globalchange  > 全球变化的国际研究计划
DOI: 10.1016/j.scitotenv.2019.04.229
WOS记录号: WOS:000468618900032
论文题名:
Drone-based Structure-from-Motion provides accurate forest canopy data to assess shading effects in river temperature models
作者: Dugdale, Stephen J.1,3; Malcolm, Lain A.2; Hannah, David M.1
通讯作者: Dugdale, Stephen J. ; Hannah, David M.
刊名: SCIENCE OF THE TOTAL ENVIRONMENT
ISSN: 0048-9697
EISSN: 1879-1026
出版年: 2019
卷: 678, 页码:326-340
语种: 英语
英文关键词: River temperature ; Structure from motion ; Process-based model ; Drones ; Unoccupied aerial systems ; Climate change
WOS关键词: SALMON SALMO-SALAR ; RIPARIAN VEGETATION SHADE ; FRESH-WATER FISHES ; STREAM TEMPERATURE ; CLIMATE-CHANGE ; BRITISH-COLUMBIA ; HEAT EXCHANGES ; IMPACTS ; LIDAR ; VARIABILITY
WOS学科分类: Environmental Sciences
WOS研究方向: Environmental Sciences & Ecology
英文摘要:

Climatic warming will increase river temperature globally, with consequences for cold water-adapted organisms. In regions with low forest cover, elevated river temperature is often associated with a lack of bankside shading. Consequently, river managers have advocated riparian tree planting as a strategy to reduce temperature extremes. However, the effect of riparian shading on river temperature varies substantially between locations. Process-based models can elucidate the relative importance of woodland and other factors driving river temperature and thus improve understanding of spatial variability of the effect of shading, but characterising the spatial distribution and height of riparian tree cover necessary to parameterise these models remains a significant challenge. Here, we document a novel approach that combines Structure-from-Motion (SfM) photogrammetry acquired from a drone to characterise the riparian canopy with a process based temperature model (Heat Source) to simulate the effects of tree shading on river temperature. Our approach was applied in the Gimock Burn, a tributary of the Aberdeenshire Dee, Scotland. Results show that SIM approximates true canopy elevation with a good degree of accuracy (R-2 = 0.96) and reveals notable spatial heterogeneity in shading. When these data were incorporated into a process-based temperature model, it was possible to simulate river temperatures with a similarly-high level of accuracy (RMSE <0.7 degrees C) to a model parameterised using 'conventional' LiDAR tree height data. We subsequently demonstrate the utility of our approach for quantifying the magnitude of shading effects on stream temperature by comparing simulated temperatures against another model from which all riparian woodland has been removed. Our findings highlight drone-based SIM as an effective tool for characterising riparian shading and improving river temperature models. This research provides valuable insights into the effects of riparian woodland on river temperature and the potential of bankside tree planting for climate change adaptation. Crown Copyright (C) 2019 Published by Elsevier B.V. All rights reserved.


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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/145482
Appears in Collections:全球变化的国际研究计划

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作者单位: 1.Univ Birmingham, Sch Geog Earth & Environm Sci, Birmingham B15 2TT, W Midlands, England
2.Marine Scotland Sci, Freshwater Fisheries Lab, Faskally PH16 5LB, Pitlochry, England
3.Univ Nottingham, Sch Geog, Univ Pk, Nottingham NG7 2RD, England

Recommended Citation:
Dugdale, Stephen J.,Malcolm, Lain A.,Hannah, David M.. Drone-based Structure-from-Motion provides accurate forest canopy data to assess shading effects in river temperature models[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2019-01-01,678:326-340
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