Scholarship & Research
Permanent URI for this communityhttps://scholarworks.montana.edu/handle/1/1
Browse
3 results
Search Results
Item Comparison of Supervised Learning and Changepoint Detection for Insect Detection in Lidar Data(MDPI AG, 2023-12) Vannoy, Trevor C.; Sweeney, Nathaniel B.; Shaw, Joseph A.; Whitaker, Bradley M.Concerns about decreases in insect population and biodiversity, in addition to the need for monitoring insects in agriculture and disease control, have led to an increased need for automated, non-invasive monitoring techniques. To this end, entomological lidar systems have been developed and successfully used for detecting and classifying insects. However, the data produced by these lidar systems create several problems from a data analysis standpoint: the data can contain millions of observations, very few observations contain insects, and the background environment is non-stationary. This study compares the insect-detection performance of various supervised machine learning and unsupervised changepoint detection algorithms and provides commentary on the relative strengths of each method. We found that the supervised methods generally perform better than the changepoint detection methods, at the cost of needing labeled data. The supervised learning method with the highest Matthew’s Correlation Coefficient score on the testing set correctly identified 99.5% of the insect-containing images and 83.7% of the non-insect images; similarly, the best changepoint detection method correctly identified 83.2% of the insect-containing images and 84.2% of the non-insect images. Our results show that both types of methods can reduce the need for manual data analysis.Item Effects of agricultural practices on microtopography in restored prairies(Montana State University - Bozeman, College of Agriculture, 2022) Cich, Karli Marie; Chairperson, Graduate Committee: Scott PowellTallgrass prairies are often restored from agricultural fields that have long since removed the natural microtopography. Therefore, our study investigates how these restored prairies recover microtopography. We used precise elevation data collected from LiDAR to compare differences in microtopography between crop fields, fallow fields, newer restored prairies (restored within the last 10 years), and older restored prairies (restored 10 to 20 years ago). We also compared plant biodiversity indices between newer and older restored prairies using nested plot vegetation sampling. Our first research question is how microtopography differs between the sites. Our results show that older restored prairies had the most microtopographic variation, followed by newly restored prairies. Our second research question is how plant biodiversity correlates with microtopographic variation. Our results show that older restored prairies had the highest biodiversity and the highest microtopographic variation. Our third research question is how microtopographic variation changes between 2011 LiDAR data to the 2021 LiDAR data. Our results show an increase in microtopographic variation after a decade once farming has ceased. Synthesis and application: Land managers that have better access to LiDAR data may want to add microtopographic variation as a tool for long-term monitoring ecosystem function in restored tallgrass prairie sites. Our study suggests that microtopographic features appear to increase with the age of a restoration site, providing a visual way to show a restoration’s success.Item Planetary boundary layer height retrieval from a diode-laser-based high spectral resolution lidar(SPIE-Intl Soc Optical Eng, 2022-04) Colberg, Luke; Cruikshank, Owen; Repasky, Kevin S.The planetary boundary layer height (PBLH) is an essential parameter for weather forecasting and climate modeling. The primary methods for obtaining the PBLH include radiosonde measurements of atmospheric parameters and lidar measurements, which track aerosol layers in the lower atmosphere. Radiosondes provide the parameters to determine the PBLH but cannot monitor changes over a diurnal cycle. Lidar instruments can track the temporal variability of the PBLH and account for spatial variability when operated in a network configuration. The networkable micropulse DIAL (MPD) instruments for thermodynamic profiling are based on diode-laser technology that is eye-safe and cost-effective and has demonstrated long-term autonomous operation. We present a retrieval algorithm for determining the PBLH from the quantitative aerosol profiling capability of the high spectral resolution channel of the MPD. The PBLH is determined using a Haar wavelet transform (HWT) method that tracks aerosol layers in the lower atmosphere. The PBLH from the lidar is compared with the PBLH determined from potential temperature profiles from radiosondes. In many cases, good agreement among the PBLH retrievals was seen. However, the radiosonde retrieval often missed the lowest inversion layer when several layers were present, while the HWT could track the lowest layer.