1. Use of remote sensing on turfgrass Soil 4213 course presentation Xi Xiong April 18, 2003

2. Why is it necessary to use sensor on turfgrass management? Stress lead to reduced turfgrass quality. Traditional turfgrass management is based on visible observation. The use of remote sensing may allow the turf manager to see stress before it becomes visible as damage to the turf.

3. Background Stress causes an increase in reflectance in the red and blue portions of the spectrum and decreased reflectance in the near infrared (NIR) region. methods of assessing plant reflectance properties include infrared thermography, multispectral radiometry, and near infrared spectroscopy.

4. Background (continued) Indices such as the Leaf Area Index (LAI) (IR reflectance/Red reflectance) and Normalized Difference Vegetative Index (NDVI) [(IR- R)/(IR+R)] have been correlated with the presence of green biomass and provide a quantitative estimate of general stress on a plant. All of these technologies and knowledge make it possible of detecting turf quality by remote sensing.

5. Use of remote sensing to detect disease on turf

7. Use infrared aerial photographs to detect Dollar Spot and Brown Patch on turfgrass. Data was analyzed by multivariate discriminant analysis as using software provided by Infrasoft International.

8. Use of remote sensing to detect disease on turf

9. From the Dollar Spot experiment, 20 out of 193 samples (10.3%) were classified incorrectly. The data from Brown Patch study showed 29 out of 337 samples (8.6%) were classified incorrectly. These results indicate that VIS-NIRS is a viable method for assessing brown patch and dollar spot severity. However, enough data should be collected before to build the threshold levels of disease which is need to determine the proper fungicide treatment.

10. Use of remote sensing to detect white grub on turf

11. Using GER 1500 field spectrometer (hand held type) to determine the damage severity. using satellite remote sensing and geo- information technologies to predict when and where pest populations are likely to develop over large geographic regions. Using Micro Air Vehicle (MAV) and Unmanned Air Vehicle ( UAV) to detect pest population within field.

12. Use of remote sensing to detect white grub on turf A small UAV that carries a multispectral imaging system with a wingspan of just 6 inches, and the cost is $300 a piece. It will be supposed to use as detecting insect infestations, nematodes, water stress and plant pathogens.

13. Use of remote sensing to detect soil compaction on turf A small hand-held unit in this study analyses 507nm, 559nm,661nm,706n m,760nm,935nm to separate soil compaction.

14. Use of remote sensing to detect soil compaction on turf The result showed that the VIR (visible range) had significant positive correlations to soil penetrometer readings, while readings in most of the NIR (near infrared) portion of the spectrum did not correlate with soil strength. One issue needed to concerned is when the turf was overseeded with other turfgrass species, the strong relationship will decrease.

15. Use of remote sensing to detect nitrogen content on turf Digital image analysis used to determine if it can quantify nitrogen levels in grasses. The technology will use the color analysis software to detect different amounts of chlorophyll in the turf, eventually aimed to reduce fertilizer inputs.

16. Use of remote sensing to detect nitrogen content on turf using vehicle- mounted optical sensors to map turf area received different N fertilizer rate. The NDVI map provide early warning of plant decline, indicate areas in need of N fertilization and the amount of fertilizer required.

17. Use of remote sensing to detect drainage patterns on Golf course A project under development in Clemson University is trying to build a three- dimensional digital elevation model (DEM) to identify drainage patterns and possible areas of runoff problems. The project will use GIS to help locating aerial maps, and eventually optimize chemical application rates and irrigation rates in golf courses.

18. Use of remote sensing to detect turfgrass quality The relationship between normalized difference vegetation index (NDVI) and tall fescue turf color on a 1 to 9 scale with 9 the deepest green and percentage live cover (PLC) on a 0 to 100% scale. Model: NDVI = 0.258 + 0.4867 x log 10 turf color + 1.053 x 10 -7 x PLC 3, R 2 = 0.80, P < 0.0001.

19. Use of remote sensing to detect turfgrass quality The relationship between normalized difference vegetation index (NDVI) and creeping bentgrass turf color on a 1 to 9 scale with 9 the deepest green and percentage live cover (PLC) on a 0 to 100% scale. Model: NDVI = 0.305 + 0.3072 x log 10 turf color + 1.1757 x 10 -7 x PLC 3, R 2 = 0.50, P < 0.0001.

20. The use of Greenseeker on turf

21. The concept of precision turfgrass management The combination of GPS, GIS, sensors and VRT (variable rate technology) will allow turfgrass managers to maintain their turf according to site specific needs, thereby reducing excessive and potentially unnecessary application of pesticides and nutrients.

22. The concept of precision turfgrass management First, accurately scouting to develop zones of management. Second, use GIS software decision-support system and data collected from the site to give a appropriate management operation for each area. Third, use application hardware to precisely deliver management operations to each selected area in the same time frame as normal maintenance operations.

23. Questions? Thank you!