Testing of disability identification tool for schools

There has been an ongoing concern about the lack of reliable data on disabled children in schools. To date there has been no consistent way of identifying and categorising disabilities. Schools in England are currentlyrequired to collect data on children with Special Educational Need (SEN), but this does not capture information about all disabled children. The lack of this information may seriously restrict capacity at all levels of policy and practice to understand and respond to the needs of disabled children and their families in line with Disability Discrimination Act (2005) and the single Equality Act (2010). The aim of the project was to test the draft tools for identifying disability and accompanying guidance in a sample of all types of maintained schools in order to assess their usability and reliability and whether they resulted in the generation of robust and consistent data that could reliably inform school returns for the annual School Census.

Disability data collection for children's services

Schools need to identify disabled pupils in accordance with their Disability Equality Duty. This research assisted in the development of suitable tools to allow them to identify disabled children in accordance with the definition set out in the Disability Discrimination Act (DDA) by surveying parents and, via the use of purpose-designed activities, the children themselves.

CHP toolkit: case study of LAIe sensitivity to discontinuity of canopy cover in fruit plantations

This paper presents an open-source canopy height profile (CHP) toolkit designed for processing small-footprint full-waveform LiDAR data to obtain the estimates of effective leaf area index (LAIe) and CHPs. The use of the toolkit is presented with a case study of LAIe estimation in discontinuous-canopy fruit plantations. The experiments are carried out in two study areas, namely, orange and almond plantations, with different percentages of canopy cover (48% and 40%, respectively). For comparison, two commonly used discrete-point LAIe estimation methods are also tested. The LiDAR LAIe values are first computed for each of the sites and each method as a whole, providing “apparent” site-level LAIe, which disregards the discontinuity of the plantations’ canopies. Since the toolkit allows for the calculation of the study area LAIe at different spatial scales, between-tree-level clumpingcan be easily accounted for and is then used to illustrate the impact of the discontinuity of canopy cover on LAIe retrieval. The LiDAR LAIe estimates are therefore computed at smaller scales as a mean of LAIe in various grid-cell sizes, providing estimates of “actual” site-level LAIe. Subsequently, the LiDAR LAIe results are compared with theoretical models of “apparent” LAIe versus “actual” LAIe, based on known percent canopy cover in each site. The comparison of those models to LiDAR LAIe derived from the smallest grid-cell sizes against the estimates of LAIe for the whole site has shown that the LAIe estimates obtained from the CHP toolkit provided values that are closest to those of theoretical models.