Vegetation‐groundwater dynamics at a former uranium mill site following invasion of a biocontrol agent: A time series analysis of Landsat normalized difference vegetation index data

Because groundwater recharge in dry regions is generally low, arid and semiarid environments have been considered well-suited for long-term isolation of hazardous materials (e.g., radioactive waste). In these dry regions, water lost (transpired) by plants and evaporated from the soil surface, collectively termed evapotranspiration (ET), is usually the primary discharge component in the water balance. Therefore, vegetation can potentially affect groundwater flow and contaminant transport at waste disposal sites. We studied vegetation health and ET dynamics at a Uranium Mill Tailings Radiation Control Act (UMTRCA) disposal site in Shiprock, New Mexico, where a floodplain alluvial aquifer was contaminated by mill effluent. Vegetation on the floodplain was predominantly deep-rooted, non-native tamarisk shrubs (Tamarix sp.). After the introduction of the tamarisk beetle (Diorhabda sp.) as a biocontrol agent, the health of the invasive tamarisk on the Shiprock floodplain declined. We used Landsat normalized difference vegetation index (NDVI) data to measure greenness and a remote sensing algorithm to estimate landscape-scale ET along the floodplain of the UMTRCA site in Shiprock prior to (2000-2009) and after (2010-2018) beetle establishment. Using groundwater level data collected from 2011 to 2014, we also assessed the role of ET in explaining seasonal variations in depth to water of the floodplain. Growing season scaled NDVI decreased 30% (p <.001), while ET decreased 26% from the pre- to post-beetle period and seasonal ET estimates were significantly correlated with groundwater levels from 2011 to 2014 (r(2) =.71; p =.009). Tamarisk greenness (a proxy for health) was significantly affected by Diorhabda but has partially recovered since 2012. Despite this, increased ET demand in the summer/fall period might reduce contaminant transport to the San Juan River during this period.Public domain articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Prototype geographic information system for agricultural water quality management

A prototype raster geographic information system (GIS) for agricultural water quality analysis was developed considering the farm as an aggregation of spatial units with homogeneous physical and management characteristics. A crop model that simulates the farm and environment response to different management scenarios was integrated with the GIS. The integrated GIS-model is then run on each homogeneous area. The results of crop yield and chemical leaching are geographically referenced for further display and analysis, and to serve as an input to the decision model. A decision model based on maximization of expected utility (MEU) was also integrated to help assess and evaluate the impacts of fertilizer application on the faun system and the environment. By using utilities for both crop yield and chemical leaching the model circumvents the issue of assigning a monetary value to the environment. Accommodating both the farmers' goals, in terms of higher yield and the well being of the environment, in terms of lower chemical leaching, the model computes the expected utility of each management scenario. The management practice with the maximum expected utility is then recommended. The integrated model was tested with an example of lettuce production in Arizona. Results were compared to published field reports, the model recommendation matched well with the field results. The prototype model was simple to use, and very well integrated, which makes it an alternative to the more complex and expensive coupling of commercial GIS and simulation models.hydrology collectio

Expert system for drip irrigation design

Drip irrigation design is a multi-step routine that has to be carried out in a step by step fashion with each step covering a part of the design process. An expert system has been developed with a set of external programs to accomplish the drip system design. The expertise used in the present expert system knowledge base was induced from engineering handbooks and articles as well as personal consultations. The expert system has been developed in such a way that a variety of cases can be handled. In addition, to simulate the human expert, a new drip irrigation design evaluation factor has been introduced (Design Success Indicator, DSI) in order to estimate the system response on field depending on the confidence of data being used. The results are very promising with respect to the expertise used. However many parts of the knowledge-base have to be fine-tuned in order to reach a highly performing expert system

Global EVI from Spring to Winter

Traditional satellite-based mapping of vegetation vigor and amount is based on the way vegetation interacts with red and infrared light. Occasionally, however, those two signals are not enough. MODIS measures light reflected from Earth at a variety of wavelengths, and the Arizona researchers incorporate the additional information into their Enhanced Vegetation Index (EVI). The EVI has increased sensitivity within very dense vegetation, and it has built-in corrections for several factors that can interfere with the satellite-based vegetation mapping, like smoke and background noise caused by light reflecting off soil. The bi-weekly and monthly vegetation index maps have wide usability by biologists, natural resources managers, and climate modelers. They can track naturally occurring fluctuations in vegetation, such as seasonal changes, as well as those that result from land use change, such as deforestation. The EVI can also monitor changes in vegetation resulting from climate change, such as expansion of deserts or extension of growing seasons. Educational levels: Undergraduate lower division, Undergraduate upper division, Graduate or professional

United States EVI from Summer, 2000 to Spring, 2001.

Traditional satellite-based mapping of vegetation vigor and amount is based on the way vegetation interacts with red and infrared light. Occasionally, however, those two signals are not enough. MODIS measures light reflected from Earth at a variety of wavelengths, and the Arizona researchers incorporate the additional information into their Enhanced Vegetation Index (EVI). The EVI has increased sensitivity within very dense vegetation, and it has built-in corrections for several factors that can interfere with the satellite-based vegetation mapping, like smoke and background noise caused by light reflecting off soil. The bi-weekly and monthly vegetation index maps have wide usability by biologists, natural resources managers, and climate modelers. They can track naturally occurring fluctuations in vegetation, such as seasonal changes, as well as those that result from land use change, such as deforestation. The EVI can also monitor changes in vegetation resulting from climate change, such as expansion of deserts or extension of growing seasons. Educational levels: Undergraduate lower division, Undergraduate upper division, Graduate or professional

The association between the incidence of Lyme disease in the USA and indicators of greenness and land cover

Lyme disease (LD) is the most common vector-borne illness in the USA. Incidence is related to specific environmental conditions such as temperature, metrics of land cover, and vertebrate species diversity. To determine whether greenness, as measured by the Normalized Difference Vegetation Index (NDVI), and other selected indices of land cover were associated with the incidence of LD in the northeastern USA for the years 2000–2018, we conducted an ecological analysis of incidence rates of LD in counties of 15 “high” incidence states and the District of Columbia for 2000–2018. Annual counts of LD by county were obtained from the US Centers for Disease Control and values of NDVI were acquired from the Moderate Resolution Imaging Spectroradiometer instrument aboard Terra and Aqua Satellites. County-specific values of human population density, area of land and water were obtained from the US Census. Using quasi-Poisson regression, multivariable associations were estimated between the incidence of LD, NDVI, land cover variables, human population density, and calendar year. We found that LD incidence increased by 7.1% per year (95% confidence interval: 6.8–8.2%). Land cover variables showed complex non-linear associations with incidence: average county-specific NDVI showed a “u-shaped” association, the standard deviation of NDVI showed a monotonic upward relationship, population density showed a decreasing trend, areas of land and water showed “n-shaped” relationships. We found an interaction between average and standard deviation of NDVI, with the highest average NDVI category; increased standard deviation of NDVI showed the greatest increase in rates. These associations cannot be interpreted as causal but indicate that certain patterns of land cover may have the potential to increase exposure to infected ticks and thereby may contribute indirectly to increased rates of LD. Public health interventions could make use of these results in informing people where risks may be high

ESTIMATION OF THE FRACTION OF CANOPY COVER FROM MULTISPECTRAL DATA TO BE USED IN A WATER SOIL EROSION PREDICTION MODEL

Using the 16 day MODIS (aboard the EOS Terra satellite) 250m NDVI and ground biophysical and spectral measurements we established simple relationships between these parameters and the canopy cover. The canopy cover is used in water erosion models to estimate the amount of soil loss under precipitation events and specific geographic conditions. Two transects, in the grassland part of the Walnut Gulch Experimental Watershed (WGEW) located near the town of Tombstone in Arizona, were established for ground data collection. Ground measurements were performed every 16 days, to coincide with the Terra Satellite overpass. Erosion, in desert environment is a contributing factor to soil degradation and subsequently desertification. Erosion is strongly related with canopy cover, soil parameters, topography, and climate variables. Although ground point estimates of canopy cover are usually used in erosion models, their temporal and spatial variability need to be accounted for. Using MODIS NDVI data, calibrated with field measurements, we were able to estimate the canopy cover using regression analysis. This technique is very simple and properly accounts for the spatial and temporal variability of the canopy cover. We tested this technique with the WEPP erosion model and we found it to be very valuabl