James Tricker, Claire Wright, Spencer Rose, Jeanine Rhemtulla, Trevor Lantz, Eric Higgs
Repeat photography offers distinctive insights into ecological change, with ground-based oblique photographs often predating early aerial images by decades. However, the oblique angle of the photographs presents challenges for extracting and analyzing ecological information using traditional remote sensing approaches. Several innovative methods have been developed for analyzing repeat photographs, but none offer a comprehensive end-to-end workflow incorporating image classification and georeferencing to produce quantifiable landcover data. In this paper, we provide an overview of two new tools, an automated deep learning classifier and intuitive georeferencing tool, and describe how they are used to derive landcover data from 19 images associated with the Mountain Legacy Project, a research team that works with the world's largest collection of systematic high-resolution historic mountain photographs. We then combined these data to produce a contemporary landcover map for a study area in Jasper National Park, Canada. We assessed georeferencing accuracy by calculating the root-mean-square error and mean displacement for a subset of the images, which was 4.6 and 3.7 m, respectively. Overall classification accuracy of the landcover map produced from oblique images was 68%, which was comparable to landcover data produced from aerial imagery using a conventional classification method. The new workflow advances the use of repeat photographs for yielding quantitative landcover data. It has several advantages over existing methods including the ability to produce quick and consistent image classifications with little human input, and accurately georeference and combine these data to generate landcover maps for large areas.
{"title":"Assessing the accuracy of georeferenced landcover data derived from oblique imagery using machine learning","authors":"James Tricker, Claire Wright, Spencer Rose, Jeanine Rhemtulla, Trevor Lantz, Eric Higgs","doi":"10.1002/rse2.379","DOIUrl":"https://doi.org/10.1002/rse2.379","url":null,"abstract":"Repeat photography offers distinctive insights into ecological change, with ground-based oblique photographs often predating early aerial images by decades. However, the oblique angle of the photographs presents challenges for extracting and analyzing ecological information using traditional remote sensing approaches. Several innovative methods have been developed for analyzing repeat photographs, but none offer a comprehensive end-to-end workflow incorporating image classification and georeferencing to produce quantifiable landcover data. In this paper, we provide an overview of two new tools, an automated deep learning classifier and intuitive georeferencing tool, and describe how they are used to derive landcover data from 19 images associated with the Mountain Legacy Project, a research team that works with the world's largest collection of systematic high-resolution historic mountain photographs. We then combined these data to produce a contemporary landcover map for a study area in Jasper National Park, Canada. We assessed georeferencing accuracy by calculating the root-mean-square error and mean displacement for a subset of the images, which was 4.6 and 3.7 m, respectively. Overall classification accuracy of the landcover map produced from oblique images was 68%, which was comparable to landcover data produced from aerial imagery using a conventional classification method. The new workflow advances the use of repeat photographs for yielding quantitative landcover data. It has several advantages over existing methods including the ability to produce quick and consistent image classifications with little human input, and accurately georeference and combine these data to generate landcover maps for large areas.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"29 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139110451","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}
Simon Hirschhofer, Felix Liechti, Peter Ranacher, Robert Weibel, Baptiste Schmid
The Alps are a natural barrier for avian broad-front migration in Central Europe. While most birds that approach the Alps are deflected and circumvent the mountains, some choose to make the crossing. Here, they are funnelled and channelled in valleys, leading to high bird densities. Many Alpine valleys are suitable locations for wind farms, potentially creating a conflict between wind energy production and bird conservation. Collisions can be reduced by temporarily shutting down wind turbines. This however requires timely coordination, either by locally monitoring migration intensity or by extrapolating and forecasting migratory fluxes from other sites. However, little is known about the timing and intensity of bird migration in valleys of the central Alps, especially during spring migration. This study presents a 2-year quantification of avian migration across the Alps. We collected terrestrial radar data at three sites: two located in Alpine valleys and one in the lowland, close to the northern foothills of the Alps. We found high migration traffic rates (MTR) during both migration seasons in the Alpine valleys, with outstanding numbers of migrants during the spring season. The strong alignment of the flight directions with the main orientation of alpine valleys highlights the importance of valleys and the connected passes in channelling migratory fluxes through the Alps. However, extrapolating migration intensities and forecasting peak migration events for inner Alpine sites is difficult, likely due to how migratory patterns and activity are influenced by the complexity of the local topography and the associated dynamic wind and weather conditions. Instead, we call for year-round on-site monitoring of migration intensities and strategies tailored to the local context to reduce the risk of bird strikes at wind turbines in the Alps.
{"title":"High-intensity bird migration along Alpine valleys calls for protective measures against anthropogenically induced avian mortality","authors":"Simon Hirschhofer, Felix Liechti, Peter Ranacher, Robert Weibel, Baptiste Schmid","doi":"10.1002/rse2.377","DOIUrl":"https://doi.org/10.1002/rse2.377","url":null,"abstract":"The Alps are a natural barrier for avian broad-front migration in Central Europe. While most birds that approach the Alps are deflected and circumvent the mountains, some choose to make the crossing. Here, they are funnelled and channelled in valleys, leading to high bird densities. Many Alpine valleys are suitable locations for wind farms, potentially creating a conflict between wind energy production and bird conservation. Collisions can be reduced by temporarily shutting down wind turbines. This however requires timely coordination, either by locally monitoring migration intensity or by extrapolating and forecasting migratory fluxes from other sites. However, little is known about the timing and intensity of bird migration in valleys of the central Alps, especially during spring migration. This study presents a 2-year quantification of avian migration across the Alps. We collected terrestrial radar data at three sites: two located in Alpine valleys and one in the lowland, close to the northern foothills of the Alps. We found high migration traffic rates (MTR) during both migration seasons in the Alpine valleys, with outstanding numbers of migrants during the spring season. The strong alignment of the flight directions with the main orientation of alpine valleys highlights the importance of valleys and the connected passes in channelling migratory fluxes through the Alps. However, extrapolating migration intensities and forecasting peak migration events for inner Alpine sites is difficult, likely due to how migratory patterns and activity are influenced by the complexity of the local topography and the associated dynamic wind and weather conditions. Instead, we call for year-round on-site monitoring of migration intensities and strategies tailored to the local context to reduce the risk of bird strikes at wind turbines in the Alps.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"157 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139091765","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}
Shukhrat Shokirov, Tommaso Jucker, Shaun R. Levick, Adrian D. Manning, Kara N. Youngentob
Remotely sensed measures of vegetation structure have been shown to explain patterns in the occurrence and diversity of several animal taxa, including birds, mammals, and invertebrates. However, very little research in this area has focused on reptiles and amphibians (herpetofauna). Moreover, most remote sensing studies on animal–habitat associations have relied on airborne or satellite data that provide coverage over relatively large areas but may not have the resolution or viewing angle necessary to measure vegetation features at scales that are meaningful to herpetofauna. Here, we combined terrestrial laser scanning (TLS), unmanned aerial vehicle laser scanning (ULS), and fused (FLS) data to provide the first test of whether vegetation structural attributes can help explain variation in herpetofauna abundance, species richness, and diversity across a woodland landscape. We identified relationships between the abundance and diversity of herpetofauna and several vegetation metrics, including canopy height, skewedness, vertical complexity, volume of vegetation, and coarse woody debris. These relationships varied across species, groups, and sensors. ULS models tended to perform as well or better than TLS or FLS models based on the methods we used in this study. In open woodland landscapes, ULS data may have some benefits over TLS data for modeling relationships between herpetofauna and vegetation structure, which we discuss. However, for some species, only TLS data identified significant predictor variables among the LiDAR-derived structural metrics. While the overall predictive power of models was relatively low (i.e., at most R2 = 0.32 for ULS overall abundance and R2 = 0.32 for abundance at the individual species level [three-toed skink (Chalcides striatus)]), the ability to identify relationships between specific LiDAR structural metrics and the abundance and diversity of herpetofauna could be useful for understanding their habitat associations and managing reptile and amphibian populations.
{"title":"Using multiplatform LiDAR to identify relationships between vegetation structure and the abundance and diversity of woodland reptiles and amphibians","authors":"Shukhrat Shokirov, Tommaso Jucker, Shaun R. Levick, Adrian D. Manning, Kara N. Youngentob","doi":"10.1002/rse2.381","DOIUrl":"https://doi.org/10.1002/rse2.381","url":null,"abstract":"Remotely sensed measures of vegetation structure have been shown to explain patterns in the occurrence and diversity of several animal taxa, including birds, mammals, and invertebrates. However, very little research in this area has focused on reptiles and amphibians (herpetofauna). Moreover, most remote sensing studies on animal–habitat associations have relied on airborne or satellite data that provide coverage over relatively large areas but may not have the resolution or viewing angle necessary to measure vegetation features at scales that are meaningful to herpetofauna. Here, we combined terrestrial laser scanning (TLS), unmanned aerial vehicle laser scanning (ULS), and fused (FLS) data to provide the first test of whether vegetation structural attributes can help explain variation in herpetofauna abundance, species richness, and diversity across a woodland landscape. We identified relationships between the abundance and diversity of herpetofauna and several vegetation metrics, including canopy height, skewedness, vertical complexity, volume of vegetation, and coarse woody debris. These relationships varied across species, groups, and sensors. ULS models tended to perform as well or better than TLS or FLS models based on the methods we used in this study. In open woodland landscapes, ULS data may have some benefits over TLS data for modeling relationships between herpetofauna and vegetation structure, which we discuss. However, for some species, only TLS data identified significant predictor variables among the LiDAR-derived structural metrics. While the overall predictive power of models was relatively low (i.e., at most <i>R</i><sup>2</sup> = 0.32 for ULS overall abundance and <i>R</i><sup>2</sup> = 0.32 for abundance at the individual species level [three-toed skink (<i>Chalcides striatus</i>)]), the ability to identify relationships between specific LiDAR structural metrics and the abundance and diversity of herpetofauna could be useful for understanding their habitat associations and managing reptile and amphibian populations.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"18 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139091743","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}
Melissa Schiele, J. Marcus Rowcliffe, Ben Clark, Paul Lepper, Tom B. Letessier
Bird colonies on islands sustain elevated productivity and biomass on adjacent reefs, through nutrient subsidies. However, the implications of this localized enhancement on higher and often more mobile trophic levels (such as sharks and rays) are unclear, as spatial trends in mobile fauna are often poorly captured by traditional underwater visual surveys. Here, we explore whether the presence of seabird colonies is associated with enhanced abundances of sharks and rays on adjacent coral reefs. We used a novel long-range water-landing fixed-wing unoccupied aerial vehicle (UAV) to survey the distribution and density of sharks, rays and any additional megafauna, on and around tropical coral islands (n = 14) in the Chagos Archipelago Marine Protected Area. We developed a computer-vision algorithm to distinguish greenery (trees and shrubs), sand and sea glitter from visible ocean to yield accurate marine megafauna density estimation. We detected elevated seabird densities over rat-free islands, with the commonest species, sooty tern, reaching densities of 932 ± 199 per km−2 while none were observed over former coconut plantation islands. Elasmobranch density around rat-free islands with seabird colonies was 6.7 times higher than around islands without seabird colonies (1.3 ± 0.63 vs. 0.2 ± SE 0.1 per km2). Our results are evidence that shark and ray distribution is sensitive to natural and localized nutrient subsidies. Correcting for non-sampled regions of images increased estimated elasmobranch density by 14%, and our openly accessible computer vision algorithm makes this correction easy to implement to generate shark and ray and other wildlife densities from any aerial imagery. The water-landing fixed-wing long-range UAV technology used in this study may provide cost effective monitoring opportunities in remote ocean locations.
岛屿上的鸟类群落通过提供营养补贴，提高了邻近珊瑚礁的生产力和生物量。然而，由于传统的水下目测往往不能很好地捕捉到移动动物的空间趋势，因此这种局部性的提高对更高、通常更机动的营养级（如鲨鱼和鳐鱼）的影响尚不清楚。在此，我们探讨了海鸟群的存在是否与邻近珊瑚礁上鲨鱼和鳐鱼数量的增加有关。我们使用了一种新型长距离水上降落固定翼无人飞行器（UAV）来调查查戈斯群岛海洋保护区热带珊瑚岛（n = 14）及其周围的鲨鱼、鳐鱼和其他巨型动物的分布和密度。我们开发了一种计算机视觉算法，从可见海洋中分辨出绿色植物（树木和灌木）、沙子和海面绒毛，从而准确估算出海洋巨型动物的密度。我们发现无鼠岛的海鸟密度较高，最常见的物种燕鸥的密度达到每平方公里 932 ± 199 只，而在前椰子种植园岛屿上则没有观察到任何海鸟。有海鸟栖息地的无鼠岛屿周围的箭鱼密度是无海鸟栖息地岛屿周围的 6.7 倍（1.3 ± 0.63 vs. 0.2 ± SE 0.1 per km2）。我们的研究结果证明，鲨鱼和鳐鱼的分布对自然和局部的营养补贴很敏感。对图像中的非采样区域进行校正后，估计的鞘鳃类密度增加了 14%，我们公开的计算机视觉算法使这种校正很容易实现，可以从任何航空图像中生成鲨鱼和鳐鱼以及其他野生动物的密度。本研究中使用的水上降落固定翼长航时无人机技术可为偏远海洋地区提供经济有效的监测机会。
{"title":"Using water-landing, fixed-wing UAVs and computer vision to assess seabird nutrient subsidy effects on sharks and rays","authors":"Melissa Schiele, J. Marcus Rowcliffe, Ben Clark, Paul Lepper, Tom B. Letessier","doi":"10.1002/rse2.378","DOIUrl":"https://doi.org/10.1002/rse2.378","url":null,"abstract":"Bird colonies on islands sustain elevated productivity and biomass on adjacent reefs, through nutrient subsidies. However, the implications of this localized enhancement on higher and often more mobile trophic levels (such as sharks and rays) are unclear, as spatial trends in mobile fauna are often poorly captured by traditional underwater visual surveys. Here, we explore whether the presence of seabird colonies is associated with enhanced abundances of sharks and rays on adjacent coral reefs. We used a novel long-range water-landing fixed-wing unoccupied aerial vehicle (UAV) to survey the distribution and density of sharks, rays and any additional megafauna, on and around tropical coral islands (n = 14) in the Chagos Archipelago Marine Protected Area. We developed a computer-vision algorithm to distinguish greenery (trees and shrubs), sand and sea glitter from visible ocean to yield accurate marine megafauna density estimation. We detected elevated seabird densities over rat-free islands, with the commonest species, sooty tern, reaching densities of 932 ± 199 per km<sup>−2</sup> while none were observed over former coconut plantation islands. Elasmobranch density around rat-free islands with seabird colonies was 6.7 times higher than around islands without seabird colonies (1.3 ± 0.63 <i>vs.</i> 0.2 ± SE 0.1 per km<sup>2</sup>). Our results are evidence that shark and ray distribution is sensitive to natural and localized nutrient subsidies. Correcting for non-sampled regions of images increased estimated elasmobranch density by 14%, and our openly accessible computer vision algorithm makes this correction easy to implement to generate shark and ray and other wildlife densities from any aerial imagery. The water-landing fixed-wing long-range UAV technology used in this study may provide cost effective monitoring opportunities in remote ocean locations.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"3 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139051004","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 Lawson, Andre Farinha, Luca Romanello, Oscar Pang, Raphael Zufferey, Mirko Kovac
Freshwater ecosystems are endangered, underfunded and understudied, making new methods such as passive acoustic monitoring (PAM) essential for improving the efficiency and effectiveness of data collection. However, many challenges are still to be addressed with PAM: difficulty in accessing research sites, the logistics of implementing large-scale studies and the invasiveness of data collection. When combined with PAM and other sensing strategies, mobile robotics are a promising solution to directly address these challenges. In this paper, we integrate water surface and underwater acoustic monitoring equipment onto a prototype unmanned aerial-aquatic vehicle (UAAV) capable of sailing and flight (SailMAV). Twelve autonomous sailing missions were run on Lake Vrana, Croatia, during which acoustic data were collected, and the ability of the UAAV to facilitate the collection of acoustic data demonstrated. Data were simultaneously collected using standard recording methods on buoys and banksides to provide a comparative approach. Acoustic indices were used to analyse the soundscape of underwater acoustic data and BirdNET (a deep artificial neural network) was used on water surface datasets to determine bird species composition. Results show higher species richness and call abundance from UAAV surveys and high site dissimilarity owing to turnover between stationary and UAAV methods. This highlights the success of the UAAV in detecting biodiversity and the complementarity of these methods in providing a broad picture of the biodiversity of freshwater ecosystems. Increased bird diversity and underwater acoustic activity in protected areas demonstrate the benefits of protecting freshwater ecosystems; however, site dissimilarity driven by turnover highlights the importance of protecting the entire ecosystem. We show how, by integrating PAM and a UAAV, we can overcome some of the current challenges in freshwater biodiversity monitoring, improving accessibility, increasing spatial scale and coverage, and reducing invasiveness.
{"title":"Use of an unmanned aerial-aquatic vehicle for acoustic sensing in freshwater ecosystems","authors":"Jenna Lawson, Andre Farinha, Luca Romanello, Oscar Pang, Raphael Zufferey, Mirko Kovac","doi":"10.1002/rse2.373","DOIUrl":"https://doi.org/10.1002/rse2.373","url":null,"abstract":"Freshwater ecosystems are endangered, underfunded and understudied, making new methods such as passive acoustic monitoring (PAM) essential for improving the efficiency and effectiveness of data collection. However, many challenges are still to be addressed with PAM: difficulty in accessing research sites, the logistics of implementing large-scale studies and the invasiveness of data collection. When combined with PAM and other sensing strategies, mobile robotics are a promising solution to directly address these challenges. In this paper, we integrate water surface and underwater acoustic monitoring equipment onto a prototype unmanned aerial-aquatic vehicle (UAAV) capable of sailing and flight (SailMAV). Twelve autonomous sailing missions were run on Lake Vrana, Croatia, during which acoustic data were collected, and the ability of the UAAV to facilitate the collection of acoustic data demonstrated. Data were simultaneously collected using standard recording methods on buoys and banksides to provide a comparative approach. Acoustic indices were used to analyse the soundscape of underwater acoustic data and BirdNET (a deep artificial neural network) was used on water surface datasets to determine bird species composition. Results show higher species richness and call abundance from UAAV surveys and high site dissimilarity owing to turnover between stationary and UAAV methods. This highlights the success of the UAAV in detecting biodiversity and the complementarity of these methods in providing a broad picture of the biodiversity of freshwater ecosystems. Increased bird diversity and underwater acoustic activity in protected areas demonstrate the benefits of protecting freshwater ecosystems; however, site dissimilarity driven by turnover highlights the importance of protecting the entire ecosystem. We show how, by integrating PAM and a UAAV, we can overcome some of the current challenges in freshwater biodiversity monitoring, improving accessibility, increasing spatial scale and coverage, and reducing invasiveness.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"70 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139038798","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}
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 (). 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.
{"title":"An automated procedure to determine construction year of roads in forested landscapes using a least-cost path and a Before-After Control-Impact approach","authors":"Denis Valle, Sami W. Rifai, Gabriel C. Carrero, Ana Y. Y. Meiga","doi":"10.1002/rse2.376","DOIUrl":"https://doi.org/10.1002/rse2.376","url":null,"abstract":"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 km<sup>2</sup> 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 (<math altimg=\"urn:x-wiley:20563485:media:rse2376:rse2376-math-0001\" display=\"inline\" location=\"graphic/rse2376-math-0001.png\" overflow=\"scroll\">\u0000<semantics>\u0000<mrow>\u0000<msub>\u0000<mi>r</mi>\u0000<mtext>Pearson</mtext>\u0000</msub>\u0000<mo>=</mo>\u0000<mn>0.77</mn>\u0000</mrow>\u0000$$ {r}_{mathrm{Pearson}}=0.77 $$</annotation>\u0000</semantics></math>). 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.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"14 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138823282","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}
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。
{"title":"Camtrap DP: an open standard for the FAIR exchange and archiving of camera trap data","authors":"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","doi":"10.1002/rse2.374","DOIUrl":"https://doi.org/10.1002/rse2.374","url":null,"abstract":"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 (<i>e.g.</i>, 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.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"61 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138562622","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}
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.
{"title":"Tracking landscape scale vegetation change in the arid zone by integrating ground, drone and satellite data","authors":"Roxane J. Francis, Richard T. Kingsford, Katherine Moseby, John Read, Reece Pedler, Adrian Fisher, Justin McCann, Rebecca West","doi":"10.1002/rse2.375","DOIUrl":"https://doi.org/10.1002/rse2.375","url":null,"abstract":"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.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"38 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138562615","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}
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.
{"title":"Selection in the third dimension: Using LiDAR derived canopy metrics to assess individual and population-level habitat partitioning of ocelots, bobcats, and coyotes","authors":"Maksim Sergeyev, Daniel A. Crawford, Joseph D. Holbrook, Jason V. Lombardi, Michael E. Tewes, Tyler A. Campbell","doi":"10.1002/rse2.369","DOIUrl":"https://doi.org/10.1002/rse2.369","url":null,"abstract":"Wildlife depends on specific landscape features to persist. Thus, characterizing the vegetation available in an area can be essential for management. The ocelot (<i>Leopardus pardalis</i>) 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 (<i>Lynx rufus</i>) and coyotes (<i>Canis latrans</i>) 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.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"15 11","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138293382","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}
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.
{"title":"Assessing plant trait diversity as an indicators of species α- and β-diversity in a subalpine grassland of the Italian Alps","authors":"Hafiz Ali Imran, Karolina Sakowska, Damiano Gianelle, Duccio Rocchini, Michele Dalponte, Michele Scotton, Loris Vescovo","doi":"10.1002/rse2.370","DOIUrl":"https://doi.org/10.1002/rse2.370","url":null,"abstract":"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.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"43 39","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71491580","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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