Detecting annual and seasonal changes in a sagebrush ecosystem with remote sensing-derived continuous fields

Abstract

Abstract. Climate change may represent the greatest future risk to the sagebrush ecosystem. Improved ways to quantify and monitor gradual change resulting from climate influences in this ecosystem are vital to its future management. For this research, the change over time of five continuous field cover components including bare ground, herbaceous, litter, sagebrush, and shrub were measured on the ground and by satellite across six seasons and four years. Ground-measured litter and herbaceous cover exhibited the highest variation annually and herbaceous cover the highest variation seasonally. Correlation of ground measurements to corresponding remote-sensing predictions indicated that annual predictions tracked ground measurements more closely than seasonal ones, and QuickBird predictions tracked ground measurements more closely than Landsat predictions. When annual linear slope values from ground plots and sensor predictions were correlated by component, the direction of ground-measured change was tracked better with QuickBird components than with Landsat components. Component predictions were correlated to annual and seasonal DAYMET precipitation. QuickBird components on average had the best response to precipitation patterns, followed by Landsat components. Overall, these results demonstrate the ability of sagebrush ecosystem components as predicted by regression trees to incrementally measure changing components of a sagebrush ecosystem.

Forecasting sagebrush ecosystem components and greater sage-grouse habitat for 2050: Learning from past climate patterns and Landsat imagery to predict the future

“…1). Site 1 is the location where comprehensive trend analysis research has been on-going for many years (Homer et al, 2013).…”

“…Remote sensing images interpreted into fractional vegetation and soil ecosystem components offer a way to quantify and regionalize subtle climate process impacts on vegetation change in a sagebrush ecosystem across time (Xian et al, 2012a,b;Homer et al, 2013). This process can draw on the Landsat (LS) archive, which offers an especially rich source of remote sensing information capable of exploring historical patterns back to 1972, using a global record of millions of images of the Earth (Loveland and Dwyer, 2012).…”

“…This process can draw on the Landsat (LS) archive, which offers an especially rich source of remote sensing information capable of exploring historical patterns back to 1972, using a global record of millions of images of the Earth (Loveland and Dwyer, 2012). The multispectral capabilities and 30-m resolution of LS are well suited for detecting and quantifying a range of vegetation attributes, as well as for detecting gradual change and the underlying ecological processes (Vogelmann et al, 2012;Homer et al, 2013).…”

Monitoring Protocols: Options, Approaches, Implementation, Benefits

“…Numerous studies have shown that interpretation or classification of high-resolution imagery can match or outperform many field-based measurements of certain attributes (e.g., Booth et al 2005;Seefeldt and Booth 2006;Cagney et al 2011;Hulet et al 2014b). Land cover classifications or predictions of vegetation attributes (e.g., cover) have been used for landscape-scale rangeland assessment and monitoring (e.g., Hunt and Miyake 2006;Marsett et al 2006;Homer et al 2013). Regression models (e.g., Homer et al 2012) or geostatisical techniques (e.g., Karl 2010) are used to predict rangeland indicators over landscapes from a set of field samples.…”

Monitoring Natural Ecosystem and Ecological Gradients: Perspectives with EnMAP

“…Moreover, the recent opening of the Landsat archive has made available a valuable dataset, which enables a detailed characterization of dynamic ecosystem processes at landscape scales, such as e.g., disturbances or long-term trends [25]. This has been followed by a wealth of studies recurring to this archive for ecosystem change monitoring [26,27]. Imaging spectroscopy (IS) data, i.e., data with a high number of contiguous and narrow spectral bands, allow a precise characterization and quantification of relevant ecosystem properties, such as vegetation physiognomies or plant functional types [29,30].…”