Sharmistha Swain

Climate forcing of wetland landscape connectivity in the Great Plains

Habitat connectivity is a landscape attribute critical to the long-term viability of many wildlife species, including migratory birds. Climate change has the potential to affect habitat connectivity within and across the three main wetland complexes in the Great Plains of North America: the prairie potholes of the northern plains, the Rainwater Basin of Nebraska, and the playas of the southern plains. Here, we use these wetlands as model systems in a graph-theory-based approach to establish links between climatic drivers and habitat connectivity for wildlife in current and projected wetland landscapes and to discern how that capacity can vary as a function of climatic forcing. We also provide a case study of macrosystems ecology to examine how the patterns and processes that determine habitat connectivity fluctuate across landscapes, regions, and continents.

Assessment of Vegetation Response to Drought in Nebraska Using Terra-MODIS Land Surface Temperature and Normalized Difference Vegetation Index

Eight-day composite Terra-MODIS cumulative LST and NDVI timeseries data were used to analyze the responses of crop and grassland cover types to drought in Nebraska. Four hundred ninety 1 km pixels that included irrigated and non-irrigated corn and soybeans and three grassland cover types were selected across the state of Nebraska. Statistical analyses revealed that the majority of the land cover pixels experienced significantly higher daytime and nighttime LSTs and lower NDVI during the drought-year growing season (p < 0.01). Among the land cover types analyzed, grassland experienced the highest increase in daytime LST and decrease in NDVI.

Relationships between vegetation indices and root zone soil moisture under maize and soybean canopies in the US Corn Belt: a comparative study using a close-range sensing approach

Understanding the relationships between root zone soil moisture and vegetation spectral signals will enhance our ability to manage water resources and monitor drought-related stress in vegetation. In this article, the relationships between vegetation indices (VIs) and in situ soil moisture under maize and soybean canopies were analysed using close-range reflectance data acquired at a rainfed cropland site in the US Corn Belt. Because of the deep rooting depths of maize plants, maize-based VIs exhibited significant correlations with soil moisture at a depth of 100 cm (P < 0.01) and kept soil moisture memory for a long period of time (45 days). Among the VIs applied to maize, the chrolophyll red-edge index (CIred-edge) correlated best with the concurrent soil moisture at 100 cm depth (P < 0.01) for up to 20 day lag periods. The same index showed a significant correlation with soil moisture at a 50 cm depth for lag periods from 10 (P < 0.05) to 60 days (P < 0.01). VIs applied to soybean resulted in statistically significant correlations with soil moisture at the shallower 10 and 25 cm depths, and the correlation coefficients declined with increasing depths. As opposed to maize, soybean held a shorter soil moisture memory as the correlations for all VIs versus soil moisture at 10 cm depth were strongest for the 5 day lag period. Wide dynamic range VI and normalized difference VI performed better in characterizing soil moisture at the 10 and 25 cm depths under soybean canopies when compared with enhanced VI and CIred-edge.

Monitoring invasive species: detecting purple loosestrife and evaluating biocontrol along the Niobrara River, Nebraska.

Geospatial tools and techniques are playing important roles in determining the location and spatial extents of invasive species infestations and in evaluating the performances of various management activities aimed at controlling their spread. In this study, hyperspectral image processing techniques were used to map purple loosestrife and to assess the effectiveness of biological control agents in controlling its infestations along the Niobrara River in Nebraska. Validation based on field survey showed an overall map accuracy of 82.1% and comparison with in situ data on biocontrol release indicated that biocontrol agents were effective in the areas where they were released.

Non-invasive estimation of relative water content in soybean leaves using infrared thermography

Infrared thermography is a useful technology for examining water status in terres- trial vegetation. This research was focused on assessing the water status of soybean plants (Glycinemax (L.) Merrill) using high resolution thermal infrared images. The plants were subjected to a range of moisture stress treatments in order to evaluate the water content in sampled leaves. The plants were irrigated with 8 different treat- ment levels (control (i.e., fully irrigated) and 1 to 7 days of water being withheld). One specific trifoliate was segmented from each of the thermal images for every plant sample, and both mean temperature and Crop Water Stress Index (CWSI) were computed for each plant. Leaf discs were taken from the same trifoliate to gravi- metrically measure relative water content (RWC). RWC had statistically significant correlation coefficients with both CWSI (r = -0.92, n = 56; p�< 0.001) and raw mean temperature (r = -0.84, n = 56; p�< 0.001). Two separate regression models were developed to predict RWC using mean raw trifoliate temperature and CWSI. Our results document that a CWSI-based regression model was better in predicting RWC than a model based on mean raw trifoliate temperature.