Remote Sensing in Ecology and Conservation最新文献

{"title":"A hierarchical, multi-sensor framework for peatland sub-class and vegetation mapping throughout the Canadian boreal forest","authors":"Nicholas Pontone, Koreen Millard, Dan K. Thompson, Luc Guindon, André Beaudoin","doi":"10.1002/rse2.384","DOIUrl":"https://doi.org/10.1002/rse2.384","url":null,"abstract":"Peatlands in the Canadian boreal forest are being negatively impacted by anthropogenic climate change, the effects of which are expected to worsen. Peatland types and sub-classes vary in their ecohydrological characteristics and are expected to have different responses to climate change. Large-scale modelling frameworks such as the Canadian Model for Peatlands, the Canadian Fire Behaviour Prediction System and the Canadian Land Data Assimilation System require peatland maps including information on sub-types and vegetation as critical inputs. Additionally, peatland class and vegetation height are critical variables for wildlife habitat management and are related to the carbon cycle and wildfire fuel loading. This research aimed to create a map of peatland sub-classes (bog, poor fen, rich fen permafrost peat complex) for the Canadian boreal forest and create an inventory of peatland vegetation height characteristics using ICESat-2. A three-stage hierarchical classification framework was developed to map peatland sub-classes within the Canadian boreal forest circa 2020. Training and validation data consisted of peatland locations derived from various sources (field data, aerial photo interpretation, measurements documented in literature). A combination of multispectral data, L-band SAR backscatter and C-Band interferometric SAR coherence, forest structure and ancillary variables was used as model predictors. Ancillary data were used to mask agricultural areas and urban regions and account for regions that may exhibit permafrost. In the first stage of the classification, wetlands, uplands and water were classified with 86.5% accuracy. In the second stage, within the wetland areas only, peatland and mineral wetlands were differentiated with 93.3% accuracy. In the third stage, constrained to only the peatland areas, bogs, rich fens, poor fens and permafrost peat complexes were classified with 71.5% accuracy. Then, ICESat-2 ATL08 spaceborne lidar data were used to describe regional variations in peatland vegetation height characteristics and regional and class-wise variations based on a boreal forest wide sample. This research introduced a comprehensive large-scale peatland sub-class mapping framework for the Canadian boreal forest, presenting the first moderate resolution map of its kind.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"8 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139957152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aggregated time-series features boost species-specific differentiation of true and false positives in passive acoustic monitoring of bird assemblages","authors":"David Singer, Jonas Hagge, Johannes Kamp, Hermann Hondong, Andreas Schuldt","doi":"10.1002/rse2.385","DOIUrl":"https://doi.org/10.1002/rse2.385","url":null,"abstract":"Passive acoustic monitoring (PAM) has gained increasing popularity to study behaviour, habitat preferences, distribution and community assembly of birds and other animals. Automated species classification algorithms like ‘BirdNET’ are capable of detecting and classifying avian vocalizations within extensive audio data, covering entire species assemblages. PAM reveals substantial potential for biodiversity monitoring that informs evidence-based conservation. Nevertheless, fully realizing this potential remains challenging, especially due to the issue of false-positive species detections. Here, we introduce an optimized thresholding framework, which incorporates contextual information extracted from the time-series of automated species detections (i.e. covariates on quality and quantity of species' detections measured at varying time intervals) to improve the differentiation of true and false positives. We verified a sample of BirdNET detections per species and modelled species-specific thresholds using conditional inference trees. These thresholds were designed to minimize false-positive detections while maximizing the preservation of true positives in the dataset. We tested this framework for a large dataset of BirdNET detections (5760 h of audio data, 60 sites) recorded over an entire breeding season. Our results revealed considerable interspecific variability of precision (percentage of true positives) within raw BirdNET data. Our optimized thresholding approach achieved high precision (≥0.9) for 70% of the 61 detected species, while species-specific thresholds solely relying on the BirdNET confidence scores achieved high precision for only 31% of the species. Conservative universal thresholds (not species-specific) reached high precision for 48% of the species. Our thresholding approach outperformed previous thresholding approaches and enhanced interspecific comparability for bird community analyses. By incorporating contextual information from the time-series of species detections, the differentiation of true and false positives was substantially improved. Our approach may enhance a straightforward application of PAM in biodiversity research, landscape planning and evidence-based conservation.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"3 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139957154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tree species diversity mapping from spaceborne optical images: The effects of spectral and spatial resolution","authors":"Xiang Liu, Julian Frey, Catalina Munteanu, Martin Denter, Barbara Koch","doi":"10.1002/rse2.383","DOIUrl":"https://doi.org/10.1002/rse2.383","url":null,"abstract":"Increasingly available spaceborne sensors provide unprecedented opportunities for large-scale, timely and continuous tree species diversity (TSD) monitoring. However, given differences in spectral and spatial resolutions, the choice of sensor is not always straightforward. In this work, we investigated the effects of spatial and spectral resolutions for four spaceborne sensors (RapidEye, Landsat-8, Sentinel-2 and PlanetScope) on TSD mapping in an area of approximately 4000 km² within the Black Forest, Germany. We employed a random forest (RF) regression model to predict Shannon–Wiener diversity based on seven types of spectral heterogeneity metrics (texture, coefficient of variation, Rao's Q, convex hull volume, spectral angle mapper, convex hull area and spectral species diversity) and a full survey dataset from 135 one-ha sample plots. We compared the RF model's performance across sensors and spatial resolutions. Our results demonstrated that the Sentinel-2-based TSD model achieved the highest accuracy (mean <i>R</i>²: 0.477, mean root-mean-square error (RMSE): 0.274). The RapidEye-based TSD model produced lower accuracy (mean <i>R</i>²: 0.346, mean RMSE: 0.303), but it was better than the PlanetScope- and Landsat-based TSD models. The 10 m (for Sentinel-2 and RapidEye) and 15 m (for PlanetScope) were the best spatial resolutions for predicting TSD. The NIR band was the most favourable spectral band for predicting TSD. Texture metrics and Rao's Q outperformed the other spectral heterogeneity metrics. Our results highlighted that spaceborne optical imagery (especially Sentinel-2) can be successfully used for large-scale TSD mapping but that the choice of sensors can significantly affect the resulting mapping accuracy in temperate montane forests.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"29 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139911333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using photographs and deep neural networks to understand flowering phenology and diversity in mountain meadows","authors":"Aji John, Elli J. Theobald, Nicoleta Cristea, Amanda Tan, Janneke Hille Ris Lambers","doi":"10.1002/rse2.382","DOIUrl":"https://doi.org/10.1002/rse2.382","url":null,"abstract":"Mountain meadows are an essential part of the alpine–subalpine ecosystem; they provide ecosystem services like pollination and are home to diverse plant communities. Changes in climate affect meadow ecology on multiple levels, for example, by altering growing season dynamics. Tracking the effects of climate change on meadow diversity through the impacts on individual species and overall growing season dynamics is critical to conservation efforts. Here, we explore how to combine crowd-sourced camera images with machine learning to quantify flowering species richness across a range of elevations in alpine meadows located in Mt. Rainier National Park, Washington, USA. We employed three machine-learning techniques (Mask R-CNN, RetinaNet and YOLOv5) to detect wildflower species in images taken during two flowering seasons. We demonstrate that deep learning techniques can detect multiple species, providing information on flowering richness in photographed meadows. The results indicate higher richness just above the tree line for most of the species, which is comparable with patterns found using field studies. We found that the two-stage detector Mask R-CNN was more accurate than single-stage detectors like RetinaNet and YOLO, with the Mask R-CNN network performing best overall with mean average precision (mAP) of 0.67 followed by RetinaNet (0.5) and YOLO (0.4). We found that across the methods using anchor box variations in multiples of 16 led to enhanced accuracy. We also show that detection is possible even when pictures are interspersed with complex backgrounds and are not in focus. We found differential detection rates depending on species abundance, with additional challenges related to similarity in flower characteristics, labeling errors and occlusion issues. Despite these potential biases and limitations in capturing flowering abundance and location-specific quantification, accuracy was notable considering the complexity of flower types and picture angles in this dataset. We, therefore, expect that this approach can be used to address many ecological questions that benefit from automated flower detection, including studies of flowering phenology and floral resources, and that this approach can, therefore, complement a wide range of ecological approaches (e.g., field observations, experiments, community science, etc.). In all, our study suggests that ecological metrics like floral richness can be efficiently monitored by combining machine learning with easily accessible publicly curated datasets (e.g., Flickr, iNaturalist).","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"6 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139911341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implications of target signal choice in passive acoustic monitoring: an example of age- and sex-dependent vocal repertoire use in African forest elephants (Loxodonta cyclotis)","authors":"Colin R. Swider, Daniela Hedwig, Peter H. Wrege, Susan E. Parks","doi":"10.1002/rse2.380","DOIUrl":"https://doi.org/10.1002/rse2.380","url":null,"abstract":"Passive acoustic monitoring (PAM) is an effective remote sensing approach for sampling acoustically active animal species and is particularly useful for elusive, visually cryptic species inhabiting remote or inaccessible habitats. Key advantages of PAM are large spatial coverage and continuous, long-term monitoring. In most cases, a signal detection algorithm is utilized to locate sounds of interest within long sequences of audio data. It is important to understand the demographic/contextual usage of call types when choosing a particular signal to use for detection. Sampling biases may result if sampling is restricted to subsets of the population, for example, when detectable vocalizations are produced only by a certain demographic class. Using the African forest elephant repertoire as a case study, we test for differences in call type usage among different age-sex classes. We identified disproportionate usage by age-sex class of four call types—roars, trumpets, rumbles, and combination calls. This differential usage of signals by demographic class has implications for the use of particular call types in PAM for this species. Our results highlight that forest elephant PAM studies that have used rumbles as target signals may have under-sampled adult males. The addition of other call types to PAM frameworks may be useful to leverage additional population demographic information from these surveys. Our research exemplifies how an examination of a species' acoustic behavior can be used to better contextualize the data and results from PAM and to strengthen the resulting inference.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"56 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139396180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the accuracy of georeferenced landcover data derived from oblique imagery using machine learning","authors":"James Tricker, Claire Wright, Spencer Rose, Jeanine Rhemtulla, Trevor Lantz, Eric Higgs","doi":"10.1002/rse2.379","DOIUrl":"https://doi.org/10.1002/rse2.379","url":null,"abstract":"Repeat photography offers distinctive insights into ecological change, with ground-based oblique photographs often predating early aerial images by decades. However, the oblique angle of the photographs presents challenges for extracting and analyzing ecological information using traditional remote sensing approaches. Several innovative methods have been developed for analyzing repeat photographs, but none offer a comprehensive end-to-end workflow incorporating image classification and georeferencing to produce quantifiable landcover data. In this paper, we provide an overview of two new tools, an automated deep learning classifier and intuitive georeferencing tool, and describe how they are used to derive landcover data from 19 images associated with the Mountain Legacy Project, a research team that works with the world's largest collection of systematic high-resolution historic mountain photographs. We then combined these data to produce a contemporary landcover map for a study area in Jasper National Park, Canada. We assessed georeferencing accuracy by calculating the root-mean-square error and mean displacement for a subset of the images, which was 4.6 and 3.7 m, respectively. Overall classification accuracy of the landcover map produced from oblique images was 68%, which was comparable to landcover data produced from aerial imagery using a conventional classification method. The new workflow advances the use of repeat photographs for yielding quantitative landcover data. It has several advantages over existing methods including the ability to produce quick and consistent image classifications with little human input, and accurately georeference and combine these data to generate landcover maps for large areas.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"29 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139110451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-intensity bird migration along Alpine valleys calls for protective measures against anthropogenically induced avian mortality","authors":"Simon Hirschhofer, Felix Liechti, Peter Ranacher, Robert Weibel, Baptiste Schmid","doi":"10.1002/rse2.377","DOIUrl":"https://doi.org/10.1002/rse2.377","url":null,"abstract":"The Alps are a natural barrier for avian broad-front migration in Central Europe. While most birds that approach the Alps are deflected and circumvent the mountains, some choose to make the crossing. Here, they are funnelled and channelled in valleys, leading to high bird densities. Many Alpine valleys are suitable locations for wind farms, potentially creating a conflict between wind energy production and bird conservation. Collisions can be reduced by temporarily shutting down wind turbines. This however requires timely coordination, either by locally monitoring migration intensity or by extrapolating and forecasting migratory fluxes from other sites. However, little is known about the timing and intensity of bird migration in valleys of the central Alps, especially during spring migration. This study presents a 2-year quantification of avian migration across the Alps. We collected terrestrial radar data at three sites: two located in Alpine valleys and one in the lowland, close to the northern foothills of the Alps. We found high migration traffic rates (MTR) during both migration seasons in the Alpine valleys, with outstanding numbers of migrants during the spring season. The strong alignment of the flight directions with the main orientation of alpine valleys highlights the importance of valleys and the connected passes in channelling migratory fluxes through the Alps. However, extrapolating migration intensities and forecasting peak migration events for inner Alpine sites is difficult, likely due to how migratory patterns and activity are influenced by the complexity of the local topography and the associated dynamic wind and weather conditions. Instead, we call for year-round on-site monitoring of migration intensities and strategies tailored to the local context to reduce the risk of bird strikes at wind turbines in the Alps.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"157 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139091765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using multiplatform LiDAR to identify relationships between vegetation structure and the abundance and diversity of woodland reptiles and amphibians","authors":"Shukhrat Shokirov, Tommaso Jucker, Shaun R. Levick, Adrian D. Manning, Kara N. Youngentob","doi":"10.1002/rse2.381","DOIUrl":"https://doi.org/10.1002/rse2.381","url":null,"abstract":"Remotely sensed measures of vegetation structure have been shown to explain patterns in the occurrence and diversity of several animal taxa, including birds, mammals, and invertebrates. However, very little research in this area has focused on reptiles and amphibians (herpetofauna). Moreover, most remote sensing studies on animal–habitat associations have relied on airborne or satellite data that provide coverage over relatively large areas but may not have the resolution or viewing angle necessary to measure vegetation features at scales that are meaningful to herpetofauna. Here, we combined terrestrial laser scanning (TLS), unmanned aerial vehicle laser scanning (ULS), and fused (FLS) data to provide the first test of whether vegetation structural attributes can help explain variation in herpetofauna abundance, species richness, and diversity across a woodland landscape. We identified relationships between the abundance and diversity of herpetofauna and several vegetation metrics, including canopy height, skewedness, vertical complexity, volume of vegetation, and coarse woody debris. These relationships varied across species, groups, and sensors. ULS models tended to perform as well or better than TLS or FLS models based on the methods we used in this study. In open woodland landscapes, ULS data may have some benefits over TLS data for modeling relationships between herpetofauna and vegetation structure, which we discuss. However, for some species, only TLS data identified significant predictor variables among the LiDAR-derived structural metrics. While the overall predictive power of models was relatively low (i.e., at most <i>R</i>² = 0.32 for ULS overall abundance and <i>R</i>² = 0.32 for abundance at the individual species level [three-toed skink (<i>Chalcides striatus</i>)]), the ability to identify relationships between specific LiDAR structural metrics and the abundance and diversity of herpetofauna could be useful for understanding their habitat associations and managing reptile and amphibian populations.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"18 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139091743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using water-landing, fixed-wing UAVs and computer vision to assess seabird nutrient subsidy effects on sharks and rays","authors":"Melissa Schiele, J. Marcus Rowcliffe, Ben Clark, Paul Lepper, Tom B. Letessier","doi":"10.1002/rse2.378","DOIUrl":"https://doi.org/10.1002/rse2.378","url":null,"abstract":"Bird colonies on islands sustain elevated productivity and biomass on adjacent reefs, through nutrient subsidies. However, the implications of this localized enhancement on higher and often more mobile trophic levels (such as sharks and rays) are unclear, as spatial trends in mobile fauna are often poorly captured by traditional underwater visual surveys. Here, we explore whether the presence of seabird colonies is associated with enhanced abundances of sharks and rays on adjacent coral reefs. We used a novel long-range water-landing fixed-wing unoccupied aerial vehicle (UAV) to survey the distribution and density of sharks, rays and any additional megafauna, on and around tropical coral islands (n = 14) in the Chagos Archipelago Marine Protected Area. We developed a computer-vision algorithm to distinguish greenery (trees and shrubs), sand and sea glitter from visible ocean to yield accurate marine megafauna density estimation. We detected elevated seabird densities over rat-free islands, with the commonest species, sooty tern, reaching densities of 932 ± 199 per km⁻² while none were observed over former coconut plantation islands. Elasmobranch density around rat-free islands with seabird colonies was 6.7 times higher than around islands without seabird colonies (1.3 ± 0.63 <i>vs.</i> 0.2 ± SE 0.1 per km²). Our results are evidence that shark and ray distribution is sensitive to natural and localized nutrient subsidies. Correcting for non-sampled regions of images increased estimated elasmobranch density by 14%, and our openly accessible computer vision algorithm makes this correction easy to implement to generate shark and ray and other wildlife densities from any aerial imagery. The water-landing fixed-wing long-range UAV technology used in this study may provide cost effective monitoring opportunities in remote ocean locations.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"3 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139051004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of an unmanned aerial-aquatic vehicle for acoustic sensing in freshwater ecosystems","authors":"Jenna Lawson, Andre Farinha, Luca Romanello, Oscar Pang, Raphael Zufferey, Mirko Kovac","doi":"10.1002/rse2.373","DOIUrl":"https://doi.org/10.1002/rse2.373","url":null,"abstract":"Freshwater ecosystems are endangered, underfunded and understudied, making new methods such as passive acoustic monitoring (PAM) essential for improving the efficiency and effectiveness of data collection. However, many challenges are still to be addressed with PAM: difficulty in accessing research sites, the logistics of implementing large-scale studies and the invasiveness of data collection. When combined with PAM and other sensing strategies, mobile robotics are a promising solution to directly address these challenges. In this paper, we integrate water surface and underwater acoustic monitoring equipment onto a prototype unmanned aerial-aquatic vehicle (UAAV) capable of sailing and flight (SailMAV). Twelve autonomous sailing missions were run on Lake Vrana, Croatia, during which acoustic data were collected, and the ability of the UAAV to facilitate the collection of acoustic data demonstrated. Data were simultaneously collected using standard recording methods on buoys and banksides to provide a comparative approach. Acoustic indices were used to analyse the soundscape of underwater acoustic data and BirdNET (a deep artificial neural network) was used on water surface datasets to determine bird species composition. Results show higher species richness and call abundance from UAAV surveys and high site dissimilarity owing to turnover between stationary and UAAV methods. This highlights the success of the UAAV in detecting biodiversity and the complementarity of these methods in providing a broad picture of the biodiversity of freshwater ecosystems. Increased bird diversity and underwater acoustic activity in protected areas demonstrate the benefits of protecting freshwater ecosystems; however, site dissimilarity driven by turnover highlights the importance of protecting the entire ecosystem. We show how, by integrating PAM and a UAAV, we can overcome some of the current challenges in freshwater biodiversity monitoring, improving accessibility, increasing spatial scale and coverage, and reducing invasiveness.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"70 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139038798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}