开发澳大利亚全国受保护种植结构图的多元方法

IF 3.2 2区 环境科学与生态学 Q2 ENVIRONMENTAL STUDIES Land Pub Date : 2023-12-14 DOI:10.3390/land12122168
Andrew Clark, Craig Shephard, Andrew Robson, Joel McKechnie, R. B. Morrison, Abbie Rankin
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引用次数: 0

摘要

随着全球人口的增长,对粮食在数量、质量和可持续生产方面的需求不断增加。保护性耕作结构(PCS)提供了可控的耕作环境,支持作物生长投入的最佳利用、更快的生产周期、每年多个生长季节和更高的产量,同时还能更好地控制病虫害和恶劣天气。在全球范围内,PCS 的应用迅速增加。然而,在提供准确和最新的空间信息以确定 PCS 的范围(位置和面积)方面,仍然存在令人担忧的知识差距。这些数据是提供衡量标准的基础,可为有关远期销售、劳动力、加工和基础设施要求、可追溯性、生物安全以及自然灾害防备和应对等方面的决策提供依据。为了满足这一需求,该项目利用遥感图像和深度学习分析、辅助数据、实地验证和行业参与,为澳大利亚绘制了一份全国多氯联苯地图。由此绘制的地图显示了面积大于 0.2 公顷的所有商业玻璃温室、多肉温室、多肉大棚、荫棚和永久性网箱的位置和范围。该项目的成果表明,深度学习技术能够准确绘制多用途温室地图,其模型的 F 值大于 0.9,并能在有合适图像的情况下加快地图绘制速度。网络制图应用程序支持的定位工具对于验证多氯联苯的位置以及建立行业意识和参与度至关重要。最终的全国 PCS 地图可通过在线仪表板公开获取,该仪表板汇总了各种规模的 PCS 结构区域,包括州/地区、地方政府区域和单个结构。该项目的成果为如何通过多层面的合作方式来实现这种水平的地图绘制设定了全球标准。
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A Multifaceted Approach to Developing an Australian National Map of Protected Cropping Structures
As the global population rises, there is an ever-increasing demand for food, in terms of volume, quality and sustainable production. Protected Cropping Structures (PCS) provide controlled farming environments that support the optimum use of crop inputs for plant growth, faster production cycles, multiple growing seasons per annum and increased yield, while offering greater control of pests, disease and adverse weather. Globally, there has been a rapid increase in the adoption of PCS. However, there remains a concerning knowledge gap in the availability of accurate and up-to-date spatial information that defines the extent (location and area) of PCS. This data is fundamental for providing metrics that inform decision making around forward selling, labour, processing and infrastructure requirements, traceability, biosecurity and natural disaster preparedness and response. This project addresses this need, by developing a national map of PCS for Australia using remotely sensed imagery and deep learning analytics, ancillary data, field validation and industry engagement. The resulting map presents the location and extent of all commercial glasshouses, polyhouses, polytunnels, shadehouses and permanent nets with an area of >0.2 ha. The outcomes of the project revealed deep learning techniques can accurately map PCS with models achieving F-Scores > 0.9 and accelerate the mapping where suitable imagery is available. Location-based tools supported by web mapping applications were critical for the validation of PCS locations and for building industry awareness and engagement. The final national PCS map is publicly available through an online dashboard which summarises the area of PCS structures at a range of scales including state/territory, local government area and individual structure. The outcomes of this project have set a global standard on how this level of mapping can be achieved through a collaborative, multifaceted approach.
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来源期刊
Land
Land ENVIRONMENTAL STUDIES-Nature and Landscape Conservation
CiteScore
4.90
自引率
23.10%
发文量
1927
期刊介绍: Land is an international and cross-disciplinary, peer-reviewed, open access journal of land system science, landscape, soil–sediment–water systems, urban study, land–climate interactions, water–energy–land–food (WELF) nexus, biodiversity research and health nexus, land modelling and data processing, ecosystem services, and multifunctionality and sustainability etc., published monthly online by MDPI. The International Association for Landscape Ecology (IALE), European Land-use Institute (ELI), and Landscape Institute (LI) are affiliated with Land, and their members receive a discount on the article processing charge.
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