Using remotely sensed imagery to estimate ecosystem services on farmland

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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.