This chapter summarizes the key questions and issues discussed by three review panels in the 9th International Symposium on Physical Measurements and Signatures in Remote Sensing (ISPMSRS) held in October 2005 in Beijing. The panels focused on remote sensing systems and sensors, modeling and inversion, and remote sensing applications. Some emerging issues in land remote sensing are presented, including sensor networks, modeling complex landscapes, machine learning techniques for inversion, and spatial scaling.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Akyildiz IF, Su WL, Sankarasubramaniam Y, Cayirci E (2002) A survey on sensor networks. IEEE Commun. Mag. 40(8): 102–114
Andrews R, Diederich J, Tickle AB (1995) Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems 8(6): 373–389
Clark JS, Gelfand AE (2006) A future for models and data in environmental science. Trends Ecol. Evol. 21(7): 375–380
Clark SD, Fearn DG (2006) The impact of advanced platform and ion propulsion technologies on small, low-cost interplanetary spacecraft. Acta Astronautica 59(8-11): 899–910
Ding XQ, Xin S (2006) Application research on extraction of rule from artificial neural networks for nonlinear regression. Dyn. Contin. Discrete Impuls. Syst. Ser. A Math. Anal. 13: 565–568
Gotway CA, Young LJ (2002) Combining incompatible spatial data. J. Am. Stat. Assoc. 97(458): 632–648
Hart JK, Martinez K (2006) Environmental Sensor Networks: A revolution in the Earth system science. Earth-Science Reviews, 78: 177–191
Krasnopolsky VM, Fox-Rabinovitz MS (2006) A new synergetic paradigm in environmental numerical modeling: hybrid models combining deterministic and machine learning components. Ecol. Model. 191(1): 5–18
Krasnopolsky VM, Schiller H (2003) Some neural network applications in environmental sciences. Part I: forward and inverse problems in geophysical remote measurements. Neural Networks 16 (3-4): 321–334
Kung HY, Hua JS, Chen CT (2006) Drought forecast model and framework using wireless sensor networks. J. Inform. Sci. Eng. 22(4): 751–769
Kyriakidis PC, Yoo EH (2005) Geostatistical prediction and simulation of point values from areal data. Geogr. Anal. 37(2): 124–151
Lemmerman L, Raymond C, Shotwell R, Chase J, Bhasin K, Connerton R (2005) Advanced platform technologies for Earth science. Acta Astronautica 56(1-2): 199–208
Liang S (2007) Recent developments in estimating land surface biogeophysical variables from optical remote sensing, Prog. Phys. Geogr. 31(5): 501–516
Lofstrom T, Johansson U, Niklasson L (2004) Rule extraction by seeing through the model, Neural Information Processing. Lecture Notes in Computer Science 3316, pp 555–560
Loyola RDG (2006) Applications of neural network methods to the processing of Earth observation satellite data. Neural Networks 19(2): 168–177
Nunez H, Angulo C, Catala A (2006) Rule-based learning systems for support vector machines. Neural Process. Lett. 24(1): 1–18
Palmer TN, Shutts GJ, Hagedorn R, Doblas-Reyes E, Jung T, Leutbecher M (2005) Representing model uncertainty in weather and climate prediction. Annu. Rev. Earth Planet. Sci. 33: 163–193
Pardo-Iguzquiza E, Chica-Olmo M, Atkinson PM (2006) Downscaling cokriging for image sharpening. Remote Sens. Environ. 102(1-2): 86–98
Paul JL (2006) Smart Sensor Web: tactical battlefield visualization using sensor fusion. IEEE Aerosp. Electron. Syst. Mag. 21(1): 13–20
Saad EW, Wunsch DC (2007) Neural network explanation using inversion. Neural Networks 20(1): 78–93
Wikle CK, Berliner LM (2005) Combining information across spatial scales. Technometrics 47(1): 80–91
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer Science+Business Media B.V
About this chapter
Cite this chapter
Liang, S. et al. (2008). Emerging Issues in Land Remote Sensing. In: Liang, S. (eds) Advances in Land Remote Sensing. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6450-0_19
Download citation
DOI: https://doi.org/10.1007/978-1-4020-6450-0_19
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-6449-4
Online ISBN: 978-1-4020-6450-0
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)