Abstract
Drylands cover 41% of the terrestrial surface and support > 36% of the world's population. However, the magnitude of dryland degradation is unknown at regional and global spatial scales and at 15-30-yr temporal scales. Historical archives of > 30 yr of Landsat satellite imagery exist and allow local to global monitoring and assessment of a landscape's natural resources in response to climatic events and human activities. Vegetation indices (VIs), i.e., proxies of vegetation characteristics such as phytomass, can be derived from the spectral properties of Landsat imagery. A dynamical systems analysis method called mean-variance analysis can be used to describe and quantify dynamic regimes of VI response to disturbance using characteristics of ecological resilience, particularly amplitude and malleability, from a change detection perspective. Amplitude is the magnitude of response of a VI to a disturbance; malleability is the degree of recovery of a resource after a disturbance. Spatially aggregate and spatially explicit (image) differencing are methods whereby a VI image or statistic from one time period is subtracted from a VI image or statistic from another time period. To illustrate this method, we used a time series of Landsat imagery from 1972 to 1987 to measure the response of vegetation communities that are managed by subsistence agropastoral communities to the severe 1982-1984 El Nino-induced drought on the Bolivian Altiplano. We found that the entire landscape had decreased vegetation cover, increased variance (diagnostic of a regime shift), and thus, increased susceptibility to soil erosion during the drought. The wet meadow vegetation cover class had the lowest amplitude and thus the most resilience relative to other vegetation cover classes. This response identified the wet meadow as a key resource, as well as a harbinger of climate change for agropastoral communities in areas where drought is an endemic stressor.
Highlights
Assessments of livestock development projects in the 1980s and 1990s concluded that the failure of these projects in developing countries was caused by an assumption of stable or equilibrium conditions in drylands and a failure to monitor the ecological and socioeconomic resilience of existing systems, during drought (Ellis and Swift 1988, Moris 1988, Buttolph and Coppock 2004)
We addressed the spectral compatibility between Thematic Mapper (TM) and Multispectral Scanner (MSS) data by direct substitution of TM bands 3 and 4 for MSS bands 2 (R) and 4 (NIR; Crist and Cicone 1984, Suits et al 1988)
With the exception of bofedal, the vegetation response appears to converge back toward 1972 conditions in 1986 and 1987 (Fig. 3). Because it is not water limited, bofedal had a higher mean transformed normalized difference vegetation index (TNDVI) and a wider range of TNDVI variance compared to the other cover classes (Fig. 3)
Summary
Assessments of livestock development projects in the 1980s and 1990s concluded that the failure of these projects in developing countries was caused by an assumption of stable or equilibrium conditions in drylands and a failure to monitor the ecological and socioeconomic resilience of existing systems, during drought (Ellis and Swift 1988, Moris 1988, Buttolph and Coppock 2004). Global assessments that were conducted between 1981 and 2001 estimated that 10–70% of drylands were degraded in terms of reduced vegetation canopy cover and accelerated soil erosion (Oldeman et al 1991, Dregne and Chou 1992, Lepers et al 2005, Millennium Ecosystem Assessment 2005) This discrepancy in the range of estimates has been attributed to multiple definitions of drylands (Lund 2007) and the failure to conduct monitoring at the appropriate spatial and temporal scales (West 2003a,b, Washington-Allen et al 2006, Zimmermann et al 2007). Drylands are usually monitored using vegetation field survey techniques that use a specific sampling design to reduce spatial variability; the costs to implement such a design at regional to global scales are temporally
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