Open AccessUpland vegetation mapping using Random Forests with optical and radar satellite data
Author(s) -
Barrett Brian,
Raab Christoph,
Cawkwell Fiona,
Green Stuart
Publication year - 2016
Publication title -
remote sensing in ecology and conservation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.191
H-Index - 21
ISSN - 2056-3485
DOI - 10.1002/rse2.32
Subject(s) - random forest , remote sensing , vegetation (pathology) , environmental science , wildlife , environmental resource management , elevation (ballistics) , habitat , radar , geography , ecology , computer science , medicine , telecommunications , geometry , mathematics , pathology , machine learning , biology
Abstract Uplands represent unique landscapes that provide a range of vital benefits to society, but are under increasing pressure from the management needs of a diverse number of stakeholders (e.g. farmers, conservationists, foresters, government agencies and recreational users). Mapping the spatial distribution of upland vegetation could benefit management and conservation programmes and allow for the impacts of environmental change (natural and anthropogenic) in these areas to be reliably estimated. The aim of this study was to evaluate the use of medium spatial resolution optical and radar satellite data, together with ancillary soil and topographic data, for identifying and mapping upland vegetation using the Random Forests ( RF ) algorithm. Intensive field survey data collected at three study sites in Ireland as part of the National Parks and Wildlife Service ( NPWS ) funded survey of upland habitats was used in the calibration and validation of different RF models. Eight different datasets were analysed for each site to compare the change in classification accuracy depending on the input variables. The overall accuracy values varied from 59.8% to 94.3% across the three study locations and the inclusion of ancillary datasets containing information on the soil and elevation further improved the classification accuracies (between 5 and 27%, depending on the input classification dataset). The classification results were consistent across the three different study areas, confirming the applicability of the approach under different environmental contexts.