Geostatistical approaches to refinement of digital elevation data

Data refinement refers to the processes by which a dataset’s resolution,in particular,the spatial one,is refined,and is thus synonymous to spatial downscaling.Spatial resolution indicates measurement scale and can be seen as an index for regular data support.As a type of change of scale,data refinement is useful for many scenarios where spatial scales of existing data,desired analyses,or specific applications need to be made commensurate and refined.As spatial data are related to certain data support,they can be conceived of as support-specific realizations of random fields,suggesting that multivariate geostatistics should be explored for refining datasets from their coarser-resolution versions to the finerresolution ones.In this paper,geostatistical methods for downscaling are described,and were implemented using GTOPO30 data and sampled Shuttle Radar Topography Mission data at a site in northwest China,with the latter’s majority grid cells used as surrogate reference data.It was found that proper structural modeling is important for achieving increased accuracy in data refinement;here,structural modeling can be done through proper decomposition of elevation fields into trends and residuals and thereafter.It was confirmed that effects of semantic differences on data refinement can be reduced through properly estimating and incorporating biases in local means.

Geometrical feature analysis and disaster assessment of the Xinmo landslide based on remote sensing data

At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle(UAV), and a digital elevation model(DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include Quick Bird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.

A Remote Sensing Model to Estimate Sunshine Duration in the Ningxia Hui Autonomous Region,China

Sunshine duration（SD） is strongly correlated with solar radiation, and is most widely used to estimate the latter. This study builds a remote sensing model on a 100 m × 100 m spatial resolution to estimate SD for the Ningxia Hui Autonomous Region, China. Digital elevation model（DEM） data are employed to reflect topography, and moderate-resolution imaging spectroradiometer（MODIS） cloud products（Aqua MYD06-L2 and Terra MOD06-L2） are used to estimate sunshine percentage. Based on the terrain（e.g.,slope, aspect, and terrain shadowing degree） and the atmospheric conditions（e.g., air molecules, aerosols,moisture, cloud cover, and cloud types）, observation data from weather stations are also incorporated into the model. Verification results indicate that the model simulations match reasonably with the observations,with the average relative error of the total daily SD being 2.21%. Further data analysis reveals that the variation of the estimated SD is consistent with that of the maximum possible SD; its spatial variation is so substantial that the estimated SD differs significantly between the south-facing and north-facing slopes,and its seasonal variation is also large throughout the year.