Using object‐oriented classification and high‐resolution imagery to map fuel types in a Mediterranean region
TL;DR: Results suggested that object-oriented classification of high-resolution imagery has the potential to create accurate and spatially precise fuel maps.
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Abstract: [1] Knowledge of fuel load and composition is critical in fighting, preventing, and understanding wildfires. Commonly, the generation of fuel maps from remotely sensed imagery has made use of medium-resolution sensors such as Landsat. This paper presents a methodology to generate fuel type maps from high spatial resolution satellite data through object-oriented classification. Fuel maps were derived from QuickBird imagery, which offers a panchromatic and four multispectral bands ranging from 0.61 to 2.44 m resolution. The image used for this paper dated from July 2002 and is located in the NW region of Madrid, Spain. The Prometheus system, a fuel type classification adapted to the ecological characteristics of the European Mediterranean basin, was adopted for this study. Viewed with high-resolution imagery, fuel-related features are often aggregations of pixels exhibiting a variety of spectral properties. Correct identification and classification of these objects requires an explicit consideration of spatial context. We used an object-oriented approach, which allowed context consideration during the classification process, as a complement to traditional pixel-based methods. The map created with this approach was assessed to have greater than 80% accuracy for the prediction of six fuel classes. Results suggested that object-oriented classification of high-resolution imagery has the potential to create accurate and spatially precise fuel maps.
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Citations
Fusion of LiDAR and imagery for estimating forest canopy fuels
Todd L. Erdody,L. Monika Moskal +1 more
TL;DR: In this paper, LiDAR and color near-infrared imagery are used to estimate the amount of available canopy fuel in the Ahtanum State Forest in eastern Washington State.
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Fire models and methods to map fuel types: The role of remote sensing
TL;DR: The main concepts and terminology associated with forest fuels and a number of fuel type classifications are reviewed and the main techniques employed to map fuel types starting with the most traditional approaches, such as field work, aerial photo interpretation or ecological modelling are summarized.
271
Multispectral and LiDAR data fusion for fuel type mapping using Support Vector Machine and decision rules
TL;DR: In this article, a two-phase classification method is proposed to discriminate the fuel classes of the Prometheus classification system, which is adapted to the ecological characteristics of the European Mediterranean basin.
189
Integration of LiDAR and QuickBird imagery for mapping riparian biophysical parameters and land cover types in Australian tropical savannas
TL;DR: In this paper, the authors integrated remotely sensed light detection and ranging (LiDAR) data and high spatial resolution satellite imagery (QuickBird-2) to estimate riparian biophysical parameters and land cover types.
105
Estimating stem volume by tree crown area and tree shadow area extracted from pan-sharpened Quickbird imagery in open Crimean juniper forests
TL;DR: In this paper, the relationship between field-measured stem volume and tree attributes, including tree crown area and tree shadow area, measured from pan-sharpened Quickbird imagery with a 0.61m resolution in a sparse Crimean juniper (Juniperus excelsa M.Bieb.) forest in south-western Turkey, was investigated.
86
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