This paper presents preliminary results of the 3D-wind retrieval algorithm developed for the multi-look airborne Doppler radar measurements using various forms of Kalman filters. The data was collected inside Hurricane Lili during NOAA's 2002 Atlantic Hurricane Ocean Winds Field Experiment with University of Massachusetts's newly developed Imaging Wind and Rain Airborne Profiler (IWRAP). Two forms of Adaptive Kalman filter are presented for 3-D wind retrieval. Simulations of different wind field and radar parameters are made to investigate the performance of the selected filter. Preliminary results of the actual 3-D wind estimates are then obtained and compared with simultaneous and independent wind vector measurements by GPS dropwindsondes, surface wind speed measurements by a microwave radiometer and flight level wind vector measurements.
This paper analyzed the damaged forest by tomicus piniperda using multiple types of remote sensing data such as TM, CBERS-1, AVHRR and MODIS data. It selected a typical region including heavy damaged and healthy forest. The region was located by GPS (Global Position System). Then the spectral features of the above remote sensing data (March, 2001) were given. It indicates that the values of healthy forest of TM NIR band (0.76-0.9 ) and SWIR band (1.55-1.75 ) are distinctly greater than those of damaged forest. The values of CBERS-1 NIR bands (0.77-0.89 ), AVHRR bands (0.725-1.0 ) and MODIS bands (0.841-0.876 ) behave in the same pattern with TM. Otherwise, the values of MODIS thermal bands (3.929-3.89 , 10.78-11.28 and 11.77-12.27 ) of damaged forest are distinctly greater than those of healthy forest. The AVHRR thermal bands are not so. Finally, two detection models were put forward according to the spectral changing characteristics. One was named Difference Rate (DR) model with NIR and VIR data, which applied for TM, CBERS-1, AVHRR and MODIS. DR is greater, the forest grow healthily. Basis on the typical sample, the different guidelines distinguished healthy and damaged forests are obtained. The other model was named Disaster Index (DI) model with thermal and NIR data, only suitable for MODIS. The guidelines of healthy and damaged forest are determined too. DI is greater the forest is stricken more badly. In conclusion, it will help monitoring and assessing the vermin occurrence and impact by remote sensing detection model.
KEYWORDS: Signal to noise ratio, Wind measurement, Filtering (signal processing), Error analysis, Doppler effect, Radar, Global Positioning System, 3D metrology, Digital filtering, Algorithm development
This paper presents preliminary results of the 3D-wind retrieval algorithm developed for the multi-look airborne Doppler radar measurements using various forms of Kalman filters. The data was collected inside Hurricane Lili during NOAA's 2002 Atlantic Hurricane Ocean Winds Field Experiment with University of Massachusetts's newly developed Imaging Wind and Rain Airborne Profiler (IWRAP). Two forms of Adaptive Kalman filter are presented for 3-D wind retrieval. Simulations of different wind field and radar parameters are made to investigate the performance of the selected filter. Preliminary results of the actual 3-D wind estimates are then obtained and compared with simultaneous and independent wind vector measurements by GPS dropwindsondes, surface wind speed measurements by a microwave radiometer and flight level wind vector measurements.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.