An Efficient Group-Based Replica Placement Policy for Large-Scale Geospatial 3D Raster Data on Hadoop.

Geospatial three-dimensional (3D) raster data have been widely used for simple representations and analysis, such as geological models, spatio-temporal satellite data, hyperspectral images, and climate data. With the increasing requirements of resolution and accuracy, the amount of geospatial 3D raster data has grown exponentially. In recent years, the processing of large raster data using Hadoop has gained popularity.

Precipitation Changes in the Three Gorges Reservoir Area and the Relationship with Water Level Change.

As the largest hydroelectric project worldwide, previous studies indicate that the Three Gorges Dam (TGD) affects the local climate because of the changes of hydrological cycle caused by the impounding and draining of the TGD. However, previous studies do not analyze the long-term precipitation changes before and after the impoundment, and the variation characteristics of local precipitation remain elusive. In this study, we use precipitation anomaly data derived from the CN05.

Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression Methods.

Leaf area index (LAI) is an important biophysical parameter, which can be effectively applied in the estimation of vegetation growth status. At present, amounts of studies just focused on the LAI estimation of a single plant type, while plant types are usually mixed rather than single distribution. In this study, the suitability of GF-1 data for multi-species LAI estimation was evaluated by using Gaussian process regression (GPR), and a look-up table (LUT) combined with a PROSAIL radiative transfer model.

Effect of different regression algorithms on the estimating leaf parameters based on selected characteristic wavelengths by using the PROSPECT model.

In this study, the characteristic wavelengths of leaf biochemical parameters (including carotenoid content, chlorophyll ${a} + { b}$a+b content, dry matter content, equivalent water thickness, and leaf structure parameter) were obtained through a sensitivity analysis based on a physical model. Then, performance of the selected characteristic wavelengths for monitoring leaf biochemical contents (LBC) was analyzed by using the following six popular regression algorithms: random forest, backpropagation neural network, support vector regression, radial basic function neural network, partial least-squares regression, and Gaussian process regression of different parameter values/kernel functions/training functions. In addition, the optimal parameters of each regression algorithm for estimating LBC were determined.

Permafrost Deformation Monitoring Along the Qinghai-Tibet Plateau Engineering Corridor Using InSAR Observations with Multi-Sensor SAR Datasets from 1997-2018.

As the highest elevation permafrost region in the world, the Qinghai-Tibet Plateau (QTP) permafrost is quickly degrading due to global warming, climate change and human activities. The Qinghai-Tibet Engineering Corridor (QTEC), located in the QTP tundra, is of growing interest due to the increased infrastructure development in the remote QTP area. The ground, including the embankment of permafrost engineering, is prone to instability, primarily due to the seasonal freezing and thawing cycles and increase in human activities.

Mapping and characterizing displacements of active loess slopes along the upstream Yellow River with multi-temporal InSAR datasets.

Landslides and debris flows in the Loess Plateau pose great threats to human lives and man-made infrastructure, such as buildings and expressways. Thus, the detection and monitoring of the stability of slopes are crucial in geohazard prevention and management. In this study, the time series synthetic aperture radar interferometry (InSAR) analysis method that combines persistent scatters (PSs) and distributed scatters (DSs) is employed to detect and map active slopes along the upstream Yellow River from the Longyang Gorge dam to the Lijia Gorge dam using one ALOS PALSAR data stack from 2006 to 2011 and two Sentinel-1 data stacks from 2015 to 2017.

Variational Pansharpening for Hyperspectral Imagery Constrained by Spectral Shape and Gram⁻Schmidt Transformation.

Image pansharpening can generate a high-resolution hyperspectral (HS) image by combining a high-resolution panchromatic image and a HS image. In this paper, we propose a variational pansharpening method for HS imagery constrained by spectral shape and Gram⁻Schmidt (GS) transformation. The main novelties of the proposed method are the additional spectral and correlation fidelity terms.

Mechanical Metamaterials Foams with Tunable Negative Poisson's Ratio for Enhanced Energy Absorption and Damage Resistance.

Systematic and deep understanding of mechanical properties of the negative Poisson's ratio convex-concave foams plays a very important role for their practical engineering applications. However, in the open literature, only a negative Poisson's ratio effect of the metamaterials convex-concave foams is simply mentioned. In this paper, through the experimental and finite element methods, effects of geometrical morphology on elastic moduli, energy absorption, and damage properties of the convex-concave foams are systematically studied.