Multi-spatial-scale observational studies of the Sierra Nevada snowpack using wireless-sensor networks and multi-platform remote-sensing data

The Sierra Nevada winter snowpack is the major water resource for the state of California. To better quantify the input of the water system, we deployed wireless-sensor networks across several basins in the Sierra Nevada. Together with operational and scientific research agencies, we also collected numerous scans of snow-on and snow-off lidar data over several basins in the high Sierra. We mined the lidar data and found how spatial patterns of snow depth are affected by topography and vegetation while elevation is the primary variable, other lidar-derived attributes slope, aspect, northness, canopy penetration fraction explained much of the remaining variance. By segmenting the vegetation into individual trees using lidar point clouds, we were able to extract tree wells from the high resolution snow-depth maps and we found the spatial snow distribution to be affected by the interactions of terrain and canopies. The snowpack is deeper at the downslope direction from the tree bole, however the snowpack at upslope direction being deeper. On sub-meter to meter scales, non-parametric machine-learning models, such as the extra-gradient boosting and the random-forest model, were found to be effective in predicting snow depth in both open and under-canopy areas. At spatial scales that are larger than 100 × 100 m2, we developed a novel approach of using the k-NN algorithm to combine the real-time wireless-sensor-network data with historical spatial products to estimate snow water equivalent spatially. The results suggest only a few historical snow-water-equivalent maps are needed if the historical maps can accurately represent the spatial distribution of snow water equivalent. The residual from the k-NN estimates can be distributed spatially using a Gaussian-process regression model. The entire estimation process can explain 90% of the variability of the spatial SWE

Multi-spatial-scale observational studies of the Sierra Nevada snowpack using wireless-sensor networks and multi-platform remote-sensing data

The Sierra Nevada winter snowpack is the major water resource for the state of California. To better quantify the input of the water system, we deployed wireless-sensor networks across several basins in the Sierra Nevada. Together with operational and scientific research agencies, we also collected numerous scans of snow-on and snow-off lidar data over several basins in the high Sierra. We mined the lidar data and found how spatial patterns of snow depth are affected by topography and vegetation while elevation is the primary variable, other lidar-derived attributes slope, aspect, northness, canopy penetration fraction explained much of the remaining variance. By segmenting the vegetation into individual trees using lidar point clouds, we were able to extract tree wells from the high resolution snow-depth maps and we found the spatial snow distribution to be affected by the interactions of terrain and canopies. The snowpack is deeper at the downslope direction from the tree bole, however the snowpack at upslope direction being deeper. On sub-meter to meter scales, non-parametric machine-learning models, such as the extra-gradient boosting and the random-forest model, were found to be effective in predicting snow depth in both open and under-canopy areas. At spatial scales that are larger than 100 × 100 m2, we developed a novel approach of using the k-NN algorithm to combine the real-time wireless-sensor-network data with historical spatial products to estimate snow water equivalent spatially. The results suggest only a few historical snow-water-equivalent maps are needed if the historical maps can accurately represent the spatial distribution of snow water equivalent. The residual from the k-NN estimates can be distributed spatially using a Gaussian-process regression model. The entire estimation process can explain 90% of the variability of the spatial SWE

Multi-spatial-scale observational studies of the Sierra Nevada snowpack using wireless-sensor networks and multi-platform remote-sensing data

The Sierra Nevada winter snowpack is the major water resource for the state of California. To better quantify the input of the water system, we deployed wireless-sensor networks across several basins in the Sierra Nevada. Together with operational and scientific research agencies, we also collected numerous scans of snow-on and snow-off lidar data over several basins in the high Sierra. We mined the lidar data and found how spatial patterns of snow depth are affected by topography and vegetation while elevation is the primary variable, other lidar-derived attributes slope, aspect, northness, canopy penetration fraction explained much of the remaining variance. By segmenting the vegetation into individual trees using lidar point clouds, we were able to extract tree wells from the high resolution snow-depth maps and we found the spatial snow distribution to be affected by the interactions of terrain and canopies. The snowpack is deeper at the downslope direction from the tree bole, however the snowpack at upslope direction being deeper. On sub-meter to meter scales, non-parametric machine-learning models, such as the extra-gradient boosting and the random-forest model, were found to be effective in predicting snow depth in both open and under-canopy areas. At spatial scales that are larger than 100 × 100 m2, we developed a novel approach of using the k-NN algorithm to combine the real-time wireless-sensor-network data with historical spatial products to estimate snow water equivalent spatially. The results suggest only a few historical snow-water-equivalent maps are needed if the historical maps can accurately represent the spatial distribution of snow water equivalent. The residual from the k-NN estimates can be distributed spatially using a Gaussian-process regression model. The entire estimation process can explain 90% of the variability of the spatial SWE

THE REVIEW OF CURRENT VERTICAL FARMS

With the population explosion and the acceleration of urbanization, the growth of land area and agriculture development has not kept pace with the development. In order to feed more people with fewer resources, vertical farms have emerged. Vertical farms refer to the cultivation of vegetables by hydroponic or aerobic methods in indoor structures such as vertically stacked layers. The advantage of a vertical structure is that it maximizes the use of space and water and soil. Vegetables produced there are fresher and easier to transport because of its urban location. Since there are no harmful insects indoors, there is no need to use pesticides. But early equipment and artificial lighting inputs created obstacles to the beginning of vertical farms. Failure to accurately match demand for continuous production is also a difficulty for farm operations. This paper reviews the current strengths and weaknesses and development status of vertical farms, and proposes possible solutions to existing problems in order to lay the foundation for its development

Canopy Effects on Snow Accumulation: Observations from Lidar, Canonical-View Photos, and Continuous Ground Measurements from Sensor Networks

A variety of canopy metrics were extracted from the snow-off airborne light detection and ranging (lidar) measurements over three study areas in the central and southern Sierra Nevada. Two of the sites, Providence and Wolverton, had wireless snow-depth sensors since 2008, with the third site, Pinecrest having sensors since 2014. At Wolverton and Pinecrest, images were captured and the sky-view factors were derived from hemispherical-view photos. We found the variation of snow accumulation across the landscape to be significantly related to canopy-cover conditions. Using a regularized regression model Elastic Net to model the normalized snow accumulation with canopy metrics as independent variables, we found that about 50 % of snow accumulation variability at each site can be explained by the canopy metrics from lidar

Quantum Secure Direct Communication Based on Dense Coding and Detecting Eavesdropping with Four-Particle Genuine Entangled State

A novel quantum secure direct communication protocol based on four-particle genuine entangled state and quantum dense coding is proposed. In this protocol, the four-particle genuine entangled state is used to detect eavesdroppers, and quantum dense coding is used to encode the message. Finally, the security of the proposed protocol is discussed. During the security analysis, the method of entropy theory is introduced, and two detection strategies are compared quantitatively by comparing the relationship between the maximal information that the eavesdroppers (Eve) can obtain, and the probability of being detected. Through the analysis we can state that our scheme is feasible and secure