RESULTS Comparing NDVI values for the three years shows a distinct visual difference between the general health of urban landscapes (See Figure 2). Of particular note, a golf course within this study area provided a very effective control. The golf course is not subject to the same regulations as are residences, and may also have access to other water sources (recycled water, ponds). While there was a drop in NDVI for the golf course, it is much less than the urban landscapes (see Figure 3). Overall NDVI values across the entire study area are less in subsequent years as displayed in Table 1. Figure captions go below each figure. Table captions go above each table. Figure 2. NDVI images for 2010 (left) and 2014 (right). Household lawns highlighted. Figure 3. The golf course maintained a high level of NDVI in each year of the study Figure 4. Detail; 2012 NDVI left, 2012 RGB center left, 2014 NDVI center right, 2014 RGB right Table 1. NDVI values as a percentage of total pixels per image. Note that pixel values were reclassified during processing. In this table, NDVI ranges from 1 (or approximately -1 NDVI) and 19 (approximately +1 NDVI). SOURCES OF ERROR The USDA photographs are taken by commercial contractors. As such, different cameras were used in the different years that these images were produced. The differences in the red and NIR bands may result in different NDVI values, which makes a direct comparison problematic. As Table 1 displays, there are much more green pixels present in This is also highlighted by figure 2 which shows the houses with a green tint in 2010, compared to 2012 and 2014 where the houses are yellow to red (figure 2 and figure 4). Other sources of error include mixed pixels, shaded areas, and diverse plant types that may respond to reduced watering differently. Without a direct method to control for all of these variables, a consistent, numerical change value is difficult. While climate data was similar, each year was progressively warmer which can play a very large role in the NDVI (see Table 2). Finally, as the USDA imagery program only provides data every two years, changes in landscape and water conservation efforts are difficult to account for during such long intervals. Table 2. Weather data for Sacramento metropolitan area (Weather-Warehouse) DISCUSSION While 2010 had the highest NDVI values, the numbers appear much higher than in 2012 and The differences in the cameras for each year may have played a very large role in the different NDVI values. Although less NDVI seems likely, it is difficult to attribute the drop directly to water conservation efforts. The difference in NDVI between the 2012 and 2014 images appears much more logical as the overall spread of the pixel values maintains a similar curve. Multi-spectral images also show that some household lawns have indeed turned yellow (figure 4). CONCLUSIONS NDVI can be utilized by many levels of government to corroborate existing water reduction data. The three images used in this project show that urban vegetation is more stressed in successive years which coincides with increasing drought conditions, water conservation efforts, and mandatory restrictions. USDA images can provide a fairly low cost and high resolution method to monitor urban landscapes. However, due to differences in camera optics and the time between photos, these images should only supplement other sources of information. Further study could be useful in determining an optimal level of the “green-ness” of urban landscapes that can coincide with water conservation efforts REFERENCES Jensen, J. R. (2007). Remote Sensing of the Environment. An Earth Resource perspective. Upper Saddle River, NJ: Pearson Prentice Hall. Johnsen, A. R. (2009). Evaluation of Remote Sensing to measure Plant Stress in Creeping Bentgrass (Agrostis Stolonifer L.) Fairways. Crop Science, 49, 2261 – National Drought Mitigation Center. (n.d.). United States Drought Monitor. Retrieved January 29, 2015, from Weather-Warehouse. (n.d.). Past Monthly Weather Data for Sacramento, CA [California] ("Sacramento Executive Arpt") : DECEMBER, Retrieved January 27, 2015, from warehouse.com/ Weier, J. a. (2000, August 30). Measuring Vegetation (NDVI and EVI). Retrieved January 27, 2015, from NASA Earth Observatory: NDVI and Effective Water Conservation Brent Freeman Department of Geosciences, Oregon State University GEO 544 Remote Sensing melt ponds Remote sensing offers governments of all levels a different capability to monitor urban water use. Current record level drought in California highlights growing concerns over water management. The effects of climate change will continue to exacerbate this problem. Local, state and national governments need a method in which they are able to quickly monitor the effects of conservation efforts and policies. While water usage may be measured by water utility companies, Normalized Difference Vegetation Index (NDVI) can provide an additional method for government agencies to monitor where the reductions are coming from. This can help governments in identifying households that overwater their landscapes as well as assist homeowners in understanding their watering habits. Additionally, in Sacramento and other cities across California, not all homes INTRODUCTION Remote sensing offers governments of all levels a different capability to monitor urban water use. Current record level drought in California highlights growing concerns over water management. The effects of climate change will continue to exacerbate this problem. Local, state and national governments need a method in which they are able to quickly monitor the effects of conservation efforts and policies. While water usage may be measured by water utility companies, Normalized Difference Vegetation Index (NDVI) can provide an additional method for government agencies to monitor where the reductions are coming from. This can help governments in identifying households that overwater their landscapes as well as assist homeowners in understanding their watering habits. Additionally, in Sacramento and other cities across California, not all homes have water meters, making enforcement of mandatory water rationing very difficult (Rogers, 2014). While the drought across California has been spreading since before 2010 (See Figure 1) water restrictions have only become mandatory since July 15, The project objectives are to: 1) Explore methods of converting multispectral imagery for use in NDVI calculation; 2) Select, apply, and assess the most feasible methods for the study area and; 3) Compare vegetation health of urban landscapes to increasing drought conditions and water conservation efforts.. Figure 1. California drought levels (2010 left, 2012 middle and 2014 right) have increased in each year that the images used in this study were taken (Drought) BACKGROUND AND PREVIOUS WORK Use of NDVI to monitor plant health has been in use for decades. NDVI takes advantage of the fact that chlorophyll, contained in green plant leaves, absorbs visible light in the spectrum of 0.35 μm μm (Jensen). When plants are under stress, chlorophyll may begin to disappear, and other pigments will begin to absorb different wavelengths of light. Through this change in color, the health of plant life can be observed. Previous studies have shown that NDVI values of turf grass decrease as the water content in the leaves decrease (Johnson et al). Although this previous work has shown the ability to use NDVI values as an indicator of the health of specific plants, it remains to be determined if this process can be translated to reflect water conservation efforts within urban communities. METHODOLOGY US Department of Agriculture produced, four-band (Red, Green, Blue and Near- IR) aerial photos for the years 2010, 2012 and 2014 were purchased and NDVI raster images were created using arcGIS mapping software. NDVI is calculated through the formula: NDVI = (NIR – Red) / (NIR + Red). NDVI values for each image were reclassified into 20 equal intervals between the minimum and maximum value as each image did not have the full range of NDVI (-1 to +1). Each of the three images were taken at approximately the same date in each year (late June - early July). Additionally, weather data was referenced and rainfall amounts remained trace in the months during and before the photos were taken, (See table 2). Abnormally Dry Drought – Moderate Drought – Severe Drought – Extreme Drought - Exceptional MayJuneJuly Precip (in) Mean Temp (Deg C) Precip Mean Temp (Deg C) Precip Mean Temp (Deg C) (in)