Paper Info
Title
Accuracy of High-Resolution Radar Images in the Estimation of Plot-Level Forest Variables
Abstract
In the present study, we used the airborne E-SAR radar to simulate the satellite-borne high-resolution TerraSAR radar data and determined the accuracy of the plot-level forest variable estimates produced. Estimation was carried out using the nonparametric k-nearest neighbour (k-nn) method. Variables studied included mean volume, tree species-specific volumes and their proportions of total volume, basal area, mean height and mean diameter. E-SAR-based estimates were compared with those obtained using aerial photographs and medium-resolution satellite image (Landsat ETM+) recording optical wavelength energy. The study area was located in Kirkkonummi, southern Finland. The relative RMSEs for E-SAR were 45%, 29%, 28% and 38% for mean volume, mean diameter, mean height and basal area, respectively. For aerial photographs these were 51%, 26%, 27% and 42%, and for Landsat ETM+ images 58%, 40%, 35% and 49%. Combined datasets outperformed all single-source datasets, with relative RMSEs of 26%, 23%, 33% and 39%. Of the single-source datasets, the E-SAR images were well suited for estimating mean volume, while for mean diameter, mean height and basal area the E-SAR and aerial photographs performed similarly and far better than Landsat ETM+. The aerial photographs succeeded well in the estimation of species-specific volumes and their proportions, but the combined dataset was still significantly better in volume proportions. Due to its good temporal resolution, satellite-borne radar imaging is a promising data source for forest inventories, both in large-area forest inventories and operative forest management planning. Future high-resolution synthetic aperture radar (SAR) images could be combined with airborne laser scanner data when estimating forest or even tree characteristics.
Year
DOI
Venue
2009
10.1007/978-3-642-00318-9_4
ADVANCES IN GISCIENCE
Keywords
Field
DocType
Forest inventory,forest planning,radar imaging,E-SAR,TerraSAR,aerial photographs,Landsat
Systems engineering,Computer science,Synthetic aperture radar,Remote sensing,Artificial intelligence,Temporal resolution,Radar,Computer vision,Radar imaging,Laser scanning,Forest inventory,Basal area,Forest management
Conference
ISSN
Citations 
PageRank 
1863-2246
2
1.05
References 
Authors
6
6
Name
Order
Citations
PageRank
Markus Holopainen135740.95
Sakari Tuominen2394.67
Mika Karjalainen3499.88
Juha Hyyppä443966.75
Mikko Vastaranta529834.91
Hannu Hyyppä6385.47