Macrobenthos habitat mapping in a tidal flat using remotely sensed data and a GIS-based probabilistic model

This paper proposes and tests a method of producing macrofauna habitat potential maps based on a weights-of-evidence model (a probabilistic approach) for the Hwangdo tidal flat, Korea. Samples of macrobenthos were collected during field work, and we considered five mollusca species for habitat mapping. A weights-of-evidence model was used to calculate the relative weights of 10 control factors that affect the macrobenthos habitat. The control factors were compiled as a spatial database from remotely sensed data combined with GIS analysis. The relative weight of each factor was integrated as a species potential index (SPI), which produced habitat potential maps. The maps were compared with the surveyed habitat locations, revealing a strong correlation between the potential maps and species locations. The combination of a GIS-based weights-of-evidence model and remote sensing techniques is an effective method in determining areas of macrobenthos habitat potential in a tidal flat setting.     

Rapid formation of hyperpycnal sediment gravity currents offshore of a semi-arid California river

Observations of sediment dispersal from the Santa Clara River of southern California during two moderately sized river discharge events suggest that river sediment rapidly formed a negatively buoyant (hyperpycnal) bottom plume along the seabed within hours of peak discharge. An array of acoustic and optical sensors were placed at three stations 1 km from the Santa Clara River mouth in 10-m water depth during January–February 2004. These combined observations suggest that fluid mud concentrations of suspended sediment (>10 g/l) and across-shore gravity currents (∼5 cm/s) were observed in the lower 20–40 cm of the water column 4–6 h after discharge events. Gravity currents were wave dominated, rather than auto-suspending, and appeared to consist of silt-to-clay sized sediment from the river. Sediment mass balances suggest that 25–50% of the discharged river sediment was transported by these hyperpycnal currents. Sediment settling purely by flocs (∼1 mm/s) cannot explain the formation of the observed hyperpycnal plumes, therefore we suggest that some enhanced sediment settling from mixing, convective instabilities, or diverging plumes occurred that would explain the formation of the gravity currents. These combined results provide field evidence that high suspended-sediment concentrations from rivers (>1 g/l) may rapidly form hyperpycnal sediment gravity currents immediately offshore of river mouths, and these pathways can explain a significant portion of the river-margin sediment budget. The fate of this sediment will be strongly influenced by bathymetry, whereas the fate of the remaining sediment will be much more influenced by ocean currents.     

Combining Lidar Elevation Data and IKONOS Multispectral Imagery for Coastal Classification Mapping

This article studies the effect of airborne lidar (surface) elevation data on the classification of multispectral IKONOS images over a coastal area. The lidar data and IKONOS images are treated as independent multiple bands to conduct the classification. To do so, the lidar elevation data is first resampled to the same ground spacing interval and stretched to the same radiometric range as the IKONOS images. An unsupervised classification based on the ISODATA algorithm is then used to determine a class schema of six classes: road, water, marsh, roof, tree, and sand. Training sites and checking sites are selected over the lidar-IKONOS merged data set for the subsequent supervised classification and quality evaluation. The complete confusion matrices and average quality indices are presented to assess and compare the classification results. It is shown that the inclusion of the lidar elevation data benefits the separation of classes that have similar spectral characteristics, such as roof and road, water and marsh. The overall classification errors, especially the false positive errors, are reduced by up to 50%. Moreover, by using the lidar elevation data, the classification results show more realistic and homogeneous distribution of geographic features. This property will benefit the subsequent vectorization of the classification maps and the integration of the vector data into a geographical information system.     

A spatiotemporal indexing approach for efficient processing of big array-based climate data with MapReduce

Climate observations and model simulations are producing vast amounts of array-based spatiotemporal data. Efficient processing of these data is essential for assessing global challenges such as climate change, natural disasters, and diseases. This is challenging not only because of the large data volume, but also because of the intrinsic high-dimensional nature of geoscience data. To tackle this challenge, we propose a spatiotemporal indexing approach to efficiently manage and process big climate data with MapReduce in a highly scalable environment. Using this approach, big climate data are directly stored in a Hadoop Distributed File System in its original, native file format. A spatiotemporal index is built to bridge the logical array-based data model and the physical data layout, which enables fast data retrieval when performing spatiotemporal queries. Based on the index, a data-partitioning algorithm is applied to enable MapReduce to achieve high data locality, as well as balancing the workload. The proposed indexing approach is evaluated using the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA) climate reanalysis dataset. The experimental results show that the index can significantly accelerate querying and processing (~10× speedup compared to the baseline test using the same computing cluster), while keeping the index-to-data ratio small (0.0328%). The applicability of the indexing approach is demonstrated by a climate anomaly detection deployed on a NASA Hadoop cluster. This approach is also able to support efficient processing of general array-based spatiotemporal data in various geoscience domains without special configuration on a Hadoop cluster.     

基于水动力条件的矿山泥石流成因与特征——以石棉县后沟为例

四川省石棉县后沟是一条典型的高频矿山泥石流沟,坡面矿渣面蚀、沟道矿渣揭底和沟口尾矿侧蚀溃决是泥石流发生的三种主要成因.水动力条件通过对矿渣提供方式的影响而导致矿山泥石流在成因、规模等方面的差异.计算表明,单纯洪水难以对后沟尾矿库稳定性构成威胁,＜10 a一遇泥石流主要由坡面矿渣面蚀和沟道矿渣揭底引发,流量＜79 m3/s,沟口尾矿库基本不参与泥石流活动,泥石流规模以小型为主;10 a一遇和更大规模泥石流开始侧蚀尾矿坝,尾矿失稳垮塌补给泥石流,泥石流规模以中型甚至大型为主;后沟矿山泥石流特征主要表现为物源补给的集中性与固定性,形成过程的易发性与频发性,运动过程的单调性与重复性,成灾过程的相互性与链式性以及防治过程的可控性与多元性五个方面.     

Integration of a Multi-Stage Bulk Materials Acquisition System from the Viewpoint of Dock Operations

Abstract

A multi-stage bulk materials acquisition system is examined from the viewpoint of dock operations. The whole process can be divided into four stages and treated as four subsystems: ships scheduling planning, material yard planning, dock arrangement, and material discharging. Since the structure and the complexity of each subsystem is different, the solution approaches applied are also different with respect to each. To increase operations efficiency and data consistency, an integration of the subsystems by coordinating the operations sequence and data communications for the four subsystems is discussed. The four subsystems are illustrated by using the material docks of China Steel Corporation, and the models developed in this research are also validated. The results indicate that the models developed are capable of deriving solutions better than the existing ones. Although the discussions are confined to China Steel Corporation, the models can be applied to other companies with similar operations.     

3-D Geovisualization of satellite images on smart devices by the integration of spatial DBMS,RESTful API and WebGL

Recent technological advancements in web-based geographic information systems have enabled access to satellite images on smart devices. The Representational State Transfer (REST) architecture overcomes difficulties that are associated with conventional data communications on the web, and the Web Graphics Library (WebGL) can be used as an alternative to web-based three-dimensional geographic visualization (3-D geovisualization) due to its efficient image processing capabilities. This paper describes a 3-D geovisualization system that was developed for satellite images on smart devices by integrating a spatial database management system (DBMS), a RESTful application programming interface (API), and WebGL. Spatiotemporal objects were constructed for time-series satellite images within a DBMS and a RESTful API was built for spatiotemporal queries to the time-series database so that the requested satellite data could be represented in 3-D on smart devices using WebGL. Satellite images that are represented in WebGL give a more realistic 3-D experience when they are combined with terrain data and provide for intuitive observations of the relationships between pixel values and associated geospatial conditions. This paper shows that a creative combination of existing technologies can be used to enhance and display satellite images on smart devices for 3-D geovisualization.