Temuulen T. Sankey — Page 2

USGS2016

Remote sensing of tamarisk biomass, insect herbivory, and defoliation: Novel methods in the Grand Canyon Region, Arizona

Tamarisk is an invasive, riparian shrub species in the southwestern USA. The northern tamarisk beetle (Diorhabda carinulata) has been introduced to several states to control tamarisk. We classified tamarisk distribution in the Glen Canyon National Recreation Area, Arizona using a 0.2 m resolution, airborne multispectral data and estimated tamarisk beetle effects (overall accuracy of 86 percent) leading to leaf defoliation in a 49,408 m2 area. We also estimated individual tamarisk tree biomass and their uncertainties using airbonre liday data (100 points/m2). On average, total above ground tamarisk biomass was 8.67 kg/m2 (SD=17.6). The tamarisk beetle defoliation resulted in a mean leaf biomass loss of 0.52 kg/m2 and an equivalent of 25,692 kg across the entire study area. Our defoliated tamarisk map and biomass estimates can help inform restoration treatments to reduce tamarisk. Continued monitoring of tamarisk and tamarisk beetle effects are recommended to understand the currently-unknown eventual equilibrium between the two species and the cascading effects on ecosystem processes.

Photogrammetric Engineering and Remote Sensing↗

USGS2012

Remote sensing of sagebrush canopy nitrogen

This paper presents a combination of techniques suitable for remotely sensing foliar Nitrogen (N) in semiarid shrublands – a capability that would significantly improve our limited understanding of vegetation functionality in dryland ecosystems. The ability to estimate foliar N distributions across arid and semi-arid environments could help answer process-driven questions related to topics such as controls on canopy photosynthesis, the influence of N on carbon cycling behavior, nutrient pulse dynamics, and post-fire recovery. Our study determined that further exploration into estimating sagebrush canopy N concentrations from an airborne platform is warranted, despite remote sensing challenges inherent to open canopy systems. Hyperspectral data transformed using standard derivative analysis were capable of quantifying sagebrush canopy N concentrations using partial least squares (PLS) regression with an R 2 value of 0.72 and an R 2 predicted value of 0.42 ( n = 35). Subsetting the dataset to minimize the influence of bare ground (n = 19) increased R 2 to 0.95 ( R 2 predicted = 0.56). Ground-based estimates of canopy N using leaf mass per unit area measurements (LMA) yielded consistently better model fits than ground-based estimates of canopy N using cover and height measurements. The LMA approach is likely a method that could be extended to other semiarid shrublands. Overall, the results of this study are encouraging for future landscape scale N estimates and represent an important step in addressing the confounding influence of bare ground, which we found to be a major influence on predictions of sagebrush canopy N from an airborne platform.

Remote Sensing of Environment↗