Study of linearized index generation using multispectral images for environmental monitoring

Jeng Jong Pan; Sheng Tun Li

Study of linearized index generation using multispectral images for environmental monitoring

Research output: Contribution to conference › Paper › peer-review

Abstract

Many indices have been used to study the environmental change. It is easy to monitor the change if an index provides a linear response to a physical or biophysical parameter. We proposed a spectral reflectance curves-based approach to generate a linearized index. Using multispectral images, we first converted DN to reflectance, used KL transform and fuzzy logic to classify pixels, then determined a mapping function to generate a linearized index. An application of this approach to generate a linearized snow age index is shown in this paper. The linearized index can be further analyzed using time-varying signal analysis techniques to explore the spatial and temporal variations.

Original language	English
Pages	117-119
Number of pages	3
Publication status	Published - 1994 Dec 1
Event	Proceedings of the 1994 International Geoscience and Remote Sensing Symposium. Vol 4 (of 4) - Pasadena, CA, USA Duration: 1994 Aug 8 → 1994 Aug 12

Other

Other	Proceedings of the 1994 International Geoscience and Remote Sensing Symposium. Vol 4 (of 4)
City	Pasadena, CA, USA
Period	94-08-08 → 94-08-12

All Science Journal Classification (ASJC) codes

Computer Science Applications
Earth and Planetary Sciences(all)

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title = "Study of linearized index generation using multispectral images for environmental monitoring",

abstract = "Many indices have been used to study the environmental change. It is easy to monitor the change if an index provides a linear response to a physical or biophysical parameter. We proposed a spectral reflectance curves-based approach to generate a linearized index. Using multispectral images, we first converted DN to reflectance, used KL transform and fuzzy logic to classify pixels, then determined a mapping function to generate a linearized index. An application of this approach to generate a linearized snow age index is shown in this paper. The linearized index can be further analyzed using time-varying signal analysis techniques to explore the spatial and temporal variations.",

author = "Pan, {Jeng Jong} and Li, {Sheng Tun}",

year = "1994",

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AU - Li, Sheng Tun

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N2 - Many indices have been used to study the environmental change. It is easy to monitor the change if an index provides a linear response to a physical or biophysical parameter. We proposed a spectral reflectance curves-based approach to generate a linearized index. Using multispectral images, we first converted DN to reflectance, used KL transform and fuzzy logic to classify pixels, then determined a mapping function to generate a linearized index. An application of this approach to generate a linearized snow age index is shown in this paper. The linearized index can be further analyzed using time-varying signal analysis techniques to explore the spatial and temporal variations.

AB - Many indices have been used to study the environmental change. It is easy to monitor the change if an index provides a linear response to a physical or biophysical parameter. We proposed a spectral reflectance curves-based approach to generate a linearized index. Using multispectral images, we first converted DN to reflectance, used KL transform and fuzzy logic to classify pixels, then determined a mapping function to generate a linearized index. An application of this approach to generate a linearized snow age index is shown in this paper. The linearized index can be further analyzed using time-varying signal analysis techniques to explore the spatial and temporal variations.

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