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An automated procedure to determine construction year of roads in forested landscapes using a least-cost path and a Before-After Control-Impact approach 使用最低成本路径和前后控制影响法确定森林景观中道路施工年份的自动程序
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2023-12-21 DOI: 10.1002/rse2.376
Denis Valle, Sami W. Rifai, Gabriel C. Carrero, Ana Y. Y. Meiga
Proximity to roads is one of the main determinants of deforestation in the Amazon basin. Determining the construction year of roads (CYR) is critical to improve the understanding of the drivers of road construction and to enable predictions of the expansion of the road network and its consequent impact on ecosystems. While recent artificial intelligence approaches have been successfully used for road extraction, they have typically relied on high spatial-resolution imagery, precluding their adoption for the determination of CYR for older roads. In this article, we developed a new approach to automate the process of determining CYR that relies on the approximate position of the current road network and a time-series of the proportion of exposed soil based on the multidecadal remote sensing imagery from the Landsat program. Starting with these inputs, our methodology relies on the Least Cost Path algorithm to co-register the road network and on a Before-After Control-Impact design to circumvent the inherent image-to-image variability in the estimated amount of exposed soil. We demonstrate this approach for a 357 000 km2 area around the Transamazon highway (BR-230) in the Brazilian Amazon, encompassing 36 240 road segments. The reliability of this approach is assessed by comparing the estimated CYR using our approach to the observed CYR based on a time-series of Landsat images. This exercise reveals a close correspondence between the estimated and observed CYR (rPearson=0.77�$$ {r}_{mathrm{Pearson}}=0.77 $$�). Finally, we show how these data can be used to assess the effectiveness of protected areas (PAs) in reducing the yearly rate of road construction and thus their vulnerability to future degradation. In particular, we find that integral protection PAs in this region were generally more effective in reducing the expansion of the road network when compared to sustainable use PAs.
靠近公路是亚马逊流域森林砍伐的主要决定因素之一。确定道路的建设年份(CYR)对于更好地了解道路建设的驱动因素、预测道路网络的扩张及其对生态系统的影响至关重要。虽然最近的人工智能方法已成功用于道路提取,但它们通常依赖于高空间分辨率的图像,因此无法用于确定旧道路的 CYR。在本文中,我们开发了一种新方法来自动确定 CYR,该方法依赖于当前道路网络的大致位置,以及基于 Landsat 计划十年期遥感图像的裸露土壤比例时间序列。从这些输入开始,我们的方法依赖于最小成本路径算法对道路网络进行共同注册,并依赖于控制-影响前后设计来规避估计裸露土壤量中固有的图像间差异。我们对巴西亚马逊地区 Transamazon 高速公路(BR-230)周围 357 000 平方公里的区域(包括 36 240 个路段)演示了这种方法。通过比较使用我们的方法估算出的 CYR 和基于陆地卫星图像时间序列观测到的 CYR,评估了这种方法的可靠性。结果表明,估算的 CYR 与观测到的 CYR 非常接近(rPearson=0.77$${r}_{mathrm{Pearson}}=0.77$$)。最后,我们展示了如何利用这些数据来评估保护区在降低道路建设年增长率方面的有效性,以及保护区在未来退化中的脆弱性。特别是,我们发现与可持续利用保护区相比,该地区的整体保护保护区在减少道路网络扩张方面通常更为有效。
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引用次数: 0
Camtrap DP: an open standard for the FAIR exchange and archiving of camera trap data 相机陷阱 DP:相机陷阱数据 FAIR 交换和存档的开放标准
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2023-12-09 DOI: 10.1002/rse2.374
Jakub W. Bubnicki, Ben Norton, Steven J. Baskauf, Tom Bruce, Francesca Cagnacci, Jim Casaer, Marcin Churski, Joris P. G. M. Cromsigt, Simone Dal Farra, Christian Fiderer, Tavis D. Forrester, Heidi Hendry, Marco Heurich, Tim R. Hofmeester, Patrick A. Jansen, Roland Kays, Dries P. J. Kuijper, Yorick Liefting, John D. C. Linnell, Matthew S. Luskin, Christopher Mann, Tanja Milotic, Peggy Newman, Jürgen Niedballa, Damiano Oldoni, Federico Ossi, Tim Robertson, Francesco Rovero, Marcus Rowcliffe, Lorenzo Seidenari, Izabela Stachowicz, Dan Stowell, Mathias W. Tobler, John Wieczorek, Fridolin Zimmermann, Peter Desmet
Camera trapping has revolutionized wildlife ecology and conservation by providing automated data acquisition, leading to the accumulation of massive amounts of camera trap data worldwide. Although management and processing of camera trap-derived Big Data are becoming increasingly solvable with the help of scalable cyber-infrastructures, harmonization and exchange of the data remain limited, hindering its full potential. There is currently no widely accepted standard for exchanging camera trap data. The only existing proposal, “Camera Trap Metadata Standard” (CTMS), has several technical shortcomings and limited adoption. We present a new data exchange format, the Camera Trap Data Package (Camtrap DP), designed to allow users to easily exchange, harmonize and archive camera trap data at local to global scales. Camtrap DP structures camera trap data in a simple yet flexible data model consisting of three tables (Deployments, Media and Observations) that supports a wide range of camera deployment designs, classification techniques (e.g., human and AI, media-based and event-based) and analytical use cases, from compiling species occurrence data through distribution, occupancy and activity modeling to density estimation. The format further achieves interoperability by building upon existing standards, Frictionless Data Package in particular, which is supported by a suite of open software tools to read and validate data. Camtrap DP is the consensus of a long, in-depth, consultation and outreach process with standard and software developers, the main existing camera trap data management platforms, major players in the field of camera trapping and the Global Biodiversity Information Facility (GBIF). Under the umbrella of the Biodiversity Information Standards (TDWG), Camtrap DP has been developed openly, collaboratively and with version control from the start. We encourage camera trapping users and developers to join the discussion and contribute to the further development and adoption of this standard.
相机陷阱通过提供自动数据采集,彻底改变了野生动物生态学和保护,从而在全球范围内积累了大量相机陷阱数据。尽管在可扩展的网络基础设施的帮助下,相机陷阱大数据的管理和处理正变得越来越容易,但数据的协调和交换仍然有限,阻碍了其潜力的充分发挥。目前还没有被广泛接受的相机陷阱数据交换标准。现有的唯一建议,即 "相机陷阱元数据标准"(CTMS),在技术上存在一些缺陷,采用范围有限。我们提出了一种新的数据交换格式--相机陷阱数据包(Camtrap DP),旨在让用户能够轻松地交换、协调和归档本地到全球范围内的相机陷阱数据。Camtrap DP将相机陷阱数据结构化为一个简单而灵活的数据模型,该模型由三个表(部署表、媒体表和观测表)组成,支持各种相机部署设计、分类技术(如人工和人工智能、基于媒体和基于事件的分类)和分析用例,从汇编物种出现数据到分布、占用和活动建模再到密度估算。该格式以现有标准为基础,特别是无摩擦数据包(Frictionless Data Package),进一步实现了互操作性。Camtrap DP是与标准和软件开发商、现有的主要相机陷阱数据管理平台、相机陷阱领域的主要参与者以及全球生物多样性信息基金(GBIF)经过长期、深入的磋商和推广过程后达成的共识。在生物多样性信息标准(TDWG)的保护下,Camtrap DP 从一开始就以开放、协作和版本控制的方式进行开发。我们鼓励相机诱捕用户和开发人员加入讨论,为进一步开发和采用该标准做出贡献。
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引用次数: 0
Tracking landscape scale vegetation change in the arid zone by integrating ground, drone and satellite data 通过整合地面、无人机和卫星数据,跟踪干旱地区景观尺度的植被变化
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2023-12-07 DOI: 10.1002/rse2.375
Roxane J. Francis, Richard T. Kingsford, Katherine Moseby, John Read, Reece Pedler, Adrian Fisher, Justin McCann, Rebecca West
A combined multiscale approach using ground, drone and satellite surveys can provide accurate landscape scale spatial mapping and monitoring. We used field observations with drone collected imagery covering 70 ha annually for a 5-year period to estimate changes in living and dead vegetation of four widespread and abundant arid zone woody shrub species. Random forest classifiers delivered high accuracy (> 95%) using object-based detection methods, with fast repeatable and transferrable processing using Google Earth Engine. Our classifiers performed well in both dominant arid zone landscape types: dune and swale, and at extremes of dry and wet years with minimal alterations. This highlighted the flexibility of the approach, potentially delivering insights into changes in highly variable environments. We also linked this classified drone vegetation to available temporally and spatially explicit Landsat satellite imagery, training a new, more accurate fractional vegetation cover model, allowing for accurate tracking of vegetation responses at large scales in the arid zone. Our method promises considerable opportunity to track vegetation dynamics including responses to management interventions, at large geographic scales, extending inference well beyond ground surveys.
采用地面、无人机和卫星调查相结合的多尺度方法可以提供精确的景观尺度空间绘图和监测。我们利用野外观测和无人机采集的图像,在 5 年内每年覆盖 70 公顷的面积,估算了 4 种广泛分布的丰富干旱区木本灌木物种的生死植被变化情况。随机森林分类器采用基于对象的检测方法,具有较高的准确率(95%),并可使用谷歌地球引擎进行快速重复和转移处理。我们的分类器在两种主要的干旱区地貌类型(沙丘和沼泽)中都表现出色,而且在干年和湿年的极端情况下,改变极小。这凸显了该方法的灵活性,有可能帮助我们深入了解多变环境中的变化。我们还将这种分类无人机植被与现有的时间和空间明确的陆地卫星图像联系起来,训练出一种新的、更精确的部分植被覆盖模型,从而能够准确跟踪干旱地区大尺度的植被反应。我们的方法为在大地理尺度上跟踪植被动态(包括对管理干预措施的反应)提供了大量机会,推断范围远远超出了地面调查。
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引用次数: 0
Selection in the third dimension: Using LiDAR derived canopy metrics to assess individual and population-level habitat partitioning of ocelots, bobcats, and coyotes 第三维度的选择:使用激光雷达衍生的冠层指标来评估豹猫、山猫和土狼的个体和种群水平的栖息地划分
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2023-11-15 DOI: 10.1002/rse2.369
Maksim Sergeyev, Daniel A. Crawford, Joseph D. Holbrook, Jason V. Lombardi, Michael E. Tewes, Tyler A. Campbell
Wildlife depends on specific landscape features to persist. Thus, characterizing the vegetation available in an area can be essential for management. The ocelot (Leopardus pardalis) is a federally endangered, medium-sized felid adapted to woody vegetation. Quantifying the characteristics of vegetation most suitable for ocelots is essential for their conservation. Furthermore, understanding differences in the selection of sympatric bobcats (Lynx rufus) and coyotes (Canis latrans) can provide insight into the mechanisms of coexistence between species. Because of differences in hunting strategy (cursorial vs. ambush) and differences in use of land cover types between species, these three carnivores may be partitioning their landscape as a function of vegetation structure. Light detection and ranging (LiDAR) is a remote sensing platform capable of quantifying the sub-canopy structure of vegetation. Using LiDAR data, we quantified the horizontal and vertical structure of vegetation cover to assess habitat selection by ocelots, bobcats, and coyotes. We captured and collared 8 ocelots, 13 bobcats, and 5 coyotes in southern Texas from 2017 to 2021. We used step selection functions to determine the selection of vegetation cover at the population and individual level for each species. Ocelots selected for vertical canopy cover and dense vegetation 0–2 m in height. Bobcats selected cover to a lesser extent and had a broader selection, while coyotes avoided under-story vegetation and selected areas with dense high canopies and relatively open understories. We observed a high degree of variation among individuals that may aid in facilitating intraspecific and interspecific coexistence. Management for ocelots should prioritize vegetation below 2 m and vertical canopy cover. We provide evidence that fine-scale habitat partitioning may facilitate coexistence between sympatric carnivores. Differences among individuals may enhance coexistence among species, as increased behavioral plasticity of individuals can reduce competition for resources. By combining accurate, fine-scale measurements derived from LiDAR data with high-frequency global positioning system locations, we provide a more thorough understanding of the habitat use of ocelots and two sympatric carnivores.
野生动物的生存依赖于特定的景观特征。因此,描述一个地区可用植被的特征对管理是至关重要的。豹猫(Leopardus pardalis)是一种联邦濒危的中型猫科动物,适应木本植被。确定最适合豹猫生长的植被特征是豹猫保护的重要内容。此外,了解同域山猫(Lynx rufus)和土狼(Canis latrans)在选择上的差异,可以深入了解物种之间共存的机制。由于不同物种之间狩猎策略的差异(游猎vs伏击)和土地覆盖类型的差异,这三种食肉动物可能会根据植被结构划分其景观。光探测与测距(LiDAR)是一种能够量化植被冠层亚结构的遥感平台。利用激光雷达数据,我们量化了植被覆盖的水平和垂直结构,以评估豹猫、山猫和土狼的栖息地选择。从2017年到2021年,我们在德克萨斯州南部捕获并捕获了8只豹猫、13只山猫和5只土狼。利用步进选择函数确定了种群和个体水平上植被覆盖度的选择。豹猫选择垂直冠层覆盖和0-2 m高度的茂密植被。山猫选择覆盖物的程度较小,选择范围更广,而土狼避开下层植被,选择茂密的高冠层和相对开阔的林下植被。我们观察到个体之间的高度差异可能有助于促进种内和种间共存。豹猫的管理应优先考虑2米以下的植被和垂直树冠覆盖。我们提供的证据表明,精细尺度的栖息地划分可能促进同域食肉动物之间的共存。个体间的差异可以促进物种间的共存,因为个体行为可塑性的增强可以减少对资源的竞争。通过结合激光雷达数据和高频全球定位系统的精确、精细测量,我们对豹猫和两种同域食肉动物的栖息地使用有了更全面的了解。
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引用次数: 0
Assessing plant trait diversity as an indicators of species α- and β-diversity in a subalpine grassland of the Italian Alps 评估植物特征多样性作为意大利阿尔卑斯山亚高山草原物种α-和β-多样性的指标
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2023-10-30 DOI: 10.1002/rse2.370
Hafiz Ali Imran, Karolina Sakowska, Damiano Gianelle, Duccio Rocchini, Michele Dalponte, Michele Scotton, Loris Vescovo
As the need for ecosystem biodiversity assessment increases within the climate crisis framework, more and more studies using spectral variation hypothesis (SVH) are proposed to assess biodiversity at various scales. The SVH implies optical diversity (also called spectral diversity) is driven by light absorption dynamics associated with plant traits (PTs) variability (which is an indicator of functional diversity) which is, in turn, determined by biodiversity. In this study, we examined the relationship between PTs variability, optical diversity and α- and β-diversity at different taxonomic ranks at the Monte Bondone grasslands, Trentino province, Italy. The results of the study showed that the PTs variability, at the α scale, was not correlated with biodiversity. On the other hand, the results observed at the community scale (β-diversity) showed that the variation of some of the investigated biochemical and biophysical PTs was associated with the β-diversity. We used the Mantel test to analyse the relationship between the PTs variability and species β-diversity. The results showed a correlation coefficient of up to 0.50 between PTs variability and species β-diversity. For higher taxonomic ranks such as family and functional groups, a slightly higher Spearman's correlation coefficient of up to 0.64 and 0.61 was observed, respectively. The SVH approach was also tested to estimate β-diversity and we found that spectral diversity calculated by Spectral Angle Mapper showed to be a better proxy of biodiversity in the same ecosystem where the spectral diversity approach failed to estimate α-diversity. These findings suggest that optical and PTs diversity approaches can be used to predict species diversity in the grasslands ecosystem where the species turnover is high.
随着在气候危机框架内对生态系统生物多样性评估的需求增加,越来越多的研究提出了使用光谱变异假说(SVH)来评估不同尺度的生物多样性。SVH意味着光学多样性(也称为光谱多样性)是由与植物性状(PT)变异性相关的光吸收动力学驱动的(这是功能多样性的指标),而功能多样性又由生物多样性决定。在本研究中,我们检验了意大利特伦蒂诺省蒙特邦通草原不同分类等级的PT变异性、光学多样性和α-和β-多样性之间的关系。研究结果表明,在α尺度上,PT的变异性与生物多样性无关。另一方面,在群落规模(β-多样性)上观察到的结果表明,一些所研究的生物化学和生物物理PT的变化与β-多样度有关。我们使用Mantel检验来分析PT变异性与物种β-多样性之间的关系。结果表明,PT变异性与物种β多样性之间的相关系数高达0.50。对于较高的分类等级,如家族和官能团,观察到的斯皮尔曼相关系数略高,分别高达0.64和0.61。SVH方法也被用于估计β-多样性,我们发现,在光谱多样性方法未能估计α-多样性的同一生态系统中,由光谱角度映射器计算的光谱多样性显示出更好的生物多样性代表。这些发现表明,光学和PT多样性方法可用于预测物种更替率高的草原生态系统中的物种多样性。
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引用次数: 0
Grassland-use intensity maps for Switzerland based on satellite time series: Challenges and opportunities for ecological applications 基于卫星时间序列的瑞士草原利用强度图:生态应用的挑战和机遇
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2023-10-27 DOI: 10.1002/rse2.372
Dominique Weber, Marcel Schwieder, Lukas Ritter, Tiziana Koch, Achilleas Psomas, Nica Huber, Christian Ginzler, Steffen Boch
Land-use intensification in grassland ecosystems (i.e. increased mowing frequency, intensified grazing) has a strong negative effect on biodiversity and ecosystem services. However, accurate information on grassland-use intensity is difficult to acquire and restricted to the local or regional level. Recent studies have shown that mowing events can be mapped for large areas using satellite image time series. The transferability of such approaches, especially to mountain areas, has been little explored, however, and the relevance for ecological applications in biodiversity and conservation has hardly been investigated. Here, we used a rule-based algorithm to produce annual maps for 2018–2021 of grassland-management events, that is, mowing and/or grazing, for Switzerland using Sentinel-2 and Landsat 8 satellite data. We assessed the detection of management events based on independent reference data, which we acquired from daily time series of publicly available webcams that are widely distributed across Switzerland. We further examined the relationships between the generated grassland-use intensity measures and plant species richness and ecological indicator values derived from a nationwide field survey. The webcam-based verification for 2020 and 2021 revealed that most detected management events were actual mowing/grazing events (≥78%), but that a substantial number of events were not detected (up to 57%), particularly grazing events at higher elevations. We found lower plant species richness and higher mean ecological indicator values for nutrients and mowing tolerance with more frequent management events and those starting earlier in the year. A large proportion of the variance was explained by our use-intensity measures. Our findings therefore highlight that remotely assessed management events can characterise land-use intensity at fine spatial and temporal resolutions across broad scales and can explain plant biodiversity patterns in grasslands.
草原生态系统的土地利用集约化(即增加割草频率、加强放牧)对生物多样性和生态系统服务产生了强烈的负面影响。然而,关于草原利用强度的准确信息很难获得,并且仅限于地方或区域层面。最近的研究表明,可以使用卫星图像时间序列绘制大面积的割草事件。然而,这种方法的可转让性,特别是对山区的可转让能力,很少有人探讨,也很少有人调查其在生物多样性和保护方面的生态应用的相关性。在这里,我们使用基于规则的算法,使用Sentinel-2和Landsat 8卫星数据为瑞士绘制了2018-2021年草原管理事件(即割草和/或放牧)的年度地图。我们根据独立参考数据评估了管理事件的检测情况,这些数据是我们从瑞士各地广泛分布的公开网络摄像头的每日时间序列中获得的。我们进一步研究了产生的草原利用强度指标与全国实地调查得出的植物物种丰富度和生态指标值之间的关系。2020年和2021年基于网络摄像头的验证显示,大多数检测到的管理事件都是实际的割草/放牧事件(≥78%),但大量事件没有检测到(高达57%),尤其是高海拔地区的放牧事件。我们发现,植物物种丰富度较低,营养物质和割草耐受性的平均生态指标值较高,管理事件更频繁,而且开始时间更早。很大一部分方差是由我们的使用强度测量来解释的。因此,我们的研究结果强调,远程评估的管理事件可以在大尺度上以精细的空间和时间分辨率表征土地利用强度,并可以解释草原中的植物生物多样性模式。
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引用次数: 0
A new way to understand migration routes of oceanic squid (Ommastrephidae) from satellite data 从卫星数据了解海洋鱿鱼迁徙路线的新方法
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2023-10-19 DOI: 10.1002/rse2.368
Fei Ji, Xinyu Guo
Visible Infrared Imaging Radiometer Suite (VIIRS) Boat Detection (VBD) data have been widely used to study the patterns of fishing grounds and their linking to fishery targets, particularly species mainly caught by jiggers. In line with most species in the Ommastrephidae family, the population of Todarodes pacificus is made up of various splinter cohorts concerning the timing and location of hatching. Therefore, the satellite-recorded fishing grounds consist of groups with complex age structures and different migration directions within cohorts. This study examined the age composition of harvestable stocks (age spectrum) of T. pacificus in the Japan Sea based on an early life history individual-based model of T. pacificus and VBD data. Using the age spectrum, we analysed the relationship between fishery effort and the age of the target group. It was found that jiggers most prefer individuals around 310 ± 20 days. Furthermore, the correlation between ambient water temperature and fishing effort revealed that T. pacificus migrated to colder waters, reaching the coldest waters at 250 ± 7.5 days before moving back towards warmer waters. We discussed a possible way to use the age-temperature relationship to analyse the flow of VBD distributions to record the movements related to the migration of the fishing target. The results show migration-like trajectories, which are initially parallel to the isotherm, gradually deflect towards lower temperature sides over several months, sharply turn for about a month and then move back with a slight angle to the isotherms. The method provides a potential framework to improve our understanding of the active migration of oceanic squid.
可见红外成像辐射计套件(VIIRS)船只探测(VBD)数据已被广泛用于研究渔场的模式及其与渔业目标的联系,特别是主要由跳汰机捕获的物种。与大蟾蜍科的大多数物种一样,太平洋蟾蜍的种群由不同的孵化时间和地点组成。因此,卫星记录的渔场由年龄结构复杂、群体内迁徙方向不同的群体组成。本研究基于太平洋锥虫的早期生活史个体模型和VBD数据,检验了日本海太平洋锥虫可采种群的年龄组成(年龄谱)。利用年龄谱,我们分析了渔业努力与目标群体年龄之间的关系。研究发现,抖动者最喜欢310左右的个体 ± 20 天。此外,环境水温和捕鱼努力之间的相关性表明,太平洋T.pacificus迁移到了较冷的水域,在250时到达了最冷的水域 ± 7.5 在返回温暖水域前几天。我们讨论了一种可能的方法,使用年龄-温度关系来分析VBD分布的流量,以记录与捕鱼目标迁移相关的运动。结果显示,最初与等温线平行的类迁移轨迹在几个月内逐渐向较低温度一侧偏转,在大约一个月内急剧转向,然后以与等温线成微小角度向后移动。该方法为提高我们对海洋鱿鱼主动迁徙的理解提供了一个潜在的框架。
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引用次数: 0
Mapping water content in drying Antarctic moss communities using UAS-borne SWIR imaging spectroscopy 使用无人机SWIR成像光谱绘制南极干燥苔藓群落的含水量
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2023-10-13 DOI: 10.1002/rse2.371
Darren Turner, Emiliano Cimoli, Arko Lucieer, Ryan S. Haynes, Krystal Randall, Melinda J. Waterman, Vanessa Lucieer, Sharon A. Robinson
Antarctic moss beds are sensitive to climatic conditions, and both their survival and community composition are particularly influenced by the availability of liquid water over summer. As Antarctic regions increasingly face climate pressures (e.g., changing hydrology and heat waves), advancing capabilities to efficiently and non-destructively monitor water content in moss communities becomes a key research priority. Because of the complexity induced by multiple micro-climatic drivers and its fragility, tracking the evolution and responses of moss bed moisture requires monitoring methods that are non-intrusive, efficient, and spatially significant, such as the use of unoccupied aerial systems (UAS). In this study, we combine a multi-species drying laboratory experiment with short-wave infrared (SWIR) spectroscopy analyses to first develop a Random Forest regression Model (RFM) capable of predicting Antarctic moss turf water content (~5% error). The RFM was then applied to UAS-borne SWIR imaging data (900–1700 nm, <16 nm spectral resolution) of the moss beds at high spatial resolution (2 cm) across three sites in the vicinity of Casey Station, Antarctica. The sites differed in terrain, snow cover, and moisture availability to evaluate method capabilities under different conditions. Optimum RFM parameters and input variables (spectral indices and reflectance spectra) were determined. Maps of moss moisture were validated via acquiring moss spectra and water content (using sponges inserted into the moss turf) collected in situ, for which an exponential correlation (R2 = 0.72) was reported. RFM further allowed investigation of the influential spectral variables to model water content in moss and associated spectral water absorption features. We demonstrated that UAS-borne SWIR imaging is a promising new tool to map and quantify water content in Antarctic moss beds. Hyperspectral mapping facilitates the exploration of the spatial variability of moss health and enables the creation of a baseline against which changes in these moss communities can be measured.
南极苔藓床对气候条件很敏感,它们的生存和群落组成尤其受到夏季液态水供应的影响。随着南极地区越来越多地面临气候压力(例如,不断变化的水文和热浪),提高高效、无损地监测苔藓群落含水量的能力成为关键的研究重点。由于多种微观气候驱动因素及其脆弱性导致的复杂性,跟踪苔藓床水分的演变和响应需要非侵入性、高效和空间意义重大的监测方法,例如使用无人驾驶的空中系统。在这项研究中,我们将多物种干燥实验室实验与短波红外(SWIR)光谱分析相结合,首次开发了一个能够预测南极苔藓草皮含水量(~5%误差)的随机森林回归模型(RFM)。然后将RFM应用于无人机携带的SWIR成像数据(900–1700 nm,<;16 nm光谱分辨率)在高空间分辨率(2 厘米)穿过南极洲凯西站附近的三个地点。这些地点在地形、积雪和水分可用性方面存在差异,以评估不同条件下的方法能力。确定了最佳RFM参数和输入变量(光谱指数和反射光谱)。通过获取原位收集的苔藓光谱和含水量(使用插入苔藓草皮的海绵)来验证苔藓水分图,其中指数相关性(R2 = 0.72)。RFM进一步允许研究有影响的光谱变量,以模拟苔藓中的含水量和相关的光谱吸水特征。我们证明,无人机SWIR成像是绘制和量化南极苔藓层含水量的一种很有前途的新工具。高光谱测绘有助于探索苔藓健康的空间变异性,并能够创建一个基线,根据该基线可以测量这些苔藓群落的变化。
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引用次数: 1
Automated visitor and wildlife monitoring with camera traps and machine learning 通过相机陷阱和机器学习自动监控游客和野生动物
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2023-08-30 DOI: 10.1002/rse2.367
Veronika Mitterwallner, A. Peters, Hendrik Edelhoff, Gregor H. Mathes, Hien Nguyen, W. Peters, M. Heurich, M. Steinbauer
As human activities in natural areas increase, understanding human–wildlife interactions is crucial. Big data approaches, like large‐scale camera trap studies, are becoming more relevant for studying these interactions. In addition, open‐source object detection models are rapidly improving and have great potential to enhance the image processing of camera trap data from human and wildlife activities. In this study, we evaluate the performance of the open‐source object detection model MegaDetector in cross‐regional monitoring using camera traps. The performance at detecting and counting humans, animals and vehicles is evaluated by comparing the detection results with manual classifications of more than 300 000 camera trap images from three study regions. Moreover, we investigate structural patterns of misclassification and evaluate the results of the detection model for typical temporal analyses conducted in ecological research. Overall, the accuracy of the detection model was very high with 96.0% accuracy for animals, 93.8% for persons and 99.3% for vehicles. Results reveal systematic patterns in misclassifications that can be automatically identified and removed. In addition, we show that the detection model can be readily used to count people and animals on images with underestimating persons by −0.05, vehicles by −0.01 and animals by −0.01 counts per image. Most importantly, the temporal pattern in a long‐term time series of manually classified human and wildlife activities was highly correlated with classification results of the detection model (Pearson's r = 0.996, p < 0.001) and diurnal kernel densities of activities were almost equivalent for manual and automated classification. The results thus prove the overall applicability of the detection model in the image classification process of cross‐regional camera trap studies without further manual intervention. Besides the great acceleration in processing speed, the model is also suitable for long‐term monitoring and allows reproducibility in scientific studies while complying with privacy regulations.
随着人类在自然区域活动的增加,了解人类与野生动物的相互作用至关重要。大数据方法,如大规模相机陷阱研究,正变得越来越适用于研究这些相互作用。此外,开源目标检测模型正在迅速改进,并且在增强来自人类和野生动物活动的相机陷阱数据的图像处理方面具有巨大的潜力。在本研究中,我们评估了开源目标检测模型MegaDetector在使用相机陷阱进行跨区域监控中的性能。通过将检测结果与来自三个研究区域的30多万张相机陷阱图像的人工分类结果进行比较,评估了该方法在检测和计数人类、动物和车辆方面的性能。此外,我们还研究了错误分类的结构模式,并评估了在生态研究中进行的典型时间分析的检测模型的结果。总体而言,该检测模型的准确率非常高,对动物的准确率为96.0%,对人的准确率为93.8%,对车辆的准确率为99.3%。结果揭示了错误分类的系统模式,可以自动识别和删除。此外,我们还表明,该检测模型可以很容易地用于对图像上的人和动物进行计数,每个图像低估了- 0.05个人,- 0.01个车辆和- 0.01个动物。最重要的是,人工分类的人类和野生动物活动的长期时间序列的时间格局与检测模型的分类结果高度相关(Pearson’s r = 0.996, p < 0.001),并且活动的日核密度在人工和自动分类中几乎相等。结果证明了该检测模型在跨区域相机陷阱研究的图像分类过程中的整体适用性,无需进一步的人工干预。除了处理速度大大加快外,该模型还适用于长期监测,并在遵守隐私法规的同时允许科学研究的可重复性。
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引用次数: 0
A semi‐automated camera trap distance sampling approach for population density estimation 一种用于人口密度估计的半自动相机陷阱距离采样方法
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2023-08-28 DOI: 10.1002/rse2.362
Maik Henrich, Mercedes Burgueño, J. Hoyer, T. Haucke, V. Steinhage, H. Kühl, M. Heurich
Camera traps have become important tools for the monitoring of animal populations. However, the study‐specific estimation of animal detection probabilities is key if unbiased abundance estimates of unmarked species are to be obtained. Since this process can be very time‐consuming, we developed the first semi‐automated workflow for animals of any size and shape to estimate detection probabilities and population densities. In order to obtain observation distances, a deep learning algorithm is used to create relative depth images that are calibrated with a small set of reference photos for each location, with distances then extracted for animals automatically detected by MegaDetector 4.0. Animal detection by MegaDetector was generally independent of the distance to the camera trap for 10 animal species at two different study sites. If an animal was detected both manually and automatically, the difference in the distance estimates was often minimal at a distance about 4 m from the camera trap. The difference increased approximately linearly for larger distances. Nonetheless, population density estimates based on manual and semi‐automated camera trap distance sampling workflows did not differ significantly. Our results show that a readily available software for semi‐automated distance estimation can reliably be used within a camera trap distance sampling workflow, reducing the time required for data processing, by >13‐fold. This greatly improves the accessibility of camera trap distance sampling for wildlife research and management.
相机捕捉器已成为监测动物种群的重要工具。然而,如果要获得无标记物种的无偏丰度估计,则动物检测概率的特定研究估计是关键。由于这个过程可能非常耗时,我们为任何大小和形状的动物开发了第一个半自动工作流程,以估计检测概率和种群密度。为了获得观察距离,使用深度学习算法创建相对深度图像,这些图像用每个位置的一小组参考照片进行校准,然后为MegaDetector 4.0自动检测到的动物提取距离。MegaDetector的动物检测通常与两个不同研究地点的10种动物到相机陷阱的距离无关。如果手动和自动检测到动物,距离估计的差异通常在距离约4 距离相机陷阱m。对于较大的距离,差异近似线性增加。尽管如此,基于手动和半自动相机陷阱距离采样工作流程的人口密度估计没有显著差异。我们的研究结果表明,一种易于使用的半自动距离估计软件可以在相机陷阱距离采样工作流程中可靠地使用,将数据处理所需的时间减少了13倍以上。这大大提高了野生动物研究和管理中相机陷阱距离采样的可及性。
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引用次数: 0
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Remote Sensing in Ecology and Conservation
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