Abstract
The ability to quantify green vegetation across space and over time is useful for studying grassland health and function and improving our understanding of the impact of land use and climate change on grasslands. Directly measuring the fraction of green vegetation cover is labor-intensive and thus only practical on relatively smaller experimental sites. Remote sensing vegetation indices, as a commonly-used method for large-area vegetation mapping, were found to produce inconsistent accuracies when mapping green vegetation in semi-arid grasslands, largely due to mixed pixels including both photosynthetic and non-photosynthetic material. The spectral mixture approach has the potential to map the fraction of green vegetation cover in a heterogeneous landscape, thanks to its ability to decompose a spectral signal from a mixed pixel into a set of fractional abundances. In this study, a time series of fractional green vegetation cover (FGVC) from 1999 to 2014 is estimated using the spectral mixture approach for a semi-arid mixed grassland, which represents a typical threatened, species-rich habitat in Central Canada. The shape of pixel clouds in each of the Landsat images is used to identify three major image endmembers (green vegetation, bare soil/litter, and water/shadow) for automated image spectral unmixing. The FGVC derived through the spectral mixture approach correlates highly with field observations (R2 = 0.86). Change in the FGVC over the study period was also mapped, and green vegetation in badlands and uplands is found to experience a slight increase, while vegetation in riparian zone shows a decrease. Only a small portion of the study area is undergoing significant changes, which is likely attributable to climate variability, bison reintroduction, and wildfire. The results of this study suggest that the automated spectral unmixing approach is promising, and the time series of medium-resolution images is capable of identifying changes in green vegetation cover in semi-arid grasslands. Further research should investigate driving forces for areas undergoing significant changes.
Highlights
Grasslands occupy nearly half of the terrestrial globe and play a critical role in supplying food, fiber, and fuel, supporting the biodiversity of animals and plants, maintaining water and air quality, and supporting ecological processes that sustain ecosystems and landscapes [1,2]
This study aims to investigate the shape of pixel clouds in a series of Landsat images for a semi-arid grassland and use the identified image endmembers for automated image spectral unmixing
It is worth noting that this study only focused on green vegetation cover, so it was not an issue to combine bare soil and litter as one endmember
Summary
Grasslands occupy nearly half of the terrestrial globe and play a critical role in supplying food, fiber, and fuel, supporting the biodiversity of animals and plants, maintaining water and air quality, and supporting ecological processes that sustain ecosystems and landscapes [1,2]. Protecting and monitoring grassland health has been a major priority for many local, national, and international agencies. Green vegetation cover is directly linked to photosynthetic activity and ecosystem productivity [5]. The ability to quantify green vegetation across space over time is useful for studying grassland health and function [6], investigating the impact of land use and climate change [7], and improving our understanding of vegetation recovery after major disturbances such as wildfire [8]. Measuring green vegetation cover is labor-intensive and only practical on relatively small experimental sites [9]. A practical means to map green vegetation cover at the landscape scale, or even global scale, is only possible with remote sensing techniques [10]
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