Vegetation Water Content Prediction: Towards More Relevant Explicatory Waveband Variables

by Eric Ariel L. Salas
Department of Fish, Wildlife and Conservation Ecology, New Mexico State University, Las Cruces, NM 88003, USA

Abstract
Although the water absorption feature (WAF) at 970 nm is not very well-defined, it may be used alongside other indices to estimate the canopy water content. The individual performance of a number of existing vegetation water content (VWC) indices against the WAF is assessed using linear regression model. We developed a new Combined Vegetation Water Index (CVWI) by merging indices to boost the weak absorption feature. CVWI showed a promise in assessing the vegetation water status derived from the 970 nm absorption wavelength. CVWI was able to differentiate two groups of dataset when regressed against the absorption feature. CVWI could be seen as an easy and robust method for vegetation water content studies using hyperspectral field data.
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Modeling the Effects of Environmental Change on Crucial Wildlife Habitat

Project Summary
This project evaluated bioclimatic-envelope models (from 19 bioclimate variables) in order to project availability of suitable bioclimatic conditions for 20 terrestrial species, identified as species of concern (SOC) in the South Central United States. We used various climate projections derived from general circulation models (GCMs) and they were post-processed via application of a simple statistical downscaling method. We compared future projected climate envelope suitability results produced from combinations of four GCMs and two greenhouse gas concentration trajectories [Representative Concentration Pathways (RCPs) 2.6 and 8.5] for two future time periods (2050: average for 2041 to 2060 and 2070: average for 2061 to 2080).
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A New Method to Compute Canopy Height Using Waveform LiDAR

Canopy Height Estimation by Characterizing Waveform LiDAR Geometry Based on Shape-Distance Metric
by: Eric Ariel L. Salas, and Geoffrey M. Henebry

Abstract
There have been few approaches developed for the estimation of height using waveform LiDAR data. Unlike any existing methods, we illustrate how the new Moment Distance (MD) framework can characterize the canopy height based on the geometry and return power of the LiDAR waveform without having to go through curve modeling processes. Our approach offers the possibilities of using the raw waveform data to capture vital information from the variety of complex waveform shapes in LiDAR. We assess the relationship of the MD metrics to the key waveform landmarks—such as locations of peaks, power of returns, canopy heights, and height metrics—using synthetic data and real Laser Vegetation Imaging Sensor (LVIS) data. In order to verify the utility of the new approach, we use field measurements obtained through the DESDynI (Deformation, Ecosystem Structure and Dynamics of Ice) campaign. Our results reveal that the MDI can capture temporal dynamics of canopy and segregate generations of stands based on curve shapes.
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