Vegetation Index Differencing for Broad-Scale Assessment of Productivity Under Drought and High Rainfall

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Spatially-explicit depictions of plant productivity over large areas are critical to monitoring landscapes in highly heterogeneous arid ecosystems. Applying radiometric change detection techniques we sought to determine whether: (1) differences between pre- and post-growing season spectral vegetation index values effectively identify areas of significant change in vegetation; and (2) areas of significant change coincide with altered ecological states. We differenced NDVI values, standardized difference values to Z-scores to identify areas of significant increase and decrease in NDVI, and examined the ecological states associated with these areas. The vegetation index differencing method and translation of growing season NDVI to Z-scores permit examination of change over large areas and can be applied by non-experts. This method identified areas with potential for vegetation/ecological state transition and serves to guide field reconnaissance efforts that may ultimately inform land management decisions for millions of acres of federal lands.

 

Distribution of ecological states (gray outlines) within areas demonstrating NDVI growing season anomalies (A) Site 1, negative change in 2003; (B) Site 1, positive change 2009; (C) Site 2, positive change in 2003; (D) Site 2, negative change 2003; (E) Site 2, positive change, 2009; (F) Site 3, negative change in 2003; (G) Site 3, positive change 2009. Gray outlines depict ecological state boundaries prior to grouping.

 

Spatially-explicit depictions of plant production that are derived in a consistent and repeatable manner are critical to monitoring landscapes in heterogeneous arid and semi-arid grassland and savanna ecosystems (hereafter “rangelands”). Change detection techniques applied to remotely sensed data provide opportunities to identify and characterize changes in land surface conditions to assist decision-making for land management and to focus field reconnaissance efforts.

Predictions for future climate are increasing temperatures and variability in the amount and timing of rainfall in the water-limited regions of the southwestern USA. These changes in conjunction with increasing pressure on resources posed by a rapidly growing human population in this region necessitate effective, consistent, and data-driven tools to guide land management decisions and envision novel scenarios. The USA encompasses approximately 312 million hectares of rangelands, 43% of which is managed by the federal government. The millions of hectares under federal jurisdiction pose a particular challenge to meeting the need for data-driven models that are linked to ecosystem function.

Understanding vegetation dynamics is central to the assessment of rangeland resources. Over the last twenty years there has been a shift to using conceptual models that integrate non-linear vegetation dynamics. These models, known as state-and-transition models (STMs), encapsulate the notion that vegetation communities are present in multiple stable states, where the term “state” refers to the physical structure and set of ecological processes associated with a particular vegetation community. Transitions between states are catalyzed by both persistent environmental pressures such as drought or soil degradation and catastrophic disturbance. STMs are constructed according to “ecological sites”. Ecological sites are part of the Land Resource Hierarchy developed by the USDA-NRCS (http://soils.usda.gov/survey/geography/hierarchy/) and are distinguished according to soil type, climate and geomorphic position. Central to ecological site classification is the potential of a place to support a given vegetation community and to exhibit specific processes that lead to transitions in vegetation composition and/or structure from the expected historic state.

State-and-transition models use various ecological site-specific terms to depict different states. We use the generalized nomenclature adopted by Steele et al. Historic state describes an historic unaltered vegetation community; an altered state describes a vegetation community that has undergone minor changes in ecosystem structure and governing ecological processes, but the soil profile remains largely intact. A degraded state describes a vegetation community that has undergone major changes in ecosystem structure and the ecological processes that govern transitions. Minor losses may be observed from the uppermost soil layer (A horizon). In many parts of the southwestern US, adverse changes in the vegetation structure that lead to degraded states are primarily associated with increases in woody cover and decreases in the cover of native perennial grasses to create shrub- or tree-dominated states. The most degraded state is the Bare-Annuals state, where there is widespread depletion of the A horizon and almost all vegetation has been removed except for annual pioneer species.

Establishing direct relationships between remotely-sensed data and ecological states or sites is extremely challenging in arid rangelands due to (i) the heterogeneity and sparseness of the vegetation and (ii) that fact that not all states exhibit distinct spectral signatures. We suggest that radiometric change detection techniques can serve as an effective method to evaluate land surface changes in the context of vegetation dynamics (i.e., state changes) predicted by STMs and to identify locations to focus field monitoring efforts. This is achieved by integrating the change surface with available spatially-explicit ancillary data.

Vegetation index differencing (VID) using moderate spatial resolution Landsat Thematic Mapper (TM) imagery is one way to achieve consistent depictions of land surface change for its multi-decadal record and ground resolved distance. VID is a radiometric method for detecting change in pixel values between dates and avoids issues associated with other change detection techniques. We sought to broadly assess land surface conditions in actively managed rangeland landscapes that occur on the interface of the Chihuahuan and Sonoran Deserts in the southwestern USA by capitalizing on the readily available Landsat 5 Thematic Mapper image archive.

We examined changes between pre- and post-growing season Normalized Difference Vegetation Index (NDVI) values to depict the change in photosynthetic biomass over the growing season. We selected imagery for a year following persistent below-average rainfall in the region (2003) and a year following two historically high rainfall years (2009; see Figure 1 below). We hypothesized that ecological states representing different degrees of degradation (i.e., Historic, Altered, Degraded) would respond differently to conditions of drought and abundant rainfall. We sought to answer the following questions: (1) What ecological states exhibit most positive and negative change in NDVI after periods of above- and below-average rainfall? and (2) What can we infer from the pattern of NDVI responses with respect to state-specific vegetation dynamics?

 

Figure 1. Climatic patterns for the Malpais Borderland Area calculated using data from five NOAA meteorological stations from 1990 through 2010. Panel (A) depicts the mean monthly precipitation (solid blue line) and temperature (black dotted line); gray arrows indicate the temporal range of Landsat TM 5 imagery used in this study. Panel (B) is a standardized difference from long-term average calculated seasonally based on standard water year (October through September). In this region, the growing season (July, August, and September; Panel (A)) occurs at the end of the water year.

 

Table 1. Ecological site and state designations within the Malpais Borderlands Area (MBA) study area. Ecological sites came from the Natural Resource Conservation Service (NRCS) Soil Survey Geographic database (SSURGO) ecological site information. Within these Major Land Resource Areas, ecological sites may be grouped according to the presence and amount of woody cover in the Historic state; Type 1 sites have little to no woody cover and Type 2 sites have a woody cover component in the Historic state.

 

Table 2. Distribution of ecological sites and states representing growing season anomalies (i.e., growing season NDVI Z-score > 1.96 or < −1.96). Percentages are based on the extent of Ecological states within the three focal areas (hatched polygons in Figure 2). Type 1 and Type 2 Ecological sites were grouped according to the presence and amount of woody cover in the Historic state; Type 1 sites have little to no woody cover and Type 2 sites have a woody cover component in the Historic state [8]. There were no patches of negative change (i.e., areas of anomalous decrease in NDVI) in 2009 within the three focal areas.

Discussion

Broad-scale monitoring of land surface conditions is a pressing need in many parts of the world as the demand for multiple uses intensifies. Remote sensing change detection provides an opportunity to evaluate and identify areas of change and assist in natural resource problem solving [21,22]. We used a multi-scale approach that integrating ecological state mapping using fine-resolution aerial photography with vegetation index differencing using moderate resolution satellite imagery to greatly enhance our interpretation of growing season responses to high and low rainfall. Moreover, interpretations of NDVI anomalies were informed by vegetation dynamics derived from state-and-transition models in an effort to facilitate ecosystem inventory and monitoring efforts.

While we can make inferences regarding NDVI changes using knowledge of vegetation dynamics in this region, this method must be developed further and correlated with field observations to verify our inferences. A traditional accuracy assessment was beyond the scope of this initial study. This is primarily because we lack ground data suitable for determining the degree of change in vegetation indicated by VID over such an expansive area of which much is difficult to access. Yet, future efforts are planned that will incorporate field visits to areas that demonstrated dynamic responses to drought and periods of high rainfall. The effort will incorporate on-the-ground data collection with on-going analysis of fine spatial resolution imagery image analysis to refine and inform the ecological state mapping process. The classification of ecological sites using fine spatial resolution imagery is a developing field that is most successfully accomplished with iterative refinement and development as field data are compiled [8]. We uphold the integrity of the ecological state maps generated in this study with expert knowledge and experience in interpreting vegetation patterns and associations with geomorphology discernible on the digital ortho-quarter quadrangle imagery; however, we acknowledge that the mapping of ecological state polygons represents a source of uncertainty. Where this expert knowledge is not available, VID interpretations could still be informed by readily-available ecological site polygons.

We present a case study to demonstrate how a multi-scale approach to change detection can effectively focus field reconnaissance efforts that in itself provides insight where field data are presently lacking. Further development can be greatly enhanced by incorporating longer time series such as Landsat data available through the Web-enabled Landsat Data (WELD) project [23]. We contend that even in the absence of field data or expert knowledge, the use of VID is valuable for prioritizing sites for field visits. Vegetation communities occurring in altered states have been shown to respond well to management intervention whereas a vegetation community in a highly degraded state is often beyond economical means of intervention. We recommend that locations where the vegetation communities are in historic or altered states and which appear as growing season NDVI anomalies (positive or negative) should be prioritized for field visits over locations showing less significant changes in NDVI. In this manner, spatially-explicit depictions of areas with potential for vegetation/ecological state transition would greatly enhance the effectiveness of field and management efforts across millions of acres of federal lands.

All remote sensing protocols designed to provide data needed for decision-making have strengths, weaknesses, and situations for which they function optimally. That the 2009 growing season NDVI VID values were not normally distributed does not compromise our ability to identify areas in the tails of the distribution to identify patterns and guide field efforts for broad-scale landscape monitoring. When research objectives or management needs require multiple depictions of land surface condition (either multiple dates or multiple sites at one time), growing season NDVI values must be standardized. If emphasis is placed on a mapping effort for a site at one point in time, the user could alternatively rank the VID values and choose those at the upper and lower ends of the distribution. This modification and/or non-parametric techniques could be used to avoid violating assumptions associated with normally distributed data.

There are research applications for which remotely sensed imagery assist, but do not fulfill decision-making needs and requirements [21] and the strengths and limitations should be duly noted. Land managers and decision-makers seek remote sensing tools that provide products relevant to and consistent with STM concepts [6,8,26]. This is a compelling challenge from two perspectives. The remote sensing community is needed to augment the knowledge regarding the accuracy and suitability of the full suite of change detection algorithms not presented here, e.g., [11,24,27] to promote understanding of which techniques are best suited for different research applications. Land managers and their technical collaborators are challenged to identify existing indicators or modifications thereof that are commensurate with products derived from remotely sensed data [25]. Only with contributions from both communities and effective dialogue between them will the full potential of remote sensing for natural resource management decision-making be realized.

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