Dopamine‐Related Genotypes and Physical Activity Change During an Intervention: The Lifestyle Interventions and Independence for Elders Study

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145351/1/jgs15369.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145351/2/jgs15369_am.pd

Alternative Dimensions: Lentil Underground: Renegade Farmers and the Future of Food in America

First paragraphs: Lentil Underground: Renegade Farmers and the Future of Food in America is author Liz Carlisle's first book and is based on ethnographic research she undertook while a doctoral student at the University of California, Berkeley. Carlisle traces the history of organic agriculture in Montana to the present day, with a richly written narrative that makes for an easy read.  In this book Carlisle explores the literal, below-the-ground workings of nitrogen fixation and legume crops, with lentils taking center stage as the stars of the show. At the same time, Carlisle explores the emergence of a community of tenacious organic farmers centered around Dave Oien, a founding farmer and CEO of Timeless Seeds. Dave Oien serves as the hardworking and tireless hero of this tale. The author selected the Timeless Seeds growers as a point of interest and resistance in the food system. She examines their work as climatic systems shift, as they navigate systems of governance, and as they connect with buyers, sellers, and consumers. For the Timeless Seeds crew, systems thinking is a central philosophy of their work, so they become engaged in shaping the physical, social, and macrolevel environments toward diversity and sustainability...

UAS TOPOGRAPHIC MAPPING WITH VELODYNE LiDAR SENSOR

Unmanned Aerial System (UAS) technology is nowadays willingly used in small area topographic mapping due to low costs and good quality of derived products. Since cameras typically used with UAS have some limitations, e.g. cannot penetrate the vegetation, LiDAR sensors are increasingly getting attention in UAS mapping. Sensor developments reached the point when their costs and size suit the UAS platform, though, LiDAR UAS is still an emerging technology. One issue related to using LiDAR sensors on UAS is the limited performance of the navigation sensors used on UAS platforms. Therefore, various hardware and software solutions are investigated to increase the quality of UAS LiDAR point clouds. This work analyses several aspects of the UAS LiDAR point cloud generation performance based on UAS flights conducted with the Velodyne laser scanner and cameras. The attention was primarily paid to the trajectory reconstruction performance that is essential for accurate point cloud georeferencing. Since the navigation sensors, especially Inertial Measurement Units (IMUs), may not be of sufficient performance, the estimated camera poses could allow to increase the robustness of the estimated trajectory, and subsequently, the accuracy of the point cloud. The accuracy of the final UAS LiDAR point cloud was evaluated on the basis of the generated DSM, including comparison with point clouds obtained from dense image matching. The results showed the need for more investigation on MEMS IMU sensors used for UAS trajectory reconstruction. The accuracy of the UAS LiDAR point cloud, though lower than for point cloud obtained from images, may be still sufficient for certain mapping applications where the optical imagery is not useful

MONITORING OF FLUVIAL TRANSPORT IN THE MOUNTAIN RIVER BED USING TERRESTRIAL LASER SCANNING

The fluvial transport is the surface process that has a strong impact on the topography changes, especially in mountain areas. Traditional hydrological measurements usually give a good understanding of the river flow, however, the information of the bedload movement in the rivers is still insufficient. In particular, there is limited knowledge about the movement of the largest clasts, i.e. boulders. This investigation addresses mentioned issues by employing Terrestrial Laser Scanning (TLS) to monitor annual changes of the mountain river bed. The vertical changes were estimated based on the Digital Elevation Model (DEM) of difference (DoD) while transported boulders were identified based on the distances between point clouds and RGB-coloured points. Combined RGB point clouds allowed also to measure 3D displacements of boulders. The results showed that the highest dynamic of the fluvial process occurred between years 2012-2013. Obtained DoD clearly indicated alternating zones of erosion and deposition of the sediment finer fractions in the local sedimentary traps. The horizontal displacement of the rock material in the river bed showed high complexity resulting in the displacement of large boulders (major axis about 0.8 m) for the distance up to 2.3 m

DETERMINING GEOMETRIC PARAMETERS OF AGRICULTURAL TREES FROM LASER SCANNING DATA OBTAINED WITH UNMANNED AERIAL VEHICLE

The estimation of dendrometric parameters has become an important issue for agriculture planning and for the efficient management of orchards. Airborne Laser Scanning (ALS) data is widely used in forestry and many algorithms for automatic estimation of dendrometric parameters of individual forest trees were developed. Unfortunately, due to significant differences between forest and fruit trees, some contradictions exist against adopting the achievements of forestry science to agricultural studies indiscriminately. In this study we present the methodology to identify individual trees in apple orchard and estimate heights of individual trees, using high-density LiDAR data (3200 points/m2) obtained with Unmanned Aerial Vehicle (UAV) equipped with Velodyne HDL32-E sensor. The processing strategy combines the alpha-shape algorithm, principal component analysis (PCA) and detection of local minima. The alpha-shape algorithm is used to separate tree rows. In order to separate trees in a single row, we detect local minima on the canopy profile and slice polygons from alpha-shape results. We successfully separated 92 % of trees in the test area. 6 % of trees in orchard were not separated from each other and 2 % were sliced into two polygons. The RMSE of tree heights determined from the point clouds compared to field measurements was equal to 0.09 m, and the correlation coefficient was equal to 0.96. The results confirm the usefulness of LiDAR data from UAV platform in orchard inventory