Multitemporal Remote Sensing: Methods and Applications

ISBN-13: 9783319470351 / Angielski / Twarda / 2016 / 445 str.

Yifang Ban

Spis treści

1. Multitemporal Remote Sensing: Current Status, Trends and Challenges

Yifang Ban

2. Change Detection Techniques: A Review

Yifang Ban and Osama Yousif

3. Change Detection in Urban Areas: Spatial and Temporal Scales

Paolo Gamba, Fabio Dell’Acqua

4. Change Detection in Multitemporal Hyperspectral Images

Lorenzo Bruzzone, Sicong Liu, Francesca Bovolo and Peijun Du

5. Object-based Change Detection in Urban Areas Using Multitemporal High Resolution SAR Images with Unsupervised Thresholding Algorithms

Osama Yousif and Yifang Ban

6. Fusion of Multitemporal Spaceborne SAR and Optical Data for Urban Mapping and Urbanization Monitoring

Yifang Ban and Alexander Jacob

7. Post-Classification Change Detection in Arctic Glaciers by Multi-Polarization SAR

Vahid Akbari, Anthony P. Doulgeris and Torbjørn Eltoft

8. Interferometric SAR MultiTemporal Processing: Techniques and Applications

Daniele Perissin

9. TIMESAT for Processing Time-Series Data from Satellite Sensors for Land Surface Monitoring

Lars Eklundh and Per Jönsson

10. PhenoSat – A Tool for Remote Sensing Based Analysis of Vegetation Dynamics

Arlete Rodrigues, André R.S. Marcal and Mário Cunha

11. Temporal Techniques in Remote Sensing of Global Vegetation

Aleixandre Verger, Sivasathivel Kandasamy and Frédéric Baret

12. Soil Moisture Dynamics Estimated from MODIS Time Series Images

Thomas Gumbricht

13. Temporal Analysis of Remotely Sensed Land Surface Shortwave Albedo

Tao He and Shunlin Liang

14. Observing the Response of Terrestrial Vegetation to Climate Variability Across a Range of Time Scales by Time Series Analysis of Land Surface Temperature 

M. Menenti, H.R. Ghafarian Malamiri, H. Shang, S.M. Alfieri, C. Maffei and L. Jia

15. A Review of Multitemporal Synthetic Aperture Radar (SAR) for Crop Monitoring

Heather McNairn and Jiali Shang

16. An Approach for Determining Relationships Between Disturbance and Habitat Selection Using Bi-Weekly Synthetic Images and Telemetry Data

Nicholas D.A. Brown, Trisalyn Nelson, Michael A. Wulder, Nicholas C. Coops, Thomas Hilker, Christopher W. Bater,  Rachel Gaulton, Gordon B. Stenhouse

17. Multitemporal Remote Sensing for Inland Water Bodies and Wetland Monitoring

Yeqiao Wang, Shuhua Qi and Jian Xu

18. Global Land Surface Water Mapping and Analysis at 30m Spatial Resolution for Years 2000 and 2010

Xin Cao, Jun Chen, Anping Liao, Lijun Chen, Jin Chen

19. Multitemporal Remote Sensing of Coastal Waters

Susanne Kratzer, Krista Alikas, Therese Harvey, José María Beltrán-Abaunza, Evgeny Morozov, Selima Ben Mustapha and Samantha Lavender

20. Monitoring Long-Term Disaster Recovery – Space And Ground Views

Tuong-Thuy Vu and Daroonwan Kamthonkiat

Blurb

Written by world renowned scientists, this book provides an excellent overview of a wide array of methods and techniques for the processing and analysis of multitemporal remotely sensed images. These methods and techniques include change detection, multitemporal data fusion, coarse-resolution time series processing, and interferometric SAR multitemporal processing, among others. A broad range of multitemporal datasets are used in their methodology demonstrations and application examples, including multispectral, hyperspectral, SAR and passive microwave data.  

This book features a variety of application examples covering both land and aquatic environments. Land applications include urban, agriculture, habitat disturbance, vegetation dynamics, soil moisture, land surface albedo, land surface temperature, glacier and disaster recovery. Aquatic applications include monitoring water quality, water surface areas and water fluctuation in wetland areas, spatial distribution patterns and temporal fluctuation trends of global land surface water, as well as evaluation of water quality in several coastal and marine environments.

This book will help scientists, practitioners, students gain a greater understanding of how multitemporal remote sensing could be effectively used to monitor our changing planet at local, regional, and global scales.