Combining LiDAR and IKONOS Data for Eco‐Hydrological Classification of an Ombrotrophic Peatland

Abstract

Remote sensing techniques have potential for peatland monitoring, but most previous work has focused on spectral approaches that often result in poor discrimination of cover types and neglect structural information. Peatlands contain structural "microtopes" (e.g., hummocks and hollows) which are linked to hydrology, biodiversity and carbon sequestration, and information on surface structure is thus a useful proxy for peatland condition. The objective of this work was to develop and test a new eco-hydrological mapping technique for ombrotrophic (rain-fed) peatlands using a combined spectral-structural remote sensing approach. The study site was Wedholme Flow, Cumbria, UK. Airborne light dectection and ranging (LiDAR) data were used with IKONOS data in a combined multispectral-structural approach for mapping peatland condition classes. LiDAR data were preprocessed so that spatial estimates of minimum and maximum land surface height, variance and semi-variance (from semi-variogram analysis) were extracted. These were assimilated alongside IKONOS data into a maximum likelihood classification procedure, and thematic outputs were compared. Ecological survey data were used to validate the results. Considerable improvements in thematic separation of peatland classes were achieved when spatially-distributed measurements of LiDAR variance or semi-variance were included. Specifically, the classification accuracy improved from 71.8% (IKONOS data only) to 88.0% when a LiDAR semi-variance product was used. Of note was the improved delineation of management classes (including Eriophorum bog, active raised bog and degraded raised bog). The application of a combined textural-optical approach can improve land cover mapping in areas where reliance on purely spectral discrimination approaches would otherwise result in considerable thematic uncertainty.

The role of remote sensing in the development of SMART indicators for ecosystem services assessment

“…Harris and Bryant, 2009), fine scale hydrological networks can be extracted (e.g. Korpela et al, 2009;Anderson et al, 2010) and peat physico-chemical properties (e.g. McMorrow et al, 2004).…”

Characterizing peatland carbon balance estimates using freely available Landsat ETM+ data

“…There is a strong driver for spatially distributed information on peatland condition because of recent policy changes that seek to place value on ecosystem services such as carbon sequestration, biodiversity and clean water provision. Various methods of surveying peatland condition, such as site surveys, aerial photography or remote sensing methods at fine spatial scales exist (Harris and Bryant, ; Anderson and Croft, ; Dissanska et al ., ; Anderson et al ., 2010a,b), but these methods are typically time consuming, costly or both. Additionally, these approaches provide data at a scale that is optimized for localized management rather than for national or international monitoring over long timescales.…”

Detecting peatland drains with Object Based Image Analysis and Geoeye-1 imagery

“…Remote sensing can be used to map and monitor peatlands over very wide areas [38] using both spectral-oriented data [39, 40] and object-oriented data [41]. Low and medium resolution multispectral imagery have been used to map the extent of peatlands.…”