, Laurent Vigliola
Given the global biodiversity crisis, there is an urgent need for new tools to monitor populations of endangered marine megafauna, like sharks. To this end, Baited Remote Underwater Video Stations (BRUVS) stand as the most effective tools for estimating shark abundance, measured using the MaxN metric. However, a bottleneck exists in manually computing MaxN from extensive BRUVS video data. Although
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Quantifying the direct and indirect effects of terrain, climate and human activity on the spatial pattern of kNDVI-based vegetation growth: A case study from the Minjiang River Basin, Southeast China Ecol. Inform. (IF 5.1) Pub Date : 2024-01-22 Zipeng Gu, Xingwei Chen, Weifang Ruan, Meiling Zheng, Kaili Gen, Xiaochen Li, Haijun Deng, Ying Chen, Meibing Liu
In the context of global change, it is vital to comprehensively understand the spatial pattern and driving mechanism of vegetation growth to maintain the stability of watershed ecosystems. Previous research has focused mainly on identifying the main drivers of vegetation growth, while the direct and indirect effects of climate, terrain, and human activity on vegetation growth have rarely been explored
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Mapping of nearshore bathymetry using Gaofen-6 images for the Yellow River Delta-Laizhou Bay, China Ecol. Inform. (IF 5.1) Pub Date : 2024-01-23 Kun Tan, Minxuan Sun, Danfeng Sun, Xiaojie Liu, Xiaohuang Liu, Bin Wang, Wenjun Dou, Haiyan Zhang, Fei Lun
Bathymetric mapping is integral to maintaining marine ecosystems, managing coastal zones, and safeguarding the environment. However, achieving accurate large-scale bathymetric maps remains a challenge in China, particularly in nearshore turbid waters. To address this gap, we leveraged seasonal Gaofen-6 (GF-6) data to conduct bathymetry mapping in the Yellow River Delta-Laizhou Bay area. In our study
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The Atlantic forest is a potentially climatic suitable habitat for four Neotropical Myrtaceae species through time Ecol. Inform. (IF 5.1) Pub Date : 2024-01-21 Ossman Barrientos-Díaz, Mabel R. Báez-Lizarazo, Fernanda Enderle, Ana Lucia Anversa Segatto, Marcelo Reginato, Andreia Carina Turchetto-Zolet
Myrtaceae is one of the most species-rich botanical families and is a critical floristic component in regions with high diversity, such as the Atlantic Forest and Cerrado. In the Neotropical region, Myrteae is the main tribe of Myrtaceae and includes the most diverse genera Eugenia, Myrcia, Psidium, Myrceugenia, and Campomanesia. Here, we investigated the climatic suitability selected Myrteae species
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Advancing equitable stormwater management: A decision support tool integrating best practices for nutrient removal and environmental justice Ecol. Inform. (IF 5.1) Pub Date : 2024-01-23 Sara Kamanmalek, Nasrin Alamdari
Stormwater runoff is a significant contributor to nutrient pollution, leading to water quality degradation and ecological imbalances. The management of stormwater runoff and nutrient pollution faces significant challenges due to inadequate assessment tools for evaluating nutrient loads across multiple watersheds at the state level and the absence of an open-source tool that can comprehensively assess
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Google Earth Engine-based mapping of land use and land cover for weather forecast models using Landsat 8 imagery Ecol. Inform. (IF 5.1) Pub Date : 2024-01-26 Mohammad Ganjirad, Hossein Bagheri
Land Use and Land Cover (LULC) maps are vital prerequisites for weather prediction models. This study proposes a framework to generate LULC maps based on the U.S. Geological Survey (USGS) 24-category scheme using Google Earth Engine. To realize a precise LULC map, a fusion of pixel-based and object-based classification strategies was implemented using various machine learning techniques across different