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Using remotely sensed imagery to estimate ecosystem services on farmland

利用遥感图像估算农田生态系统服务

基本信息

批准号：
BB/J005851/1
负责人：
Timothy Benton
金额：
$ 41.99万
依托单位：
University of Leeds
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2012
资助国家：
英国
起止时间：
2012 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FJ005851%2F1
关键词：
Using remotely sensed imagery estimate

项目摘要

The world population is projected to reach over 9 billion by 2050. To meet the demand of an increasing population that is also increasing its per capita demand for food the FAO estimates almost a doubling of global food production will be required. This will increase the pressure on land which not only provides food, but also other "ecosystem services" such as the production of fibre, clean water, fertile soils, pollination or natural pest control services as well as cultural services such as maintaining biodiversity or land for recreational use. Increasing food production - whilst not impacting on the other services - is widely recognized as one of the major societal challenges for the 21st century. Recent research suggests that in intensively farmed areas, a landscape that combines land farmed to maximise yields with land managed for nature could provide both greater yields and more biodiversity (or ecosystem services in general) than were the whole landscape farmed with what is traditionally seen as sustainable farming methods (e.g. organic farming). This is because if yields are high, a smaller land area is needed, allowing some land to be "spared" for supporting ecosystem services. Maintaining high ecosystem service levels (e.g. pollination and natural pest control services) by proper management of non-cropped areas can also produce an increase in yield. The "optimal" mix of farm land and non-farm land to maximise yields and services is specific to each landscape and depends on the achievable yields and the background biodiversity. Given such data it is possible to assess each landscape and advise on the best way to manage land for biodiversity whilst maintaining production systems. However, whilst yield data is available, as farmers record this, data on the amount and quality of habitat, and its associated biodiversity is currently prohibitively expensive to obtain as they are typically collected by field surveys. Remote sensing can offer a cost effective alternative. Reliably mapping of biodiversity could be possible if remote sensing can 1) distinguish non-cropped land from cropland (the former typically contains a higher biodiversity than neighbouring cropland) 2) map the spatial configuration of non-cropped land (and so provide information on habitat connectivity or hotspots of suitable habitat, etc.), 3) map habitat type and 4) map vegetation structure (habitat type and structure are often associated with certain animal groups). Until now, the major constraint has been the limited ecological detail that habitat types mapped from remotely sensed data contained. Good mapping of vegetation communities (detailed information on plant species composition) has been difficult to achieve. Recently, the applicants completed the first study that classified National Vegetation Classification (NVC) communities at a high resolution (5 m) for a large extent with a high accuracy of 87-92% in an upland area of the UK. If mapping at this level of detail and accuracy can be replicated in lowland agricultural areas, mapping biodiversity for large areas could become possible and so ultimately the modelling of designs that optimise both yield and ecosystem services.In the first step, we will map the amount and layout of non-cropped features from remotely sensed imagery. This requires mapping at a high resolution to resolve small, but widespread features such as field margins. In the second step, we will develop our existing methodology for the lowland agricultural areas to estimate vegetation composition and structure. In the third step, we will estimate biodiversity and associated ecosystem services using the habitat maps from previous steps and ecological knowledge of processes at multiple spatial scales. Throughout the grant, we will seek to use cost-effective imagery (e.g. aerial is cheaper than LiDAR imagery) and explore the contribution of different imagery (e.g. satellite versus aerial) to achieving the above objectives.

预计到 2050 年，世界人口将超过 90 亿。为了满足不断增长的人口的需求以及人均粮食需求的增加，粮农组织估计全球粮食产量将需要增加近一倍。这将增加土地的压力，土地不仅提供食物，还提供其他“生态系统服务”，如生产纤维、清洁水、肥沃的土壤、授粉或自然害虫控制服务，以及文化服务，如维持生物多样性或娱乐用地。增加粮食产量——同时不影响其他服务——被广泛认为是 21 世纪的主要社会挑战之一。最近的研究表明，在集约化耕作地区，将旨在最大化产量的耕作土地与自然管理土地相结合的景观，可以比传统上被视为可持续耕作方法（例如有机耕作）的整个景观提供更高的产量和更多的生物多样性（或一般的生态系统服务）。这是因为如果产量高，则需要较小的土地面积，从而可以“节省”一些土地来支持生态系统服务。通过对非作物地区的适当管理来维持较高的生态系统服务水平（例如授粉和自然害虫防治服务）也可以提高产量。为了最大限度地提高产量和服务而进行的农田和非农田的“最佳”组合针对每种景观而定，并且取决于可实现的产量和背景生物多样性。有了这些数据，就可以评估每种景观，并就在维持生产系统的同时管理生物多样性土地的最佳方式提出建议。然而，虽然产量数据是可用的，但正如农民记录的那样，有关栖息地的数量和质量及其相关生物多样性的数据目前获取起来非常昂贵，因为它们通常是通过实地调查收集的。遥感可以提供一种具有成本效益的替代方案。如果遥感能够 1) 区分非耕地和农田（前者通常比邻近农田具有更高的生物多样性），2) 绘制非耕地的空间配置图（从而提供有关栖息地连通性或合适栖息地热点等的信息），3) 绘制栖息地类型图，以及 4) 绘制植被结构图（栖息地类型和结构通常与某些动物群体相关），则可以可靠地绘制生物多样性图。到目前为止，主要的限制是根据遥感数据绘制的栖息地类型所包含的生态细节有限。植被群落的良好绘图（植物物种组成的详细信息）一直难以实现。近日，申请人完成了第一项在英国高地地区以高分辨率（5 m）对国家植被分类（NVC）群落进行大范围分类的研究，准确率高达87-92%。如果这种详细程度和准确性的地图可以在低地农业地区复制，那么绘制大面积的生物多样性地图就成为可能，因此最终可以对优化产量和生态系统服务的设计进行建模。第一步，我们将根据遥感图像绘制非作物特征的数量和布局。这需要以高分辨率进行映射，以解决微小但广泛的特征，例如场边缘。第二步，我们将开发低地农业地区现有的方法来估计植被组成和结构。在第三步中，我们将使用先前步骤中的栖息地地图和多个空间尺度的过程的生态知识来估计生物多样性和相关的生态系统服务。在整个拨款过程中，我们将寻求使用具有成本效益的图像（例如，航拍图像比激光雷达图像便宜），并探索不同图像（例如卫星与航拍图像）对实现上述目标的贡献。