Remote Sensing in Ecology and Conservation最新文献_第6页

Deep learning‐based training data augmentation combined with post‐classification improves the classification accuracy for dominant and scattered invasive forest tree species 基于深度学习的训练数据增强与后分类相结合，提高了优势和分散入侵树种的分类精度

IF 5.5 2区环境科学与生态学

Remote Sensing in Ecology and Conservation Pub Date : 2023-08-09 DOI: 10.1002/rse2.365

Szilárd Balázs Likó, I. Holb, Viktor Oláh, P. Burai, Szilárd Szabó

{"title":"Deep learning‐based training data augmentation combined with post‐classification improves the classification accuracy for dominant and scattered invasive forest tree species","authors":"Szilárd Balázs Likó, I. Holb, Viktor Oláh, P. Burai, Szilárd Szabó","doi":"10.1002/rse2.365","DOIUrl":"https://doi.org/10.1002/rse2.365","url":null,"abstract":"Species composition of forests is a very important component from the point of view of nature conservation and forestry. We aimed to identify 10 tree species in a hilly forest stand using a hyperspectral aerial image with a particular focus on two invasive species, namely Ailanthus tree and black locust. Deep learning‐based training data augmentation (TDA) and post‐classification techniques were tested with Random Forest and Support Vector Machine (SVM) classifiers. SVM had better performance with 81.6% overall accuracy (OA). TDA increased the OA to 82.5% and post‐classification with segmentation improved the total accuracy to 86.2%. The class‐level performance was more convincing: the invasive Ailanthus trees were identified with 40% higher producer's and user's accuracies (PA and UA) to 70% related to the common technique (using a training dataset and classifying the trees). The PA and UA did not change in the case of the other invasive species, black locust. Accordingly, this new method identifies well Ailanthus, a sparsely distributed species in the area; while it was less efficient with black locust that dominates larger patches in the stand. The combination of the two ancillary steps of hyperspectral image classification proved to be reasonable and can support forest management planning and nature conservation in the future.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41461848","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}

引用次数: 0

Remote sensing of Antarctic polychaete reefs (Serpula narconensis): reproducible workflows for quantifying benthic structural complexity with action cameras, remotely operated vehicles and structure‐from‐motion photogrammetry 南极多毛类珊瑚礁的遥感：用动作相机、遥控飞行器和结构自运动摄影测量量化海底结构复杂性的可复制工作流程

IF 5.5 2区环境科学与生态学

Remote Sensing in Ecology and Conservation Pub Date : 2023-07-25 DOI: 10.1002/rse2.358

Juan C. Montes‐Herrera, N. Hill, V. Cummings, G. Johnstone, J. Stark, V. Lucieer

引用次数: 1

Estimating animal density using the Space‐to‐Event model and bootstrap resampling with motion‐triggered camera‐trap data 利用空间-事件模型估算动物密度，并利用运动触发的相机陷阱数据进行自举重采样

IF 5.5 2区环境科学与生态学

Remote Sensing in Ecology and Conservation Pub Date : 2023-07-24 DOI: 10.1002/rse2.361

A. Lyet, Scott Waller, T. Chambert, P. Acevedo, E. Howe, H. Kühl, R. Naidoo, T. O'Brien, Pablo Palencia, Svetlana V. Soutyrina, J. Vicente, Oliver R. Wearn, T. Gray

引用次数: 0

High‐resolution thermal imagery reveals how interactions between crown structure and genetics shape plant temperature 高分辨率热成像揭示了树冠结构和遗传之间的相互作用如何影响植物温度

IF 5.5 2区环境科学与生态学

Remote Sensing in Ecology and Conservation Pub Date : 2023-07-21 DOI: 10.1002/rse2.359

Peter J. Olsoy, A. Zaiats, D. Delparte, M. Germino, B. Richardson, Spencer Roop, A. Roser, J. Forbey, M. Cattau, S. Buerki, K. Reinhardt, T. T. Caughlin

引用次数: 0

Mapping tree cover expansion in Montana, U.S.A. rangelands using high-resolution historical aerial imagery 使用高分辨率历史航空图像绘制美国蒙大拿州牧场树木覆盖扩张图

IF 5.5 2区环境科学与生态学

Remote Sensing in Ecology and Conservation Pub Date : 2023-07-19 DOI: 10.1002/rse2.357

Scott L. Morford, Brady W. Allred, Eric R. Jensen, Jeremy D. Maestas, Kristopher R. Mueller, Catherine L. Pacholski, Joseph T. Smith, Jason D. Tack, Kyle N. Tackett, David E. Naugle

{"title":"Mapping tree cover expansion in Montana, U.S.A. rangelands using high-resolution historical aerial imagery","authors":"Scott L. Morford, Brady W. Allred, Eric R. Jensen, Jeremy D. Maestas, Kristopher R. Mueller, Catherine L. Pacholski, Joseph T. Smith, Jason D. Tack, Kyle N. Tackett, David E. Naugle","doi":"10.1002/rse2.357","DOIUrl":"https://doi.org/10.1002/rse2.357","url":null,"abstract":"Worldwide, trees are colonizing rangelands with high conservation value. The introduction of trees into grasslands and shrublands causes large-scale changes in ecosystem structure and function, which have cascading impacts on ecosystem services, biodiversity, and agricultural economies. Satellites are increasingly being used to track tree cover at continental to global scales, but these methods can only provide reliable estimates of change over recent decades. Given the slow pace of tree cover expansion, remote sensing techniques that can extend this historical record provide critical insights into the magnitude of environmental change. Here, we estimate conifer expansion in rangelands of the northern Great Plains, United States, North America, using historical aerial imagery from the mid-20th century and modern aerial imagery. We analyzed 19.3 million hectares of rangelands in Montana, USA, using a convolutional neural network (U-Net architecture) and cloud computing to detect tree features and tree cover change. Our bias-corrected results estimate 3.0 ± 0.2 million hectares of conifer tree cover expansion in Montana rangelands, which accounts for 15.4% of the total study area. Overall accuracy was >91%, but the producer's accuracy was lower than the user's accuracy (0.60 vs. 0.88) for areas of tree cover expansion. Nonetheless, the omission errors were not spatially clustered, suggesting that the method is reliable for identifying the regions of Montana where substantial tree expansion has occurred. Using the model results in conjunction with historical and modern imagery allows for effective communication of the scale of tree expansion while overcoming the recency effect caused by shifting environmental baselines.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"157 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138532768","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}

引用次数: 0

Recurring bleaching events disrupt the spatial properties of coral reef benthic communities across scales 反复发生的白化事件破坏了珊瑚礁底栖生物群落的空间特性

IF 5.5 2区环境科学与生态学

Remote Sensing in Ecology and Conservation Pub Date : 2023-07-10 DOI: 10.1002/rse2.355

Helen V. Ford, J. Gove, J. Healey, A. Davies, N. A. Graham, Gareth J. Williams

引用次数: 0

Both Landsat‐ and LiDAR‐derived measures predict forest bee response to large‐scale wildfire 陆地卫星和激光雷达测量都预测了森林蜜蜂对大规模野火的反应

IF 5.5 2区环境科学与生态学

Remote Sensing in Ecology and Conservation Pub Date : 2023-07-10 DOI: 10.1002/rse2.354

Sara M. Galbraith, Jonathon J. Valente, Christopher J. C. Dunn, J. Rivers

引用次数: 0

Efficacy of machine learning image classification for automated occupancy‐based monitoring 机器学习图像分类在基于占用率的自动监控中的有效性

IF 5.5 2区环境科学与生态学

Remote Sensing in Ecology and Conservation Pub Date : 2023-07-10 DOI: 10.1002/rse2.356

Robert C. Lonsinger, Marlin M. Dart, R. Larsen, Robert N. Knight

引用次数: 0

Invasion in the Niger Delta: remote sensing of mangrove conversion to invasive Nypa fruticans from 2015 to 2020 尼日尔三角洲的入侵:2015 - 2020年红树林向入侵的尼帕果树转化的遥感

IF 5.5 2区环境科学与生态学

Remote Sensing in Ecology and Conservation Pub Date : 2023-07-03 DOI: 10.1002/rse2.353

Abigail Barenblitt, L. Fatoyinbo, N. Thomas, A. Stovall, Celio de Sousa, Chukwuebuka Nwobi, L. Duncanson

引用次数: 0

Gray whale detection in satellite imagery using deep learning 利用深度学习在卫星图像中检测灰鲸

IF 5.5 2区环境科学与生态学

Remote Sensing in Ecology and Conservation Pub Date : 2023-06-22 DOI: 10.1002/rse2.352

Katherine M. Green, Mala Virdee, H. Cubaynes, Angelica I. Avilés-Rivero, P. Fretwell, P. Gray, D. Johnston, C. Schönlieb, L. Torres, Jennifer A. Jackson

引用次数: 1