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Leveraging the next generation of spaceborne Earth observations for fuel monitoring and wildland fire management 利用下一代空间地球观测进行燃料监测和野地火灾管理
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-08-17 DOI: 10.1002/rse2.416
Rodrigo V. Leite, Cibele Amaral, Christopher S. R. Neigh, Diogo N. Cosenza, Carine Klauberg, Andrew T. Hudak, Luiz Aragão, Douglas C. Morton, Shane Coffield, Tempest McCabe, Carlos A. Silva
Managing fuels is a key strategy for mitigating the negative impacts of wildfires on people and the environment. The use of satellite‐based Earth observation data has become an important tool for managers to optimize fuel treatment planning at regional scales. Fortunately, several new sensors have been launched in the last few years, providing novel opportunities to enhance fuel characterization. Herein, we summarize the potential improvements in fuel characterization at large scale (i.e., hundreds to thousands of km2) with high spatial and spectral resolution arising from the use of new spaceborne instruments with near‐global, freely‐available data. We identified sensors at spatial resolutions suitable for fuel treatment planning, featuring: lidar data for characterizing vegetation structure; hyperspectral sensors for retrieving chemical compounds and species composition; and dense time series derived from multispectral and synthetic aperture radar sensors for mapping phenology and moisture dynamics. We also highlight future hyperspectral and radar missions that will deliver valuable and complementary information for a new era of fuel load characterization from space. The data volume that is being generated may still challenge the usability by a diverse group of stakeholders. Seamless cyberinfrastructure and community engagement are paramount to guarantee the use of these cutting‐edge datasets for fuel monitoring and wildland fire management across the world.
管理燃料是减轻野火对人类和环境负面影响的关键策略。使用基于卫星的地球观测数据已成为管理者在区域范围内优化燃料处理规划的重要工具。幸运的是,过去几年中发射了几个新的传感器,为加强燃料特征描述提供了新的机会。在此,我们总结了在大尺度(即数百到数千平方公里)、高空间分辨率和光谱分辨率的燃料特征描述方面的潜在改进,这些改进源于使用新的空间仪器和近全球、免费提供的数据。我们确定了适用于燃料处理规划的空间分辨率传感器,其特点是:激光雷达数据用于确定植被结构特征;高光谱传感器用于检索化合物和物种组成;多光谱和合成孔径雷达传感器产生的密集时间序列用于绘制物候和水分动态图。我们还重点介绍了未来的高光谱和雷达任务,这些任务将为新时代的太空燃料负荷特征描述提供宝贵的补充信息。正在生成的数据量可能仍会对不同利益相关者的可用性构成挑战。无缝网络基础设施和社区参与对于确保将这些前沿数据集用于世界各地的燃料监测和野地火灾管理至关重要。
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引用次数: 0
The application of unoccupied aerial systems (UAS) for monitoring intertidal oyster density and abundance 应用无人机系统(UAS)监测潮间带牡蛎的密度和丰度
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-08-13 DOI: 10.1002/rse2.417
Jenny Bueno, Sarah E. Lester, Joshua L. Breithaupt, Sandra Brooke
The eastern oyster (Crassostrea virginica) is a coastal foundation species currently under threat from anthropogenic activities both globally and in the Apalachicola Bay region of north Florida. Oysters provide numerous ecosystem services, and it is important to establish efficient and reliable methods for their effective monitoring and management. Traditional monitoring techniques, such as quadrat density sampling, can be labor‐intensive, destructive of both oysters and reefs, and may be spatially limited. In this study, we demonstrate how unoccupied aerial systems (UAS) can be used to efficiently generate high‐resolution geospatial oyster reef condition data over large areas. These data, with appropriate ground truthing and minimal destructive sampling, can be used to effectively monitor the size and abundance of oyster clusters on intertidal reefs. Utilizing structure‐from‐motion photogrammetry techniques to create three‐dimensional topographic models, we reconstructed the distribution, spatial density and size of oyster clusters on intertidal reefs in Apalachicola Bay. Ground truthing revealed 97% accuracy for cluster presence detection by UAS products and we confirmed that live oysters are predominately located within clusters, supporting the use of cluster features to estimate oyster population status. We found a positive significant relationship between cluster size and live oyster counts. These findings allowed us to extract clusters from geospatial products and predict live oyster abundance and spatial density on 138 reefs covering 138 382 m2 over two locations. Oyster densities varied between sites, with higher live oyster densities occurring at one site within the Apalachicola Bay bounds, and lower oyster densities in areas adjacent to Apalachicola Bay. Repeated monitoring at one site in 2022 and 2023 revealed a relatively stable oyster density over time. This study demonstrated the successful application of high‐resolution drone imagery combined with cluster sampling, providing a repeatable method for mapping and monitoring to inform conservation, restoration and management strategies for intertidal oyster populations.
东部牡蛎(Crassostrea virginica)是一种沿海基础物种,目前正受到全球和佛罗里达州北部阿帕拉奇科拉湾地区人为活动的威胁。牡蛎为生态系统提供了大量服务,因此建立高效可靠的方法对其进行有效监测和管理非常重要。传统的监测技术,如四分密度取样,可能需要大量人力,对牡蛎和礁石都有破坏作用,而且在空间上可能受到限制。在本研究中,我们展示了如何利用无人机系统(UAS)有效生成大面积高分辨率地理空间牡蛎礁状况数据。这些数据经过适当的地面实况核实和最少的破坏性取样,可用于有效监测潮间带礁石上牡蛎群的大小和丰度。利用运动结构摄影测量技术创建三维地形模型,我们重建了阿帕拉奇科拉湾潮间带礁石上牡蛎群的分布、空间密度和大小。地面实况调查显示,无人机系统产品对集群存在检测的准确率为 97%,我们证实活牡蛎主要位于集群内,支持使用集群特征来估计牡蛎种群状况。我们发现集群大小与活牡蛎数量之间存在正相关关系。这些发现使我们能够从地理空间产品中提取聚类,并预测两个地点 138 个礁石(面积 138 382 平方米)上的活牡蛎丰度和空间密度。不同地点的牡蛎密度各不相同,阿帕拉契科拉湾范围内的一个地点活牡蛎密度较高,而阿帕拉契科拉湾附近地区的牡蛎密度较低。2022 年和 2023 年在一个地点的重复监测显示,牡蛎密度随着时间的推移相对稳定。这项研究证明了高分辨率无人机图像与集群取样相结合的成功应用,提供了一种可重复的绘图和监测方法,为潮间带牡蛎种群的保护、恢复和管理策略提供了信息。
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引用次数: 0
Detecting selective logging in tropical forests with optical satellite data: an experiment in Peru shows texture at 3 m gives the best results 利用光学卫星数据检测热带森林中的选择性砍伐:秘鲁的一项实验表明,3 米处的纹理效果最佳
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-07-31 DOI: 10.1002/rse2.414
Chiara Aquino, Edward T. A. Mitchard, Iain M. McNicol, Harry Carstairs, Andrew Burt, Beisit L. P. Vilca, Sylvia Mayta, Mathias Disney
Selective logging is known to be widespread in the tropics, but is currently very poorly mapped, in part because there is little quantitative data on which satellite sensor characteristics and analysis methods are best at detecting it. To improve this, we used data from the Tropical Forest Degradation Experiment (FODEX) plots in the southern Peruvian Amazon, where different numbers of trees had been removed from four plots of 1 ha each, carefully inventoried by hand and terrestrial laser scanning before and after the logging to give a range of biomass loss (∆AGB) values. We conducted a comparative study of six multispectral optical satellite sensors at 0.3–30 m spatial resolution, to find the best combination of sensor and remote sensing indicator for change detection. Spectral reflectance, the normalised difference vegetation index (NDVI) and texture parameters were extracted after radiometric calibration and image preprocessing. The strength of the relationships between the change in these values and field‐measured ∆AGB (computed in % ha−1) was analysed. The results demonstrate that: (a) texture measures correlates more with ∆AGB than simple spectral parameters; (b) the strongest correlations are achieved for those sensors with spatial resolutions in the intermediate range (1.5–10 m), with finer or coarser resolutions producing worse results, and (c) when texture is computed using a moving square window ranging between 9 and 14 m in length. Maps predicting ∆AGB showed very promising results using a NIR‐derived texture parameter for 3 m resolution PlanetScope (R2 = 0.97 and root mean square error (RMSE) = 1.91% ha−1), followed by 1.5 m SPOT‐7 (R2 = 0.76 and RMSE = 5.06% ha−1) and 10 m Sentinel‐2 (R2 = 0.79 and RMSE = 4.77% ha−1). Our findings imply that, at least for lowland Peru, low‐medium intensity disturbance can be detected best in optical wavelengths using a texture measure derived from 3 m PlanetScope data.
众所周知,选择性采伐在热带地区非常普遍,但目前的测绘工作却非常薄弱,部分原因是几乎没有定量数据说明哪种卫星传感器特性和分析方法最适合检测选择性采伐。为了改善这一情况,我们使用了秘鲁亚马逊南部热带森林退化实验(FODEX)地块的数据,在这些地块中,每块 1 公顷的四块土地上都有不同数量的树木被移除,在采伐前后,我们通过人工和地面激光扫描进行了仔细的清点,得出了一系列生物量损失(ΔAGB)值。我们对六种空间分辨率为 0.3-30 米的多光谱光学卫星传感器进行了比较研究,以找到传感器和遥感指标的最佳组合,用于变化检测。经过辐射校准和图像预处理后,提取了光谱反射率、归一化差异植被指数(NDVI)和纹理参数。分析了这些值的变化与实地测量的 ∆AGB(以 % ha-1 计算)之间的关系强度。结果表明(a) 与简单的光谱参数相比,纹理测量值与∆AGB 的相关性更高;(b) 空间分辨率在中间范围(1.5-10 米)的传感器的相关性最强,更细或更粗的分辨率产生的结果更差;(c) 使用长度在 9-14 米之间的移动方窗计算纹理时,相关性最强。使用近红外纹理参数预测 ∆AGB 的地图显示,3 米分辨率的 PlanetScope(R2 = 0.97,均方根误差 (RMSE) = 1.91% ha-1)结果非常好,其次是 1.5 米分辨率的 SPOT-7(R2 = 0.76,均方根误差 (RMSE) = 5.06% ha-1)和 10 米分辨率的 Sentinel-2(R2 = 0.79,均方根误差 (RMSE) = 4.77% ha-1)。我们的研究结果表明,至少对秘鲁低地而言,利用 3 m PlanetScope 数据得出的纹理测量结果,在光波长中检测中低强度干扰的效果最佳。
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引用次数: 0
Quantifying vegetation cover on coastal active dunes using nationwide aerial image analysis 利用全国航空图像分析量化沿海活动沙丘的植被覆盖率
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-07-16 DOI: 10.1002/rse2.410
Cate Ryan, Hannah L. Buckley, Craig D. Bishop, Graham Hinchliffe, Bradley C. Case
Coastal active dunes provide vital biodiversity, habitat, and ecosystem services, yet they are one of the most endangered and understudied ecosystems worldwide. Therefore, monitoring the status of these systems is essential, but field vegetation surveys are time‐consuming and expensive. Remotely sensed aerial imagery offers spatially continuous, low‐cost, high‐resolution coverage, allowing for vegetation mapping across larger areas than traditional field surveys. Taking Aotearoa New Zealand as a case study, we used a nationally representative sample of coastal active dunes to classify vegetation from red‐green‐blue (RGB) high‐resolution (0.075–0.75 m) aerial imagery with object‐based image analysis. The mean overall accuracy was 0.76 across 21 beaches for aggregated classes, and key cover classes, such as sand, sandbinders, and woody vegetation, were discerned. However, differentiation among woody vegetation species on semi‐stable and stable dunes posed a challenge. We developed a national cover typology from the classification, comprising seven vegetation types. Classification tree models showed that where human activity was higher, it was more important than geomorphic factors in influencing the relative percent cover of the different active dune cover classes. Our methods provide a quantitative approach to characterizing the cover classes on active dunes at a national scale, which are relevant for conservation management, including habitat mapping, determining species occupancy, indigenous dominance, and the representativeness of remaining active dunes.
沿海活跃沙丘提供了重要的生物多样性、栖息地和生态系统服务,但它们却是全世界最濒危和研究最不充分的生态系统之一。因此,监测这些系统的状况至关重要,但实地植被调查既耗时又昂贵。遥感航空图像具有空间连续性、低成本、高分辨率的覆盖范围,与传统的实地调查相比,可以绘制更大范围的植被图。以新西兰奥特亚罗瓦为例,我们利用具有全国代表性的沿海活跃沙丘样本,通过基于对象的图像分析,对红绿蓝(RGB)高分辨率(0.075-0.75 米)航空图像中的植被进行了分类。在 21 个海滩上,总体分类的平均准确率为 0.76。然而,要区分半稳定和稳定沙丘上的木本植被物种则是一项挑战。我们根据分类结果建立了全国植被类型,包括七种植被类型。分类树模型显示,在人类活动较多的地方,人类活动比地貌因素更能影响不同活跃沙丘植被类型的相对覆盖率。我们的方法提供了一种定量方法来描述全国范围内活跃沙丘的植被类型,这与保护管理有关,包括绘制栖息地地图、确定物种占有率、本地优势以及剩余活跃沙丘的代表性。
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引用次数: 0
Highly precise community science annotations of video camera‐trapped fauna in challenging environments 在充满挑战的环境中对摄像捕获的动物群落进行高度精确的群落科学注释
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-06-25 DOI: 10.1002/rse2.402
Mimi Arandjelovic, Colleen R. Stephens, Paula Dieguez, Nuria Maldonado, Gaëlle Bocksberger, Marie‐Lyne Després‐Einspenner, Benjamin Debetencourt, Vittoria Estienne, Ammie K. Kalan, Maureen S. McCarthy, Anne‐Céline Granjon, Veronika Städele, Briana Harder, Lucia Hacker, Anja Landsmann, Laura K. Lynn, Heidi Pfund, Zuzana Ročkaiová, Kristeena Sigler, Jane Widness, Heike Wilken, Antonio Buzharevski, Adeelia S. Goffe, Kristin Havercamp, Lydia L. Luncz, Giulia Sirianni, Erin G. Wessling, Roman M. Wittig, Christophe Boesch, Hjalmar S. Kühl
As camera trapping grows in popularity and application, some analytical limitations persist including processing time and accuracy of data annotation. Typically images are recorded by camera traps although videos are becoming increasingly collected even though they require much more time for annotation. To overcome limitations with image annotation, camera trap studies are increasingly linked to community science (CS) platforms. Here, we extend previous work on CS image annotations to camera trap videos from a challenging environment; a dense tropical forest with low visibility and high occlusion due to thick canopy cover and bushy undergrowth at the camera level. Using the CS platform Chimp&See, established for classification of 599 956 video clips from tropical Africa, we assess annotation precision and accuracy by comparing classification of 13 531 1‐min video clips by a professional ecologist (PE) with output from 1744 registered, as well as unregistered, Chimp&See community scientists. We considered 29 classification categories, including 17 species and 12 higher‐level categories, in which phenotypically similar species were grouped. Overall, annotation precision was 95.4%, which increased to 98.2% when aggregating similar species groups together. Our findings demonstrate the competence of community scientists working with camera trap videos from even challenging environments and hold great promise for future studies on animal behaviour, species interaction dynamics and population monitoring.
随着相机诱捕技术的普及和应用,一些分析方面的限制因素依然存在,包括处理时间和数据标注的准确性。通常情况下,照相机诱捕器记录的是图像,尽管视频的收集也越来越多,但它们需要更多的注释时间。为了克服图像标注的局限性,相机陷阱研究越来越多地与社区科学(CS)平台联系起来。在这里,我们将以前的 CS 图像注释工作扩展到了具有挑战性的环境中的相机捕捉器视频上;这是一片茂密的热带森林,由于树冠覆盖厚实,相机水平上灌木丛生,能见度低,遮蔽率高。利用为热带非洲 599 956 个视频片段分类而建立的 CS 平台 Chimp&See,我们通过比较专业生态学家(PE)对 13 531 个 1 分钟视频片段的分类与 1744 名注册和未注册 Chimp&See 社区科学家的输出结果,评估了注释的精确度和准确性。我们考虑了 29 个分类类别,包括 17 个物种和 12 个更高层次的类别,其中表型相似的物种被归为一类。总体而言,注释精确度为 95.4%,将相似物种分组汇总后,精确度提高到 98.2%。我们的研究结果表明,社区科学家即使在具有挑战性的环境中也有能力使用相机陷阱视频,这为未来的动物行为、物种相互作用动态和种群监测研究带来了巨大希望。
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引用次数: 0
Approaching a population‐level assessment of body size in pinnipeds using drones, an early warning of environmental degradation 利用无人机对针足类动物的体型进行种群级评估,这是环境退化的预警手段
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-06-25 DOI: 10.1002/rse2.413
Daire Carroll, Eduardo Infantes, Eva V. Pagan, Karin C. Harding
Body mass is a fundamental indicator of animal health closely linked to survival and reproductive success. Systematic assessment of body mass for a large proportion of a population can allow early detection of changes likely to impact population growth, facilitating responsive management and a mechanistic understanding of ecological trends. One challenge with integrating body mass assessment into monitoring is sampling enough animals to detect trends and account for individual variation. Harbour seals (Phoca vitulina) are philopatric marine mammals responsive to regional environmental changes, resulting in their use as an indicator species. We present a novel method for the non‐invasive and semi‐automatic assessment of harbour seal body condition, using unoccupied aerial vehicles (UAVs/drones). Morphological parameters are automatically measured in georeferenced images and used to estimate volume, which is then translated to estimated mass. Remote observations of known individuals are utilized to calibrate the method. We achieve a high level of accuracy (mean absolute error of 4.5 kg or 10.5% for all seals and 3.2 kg or 12.7% for pups‐of‐the‐year). We systematically apply the method to wild seals during the Spring pupping season and Autumn over 2 years, achieving a near‐population‐level assessment for pups on land (82.5% measured). With reference to previous mark‐recapture work linking Autumn pup weights to survival, we estimate mean expected probability of over‐winter survival (mean = 0.89, standard deviation = 0.08). This work marks a significant step forward for the non‐invasive assessment of body condition in pinnipeds and could provide daily estimates of body mass for thousands of individuals. It can act as an early warning for deteriorating environmental conditions and be utilized as an integrative tool for wildlife monitoring. It also enables estimation of yearly variation in demographic rates which can be utilized in parameterizing models of population growth with relevance for conservation and evolutionary biology.
体重是动物健康的基本指标,与存活率和繁殖成功率密切相关。对很大一部分种群的体重进行系统评估,可以及早发现可能影响种群增长的变化,从而促进有针对性的管理和对生态趋势的机理理解。将体重评估纳入监测工作的一个挑战是对足够多的动物进行采样,以检测趋势并考虑个体差异。港海豹(Phoca vitulina)是一种亲缘性海洋哺乳动物,对区域环境变化反应灵敏,因此被用作指示物种。我们提出了一种利用无人飞行器(UAV/无人机)对海豹身体状况进行非侵入式半自动评估的新方法。在地理参照图像中自动测量形态参数并用于估算体积,然后将其转换为估算质量。利用对已知个体的远程观测来校准该方法。我们的方法具有很高的准确性(所有海豹的平均绝对误差为 4.5 千克或 10.5%,幼年海豹的平均绝对误差为 3.2 千克或 12.7%)。我们将该方法系统地应用于春季和秋季的野生海豹,历时两年,对陆地上的幼崽进行了接近种群水平的评估(测量率为 82.5%)。参照之前将秋季幼崽体重与存活率联系起来的标记重捕工作,我们估算出了越冬存活率的平均预期概率(平均值 = 0.89,标准偏差 = 0.08)。这项工作标志着在非侵入式评估针鼹身体状况方面迈出了重要一步,可提供数千只个体的每日体重估计值。它可以作为环境条件恶化的早期预警,并可用作野生动物监测的综合工具。它还能估算人口统计率的年度变化,可用于确定与保护和进化生物学相关的种群增长模型的参数。
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引用次数: 0
Quantifying nocturnal thrush migration using sensor data fusion between acoustics and vertical‐looking radar 利用声学和垂直探测雷达之间的传感器数据融合量化夜间鸫鸟迁徙
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-06-20 DOI: 10.1002/rse2.397
Silvia Giuntini, Juha Saari, Adriano Martinoli, Damiano G. Preatoni, Birgen Haest, Baptiste Schmid, Nadja Weisshaupt
Studying nocturnal bird migration is challenging because direct visual observations are difficult during darkness. Radar has been the means of choice to study nocturnal bird migration for several decades, but provides limited taxonomic information. Here, to ascertain the feasibility of enhancing the taxonomic resolution of radar data, we combined acoustic data with vertical‐looking radar measurements to quantify thrush (Family: Turdidae) migration. Acoustic recordings, collected in Helsinki between August and October of 2021–2022, were used to identify likely nights of high and low thrush migration. Then, we built a random forest classifier that used recorded radar signals from those nights to separate all migrating passerines across the autumn migration season into thrushes and non‐thrushes. The classifier had a high overall accuracy (≈0.82), with wingbeat frequency and bird size being key for separation. The overall estimated thrush autumn migration phenology was in line with known migratory patterns and strongly correlated (Pearson correlation coefficient ≈0.65) with the phenology of the acoustic data. These results confirm how the joint application of acoustic and vertical‐looking radar data can, under certain migratory conditions and locations, be used to quantify ‘family‐level’ bird migration.
研究夜间鸟类迁徙具有挑战性,因为在黑暗中很难进行直接肉眼观察。几十年来,雷达一直是研究夜间鸟类迁徙的首选手段,但其提供的分类信息有限。为了确定提高雷达数据分类分辨率的可行性,我们将声学数据与垂直雷达测量相结合,对鸫科(Turdidae)鸟类的迁徙进行量化。我们利用2021-2022年8月至10月期间在赫尔辛基采集的声学记录来确定鸫鸟迁徙高峰和低谷的可能夜晚。然后,我们建立了一个随机森林分类器,利用这些夜晚记录的雷达信号将秋季迁徙季节的所有迁徙鸟类分为鸫类和非鸫类。该分类器的总体准确率很高(≈0.82),其中振翅频率和鸟的大小是区分的关键。对鸫鸟秋季迁徙物候的总体估计符合已知的迁徙模式,并且与声学数据的物候密切相关(皮尔逊相关系数≈0.65)。这些结果证实了在某些迁徙条件和地点下,声学和垂直探测雷达数据的联合应用可用于量化 "家族级 "鸟类迁徙。
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引用次数: 0
Mapping emergent coral reefs: a comparison of pixel‐ and object‐based methods 绘制新出现的珊瑚礁:基于像素和对象的方法比较
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-05-29 DOI: 10.1002/rse2.401
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。
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引用次数: 0
Uncovering mangrove range limits using very high resolution satellite imagery to detect fine‐scale mangrove and saltmarsh habitats in dynamic coastal ecotones 利用甚高分辨率卫星图像探测动态沿海生态带中红树林和盐沼生境的精细尺度,揭示红树林的范围极限
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-05-24 DOI: 10.1002/rse2.394
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.
红树林是沿海生物多样性、恢复力和碳动态的重要生态系统,在全球范围内正受到人类压力和气候变化的威胁。然而,在热带-温带过渡带的几个地理范围极限,红树林生态系统正随着宏观气候驱动因素的变化向极地扩展。靠近分布范围极限的红树林通常生长得较小,并与其他沿海栖息地形成动态的斑块分布,这很难用中等分辨率(30 米)的卫星图像进行测绘。因此,全球分布图中缺少许多这样的红树林区域。为了更好地绘制小型灌丛红树林地图,我们将 Landsat(30 米)和 Sentinel(10 米)与甚高分辨率(VHR)Planet(3 米)和 WorldView(1.8 米)图像进行了对比测试,并评估了机器学习分类方法在美国佛罗里达州东海岸快速变化的生态区中将当前(2022 年)红树林和盐沼与其他沿海生境区分开来的准确性。我们的目标是:(1) 量化红树林和盐沼斑块的景观组成和复杂性、类别优势和空间属性因图像分辨率而产生的可测绘差异;(2) 解决该地区测绘的不确定性。我们发现,由于红树林的面积和范围太小,无法进行探测(准确率为 50%),因此大地遥感卫星绘制红树林分布图的能力受到了影响。WorldView 在区分红树林和其他湿地生境方面最为成功(准确率 84%),紧随其后的是 Planet(82%)和 Sentinel(81%)。利用 WorldView,我们在佛罗里达州范围限制研究区域内发现了 800 公顷的红树林,比利用 Planet 发现的红树林多 35%,比 Sentinel 多 114%,比 Landsat 多 537%。更高分辨率的图像有助于揭示景观指标的更多变化,这些指标量化了红树林和盐沼栖息地在景观、等级和斑块尺度上的多样性、空间配置和连接性。总体而言,VHR 卫星图像提高了我们绘制红树林分布范围界限图的能力,有助于补充中等分辨率的全球分布图和过时的区域图。
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引用次数: 0
Walruses from space: walrus counts in simultaneous remotely piloted aircraft system versus very high‐resolution satellite imagery 来自太空的海象:同步遥控飞机系统与超高分辨率卫星图像中的海象数量对比
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-05-22 DOI: 10.1002/rse2.391
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.
有必要对海象(Odobenus rosmarus)在泛北极地区的分布进行定期计数,以确定准确的种群趋势,进而了解当前海象栖息地的快速变化(如海冰消失)对海象的影响。然而,用船只或飞机对像北极这样广袤而偏远的地区进行调查是一项艰巨的后勤挑战,限制了实地调查的频率和空间覆盖范围。使用超高分辨率 (VHR) 卫星图像的替代方法已被证明是探测海象的有用工具,但准确计数海象个体的可行性尚未得到解决。在这里,我们比较了通过 VHR WorldView-3 卫星图像获得的海象计数和使用遥控飞机系统 (RPAS) 同步获得的地面计数。我们估计了海象计数的准确性,这取决于:(1) VHR 卫星图像的空间分辨率,以三种不同的空间分辨率(即每像素 50、30 和 15 厘米)向评估者提供相同的 WorldView-3 图像;(2) 评估者的专业知识水平(专家与混合经验水平--代表公民科学家)。后一方面的研究对全球评估计划的效率和成果非常重要,因为有公民科学活动邀请公众在 VHR 卫星图像中计数海象。我们的 RPAS "对照 "图像中有 73 只海象。我们的结果表明,在所有空间分辨率和所有评估者专业水平下,海象在 VHR 卫星图像中的计数都偏低。空间分辨率为 30 厘米的 VHR 卫星图像中的海象数量最为准确,而且不同专业水平的海象数量差异最小。这是用近乎同步的现场数据验证 VHR 计数的首次成功尝试,但还需要对不同密度和结构、不同底质的海象聚集地进行进一步评估。
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引用次数: 0
期刊
Remote Sensing in Ecology and Conservation
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