# Development of a GPS Forest Signal Absorption Coefficient Index
## 5 Conclusions  
In this study, four different GPS SNR models were presented: one model predicts GPS SNR in the open and three models provide methods to predict GPS SNR in forest conditions. Previous work shows how applying predicted SNR values can easily be applied to determine estimated attenuation. While we expected all models to perform well, we were initially surprised that the CCPM did not perform well when modeling all forests together. However, when considering that the CCPM uses only CC to describe the Beer's Law component of signal loss, these results reflect that absorption variation is significant between different tree species and environmental conditions. The SSFIM accounts for this variation nicely according to its associated results.  
This work specifically investigates GPS signal attenuation in different forest conditions. However, gaining a better understanding of techniques to model GPS signal attenuation will lead us to understand how other signals belonging to other technologies may be influenced. Technologies dependent on different cell phone frequencies, satellite communications, Bluetooth, and AM or FM radio transmissions are just a few of the different signals that could benefit from the presented predictive models. This work could also benefit forest growth research that uses light interception predictions.  
When this research began, it was our desire to build on the knowledge of how GPS signals are attenuated in different forest environments. There was no desire or requirement to limit our modeling efforts to any specific technologies, only the desire to try as many different techniques available to us and identify the optimal modeling approach. The use of HSOPs in the modeling process proved fruitful from the beginning of our work. The historic use of HSOPs to estimate LAI led us to using HSOPs in our modeling process. We explored the use of LAI values derived from the HSOPs during the modeling process. However, for each photo there is just one LAI value. In contrast, using the HSOPs to calculate CC values at particular angles from zenith became an additional consideration and proved effective. The results of the research suggest that the only needed measurements are HSOPs and a calibration of the model using GPS observations from a specific forest. Another approach to estimate signal loss in a forest is to reference a given forest to the SSFIM index. Finding forests within the index that have similar attributes would guide the user towards selecting an appropriate model absorption coefficient.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data and wrote the paper.  
This work was supported by the National Geospatial Intelligence Agency under Grant Number NIB8G15107 GS71. We are also grateful to the Forest Biology Research Cooperative and [PERSON] and [PERSON] for their assistance in providing access to the experimental IMPAC study site. A special thanks to the following sites: Camp San Luis Obispo, California; California Polytechnic's Swanton Pacific Ranch Research Center in Davenport, California; and the University of California Berkeley's Sagehen Research center near Truckee, California. The authors declare no conflict of interest and the founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.