Skip to main content
ARS Home » Plains Area » Las Cruces, New Mexico » Range Management Research » Research » Publications at this Location » Publication #287530
Title: Vegetation index differencing for broad-scale assessment of productivity under prolonged drought and sequential high rainfall conditions

Author
item Browning, Dawn
item STEELE, CAITI - New Mexico State University

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/16/2013
Publication Date: 1/7/2013
Citation: Browning, D.M., Steele, C. 2013. Vegetation index differencing for broad-scale assessment of productivity under prolonged drought and sequential high rainfall conditions. Remote Sensing. 5:327-341.

Interpretive Summary: 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 holds promise for enhancing the effectiveness of field and management efforts across millions of acres of federal lands.

Technical Abstract: The ability to evaluate large tracts of land with in a consistent manner using readily available moderate resolution satellite data is a highly valuable tool for land resource managers. Herein we present a multi-scale approach using remotely sensed imagery that holds the potential to effectively prioritize sites for assessment and monitoring and identify those areas susceptible to degradation and possible candidates for management intervention. There are research applications for which remotely sensed imagery assist, but do not fulfill decision-making needs and requirements 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. 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 to promote understand 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. 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.