Amy Stone, Sharyn Hickey, Ben Radford, Mary Wakeford
Although emergent coral reefs represent a significant proportion of overall reef habitat, they are often excluded from monitoring projects due to their shallow and exposed setting that makes them challenging to access. Using drones to survey emergent reefs overcomes issues around access to this habitat type; however, methods for deriving robust monitoring metrics, such as coral cover, are not well developed for drone imagery. To address this knowledge gap, we compare the effectiveness of two remote sensing methods in quantifying broad substrate groups, such as coral cover, on a lagoon bommie, namely a pixel‐based (PB) model versus an object‐based (OB) model. For the OB model, two segmentation methods were considered: an optimized mean shift segmentation and the fully automated Segment Anything Model (SAM). Mean shift segmentation was assessed as the preferred method and applied in the final OB model (SAM exhibited poor identification of coral patches on the bommie). While good cross‐validation accuracies were achieved for both models, the PB had generally higher overall accuracy (mean accuracy PB = 75%, OB = 70%) and kappa (mean kappa PB = 0.69, OB = 0.63), making it the preferred method for monitoring coral cover. Both models were limited by the low contrast between Coral features and the bommie substrate in the drone imagery, causing indistinct segment boundaries in the OB model that increased misclassification. For both models, the inclusion of a drone‐derived digital surface model and multiscale derivatives was critical to predicting coral habitat. Our success in creating emergent reef habitat models with high accuracy demonstrates the niche role drones could play in monitoring these habitat types, which are particularly vulnerable to rising sea surface and air temperatures, as well as sea level rise which is predicted to outpace reef vertical accretion rates.
虽然新生珊瑚礁在整个珊瑚礁栖息地中占很大比例，但由于其位置较浅且暴露在外，难以进入，因此常常被排除在监测项目之外。使用无人机勘测突起珊瑚礁克服了进入这种生境类型的问题；但是，无人机图像得出珊瑚覆盖率等可靠监测指标的方法并不完善。为了填补这一知识空白，我们比较了两种遥感方法（即基于像素（PB）的模型和基于对象（OB）的模型）在量化泻湖礁石上珊瑚覆盖率等广泛基质群方面的效果。对于 OB 模型，考虑了两种分割方法：优化的均值偏移分割法和全自动的 "任意分割模型"（SAM）。平均移位分割法被认为是首选方法，并被应用于最终的 OB 模型中（SAM 对 Bommie 上珊瑚斑块的识别能力较差）。虽然两个模型都达到了良好的交叉验证精度，但 PB 的总体精度（平均精度 PB = 75%，OB = 70%）和卡帕值（平均卡帕值 PB = 0.69，OB = 0.63）普遍较高，因此成为监测珊瑚覆盖率的首选方法。两种模型都受到了无人机图像中珊瑚特征与鲂鱼底质之间对比度低的限制，导致 OB 模型中的区段边界不清晰，从而增加了误分类。对于这两个模型来说，包含无人机数字表面模型和多尺度衍生物对于预测珊瑚栖息地至关重要。我们成功创建了高精度的新兴珊瑚礁栖息地模型，这表明无人机在监测这些栖息地类型方面可以发挥利基作用，因为这些栖息地特别容易受到海面和气温上升以及海平面上升的影响，而海平面上升的速度预计将超过珊瑚礁垂直增生的速度。
{"title":"Mapping emergent coral reefs: a comparison of pixel‐ and object‐based methods","authors":"Amy Stone, Sharyn Hickey, Ben Radford, Mary Wakeford","doi":"10.1002/rse2.401","DOIUrl":"https://doi.org/10.1002/rse2.401","url":null,"abstract":"Although emergent coral reefs represent a significant proportion of overall reef habitat, they are often excluded from monitoring projects due to their shallow and exposed setting that makes them challenging to access. Using drones to survey emergent reefs overcomes issues around access to this habitat type; however, methods for deriving robust monitoring metrics, such as coral cover, are not well developed for drone imagery. To address this knowledge gap, we compare the effectiveness of two remote sensing methods in quantifying broad substrate groups, such as coral cover, on a lagoon bommie, namely a pixel‐based (PB) model versus an object‐based (OB) model. For the OB model, two segmentation methods were considered: an optimized mean shift segmentation and the fully automated Segment Anything Model (SAM). Mean shift segmentation was assessed as the preferred method and applied in the final OB model (SAM exhibited poor identification of coral patches on the bommie). While good cross‐validation accuracies were achieved for both models, the PB had generally higher overall accuracy (mean accuracy PB = 75%, OB = 70%) and kappa (mean kappa PB = 0.69, OB = 0.63), making it the preferred method for monitoring coral cover. Both models were limited by the low contrast between Coral features and the bommie substrate in the drone imagery, causing indistinct segment boundaries in the OB model that increased misclassification. For both models, the inclusion of a drone‐derived digital surface model and multiscale derivatives was critical to predicting coral habitat. Our success in creating emergent reef habitat models with high accuracy demonstrates the niche role drones could play in monitoring these habitat types, which are particularly vulnerable to rising sea surface and air temperatures, as well as sea level rise which is predicted to outpace reef vertical accretion rates.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"31 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141177384","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}
Cheryl L. Doughty, Kyle C. Cavanaugh, Samantha Chapman, Lola Fatoyinbo
Mangroves are important ecosystems for coastal biodiversity, resilience and carbon dynamics that are being threatened globally by human pressures and the impacts of climate change. Yet, at several geographic range limits in tropical–temperate transition zones, mangrove ecosystems are expanding poleward in response to changing macroclimatic drivers. Mangroves near range limits often grow to smaller statures and form dynamic, patchy distributions with other coastal habitats, which are difficult to map using moderate‐resolution (30‐m) satellite imagery. As a result, many of these mangrove areas are missing in global distribution maps. To better map small, scrub mangroves, we tested Landsat (30‐m) and Sentinel (10‐m) against very high resolution (VHR) Planet (3‐m) and WorldView (1.8‐m) imagery and assessed the accuracy of machine learning classification approaches in discerning current (2022) mangrove and saltmarsh from other coastal habitats in a rapidly changing ecotone along the east coast of Florida, USA. Our aim is to (1) quantify the mappable differences in landscape composition and complexity, class dominance and spatial properties of mangrove and saltmarsh patches due to image resolution; and (2) to resolve mapping uncertainties in the region. We found that the ability of Landsat to map mangrove distributions at the leading range edge was hampered by the size and extent of mangrove stands being too small for detection (50% accuracy). WorldView was the most successful in discerning mangroves from other wetland habitats (84% accuracy), closely followed by Planet (82%) and Sentinel (81%). With WorldView, we detected 800 ha of mangroves within the Florida range‐limit study area, 35% more mangroves than were detected with Planet, 114% more than Sentinel and 537% more than Landsat. Higher‐resolution imagery helped reveal additional variability in landscape metrics quantifying diversity, spatial configuration and connectedness among mangrove and saltmarsh habitats at the landscape, class and patch scales. Overall, VHR satellite imagery improved our ability to map mangroves at range limits and can help supplement moderate‐resolution global distributions and outdated regional maps.
{"title":"Uncovering mangrove range limits using very high resolution satellite imagery to detect fine‐scale mangrove and saltmarsh habitats in dynamic coastal ecotones","authors":"Cheryl L. Doughty, Kyle C. Cavanaugh, Samantha Chapman, Lola Fatoyinbo","doi":"10.1002/rse2.394","DOIUrl":"https://doi.org/10.1002/rse2.394","url":null,"abstract":"Mangroves are important ecosystems for coastal biodiversity, resilience and carbon dynamics that are being threatened globally by human pressures and the impacts of climate change. Yet, at several geographic range limits in tropical–temperate transition zones, mangrove ecosystems are expanding poleward in response to changing macroclimatic drivers. Mangroves near range limits often grow to smaller statures and form dynamic, patchy distributions with other coastal habitats, which are difficult to map using moderate‐resolution (30‐m) satellite imagery. As a result, many of these mangrove areas are missing in global distribution maps. To better map small, scrub mangroves, we tested Landsat (30‐m) and Sentinel (10‐m) against very high resolution (VHR) Planet (3‐m) and WorldView (1.8‐m) imagery and assessed the accuracy of machine learning classification approaches in discerning current (2022) mangrove and saltmarsh from other coastal habitats in a rapidly changing ecotone along the east coast of Florida, USA. Our aim is to (1) quantify the mappable differences in landscape composition and complexity, class dominance and spatial properties of mangrove and saltmarsh patches due to image resolution; and (2) to resolve mapping uncertainties in the region. We found that the ability of Landsat to map mangrove distributions at the leading range edge was hampered by the size and extent of mangrove stands being too small for detection (50% accuracy). WorldView was the most successful in discerning mangroves from other wetland habitats (84% accuracy), closely followed by Planet (82%) and Sentinel (81%). With WorldView, we detected 800 ha of mangroves within the Florida range‐limit study area, 35% more mangroves than were detected with Planet, 114% more than Sentinel and 537% more than Landsat. Higher‐resolution imagery helped reveal additional variability in landscape metrics quantifying diversity, spatial configuration and connectedness among mangrove and saltmarsh habitats at the landscape, class and patch scales. Overall, VHR satellite imagery improved our ability to map mangroves at range limits and can help supplement moderate‐resolution global distributions and outdated regional maps.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"26 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096665","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}
Hannah C. Cubaynes, Jaume Forcada, Kit M. Kovacs, Christian Lydersen, Rod Downie, Peter T. Fretwell
Regular counts of walruses (Odobenus rosmarus) across their pan‐Arctic range are necessary to determine accurate population trends and in turn understand how current rapid changes in their habitat, such as sea ice loss, are impacting them. However, surveying a region as vast and remote as the Arctic with vessels or aircraft is a formidable logistical challenge, limiting the frequency and spatial coverage of field surveys. An alternative methodology involving very high‐resolution (VHR) satellite imagery has proven to be a useful tool to detect walruses, but the feasibility of accurately counting individuals has not been addressed. Here, we compare walrus counts obtained from a VHR WorldView‐3 satellite image, with a simultaneous ground count obtained using a remotely piloted aircraft system (RPAS). We estimated the accuracy of the walrus counts depending on (1) the spatial resolution of the VHR satellite imagery, providing the same WorldView‐3 image to assessors at three different spatial resolutions (i.e., 50, 30 and 15 cm per pixel) and (2) the level of expertise of the assessors (experts vs. a mixed level of experience – representative of citizen scientists). This latter aspect of the study is important to the efficiency and outcomes of the global assessment programme because there are citizen science campaigns inviting the public to count walruses in VHR satellite imagery. There were 73 walruses in our RPAS ‘control’ image. Our results show that walruses were under‐counted in VHR satellite imagery at all spatial resolutions and across all levels of assessor expertise. Counts from the VHR satellite imagery with 30 cm spatial resolution were the most accurate and least variable across levels of expertise. This was a successful first attempt at validating VHR counts with near‐simultaneous, in situ, data but further assessments are required for walrus aggregations with different densities and configurations, on different substrates.
{"title":"Walruses from space: walrus counts in simultaneous remotely piloted aircraft system versus very high‐resolution satellite imagery","authors":"Hannah C. Cubaynes, Jaume Forcada, Kit M. Kovacs, Christian Lydersen, Rod Downie, Peter T. Fretwell","doi":"10.1002/rse2.391","DOIUrl":"https://doi.org/10.1002/rse2.391","url":null,"abstract":"Regular counts of walruses (<jats:italic>Odobenus rosmarus</jats:italic>) across their pan‐Arctic range are necessary to determine accurate population trends and in turn understand how current rapid changes in their habitat, such as sea ice loss, are impacting them. However, surveying a region as vast and remote as the Arctic with vessels or aircraft is a formidable logistical challenge, limiting the frequency and spatial coverage of field surveys. An alternative methodology involving very high‐resolution (VHR) satellite imagery has proven to be a useful tool to detect walruses, but the feasibility of accurately counting individuals has not been addressed. Here, we compare walrus counts obtained from a VHR WorldView‐3 satellite image, with a simultaneous ground count obtained using a remotely piloted aircraft system (RPAS). We estimated the accuracy of the walrus counts depending on (1) the spatial resolution of the VHR satellite imagery, providing the same WorldView‐3 image to assessors at three different spatial resolutions (i.e., 50, 30 and 15 cm per pixel) and (2) the level of expertise of the assessors (experts vs. a mixed level of experience – representative of citizen scientists). This latter aspect of the study is important to the efficiency and outcomes of the global assessment programme because there are citizen science campaigns inviting the public to count walruses in VHR satellite imagery. There were 73 walruses in our RPAS ‘control’ image. Our results show that walruses were under‐counted in VHR satellite imagery at all spatial resolutions and across all levels of assessor expertise. Counts from the VHR satellite imagery with 30 cm spatial resolution were the most accurate and least variable across levels of expertise. This was a successful first attempt at validating VHR counts with near‐simultaneous, in situ, data but further assessments are required for walrus aggregations with different densities and configurations, on different substrates.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"26 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141085525","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}
Beibei Zhang, Fabian J. Fischer, Suzanne M. Prober, Paul B. Yeoh, Carl R. Gosper, Katherine Zdunic, Tommaso Jucker
LiDAR data acquired from airplanes and helicopters – known as airborne laser scanning (ALS) – are widely regarded as the gold standard for characterizing the 3D structure of forests at scale. But in the last decade, advances in unoccupied aerial vehicle (UAV) technologies have led to a rapid rise in the use of UAV laser scanning (ULS) for mapping forest structure. As both ALS and ULS data become increasingly available, they are being used to derive an ever‐growing number of metrics designed to measure different facets of canopy structure. However, which metrics can be robustly retrieved from both ALS and ULS platforms remains unclear. To address this question, we acquired coincident, high‐density ALS and ULS scans covering 115 plots (4‐ha in size) in an open‐canopy temperate ecosystem in Western Australia. Using this unique dataset, we quantified 32 canopy structural metrics related to canopy height, openness and heterogeneity, including metrics calculated directly from the point clouds and ones measured from derived canopy height models (CHM). Overall, we found that ALS and ULS‐derived metrics were strongly correlated (r2 = 0.90). However, this high degree of correlation masked considerable systematic differences between platforms. Specifically, point cloud metrics were less strongly (r2 = 0.87) correlated and had higher bias (10.7%) compared to CHM‐derived ones (r2 = 0.98; bias = 2.5%). Similarly, metrics of canopy openness and heterogeneity were less strongly correlated (r2 = 0.84 and 0.87) and exhibited greater bias (14.4 and 7.9%) than ones relating to canopy height (r2 = 0.96; bias = 3.8%). Our results indicate that only a small subset of the 32 metrics we tested were directly comparable between ALS and ULS platforms. Consequently, future efforts to combine laser scanning data across platforms and instruments should think carefully about which metrics are most appropriate, especially when working with point cloud data.
从飞机和直升机上获取的激光雷达数据--即机载激光扫描（ALS）--被广泛认为是描述森林三维结构的黄金标准。但在过去的十年中，无人飞行器（UAV）技术的进步使用于绘制森林结构图的无人飞行器激光扫描（ULS）技术迅速崛起。随着 ALS 和 ULS 数据越来越多地可用，它们被用来推导出越来越多的指标，这些指标旨在测量冠层结构的不同方面。然而，哪些指标可以同时从 ALS 和 ULS 平台上稳健地检索到仍不清楚。为了解决这个问题，我们在西澳大利亚的一个开阔树冠温带生态系统中获取了重合、高密度的 ALS 和 ULS 扫描，覆盖了 115 个地块（面积为 4 公顷）。利用这一独特的数据集，我们量化了与冠层高度、开阔度和异质性有关的 32 个冠层结构指标，包括直接从点云计算得出的指标和从衍生冠层高度模型（CHM）测量得出的指标。总体而言，我们发现 ALS 和 ULS 得出的指标具有很强的相关性（r2 = 0.90）。然而，这种高度相关性掩盖了不同平台之间存在的相当大的系统性差异。具体而言，点云指标的相关性较弱（r2 = 0.87），与 CHM 衍生指标（r2 = 0.98；偏差 = 2.5%）相比，偏差更高（10.7%）。同样，与冠层高度相关指标（r2 = 0.96；偏差 = 3.8%）相比，冠层开阔度和异质性指标的相关性较弱（r2 = 0.84 和 0.87），偏差较大（14.4 和 7.9%）。我们的结果表明，在我们测试的 32 项指标中，只有一小部分在 ALS 和 ULS 平台之间具有直接可比性。因此，未来在跨平台和仪器组合激光扫描数据时，应仔细考虑哪些指标是最合适的，尤其是在处理点云数据时。
{"title":"Robust retrieval of forest canopy structural attributes using multi‐platform airborne LiDAR","authors":"Beibei Zhang, Fabian J. Fischer, Suzanne M. Prober, Paul B. Yeoh, Carl R. Gosper, Katherine Zdunic, Tommaso Jucker","doi":"10.1002/rse2.398","DOIUrl":"https://doi.org/10.1002/rse2.398","url":null,"abstract":"LiDAR data acquired from airplanes and helicopters – known as airborne laser scanning (ALS) – are widely regarded as the gold standard for characterizing the 3D structure of forests at scale. But in the last decade, advances in unoccupied aerial vehicle (UAV) technologies have led to a rapid rise in the use of UAV laser scanning (ULS) for mapping forest structure. As both ALS and ULS data become increasingly available, they are being used to derive an ever‐growing number of metrics designed to measure different facets of canopy structure. However, which metrics can be robustly retrieved from both ALS and ULS platforms remains unclear. To address this question, we acquired coincident, high‐density ALS and ULS scans covering 115 plots (4‐ha in size) in an open‐canopy temperate ecosystem in Western Australia. Using this unique dataset, we quantified 32 canopy structural metrics related to canopy height, openness and heterogeneity, including metrics calculated directly from the point clouds and ones measured from derived canopy height models (CHM). Overall, we found that ALS and ULS‐derived metrics were strongly correlated (<jats:italic>r</jats:italic><jats:sup>2</jats:sup> = 0.90). However, this high degree of correlation masked considerable systematic differences between platforms. Specifically, point cloud metrics were less strongly (<jats:italic>r</jats:italic><jats:sup>2</jats:sup> = 0.87) correlated and had higher bias (10.7%) compared to CHM‐derived ones (<jats:italic>r</jats:italic><jats:sup>2</jats:sup> = 0.98; bias = 2.5%). Similarly, metrics of canopy openness and heterogeneity were less strongly correlated (<jats:italic>r</jats:italic><jats:sup>2</jats:sup> = 0.84 and 0.87) and exhibited greater bias (14.4 and 7.9%) than ones relating to canopy height (<jats:italic>r</jats:italic><jats:sup>2</jats:sup> = 0.96; bias = 3.8%). Our results indicate that only a small subset of the 32 metrics we tested were directly comparable between ALS and ULS platforms. Consequently, future efforts to combine laser scanning data across platforms and instruments should think carefully about which metrics are most appropriate, especially when working with point cloud data.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"28 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140953879","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}
Jordan B. Stewart, Justine M. Hudson, Bryanna A. H. Sherbo, Cortney A. Watt
Routine monitoring of cetaceans is imperative for understanding their population trends and making informed management decisions. However, the inherent nature of cetaceans and the marine ecosystems they inhabit make annual population surveys logistically and economically challenging with current survey methods. One emerging solution is utilizing very high‐resolution (VHR) satellite imagery, which is a logistically efficient method for providing an instantaneous view of areas spanning hundreds of square kilometers. The objective of this study was to determine two factors required to reliably conduct beluga whale population abundance estimates with VHR satellite imagery: (1) depths that beluga whales are visible in VHR satellite images, which are used to define availability bias correction factors, and (2) a comparison of abundance estimates in VHR satellite imagery to current aerial methods. We submerged beluga whale models to different depths in two different water clarities and determined that beluga whales are distinguished only at the surface in turbid water (Secchi depth: 2.56 m) and at depths of 0–2 m in clear water (Secchi depth: 4.04 m). Based on the proportion of time beluga whales spend at these depths, an availability bias correction factor for Western Hudson Bay beluga whales was defined as 2.40 ± 0.16 for turbid water and 1.89 ± 0.05 for clear water. Synchronous ground‐validation surveys determined availability corrected beluga whale abundance estimates in 0.31 m VHR satellite imagery (n = 173 beluga whales) and imagery that was HD sharpened using a proprietary algorithm to approximate 0.15 m resolution (n = 170) to be comparable to drone imagery (n = 164). VHR satellite imagery has the potential to increase the frequency of beluga whale population surveys, which has become increasingly important as beluga whales face rapid ecosystem changes and increased anthropogenic disturbances.
{"title":"Estimating beluga whale abundance from space: using drones to ground‐validate VHR satellite imagery","authors":"Jordan B. Stewart, Justine M. Hudson, Bryanna A. H. Sherbo, Cortney A. Watt","doi":"10.1002/rse2.396","DOIUrl":"https://doi.org/10.1002/rse2.396","url":null,"abstract":"Routine monitoring of cetaceans is imperative for understanding their population trends and making informed management decisions. However, the inherent nature of cetaceans and the marine ecosystems they inhabit make annual population surveys logistically and economically challenging with current survey methods. One emerging solution is utilizing very high‐resolution (VHR) satellite imagery, which is a logistically efficient method for providing an instantaneous view of areas spanning hundreds of square kilometers. The objective of this study was to determine two factors required to reliably conduct beluga whale population abundance estimates with VHR satellite imagery: (1) depths that beluga whales are visible in VHR satellite images, which are used to define availability bias correction factors, and (2) a comparison of abundance estimates in VHR satellite imagery to current aerial methods. We submerged beluga whale models to different depths in two different water clarities and determined that beluga whales are distinguished only at the surface in turbid water (Secchi depth: 2.56 m) and at depths of 0–2 m in clear water (Secchi depth: 4.04 m). Based on the proportion of time beluga whales spend at these depths, an availability bias correction factor for Western Hudson Bay beluga whales was defined as 2.40 ± 0.16 for turbid water and 1.89 ± 0.05 for clear water. Synchronous ground‐validation surveys determined availability corrected beluga whale abundance estimates in 0.31 m VHR satellite imagery (<jats:italic>n</jats:italic> = 173 beluga whales) and imagery that was HD sharpened using a proprietary algorithm to approximate 0.15 m resolution (<jats:italic>n</jats:italic> = 170) to be comparable to drone imagery (<jats:italic>n</jats:italic> = 164). VHR satellite imagery has the potential to increase the frequency of beluga whale population surveys, which has become increasingly important as beluga whales face rapid ecosystem changes and increased anthropogenic disturbances.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"2014 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140895391","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}
Jenna M. Keany, Patrick Burns, Andrew J. Abraham, Patrick Jantz, Loic Makaga, Sassan Saatchi, Fiona Maisels, Katharine Abernethy, Christopher E. Doughty
Recently classified as a unique species by the IUCN, African forest elephants (Loxodonta cyclotis) are critically endangered due to severe poaching. With limited knowledge about their ecological role due to the dense tropical forests they inhabit in central Africa, it is unclear how the Afrotropics are influenced by elephants. Although their role as seed dispersers is well known, they may also drive large‐scale processes that determine forest structure through the creation of elephant trails and browsing the understory, allowing larger, carbon‐dense trees to succeed. Multiple scales of lidar were collected by NASA in Lopé National Park, Gabon from 2015 to 2022. Utilizing two airborne lidar datasets in an African forest elephant stronghold, detailed canopy structural information was used in conjunction with elephant trail data to determine how forest structure varies on and off trails. Forest along elephant trails displayed different structural characteristics than forested areas off trails, with lower canopy height, canopy cover, and different vertical distribution of plant density. Less plant area density was found on trails at 1 m in height, while more vegetation was found at 12 m, compared to off trail locations. Trails in forest areas with previous logging history had lower plant area in the top of the canopy. Forest elephants can be considered as “logging light” ecosystem engineers, affecting canopy structure through browsing and movement. Both airborne lidar scales were able to capture elephant impact along trails, with the high‐resolution discrete return lidar performing higher than waveform lidar.
{"title":"Using multiscale lidar to determine variation in canopy structure from African forest elephant trails","authors":"Jenna M. Keany, Patrick Burns, Andrew J. Abraham, Patrick Jantz, Loic Makaga, Sassan Saatchi, Fiona Maisels, Katharine Abernethy, Christopher E. Doughty","doi":"10.1002/rse2.395","DOIUrl":"https://doi.org/10.1002/rse2.395","url":null,"abstract":"Recently classified as a unique species by the IUCN, African forest elephants (<jats:italic>Loxodonta cyclotis</jats:italic>) are critically endangered due to severe poaching. With limited knowledge about their ecological role due to the dense tropical forests they inhabit in central Africa, it is unclear how the Afrotropics are influenced by elephants. Although their role as seed dispersers is well known, they may also drive large‐scale processes that determine forest structure through the creation of elephant trails and browsing the understory, allowing larger, carbon‐dense trees to succeed. Multiple scales of lidar were collected by NASA in Lopé National Park, Gabon from 2015 to 2022. Utilizing two airborne lidar datasets in an African forest elephant stronghold, detailed canopy structural information was used in conjunction with elephant trail data to determine how forest structure varies on and off trails. Forest along elephant trails displayed different structural characteristics than forested areas off trails, with lower canopy height, canopy cover, and different vertical distribution of plant density. Less plant area density was found on trails at 1 m in height, while more vegetation was found at 12 m, compared to off trail locations. Trails in forest areas with previous logging history had lower plant area in the top of the canopy. Forest elephants can be considered as “logging light” ecosystem engineers, affecting canopy structure through browsing and movement. Both airborne lidar scales were able to capture elephant impact along trails, with the high‐resolution discrete return lidar performing higher than waveform lidar.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"8 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140895575","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}
Mike P. Shewring, Nicholas I. Wilkinson, Emma L. Teuten, Graeme M. Buchanan, Patrick Thompson, David J. T. Douglas
In the UK uplands, prescribed burning of unenclosed heath, grass and blanket bog (‘moorland’) is used to support game shooting and grazing. Burning on moorland is contentious due to its impact on peat soils, hydrology and habitat condition. There is little information on spatial and temporal patterns of burning, the overlap with soil carbon and sensitive habitats and, importantly, whether these patterns are changing. This information is required to assess the sustainability of burning and the effectiveness of new legislation. We developed a method for semi‐automated detection of burning using satellite imagery – our best performing model has a balanced accuracy of 84.9%. We identified annual burn areas in Great Britain in five burning seasons from 2017/18 to 2021/22 of 8333 to 20 974 ha (average 15 250 ha year−1). Annual extent in England in 2021/22 was 73% lower than the average of the four previous seasons. Burning was identified over carbon‐rich soils (mean 5150 ha or 34% by area of all burning annually) and on steep slopes – 915 ha across the five seasons (1.3%), contravening guidance. Burning (>1 ha) was recorded in 14% of UK protected areas (PAs) and, within these, the percentage area of moorland burned varied from 2 to 31%. In England in some years, the percentage area of moorland burned inside PAs was higher than outside, while this was not the case in Scotland. Burning in sensitive alpine habitats totalled 158 ha across the five seasons. The reduction in burned area in England in 2021/22 could relate to England‐specific legislation, introduced in May 2021, to prohibit burning on deep peat in PAs. This suggests that regulation can be effective. However, the continued overlap with sensitive features suggests that burning falls short of sustainable practices. Our method will enable repeatable re‐assessment of burning extents and overlap with ecosystem services.
{"title":"Annual extent of prescribed burning on moorland in Great Britain and overlap with ecosystem services","authors":"Mike P. Shewring, Nicholas I. Wilkinson, Emma L. Teuten, Graeme M. Buchanan, Patrick Thompson, David J. T. Douglas","doi":"10.1002/rse2.389","DOIUrl":"https://doi.org/10.1002/rse2.389","url":null,"abstract":"In the UK uplands, prescribed burning of unenclosed heath, grass and blanket bog (‘moorland’) is used to support game shooting and grazing. Burning on moorland is contentious due to its impact on peat soils, hydrology and habitat condition. There is little information on spatial and temporal patterns of burning, the overlap with soil carbon and sensitive habitats and, importantly, whether these patterns are changing. This information is required to assess the sustainability of burning and the effectiveness of new legislation. We developed a method for semi‐automated detection of burning using satellite imagery – our best performing model has a balanced accuracy of 84.9%. We identified annual burn areas in Great Britain in five burning seasons from 2017/18 to 2021/22 of 8333 to 20 974 ha (average 15 250 ha year<jats:sup>−1</jats:sup>). Annual extent in England in 2021/22 was 73% lower than the average of the four previous seasons. Burning was identified over carbon‐rich soils (mean 5150 ha or 34% by area of all burning annually) and on steep slopes – 915 ha across the five seasons (1.3%), contravening guidance. Burning (>1 ha) was recorded in 14% of UK protected areas (PAs) and, within these, the percentage area of moorland burned varied from 2 to 31%. In England in some years, the percentage area of moorland burned inside PAs was higher than outside, while this was not the case in Scotland. Burning in sensitive alpine habitats totalled 158 ha across the five seasons. The reduction in burned area in England in 2021/22 could relate to England‐specific legislation, introduced in May 2021, to prohibit burning on deep peat in PAs. This suggests that regulation can be effective. However, the continued overlap with sensitive features suggests that burning falls short of sustainable practices. Our method will enable repeatable re‐assessment of burning extents and overlap with ecosystem services.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"9 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140814580","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}
Emma A. Higgins, Doreen S. Boyd, Tom W. Brown, Sarah C. Owen, Geertje M. F. van der Heijden, Adam C. Algar
To understand how ectotherms will respond to warming temperatures, we require information on thermal habitat quality at spatial resolutions and extents relevant to the organism. Measuring thermal habitat quality is either limited to small spatial extents, such as with ground‐based 3D operative temperature (Te) replicas, representing the temperature of the animal at equilibrium with its environment, or is based on microclimate derived from physical models that use land cover variables and downscale coarse climate data. We draw on aspects of both these approaches and test the ability of unoccupied aerial vehicle (UAV) data (optical RGB) to predict fine‐scale heterogeneity in sub‐canopy lizard (Anolis bicaorum) Te in tropical forest using random forest models. Anolis bicaorum is an endemic, critically endangered, species, facing significant threats of habitat loss and degradation, and work was conducted as part of a larger project. Our findings indicate that a model incorporating solely air temperature, measured at the centre of the 20 × 20 m plot, and ground‐based leaf area index (LAI) measurements, measured at directly above the 3D replica, predicted Te well. However, a model with air temperature and UAV‐derived canopy metrics performed slightly better with the added advantage of enabling the mapping of Te with continuous spatial extent at high spatial resolutions, across the whole of the UAV orthomosaic, allowing us to capture and map Te across the whole of the survey plot, rather than purely at 3D replica locations. Our work provides a feasible workflow to map sub‐canopy lizard Te in tropical environments at spatial scales relevant to the organism, and across continuous areas. This can be applied to other species and can represent species within the same community that have evolved a similar thermal niche. Such methods will be imperative in risk modelling of such species to anthropogenic land cover and climate change.
要了解外温动物如何应对气温变暖，我们需要获得与生物体相关的空间分辨率和范围内的热栖息地质量信息。测量热栖息地质量要么局限于较小的空间范围，如使用地面三维工作温度（Te）复制品，代表动物与其环境平衡时的温度；要么基于使用土地覆盖变量和降尺度粗气候数据的物理模型得出的小气候。我们借鉴了这两种方法的各个方面，并利用随机森林模型测试了无人飞行器（UAV）数据（光学 RGB）预测热带森林亚冠蜥蜴（Anolis bicaorum）Te 的细尺度异质性的能力。Anolis bicaorum 是一种特有的极度濒危物种，面临栖息地丧失和退化的严重威胁。我们的研究结果表明，仅包含在 20 × 20 米地块中心测量的气温和在三维复制品正上方测量的地面叶面积指数（LAI）的模型就能很好地预测 Te。然而，包含气温和无人机树冠指标的模型表现略好，其额外优势是能够以高空间分辨率绘制整个无人机正射影像图的连续空间范围的 Te 图，使我们能够捕捉和绘制整个调查地块的 Te 图，而不仅仅是三维复制品位置的 Te 图。我们的工作提供了一个可行的工作流程，可以在热带环境中以与生物体相关的空间尺度绘制亚冠蜥Te图，并绘制整个连续区域的Te图。这可以应用于其他物种，并代表同一群落中进化出类似热生态位的物种。这种方法对于建立此类物种对人为土地覆盖和气候变化的风险模型至关重要。
{"title":"Unoccupied aerial vehicles as a tool to map lizard operative temperature in tropical environments","authors":"Emma A. Higgins, Doreen S. Boyd, Tom W. Brown, Sarah C. Owen, Geertje M. F. van der Heijden, Adam C. Algar","doi":"10.1002/rse2.393","DOIUrl":"https://doi.org/10.1002/rse2.393","url":null,"abstract":"To understand how ectotherms will respond to warming temperatures, we require information on thermal habitat quality at spatial resolutions and extents relevant to the organism. Measuring thermal habitat quality is either limited to small spatial extents, such as with ground‐based 3D operative temperature (<jats:italic>T</jats:italic><jats:sub><jats:italic>e</jats:italic></jats:sub>) replicas, representing the temperature of the animal at equilibrium with its environment, or is based on microclimate derived from physical models that use land cover variables and downscale coarse climate data. We draw on aspects of both these approaches and test the ability of unoccupied aerial vehicle (UAV) data (optical RGB) to predict fine‐scale heterogeneity in sub‐canopy lizard (<jats:italic>Anolis bicaorum</jats:italic>) <jats:italic>T</jats:italic><jats:sub><jats:italic>e</jats:italic></jats:sub> in tropical forest using random forest models. <jats:italic>Anolis bicaorum</jats:italic> is an endemic, critically endangered, species, facing significant threats of habitat loss and degradation, and work was conducted as part of a larger project. Our findings indicate that a model incorporating solely air temperature, measured at the centre of the 20 × 20 m plot, and ground‐based leaf area index (LAI) measurements, measured at directly above the 3D replica, predicted <jats:italic>T</jats:italic><jats:sub><jats:italic>e</jats:italic></jats:sub> well. However, a model with air temperature and UAV‐derived canopy metrics performed slightly better with the added advantage of enabling the mapping of <jats:italic>T</jats:italic><jats:sub><jats:italic>e</jats:italic></jats:sub> with continuous spatial extent at high spatial resolutions, across the whole of the UAV orthomosaic, allowing us to capture and map <jats:italic>T</jats:italic><jats:sub><jats:italic>e</jats:italic></jats:sub> across the whole of the survey plot, rather than purely at 3D replica locations. Our work provides a feasible workflow to map sub‐canopy lizard <jats:italic>T</jats:italic><jats:sub><jats:italic>e</jats:italic></jats:sub> in tropical environments at spatial scales relevant to the organism, and across continuous areas. This can be applied to other species and can represent species within the same community that have evolved a similar thermal niche. Such methods will be imperative in risk modelling of such species to anthropogenic land cover and climate change.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"37 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140651662","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}
Wahaj Habib, Rémi Cresson, Kevin McGuinness, John Connolly
Peatlands, constituting over half of terrestrial wetland ecosystems across the globe, hold critical ecological significance and are large stores of carbon (C). Irish oceanic raised bogs are a rare peatland ecosystem offering numerous ecosystem services, including C storage, biodiversity support and water regulation. However, they have been degraded over the centuries due to artificial drainage, followed by peat extraction, afforestation and agriculture. This has an overall negative impact on the functioning of peatlands, shifting them from a moderate C sink to a large C source. Recognizing the importance of these ecosystems, efforts are underway for conservation (rewetting and rehabilitation), while accurately accounting for C stock and greenhouse gas (GHG) emissions. However, the implementation of these efforts requires accurate identification and mapping of artificial drainage ditches. This study utilized very high‐resolution (25 cm) aerial imagery, and a deep learning (U‐Net) approach to map the visible artificial drainage (unobstructed by vegetation or infill) in raised bogs at a national scale. The results show that artificial drainage is widespread, with ~20 000 km of drains mapped. The overall accuracy of the model was 80% on an independent testing dataset. The data were also used to derive the Fracditch which was 0.03 (fraction of artificial drainage on industrial peat extraction sites). This is lower than IPCC Tier 1 Fracditch and can aid in IPCC Tier 2 reporting for Ireland. This is the first study to map drains with diverse sizes and patterns on Irish‐raised bogs using optical aerial imagery and deep learning methods. The map will serve as an important baseline dataset for evaluating the artificial drainage ditch conditions. It will prove useful for sustainable management, conservation and refined estimations of GHG emissions. The model's capacity for generalization implies its potential in mapping artificial drains in peatlands at a regional and global scale, thereby enhancing the comprehension of the global effects of artificial drainage ditches on peatlands.
{"title":"Mapping artificial drains in peatlands—A national‐scale assessment of Irish raised bogs using sub‐meter aerial imagery and deep learning methods","authors":"Wahaj Habib, Rémi Cresson, Kevin McGuinness, John Connolly","doi":"10.1002/rse2.387","DOIUrl":"https://doi.org/10.1002/rse2.387","url":null,"abstract":"Peatlands, constituting over half of terrestrial wetland ecosystems across the globe, hold critical ecological significance and are large stores of carbon (C). Irish oceanic raised bogs are a rare peatland ecosystem offering numerous ecosystem services, including C storage, biodiversity support and water regulation. However, they have been degraded over the centuries due to artificial drainage, followed by peat extraction, afforestation and agriculture. This has an overall negative impact on the functioning of peatlands, shifting them from a moderate C sink to a large C source. Recognizing the importance of these ecosystems, efforts are underway for conservation (rewetting and rehabilitation), while accurately accounting for C stock and greenhouse gas (GHG) emissions. However, the implementation of these efforts requires accurate identification and mapping of artificial drainage ditches. This study utilized very high‐resolution (25 cm) aerial imagery, and a deep learning (U‐Net) approach to map the visible artificial drainage (unobstructed by vegetation or infill) in raised bogs at a national scale. The results show that artificial drainage is widespread, with ~20 000 km of drains mapped. The overall accuracy of the model was 80% on an independent testing dataset. The data were also used to derive the Frac<jats:sub>ditch</jats:sub> which was 0.03 (fraction of artificial drainage on industrial peat extraction sites). This is lower than IPCC Tier 1 Frac<jats:sub>ditch</jats:sub> and can aid in IPCC Tier 2 reporting for Ireland. This is the first study to map drains with diverse sizes and patterns on Irish‐raised bogs using optical aerial imagery and deep learning methods. The map will serve as an important baseline dataset for evaluating the artificial drainage ditch conditions. It will prove useful for sustainable management, conservation and refined estimations of GHG emissions. The model's capacity for generalization implies its potential in mapping artificial drains in peatlands at a regional and global scale, thereby enhancing the comprehension of the global effects of artificial drainage ditches on peatlands.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"217 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140640323","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}
Gustavo Perez, Wenlong Zhao, Zezhou Cheng, Maria Carolina T. D. Belotti, Yuting Deng, Victoria F. Simons, Elske Tielens, Jeffrey F. Kelly, Kyle G. Horton, Subhransu Maji, Daniel Sheldon
The exodus of flying animals from their roosting locations is often visible as expanding ring‐shaped patterns in weather radar data. The NEXRAD network, for example, archives more than 25 years of data across 143 contiguous US radar stations, providing opportunities to study roosting locations and times and the ecosystems of birds and bats. However, access to this information is limited by the cost of manually annotating millions of radar scans. We develop and deploy an AI‐assisted system to annotate roosts in radar data. We build datasets with roost annotations to support the training and evaluation of automated detection models. Roosts are detected, tracked, and incorporated into our developed web‐based interface for human screening to produce research‐grade annotations. We deploy the system to collect swallow and martin roost information from 12 radar stations around the Great Lakes spanning 21 years. After verifying the practical value of the system, we propose to improve the detector by incorporating both spatial and temporal channels from volumetric radar scans. The deployment on Great Lakes radar scans allows accelerated annotation of 15 628 roost signatures in 612 786 radar scans with 183.6 human screening hours, or 1.08 s per radar scan. We estimate that the deployed system reduces human annotation time by ~7×. The temporal detector model improves the average precision at intersection‐over‐union threshold 0.5 (APIoU = .50) by 8% over the previous model (48%→56%), further reducing human screening time by 2.3× in its pilot deployment. These data contain critical information about phenology and population trends of swallows and martins, aerial insectivore species experiencing acute declines, and have enabled novel research. We present error analyses, lay the groundwork for continent‐scale historical investigation about these species, and provide a starting point for automating the detection of other family‐specific phenomena in radar data, such as bat roosts and mayfly hatches.
{"title":"Using spatiotemporal information in weather radar data to detect and track communal roosts","authors":"Gustavo Perez, Wenlong Zhao, Zezhou Cheng, Maria Carolina T. D. Belotti, Yuting Deng, Victoria F. Simons, Elske Tielens, Jeffrey F. Kelly, Kyle G. Horton, Subhransu Maji, Daniel Sheldon","doi":"10.1002/rse2.388","DOIUrl":"https://doi.org/10.1002/rse2.388","url":null,"abstract":"The exodus of flying animals from their roosting locations is often visible as expanding ring‐shaped patterns in weather radar data. The NEXRAD network, for example, archives more than 25 years of data across 143 contiguous US radar stations, providing opportunities to study roosting locations and times and the ecosystems of birds and bats. However, access to this information is limited by the cost of manually annotating millions of radar scans. We develop and deploy an AI‐assisted system to annotate roosts in radar data. We build datasets with roost annotations to support the training and evaluation of automated detection models. Roosts are detected, tracked, and incorporated into our developed web‐based interface for human screening to produce research‐grade annotations. We deploy the system to collect swallow and martin roost information from 12 radar stations around the Great Lakes spanning 21 years. After verifying the practical value of the system, we propose to improve the detector by incorporating both spatial and temporal channels from volumetric radar scans. The deployment on Great Lakes radar scans allows accelerated annotation of 15 628 roost signatures in 612 786 radar scans with 183.6 human screening hours, or 1.08 s per radar scan. We estimate that the deployed system reduces human annotation time by ~7×. The temporal detector model improves the average precision at intersection‐over‐union threshold 0.5 (AP<jats:sup>IoU = .50</jats:sup>) by 8% over the previous model (48%→56%), further reducing human screening time by 2.3× in its pilot deployment. These data contain critical information about phenology and population trends of swallows and martins, aerial insectivore species experiencing acute declines, and have enabled novel research. We present error analyses, lay the groundwork for continent‐scale historical investigation about these species, and provide a starting point for automating the detection of other family‐specific phenomena in radar data, such as bat roosts and mayfly hatches.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"49 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140607842","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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