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Examining wildfire dynamics using ECOSTRESS data with machine learning approaches: the case of South‐Eastern Australia's black summer 利用 ECOSTRESS 数据和机器学习方法研究野火动态:澳大利亚东南部黑色夏季的案例
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-11-05 DOI: 10.1002/rse2.422
Yuanhui Zhu, Shakthi B. Murugesan, Ivone K. Masara, Soe W. Myint, Joshua B. Fisher
Wildfires are increasing in risk and prevalence. The most destructive wildfires in decades in Australia occurred in 2019–2020. However, there is still a challenge in developing effective models to understand the likelihood of wildfire spread (susceptibility) and pre‐fire vegetation conditions. The recent launch of NASA's ECOSTRESS presents an opportunity to monitor fire dynamics with a high resolution of 70 m by measuring ecosystem stress and drought conditions preceding wildfires. We incorporated ECOSTRESS data, vegetation indices, rainfall, and topographic data as independent variables and fire events as dependent variables into machine learning algorithms applied to the historic Australian wildfires of 2019–2020. With these data, we predicted over 90% of all wildfire occurrences 1 week ahead of these wildfire events. Our models identified vegetation conditions with a 3‐week time lag before wildfire events in the fourth week and predicted the probability of wildfire occurrences in the subsequent week (fifth week). ECOSTRESS water use efficiency (WUE) consistently emerged as the leading factor in all models predicting wildfires. Results suggest that the pre‐fire vegetation was affected by wildfires in areas with WUE above 2 g C kg−1 H₂O at 95% probability level. Additionally, the ECOSTRESS evaporative stress index and topographic slope were identified as significant contributors in predicting wildfire susceptibility. These results indicate a significant potential for ECOSTRESS data to predict and analyze wildfires and emphasize the crucial role of drought conditions in wildfire events, as evident from ECOSTRESS data. Our approaches developed in this study and outcome can help policymakers, fire managers, and city planners assess, manage, prepare, and mitigate wildfires in the future.
野火的风险和发生率都在增加。澳大利亚几十年来破坏性最大的野火发生在 2019-2020 年。然而,在开发有效模型以了解野火蔓延的可能性(易感性)和火前植被状况方面仍存在挑战。美国国家航空航天局(NASA)最近发射的 ECOSTRESS 提供了一个机会,可以通过测量野火发生前的生态系统压力和干旱状况,以 70 米的高分辨率监测火灾动态。我们将 ECOSTRESS 数据、植被指数、降雨量和地形数据作为自变量,将火灾事件作为因变量纳入机器学习算法,并将其应用于 2019-2020 年历史上的澳大利亚野火。利用这些数据,我们在这些野火事件发生前一周预测了90%以上的野火事件。我们的模型确定了第四周野火事件发生前 3 周的植被状况,并预测了随后一周(第五周)发生野火的概率。在所有预测野火的模型中,ECOSTRESS 水利用效率(WUE)始终是最主要的因素。结果表明,在 WUE 超过 2 g C kg-1 H₂O 的地区,火灾前植被受野火影响的概率为 95%。此外,ECOSTRESS 蒸发压力指数和地形坡度也是预测野火易感性的重要因素。这些结果表明了 ECOSTRESS 数据在预测和分析野火方面的巨大潜力,并强调了 ECOSTRESS 数据所显示的干旱条件在野火事件中的关键作用。我们在这项研究中开发的方法和成果可以帮助政策制定者、火灾管理者和城市规划者在未来评估、管理、准备和缓解野火。
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引用次数: 0
Amazonian manatee critical habitat revealed by artificial intelligence‐based passive acoustic techniques 基于人工智能的被动声学技术揭示亚马逊海牛关键栖息地
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-10-31 DOI: 10.1002/rse2.418
Florence Erbs, Mike van der Schaar, Miriam Marmontel, Marina Gaona, Emiliano Ramalho, Michel André
For many species at risk, monitoring challenges related to low visual detectability and elusive behavior limit the use of traditional visual surveys to collect critical information, hindering the development of sound conservation strategies. Passive acoustics can cost‐effectively acquire terrestrial and underwater long‐term data. However, to extract valuable information from large datasets, automatic methods need to be developed, tested and applied. Combining passive acoustics with deep learning models, we developed a method to monitor the secretive Amazonian manatee over two consecutive flooded seasons in the Brazilian Amazon floodplains. Subsequently, we investigated the vocal behavior parameters based on vocalization frequencies and temporal characteristics in the context of habitat use. A Convolutional Neural Network model successfully detected Amazonian manatee vocalizations with a 0.98 average precision on training data. Similar classification performance in terms of precision (range: 0.83–1.00) and recall (range: 0.97–1.00) was achieved for each year. Using this model, we evaluated manatee acoustic presence over a total of 226 days comprising recording periods in 2021 and 2022. Manatee vocalizations were consistently detected during both years, reaching 94% daily temporal occurrence in 2021, and up to 11 h a day with detections during peak presence. Manatee calls were characterized by a high emphasized frequency and high repetition rate, being mostly produced in rapid sequences. This vocal behavior strongly indicates an exchange between females and their calves. Combining passive acoustic monitoring with deep learning models, and extending temporal monitoring and increasing species detectability, we demonstrated that the approach can be used to identify manatee core habitats according to seasonality. The combined method represents a reliable, cost‐effective, scalable ecological monitoring technique that can be integrated into long‐term, standardized survey protocols of aquatic species. It can considerably benefit the monitoring of inaccessible regions, such as the Amazonian freshwater systems, which are facing immediate threats from increased hydropower construction.
对于许多濒危物种来说,由于视觉可探测性低和行为难以捉摸,传统的视觉调查在收集关键信息方面受到限制,从而阻碍了合理保护战略的制定。被动声学可以经济有效地获取陆地和水下的长期数据。然而,要从大型数据集中提取有价值的信息,需要开发、测试和应用自动方法。我们将被动声学与深度学习模型相结合,开发出一种方法,在巴西亚马逊洪泛平原连续两个汛期监测神秘的亚马逊海牛。随后,我们根据发声频率和时间特征研究了栖息地使用背景下的发声行为参数。卷积神经网络模型成功地检测到了亚马逊海牛的发声,训练数据的平均精度为 0.98。在精确度(范围:0.83-1.00)和召回率(范围:0.97-1.00)方面,每年都取得了相似的分类效果。利用该模型,我们对 2021 年和 2022 年共计 226 天的海牛声学存在进行了评估。在这两年中,海牛的叫声一直都能被探测到,2021 年海牛叫声的日出现率高达 94%,在海牛出现高峰期,每天的探测时间长达 11 小时。海牛叫声的特点是强调频率高、重复率高,大多以快速序列发出。这种发声行为强烈表明雌海牛与幼海牛之间存在交流。我们将被动声学监测与深度学习模型相结合,并扩大了时间监测范围,提高了物种可探测性,证明该方法可用于根据季节性识别海牛的核心栖息地。这种综合方法是一种可靠、经济、可扩展的生态监测技术,可纳入长期、标准化的水生物种调查方案。它可以极大地促进对亚马逊淡水系统等难以进入地区的监测,这些地区正面临着水电建设增加所带来的直接威胁。
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引用次数: 0
Combining satellite and field data reveals Congo's forest types structure, functioning and composition 结合卫星和实地数据揭示刚果森林类型的结构、功能和组成
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-10-12 DOI: 10.1002/rse2.419
Juliette Picard, Maïalicah M. Nungi‐Pambu Dembi, Nicolas Barbier, Guillaume Cornu, Pierre Couteron, Eric Forni, Gwili Gibbon, Felix Lim, Pierre Ploton, Robin Pouteau, Paul Tresson, Tom van Loon, Gaëlle Viennois, Maxime Réjou‐Méchain
Tropical moist forests are not the homogeneous green carpet often illustrated in maps or considered by global models. They harbour a complex mixture of forest types organized at different spatial scales that can now be more accurately mapped thanks to remote sensing products and artificial intelligence. In this study, we built a large‐scale vegetation map of the North of Congo and assessed the environmental drivers of the main forest types, their forest structure, their floristic and functional compositions and their faunistic composition. To build the map, we used Sentinel‐2 satellite images and recent deep learning architectures. We tested the effect of topographically determined water availability on vegetation type distribution by linking the map with a water drainage depth proxy (HAND, height above the nearest drainage index). We also described vegetation type structure and composition (floristic, functional and associated fauna) by linking the map with data from large inventories and derived from satellite images. We found that water drainage depth is a major driver of forest type distribution and that the different forest types are characterized by different structure, composition and functions, bringing new insights about their origins and successional dynamics. We discuss not only the crucial role of soil–water depth, but also the importance of consistently reproducing such maps through time to develop an accurate monitoring of tropical forest types and functions, and we provide insights on peculiar forest types (Marantaceae forests and monodominant Gilbertiodendron forests) on which future studies should focus more. Under the current context of global change, expected to trigger major forest structural and compositional changes in the tropics, an appropriate monitoring strategy of the spatio‐temporal dynamics of forest types and their associated floristic and faunistic composition would considerably help anticipate detrimental shifts.
热带潮湿森林并不是地图上经常标示的或全球模型所认为的均匀的绿色地毯。在遥感产品和人工智能的帮助下,现在可以更精确地绘制出不同空间尺度的森林类型。在这项研究中,我们绘制了刚果北部大尺度植被图,并评估了主要森林类型的环境驱动因素、森林结构、花卉和功能组成以及动物组成。为了绘制该地图,我们使用了 Sentinel-2 卫星图像和最新的深度学习架构。我们通过将地图与排水深度代用指标(HAND,最近排水指数以上的高度)相连接,测试了由地形确定的水源对植被类型分布的影响。我们还通过将地图与来自大型清单和卫星图像的数据相连接,描述了植被类型的结构和组成(植物学、功能和相关动物群)。我们发现,排水深度是森林类型分布的主要驱动因素,不同的森林类型具有不同的结构、组成和功能,这为我们了解其起源和演替动态提供了新的视角。我们不仅讨论了土壤水深度的关键作用,还讨论了随着时间的推移不断复制此类地图对准确监测热带森林类型和功能的重要性,并就未来研究应更加关注的特殊森林类型(马缨丹科森林和单优势吉尔伯特碘龙森林)提出了见解。在当前全球变化的背景下,预计热带地区的森林结构和组成将发生重大变化,对森林类型的时空动态及其相关的花卉和动物组成进行适当的监测战略将大大有助于预测有害的变化。
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引用次数: 0
Early spectral dynamics are indicative of distinct growth patterns in post‐wildfire forests 早期光谱动态显示了野火后森林的独特生长模式
IF 5.5 2区 环境科学与生态学 Q1 ECOLOGY Pub Date : 2024-09-18 DOI: 10.1002/rse2.420
Sarah Smith‐Tripp, Nicholas C. Coops, Christopher Mulverhill, Joanne C. White, Sarah Gergel
Western North America has seen a recent dramatic increase in large and often high‐severity wildfires. After forest fire, understanding patterns of structural recovery is important, as recovery patterns impact critical ecosystem services. Continuous forest monitoring provided by satellite observations is particularly beneficial to capture the pivotal post‐fire period when forest recovery begins. However, it is challenging to optimize optical satellite imagery to both interpolate current and extrapolate future forest structure and composition. We identified a need to understand how early spectral dynamics (5 years post‐fire) inform patterns of structural recovery after fire disturbance. To create these structural patterns, we collected metrics of forest structure using high‐density Remotely Piloted Aircraft (RPAS) lidar (light detection and ranging). We employed a space‐for‐time substitution in the highly fire‐disturbed forests of interior British Columbia. In this region, we collected RPAS lidar and corresponding field plot data 5‐, 8‐, 11‐,12‐, and 16‐years postfire to predict structural attributes relevant to management, including the percent bare ground, the proportion of coniferous trees, stem density, and basal area. We compared forest structural attributes with unique early spectral responses, or trajectories, derived from Landsat time series data 5 years after fire. A total of eight unique spectral recovery trajectories were identified from spectral responses of seven vegetation indices (NBR, NDMI, NDVI, TCA, TCB, TCG, and TCW) that described five distinct patterns of structural recovery captured with RPAS lidar. Two structural patterns covered more than 80% of the study area. Both patterns had strong coniferous regrowth, but one had a higher basal area with more bare ground and the other pattern had a high stem density, but a low basal area and a higher deciduous proportion. Our approach highlights the ability to use early spectral responses to capture unique spectral trajectories and their associated distinct structural recovery patterns.
北美西部最近发生的大规模野火急剧增加,而且往往非常严重。森林火灾后,了解结构恢复的模式非常重要,因为恢复模式会影响关键的生态系统服务。卫星观测提供的连续森林监测特别有利于捕捉火灾后森林开始恢复的关键时期。然而,优化光学卫星图像以推断当前和未来的森林结构和组成是一项挑战。我们发现有必要了解早期光谱动态(火灾后 5 年)如何为火灾干扰后的结构恢复模式提供信息。为了创建这些结构模式,我们使用高密度遥控飞机(RPAS)激光雷达(光探测和测距)收集了森林结构指标。我们在不列颠哥伦比亚省内陆受火灾严重干扰的森林中采用了空间换时间的方法。在该地区,我们收集了 RPAS 激光雷达和相应的野外地块数据,用于预测火灾后 5、8、11、12 和 16 年与管理相关的结构属性,包括裸地百分比、针叶树比例、茎干密度和基部面积。我们将森林结构属性与火灾发生 5 年后从 Landsat 时间序列数据中得出的独特早期光谱响应或轨迹进行了比较。从七种植被指数(NBR、NDMI、NDVI、TCA、TCB、TCG 和 TCW)的光谱响应中,共识别出八种独特的光谱恢复轨迹,它们描述了用 RPAS 激光雷达捕捉到的五种不同的结构恢复模式。两种结构模式覆盖了 80% 以上的研究区域。这两种模式都有较强的针叶树再生,但其中一种模式的基部面积较大,裸地较多;另一种模式的茎干密度较高,但基部面积较小,落叶比例较高。我们的方法强调了利用早期光谱响应捕捉独特光谱轨迹及其相关的独特结构恢复模式的能力。
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引用次数: 0
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 Agricultural and Biological Sciences 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 Agricultural and Biological Sciences 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
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Remote Sensing in Ecology and Conservation
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