Robert A. Washington-Allen

A Protocol for Retrospective Remote Sensing–Based Ecological Monitoring of Rangelands

Abstract The degree of rangeland degradation in the United States is unknown due to the failure of traditional field-based monitoring to capture the range of variability of ecological indicators and disturbances, including climatic effects and land use practices, at regional to national spatial scales, and temporal scales of decades. Here, a protocol is presented for retrospective monitoring and assessment of rangeland degradation using historical time series of remote sensing data and catastrophe theory as an ecological framework to account for both gradual and rapid changes of state. This protocol 1) justifies the use of time-series satellite imagery in terms of the spatial and temporal scale of data collection; 2) briefly explains how to acquire, process, and transform the data into ecological indicators; 3) discusses the use of time-series analysis as the appropriate procedure for detecting significant change; and 4) explains what reference conditions are appropriate. Landsat data have been collected and archived since 1972, and include complete coverage of US rangelands. Characteristics of land degradation can be retrospectively measured for a nearly 33-year trend using surrogate remote sensing–based indicators that correlate with changes in life-form composition (time series of thematic maps), declines in vegetation productivity (vegetation indices), accelerated soil erosion (soil indices), declines in soil quality (piospheric analysis), and changes in landscape configuration (time series of thematic maps). Aspects of 2 retrospective studies are presented as examples of application of the protocol to considerations of the land use impacts from military training and testing and ranching activities on rangelands.

Quantification of the Ecological Resilience of Drylands Using Digital Remote Sensing

Drylands cover 41% of the terrestrial surface and support > 36% of the worlds 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 landscapes 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.

The Role of Soil Classification in Geographic Information System Modeling of Habitat Pattern: Threatened Calcareous Ecosystems

ABSTRACT Maps of potential habitat distribution are needed for regional population models of rare species, but reliable information from ground surveys is not always available. Existing data sources from disciplines other than ecological research often are underused. In this article, we discuss the development of a geographic information system (GIS) model that predicts potential habitats from ecosystem information contained in the US soil classification and soil survey. Soil classification and survey were used in the GIS model in an earlier study on the US Department of Energys Oak Ridge Reservation, Tennessee, to predict threatened calcareous habitats. The model predicts potential habitats from the combination of (a) soil taxon as an indication of long-term ecosystem processes; (b) geologic parent material; and (c) slope class. Satellite imagery was added to indicate current successional state. In this study, we tested the models predictive ability by using data from the Cedar Creek Slope Glades Preserve at the 44,000-ha US Department of Defense Fort Knox Military Reservation, Kentucky. We then used the model to predict occurrences of potential suitable habitat on the remainder of the Fort Knox reservation, including heavily impacted ordnance and tank training areas that are unsafe for public access. The soil component of the model also was applied to a 1.2 × 106–km2 region of the US, by using the US Department of Agriculture–National Resources Conservation Service (USDA-NRCS) State Soil Geographic Database (STATSGO) combined with official soil series descriptions. Soil taxa from the USDA-NRCS Soil Taxonomy were demonstrated to be associated with threatened calcareous habitats of rare plant species. These soil taxa were lithic mollisols (rendolls and udolls; Food and Agriculture Organization of the United Nations (FAO) rendzinas and chernozems) and alfisols (udalfs; FAO luvisols). The combined soil/geology/slope GIS approach has potential for prediction of rare ecosystems with narrow edaphic constraints. The approach would be useful in long-term planning for conservation management and restoration, especially where intensive ground surveys are expensive and/or impractical and where disturbance history obscures patterns of historical distribution.

Tree mortality from an exceptional drought spanning mesic to semiarid ecoregions.

Significant areas of the southern USA periodically experience intense drought that can lead to episodic tree mortality events. Because drought tolerance varies among species and size of trees, such events can alter the structure and function of terrestrial ecosystem in ways that are difficult to detect with local data sets or solely with remote-sensing platforms. We investigated a widespread tree mortality event that resulted from the worst 1-year drought on record for the state of Texas, USA. The drought affected ecoregions spanning mesic to semiarid climate zones and provided a unique opportunity to test hypotheses related to how trees of varying genus and size were affected. The study was based on an extensive set of 599 distributed plots, each 0.16 ha, surveyed in the summer following the drought. In each plot, dead trees larger than 12.7 cm in diameter were counted, sized, and identified to the genus level. Estimates of total mortality were obtained for each of 10 regions using a combination of design-based estimators and calibrated remote sensing using MODIS 1-yr change in normalized difference vegetation index products developed by the U.S. Forest Service. As compared with most of the publicized extreme die-off events, this study documents relatively low rates of mortality occurring over a very large area. However, statewide, regional tree mortality was massive, with an estimated 6.2% of the live trees perishing, nearly nine times greater than normal annual mortality. Dead tree diameters averaged larger than the live trees for most ecoregions, and this trend was most pronounced in the wetter climate zones, suggesting a potential re-ordering of species dominance and downward trend in tree size that was specific to climatic regions. The net effect on carbon storage was estimated to be a redistribution of 24-30 Tg C from the live tree to dead tree carbon pool. The dead tree survey documented drought mortality in more than 29 genera across all regions, and surprisingly, drought resistant and sensitive species fared similarly in some regions. Both angiosperms and gymnosperms were affected. These results highlight that drought-driven mortality alters forest structure differently across climatic regions and genera.

Perspectives on delineating management zones for variable rate irrigation

Up to 40% of soil available water content variance was explained by pie shape zoning.Dynamic zoning strategy may be needed if soil spatial arrangement varies by depth.Soil ECa and satellite images were useful attributes for irrigation zone delineation. This study aimed at investigating the performance of multiple irrigation zoning scenarios on a 73ha irrigated field located in west Tennessee along the Mississippi river. Different clustering methods, including k-means, ISODATA and Gaussian Mixture, were selected. In addition, a new zoning method, based on integer linear programming, was designed and evaluated for center pivot irrigation systems with limited speed control capability. The soil available water content was used as the main attribute for zoning while soil apparent electrical conductivity (ECa), space-borne satellite images and yield data were required as ancillary data. A good agreement was observed among delineated zones by different clustering methods. The new zoning method explained up to 40% of available water content variance underneath center pivot irrigation systems. The ECa achieved the highest Kappa coefficient (=0.79) among ancillary attributes, hence exhibited a considerable potential for irrigation zoning.

Time-Series Analysis of Land Cover Using Landscape Metrics

Time series of thematic land-cover maps are used to measure changes in land cover over time. However, pixel-to-pixel comparisons of such maps are often not advisable when these maps are generated from different sources (i.e., satellite data, aerial photography, or historical land survey data). The purpose of this study was to examine the historical changes in land cover from 1827 to 1999 using landscape metrics calculated on maps created from different sources. Regression and power law analyses were conducted to identify significant trends and threshold effects associated with land-cover change at Fort Benning, Georgia. Results indicated that since 1827 the landscape has become more fragmented with the introduction of farming, military training, and forest management practices.

Introduction to Special Feature on Catastrophic Thresholds, Perspectives, Definitions, and Applications

The contributions to this special feature focus on several conceptual and operational applications for understanding non-linear behavior of complex systems with various ecological criteria at unique levels of organization. The organizing theme of the feature emphasizes alternative stable states or regimes and intervening thresholds that possess great relevance to ecology and natural resource management. The authors within this special feature address the conceptual models of catastrophe theory, self- organization, cross-scale interactions and time-scale calculus; develop operational definitions and procedures for understanding the occurrence of dynamic regimes or multiple stable states and thresholds; suggest diagnostics tools for detection of states and thresholds and contribute to the development of scaling laws; and finally, demonstrate applications that promote both greater ecological understanding and management prescriptions for insect and disease outbreaks, resource island formation, and characterization of ecological resilience. This Special Feature concludes with a synthesis of the commonalities and disparities of concepts and interpretations among the contributed papers to identify issues and approaches that merit further research emphasis.

Studying uniform and variable rate center pivot irrigation strategies with the aid of site-specific water production functions

Novel site-specific water production functions (WPFs) were developed and tested.New zoning procedures for variable rate irrigation were established.The k-NN WPF accurately predicted cotton yield under supplemental irrigation.Sector zoning was predicted to enhance cotton yield under supplemental irrigation. Irrigation management has evolved into a top priority issue since available fresh water resources are limited. Water production functions (WPFs), mathematical relationships between applied water and crop yield, are useful tools for irrigation management and economic analysis of yield reduction due to deficit irrigation. This study aimed at (i) designing and evaluating site-specific WPFs (using k nearest neighbors (k-NN), multiple linear regression, and neural networks), (ii) simulating yield maps for uniform, sector control VRI, and zone control VRI center pivot systems using the site-specific WPFs, (iii) using the best WPF to investigate different cotton irrigation and zoning strategies using integer linear programming, and (iv) comparing soil-based and WPF-based zones for sector control VRI systems. A two-year cotton irrigation experiment (2013-2014) was implemented to study irrigation-cotton lint yield relationship across different soil types. The site-specific k-NN WPFs showed the highest performance with root mean square error equal to 0.131Mgha-1 and 0.194Mgha-1 in 2013 and 2014, respectively. The result indicated that variable rate irrigation with limited sector control capability could enhance cotton lint yield under supplemental irrigation when field-level spatial soil heterogeneity is significant. The temporal changes in climate and rainfall patterns, however, had a great impact on cotton response to irrigation in west Tennessee, a moderately humid region with short season environment. We believe site-specific WPFs are useful empirical tools for on-farm irrigation research.

Detection of Harbingers of Catastrophic Regime Shifts in Drylands

Harbingers are early warnings of imminent ecosystem collapse and thus are aids to preventing land degradation. Dynamic systems are hypothesized to exhibit dampening or inflation of critical attributes at or near a threshold, which is a decreasing or increasing spatial or temporal trend, respectively. This behavior is diagnostic of a state change and can be operationalized as an early detection system. Consequently, we used a time series from 1972 to 1997 of seasonal soil-adjusted vegetation index (SAVI) data, a proxy for canopy cover that was derived from Landsat imagery of the Marine Corps Air Ground Combat Center. We used dynamical, trend, and autocorrelation function (ACF) time series analysis to find that the time series had an increasing linear trend that correlated with wet periods of the Pacific Decadal Oscillation (PDO) and the El Nino-Southern Oscillation (ENSO). High and low SAVI values were in wet and dry basins of attraction with a rapid shift from dry to wet period from 1981 to 1982. Mean SAVI dampening appears to have occurred 2–3 years prior to the shift. Consequently, this study suggests that this dampening trend of the mean SAVI can be used as a harbinger of land degradation.