Mapping the global distribution of forest canopy height is important for estimating forest biomass and terrestrial carbon flux. In this study, we present a global map of mean forest canopy height at 500 m spatial resolution obtained by combining Geoscience Laser Altimeter System (GLAS) data acquired from 2005 to 2006 and 13 ancillary variables, including seven climatic variables and six remote sensing variables (nadir BRDF-adjusted reflectance at red and NIR bands, tree cover, anisotropic factor, accumulated Enhanced Vegetation Index, and elevation). The original contributions of this study include the following: (1) The wavelet method was applied to complement the GLA14 product to identify the ground peak and the top-canopy peak. We found that it was useful for dealing with waveforms with low reconstruction accuracy. (2) GLAS data from the leafless season were not used for nonevergreen forest because the height retrieval results exhibited underestimation and strong variations. (3) The anisotropic factor (ANIF), an indicator related to surface structure, was included as an ancillary variable for the first time and was determined to be important for height modeling in the Asian and North American regions. (4) The balanced random forest (BRF) algorithm was applied to register GLAS mean forest canopy height to a 500 m grid considering the small proportion of extreme height classes (tall and short trees), and it achieved good performance in terms of modeling accuracy (RMSE = 2.75 to 4.45 m) and preserving data variation. An inter-comparison among three global forest height maps [the present study, Lefsky (2010), andSimard et al. (2011)] was implemented in a pixel-by-pixel manner. High agreement (R 2 = 0.73, RMSE = 4.49 m) was determined between the present study and Simard et al., whereas the result from Lefsky was notably different from the other two results (R 2 = 0.14, RMSE = 8.92 m, compared with the present study; R 2 = 0.11, RMSE = 11.19 m, compared with Simard et al.). Large disparities were generally associated with evergreen broadleaf forests in South America, deciduous needleleaf forests in Europe and Russian North Asia, and evergreen needleleaf forests on the West Coast of North America. Differences in the height metric were a main factor affecting the disparities among the three results. Validation against field survey data acquired from the Distributed Active Archive Center indicated the accuracy of our mean forest canopy height map (R 2 = 0.63, RMSE = 4.68 m, n = 59).
In the data of the Mars Advanced Radar for Subsurface and Ionosphere Sounding on board the European Space Agency (ESA) mission Mars Express (MEX), a distinctive type of signals (called the “epsilon signature”), which is similar to that previously detected during radio sounding of the terrestrial F region ionosphere, is found. The signature is interpreted to originate from multiple reflections of electromagnetic waves propagating along sounder pulse‐created, crustal magnetic field‐aligned plasma bubbles (waveguides). The signatures have a low (below 0.5%) occurrence rate and apparent cutoff frequencies 3–5 times higher than the theoretical one for an ordinary mode wave. These properties are explained by the influence of the perpendicular ionospheric plasma density gradient and the sounder pulse frequency on the formation of waveguides.
a b s t r a c tIn this paper, the morphological variations of the M2 layer of the martian ionosphere with the martian seasons and solar zenith angle (SZA) at the terminator are investigated. The data used are the MARSIS (Mars Advanced Radar for Subsurface and Ionosphere Sounding) measurements (approximately 5000 ionograms) that were acquired from 2005 to 2012, which have a SZA P 85°and detect the topside transient layers. A simple, effective data inversion method is developed for the situation in which the upper portion of the height profile is non-monotonic and the observed data are insufficient for adequate reduction. The inverted parameters are subsequently explored using a statistical approach. The results reveal that the main body of the M2 layer (approximately 10 km below the first topside layer) can be well-characterized as a Chapman layer near the terminator (SZA = 85-98°), notwithstanding the high SZA and the presence of the topside layers. The height of the first topside layer tends to be concentrated approximately 60 km (with a standard deviation of $20 km) above the main density peak. The peak density and height of the first topside layer are positively correlated to the density and height of the main peak, respectively. The density and height of the first topside layer appear to be independent of the SZA, but possess seasonal variations that are similar to those of the main layer. The height of the topside layer is greater (by $10 km on average) in the southern spring and summer than in the southern autumn and winter, coinciding with the observation that, in the southern spring and summer, the underlying atmosphere is warmer due to dust heating (e.g., Smith, M.D. [2004]. Icarus 167,[148][149][150][151][152][153][154][155][156][157][158][159][160][161][162][163][164][165]. The statistical regularities of the parameters suggest a possibility that the formation of the topside layers are closely related to the processes of photoionization and diffusion that occur on the topside of the M2 layer. We propose that development of beam-plasma instabilities in the transitional region (between the lower Chapman region and the upper transport-dominating region) is possibly a mechanism that is responsible for the occurrences of the topside layers.
According to the U.S. Department of Energy, a significant portion of energy used in buildings is wasted. If the occupancy quantity in a pre-determined thermal zone is aware, a building automation system (BAS) is able to intelligently adjust the building operation to provide “just-enough” heating, cooling, and ventilation capacities to building users. Therefore, an occupancy counting device that can be widely deployed at low prices with low failure rate, small form-factor, good usability, and conserved user privacy is highly desirable. Existing occupancy detection or recognition sensors (e.g., passive infrared, camera, acoustic, RFID, CO2) cannot meet all these above system requirements. In this work, we present an IoT (Internet of Things) prototype that collects room occupancy information to assist in the operation of energy-efficient buildings. The proposed IoT prototype consists of Lattice iCE40-HX1K stick FPGA boards and Raspberry Pi modules. Two pairs of our prototypes are installed at a door frame. When a person walks through this door frame, blocking of active infrared streams between both pairs of IoT prototypes is detected. The direction of human movement is obtained through comparing occurrence time instances of two obstructive events. Thus, the change in occupancy quantity of a thermal zone is calculated and updated. Besides, an open-source application user interface is developed to allow anonymous users or building automation systems to easily acquire room occupancy information. We carry out a three-month random test of human entry and exit of a thermal zone, and find that the occupancy counting accuracy is 97%. The proposed design is completely made of off-the-shelf electronic components and the estimated cost is less than $160. To investigate the impact on building energy savings, we conduct a building energy simulation using EnergyPlus and find the payback period is approximately 4 months. In summary, the proposed design is miniature, non-intrusive, ease of use, low failure rate, and cost-effective for smart buildings.
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