NCEO NC ODA Full

NCEO NC ODA 完整

• 批准号: NE/R000115/1
• 负责人: John Remedios
• 金额: $ 112.42万
• 依托单位: University of Leicester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FR000115%2F1
• 关键词: NCEO; NC; ODA; Full

The NCEO NC-ODA programme is focussed on a series of generic science issues that are particularly relevant to development challenges: characterisation and forecasting of land surface state including vegetation change and soil moisture; the evolution of forest carbon and characterisation of carbon fluxes arising from deforestation and degradation; the dynamic nature of fires, their emissions into the atmosphere and the development of large-scale air pollution; the development of a cadre of researchers and applications specialists trained in state-of-the-art Earth Observation (EO).We will address specific problems faced by DAC countries: the vulnerability of crop yields in semi-arid regions in Africa to drought, the challenge of protecting and enhancing Kenya's forest resources to mitigate climate change, the forecast skill necessary to capture hazardous air quality in South-East Asia stemming from open biomass burning, and the current lack of capacity of many African nations to make effective use of satellite EO data. The programme is structured into four WPs. Relevant UN Sustainable Development Goals are: 2 (Zero hunger), 3 (Good health and well-being), 13 (Climate action), 15 (Life on Land), and 17 (Partnership for the goals).WP 1 will improve crop yield modelling in Ghana and potentially Ethiopia through data assimilation of multiple EO data streams, for example effective leaf area index and soil moisture. The research will yield new knowledge on the value of accurate EO data parameters in a data-model system, to better characterise crop change and increase predictive skill, to examine upscaling from landscape to country scale, and improve soil moisture forecast skill [SGDs 2, 15].WP 2 will establish a baseline of carbon emissions from deforestation in Kenya, identify different types of deforestation and degradation from synthetic aperture radar, optical and laser ranging (LiDAR) data, and establish areas that are suitable for afforestation to support the Vision 2030 of the Kenyan Government that aims to increase forest cover from 6 to 10 per cent by 2030. The work will establish forest reference emission levels and above-ground carbon stocks. This research is key to understanding carbon cycling estimates in a REDD+ policy context. [SGDs 13, 15].WP 3 will develop and demonstrate new data sources that can improve forecast accuracy for large-scale air pollution during fire events. Currently, forecast models use estimates of fires that fail to capture the magnitude and variability of dynamic large forest and peatland fires and fires due to agricultural residue burning. The research will improve pollutant emissions estimates from fires, EO-based retrievals of smoke plume aerosols and auto-identification of biomass-burning plumes. We will work with stakeholders in the ASEAN countries to co-develop and demonstrate new systems, characterise improvements and train staff in the interpretation of complex EO data [SDGs 3, 13]. WP 4 will build capacity through international EO-related initiatives, including the Group on Earth Observations (GEO) AfriGEOSS initiative and the Committee on Earth Observation Satellites (CEOS) Working Group on Capacity Building & Data Democracy, to improve access to and use of contemporary EO datasets in African nations and other DAC nations. Work will include scoping of UK-related EO projects and experts related to AfriGEOSS identified needs, extension of the training of WP1-3 to wider DAC countries and to strategic capacity building, co-ordinated work with the relevant GEO initiatives of GFOI and GEOGLAM, and support of access to EO data for DAC countries. These actions will also benefit UK national priorities such as the monitoring of projects supported by the GNU partnership (Germany, Norway and UK), which is making US$5 billion available between 2015 and 2020 for REDD+ early movers, and the Biocarbon Fund's Initiative for Sustainable Forest Landscapes (ISFL) [SDG 17].

NCEO NC-ODA 计划重点关注一系列与发展挑战特别相关的通用科学问题：包括植被变化和土壤湿度在内的地表状态的表征和预测；森林碳的演变以及森林砍伐和退化引起的碳通量特征；火灾的动态性质、火灾排放到大气中以及大规模空气污染的发展；培养一支受过最先进地球观测（EO）培训的研究人员和应用专家骨干队伍。我们将解决发展援助委员会国家面临的具体问题：非洲半干旱地区农作物产量对干旱的脆弱性，保护和增强肯尼亚森林资源以减缓气候变化的挑战、捕捉东南亚露天生物质燃烧造成的有害空气质量所需的预报技能以及许多非洲国家目前缺乏有效利用卫星的能力环氧乙烷数据。该计划分为四个工作计划。相关联合国可持续发展目标为：2（零饥饿）、3（良好健康和福祉）、13（气候行动）、15（陆地生命）和 17（目标伙伴关系）。WP 1 将改善作物通过对多个 EO 数据流（例如有效叶面积指数和土壤湿度）的数据同化，对加纳和可能的埃塞俄比亚进行产量建模。该研究将产生关于数据模型系统中准确的 EO 数据参数价值的新知识，以更好地描述作物变化并提高预测技能，检查从景观到国家尺度的升级，并提高土壤湿度预测技能 [SGDs 2， 15].WP 2将建立肯尼亚森林砍伐造成的碳排放基线，根据合成孔径雷达、光学和激光测距（LiDAR）数据识别不同类型的森林砍伐和退化，并建立适合造林的区域以支持肯尼亚政府的“2030 年愿景”旨在到 2030 年将森林覆盖率从 6% 增加到 10%。这项工作将确定森林参考排放水平和地上碳储量。这项研究对于理解 REDD+ 政策背景下的碳循环估算至关重要。 [SGDs 13, 15].WP 3 将开发和演示新的数据源，可以提高火灾事件期间大规模空气污染的预测准确性。目前，预测模型使用的火灾估计无法捕捉动态大型森林和泥炭地火灾以及农业残留物燃烧引起的火灾的规模和变化。该研究将改进火灾污染物排放估算、基于环氧乙烷的烟羽气溶胶检索以及生物质燃烧羽流的自动识别。我们将与东盟国家的利益相关者合作，共同开发和演示新系统，描述改进的特征并培训工作人员解释复杂的EO数据[SDGs 3, 13]。 WP 4 将通过与 EO 相关的国际举措进行能力建设，包括地球观测组织 (GEO) AfriGEOSS 举措和地球观测卫星委员会 (CEOS) 能力建设和数据民主工作组，以改善当代数据的获取和使用。非洲国家和其他 DAC 国家的 EO 数据集。工作将包括确定与 AfriGEOSS 已确定需求相关的英国相关 EO 项目和专家的范围，将 WP1-3 的培训扩展到更广泛的 DAC 国家以及战略能力建设，与 GFOI 和 GEOGLAM 的相关 GEO 倡议协调工作，并支持 DAC 国家获取 EO 数据。这些行动还将有利于英国的国家优先事项，例如监测 GNU 伙伴关系（德国、挪威和英国）支持的项目，该伙伴关系将在 2015 年至 2020 年间为 REDD+ 早期推动者提供 50 亿美元，以及生物碳基金的可持续发展倡议森林景观 (ISFL) [SDG 17]。

项目成果

Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks

利用卫星数据集和气候变量对每小时全球水平辐照度进行建模，作为人工神经网络的新输入

• DOI: 10.3390/en12010148
• 发表时间: 2019-01-02
• 期刊: Energies
• 影响因子: 3.2
• 作者: Bikhtiyar Ameen;H. Balzter;C. Jarvis;J. Wheeler
• 通讯作者: J. Wheeler

Validation of Hourly Global Horizontal Irradiance for Two Satellite-Derived Datasets in Northeast Iraq

伊拉克东北部两个卫星数据集每小时全球水平辐照度的验证

• DOI: 10.3390/rs10101651
• 发表时间: 2018-10-17
• 期刊: Remote. Sens.
• 作者: Bikhtiyar Ameen;H. Balzter;C. Jarvis;E. Wey;Claire Thomas;M. March
• 通讯作者: M. March

A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps

评估全球地上生物量图准确性和不确定性的综合框架

• DOI: http://dx.10.1016/j.rse.2022.112917
• 发表时间: 2022
• 期刊: Remote Sensing of Environment
• 影响因子: 13.5
• 作者: Araza A

The infiltration of wildfire smoke and its potential dose on pregnant women: Lessons learned from Indonesia wildfires in 2019.

野火烟雾的渗透及其对孕妇的潜在剂量：2019 年印度尼西亚野火的经验教训。

• DOI: http://dx.10.1016/j.heliyon.2023.e18513
• 发表时间: 2023
• 期刊: Heliyon
• 影响因子: 4
• 作者: Ardiyani V

TAMSAT-ALERT v1: a new framework for agricultural decision support

TAMSAT-ALERT v1：农业决策支持的新框架

• DOI: http://dx.10.5194/gmd-11-2353-2018
• 发表时间: 2018
• 期刊: Geoscientific Model Development
• 影响因子: 5.1
• 作者: Asfaw D