This paper presents a systematic approach that integrates spline curve fitting and geometry analysis to extract full-waveform LiDAR features for land-cover classification. The cubic smoothing spline algorithm is used to fit the waveform curve of the received LiDAR signals. After that, the local peak locations of the waveform curve are detected using a second derivative method. According to the detected local peak locations, commonly used full-waveform features such as full width at half maximum (FWHM) and amplitude can then be obtained. In addition, the number of peaks, time difference between the first and last peaks, and the average amplitude are also considered as features of LiDAR waveforms with multiple returns. Based on the waveform geometry, dynamic time-warping (DTW) is applied to measure the waveform similarity. The sum of the absolute amplitude differences that remain after time-warping can be used as a similarity feature in a classification procedure. An airborne full-waveform LiDAR data set was used to test the performance of the developed feature extraction method for land-cover classification. Experimental results indicate that the developed spline curve- fitting algorithm and geometry analysis can extract helpful full-waveform LiDAR features to produce better land-cover classification than conventional LiDAR data and feature extraction methods. In particular, the multiple-return features and the dynamic time-warping index can improve the classification results significantly.
This paper presents a systematic approach to utilize multi-temporal remote sensing images and spatial analysis for the
detection, investigation, and long-term monitoring of landslide hazards in Taiwan. Rigorous orthorectification of satellite
images are achieved by correction of sensor orbits and backward projections with ground control points of digital elevation
models. Individual images are also radiometrically corrected according to sensor calibration factors. In addition, multi-temporal
images are further normalized based on pseudo-invariant features identified from the images. Probable landslides
are automatically detected with a change-detection procedure that combines NDVI filtering and Change-Vector Analysis.
A spatial analysis system is also developed to further edit and analyze detected landslides and to produce landslide maps
and other helpful outputs such as field-investigation forms and statistical reports. The developed landslide detection and
monitoring system was applied to a study of large-scale landslide mapping and analysis in southern Taiwan and to the
long-term monitoring of landslides in the watershed of Shimen Reservoir in northern Taiwan. Both application examples
indicate that the proposed approach is viable. It can detect landslides effectively and with high accuracy. The data produced
with the developed spatial analysis system are also helpful for hazard investigation, reconstruction, and mitigation.
Long range transport leads mineral dusts to internally/externally mix with the ambient aerosols, such as soot particles,
naturally. The physicochemical characteristics of dust particles thus are dramatically altered after mixing with soot
aggregates. Therefore, the investigation on the optical properties of mineral dust along with their pathway causes a
significant topic for understanding the impacts of Asian dust storm on regional air quality, environment and climate.
Unfortunately, the previous researches regarding to the optical properties of dust/soot mixture for satellite remote sensing
are scarce. Consequently, the objective of this study is to simulate the effects of mixing with soot aggregates on the
optical properties of dust particles for satellite observations based on the well developed models. A tri-axial ellipsoidal
model for dust particles by introducing the third morphological freedom to improve the symmetry of spheroids has been
developed and showed in good agreement for the retrievals of dust optical properties from remote sensing measurements
and ground based observations. For the model of soot aggregation, the scattering properties of fractal aggregates can be
obtained with the Rayleigh-Debye-Gans (RDG), superposition T-matrix and Generalized Multiple Mie (GMM) methods.
The results show that the AOD (aerosol optical depth) retrievals of dust particle will be underestimated while the SSA
(single scattering albedo) will be overestimated when neglecting the combination of soot aggregates. The simulations
also suggest that simultaneously retrieve AOD and SSA based on the apparent reflectance may induce large uncertainty
for the dust/soot mixtures.