Landcover classification of small-footprint, full-waveform lidar data

2009 ◽  
Vol 3 (1) ◽  
pp. 033544 ◽  
Author(s):  
Amy L. Neuenschwander
Keyword(s):  
Lidar Data ◽  
Full Waveform ◽  
Landcover Classification ◽  
Small Footprint ◽  
Waveform Lidar ◽  
Full Waveform Lidar

scholarly journals Terrain surfaces and 3-D landcover classification from small footprint full-waveform lidar data: application to badlands

2009 ◽  
Vol 6 (1) ◽  
pp. 151-205 ◽  
Author(s):  
F. Bretar ◽  
A. Chauve ◽  
J.-S. Bailly ◽  
C. Mallet ◽  
A. Jacome
Keyword(s):  
Remote Sensing Data ◽  
Field Measurements ◽  
Support Vector ◽  
Lidar Data ◽  
Active Sensors ◽  
Natural Hazard Mitigation ◽  
Full Waveform ◽  
Landcover Classification ◽  
Waveform Lidar ◽  
Full Waveform Lidar

Abstract. This article presents the use of new remote sensing data acquired from airborne full-waveform lidar systems. They are active sensors which record altimeter profiles. This paper introduces a set of methodologies for processing these data. These techniques are then applied to a particular landscape, the badlands, but the methodologies are designed to be applied to any other landscape. Indeed, the knowledge of an accurate topography and a landcover classification is a prior knowledge for any hydrological and erosion model. Badlands tend to be the most significant areas of erosion in the world with the highest erosion rate values. Monitoring and predicting erosion within badland mountainous catchments is highly strategic due to the arising downstream consequences and the need for natural hazard mitigation engineering. Additionaly, beyond the altimeter information, full-waveform lidar data are processed to extract intensity and width of echoes. They are related to the target reflectance and geometry. Wa will investigate the relevancy of using lidar-derived Digital Terrain Models (DTMs) and to investigate the potentiality of the intensity and width information for 3-D landcover classification. Considering the novelty and the complexity of such data, they are presented in details as well as guidelines to process them. DTMs are then validated with field measurements. The morphological validation of DTMs is then performed via the computation of hydrological indexes and photo-interpretation. Finally, a 3-D landcover classification is performed using a Support Vector Machine classifier. The introduction of an ortho-rectified optical image in the classification process as well as full-waveform lidar data for hydrological purposes is then discussed.

scholarly journals Terrain surfaces and 3-D landcover classification from small footprint full-waveform lidar data: application to badlands

2009 ◽  
Vol 13 (8) ◽  
pp. 1531-1544 ◽  
Author(s):  
F. Bretar ◽  
A. Chauve ◽  
J.-S. Bailly ◽  
C. Mallet ◽  
A. Jacome
Keyword(s):  
Remote Sensing Data ◽  
Support Vector ◽  
Lidar Data ◽  
Digital Terrain ◽  
Natural Hazard Mitigation ◽  
Full Waveform ◽  
Mountainous Catchments ◽  
Landcover Classification ◽  
Waveform Lidar ◽  
Full Waveform Lidar

Abstract. This article presents the use of new remote sensing data acquired from airborne full-waveform lidar systems for hydrological applications. Indeed, the knowledge of an accurate topography and a landcover classification is a prior knowledge for any hydrological and erosion model. Badlands tend to be the most significant areas of erosion in the world with the highest erosion rate values. Monitoring and predicting erosion within badland mountainous catchments is highly strategic due to the arising downstream consequences and the need for natural hazard mitigation engineering. Additionally, beyond the elevation information, full-waveform lidar data are processed to extract the amplitude and the width of echoes. They are related to the target reflectance and geometry. We will investigate the relevancy of using lidar-derived Digital Terrain Models (DTMs) and the potentiality of the amplitude and the width information for 3-D landcover classification. Considering the novelty and the complexity of such data, they are presented in details as well as guidelines to process them. The morphological validation of DTMs is then performed via the computation of hydrological indexes and photo-interpretation. Finally, a 3-D landcover classification is performed using a Support Vector Machine classifier. The use of an ortho-rectified optical image in the classification process as well as full-waveform lidar data for hydrological purposes is finally discussed.

scholarly journals Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data

2014 ◽  
Vol 23 (2) ◽  
pp. 224 ◽  
Author(s):  
Txomin Hermosilla ◽  
Luis A. Ruiz ◽  
Alexandra N. Kazakova ◽  
Nicholas C. Coops ◽  
L. Monika Moskal
Keyword(s):  
Forest Structure ◽  
Forest Canopy ◽  
Structural Parameters ◽  
Mixed Forest ◽  
Lidar Data ◽  
Full Waveform ◽  
Precise Knowledge ◽  
Small Footprint ◽  
Waveform Lidar ◽  
Full Waveform Lidar

Precise knowledge of fuel conditions is important for predicting fire hazards and simulating fire growth and intensity across the landscape. We present a methodology to retrieve and map forest canopy fuel and other forest structural parameters using small-footprint full-waveform airborne light detection and ranging (LiDAR) data. Full-waveform LiDAR sensors register the complete returned backscattered signal through time and can describe physical properties of the intercepted objects. This study was undertaken in a mixed forest dominated by Douglas-fir, occasionally mixed with other conifers, in north-west Oregon (United States). We extracted two sets of LiDAR metrics using pulse detection and waveform modelling and then constructed several predictive models using forward stepwise multiple linear regression. The resulting models explained ~80% of the variability for many of the canopy fuel and forest structure parameters: aboveground biomass (R2=0.84), quadratic mean diameter (R2=0.82), canopy height (R2=0.79), canopy base height (R2=0.78) and canopy fuel load (R2=0.79). The lowest performing models included basal area (R2=0.76), stand volume (R2=0.73), canopy bulk density (R2=0.67) and stand density index (R2=0.66). Our results indicate that full-waveform LiDAR systems show promise in systematically characterising the structure and canopy fuel loads of forests, which may enable accurate fire behaviour forecasting that in turn supports the development of prevention and planning policies.

Contextual Classification of Full Waveform Lidar Data in the Wadden Sea

2014 ◽  
Vol 11 (9) ◽  
pp. 1614-1618 ◽  
Author(s):  
Alena Schmidt ◽  
Joachim Niemeyer ◽  
Franz Rottensteiner ◽  
Uwe Soergel
Keyword(s):  
Wadden Sea ◽  
Lidar Data ◽  
Full Waveform ◽  
Waveform Lidar ◽  
Full Waveform Lidar

scholarly journals Deriving pseudo-vertical waveforms from small-footprint full-waveform LiDAR data

2014 ◽  
Vol 5 (4) ◽  
pp. 332-341 ◽  
Author(s):  
T. Hermosilla ◽  
N.C. Coops ◽  
L.A. Ruiz ◽  
L.M. Moskal
Keyword(s):  
Lidar Data ◽  
Full Waveform ◽  
Small Footprint ◽  
Waveform Lidar ◽  
Full Waveform Lidar
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