Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing.

ABSTRACT: Natural and semi-natural habitats in agricultural landscapes are likely to come under increasing pressure with the global population set to exceed 9 billion by 2050. These non-cropped habitats are primarily made up of trees, hedgerows and grassy margins and their amount, quality and spatial configuration can have strong implications for the delivery and sustainability of various ecosystem services. In this study high spatial resolution (0.5 m) colour infrared aerial photography (CIR) was used in object based image analysis for the classification of non-cropped habitat in a 10,029 ha area of southeast England. Three classification scenarios were devised using 4 and 9 class scenarios. The machine learning algorithm Random Forest (RF) was used to reduce the number of variables used for each c

SUBMITTER: O'Connell J 

PROVIDER: S-EPMC4643754 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature