Maik Henrich, Christian Fiderer, Alisa Klamm, Anja Schneider, Axel Ballmann, Jürgen Stein, Raffael Kratzer, Rudolf Reiner, Sina Greiner, Sönke Twietmeyer, Tobias Rönitz, Volker Spicher, Simon Chamaillé‐Jammes, Vincent Miele, Gaspard Dussert, Marco Heurich
Automated detectors such as camera traps allow the efficient collection of large amounts of data for the monitoring of animal populations, but data processing and classification are a major bottleneck. Deep learning algorithms have gained increasing attention in this context, as they have the potential to dramatically decrease the time and effort required to obtain population density estimates. However, the robustness of such an approach has not yet been evaluated across a wide range of species and study areas. This study evaluated the application of DeepFaune, an open‐source deep learning algorithm for the classification of European animal species and camera trap distance sampling (CTDS) to a year‐round dataset containing 895,019 manually classified photos from 10 protected areas across Germany. For all wild animal species and higher taxonomic groups on which DeepFaune was trained, the algorithm achieved an overall accuracy of 90%. The 95% confidence interval (CI) of the difference between the CTDS estimates based on manual and automated image classification contained zero for all species and seasons with a minimum sample size of 20 independent observations per study area, except for two. Meta‐regression revealed an average difference between the classification methods of −0.005 (95% CI: −0.205–0.196) animals/km2. Classification success correlated with the divergence of the population density estimates, but false negative and false positive detections had complex effects on the density estimates via different CTDS parameters. Therefore, metrics of classification performance alone are insufficient to assess the effect of deep learning classifiers on the population density estimation process, which should instead be followed through entirely for proper validation. In general, however, our results demonstrate that readily available deep learning algorithms can be used in largely unsupervised workflows for estimating population densities from camera trap data.
{"title":"Camera traps and deep learning enable efficient large‐scale density estimation of wildlife in temperate forest ecosystems","authors":"Maik Henrich, Christian Fiderer, Alisa Klamm, Anja Schneider, Axel Ballmann, Jürgen Stein, Raffael Kratzer, Rudolf Reiner, Sina Greiner, Sönke Twietmeyer, Tobias Rönitz, Volker Spicher, Simon Chamaillé‐Jammes, Vincent Miele, Gaspard Dussert, Marco Heurich","doi":"10.1002/rse2.70030","DOIUrl":"https://doi.org/10.1002/rse2.70030","url":null,"abstract":"Automated detectors such as camera traps allow the efficient collection of large amounts of data for the monitoring of animal populations, but data processing and classification are a major bottleneck. Deep learning algorithms have gained increasing attention in this context, as they have the potential to dramatically decrease the time and effort required to obtain population density estimates. However, the robustness of such an approach has not yet been evaluated across a wide range of species and study areas. This study evaluated the application of DeepFaune, an open‐source deep learning algorithm for the classification of European animal species and camera trap distance sampling (CTDS) to a year‐round dataset containing 895,019 manually classified photos from 10 protected areas across Germany. For all wild animal species and higher taxonomic groups on which DeepFaune was trained, the algorithm achieved an overall accuracy of 90%. The 95% confidence interval (CI) of the difference between the CTDS estimates based on manual and automated image classification contained zero for all species and seasons with a minimum sample size of 20 independent observations per study area, except for two. Meta‐regression revealed an average difference between the classification methods of −0.005 (95% CI: −0.205–0.196) animals/km<jats:sup>2</jats:sup>. Classification success correlated with the divergence of the population density estimates, but false negative and false positive detections had complex effects on the density estimates via different CTDS parameters. Therefore, metrics of classification performance alone are insufficient to assess the effect of deep learning classifiers on the population density estimation process, which should instead be followed through entirely for proper validation. In general, however, our results demonstrate that readily available deep learning algorithms can be used in largely unsupervised workflows for estimating population densities from camera trap data.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"158 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145035725","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}
Johannes N. Wiegers, Kathryn E. Barry, Marijke van Kuijk
Camera trapping is a vital tool for wildlife monitoring. Accurately estimating a camera's detection zone, the area where animals are detected, is essential, particularly for calculating population densities of unmarked species. However, obtaining enough detection events to estimate detection zones accurately remains difficult, particularly for rare species. Given that detection zones are influenced by species‐ and camera‐specific traits, it may be possible to infer detection zones from these traits when data are scarce. We conducted a meta‐level analysis to assess how the number of detection events, species traits and site‐specific variables influence the estimation of the effective camera trap detection distance and angle. We reviewed published studies on detection zones, performed a power analysis to estimate the sample sizes required for accurate and precise estimates and used mixed‐effects models to test whether detection zones can be predicted from biological and technical traits. Our results show that c. 50 detection events are needed to achieve error rates below 10%. The mixed‐effects models explained 81% and 85% of the variation in effective detection distance and angle, respectively. Key predictors of detection distance included body mass, right‐truncation distance and camera brand, while angle was predicted by camera brand and installation height. Importantly, we demonstrate that combining model‐based predictions with limited empirical data (fewer than 25 detections) can reduce estimation error to below 15% for rare species. This study highlights that detection zones can be predicted not only within, but also across, studies using shared traits and that the right‐truncation distance is a useful metric to account for habitat‐specific visibility. These findings enhance the utility of detection zones in ecological studies and support better study design, especially for rare or understudied species.
{"title":"Inferring camera trap detection zones for rare species using species‐ and camera‐specific traits: a meta‐level analysis","authors":"Johannes N. Wiegers, Kathryn E. Barry, Marijke van Kuijk","doi":"10.1002/rse2.70027","DOIUrl":"https://doi.org/10.1002/rse2.70027","url":null,"abstract":"Camera trapping is a vital tool for wildlife monitoring. Accurately estimating a camera's detection zone, the area where animals are detected, is essential, particularly for calculating population densities of unmarked species. However, obtaining enough detection events to estimate detection zones accurately remains difficult, particularly for rare species. Given that detection zones are influenced by species‐ and camera‐specific traits, it may be possible to infer detection zones from these traits when data are scarce. We conducted a meta‐level analysis to assess how the number of detection events, species traits and site‐specific variables influence the estimation of the effective camera trap detection distance and angle. We reviewed published studies on detection zones, performed a power analysis to estimate the sample sizes required for accurate and precise estimates and used mixed‐effects models to test whether detection zones can be predicted from biological and technical traits. Our results show that c. 50 detection events are needed to achieve error rates below 10%. The mixed‐effects models explained 81% and 85% of the variation in effective detection distance and angle, respectively. Key predictors of detection distance included body mass, right‐truncation distance and camera brand, while angle was predicted by camera brand and installation height. Importantly, we demonstrate that combining model‐based predictions with limited empirical data (fewer than 25 detections) can reduce estimation error to below 15% for rare species. This study highlights that detection zones can be predicted not only within, but also across, studies using shared traits and that the right‐truncation distance is a useful metric to account for habitat‐specific visibility. These findings enhance the utility of detection zones in ecological studies and support better study design, especially for rare or understudied species.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"31 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145002780","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}
Lindsay Veazey, Christopher Latty, Zoey Chapman, Tuula E. Hollmen
Advances in camera and data storage technology have revolutionized the ability of scientists to acquire large volumes of finely resolved wildlife monitoring data. This is especially valuable for breeding bird research, which often requires and benefits from continuous nest monitoring, which may extend a month or more. Though high‐quality imagery may yield valuable insights, the sheer volume of data can create processing bottlenecks. Furthermore, achieving uniformity across projects and years is difficult given individual‐level differences in data processing by manual reviewers. To address this problem, we paired a custom trained You Only Look Once version 7 (YOLOv7) model with the StrongSORT tracking algorithm to analyze videos of nesting common eiders (Somateria mollissima) collected from barrier islands along the Beaufort Sea coast in Alaska. We used our computer vision pipeline to process footage three times faster than manual review while matching human observer accuracy in recording nest attendance and disturbances. To evaluate the effectiveness of our trained pipeline, we analyzed novel footage from a different year. The automated part of the pipeline performed well when birds were relatively large in the frame. However, performance declined for birds occupying a small frame area, which occurred when the camera was farther away from the nest and not zoomed. When birds are smaller in the frame, they are more susceptible to being obscured by rain or fog on the lens, as well as by other birds positioned in front of them. Additionally, detecting birds that occupy a small area of the frame can be more challenging in complex backgrounds, particularly under difficult lighting conditions, such as when the sun backlights the bird, or due to specific behaviors, like when birds hunker down to minimize their silhouette in response to perceived threats. To enhance performance, we recommend that researchers position cameras closer to nests whenever feasible or utilize zoom lenses. Importantly, our pipeline is designed to be species‐agnostic, allowing for easy adaptation to various nesting bird species.
相机和数据存储技术的进步已经彻底改变了科学家获取大量精细分辨率的野生动物监测数据的能力。这对于鸟类繁殖研究尤其有价值，因为这通常需要持续的鸟巢监测，这可能会持续一个月或更长时间。虽然高质量的图像可能产生有价值的见解，但庞大的数据量可能会造成处理瓶颈。此外，考虑到人工审稿人在数据处理方面的个体水平差异，实现跨项目和年份的一致性是困难的。为了解决这个问题，我们将一个定制的训练过的You Only Look Once version 7 （YOLOv7）模型与StrongSORT跟踪算法配对，以分析从阿拉斯加波弗特海岸的屏障岛上收集的筑巢普通绒鸭（Somateria mollissima）的视频。我们使用计算机视觉管道处理镜头的速度比人工审查快三倍，同时在记录鸟巢出勤率和干扰方面与人类观察者的准确性相匹配。为了评估我们训练有素的流水线的有效性，我们分析了不同年份的新镜头。当鸟在框架中相对较大时，管道的自动化部分表现良好。然而，当相机离鸟巢较远且没有变焦时，占据小帧区域的鸟类的性能下降。当鸟在画面中比较小的时候，它们更容易被镜头上的雨或雾所遮挡，以及被其他在它们前面的鸟所遮挡。此外，在复杂的背景下，探测占据框架一小块区域的鸟类可能更具挑战性，特别是在困难的照明条件下，例如当太阳背光照射鸟类时，或者由于特定的行为，例如当鸟类蹲下以最小化其轮廓以响应感知到的威胁时。为了提高性能，我们建议研究人员在可行的情况下将摄像机放置在离鸟巢更近的地方，或者使用变焦镜头。重要的是，我们的管道设计为物种不可知，允许轻松适应各种筑巢鸟类。
{"title":"Applying computer vision to accelerate monitoring and analysis of bird incubation behaviors: a case study using common eider nest camera footage","authors":"Lindsay Veazey, Christopher Latty, Zoey Chapman, Tuula E. Hollmen","doi":"10.1002/rse2.70022","DOIUrl":"https://doi.org/10.1002/rse2.70022","url":null,"abstract":"Advances in camera and data storage technology have revolutionized the ability of scientists to acquire large volumes of finely resolved wildlife monitoring data. This is especially valuable for breeding bird research, which often requires and benefits from continuous nest monitoring, which may extend a month or more. Though high‐quality imagery may yield valuable insights, the sheer volume of data can create processing bottlenecks. Furthermore, achieving uniformity across projects and years is difficult given individual‐level differences in data processing by manual reviewers. To address this problem, we paired a custom trained You Only Look Once version 7 (YOLOv7) model with the StrongSORT tracking algorithm to analyze videos of nesting common eiders (<jats:italic>Somateria mollissima</jats:italic>) collected from barrier islands along the Beaufort Sea coast in Alaska. We used our computer vision pipeline to process footage three times faster than manual review while matching human observer accuracy in recording nest attendance and disturbances. To evaluate the effectiveness of our trained pipeline, we analyzed novel footage from a different year. The automated part of the pipeline performed well when birds were relatively large in the frame. However, performance declined for birds occupying a small frame area, which occurred when the camera was farther away from the nest and not zoomed. When birds are smaller in the frame, they are more susceptible to being obscured by rain or fog on the lens, as well as by other birds positioned in front of them. Additionally, detecting birds that occupy a small area of the frame can be more challenging in complex backgrounds, particularly under difficult lighting conditions, such as when the sun backlights the bird, or due to specific behaviors, like when birds hunker down to minimize their silhouette in response to perceived threats. To enhance performance, we recommend that researchers position cameras closer to nests whenever feasible or utilize zoom lenses. Importantly, our pipeline is designed to be species‐agnostic, allowing for easy adaptation to various nesting bird species.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"9 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144906153","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}
Rachael S. Leeman, Robert S. Davis, Antonio Uzal, Heinrich Neumeyer, Rebecca A. Garbett, Joshua P. Twining, Richard W. Yarnell
Spatial capture‐recapture (SCR) provides the gold standard for robust population estimates where animals are individually identifiable. Sampling for large carnivores is often conducted over short timeframes to meet assumptions of population closure. As large carnivores are often elusive and found at low densities, surveys often result in low numbers of unique individuals captured and limited spatial recaptures, which can lead to convergence and parameter identifiability issues. In areas of high tourism footfall, additional spatial capture information can be provided by tourists. We supplemented individual encounter history data from a camera trap‐based monitoring programme for leopards (Panthera pardus) with tourist sighting data within multi‐session SCR models; we evaluated the benefits of combining multiple data sources. Integrating tourist observations improved the precision of estimates (Half Relative Confidence Interval Width: Combined = 23.1%), resulting in an overall density estimate of 7.02 leopards per 100 km2 (95% CI: 5.59–8.84 per 100 km2). Tourist‐derived methods were 92.5% cheaper than camera trapping, highlighting the cost‐efficiency of supplementing camera trap surveys with this source of data in areas with high tourism activity. This study demonstrates that combining structured survey data from camera traps with unstructured tourist‐derived images improves resultant density estimates compared to using either method alone. Supplementing structured camera trapping data with tourist images in areas of high tourism activity can offer improvements in scalability by increasing spatial and temporal coverage of sampling, with limited additional costs and improved precision in density estimates. To further enhance the reliability of these methods, we provide recommendations for improving citizen science reporting for integration into SCR frameworks.
{"title":"Tourist sightings improve the precision of camera trap‐derived density estimates using spatial capture‐recapture models","authors":"Rachael S. Leeman, Robert S. Davis, Antonio Uzal, Heinrich Neumeyer, Rebecca A. Garbett, Joshua P. Twining, Richard W. Yarnell","doi":"10.1002/rse2.70025","DOIUrl":"https://doi.org/10.1002/rse2.70025","url":null,"abstract":"Spatial capture‐recapture (SCR) provides the gold standard for robust population estimates where animals are individually identifiable. Sampling for large carnivores is often conducted over short timeframes to meet assumptions of population closure. As large carnivores are often elusive and found at low densities, surveys often result in low numbers of unique individuals captured and limited spatial recaptures, which can lead to convergence and parameter identifiability issues. In areas of high tourism footfall, additional spatial capture information can be provided by tourists. We supplemented individual encounter history data from a camera trap‐based monitoring programme for leopards (<jats:italic>Panthera pardus</jats:italic>) with tourist sighting data within multi‐session SCR models; we evaluated the benefits of combining multiple data sources. Integrating tourist observations improved the precision of estimates (Half Relative Confidence Interval Width: Combined = 23.1%), resulting in an overall density estimate of 7.02 leopards per 100 km<jats:sup>2</jats:sup> (95% CI: 5.59–8.84 per 100 km<jats:sup>2</jats:sup>). Tourist‐derived methods were 92.5% cheaper than camera trapping, highlighting the cost‐efficiency of supplementing camera trap surveys with this source of data in areas with high tourism activity. This study demonstrates that combining structured survey data from camera traps with unstructured tourist‐derived images improves resultant density estimates compared to using either method alone. Supplementing structured camera trapping data with tourist images in areas of high tourism activity can offer improvements in scalability by increasing spatial and temporal coverage of sampling, with limited additional costs and improved precision in density estimates. To further enhance the reliability of these methods, we provide recommendations for improving citizen science reporting for integration into SCR frameworks.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"15 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144906088","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}
Temuulen Tsagaan Sankey, Thu Ya Kyaw, Julia Tatum, George W. Koch, Thomas Kolb, Rayni Lewis, Helen M. Poulos, Andrew M. Barton, Blase LaSala, Andrea Thode
Southwestern US forests are experiencing increasing wildfire activity, and land managers are implementing large‐scale forest thinning treatments. We investigated semi‐arid ponderosa pine forest thinning treatment and regional drought impacts on ECOSTRESS land surface temperature (LST) and evapotranspiration (ET). Our study period at a northern Arizona study site included an average precipitation year, 2019, a regional drought period of 2020–2022, and a record winter snowfall year 2023. We examined ECOSTRESS LST and ET during spring seasons when the region experiences an annual dry period, and plant water stress is heightened. Our results indicate that ECOSTRESS LST data are sensitive to forest thinning, regional drought and their interaction. Consistent with high‐resolution UAV images, ECOSTRESS LST data indicate the thinned forest had significantly greater temperature across years, regardless of precipitation patterns. During drought, ECOSTRESS LST increased in both thinned and non‐thinned forests (by up to 10°C) and then declined in 2023. ECOSTRESS ET was similarly sensitive to forest thinning and regional drought. Consistent with in situ ET measurements, ECOSTRESS ET was significantly greater in the non‐thinned forest compared to the thinned forest. ECOSTRESS ET significantly decreased during drought in both forests. Our analysis of EMIT data indicates that EMIT trends are not consistent with ground‐based hyperspectral data that documented thinned forest moisture content is greater than that of the non‐thinned forest. While quality filtering reduces ECOSTRESS data temporal resolution, both ECOSTRESS LST and ET data can be used across large spatial extents to examine impacts of regional drought and management treatments in semi‐arid ponderosa pine forests.
{"title":"ECOSTRESS‐derived semi‐arid forest temperature and evapotranspiration estimates demonstrate drought and thinning impacts","authors":"Temuulen Tsagaan Sankey, Thu Ya Kyaw, Julia Tatum, George W. Koch, Thomas Kolb, Rayni Lewis, Helen M. Poulos, Andrew M. Barton, Blase LaSala, Andrea Thode","doi":"10.1002/rse2.70026","DOIUrl":"https://doi.org/10.1002/rse2.70026","url":null,"abstract":"Southwestern US forests are experiencing increasing wildfire activity, and land managers are implementing large‐scale forest thinning treatments. We investigated semi‐arid ponderosa pine forest thinning treatment and regional drought impacts on ECOSTRESS land surface temperature (LST) and evapotranspiration (ET). Our study period at a northern Arizona study site included an average precipitation year, 2019, a regional drought period of 2020–2022, and a record winter snowfall year 2023. We examined ECOSTRESS LST and ET during spring seasons when the region experiences an annual dry period, and plant water stress is heightened. Our results indicate that ECOSTRESS LST data are sensitive to forest thinning, regional drought and their interaction. Consistent with high‐resolution UAV images, ECOSTRESS LST data indicate the thinned forest had significantly greater temperature across years, regardless of precipitation patterns. During drought, ECOSTRESS LST increased in both thinned and non‐thinned forests (by up to 10°C) and then declined in 2023. ECOSTRESS ET was similarly sensitive to forest thinning and regional drought. Consistent with <jats:italic>in situ</jats:italic> ET measurements, ECOSTRESS ET was significantly greater in the non‐thinned forest compared to the thinned forest. ECOSTRESS ET significantly decreased during drought in both forests. Our analysis of EMIT data indicates that EMIT trends are not consistent with ground‐based hyperspectral data that documented thinned forest moisture content is greater than that of the non‐thinned forest. While quality filtering reduces ECOSTRESS data temporal resolution, both ECOSTRESS LST and ET data can be used across large spatial extents to examine impacts of regional drought and management treatments in semi‐arid ponderosa pine forests.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"25 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144898131","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}
Barbara D'hont, Kim Calders, Alexandre Antonelli, Thomas Berg, Wout Cherlet, Karun Dayal, Olivia Jayne Fitzpatrick, Leonard Hambrecht, Maurice Leponce, Arko Lucieer, Olivier Pascal, Pasi Raumonen, Hans Verbeeck
Large old trees provide multiple ecosystem services and contribute disproportionately to forest biomass and biodiversity. Yet their canopies remain among the least‐explored terrestrial habitats, despite their structural influence on key ecological processes such as light interception, moisture regulation, carbon storage and habitat formation. While terrestrial laser scanning (TLS) captures tree structure primarily from the ground, it struggles with occlusion and reduced precision in dense upper canopies, limiting information on fine‐scale branches and canopy vegetation. To address this, we introduce canopy laser scanning (CLS). We lifted a high‐end laser scanner into the canopy of six large, old trees by using scaffolding or climbers. Four trees are in diverse tropical rainforests in Colombia, Brazil and Peru and have large complex crowns with dense foliage. Two ‘giant’ trees stand out in Tasmania's wet, temperate eucalypt forests. Combining canopy and terrestrial scans resulted in a consistent high point cloud quality. The combined point clouds exhibited uniform point densities throughout the entire tree (downsampled to 1 cm), enabling a thorough examination of both the tree structure and its associated vegetation. Quantitative Structure Models (QSMs) showed, on average, a 20% increase (compared to TLS) in estimated branch volume and length, particularly concentrated in the upper crown region. We identified key epiphytic groups for a 5 × 5 × 5 m3 subset of a tree. Our results show that CLS improves point cloud precision and reduces occlusion, enabling more accurate assessments of tree architecture and canopy biodiversity. Where feasible, this advancement creates new opportunities for 3D modelling of microhabitats, estimating aboveground carbon stocks, monitoring species and studying ecological dynamics.
{"title":"Integrating terrestrial and canopy laser scanning for comprehensive analysis of large old trees: Implications for single tree and biodiversity research","authors":"Barbara D'hont, Kim Calders, Alexandre Antonelli, Thomas Berg, Wout Cherlet, Karun Dayal, Olivia Jayne Fitzpatrick, Leonard Hambrecht, Maurice Leponce, Arko Lucieer, Olivier Pascal, Pasi Raumonen, Hans Verbeeck","doi":"10.1002/rse2.70021","DOIUrl":"https://doi.org/10.1002/rse2.70021","url":null,"abstract":"Large old trees provide multiple ecosystem services and contribute disproportionately to forest biomass and biodiversity. Yet their canopies remain among the least‐explored terrestrial habitats, despite their structural influence on key ecological processes such as light interception, moisture regulation, carbon storage and habitat formation. While terrestrial laser scanning (TLS) captures tree structure primarily from the ground, it struggles with occlusion and reduced precision in dense upper canopies, limiting information on fine‐scale branches and canopy vegetation. To address this, we introduce canopy laser scanning (CLS). We lifted a high‐end laser scanner into the canopy of six large, old trees by using scaffolding or climbers. Four trees are in diverse tropical rainforests in Colombia, Brazil and Peru and have large complex crowns with dense foliage. Two ‘giant’ trees stand out in Tasmania's wet, temperate eucalypt forests. Combining canopy and terrestrial scans resulted in a consistent high point cloud quality. The combined point clouds exhibited uniform point densities throughout the entire tree (downsampled to 1 cm), enabling a thorough examination of both the tree structure and its associated vegetation. Quantitative Structure Models (QSMs) showed, on average, a 20% increase (compared to TLS) in estimated branch volume and length, particularly concentrated in the upper crown region. We identified key epiphytic groups for a 5 × 5 × 5 m<jats:sup>3</jats:sup> subset of a tree. Our results show that CLS improves point cloud precision and reduces occlusion, enabling more accurate assessments of tree architecture and canopy biodiversity. Where feasible, this advancement creates new opportunities for 3D modelling of microhabitats, estimating aboveground carbon stocks, monitoring species and studying ecological dynamics.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"14 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144897861","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}
Tyler C. Coverdale, Peter B. Boucher, Jenia Singh, Andrew B. Davies
Grassy ecosystems cover >25% of the world's land surface area. The abundance of herbaceous vegetation in these systems directly impacts a variety of ecological processes, including carbon sequestration, regulation of water and nutrient cycling, and support of grazing wildlife and livestock. Efforts to quantify herbaceous biomass, however, are often limited by a trade‐off between accuracy and spatial scale. Here, we describe a method for using Light Detection and Ranging (LiDAR) to estimate continuous aboveground biomass (AGB) at sub‐meter resolutions over large (10–10 000 ha) spatial scales. Across two African savanna ecosystems, we compared field‐ and LiDAR‐derived structural metrics—including measures of vegetation height and volume—with destructively harvested AGB by aligning our geospatial data with the location of harvested quadrats. Using this combination of approaches, we develop scaling equations to estimate spatially continuous herbaceous AGB over large areas. We demonstrate the utility of this method using a long‐term, large herbivore exclosure experiment as a case study and comprehensively compare common field‐ and LiDAR‐derived metrics for estimating herbaceous AGB. Our results indicate that UAV‐borne LiDAR provides comparable accuracy to standard field methods but over considerably larger areas. Nearly every measure of vegetation structure we quantified using LiDAR provided estimates of AGB that were comparable in accuracy (R2 > 0.6) to the suite of common field methods we evaluated. However, marked differences between our two sites indicate that, for applications where accurate estimation of absolute biomass is a priority, site‐specific parameterization with destructive harvesting is necessary regardless of methodology. With the increasing availability of high‐resolution remote sensing data globally, our results indicate that many measures of herbaceous vegetation structure can be used to accurately compare AGB, even in the absence of complementary field data.
{"title":"Quantifying aboveground herbaceous biomass in grassy ecosystems: a comparison of field and high‐resolution UAV‐LiDAR approaches","authors":"Tyler C. Coverdale, Peter B. Boucher, Jenia Singh, Andrew B. Davies","doi":"10.1002/rse2.70023","DOIUrl":"https://doi.org/10.1002/rse2.70023","url":null,"abstract":"Grassy ecosystems cover >25% of the world's land surface area. The abundance of herbaceous vegetation in these systems directly impacts a variety of ecological processes, including carbon sequestration, regulation of water and nutrient cycling, and support of grazing wildlife and livestock. Efforts to quantify herbaceous biomass, however, are often limited by a trade‐off between accuracy and spatial scale. Here, we describe a method for using Light Detection and Ranging (LiDAR) to estimate continuous aboveground biomass (AGB) at sub‐meter resolutions over large (10–10 000 ha) spatial scales. Across two African savanna ecosystems, we compared field‐ and LiDAR‐derived structural metrics—including measures of vegetation height and volume—with destructively harvested AGB by aligning our geospatial data with the location of harvested quadrats. Using this combination of approaches, we develop scaling equations to estimate spatially continuous herbaceous AGB over large areas. We demonstrate the utility of this method using a long‐term, large herbivore exclosure experiment as a case study and comprehensively compare common field‐ and LiDAR‐derived metrics for estimating herbaceous AGB. Our results indicate that UAV‐borne LiDAR provides comparable accuracy to standard field methods but over considerably larger areas. Nearly every measure of vegetation structure we quantified using LiDAR provided estimates of AGB that were comparable in accuracy (<jats:italic>R</jats:italic><jats:sup>2</jats:sup> > 0.6) to the suite of common field methods we evaluated. However, marked differences between our two sites indicate that, for applications where accurate estimation of absolute biomass is a priority, site‐specific parameterization with destructive harvesting is necessary regardless of methodology. With the increasing availability of high‐resolution remote sensing data globally, our results indicate that many measures of herbaceous vegetation structure can be used to accurately compare AGB, even in the absence of complementary field data.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"15 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144792391","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}
Jacob Virtue, Darren Turner, Guy Williams, Stephanie Zeliadt, Arko Lucieer
Monitoring seabird populations is increasingly urgent as numerous species become more vulnerable to climate change and urbanisation. Surveying burrow‐nesting seabirds is challenging due to their nocturnal behaviour, the inaccessibility of colonies, and the disturbance that monitoring poses to nesting sites. Traditional survey methods, which are manual transects conducted by researchers (~200 m), extrapolate this data to derive the population estimates of entire colonies. To enhance the accuracy beyond interpolated data, a survey method was developed using Unoccupied Aerial Systems (UAS) equipped with thermal sensors to survey short‐tailed shearwaters (Ardenna tenuirostris). Thermal imagery of breeding colonies was collected from 2019 to 2024, providing comprehensive coverage capturing all occupied burrows (chick presence) at each colony. Occupied burrow densities decreased from 0.28 to 0.18 burrows per m2 over this period. Chick numbers decreased by 27% from 2019 (6129) to 2024 (4445). Burrow occupancy counts varied widely (0%–66%) with transect location, highlighting the advantages of using UAS‐mounted thermal sensors for providing spatially complete data. This indicates that counts are not uniform, highlighting the bias of using transect data to estimate chick production. A series of simulated transects were imposed over the thermal imagery to compare whole colony chick counts with extrapolated counts. Using data from this study, we estimated that the global breeding population of short‐tailed shearwaters is currently 13.5 million, which is approximately 41% less than the last reported global estimate in 1985 of 23 million. This study highlights the utility of emerging technology that addresses the challenges of studying species that are nocturnally active or in remote/inaccessible habitats.
{"title":"Thermal drone observations capture fine‐scale population decline of short‐tailed shearwaters","authors":"Jacob Virtue, Darren Turner, Guy Williams, Stephanie Zeliadt, Arko Lucieer","doi":"10.1002/rse2.70020","DOIUrl":"https://doi.org/10.1002/rse2.70020","url":null,"abstract":"Monitoring seabird populations is increasingly urgent as numerous species become more vulnerable to climate change and urbanisation. Surveying burrow‐nesting seabirds is challenging due to their nocturnal behaviour, the inaccessibility of colonies, and the disturbance that monitoring poses to nesting sites. Traditional survey methods, which are manual transects conducted by researchers (~200 m), extrapolate this data to derive the population estimates of entire colonies. To enhance the accuracy beyond interpolated data, a survey method was developed using Unoccupied Aerial Systems (UAS) equipped with thermal sensors to survey short‐tailed shearwaters (<jats:italic>Ardenna tenuirostris</jats:italic>). Thermal imagery of breeding colonies was collected from 2019 to 2024, providing comprehensive coverage capturing all occupied burrows (chick presence) at each colony. Occupied burrow densities decreased from 0.28 to 0.18 burrows per m<jats:sup>2</jats:sup> over this period. Chick numbers decreased by 27% from 2019 (6129) to 2024 (4445). Burrow occupancy counts varied widely (0%–66%) with transect location, highlighting the advantages of using UAS‐mounted thermal sensors for providing spatially complete data. This indicates that counts are not uniform, highlighting the bias of using transect data to estimate chick production. A series of simulated transects were imposed over the thermal imagery to compare whole colony chick counts with extrapolated counts. Using data from this study, we estimated that the global breeding population of short‐tailed shearwaters is currently 13.5 million, which is approximately 41% less than the last reported global estimate in 1985 of 23 million. This study highlights the utility of emerging technology that addresses the challenges of studying species that are nocturnally active or in remote/inaccessible habitats.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"119 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763123","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}
José Luis Hernández‐Stefanoni, Luis A. Hernández‐Martínez, Juan Andres‐Mauricio, Víctor Alexis Peña‐Lara, Karina Elizabeth González‐Muñoz, Fernando Tun‐Dzul, Carlos A. Portillo‐Quintero, Eric Antonio Gamboa‐Blanco, Stephanie George‐Chacon
Accurate assessment of forest aboveground biomass density (AGBD) is essential for understanding the role of vegetation in climate change mitigation and developing forest management and environmental policies at national and regional levels. The Global Ecosystem Dynamics Investigation (GEDI) uses full‐waveform LiDAR and provides a valuable tool for estimating AGBD. Calibrating GEDI biomass products with local field data is vital for improving model accuracy, as current estimates rely on global datasets. Additionally, evaluating key factors that influence biomass estimation is essential to refine GEDI‐based models. In this research, we calibrated linear models with field AGBD as the dependent variable and GEDI metrics as independent variables, and compared the performance against the GEDI L4A product across forest types. Additionally, we evaluated the effects of terrain slope, forest structural complexity, and forest type on the accuracy of the models. Finally, we mapped AGBD in Mexico by aggregating footprint‐level estimates with local models and compared it with the GEDI AGBD map (L4B product). Model validation showed R2 values from 0.35 to 0.46 across forest types, with most models having %RMSE below 52.0. Errors were 32.7 to 34.2% lower than GEDI L4A, highlighting a notable accuracy improvement. The total carbon stocks in Mexico estimated here are approximately 1.78 Gt, aligning closely with official FAO estimates, whereas GEDI estimates are 33.5% higher than the official estimate. Biomass estimation with GEDI is most accurate in areas with moderate slopes and low forest structural complexity. Coniferous and tropical forests showed the lowest errors in estimating AGBD with GEDI (46.7 and 47.3 of %RMSE, respectively) likely due to the widespread presence of uniformly structured coniferous trees and the moderate terrain slopes found in tropical forests. Our findings highlight the importance of calibrating local AGBD data with GEDI forest structure metrics to improve biomass estimations at the footprint and national levels.
{"title":"Spatial distribution and drivers of aboveground forest biomass in Mexico using GEDI and national forest inventory data","authors":"José Luis Hernández‐Stefanoni, Luis A. Hernández‐Martínez, Juan Andres‐Mauricio, Víctor Alexis Peña‐Lara, Karina Elizabeth González‐Muñoz, Fernando Tun‐Dzul, Carlos A. Portillo‐Quintero, Eric Antonio Gamboa‐Blanco, Stephanie George‐Chacon","doi":"10.1002/rse2.70019","DOIUrl":"https://doi.org/10.1002/rse2.70019","url":null,"abstract":"Accurate assessment of forest aboveground biomass density (AGBD) is essential for understanding the role of vegetation in climate change mitigation and developing forest management and environmental policies at national and regional levels. The Global Ecosystem Dynamics Investigation (GEDI) uses full‐waveform LiDAR and provides a valuable tool for estimating AGBD. Calibrating GEDI biomass products with local field data is vital for improving model accuracy, as current estimates rely on global datasets. Additionally, evaluating key factors that influence biomass estimation is essential to refine GEDI‐based models. In this research, we calibrated linear models with field AGBD as the dependent variable and GEDI metrics as independent variables, and compared the performance against the GEDI L4A product across forest types. Additionally, we evaluated the effects of terrain slope, forest structural complexity, and forest type on the accuracy of the models. Finally, we mapped AGBD in Mexico by aggregating footprint‐level estimates with local models and compared it with the GEDI AGBD map (L4B product). Model validation showed <jats:italic>R</jats:italic><jats:sup>2</jats:sup> values from 0.35 to 0.46 across forest types, with most models having %RMSE below 52.0. Errors were 32.7 to 34.2% lower than GEDI L4A, highlighting a notable accuracy improvement. The total carbon stocks in Mexico estimated here are approximately 1.78 Gt, aligning closely with official FAO estimates, whereas GEDI estimates are 33.5% higher than the official estimate. Biomass estimation with GEDI is most accurate in areas with moderate slopes and low forest structural complexity. Coniferous and tropical forests showed the lowest errors in estimating AGBD with GEDI (46.7 and 47.3 of %RMSE, respectively) likely due to the widespread presence of uniformly structured coniferous trees and the moderate terrain slopes found in tropical forests. Our findings highlight the importance of calibrating local AGBD data with GEDI forest structure metrics to improve biomass estimations at the footprint and national levels.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"14 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144669664","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}
Francesco D'Adamo, Rebecca Spake, James M. Bullock, Booker Ogutu, Jadunandan Dash, Felix Eigenbrod
Identifying the drivers of ecosystem dynamics, and how responses vary spatially and temporally, is a critical challenge in the face of global change. Grasslands in sub‐Saharan Africa are vital ecosystems supporting biodiversity, carbon storage, and livelihoods through grazing. However, despite their importance, the processes driving change in these systems remain poorly understood, as cross‐scale interactions among drivers produce complex, context‐dependent dynamics that vary across space and time. This is particularly relevant for woody vegetation dynamics, which are often linked to degradation processes (e.g., woody encroachment), with consequences for biodiversity, forage availability, and fire regimes. Here, we used satellite data and structural equation models to investigate the effects of rainfall, temperature, fire, and population density on woody vegetation dynamics in four African grassland regions (the Sahel grasslands, Greater Karoo and Kalahari drylands, Southeast African subtropical grasslands, and Madagascar) during 1997–2016. Across all regions, rainfall was consistently positively correlated with increased woody vegetation, while higher temperatures were associated with decreased woody vegetation, suggesting that water availability promotes woody plant growth, whereas rising aridity limits it. Unexpectedly, fire had a negative effect on woody cover only in the Greater Karoo and Kalahari drylands, while in Madagascar, higher temperatures and greater population density reduced fire; yet these relationships did not translate into significant indirect effects on woody vegetation. These findings illustrate the complex ways by which environmental and anthropogenic drivers shape woody vegetation dynamics in grasslands across sub‐Saharan Africa. Compared to savannas, fire plays a weaker and more region‐specific role in grasslands, where its feedback with woody cover is less consistent. The opposing effects of rainfall and temperature may currently constrain woody expansion, but climate change could disrupt this balance and further weaken fire's limited regulatory role. These differences highlight the need for management strategies tailored to the distinct climate–vegetation dynamics of grassland systems.
{"title":"Precipitation and temperature drive woody vegetation dynamics in the grasslands of sub‐Saharan Africa","authors":"Francesco D'Adamo, Rebecca Spake, James M. Bullock, Booker Ogutu, Jadunandan Dash, Felix Eigenbrod","doi":"10.1002/rse2.70018","DOIUrl":"https://doi.org/10.1002/rse2.70018","url":null,"abstract":"Identifying the drivers of ecosystem dynamics, and how responses vary spatially and temporally, is a critical challenge in the face of global change. Grasslands in sub‐Saharan Africa are vital ecosystems supporting biodiversity, carbon storage, and livelihoods through grazing. However, despite their importance, the processes driving change in these systems remain poorly understood, as cross‐scale interactions among drivers produce complex, context‐dependent dynamics that vary across space and time. This is particularly relevant for woody vegetation dynamics, which are often linked to degradation processes (e.g., woody encroachment), with consequences for biodiversity, forage availability, and fire regimes. Here, we used satellite data and structural equation models to investigate the effects of rainfall, temperature, fire, and population density on woody vegetation dynamics in four African grassland regions (the Sahel grasslands, Greater Karoo and Kalahari drylands, Southeast African subtropical grasslands, and Madagascar) during 1997–2016. Across all regions, rainfall was consistently positively correlated with increased woody vegetation, while higher temperatures were associated with decreased woody vegetation, suggesting that water availability promotes woody plant growth, whereas rising aridity limits it. Unexpectedly, fire had a negative effect on woody cover only in the Greater Karoo and Kalahari drylands, while in Madagascar, higher temperatures and greater population density reduced fire; yet these relationships did not translate into significant indirect effects on woody vegetation. These findings illustrate the complex ways by which environmental and anthropogenic drivers shape woody vegetation dynamics in grasslands across sub‐Saharan Africa. Compared to savannas, fire plays a weaker and more region‐specific role in grasslands, where its feedback with woody cover is less consistent. The opposing effects of rainfall and temperature may currently constrain woody expansion, but climate change could disrupt this balance and further weaken fire's limited regulatory role. These differences highlight the need for management strategies tailored to the distinct climate–vegetation dynamics of grassland systems.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"13 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144629804","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}
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