Prediction of Soil Water Content and Electrical Conductivity using Random Forest Methods with UAV Multispectral and Ground-Coupled Geophysical Data

The volumetric water content (VWC) of soil is a critical parameter in agriculture, as VWC strongly influences crop yield, provides nutrients to plants, and maintains the microbes that are needed for the biological health of the soil. Measuring VWC is difficult, as it is spatially and tempo-rally heterogeneous, and most agricultural producers use point measurements that cannot fully capture this parameter. Electrical conductivity (EC) is another soil parameter that is useful in agricul-ture, since it can be used to indicate soil salinity, soil texture, and plant nutrient availability. Soil EC is also very heterogeneous; measuring EC using conventional soil sampling techniques is very time consuming and often fails to capture the variability in EC at a site. In contrast to the point-based methods used to measure VWC and EC, multispectral data acquired with unmanned aerial vehicles (UAV) can cover large areas with high resolution. In agriculture, multispectral data are often used to calculate vegetation indices (VIs). In this research UAV-acquired VIs and raw multispectral data were used to predict soil VWC and EC. High-resolution geophysical methods were used to acquire more than 41,000 measurements of VWC and 8,000 measurements of EC in 18 traverses across a field that contained 56 experimental plots. The plots varied by crop type (corn, soybeans, and al-falfa) and drainage (no drainage, moderate drainage, high drainage). Machine learning was per-formed using the random forest method to predict VWC and EC using VIs and multispectral data. Prediction accuracy was determined for several scenarios that assumed different levels of knowledge about crop type or drainage. Results showed that multispectral data improved prediction of VWC and EC, and the best predictions occurred when both the crop type and degree of drainage were known, but drainage was a more important input than crop type. Predictions were most accurate in drier soil, which may be due to the lower overall variability of VWC and EC under these conditions. An analysis of which multispectral data were most important showed that NDRE, VARI, and blue band data improved predictions the most. The final conclusions of this study are that inexpensive UAV-based multispectral data can be used to improve estimation of heterogenous soil properties, such as VWC and EC in active agricultural fields. In this study, the best estimates of these properties were obtained when the agriculture parameters in a field were fairly homogeneous (one crop type and the same type of drainage throughout the field), although improvements were observed even when these conditions were not met. The multispectral data that were most useful for prediction were those that penetrated deeper into the soil canopy or were sensitive to bare soil

Prediction of Soil Water Content and Electrical Conductivity using Random Forest Methods with UAV Multispectral and Ground-Coupled Geophysical Data

The volumetric water content (VWC) of soil is a critical parameter in agriculture, as VWC strongly influences crop yield, provides nutrients to plants, and maintains the microbes that are needed for the biological health of the soil. Measuring VWC is difficult, as it is spatially and tempo-rally heterogeneous, and most agricultural producers use point measurements that cannot fully capture this parameter. Electrical conductivity (EC) is another soil parameter that is useful in agricul-ture, since it can be used to indicate soil salinity, soil texture, and plant nutrient availability. Soil EC is also very heterogeneous; measuring EC using conventional soil sampling techniques is very time consuming and often fails to capture the variability in EC at a site. In contrast to the point-based methods used to measure VWC and EC, multispectral data acquired with unmanned aerial vehicles (UAV) can cover large areas with high resolution. In agriculture, multispectral data are often used to calculate vegetation indices (VIs). In this research UAV-acquired VIs and raw multispectral data were used to predict soil VWC and EC. High-resolution geophysical methods were used to acquire more than 41,000 measurements of VWC and 8,000 measurements of EC in 18 traverses across a field that contained 56 experimental plots. The plots varied by crop type (corn, soybeans, and al-falfa) and drainage (no drainage, moderate drainage, high drainage). Machine learning was per-formed using the random forest method to predict VWC and EC using VIs and multispectral data. Prediction accuracy was determined for several scenarios that assumed different levels of knowledge about crop type or drainage. Results showed that multispectral data improved prediction of VWC and EC, and the best predictions occurred when both the crop type and degree of drainage were known, but drainage was a more important input than crop type. Predictions were most accurate in drier soil, which may be due to the lower overall variability of VWC and EC under these conditions. An analysis of which multispectral data were most important showed that NDRE, VARI, and blue band data improved predictions the most. The final conclusions of this study are that inexpensive UAV-based multispectral data can be used to improve estimation of heterogenous soil properties, such as VWC and EC in active agricultural fields. In this study, the best estimates of these properties were obtained when the agriculture parameters in a field were fairly homogeneous (one crop type and the same type of drainage throughout the field), although improvements were observed even when these conditions were not met. The multispectral data that were most useful for prediction were those that penetrated deeper into the soil canopy or were sensitive to bare soil

Locomotion and Behavior of the Ancient Whale \u3cem\u3eGeorgiacetus\u3c/em\u3e

Georgiacetus vogtlensis is one of the most primitive archaeocetes (ancient whales) from North America. Discovered in the northern Atlantic Coastal Plain of Georgia in 1983, it has been interpreted as semi-aquatic, but shows important adaptions toward fully aquatic behavior, including the lack of articulation between the pelvis and sacral vertebrae. Among all protocetids, Georgiacetus is most closely related to the clade Pelagiceti, which includes the common ancestor of all fully aquatic archaeocetes and their descendants, including modern whales. The goal of this study is to elucidate aspects of Georgiacetus locomotion and behavior that are closely related to both semi-aquatic and aquatic lifestyles by comparing the skeletal morphology of the lumbar region of Georgiacetus to modern mammals of a known lifestyle. Unlike previous studies using muscle scarring, this investigation into those changes in skeletal morphology that occurred during the land-to-sea transition of whale evolution was achieved via multivariate analyses of 17 measurements of lumbar vertebrae from more than 30 modern and extinct mammals. Lumbar vertebrae were used because they likely underwent the most drastic changes during the early stages of whale evolution, as dorsomobile archaecetes evolved relatively quickly from dorsostable artiodactyls. To investigate the relative mobility of its lumbar vertebrae, Georgiacetus was included in a Principal Components Analysis (PCA) of lumbar vertebrae from modern and extinct aquatic, semi-aquatic, and terrestrial species. Four PCAs were conducted, one for each of the best preserved lumbar for Georgiacetus. Based upon its close relationship with Pelagiceti, we anticipated that mobility in the lumbar region would be more like that of fully aquatic animals than that of any presumed semi-aquatic animals in the PCA. The results, however, revealed that the lumbar region of Georgiacetus, while similar to those of a semi-aquatic lifestyle, was more dorsostable than other archaeocetes used in the study. Future work will include, a Discriminant Function Analysis to classify Georgiacetus into a semiaquatic or fully aquatic group based upon modern mammals of a known lifestyle, as well as the addition of more aquatic species in the PCA. We hypothesize that Georgiacetus will be classified as semi-aquatic using this method, but that the lumbar vertebrae will show adaptations toward a more fully aquatic behavior