UAV visual imagery-based evaluation of blue carbon as seagrass beds on a tidal flat scale

IF 3.8 Q2 ENVIRONMENTAL SCIENCES
Takuya Akinaga , Mitsuyo Saito , Shin-ichi Onodera , Fujio Hyodo
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Abstract

Seagrass and seaweed beds (SSBs) have a high carbon sequestration function (blue carbon) in shallow coastal waters. Unmanned aerial vehicles (UAVs) are a highly useful tool for monitoring SSBs because of their ease of use and ability to acquire high-resolution photographs. In many previous studies using UAV, surveys of SSBs have been based on area alone, but it is insufficient to properly assess the habitat and carbon fixation of SSBs.
In this study, we estimated above-ground biomass and carbon of eelgrass in shallow coastal waters by combining aerial photography of visible images, quadrat surveys, and sampling of eelgrass. The analysis area was a tidal flat on an island located in the Seto Inland Sea in western Japan. Aerial photography was conducted by UAV to acquire high-resolution RGB visual images of the area. The quadrat survey and sampling were used to develop regression formulas for estimating biomass and carbon of eelgrass. The former was conducted to investigate the relationship between the coverage and Leaf Area Index (LAI), and the latter was conducted to investigate the relationship between leaf area and biomass, carbon of eelgrass. Those showed clear relationship between coverage and LAI (R2 = 0.97) and between leaf area and biomass, carbon (biomass: R2 = 0.98, carbon: R2 = 0.98).
To identify eelgrass beds, the maximum likelihood classification was adapted. After calculating the coverage from the distribution, biomass and carbon were estimated by adapting regression formulas developed by quadrat survey and sampling.
The proposed method can be easily adapted from visible images taken by UAVs and robust to the effects of water, which provides high adaptability regarding the estimation for biomass and carbon of eelgrass on the tidal flat.
基于无人机视觉图像的潮滩尺度海草床蓝碳评价
海草和海藻床在浅海水域具有较高的固碳功能(蓝碳)。无人驾驶飞行器(uav)是监测ssb的一个非常有用的工具,因为它们易于使用和能够获取高分辨率照片。在以往的研究中,利用无人机对浮游生物的调查多是基于面积的,不足以正确评估浮游生物的生境和固碳能力。本研究采用航拍影像、样方调查和大叶藻采样相结合的方法,估算了浅海近岸大叶藻的地上生物量和碳含量。分析区域是位于日本西部濑户内海的一个岛屿上的潮滩。通过无人机进行航空摄影以获取该地区的高分辨率RGB视觉图像。采用样方调查和抽样方法,建立了估算大叶藻生物量和碳的回归公式。前者研究大叶藻盖度与叶面积指数(LAI)的关系,后者研究大叶藻叶面积与生物量、碳的关系。盖度与LAI (R2 = 0.97)、叶面积与生物量、碳(生物量:R2 = 0.98,碳:R2 = 0.98)呈显著相关。采用最大似然分类法识别大叶藻床。在计算盖度后,采用样方调查和抽样建立的回归公式估算生物量和碳。该方法可以很容易地适应无人机拍摄的可见光图像,并且对水的影响具有很强的鲁棒性,对滩涂大叶藻生物量和碳的估算具有很高的适应性。
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来源期刊
CiteScore
8.00
自引率
8.50%
发文量
204
审稿时长
65 days
期刊介绍: The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems
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