NCEO NC ODA Extension 2020-2021

NCEO NC ODA 延期 2020-2021

• 批准号: NE/T012447/1
• 负责人: John Remedios
• 金额: $ 55.86万
• 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: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FT012447%2F1
• 关键词: NCEO; NC; ODA; Extension; 2020

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 forest resources from Kenya across sub-Saharan Africa, 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 then across sub-Saharan Africa 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]. It will support an improved TAMSAT-ALERT system.WP 2 will extend a baseline of carbon emissions from deforestation across sub-Saharan Africa, 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. It will test new datasets in Kenya to ensure validity and to continue 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 in a regional context. 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. We will implement the fire component of WP3 in sub-Saharan Africa improving carbon estimates and coupling drought with fire knowledge. [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 AfriGEO 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.

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：非洲半干旱地区作物产量对干旱的脆弱性、保护和增强肯尼亚整个撒哈拉以南非洲森林资源的挑战、减缓气候变化、必要的预测技能捕捉东南亚因露天生物质燃烧而产生的有害空气质量，以及许多非洲国家目前缺乏有效利用卫星地球观测数据的能力。该计划分为四个工作计划。相关的联合国可持续发展目标包括：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 将通过多个 EO 数据流（例如有效叶面积指数和土壤湿度）的数据同化，改进加纳乃至撒哈拉以南非洲地区的作物产量模型。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]。它将支持改进的 TAMSAT-ALERT 系统。WP 2 将扩展撒哈拉以南非洲地区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，并建立适合造林的地区。它将在肯尼亚测试新的数据集，以确保有效性并继续支持肯尼亚政府的“2030 年愿景”，即到 2030 年将森林覆盖率从 6% 增加到 10%。这项工作将建立森林参考排放水平和地上碳区域背景下的股票。这项研究对于理解 REDD+ 政策背景下的碳循环估算至关重要。 [新元 13、15]。 WP 3 将开发并演示新的数据源，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。我们将在撒哈拉以南非洲实施 WP3 的火灾部分，改进碳估算并将干旱与火灾知识结合起来。 [可持续发展目标 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 AfriGEO 确定的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。

项目成果

Dynamic Landscapes in the UK Driven by Pressures from Energy Production and Forestry-Results of the CORINE Land Cover Map 2018

能源生产和林业压力驱动的英国动态景观 - 2018 年 CORINE 土地覆盖图结果

• DOI: 10.3390/land11020192
• 发表时间: 2022
• 期刊: Land
• 影响因子: 3.9
• 作者: Cole B

Hyperspectral Remote Sensing

高光谱遥感

• DOI: 10.1016/b978-0-08-102894-0.00026-7
• 发表时间: 2020
• 作者: Anand A

Satellite observations and modelling of hydrogen cyanide in the Earth's atmosphere

地球大气中氰化氢的卫星观测和建模

• 发表时间: 2022
• 期刊: IL NUOVO CIMENTO
• 作者: Bruno AG

Estimating Ecological Responses to Climatic Variability on Reclaimed and Unmined Lands Using Enhanced Vegetation Index

• DOI: 10.3390/rs13061100
• 发表时间: 2021
• 期刊: Remote. Sens.
• 作者: Xiang Fan;Yongze Song;Chuxin Zhu;H. Balzter;Z. Bai

Evaluating two land surface models for Brazil using a full carbon cycle benchmark with uncertainties

• DOI: 10.1002/cli2.10
• 发表时间: 2022-01-01
• 期刊: Climate Resilience and Sustainability
• 作者: Caen, Auguste;Smallman, T. Luke;Williams, Mathew
• 通讯作者: Williams, Mathew