Mr. Dharmendra G. Trivedi Profile

Dharmendra G. Trivedi

at Gujarat Univ

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Proceedings Article | 9 December 2006 Paper

Ku-band scatterometer time series analysis over Indian tropical moist deciduous forests

C. Singh, D. Trivedi, Shiv Mohan, . Ajai

Proceedings Volume 6410, 64100V (2006) https://doi.org/10.1117/12.693972

KEYWORDS: Backscatter, Vegetation, Ku band, Scattering, Radar, Time series analysis, Microwave radiation, Image resolution, Antennas, Polarization

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Terrestrial vegetation exhibits strong seasonal and inter-annual fluctuations, which are associated with changes in the environmental variables. The understanding of the vegetation dynamics on a long-term basis requires repetitive observations over many years at temporal and spatial scale compatible with the regional processes. Space-borne scatterometer though primarily designed for ocean study, have proved advantageous in studying vegetation dynamics at regional scale. This could be possible due to availability of high repeat cycle data in addition to availability of processed scatterometer data by SIR algorithm. In the present study, the temporal behavior of Indian tropical-moist-deciduous forests has been studied using QuikScat (Ku-band) scatterometer data of year 2000. The impact of vegetation growth and senescence was investigated by comparing σ⁰ time-series to NDVI and rainfall observations. It was observed that σ⁰ varies with different scale than the NDVI. The low correlation coefficients also indicate that, the direct relationship between σ⁰ and NDVI is either very week or absent. Therefore, inferring that NDVI, which is a measure of the vegetation greenness or vigor rather than plant water-content or height, is not important for the explanation of the backscattering behavior. Further, σ⁰ was minimum during the deciduous period with prevailing high temperature and no rainfall condition. The temporal changes in backscattering coefficient were modeled for understanding the vegetation dynamics. The study suggests the suitability of Ku-band space-borne scatterometer data for understanding the seasonal dynamics of different forest types.

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