基于深度学习应用和Sentinel-1A数据的河内市洪水对水稻生物量的影响

IF 2.3 Q2 REMOTE SENSING
Anh Ngoc Thi Do, Tuyet Anh Thi Do
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引用次数: 0

摘要

尽管是越南最大的城市,河内的经济仍然依赖农业。最近的天气事件,如洪水,严重影响了水稻生物量。利用合成孔径雷达(SAR)数据和人工蜂群-深度神经网络(ABC-DNN)对水稻生长进行测绘和监测,可以为水稻生产受洪涝影响提供可靠的数据。2022年1月至10月的Sentinel-1卫星图像显示,VH偏振比VV偏振提供了更详细的信息。田间数据和支持向量机(SVM)分类估计水稻冬春种植面积约为81公顷,夏秋种植面积约为77公顷,准确率超过90%。ABC-DNN模型预测地上生物量(AGB)的决定系数(R2)在0.722 ~ 0.745之间。该模型有效地确定了易受洪水影响的地区,帮助决策者制定减轻农业损失的战略,特别是在河内的低地地区。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Effects of flooding on rice biomass in Hanoi city on the basis of deep learning application and Sentinel-1A data

Effects of flooding on rice biomass in Hanoi city on the basis of deep learning application and Sentinel-1A data

Effects of flooding on rice biomass in Hanoi city on the basis of deep learning application and Sentinel-1A data

Despite being Vietnam's largest city, Hanoi's economy still relies on agriculture. Recent weather events, like floods, have significantly impacted rice biomass. Mapping and monitoring rice growth using synthetic aperture radar (SAR) data and the Artificial Bee Colony—Deep Neural Network (ABC-DNN) can provide reliable data on rice production affected by floods. Sentinel-1 satellite images from January to October 2022 showed that VH polarization yielded more detailed information than VV polarization. Field data and Support Vector Machine (SVM) classification estimated rice cultivation areas at approximately 81 ha for Winter-Spring and 77 ha for Summer-Autumn crops, with over 90% accuracy. The ABC-DNN model predicted aboveground biomass (AGB) with coefficients of determination (R2) ranging from 0.722 to 0.745. The model effectively identified flood-prone areas, aiding policymakers in developing strategies to mitigate agricultural damage, particularly in lowland regions of Hanoi.

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来源期刊
Applied Geomatics
Applied Geomatics REMOTE SENSING-
CiteScore
5.40
自引率
3.70%
发文量
61
期刊介绍: Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences. The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology. Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements
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