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}
Dominique Weber, Marcel Schwieder, Lukas Ritter, Tiziana Koch, Achilleas Psomas, Nica Huber, Christian Ginzler, Steffen Boch
Land-use intensification in grassland ecosystems (i.e. increased mowing frequency, intensified grazing) has a strong negative effect on biodiversity and ecosystem services. However, accurate information on grassland-use intensity is difficult to acquire and restricted to the local or regional level. Recent studies have shown that mowing events can be mapped for large areas using satellite image time series. The transferability of such approaches, especially to mountain areas, has been little explored, however, and the relevance for ecological applications in biodiversity and conservation has hardly been investigated. Here, we used a rule-based algorithm to produce annual maps for 2018–2021 of grassland-management events, that is, mowing and/or grazing, for Switzerland using Sentinel-2 and Landsat 8 satellite data. We assessed the detection of management events based on independent reference data, which we acquired from daily time series of publicly available webcams that are widely distributed across Switzerland. We further examined the relationships between the generated grassland-use intensity measures and plant species richness and ecological indicator values derived from a nationwide field survey. The webcam-based verification for 2020 and 2021 revealed that most detected management events were actual mowing/grazing events (≥78%), but that a substantial number of events were not detected (up to 57%), particularly grazing events at higher elevations. We found lower plant species richness and higher mean ecological indicator values for nutrients and mowing tolerance with more frequent management events and those starting earlier in the year. A large proportion of the variance was explained by our use-intensity measures. Our findings therefore highlight that remotely assessed management events can characterise land-use intensity at fine spatial and temporal resolutions across broad scales and can explain plant biodiversity patterns in grasslands.
{"title":"Grassland-use intensity maps for Switzerland based on satellite time series: Challenges and opportunities for ecological applications","authors":"Dominique Weber, Marcel Schwieder, Lukas Ritter, Tiziana Koch, Achilleas Psomas, Nica Huber, Christian Ginzler, Steffen Boch","doi":"10.1002/rse2.372","DOIUrl":"https://doi.org/10.1002/rse2.372","url":null,"abstract":"Land-use intensification in grassland ecosystems (i.e. increased mowing frequency, intensified grazing) has a strong negative effect on biodiversity and ecosystem services. However, accurate information on grassland-use intensity is difficult to acquire and restricted to the local or regional level. Recent studies have shown that mowing events can be mapped for large areas using satellite image time series. The transferability of such approaches, especially to mountain areas, has been little explored, however, and the relevance for ecological applications in biodiversity and conservation has hardly been investigated. Here, we used a rule-based algorithm to produce annual maps for 2018–2021 of grassland-management events, that is, mowing and/or grazing, for Switzerland using Sentinel-2 and Landsat 8 satellite data. We assessed the detection of management events based on independent reference data, which we acquired from daily time series of publicly available webcams that are widely distributed across Switzerland. We further examined the relationships between the generated grassland-use intensity measures and plant species richness and ecological indicator values derived from a nationwide field survey. The webcam-based verification for 2020 and 2021 revealed that most detected management events were actual mowing/grazing events (≥78%), but that a substantial number of events were not detected (up to 57%), particularly grazing events at higher elevations. We found lower plant species richness and higher mean ecological indicator values for nutrients and mowing tolerance with more frequent management events and those starting earlier in the year. A large proportion of the variance was explained by our use-intensity measures. Our findings therefore highlight that remotely assessed management events can characterise land-use intensity at fine spatial and temporal resolutions across broad scales and can explain plant biodiversity patterns in grasslands.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"43 40","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71491579","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}
Visible Infrared Imaging Radiometer Suite (VIIRS) Boat Detection (VBD) data have been widely used to study the patterns of fishing grounds and their linking to fishery targets, particularly species mainly caught by jiggers. In line with most species in the Ommastrephidae family, the population of Todarodes pacificus is made up of various splinter cohorts concerning the timing and location of hatching. Therefore, the satellite-recorded fishing grounds consist of groups with complex age structures and different migration directions within cohorts. This study examined the age composition of harvestable stocks (age spectrum) of T. pacificus in the Japan Sea based on an early life history individual-based model of T. pacificus and VBD data. Using the age spectrum, we analysed the relationship between fishery effort and the age of the target group. It was found that jiggers most prefer individuals around 310 ± 20 days. Furthermore, the correlation between ambient water temperature and fishing effort revealed that T. pacificus migrated to colder waters, reaching the coldest waters at 250 ± 7.5 days before moving back towards warmer waters. We discussed a possible way to use the age-temperature relationship to analyse the flow of VBD distributions to record the movements related to the migration of the fishing target. The results show migration-like trajectories, which are initially parallel to the isotherm, gradually deflect towards lower temperature sides over several months, sharply turn for about a month and then move back with a slight angle to the isotherms. The method provides a potential framework to improve our understanding of the active migration of oceanic squid.
{"title":"A new way to understand migration routes of oceanic squid (Ommastrephidae) from satellite data","authors":"Fei Ji, Xinyu Guo","doi":"10.1002/rse2.368","DOIUrl":"https://doi.org/10.1002/rse2.368","url":null,"abstract":"Visible Infrared Imaging Radiometer Suite (VIIRS) Boat Detection (VBD) data have been widely used to study the patterns of fishing grounds and their linking to fishery targets, particularly species mainly caught by jiggers. In line with most species in the Ommastrephidae family, the population of <i>Todarodes pacificus</i> is made up of various splinter cohorts concerning the timing and location of hatching. Therefore, the satellite-recorded fishing grounds consist of groups with complex age structures and different migration directions within cohorts. This study examined the age composition of harvestable stocks (age spectrum) of <i>T. pacificus</i> in the Japan Sea based on an early life history individual-based model of <i>T. pacificus</i> and VBD data. Using the age spectrum, we analysed the relationship between fishery effort and the age of the target group. It was found that jiggers most prefer individuals around 310 ± 20 days. Furthermore, the correlation between ambient water temperature and fishing effort revealed that <i>T. pacificus</i> migrated to colder waters, reaching the coldest waters at 250 ± 7.5 days before moving back towards warmer waters. We discussed a possible way to use the age-temperature relationship to analyse the flow of VBD distributions to record the movements related to the migration of the fishing target. The results show migration-like trajectories, which are initially parallel to the isotherm, gradually deflect towards lower temperature sides over several months, sharply turn for about a month and then move back with a slight angle to the isotherms. The method provides a potential framework to improve our understanding of the active migration of oceanic squid.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"44 11","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71491460","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}
Darren Turner, Emiliano Cimoli, Arko Lucieer, Ryan S. Haynes, Krystal Randall, Melinda J. Waterman, Vanessa Lucieer, Sharon A. Robinson
Antarctic moss beds are sensitive to climatic conditions, and both their survival and community composition are particularly influenced by the availability of liquid water over summer. As Antarctic regions increasingly face climate pressures (e.g., changing hydrology and heat waves), advancing capabilities to efficiently and non-destructively monitor water content in moss communities becomes a key research priority. Because of the complexity induced by multiple micro-climatic drivers and its fragility, tracking the evolution and responses of moss bed moisture requires monitoring methods that are non-intrusive, efficient, and spatially significant, such as the use of unoccupied aerial systems (UAS). In this study, we combine a multi-species drying laboratory experiment with short-wave infrared (SWIR) spectroscopy analyses to first develop a Random Forest regression Model (RFM) capable of predicting Antarctic moss turf water content (~5% error). The RFM was then applied to UAS-borne SWIR imaging data (900–1700 nm, <16 nm spectral resolution) of the moss beds at high spatial resolution (2 cm) across three sites in the vicinity of Casey Station, Antarctica. The sites differed in terrain, snow cover, and moisture availability to evaluate method capabilities under different conditions. Optimum RFM parameters and input variables (spectral indices and reflectance spectra) were determined. Maps of moss moisture were validated via acquiring moss spectra and water content (using sponges inserted into the moss turf) collected in situ, for which an exponential correlation (R2 = 0.72) was reported. RFM further allowed investigation of the influential spectral variables to model water content in moss and associated spectral water absorption features. We demonstrated that UAS-borne SWIR imaging is a promising new tool to map and quantify water content in Antarctic moss beds. Hyperspectral mapping facilitates the exploration of the spatial variability of moss health and enables the creation of a baseline against which changes in these moss communities can be measured.
{"title":"Mapping water content in drying Antarctic moss communities using UAS-borne SWIR imaging spectroscopy","authors":"Darren Turner, Emiliano Cimoli, Arko Lucieer, Ryan S. Haynes, Krystal Randall, Melinda J. Waterman, Vanessa Lucieer, Sharon A. Robinson","doi":"10.1002/rse2.371","DOIUrl":"https://doi.org/10.1002/rse2.371","url":null,"abstract":"Antarctic moss beds are sensitive to climatic conditions, and both their survival and community composition are particularly influenced by the availability of liquid water over summer. As Antarctic regions increasingly face climate pressures (e.g., changing hydrology and heat waves), advancing capabilities to efficiently and non-destructively monitor water content in moss communities becomes a key research priority. Because of the complexity induced by multiple micro-climatic drivers and its fragility, tracking the evolution and responses of moss bed moisture requires monitoring methods that are non-intrusive, efficient, and spatially significant, such as the use of unoccupied aerial systems (UAS). In this study, we combine a multi-species drying laboratory experiment with short-wave infrared (SWIR) spectroscopy analyses to first develop a Random Forest regression Model (RFM) capable of predicting Antarctic moss turf water content (~5% error). The RFM was then applied to UAS-borne SWIR imaging data (900–1700 nm, <16 nm spectral resolution) of the moss beds at high spatial resolution (2 cm) across three sites in the vicinity of Casey Station, Antarctica. The sites differed in terrain, snow cover, and moisture availability to evaluate method capabilities under different conditions. Optimum RFM parameters and input variables (spectral indices and reflectance spectra) were determined. Maps of moss moisture were validated <i>via</i> acquiring moss spectra and water content (using sponges inserted into the moss turf) collected in situ, for which an exponential correlation (<i>R</i><sup>2</sup> = 0.72) was reported. RFM further allowed investigation of the influential spectral variables to model water content in moss and associated spectral water absorption features. We demonstrated that UAS-borne SWIR imaging is a promising new tool to map and quantify water content in Antarctic moss beds. Hyperspectral mapping facilitates the exploration of the spatial variability of moss health and enables the creation of a baseline against which changes in these moss communities can be measured.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"44 23","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71491948","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}
Veronika Mitterwallner, A. Peters, Hendrik Edelhoff, Gregor H. Mathes, Hien Nguyen, W. Peters, M. Heurich, M. Steinbauer
As human activities in natural areas increase, understanding human–wildlife interactions is crucial. Big data approaches, like large‐scale camera trap studies, are becoming more relevant for studying these interactions. In addition, open‐source object detection models are rapidly improving and have great potential to enhance the image processing of camera trap data from human and wildlife activities. In this study, we evaluate the performance of the open‐source object detection model MegaDetector in cross‐regional monitoring using camera traps. The performance at detecting and counting humans, animals and vehicles is evaluated by comparing the detection results with manual classifications of more than 300 000 camera trap images from three study regions. Moreover, we investigate structural patterns of misclassification and evaluate the results of the detection model for typical temporal analyses conducted in ecological research. Overall, the accuracy of the detection model was very high with 96.0% accuracy for animals, 93.8% for persons and 99.3% for vehicles. Results reveal systematic patterns in misclassifications that can be automatically identified and removed. In addition, we show that the detection model can be readily used to count people and animals on images with underestimating persons by −0.05, vehicles by −0.01 and animals by −0.01 counts per image. Most importantly, the temporal pattern in a long‐term time series of manually classified human and wildlife activities was highly correlated with classification results of the detection model (Pearson's r = 0.996, p < 0.001) and diurnal kernel densities of activities were almost equivalent for manual and automated classification. The results thus prove the overall applicability of the detection model in the image classification process of cross‐regional camera trap studies without further manual intervention. Besides the great acceleration in processing speed, the model is also suitable for long‐term monitoring and allows reproducibility in scientific studies while complying with privacy regulations.
随着人类在自然区域活动的增加，了解人类与野生动物的相互作用至关重要。大数据方法，如大规模相机陷阱研究，正变得越来越适用于研究这些相互作用。此外，开源目标检测模型正在迅速改进，并且在增强来自人类和野生动物活动的相机陷阱数据的图像处理方面具有巨大的潜力。在本研究中，我们评估了开源目标检测模型MegaDetector在使用相机陷阱进行跨区域监控中的性能。通过将检测结果与来自三个研究区域的30多万张相机陷阱图像的人工分类结果进行比较，评估了该方法在检测和计数人类、动物和车辆方面的性能。此外，我们还研究了错误分类的结构模式，并评估了在生态研究中进行的典型时间分析的检测模型的结果。总体而言，该检测模型的准确率非常高，对动物的准确率为96.0%，对人的准确率为93.8%，对车辆的准确率为99.3%。结果揭示了错误分类的系统模式，可以自动识别和删除。此外，我们还表明，该检测模型可以很容易地用于对图像上的人和动物进行计数，每个图像低估了- 0.05个人，- 0.01个车辆和- 0.01个动物。最重要的是，人工分类的人类和野生动物活动的长期时间序列的时间格局与检测模型的分类结果高度相关(Pearson’s r = 0.996, p < 0.001)，并且活动的日核密度在人工和自动分类中几乎相等。结果证明了该检测模型在跨区域相机陷阱研究的图像分类过程中的整体适用性，无需进一步的人工干预。除了处理速度大大加快外，该模型还适用于长期监测，并在遵守隐私法规的同时允许科学研究的可重复性。
{"title":"Automated visitor and wildlife monitoring with camera traps and machine learning","authors":"Veronika Mitterwallner, A. Peters, Hendrik Edelhoff, Gregor H. Mathes, Hien Nguyen, W. Peters, M. Heurich, M. Steinbauer","doi":"10.1002/rse2.367","DOIUrl":"https://doi.org/10.1002/rse2.367","url":null,"abstract":"As human activities in natural areas increase, understanding human–wildlife interactions is crucial. Big data approaches, like large‐scale camera trap studies, are becoming more relevant for studying these interactions. In addition, open‐source object detection models are rapidly improving and have great potential to enhance the image processing of camera trap data from human and wildlife activities. In this study, we evaluate the performance of the open‐source object detection model MegaDetector in cross‐regional monitoring using camera traps. The performance at detecting and counting humans, animals and vehicles is evaluated by comparing the detection results with manual classifications of more than 300 000 camera trap images from three study regions. Moreover, we investigate structural patterns of misclassification and evaluate the results of the detection model for typical temporal analyses conducted in ecological research. Overall, the accuracy of the detection model was very high with 96.0% accuracy for animals, 93.8% for persons and 99.3% for vehicles. Results reveal systematic patterns in misclassifications that can be automatically identified and removed. In addition, we show that the detection model can be readily used to count people and animals on images with underestimating persons by −0.05, vehicles by −0.01 and animals by −0.01 counts per image. Most importantly, the temporal pattern in a long‐term time series of manually classified human and wildlife activities was highly correlated with classification results of the detection model (Pearson's r = 0.996, p < 0.001) and diurnal kernel densities of activities were almost equivalent for manual and automated classification. The results thus prove the overall applicability of the detection model in the image classification process of cross‐regional camera trap studies without further manual intervention. Besides the great acceleration in processing speed, the model is also suitable for long‐term monitoring and allows reproducibility in scientific studies while complying with privacy regulations.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43447596","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}
Maik Henrich, Mercedes Burgueño, J. Hoyer, T. Haucke, V. Steinhage, H. Kühl, M. Heurich
Camera traps have become important tools for the monitoring of animal populations. However, the study‐specific estimation of animal detection probabilities is key if unbiased abundance estimates of unmarked species are to be obtained. Since this process can be very time‐consuming, we developed the first semi‐automated workflow for animals of any size and shape to estimate detection probabilities and population densities. In order to obtain observation distances, a deep learning algorithm is used to create relative depth images that are calibrated with a small set of reference photos for each location, with distances then extracted for animals automatically detected by MegaDetector 4.0. Animal detection by MegaDetector was generally independent of the distance to the camera trap for 10 animal species at two different study sites. If an animal was detected both manually and automatically, the difference in the distance estimates was often minimal at a distance about 4 m from the camera trap. The difference increased approximately linearly for larger distances. Nonetheless, population density estimates based on manual and semi‐automated camera trap distance sampling workflows did not differ significantly. Our results show that a readily available software for semi‐automated distance estimation can reliably be used within a camera trap distance sampling workflow, reducing the time required for data processing, by >13‐fold. This greatly improves the accessibility of camera trap distance sampling for wildlife research and management.
{"title":"A semi‐automated camera trap distance sampling approach for population density estimation","authors":"Maik Henrich, Mercedes Burgueño, J. Hoyer, T. Haucke, V. Steinhage, H. Kühl, M. Heurich","doi":"10.1002/rse2.362","DOIUrl":"https://doi.org/10.1002/rse2.362","url":null,"abstract":"Camera traps have become important tools for the monitoring of animal populations. However, the study‐specific estimation of animal detection probabilities is key if unbiased abundance estimates of unmarked species are to be obtained. Since this process can be very time‐consuming, we developed the first semi‐automated workflow for animals of any size and shape to estimate detection probabilities and population densities. In order to obtain observation distances, a deep learning algorithm is used to create relative depth images that are calibrated with a small set of reference photos for each location, with distances then extracted for animals automatically detected by MegaDetector 4.0. Animal detection by MegaDetector was generally independent of the distance to the camera trap for 10 animal species at two different study sites. If an animal was detected both manually and automatically, the difference in the distance estimates was often minimal at a distance about 4 m from the camera trap. The difference increased approximately linearly for larger distances. Nonetheless, population density estimates based on manual and semi‐automated camera trap distance sampling workflows did not differ significantly. Our results show that a readily available software for semi‐automated distance estimation can reliably be used within a camera trap distance sampling workflow, reducing the time required for data processing, by >13‐fold. This greatly improves the accessibility of camera trap distance sampling for wildlife research and management.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49207353","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}
Tytti Jussila, R. Heikkinen, S. Anttila, K. Aapala, M. Kervinen, J. Aalto, P. Vihervaara
Aapa mires are waterlogged northern peatland ecosystems characterized by a patterned surface structure where water‐filled depressions (‘flarks’) alternate with drier hummock strings. As one of the EU Habitat Directive priority habitats, aapa mires are important for biodiversity and carbon cycling, harbouring several red‐listed species and supporting unique species communities. Due to their sensitivity to hydrological disturbances, reliable, up‐to‐date and systematic information on the hydrological condition and responses of mires is crucial and required for multiple purposes ranging from carbon exchange modelling to EU Habitats Directive reporting and conservation and ecosystem restoration planning. Here, we demonstrate the usability of Sentinel‐2 satellite data in a semi‐automatic cloud‐based approach to retrieve large‐scale information on aapa mire hydrological variability. Two satellite‐derived metrics, soil moisture index and the extent of water‐saturated surfaces based on pixel‐wise classification, are used to quantify monthly and interannual wetness variation between 2017 and 2020 across Natura 2000 aapa mires in Finland, including responses to the extreme drought of 2018. The results revealed high temporal variability in wetness, particularly in the southern parts of the aapa mire zone and generally in the late summer months interannually. Observations from the drought summer showed that one third of usually year‐round wet flark surfaces may be exposed to drying during climatic extremes. Responses varied between sites and regions, implicating the significance of environmental factors for drought resistance: some sites maintained high levels of moisture, whereas others lost wet surfaces completely. Our study provides the first comprehensive national‐level representation of seasonal and interannual wetness variability and drought‐sensitivity of pristine aapa mire sites. The approach and methods used here can be directly upscaled outside protected areas and to other EU countries. Thus, they provide a means for harmonized, systematic large‐scale monitoring of this priority habitat, as well as valuable information for other applications supporting peatland conservation and research.
{"title":"Quantifying wetness variability in aapa mires with Sentinel‐2: towards improved monitoring of an EU priority habitat","authors":"Tytti Jussila, R. Heikkinen, S. Anttila, K. Aapala, M. Kervinen, J. Aalto, P. Vihervaara","doi":"10.1002/rse2.363","DOIUrl":"https://doi.org/10.1002/rse2.363","url":null,"abstract":"Aapa mires are waterlogged northern peatland ecosystems characterized by a patterned surface structure where water‐filled depressions (‘flarks’) alternate with drier hummock strings. As one of the EU Habitat Directive priority habitats, aapa mires are important for biodiversity and carbon cycling, harbouring several red‐listed species and supporting unique species communities. Due to their sensitivity to hydrological disturbances, reliable, up‐to‐date and systematic information on the hydrological condition and responses of mires is crucial and required for multiple purposes ranging from carbon exchange modelling to EU Habitats Directive reporting and conservation and ecosystem restoration planning. Here, we demonstrate the usability of Sentinel‐2 satellite data in a semi‐automatic cloud‐based approach to retrieve large‐scale information on aapa mire hydrological variability. Two satellite‐derived metrics, soil moisture index and the extent of water‐saturated surfaces based on pixel‐wise classification, are used to quantify monthly and interannual wetness variation between 2017 and 2020 across Natura 2000 aapa mires in Finland, including responses to the extreme drought of 2018. The results revealed high temporal variability in wetness, particularly in the southern parts of the aapa mire zone and generally in the late summer months interannually. Observations from the drought summer showed that one third of usually year‐round wet flark surfaces may be exposed to drying during climatic extremes. Responses varied between sites and regions, implicating the significance of environmental factors for drought resistance: some sites maintained high levels of moisture, whereas others lost wet surfaces completely. Our study provides the first comprehensive national‐level representation of seasonal and interannual wetness variability and drought‐sensitivity of pristine aapa mire sites. The approach and methods used here can be directly upscaled outside protected areas and to other EU countries. Thus, they provide a means for harmonized, systematic large‐scale monitoring of this priority habitat, as well as valuable information for other applications supporting peatland conservation and research.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48626698","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}
Shuo Zong, Jeanine Brantschen, Xiaowei Zhang, C. Albouy, A. Valentini, Heng Zhang, F. Altermatt, L. Pellissier
Biodiversity loss in river ecosystems is much faster and more severe than in terrestrial systems, and spatial conservation and restoration plans are needed to halt this erosion. Reliable and highly resolved data on the state of and change in biodiversity and species distributions are critical for effective measures. However, high‐resolution maps of fish distribution remain limited for large riverine systems. Coupling data from global satellite sensors with broad‐scale environmental DNA (eDNA) and machine learning could enable rapid and precise mapping of the distribution of river organisms. Here, we investigated the potential for combining these methods using a fish eDNA dataset from 110 sites sampled along the full length of the Rhone River in Switzerland and France. Using Sentinel 2 and Landsat 8 images, we generated a set of ecological variables describing both the aquatic and the terrestrial habitats surrounding the river corridor. We combined these variables with eDNA‐based presence and absence data on 29 fish species and used three machine‐learning models to assess environmental suitability for these species. Most models showed good performance, indicating that ecological variables derived from remote sensing can approximate the ecological determinants of fish species distributions, but water‐derived variables had stronger associations than the terrestrial variables surrounding the river. The species range mapping indicated a significant transition in the species occupancy along the Rhone, from its source in the Swiss Alps to outlet into the Mediterranean Sea in southern France. Our study demonstrates the feasibility of combining remote sensing and eDNA to map species distributions in a large river. This method can be expanded to any large river to support conservation schemes.
{"title":"Combining environmental DNA with remote sensing variables to map fish species distributions along a large river","authors":"Shuo Zong, Jeanine Brantschen, Xiaowei Zhang, C. Albouy, A. Valentini, Heng Zhang, F. Altermatt, L. Pellissier","doi":"10.1002/rse2.366","DOIUrl":"https://doi.org/10.1002/rse2.366","url":null,"abstract":"Biodiversity loss in river ecosystems is much faster and more severe than in terrestrial systems, and spatial conservation and restoration plans are needed to halt this erosion. Reliable and highly resolved data on the state of and change in biodiversity and species distributions are critical for effective measures. However, high‐resolution maps of fish distribution remain limited for large riverine systems. Coupling data from global satellite sensors with broad‐scale environmental DNA (eDNA) and machine learning could enable rapid and precise mapping of the distribution of river organisms. Here, we investigated the potential for combining these methods using a fish eDNA dataset from 110 sites sampled along the full length of the Rhone River in Switzerland and France. Using Sentinel 2 and Landsat 8 images, we generated a set of ecological variables describing both the aquatic and the terrestrial habitats surrounding the river corridor. We combined these variables with eDNA‐based presence and absence data on 29 fish species and used three machine‐learning models to assess environmental suitability for these species. Most models showed good performance, indicating that ecological variables derived from remote sensing can approximate the ecological determinants of fish species distributions, but water‐derived variables had stronger associations than the terrestrial variables surrounding the river. The species range mapping indicated a significant transition in the species occupancy along the Rhone, from its source in the Swiss Alps to outlet into the Mediterranean Sea in southern France. Our study demonstrates the feasibility of combining remote sensing and eDNA to map species distributions in a large river. This method can be expanded to any large river to support conservation schemes.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"1 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41338444","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}
B. Mugerwa, Jürgen Niedballa, A. Planillo, D. Sheil, S. Kramer‐Schadt, A. Wilting
Quantifying and monitoring the risk of defaunation and extinction require assessing and monitoring biodiversity in impacted regions. Camera traps that photograph animals as they pass sensors have revolutionized wildlife assessment and monitoring globally. We conducted a global review of camera trap research on terrestrial mammals over the last two decades. We assessed if the spatial distribution of 3395 camera trap research locations from 2324 studies overlapped areas with high defaunation risk. We used a geospatial distribution modeling approach to predict the spatial allocation of camera trap research on terrestrial mammals and to identify its key correlates. We show that camera trap research over the past two decades has not targeted areas where defaunation risk is highest and that 76.8% of the global research allocation can be attributed to country income, biome, terrestrial mammal richness, and accessibility. The lowest probabilities of camera trap research allocation occurred in low‐income countries. The Amazon and Congo Forest basins – two highly biodiverse ecosystems facing unprecedented anthropogenic alteration – received inadequate camera trap research attention. Even within the best covered regions, most of the research (64.2%) was located outside the top 20% areas where defaunation risk was greatest. To monitor terrestrial mammal populations and assess the risk of extinction, more research should be extended to regions with high defaunation risk but have received low camera trap research allocation.
{"title":"Global disparity of camera trap research allocation and defaunation risk of terrestrial mammals","authors":"B. Mugerwa, Jürgen Niedballa, A. Planillo, D. Sheil, S. Kramer‐Schadt, A. Wilting","doi":"10.1002/rse2.360","DOIUrl":"https://doi.org/10.1002/rse2.360","url":null,"abstract":"Quantifying and monitoring the risk of defaunation and extinction require assessing and monitoring biodiversity in impacted regions. Camera traps that photograph animals as they pass sensors have revolutionized wildlife assessment and monitoring globally. We conducted a global review of camera trap research on terrestrial mammals over the last two decades. We assessed if the spatial distribution of 3395 camera trap research locations from 2324 studies overlapped areas with high defaunation risk. We used a geospatial distribution modeling approach to predict the spatial allocation of camera trap research on terrestrial mammals and to identify its key correlates. We show that camera trap research over the past two decades has not targeted areas where defaunation risk is highest and that 76.8% of the global research allocation can be attributed to country income, biome, terrestrial mammal richness, and accessibility. The lowest probabilities of camera trap research allocation occurred in low‐income countries. The Amazon and Congo Forest basins – two highly biodiverse ecosystems facing unprecedented anthropogenic alteration – received inadequate camera trap research attention. Even within the best covered regions, most of the research (64.2%) was located outside the top 20% areas where defaunation risk was greatest. To monitor terrestrial mammal populations and assess the risk of extinction, more research should be extended to regions with high defaunation risk but have received low camera trap research allocation.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47332556","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}
Alpine ecosystems are particularly vulnerable to climate change. Monitoring the distribution of alpine vegetation is required to plan practical conservation activities. However, conventional field observations, airborne and satellite remote sensing are difficult in terms of coverage, cost and resolution in alpine areas. Ground‐based time‐lapse cameras have been used to observe the regions' snowmelt and vegetation phenology and offer significant advantages in terms of cost, resolution and frequency. However, they have not been used in research monitoring of vegetation distribution patterns. This study proposes a novel method for drawing georeferenced vegetation classification maps from ground‐based imagery of alpine regions. Our approach had two components: vegetation classification and georectification. The proposed vegetation classification method uses a pixel time series acquired from fall images, utilizing the fall leaf color patterns. We demonstrated that the performance of the vegetation classification could be improved using time‐lapse imagery and a Recurrent Neural Network. We also developed a novel method to accurately transform ground‐based images into georeferenced data. We propose the following approaches: (1) an automated procedure to acquire Ground Control Points and (2) a camera model that considers lens distortions for accurate georectification. We demonstrated that the proposed approach outperforms conventional methods, in addition to achieving sufficient accuracy to observe the vegetation distribution on a plant‐community scale. The evaluation revealed an F1 score and root‐mean‐square error of 0.937 and 3.4 m in the vegetation classification and georectification, respectively. Our results highlight the potential of inexpensive time‐lapse cameras to monitor the distribution of alpine vegetation. The proposed method can significantly contribute to the effective conservation planning of alpine ecosystems.
{"title":"Automatically drawing vegetation classification maps using digital time‐lapse cameras in alpine ecosystems","authors":"Ryotaro Okamoto, R. Ide, H. Oguma","doi":"10.1002/rse2.364","DOIUrl":"https://doi.org/10.1002/rse2.364","url":null,"abstract":"Alpine ecosystems are particularly vulnerable to climate change. Monitoring the distribution of alpine vegetation is required to plan practical conservation activities. However, conventional field observations, airborne and satellite remote sensing are difficult in terms of coverage, cost and resolution in alpine areas. Ground‐based time‐lapse cameras have been used to observe the regions' snowmelt and vegetation phenology and offer significant advantages in terms of cost, resolution and frequency. However, they have not been used in research monitoring of vegetation distribution patterns. This study proposes a novel method for drawing georeferenced vegetation classification maps from ground‐based imagery of alpine regions. Our approach had two components: vegetation classification and georectification. The proposed vegetation classification method uses a pixel time series acquired from fall images, utilizing the fall leaf color patterns. We demonstrated that the performance of the vegetation classification could be improved using time‐lapse imagery and a Recurrent Neural Network. We also developed a novel method to accurately transform ground‐based images into georeferenced data. We propose the following approaches: (1) an automated procedure to acquire Ground Control Points and (2) a camera model that considers lens distortions for accurate georectification. We demonstrated that the proposed approach outperforms conventional methods, in addition to achieving sufficient accuracy to observe the vegetation distribution on a plant‐community scale. The evaluation revealed an F1 score and root‐mean‐square error of 0.937 and 3.4 m in the vegetation classification and georectification, respectively. Our results highlight the potential of inexpensive time‐lapse cameras to monitor the distribution of alpine vegetation. The proposed method can significantly contribute to the effective conservation planning of alpine ecosystems.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44575993","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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