Jiale Chen, Li Wang, T. Jucker, Hongzhi Da, Zhaochen Zhang, Jianbo Hu, Qingsong Yang, Xihua Wang, Yuchu Qin, Guochun Shen, Li Shu, Jian Zhang
Accurate and efficient detection of canopy gaps is essential for understanding species regeneration and community dynamics in forests. Unoccupied aerial vehicles (UAVs) equipped with visible light (e.g., RGB) cameras have the potential to be one of the most cost‐effective approaches for detecting gaps. However, current gap‐detection methods based on spectral, textural, and/or structural information derived from UAV RGB imagery are unreliable in species‐rich forests with complex terrain due to high spectral complexity and topographic shadowing. Here, we compared the performance of four methods, including pixel‐based supervised classification (PBSC), object‐based classification (OBIA), Canopy Height Model thresholding classification, and HSTAC [a novel method we developed which combines Photographic Height (H), Spectral (S), and Textural (T) information for Automatic Classification (AC)] for characterizing canopy gaps in a 20‐ha permanent subtropical forest plot of eastern China. All classification results were evaluated through a comparison with canopy gaps detected from both field surveys and UAV‐borne LiDAR data. Among the four classification methods, HSTAC performed best in terms of detection efficiency (96% overall accuracy when compared to field data and 85% when compared to the LiDAR data), classification accuracy (3–18% improvement compared to alternative methods), and speed (1–1.5 h faster on the same machine). Of the four topographic factors (elevation, slope, aspect, and convexity), elevation was the one that most affected the accuracy of canopy gap detection. The errors of PBSC classification mainly came from the gaps at low elevations, while OBIA located the position of gaps well but overestimated their sizes. Overall, HSTAC avoids many of the inherent limitations of current state‐of‐the‐art methods and can accurately map canopy gaps in diverse subtropical forests with complex terrain. Our study provides a suitable way for long‐term forest canopy monitoring, real‐time applications, and contributes to a better understanding of forest plant community assembly and succession dynamics.
{"title":"Detecting forest canopy gaps using unoccupied aerial vehicle\u0000 RGB\u0000 imagery in a species‐rich subtropical forest","authors":"Jiale Chen, Li Wang, T. Jucker, Hongzhi Da, Zhaochen Zhang, Jianbo Hu, Qingsong Yang, Xihua Wang, Yuchu Qin, Guochun Shen, Li Shu, Jian Zhang","doi":"10.1002/rse2.336","DOIUrl":"https://doi.org/10.1002/rse2.336","url":null,"abstract":"Accurate and efficient detection of canopy gaps is essential for understanding species regeneration and community dynamics in forests. Unoccupied aerial vehicles (UAVs) equipped with visible light (e.g., RGB) cameras have the potential to be one of the most cost‐effective approaches for detecting gaps. However, current gap‐detection methods based on spectral, textural, and/or structural information derived from UAV RGB imagery are unreliable in species‐rich forests with complex terrain due to high spectral complexity and topographic shadowing. Here, we compared the performance of four methods, including pixel‐based supervised classification (PBSC), object‐based classification (OBIA), Canopy Height Model thresholding classification, and HSTAC [a novel method we developed which combines Photographic Height (H), Spectral (S), and Textural (T) information for Automatic Classification (AC)] for characterizing canopy gaps in a 20‐ha permanent subtropical forest plot of eastern China. All classification results were evaluated through a comparison with canopy gaps detected from both field surveys and UAV‐borne LiDAR data. Among the four classification methods, HSTAC performed best in terms of detection efficiency (96% overall accuracy when compared to field data and 85% when compared to the LiDAR data), classification accuracy (3–18% improvement compared to alternative methods), and speed (1–1.5 h faster on the same machine). Of the four topographic factors (elevation, slope, aspect, and convexity), elevation was the one that most affected the accuracy of canopy gap detection. The errors of PBSC classification mainly came from the gaps at low elevations, while OBIA located the position of gaps well but overestimated their sizes. Overall, HSTAC avoids many of the inherent limitations of current state‐of‐the‐art methods and can accurately map canopy gaps in diverse subtropical forests with complex terrain. Our study provides a suitable way for long‐term forest canopy monitoring, real‐time applications, and contributes to a better understanding of forest plant community assembly and succession dynamics.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48966663","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}
Na Chen, N. Tsendbazar, Daniela Requena Suarez, J. Verbesselt, M. Herold
Characterization of regrowing forests is vital for understanding forest dynamics to assess the impacts on carbon stocks and to support sustainable forest management. Although remote sensing is a key tool for understanding and monitoring forest dynamics, the use of exclusively remotely sensed data to explore the effects of different variables on regrowing forests across all biomes in Brazil has rarely been investigated. Here, we analyzed how environmental and human factors affect regrowing forests. Based on Brazil's secondary forest age map, 3060 locations disturbed between 1984 and 2018 were sampled, interpreted and analyzed in different biomes. We interpreted the time since disturbance for the sampled pixels in Google Earth Engine. Elevation, slope, climatic water deficit (CWD), the total Nitrogen of soil, cation exchange capacity (CEC) of soil, surrounding tree cover, distance to roads, distance to settlements and fire frequency were analyzed in their importance for predicting aboveground biomass (AGB) and tree cover derived from global forest aboveground biomass map and tree cover map, respectively. Results show that time since disturbance interpreted from satellite time series is the most important predictor for characterizing AGB and tree cover of regrowing forests. AGB increased with increasing time since disturbance, surrounding tree cover, soil total N, slope, distance to roads, distance to settlements and decreased with larger fire frequency, CWD and CEC of soil. Tree cover increased with larger time since disturbance, soil total N, surrounding tree cover, distance to roads, distance to settlements, slope and decreased with increasing elevation and CWD. These results emphasize the importance of remotely sensing products as key opportunities to improve the characterization of forest regrowth and to reduce data gaps and uncertainties related to forest carbon sink estimation. Our results provide a better understanding of regional forest dynamics, toward developing and assessing effective forest‐related restoration and climatic mitigation strategies.
{"title":"Characterizing aboveground biomass and tree cover of regrowing forests in Brazil using multi‐source remote sensing data","authors":"Na Chen, N. Tsendbazar, Daniela Requena Suarez, J. Verbesselt, M. Herold","doi":"10.1002/rse2.328","DOIUrl":"https://doi.org/10.1002/rse2.328","url":null,"abstract":"Characterization of regrowing forests is vital for understanding forest dynamics to assess the impacts on carbon stocks and to support sustainable forest management. Although remote sensing is a key tool for understanding and monitoring forest dynamics, the use of exclusively remotely sensed data to explore the effects of different variables on regrowing forests across all biomes in Brazil has rarely been investigated. Here, we analyzed how environmental and human factors affect regrowing forests. Based on Brazil's secondary forest age map, 3060 locations disturbed between 1984 and 2018 were sampled, interpreted and analyzed in different biomes. We interpreted the time since disturbance for the sampled pixels in Google Earth Engine. Elevation, slope, climatic water deficit (CWD), the total Nitrogen of soil, cation exchange capacity (CEC) of soil, surrounding tree cover, distance to roads, distance to settlements and fire frequency were analyzed in their importance for predicting aboveground biomass (AGB) and tree cover derived from global forest aboveground biomass map and tree cover map, respectively. Results show that time since disturbance interpreted from satellite time series is the most important predictor for characterizing AGB and tree cover of regrowing forests. AGB increased with increasing time since disturbance, surrounding tree cover, soil total N, slope, distance to roads, distance to settlements and decreased with larger fire frequency, CWD and CEC of soil. Tree cover increased with larger time since disturbance, soil total N, surrounding tree cover, distance to roads, distance to settlements, slope and decreased with increasing elevation and CWD. These results emphasize the importance of remotely sensing products as key opportunities to improve the characterization of forest regrowth and to reduce data gaps and uncertainties related to forest carbon sink estimation. Our results provide a better understanding of regional forest dynamics, toward developing and assessing effective forest‐related restoration and climatic mitigation strategies.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42443337","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}
Zhongqi Miao, Stella X. Yu, K. Landolt, M. Koneff, Timothy P. White, Luke J. Fara, E. Hlavacek, B. Pickens, Travis J. Harrison, W. Getz
Remote aerial sensing provides a non‐invasive, large geographical‐scale technology for avian monitoring, but the manual processing of images limits its development and applications. Artificial Intelligence (AI) methods can be used to mitigate this manual image processing requirement. The implementation of AI methods, however, has several challenges: (1) imbalanced (i.e., long‐tailed) data distribution, (2) annotation uncertainty in categorization, and (3) dataset discrepancies across different study sites. Here we use aerial imagery data of waterbirds around Cape Cod and Lake Michigan in the United States to examine how these challenges limit avian recognition performance. We review existing solutions and demonstrate as use cases how methods like Label Distribution Aware Marginal Loss with Deferred Re‐Weighting, hierarchical classification, and FixMatch address the three challenges. We also present a new approach to tackle the annotation uncertainty challenge using a Soft‐fine Pseudo‐Label methodology. Finally, we aim with this paper to increase awareness in the ecological remote sensing community of these challenges and bridge the gap between ecological applications and state‐of‐the‐art computer science, thereby opening new doors to future research.
{"title":"Challenges and solutions for automated avian recognition in aerial imagery","authors":"Zhongqi Miao, Stella X. Yu, K. Landolt, M. Koneff, Timothy P. White, Luke J. Fara, E. Hlavacek, B. Pickens, Travis J. Harrison, W. Getz","doi":"10.1002/rse2.318","DOIUrl":"https://doi.org/10.1002/rse2.318","url":null,"abstract":"Remote aerial sensing provides a non‐invasive, large geographical‐scale technology for avian monitoring, but the manual processing of images limits its development and applications. Artificial Intelligence (AI) methods can be used to mitigate this manual image processing requirement. The implementation of AI methods, however, has several challenges: (1) imbalanced (i.e., long‐tailed) data distribution, (2) annotation uncertainty in categorization, and (3) dataset discrepancies across different study sites. Here we use aerial imagery data of waterbirds around Cape Cod and Lake Michigan in the United States to examine how these challenges limit avian recognition performance. We review existing solutions and demonstrate as use cases how methods like Label Distribution Aware Marginal Loss with Deferred Re‐Weighting, hierarchical classification, and FixMatch address the three challenges. We also present a new approach to tackle the annotation uncertainty challenge using a Soft‐fine Pseudo‐Label methodology. Finally, we aim with this paper to increase awareness in the ecological remote sensing community of these challenges and bridge the gap between ecological applications and state‐of‐the‐art computer science, thereby opening new doors to future research.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42799043","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}
Climate change modifies plant phenology through shifts in seasonal temperature and precipitation. Because the timing of plant growth can limit herbivore population dynamics, climatic alteration of historical patterns of vegetation seasonality may alter population trajectories in such taxa. Thus, sound management decisions may depend on understanding how plant growth varies across a landscape within and among distinct management units or protected areas. Here, we examine spatial variation in the timing of spring plant growth, measured using a network of automated time‐lapse cameras distributed across the range of endangered Sierra Nevada bighorn sheep (Ovis canadensis sierrae) in California, USA. We tracked greenness of individual plants across 2 years to compare spatial patterns of forage phenology in snowy and drought years. Green‐up timing was derived for individual plants across the camera network and compared with local estimates of green‐up timing from satellite data. Satellite‐derived estimates of green‐up timing showed strong correspondence with camera‐derived estimates in areas with dense vegetation cover and weak correspondence in areas with sparse vegetation cover. Daily time‐lapse imagery revealed consistent variation in green‐up timing across elevation, both among latitudinal zones and among individual plant species. Green‐up timing was earlier in 2020 than in 2019, reflecting differences in the end of the snowy season. Because bighorn forage seasonally on alpine species with a brief growing period, spring migration of bighorn may be linked to variation in snowmelt and plant growth across elevational gradients.
{"title":"Fine‐scale landscape phenology revealed through time‐lapse imagery: implications for conservation and management of an endangered migratory herbivore","authors":"C. John, Jeffrey T. Kerby, T. Stephenson, E. Post","doi":"10.1002/rse2.331","DOIUrl":"https://doi.org/10.1002/rse2.331","url":null,"abstract":"Climate change modifies plant phenology through shifts in seasonal temperature and precipitation. Because the timing of plant growth can limit herbivore population dynamics, climatic alteration of historical patterns of vegetation seasonality may alter population trajectories in such taxa. Thus, sound management decisions may depend on understanding how plant growth varies across a landscape within and among distinct management units or protected areas. Here, we examine spatial variation in the timing of spring plant growth, measured using a network of automated time‐lapse cameras distributed across the range of endangered Sierra Nevada bighorn sheep (Ovis canadensis sierrae) in California, USA. We tracked greenness of individual plants across 2 years to compare spatial patterns of forage phenology in snowy and drought years. Green‐up timing was derived for individual plants across the camera network and compared with local estimates of green‐up timing from satellite data. Satellite‐derived estimates of green‐up timing showed strong correspondence with camera‐derived estimates in areas with dense vegetation cover and weak correspondence in areas with sparse vegetation cover. Daily time‐lapse imagery revealed consistent variation in green‐up timing across elevation, both among latitudinal zones and among individual plant species. Green‐up timing was earlier in 2020 than in 2019, reflecting differences in the end of the snowy season. Because bighorn forage seasonally on alpine species with a brief growing period, spring migration of bighorn may be linked to variation in snowmelt and plant growth across elevational gradients.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44936886","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}
{"title":"Issue Information","authors":"","doi":"10.1002/rse2.280","DOIUrl":"https://doi.org/10.1002/rse2.280","url":null,"abstract":"","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43277761","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}
Lisa Mandl, A. Stritih, R. Seidl, C. Ginzler, Cornelius Senf
The launch of NASA's Global Ecosystem Dynamics Investigation (GEDI) mission in 2018 opens new opportunities to quantitatively describe forest ecosystems across large scales. While GEDI's height‐related metrics have already been extensively evaluated, the utility of GEDI for assessing the full spectrum of structural variability—particularly in topographically complex terrain—remains incompletely understood. Here, we quantified GEDI's potential to estimate forest structure in mountain landscapes at the plot and landscape level, with a focus on variables of high relevance in ecological applications. We compared five GEDI metrics including relative height percentiles, plant area index, cover and understory cover to airborne laser scanning (ALS) data in two contrasting mountain landscapes in the European Alps. At the plot level, we investigated the impact of leaf phenology and topography on GEDI's accuracy. At the landscape‐scale, we evaluated the ability of GEDIs sample‐based approach to characterize complex mountain landscapes by comparing it to wall‐to‐wall ALS estimates and evaluated the capacity of GEDI to quantify important indicators of ecosystem functions and services (i.e., avalanche protection, habitat provision, carbon storage). Our results revealed only weak to moderate agreement between GEDI and ALS at the plot level (R2 from 0.03 to 0.61), with GEDI uncertainties increasing with slope. At the landscape‐level, however, the agreement between GEDI and ALS was generally high, with R2 values ranging between 0.51 and 0.79. Both GEDI and ALS agreed in identifying areas of high avalanche protection, habitat provision, and carbon storage, highlighting the potential of GEDI for landscape‐scale analyses in the context of ecosystem dynamics and management.
{"title":"Spaceborne\u0000 LiDAR\u0000 for characterizing forest structure across scales in the European Alps","authors":"Lisa Mandl, A. Stritih, R. Seidl, C. Ginzler, Cornelius Senf","doi":"10.1002/rse2.330","DOIUrl":"https://doi.org/10.1002/rse2.330","url":null,"abstract":"The launch of NASA's Global Ecosystem Dynamics Investigation (GEDI) mission in 2018 opens new opportunities to quantitatively describe forest ecosystems across large scales. While GEDI's height‐related metrics have already been extensively evaluated, the utility of GEDI for assessing the full spectrum of structural variability—particularly in topographically complex terrain—remains incompletely understood. Here, we quantified GEDI's potential to estimate forest structure in mountain landscapes at the plot and landscape level, with a focus on variables of high relevance in ecological applications. We compared five GEDI metrics including relative height percentiles, plant area index, cover and understory cover to airborne laser scanning (ALS) data in two contrasting mountain landscapes in the European Alps. At the plot level, we investigated the impact of leaf phenology and topography on GEDI's accuracy. At the landscape‐scale, we evaluated the ability of GEDIs sample‐based approach to characterize complex mountain landscapes by comparing it to wall‐to‐wall ALS estimates and evaluated the capacity of GEDI to quantify important indicators of ecosystem functions and services (i.e., avalanche protection, habitat provision, carbon storage). Our results revealed only weak to moderate agreement between GEDI and ALS at the plot level (R2 from 0.03 to 0.61), with GEDI uncertainties increasing with slope. At the landscape‐level, however, the agreement between GEDI and ALS was generally high, with R2 values ranging between 0.51 and 0.79. Both GEDI and ALS agreed in identifying areas of high avalanche protection, habitat provision, and carbon storage, highlighting the potential of GEDI for landscape‐scale analyses in the context of ecosystem dynamics and management.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44322751","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}
Pippa H. Lewis, B. Roberts, P. Moore, Samuel Pike, A. Scarth, K. Medcalf, I. Cameron
Brown macroalgae habitats provide a range of ecosystem services, offering coastal protection, supporting and increasing biodiversity, and more recently have been recognized for their potential role as blue carbon habitats. Consequently, accurate areal estimates of these habitats are vitally important. Satellite imagery is often utilized for areal estimates of vegetated habitats due to their ability to capture vast areas but are disadvantaged by their lower resolution. In contrast, imagery collected by unmanned aerial vehicles (UAV) provide high‐resolution datasets but are unable to cover the necessary spatial scale required for calculating areal estimates at regional, national or international scales. This study successfully and accurately corrects the outputs from low‐resolution Sentinel 2 imagery to the standard of high‐resolution UAV imagery by using a novel brown algae index and a simple regression model to provide accurate spatial estimates. This model was applied to rocky shores across Wales, UK to predict a spatial extent of 6.2 km2 for three fucoid macroalgae species; Ascophyllum nodosum, Fucus vesiculosus and F. serratus. The regression model was validated in two ways. First, the data used to create the regression model was split to train and test (50:50) the model, with a root mean square error of ~8%–14%. Secondly, spatial estimates of fucoids in independent aerial imagery were assessed using aerial photography interpretation and compared to that of the regression model (7% difference). The carbon standing stock of fucoids calculated from the spatial estimate (6.2 km2) was found to be significantly lower than that of other marine carbon stores, indicating that fucoids do not significantly contribute as a blue carbon habitat based on biomass alone. This study produces a robust and accurate remote sensing technique to estimate spatial extent of macroalgae at large spatial scales, with possible worldwide applicability.
{"title":"Combining unmanned aerial vehicles and satellite imagery to quantify areal extent of intertidal brown canopy‐forming macroalgae","authors":"Pippa H. Lewis, B. Roberts, P. Moore, Samuel Pike, A. Scarth, K. Medcalf, I. Cameron","doi":"10.1002/rse2.327","DOIUrl":"https://doi.org/10.1002/rse2.327","url":null,"abstract":"Brown macroalgae habitats provide a range of ecosystem services, offering coastal protection, supporting and increasing biodiversity, and more recently have been recognized for their potential role as blue carbon habitats. Consequently, accurate areal estimates of these habitats are vitally important. Satellite imagery is often utilized for areal estimates of vegetated habitats due to their ability to capture vast areas but are disadvantaged by their lower resolution. In contrast, imagery collected by unmanned aerial vehicles (UAV) provide high‐resolution datasets but are unable to cover the necessary spatial scale required for calculating areal estimates at regional, national or international scales. This study successfully and accurately corrects the outputs from low‐resolution Sentinel 2 imagery to the standard of high‐resolution UAV imagery by using a novel brown algae index and a simple regression model to provide accurate spatial estimates. This model was applied to rocky shores across Wales, UK to predict a spatial extent of 6.2 km2 for three fucoid macroalgae species; Ascophyllum nodosum, Fucus vesiculosus and F. serratus. The regression model was validated in two ways. First, the data used to create the regression model was split to train and test (50:50) the model, with a root mean square error of ~8%–14%. Secondly, spatial estimates of fucoids in independent aerial imagery were assessed using aerial photography interpretation and compared to that of the regression model (7% difference). The carbon standing stock of fucoids calculated from the spatial estimate (6.2 km2) was found to be significantly lower than that of other marine carbon stores, indicating that fucoids do not significantly contribute as a blue carbon habitat based on biomass alone. This study produces a robust and accurate remote sensing technique to estimate spatial extent of macroalgae at large spatial scales, with possible worldwide applicability.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47364746","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}
J. Hentati‐Sundberg, Agnes B. Olin, Sheetal Reddy, Per‐Arvid Berglund, Erik Svensson, M. Reddy, Siddharta Kasarareni, A. Carlsen, Matilda Hanes, Shreyash Kad, O. Olsson
Ecological research and monitoring need to be able to rapidly convey information that can form the basis of scientifically sound management. Automated sensor systems, especially if combined with artificial intelligence, can contribute to such rapid high‐resolution data retrieval. Here, we explore the prospects of automated methods to generate insights for seabirds, which are often monitored for their high conservation value and for being sentinels for marine ecosystem changes. We have developed a system of video surveillance combined with automated image processing, which we apply to common murres Uria aalge. The system uses a deep learning algorithm for object detection (YOLOv5) that has been trained on annotated images of adult birds, chicks and eggs, and outputs time, location, size and confidence level of all detections, frame‐by‐frame, in the supplied video material. A total of 144 million bird detections were generated from a breeding cliff over three complete breeding seasons (2019–2021). We demonstrate how object detection can be used to accurately monitor breeding phenology and chick growth. Our automated monitoring approach can also identify and quantify rare events that are easily missed in traditional monitoring, such as disturbances from predators. Further, combining automated video analysis with continuous measurements from a temperature logger allows us to study impacts of heat waves on nest attendance in high detail. Our automated system thus produces comparable, and in several cases significantly more detailed, data than those generated from observational field studies. By running in real time on the camera streams, it has the potential to supply researchers and managers with high‐resolution up‐to‐date information on seabird population status. We describe how the system can be modified to fit various types of ecological research and monitoring goals and thereby provide up‐to‐date support for conservation and ecosystem management.
{"title":"Seabird surveillance: combining CCTV and artificial intelligence for monitoring and research","authors":"J. Hentati‐Sundberg, Agnes B. Olin, Sheetal Reddy, Per‐Arvid Berglund, Erik Svensson, M. Reddy, Siddharta Kasarareni, A. Carlsen, Matilda Hanes, Shreyash Kad, O. Olsson","doi":"10.1002/rse2.329","DOIUrl":"https://doi.org/10.1002/rse2.329","url":null,"abstract":"Ecological research and monitoring need to be able to rapidly convey information that can form the basis of scientifically sound management. Automated sensor systems, especially if combined with artificial intelligence, can contribute to such rapid high‐resolution data retrieval. Here, we explore the prospects of automated methods to generate insights for seabirds, which are often monitored for their high conservation value and for being sentinels for marine ecosystem changes. We have developed a system of video surveillance combined with automated image processing, which we apply to common murres Uria aalge. The system uses a deep learning algorithm for object detection (YOLOv5) that has been trained on annotated images of adult birds, chicks and eggs, and outputs time, location, size and confidence level of all detections, frame‐by‐frame, in the supplied video material. A total of 144 million bird detections were generated from a breeding cliff over three complete breeding seasons (2019–2021). We demonstrate how object detection can be used to accurately monitor breeding phenology and chick growth. Our automated monitoring approach can also identify and quantify rare events that are easily missed in traditional monitoring, such as disturbances from predators. Further, combining automated video analysis with continuous measurements from a temperature logger allows us to study impacts of heat waves on nest attendance in high detail. Our automated system thus produces comparable, and in several cases significantly more detailed, data than those generated from observational field studies. By running in real time on the camera streams, it has the potential to supply researchers and managers with high‐resolution up‐to‐date information on seabird population status. We describe how the system can be modified to fit various types of ecological research and monitoring goals and thereby provide up‐to‐date support for conservation and ecosystem management.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43254246","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}
E. Agrillo, F. Filipponi, R. Salvati, Alice Pezzarossa, L. Casella
Earth observation (EO) data, derived from remote sensing and unmanned aerial vehicle (UAV), have been recently demonstrated to be essential tools for the ecosystem monitoring and habitat mapping, combining high technological and methodological procedures for applied ecology. However, research based on EO data analyses often tend to focus on image processing techniques, neglecting the development of a detailed sampling design scheme needed for an exhaustive habitat detection. This paper shows the results of a novel approach for mapping coastal dune habitats at a fine scale, using a supervised machine learning model, through the combination of vegetation plot sampling scheme, synergic use of multi‐sensor spectral imagery (UAV‐VHR) and environmental predictors (e.g., LiDAR), object‐based image analysis, and landscape metrics analysis. Proposed approach was tested in a protected area, established to preserve notable habitats along the Italian Tyrrhenian coast. A detailed sampling scheme was designed and carried out during spring and summer of 2019, combining simultaneously UAV flight acquisition and field vegetation survey data, collected at high precision positioning. The calibrated classification model achieved an overall accuracy of 78.6% (standard error 4.33), allowing us to accurately classify and map five coastal habitats, according to EUNIS (European Nature Information System) classification, which were further verified through a fully independent validation field survey. Results demonstrate that VHR imageries, combined with specific field survey schemes, can be exploited to train classification models used for the detection of plant communities (i.e., meso‐habitat) and plant species at local scale. Our findings demonstrate that UAV‐VHR data is a valid tool to produce high spatial resolution information in sand beach ecosystems, giving ecology research a new way for responsive, timely, and cost‐effective ecosystem monitoring.
{"title":"Modeling approach for coastal dune habitat detection on coastal ecosystems combining very high‐resolution UAV imagery and field survey","authors":"E. Agrillo, F. Filipponi, R. Salvati, Alice Pezzarossa, L. Casella","doi":"10.1002/rse2.308","DOIUrl":"https://doi.org/10.1002/rse2.308","url":null,"abstract":"Earth observation (EO) data, derived from remote sensing and unmanned aerial vehicle (UAV), have been recently demonstrated to be essential tools for the ecosystem monitoring and habitat mapping, combining high technological and methodological procedures for applied ecology. However, research based on EO data analyses often tend to focus on image processing techniques, neglecting the development of a detailed sampling design scheme needed for an exhaustive habitat detection. This paper shows the results of a novel approach for mapping coastal dune habitats at a fine scale, using a supervised machine learning model, through the combination of vegetation plot sampling scheme, synergic use of multi‐sensor spectral imagery (UAV‐VHR) and environmental predictors (e.g., LiDAR), object‐based image analysis, and landscape metrics analysis. Proposed approach was tested in a protected area, established to preserve notable habitats along the Italian Tyrrhenian coast. A detailed sampling scheme was designed and carried out during spring and summer of 2019, combining simultaneously UAV flight acquisition and field vegetation survey data, collected at high precision positioning. The calibrated classification model achieved an overall accuracy of 78.6% (standard error 4.33), allowing us to accurately classify and map five coastal habitats, according to EUNIS (European Nature Information System) classification, which were further verified through a fully independent validation field survey. Results demonstrate that VHR imageries, combined with specific field survey schemes, can be exploited to train classification models used for the detection of plant communities (i.e., meso‐habitat) and plant species at local scale. Our findings demonstrate that UAV‐VHR data is a valid tool to produce high spatial resolution information in sand beach ecosystems, giving ecology research a new way for responsive, timely, and cost‐effective ecosystem monitoring.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43827404","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}
Steven Guidos, J. van Dijk, Geir H. R. Systad, A. Landa
Although nesting in colonies can offer substantial reproductive benefits for many seabird species, increased visibility to predators remains a significant disadvantage for most colony‐breeders. To counteract this, some seabird species have evolved aggressive nest defense strategies to protect vulnerable eggs and chicks. Here, we used an experimental approach to test whether colony inhabitance by breeding gulls Larus spp. in western Norway impacts visitation rates of a native, mammalian predator, the Eurasian otter Lutra lutra during the breeding season. Camera traps were placed inside of and on the periphery of seabird colonies prior to the breeding season and left to run for one continuous year. Sighting frequency of otters on these cameras was compared to a control region free of gull nesting. We found that otter activity was significantly reduced in the colonies when gulls were incubating and rearing chicks, compared to time periods when gulls were building nests and absent from the colonies. Rhythmic activity patterns did not seem to be significantly impacted by the presence of gulls. This study provides clear evidence that certain colony‐nesting species can have a direct, negative impact on visitation rates of a native carnivore. Seasonal carnivore activity patterns are likely to be highly dependent on differing nesting strategies and level of nest defense by seabirds.
{"title":"Colony‐nesting gulls restrict activity levels of a native top carnivore during the breeding season","authors":"Steven Guidos, J. van Dijk, Geir H. R. Systad, A. Landa","doi":"10.1002/rse2.326","DOIUrl":"https://doi.org/10.1002/rse2.326","url":null,"abstract":"Although nesting in colonies can offer substantial reproductive benefits for many seabird species, increased visibility to predators remains a significant disadvantage for most colony‐breeders. To counteract this, some seabird species have evolved aggressive nest defense strategies to protect vulnerable eggs and chicks. Here, we used an experimental approach to test whether colony inhabitance by breeding gulls Larus spp. in western Norway impacts visitation rates of a native, mammalian predator, the Eurasian otter Lutra lutra during the breeding season. Camera traps were placed inside of and on the periphery of seabird colonies prior to the breeding season and left to run for one continuous year. Sighting frequency of otters on these cameras was compared to a control region free of gull nesting. We found that otter activity was significantly reduced in the colonies when gulls were incubating and rearing chicks, compared to time periods when gulls were building nests and absent from the colonies. Rhythmic activity patterns did not seem to be significantly impacted by the presence of gulls. This study provides clear evidence that certain colony‐nesting species can have a direct, negative impact on visitation rates of a native carnivore. Seasonal carnivore activity patterns are likely to be highly dependent on differing nesting strategies and level of nest defense by seabirds.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42176626","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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