Mapping the countryside : information for policy and management

There is an increasing demand for information for the rational assessment and reporting of the state the environment, to detect change and to assess the effectiveness of policy or management measures. The research investigated the use of information by conservation organisations through case studies in the Statutory Nature Conservation Agencies and the North York Moors National Park. The results highlighted a number of key problems in the organisational use of information and in the content and utility of the data available. These included the lack of an organisational culture of information use, imperfect knowledge and utilisation of available data, the need to meet changing information demands and the requirement to produce comparable local, regional and national habitat stock estimates. Many of the data deficiencies highlighted would appear to be met by the Countryside Survey (CS) initiative. Despite offering potentially suitable data, with a combination of an environmental stratification (the ITE land class system), field survey and remotely sensed data, this source was little used. Thus, the study sought to assess the scope for comparing CS data with other habitat estimates and for improving the accuracy of these data through the use of Geographical Information Systems (GIS). Three main techniques were employed, modified areal weighting, modified areal weighting with control zones and 'intelligent weighting' a hybrid approach in which Land Cover Map of Great Britain (LCMGB) data were employed to redistribute Countryside Survey 1990 (CS90) totals within ITE land classes. The research found that sub-land class estimates from CS90 data could be improved in some circumstances. In most cases, LCMGB provided better estimates of habitat location and quantity than CS90. In a few cases, the intelligent weighting method improved the interpolation of CS90 estimates. It is suggested that regional habitat estimates may be improved further through greater within-land class differentiation, an increase in within-land class sampling intensity or stratification and the further development of the LCMGB. The problems faced in integrating, analysing and using available geographic data are considered and conclusions presented.