RT info:eu-repo/semantics/article
T1 Using remote sensing products to classify landscape. A multi-spatial resolution approach
A1 García Llamas, Paula
A1 Calvo Galván, María Leonor
A1 Álvarez Martínez, José Manuel
A1 Suárez Seoane, Susana
A2 Ecologia
K1 Ecología. Medio ambiente
K1 Geodinámica
K1 Topografía
K1 CORINE
K1 Land cover
K1 NDVI
K1 NOAA
K1 Uncertainty
AB The European Landscape Convention encourages the inventory and characterization of landscapes for environmental management and planning actions. Among the range of data sources available for landscape classification, remote sensing has substantial applicability, although difficulties might arise when available data are not at the spatial resolution of operational interest. We evaluated the applicability of two remote sensing products informing on land cover (the categorical CORINE map at 30 m resolution and the continuous NDVI spectral index at 1 km resolution) in landscape classification across a range of spatial resolutions (30 m, 90 m, 180 m, 1 km), using the Cantabrian Mountains (NW Spain) as study case. Separate landscape classifications (using topography, urban influence and land cover as inputs) were accomplished, one per each land cover dataset and spatial resolution. Classification accuracy was estimated through confusion matrixes and uncertainty in terms of both membership probability and confusion indices. Regarding landscape classifications based on CORINE, both typology and number of landscape classes varied across spatial resolutions. Classification accuracy increased from 30 m (the original resolution of CORINE) to 90m, decreasing towards coarser resolutions. Uncertainty followed the opposite pattern. In the case of landscape classifications based on NDVI, the identified landscape patterns were geographically structured and showed little sensitivity to changes across spatial resolutions. Only the change from 1 km (the original resolution of NDVI) to 180 m improved classification accuracy. The value of confusion indices increased with resolution. We highlight the need for greater effort in selecting data sources at the suitable spatial resolution, matching regional peculiarities and minimizing error and uncertainty.
PB Elsevier
YR 2018
FD 2018-03-16
LK http://hdl.handle.net/10612/7484
UL http://hdl.handle.net/10612/7484
NO International journal of applied earth observation and geoinformation, 2016, vol. 50
NO P. 95-105
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 06-jun-2024