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Measuring biophysical aspects of aquatic macrophytes using in-situ hyperspectral and airborne remote sensing
Abstract
Wetlands are sites of important biogeochemical processes and remote sensing because of its capability for collecting data at various spatial scales, frequent intervals, and in several parts of electromagnetic spectrum, has been used by scientists to examine wetlands. This technology is especially useful in regions which are inaccessible and/or where data collection with conventional methods is not cost effective. Yet, various research questions, related to wetlands in general and inland freshwater wetlands in particular, have been overlooked and are still unanswered. This research was focused on three aspects of wetland vegetation: (1) to provide a basic understanding of the spectral responses of the wetland vegetation over an entire growing season; (2) to understand the link between the spectral responses acquired in-situ by means of a hand-held sensor and an airborne sensor; and (3) to use spectral responses from both close range and airborne sensors in estimating leaf area index (LAI) for aquatic macrophytes. It was observed that the amount of reflectance for all species changed over the growing season. In general, spectral responses for both individual species and mixed groups were statistically significantly different from one another in both the visible and NIR regions. The results also show that the best stage to distinguish species is flowering. It was found that the reflectance, in most cases, increased with an increase in the field of view (IFOV) of the sensor, and this change is statistically significant. Also, converting the radiometric resolution of the data from 16 bits to 8 bits resulted in an aggregation and loss of some information. In general, it can be concluded that close range hyperspectral data can be linked with airborne data spatially, radiometrically, and spectrally. Finally, it was determined that the near infrared (NIR) band and green normalized difference vegetation index (GNDVI) are the best single band and best vegetation index, respectively, to estimate LAI for wetland vegetation. Also, the best month of the year to predict LAI, in the wetland I studied, is July.
Subject Area
Geography
Recommended Citation
Ullah, Asad, "Measuring biophysical aspects of aquatic macrophytes using in-situ hyperspectral and airborne remote sensing" (2000). ETD collection for University of Nebraska-Lincoln. AAI9977029.
https://digitalcommons.unl.edu/dissertations/AAI9977029