Cheng-Zhi Qin

An adaptive approach to selecting a flow-partition exponent for a multiple-flow-direction algorithm

Most multiple‐flow‐direction algorithms (MFDs) use a flow‐partition coefficient (exponent) to determine the fractions draining to all downslope neighbours. The commonly used MFD often employs a fixed exponent over an entire watershed. The fixed coefficient strategy cannot effectively model the impact of local terrain conditions on the dispersion of local flow. This paper addresses this problem based on the idea that dispersion of local flow varies over space due to the spatial variation of local terrain conditions. Thus, the flow‐partition exponent of an MFD should also vary over space. We present an adaptive approach for determining the flow‐partition exponent based on local topographic attribute which controls local flow partitioning. In our approach, the influence of local terrain on flow partition is modelled by a flow‐partition function which is based on local maximum downslope gradient (we refer to this approach as MFD based on maximum downslope gradient, MFD‐md for short). With this new approach, a steep terrain which induces a convergent flow condition can be modelled using a large value for the flow‐partition exponent. Similarly, a gentle terrain can be modelled using a small value for the flow‐partition exponent. MFD‐md is quantitatively evaluated using four types of mathematical surfaces and their theoretical ‘true’ value of Specific Catchment Area (SCA). The Root Mean Square Error (RMSE) shows that the error of SCA computed by MFD‐md is lower than that of SCA computed by the widely used SFD and MFD algorithms. Application of the new approach using a real DEM of a watershed in Northeast China shows that the flow accumulation computed by MFD‐md is better adapted to terrain conditions based on visual judgement.

Parallelizing flow-accumulation calculations on graphics processing units-From iterative DEM preprocessing algorithm to recursive multiple-flow-direction algorithm

As one of the important tasks in digital terrain analysis, the calculation of flow accumulations from gridded digital elevation models (DEMs) usually involves two steps in a real application: (1) using an iterative DEM preprocessing algorithm to remove the depressions and flat areas commonly contained in real DEMs, and (2) using a recursive flow-direction algorithm to calculate the flow accumulation for every cell in the DEM. Because both algorithms are computationally intensive, quick calculation of the flow accumulations from a DEM (especially for a large area) presents a practical challenge to personal computer (PC) users. In recent years, rapid increases in hardware capacity of the graphics processing units (GPUs) provided in modern PCs have made it possible to meet this challenge in a PC environment. Parallel computing on GPUs using a compute-unified-device-architecture (CUDA) programming model has been explored to speed up the execution of the single-flow-direction algorithm (SFD). However, the parallel implementation on a GPU of the multiple-flow-direction (MFD) algorithm, which generally performs better than the SFD algorithm, has not been reported. Moreover, GPU-based parallelization of the DEM preprocessing step in the flow-accumulation calculations has not been addressed. This paper proposes a parallel approach to calculate flow accumulations (including both iterative DEM preprocessing and a recursive MFD algorithm) on a CUDA-compatible GPU. For the parallelization of an MFD algorithm (MFD-md), two different parallelization strategies using a GPU are explored. The first parallelization strategy, which has been used in the existing parallel SFD algorithm on GPU, has the problem of computing redundancy. Therefore, we designed a parallelization strategy based on graph theory. The application results show that the proposed parallel approach to calculate flow accumulations on a GPU performs much faster than either sequential algorithms or other parallel GPU-based algorithms based on existing parallelization strategies.

Purposive Sampling for Digital Soil Mapping for Areas with Limited Data

Digital soil mapping requires two basic pieces of information: spatial information on the environmental conditions which co-vary with the soil conditions and the information on relationship between the set of environment covariates and soil conditions. The former falls into the category of GIS/remote sensing analysis. The latter is often obtained through extensive field sampling. Extensive field sampling is very labor intensive and costly. It is particularly problematic for areas with limited data. This chapter explores a purposive sampling approach to improve the efficiency of field sampling for digital soil mapping. We believe that unique soil conditions (soil types or soil properties) can be associated with unique combination (configuration) of environmental conditions. We used the fuzzy c-means classification to identify these unique combinations and their spatial locations. Field sampling efforts were then allocated to investigate the soil at the typical locations of these combinations for establishing the relationships between soil conditions and environmental conditions. The established relationships were then used to map the spatial distribution of soil conditions. A case study in China using this approach showed that this approach was effective for digital soil mapping with limited data.

A new approach to the nearest‐neighbour method to discover cluster features in overlaid spatial point processes

When two spatial point processes are overlaid, the one with the higher rate is shown as clustered points, and the other one with the lower rate is often perceived to be background. Usually, we consider the clustered points as feature and the background as noise. Revealing these point clusters allows us to further examine and understand the spatial point process. Two important aspects in discerning spatial cluster features from a set of points are the removal of noise and the determination of the number of spatial clusters. Until now, few methods were able to deal with these two aspects at the same time in an automated way. In this study, we combine the nearest‐neighbour (NN) method and the concept of density‐connected to address these two aspects. First, the removal of noise can be achieved using the NN method; then, the number of clusters can be determined by finding the density‐connected clusters. The complexity for finding density‐connected clusters is reduced in our algorithm. Since the number of clusters depends on the value of k (the kth nearest neighbour), we introduce the concept of lifetime for the number of clusters in order to measure how stable the segmentation results (or number of clusters) are. The number of clusters with the longest lifetime is considered to be the final number of clusters. Finally, a seismic example of the west part of China is used as a case study to examine the validity of our method. In this seismic case study, we discovered three seismic clusters: one as the foreshocks of the Songpan quake (M = 7.2), and the other two as aftershocks related to the Kangding‐Jiulong (M = 6.2) quake and Daguan quake (M = 7.1), respectively. Through this case study, we conclude that the approach we proposed is effective in removing noise and determining the number of feature clusters.

How to Apply the Geospatial Data Abstraction Library (GDAL) Properly to Parallel Geospatial Raster I/O?

Input/output (I/O) of geospatial raster data often becomes the bottleneck of parallel geospatial processing due to the large data size and diverse formats of raster data. The open-source Geospatial Data Abstraction Library (GDAL), which has been widely used to access diverse formats of geospatial raster data, has been applied recently to parallel geospatial raster processing. This article first explores the efficiency and feasibility of parallel raster I/O using GDAL under three common ways of domain decomposition: row-wise, column-wise, and block-wise. Experimental results show that parallel raster I/O using GDAL under column-wise or block-wise domain decomposition is highly inefficient and cannot achieve correct output, although GDAL performs well under row-wise domain decomposition. The reasons for this problem with GDAL are then analyzed and a two-phase I/O strategy is proposed, designed to overcome this problem. A data redistribution module based on the proposed I/O strategy is implemented for GDAL using a message-passing-interface (MPI) programming model. Experimental results show that the data redistribution module is effective.

A strategy for raster-based geocomputation under different parallel computing platforms

The demand for parallel geocomputation based on raster data is constantly increasing with the increase of the volume of raster data for applications and the complexity of geocomputation processing. The difficulty of parallel programming and the poor portability of parallel programs between different parallel computing platforms greatly limit the development and application of parallel raster-based geocomputation algorithms. A strategy that hides the parallel details from the developer of raster-based geocomputation algorithms provides a promising way towards solving this problem. However, existing parallel raster-based libraries cannot solve the problem of the poor portability of parallel programs. This paper presents such a strategy to overcome the poor portability, along with a set of parallel raster-based geocomputation operators (PaRGO) designed and implemented under this strategy. The developed operators are compatible with three popular types of parallel computing platforms: graphics processing unit supported by compute unified device architecture, Beowulf cluster supported by message passing interface (MPI), and symmetrical multiprocessing cluster supported by MPI and open multiprocessing, which make the details of the parallel programming and the parallel hardware architecture transparent to users. By using PaRGO in a style similar to sequential program coding, geocomputation developers can quickly develop parallel raster-based geocomputation algorithms compatible with three popular parallel computing platforms. Practical applications in implementing two algorithms for digital terrain analysis show the effectiveness of PaRGO.

Windowed nearest neighbour method for mining spatio-temporal clusters in the presence of noise

In a spatio-temporal data set, identifying spatio-temporal clusters is difficult because of the coupling of time and space and the interference of noise. Previous methods employ either the window scanning technique or the spatio-temporal distance technique to identify spatio-temporal clusters. Although easily implemented, they suffer from the subjectivity in the choice of parameters for classification. In this article, we use the windowed kth nearest (WKN) distance (the geographic distance between an event and its kth geographical nearest neighbour among those events from which to the event the temporal distances are no larger than the half of a specified time window width [TWW]) to differentiate clusters from noise in spatio-temporal data. The windowed nearest neighbour (WNN) method is composed of four steps. The first is to construct a sequence of TWW factors, with which the WKN distances of events can be computed at different temporal scales. Second, the appropriate values of TWW (i.e. the appropriate temporal scales, at which the number of false positives may reach the lowest value when classifying the events) are indicated by the local maximum values of densities of identified clustered events, which are calculated over varying TWW by using the expectation-maximization algorithm. Third, the thresholds of the WKN distance for classification are then derived with the determined TWW. In the fourth step, clustered events identified at the determined TWW are connected into clusters according to their density connectivity in geographic–temporal space. Results of simulated data and a seismic case study showed that the WNN method is efficient in identifying spatio-temporal clusters. The novelty of WNN is that it can not only identify spatio-temporal clusters with arbitrary shapes and different spatio-temporal densities but also significantly reduce the subjectivity in the classification process.

An experiment using a circular neighborhood to calculate slope gradient from a DEM

The traditional 3 � 3 cell neighborhood used in a focal operation on a raster layer has a square shape that results in a dimensional neighborhood of which the orientation is eventually arbitrary to the physical features represented. This paper presents an experiment using a circular neighborhood to calculate slope gradient. Comparisons of the results from a circular neighborhood with the results from some traditional methods show that (a) for a smooth surface, the result from a circular neighborhood is more accurate than that from a square neighborhood, (b) a circular neighborhood is generally more sensitive to noise in the input DEM than a square neighborhood, and (c) in a validation using field measurements, the circular neighborhood performs better than the square neighborhood when the ratio of user-specified neighborhood size to cell size is high.

A layered approach to parallel computing for spatially distributed hydrological modeling

Distributed hydrological simulations over large watersheds usually require an extensive amount of computation, which necessitates the use of parallel computing. Each type of hydrological model has its own computational characteristics and therefore needs a distinct parallel-computing strategy. In this paper, we focus on one type of hydrological model in which both overland flow routing and channel flow routing are performed sequentially from upstream simulation units to downstream simulation units (referred to as Fully Sequential Dependent Hydrological Models, or FSDHM). There has been little published work on parallel computing for this type of model. In this paper, a layered approach to parallel computing is proposed. This approach divides simulation units into layers according to flow direction. In each layer, there are no upstream or downstream relationships among simulation units. Thus, the calculations on simulation units in the same layer are independent and can be conducted in parallel. A grid-based FSDHM was parallelized with the Open Multi-Processing (OpenMP) library to illustrate the implementation of the proposed approach. Experiments on the performance of this parallel model were conducted on a computer with multi-core Central Processing Units (CPUs) using datasets of different resolutions (30?m, 90?m and 270?m, respectively). The results showed that the parallel performance was higher for simulations with large datasets than with small datasets and the maximum speedup ratio reached 12.49 under 24 threads for the 30?m dataset. A layered approach to parallel computing for distributed hydrological modeling.This approach divides simulation units into layers according to flow direction.In each layer, there are no upstream or downstream relationships among units.A grid-based model was parallelized to illustrate this approach.The speedup ratio reached 12.49 under 24 threads.

Spatial statistical properties and scale transform analyses on the topographic index derived from DEMs in China

The topographic index (TI), frequently used in approximately characterizing the spatial distribution of variable source areas within a watershed, has been widely applied in topography-based land-surface process schemes coupled in regional or global climatic models. The TI concept, however, was originally developed for studying hill-slope scale hydrological processes and was most commonly used in simulations from small- to medium-sized watersheds. It is still questionable whether the TI computed from coarse-resolution digital elevation models (DEMs) for large-scale hydrology and climate studies can effectively reflect the spatial distribution of soil moisture, surface saturation, and runoff generation processes in most areas. In this study, we first proposed an improved multiple flow direction algorithm (IMFD) for accurately computing the TI distribution. We then evaluated the IMFD algorithm quantitatively by using four types of artificial mathematical surfaces. Subsequently, we conducted statistical analyses on the TI distributions computed with IMFD from 90x90m^2 and 1000x1000m^2 resolution DEM blocks sampled from across the whole of China. We found there are linear relationships between the mean TI values computed from the two different resolution DEMs in three sampled blocks of different sizes, i.e., 0.1^ox0.1^o, 0.5^ox0.5^o and 1^ox1^o. Systematic analyses further suggested that the forms of these linear relationships are evidently affected by the algorithm used for the TI computation, while the size, location, and number of the selected TI samples have minor effects on them. Finally, we investigated the influence of DEM resolution on the spatial statistical properties of TI. From the viewpoint of terrain discretization and smoothing effects, we also discussed the mechanism and the reasons causing the similarity on TI at different spatial resolutions.