Optimization of Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) imagery,in situ data with chemometrics to evaluate nutrients in the Shenandoah River,Virginia

Abstract

Phosphorus and nitrogen have a strong influence on water resource and remote sensing technology has demonstrated that water quality monitoring over a greater range of temporal and spatial scales can be used to overcome these constraints. This research was designed to demonstrate the feasibility of combining remotely-sensed water quality observation and chemometric techniques to estimate water quality in the Shenandoah River. We used Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) imagery, combined with a partial least squares analysis to characterize the spatial distribution of nutrients in the Sheanadoah river. ARCHER retrievals for phosphorous with cross-validation show high sensitivity in estimating water quality in the Shenandoah River with the Bentonville in the South Fork, with an R2 of 0.93 sensitivity. Using the significance level of 0.05, data from the summer of 2014 showed that the p-value was 0.00 for both nitrogen and phosphorous. Results show retrieval method is transferable.     

Retrieving aboveground biomass of wetland Phragmites australis (common reed) using a combination of airborne discrete-return LiDAR and hyperspectral data

Wetland biomass is essential for monitoring the stability and productivity of wetland ecosystems. Conventional field methods to measure or estimate wetland biomass are accurate and reliable, but expensive, time consuming and labor intensive. This research explored the potential for estimating wetland reed biomass using a combination of airborne discrete-return Light Detection and Ranging (LiDAR) and hyperspectral data. To derive the optimal predictor variables of reed biomass, a range of LiDAR and hyperspectral metrics at different spatial scales were regressed against the field-observed biomasses. The results showed that the LiDAR-derived H_p99 (99th percentile of the LiDAR height) and hyperspectral-calculated modified soil-adjusted vegetation index (MSAVI) were the best metrics for estimating reed biomass using the single regression model. Although the LiDAR data yielded a higher estimation accuracy compared to the hyperspectral data, the combination of LiDAR and hyperspectral data produced a more accurate prediction model for reed biomass (R² = 0.648, RMSE = 167.546 g/m², RMSE_r = 20.71%) than LiDAR data alone. Thus, combining LiDAR data with hyperspectral data has a great potential for improving the accuracy of aboveground biomass estimation.     

Creation of DTM with ASTER Data and Statistical Verification of the Accuracy of the Model (Western Peloponnese,Greece)

Abstract

In this paper we present the use of ASTER data for the creation of a Digital Terrain Model (DMT) of high accuracy. Using a stereo pair of ASTER satellite images with 15m resolution we created two DMTs: one with a 30m pixel size and another one with a 15m pixel size. Then we made a statistical verification of the two DTMs accuracy. We created another DTM with 30m pixel size from digitized contours of 1:50000 scale topographic maps. We first made an optical comparison of the two DTMs with 30m pixel size. Then we subtracted the two DTMs and we presented their difference. Finally, we verified the DTMs accuracy using 68 points of a well‐known elevation. All the results demonstrated that DTMs derived from ASTER data have very good accuracy.