Land Cover Classification Accuracy Assessment Using Full-Waveform LiDAR Data

Abstract

The geomorphology of Taiwan is characterized by marked changes in terrain, geological fractures, and frequent natural disasters. Because of sustained economic growth, urbanization and land development, the land cover in Taiwan has undergone frequent use changes. Among the various technologies for monitoring changes in land cover, remote sensing technologies, such as LiDAR, are efficient tools for collecting a broad range of spectral and spatial data. Two types of airborne LiDAR systems exist; full-waveform (FW) LiDAR and traditional discrete-echo LiDAR. Because reflected waveforms are affected by the land object material type and properties, the waveform features can be applied to analyze the characteristics specifically associated with land-cover classification (LCC). Five types of land cover that characterize the volcanic Guishan Island were investigated. The automatic LCC method was used to elucidate the spectral, geomorphometric and textural characteristics. Interpretation keys accompanied by additional information were extracted from the FW LiDAR data for subsequent statistical and separation analyses. The results show that the Gabor texture and geomorphometric features, such as the normalized digital surface model (nDSM) and slopes can enhance the overall LCC accuracy to higher than 90%. Moreover, both the producer and user accuracy can be higher than 92% for forest and built-up types using amplitude and pulse width. Although the waveform characteristics did not perform as well as anticipated due to the waveform data sampling rate, the data provides suitable training samples for testing the waveform feature effects.

Cross-estimation of Soil Moisture Using Thermal Infrared Images with Different Resolutions

“…(6,7) Among the various technologies used to monitor changes in land cover, light detection and ranging are effective tools for obtaining large-bandwidth spectra and spatial data. (8) In this study, we compared data obtained from satellite images (from satellite Landsat 8) and data (i.e., aerial images, infrared inertial images, and multispectral images) obtained from a UAV with on-site measurement data. Numerous parameters were employed to estimate the moisture in surface soil using empirical equations for soil moisture.…”

A Novel De-Noising Method for Improving the Performance of Full-Waveform LiDAR Using Differential Optical Path

“…By substituting Equation (11) in to Equation (10), we obtain the SNR expression of the traditional method as…”

“…LiDAR has received much attention in recent years due to its simplicity, high accuracy, and utility in many applications, such as 3D city modeling, cartography, forest inventories, target recognition, and the digital terrain model [6][7][8][9]. To the best of our knowledge, LiDAR can be divided into two categories, i.e., discrete-echo LiDAR and full-waveform LiDAR [10,11]. A discrete-echo LiDAR records only a few discrete-echo backscattered waveform signals for each transmitted laser pulse and provides only 3D coordinates and range information about objects [8,12].…”