Examining the strength of the newly-launched Sentinel 2 MSI sensor in detecting and discriminating subtle differences between C3 and C4 grass species

摘要

C3 and C4 grass species discrimination has increasingly become relevant in understanding their response to environmental changes and to monitor their integrity in providing goods and services. While remotely sensed data provide robust, cost-effective and repeatable monitoring tools for C3 and C4 grasses, this has been largely limited by the scarcity of sensors with better earth imaging characteristics. The recent launch of the advanced Sentinel 2 MultiSpectral Instrument (MSI) presents a new prospect for discriminating C3 and C4 grasses. The present study tested the potential of Sentinel 2, characterized by refined spatial resolution and more unique spectral bands in discriminating between Festuca (C3) and Themeda (C4) grasses. To evaluate the performance of Sentinel 2 MSI; spectral bands, vegetation indices and spectral bands plus indices were used. Findings from Sentinel 2 were compared with those derived from the widely-used Worldview 2 commercial sensor and the Landsat 8 Operational Land Imager (OLI). Overall classification accuracies have shown that Sentinel 2 bands have potential (90.36%), than indices (85.54%) and combined variables (88.61%). The results were comparable to Worldview 2 sensor, which produced slightly higher accuracies using spectral bands (95.69%), indices (86.02%) and combined variables (87.09%), and better than Landsat 8 OLI spectral bands (75.26%), indices (82.79%) and combined variables (86.02%). Sentinel 2 bands produced lower errors of commission and omission (between 4.76 and 14.63%), comparable to Worldview 2 (between 1.96 and 7.14%), than Landsat 8 (between 18.18 and 30.61%), when classifying the two species. The classification accuracy from Sentinel 2 also did not differ significantly (z = 1.34) from Worldview 2, using standard bands; it was significantly (z > 1.96) different using indices and combined variables, whereas when compared to Landsat 8, Sentinel 2 accuracies were significantly different (z> 1.96) using all variables. These results demonstrated that key vegetation species discrimination could be improved by the use of the freely and improved Sentinel 2 MSI data. (C) 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.