Conditional simulation of remotely sensed rainfall data using a non-Gaussian v-transformed copula

Quantification of rainfall and its spatial and temporal variability is extremely important for reliable hydrological and meteorological modeling. While rain gauge measurements do not provide reasonable areal representation of rainfall, remotely sensed precipitation estimates offer much higher spatial resolution. However, uncertainties associated with remotely sensed rainfall estimates are not well quantified. This issue is important considering the fact that uncertainties in input rainfall are the main sources of error in hydrologic processes. Using an ensemble of rainfall estimates that resembles multiple realizations of possible true rainfall, one can assess uncertainties associated with remotely sensed rainfall data. In this paper, ensembles are generated by imposing rainfall error fields over remotely sensed rainfall estimates. A non-Gaussian copula-based model is introduced for simulation of rainfall error fields. The v-transformed copula is employed to describe the dependence structure of rainfall error estimates without the influence of the marginal distribution. Simulations using this model can be performed unconditionally or conditioned on ground reference measurements such that rain gauge data are honored at their locations. The presented model is implemented for simulation of rainfall ensembles across the Little Washita watershed, Oklahoma. The results indicate that the model generates rainfall fields with similar spatio-temporal characteristics and stochastic properties to those of observed rainfall data.     

Asphalt mounds and associated biota on the Angolan margin

Release of hydrocarbons from sediments is important in increasing habitat heterogeneity on deep ocean margins. Heterogeneity arises from variation in abiotic and biotic conditions, including changes in substratum, geochemistry, fluid flow, biological communities and ecological interactions. The seepage of heavy hydrocarbons to the seafloor is less well studied than most other cold seep systems and may lead to the formation of asphalt mounds. These have been described from several regions, particularly the Gulf of Mexico. Here, we describe the structure, potential formation and biology of a large asphalt mound province in Block 31SE Angola. A total of 2254 distinct mound features was identified by side-scan sonar, covering a total area of 3.7 km² of seafloor. The asphalt mounds took a number of forms from small (<0.5 m diameter; 13% observations) mounds to large extensive (<50 m diameter) structures. Some of the observed mounds were associated with authigenic carbonate and active seepage (living chemosynthetic fauna present in addition to the asphalt). The asphalt mounds are seabed accumulations of heavy hydrocarbons formed from subsurface migration and fractionation of reservoir hydrocarbons primarily through a network of faults. In Angola these processes are controlled by subsurface movement of salt structures. The asphalt mounds were typically densely covered with epifauna (74.5% of mounds imaged had visible epifauna) although individual mounds varied considerably in epifaunal coverage. Of the 49 non-chemosynthetic megafaunal taxa observed, 19 taxa were only found on hard substrata (including asphalt mounds), 2 fish species inhabited the asphalt mounds preferentially and 27 taxa were apparently normal soft-sediment fauna. Antipatharians (3.6±2.3% s.e.) and poriferans (2.6±1.9% s.e.) accounted for the highest mean percentage of the observed cover, with actinarians (0.9±0.4% s.e.) and alcyonaceans (0.4±0.2% s.e.) covering smaller proportions of the area. Asphalt mounds represent a common and important habitat on several margin systems globally and should be recognised in future environmental assessment and management of these areas.     

Spatiotemporal analysis of vegetation index after typhoons in the mountainous watershed

An extensive land cover change was triggered by a series of typhoons, especially Typhoon Morakot in 2009 in Taiwan. The normalized difference vegetation index (NDVI) series from multiple satellite images were applied to monitor the change processes of land cover. This study applied spatiotemporal analysis tools, including empirical orthogonal functions (EOF), and multiple variograms in analyzing space–time NDVI data, and detected the effects of large chronological disturbances in the characteristics of land cover changes. Spatiotemporal analysis delineated the temporal patterns and spatial variability of NDVI caused by these large typhoons. Results showed that mean of NDVI decreased but spatial variablity of NDVI increased after typhoons in the study area. The EOF can clarify the major component of NDVI variations and identify the core area of the NDVI changes. Various approaches showed consistent results that Typhoon Morakot significantly lowered the NDVI in land cover change process. Furthermore, the spatiotemporal analysis is an effective monitoring tool, which advocates the use of the index for the quantification of land cover change and resilience.     

宽河道线性基元无人机遥感影像镶嵌配准方法

无人机遥感影像由于数量多、单张覆盖空间范围小、几何变形大以及河道内缺少配准控制点等特点,传统的基于特征点的影像镶嵌配准方法在宽河道无人机遥感影像镶嵌配准应用中受到限制.论文根据河道内线性地物丰富的特点,提出基于线性特征的无人机遥感影像镶嵌配准方法.该方法以线性地物边缘直线段为配准基元,通过MIHT算法估算待配准影像与基准影像的变换模型参数.试验结果表明,该方法很好地满足了宽河道无人机遥感影像镶嵌配准的要求,为沿河地质灾害调查提供了影像质量保证.