Vegetation Cover Analysis of Hazardous Waste Sites in Utah and Arizona Using Hyperspectral Remote Sensing

Abstract

This study investigated the usability of hyperspectral remote sensing for characterizing vegetation at hazardous waste sites. The specific objectives of this study were to: (1) estimate leaf-area-index (LAI) of the vegetation using three different methods (i.e., vegetation indices, red-edge positioning (REP), and machine learning regression trees), and (2) map the vegetation cover using machine learning decision trees based on either the scaled reflectance data or mixture tuned matched filtering (MTMF)-derived metrics and vegetation indices. HyMap airborne data (126 bands at 2.3 × 2.3 m spatial resolution), collected over the U.S. Department of Energy uranium processing sites near Monticello, Utah and Monument Valley, Arizona, were used. Grass and shrub species were mixed on an engineered disposal cell cover at the Monticello site while shrub species were dominant in the phytoremediation plantings at the Monument Valley site. Regression trees resulted in the best calibration performance of LAI estimation (R 2 > 0.80. The use of REPs failed to accurately predict LAI (R 2 < 0.2). The use of the MTMF-derived metrics (matched OPEN ACCESSRemote Sens. 2012, 4 328 filter scores and infeasibility) and a range of vegetation indices in decision trees improved the vegetation mapping when compared to the decision tree classification using just the scaled reflectance. Results suggest that hyperspectral imagery are useful for characterizing biophysical characteristics (LAI) and vegetation cover on capped hazardous waste sites. However, it is believed that the vegetation mapping would benefit from the use of higher spatial resolution hyperspectral data due to the small size of many of the vegetation patches (<1 m) found on the sites.

Trait Estimation in Herbaceous Plant Assemblages from in situ Canopy Spectra

“…Nonetheless, our results are corroborated by earlier work where chlorophyll content per canopy surface unit was estimated more accurately compared to content per leaf surface [8,21]. These results were attributed to the fact that Chl canopy traits convey information on both LAI and chlorophyll content, which are both known drivers of canopy reflectance [55]. While this plausibly explains why Chl b canopy , total Chl canopy and SWC canopy were more accurately estimated when expressed on canopy surface basis then on leaf surface, it leaves the question why the remaining traits perform poorer when expressed on canopy surface basis.…”

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

“…Pruning to avoid overfitting is often applied when a decision tree is generated. One of the merits of See5 compared to other DT algorithms is that the generated tree can be reproduced with multiple if-then rules, which makes it easier to interpret the results than the original tree (Jensen et al, 2007;Rhee et al, 2008;Im et al, 2012Im et al, , 2008Kim et al, 2015).…”

Mapping Vegetation Density in a Heterogeneous River Floodplain Ecosystem Using Pointable CHRIS/PROBA Data

“…Implementing spatially-explicit LAI estimates in flow resistance calculation schemes may therefore bypass the need for many elaborative field measurements [20,21]. The retrieval of LAI from EO data is often based on empirical relationships between spectral vegetation indices and ground-based measurements (e.g., [23][24][25]). These relationships work well under particular viewing and illumination geometry and for specific vegetation phenology, but they tend to produce inaccurate results when applied over a broad range of land cover types and optical and geometric conditions encountered in satellite images [26].…”