A REMOTE SENSING APPROACH TO UNDERSTANDING THE HEALTH OF CLARK COUNTY WETLANDS PARK, NV USA

We present goals, methods, and results of a remote sensing module for introductory environmental science and geology courses at Nevada Stage College (NSC), Henderson NV USA. Funded by an Award from the Nevada NASA Space Grant Consortium office ( award # NCE NASA Space Grant NNX15AI02H), the students of NSC’s ENV 101 and GEOL 101 in fall semester 2016 and spring semester 2017 conducted a variety of activities in classroom and field related to remote sensing of the Clark County Wetlands Park (CCWP), NV. These included collection of new spectral data using a hand help portable field spectrometer and analysis and interpretation of Landsat image data over the selected area. The research presented here represents one part of that ongoing undergraduate teaching and research agenda.

The research component has 2 separate parts: 1) field based collection of data on background and target materials using an ASD portable field spectrometer, and 2) analysis of satellite and aircraft images collected at different seasons and across multiple years over the study site, the CCWP. The research goals are to understand the “health” of the CCWP over time, and to compile a spectral library of target and background materials that will be useful for understanding changes in ecosystem health and productivity.

We collected spectra of a variety of materials from study plots in the CCWP. These include soils, rocks living and dead vegetation of different species, and other materials likely to appear in images. Reflectance spectra were collected in the range from 350-2500 nm using the ASD instrument. In particular, we calculated the NDVI of selected study plots in different years to determine the “health” of the CCWP. We examined a number of Landsat scenes of the study area taken at about the same time year over a period of ~ 25 years. This was measured in 2 ways: 1) the total area covered by green vegetation from year to year as measured by NDVI; and 2) the area covered by the highest fraction of NDVI (the upper 25% of those pixels with the highest NDVI reading