Using remotely sensed imagery to estimate ecosystem services on farmland

到2050年，世界人口预计将超过90亿。为了满足不断增长的人口对粮食的需求，粮农组织估计，全球粮食产量将需要增加近一倍。这将增加对土地的压力，因为土地不仅提供粮食，而且还提供其他“生态系统服务”，如生产纤维、清洁水、肥沃土壤、授粉或自然虫害防治服务，以及维持生物多样性或娱乐用地等文化服务。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，与传统上被视为可持续耕作方法（例如有机耕作）的整个景观相比，将产量最大化耕种的土地与自然管理的土地相结合的景观可以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，则需要较少的土地面积，从而允许一些土地“保留”用于支持生态系统服务。通过对非种植区的适当管理，保持较高的生态系统服务水平（例如授粉和自然虫害防治服务）也可以提高产量。使产量和服务最大化的农田和非农业土地的“最佳”组合是特定于每种景观的，并取决于可实现的产量和背景生物多样性。有了这些数据，就有可能评估每一种景观，并就在维持生产系统的同时管理土地生物多样性的最佳方式提出建议。然而，虽然产量数据是可用的，但在农民记录这些数据时，关于栖息地数量和质量及其相关生物多样性的数据目前难以获得，因为这些数据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)区分非耕地和耕地（前者通常比邻近的耕地具有更高的生物多样性），2)绘制非耕地的空间结构（从而提供栖息地连通性或适宜栖息地热点等信息），3)绘制栖息地类型，4)绘制植被结构（栖息地类型和结构通常与某些动物类群有关），就有可能可靠地绘制生物多样性地图。到目前为止，主要的制约因素是遥感数据绘制的栖息地类型所包含的有限的生态细节。良好的植被群落制图（植物物种组成的详细信息）一直难以实现。最近，申请人完成了第一个在英国高地地区以高分辨率（5米）对国家植被分类（NVC）群落进行大范围分类的研究，精度高达87-92%。如果能在低地农业区复制这种详细和精确的制图，那么绘制大面积的生物多样性就成为可能，从而最终建立优化产量和生态系统服务的设计模型。在第一步中，我们将绘制遥感图像中未裁剪特征的数量和布局。这需要以高分辨率进行映射，以解决小而广泛的特征，如字段边缘。在第二步，我们将发展现有的低地农业区植被组成和结构估算方法。在第三步，我们将利用前一步的栖息地地图和多个空间尺度上的过程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，空中图像比激光雷达图像便宜），并探索不同图像（例如，卫星与空中图像）对实现上述目标的贡献。

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.