Water Cycle Analysis of Social and Economic System in Tarim River Basin

Water cycle includes natural water circulation and social economic system water cycle. The concept of virtual water provides a new method and means for studying social water circulation. This paper is based on the theory of water circulation in social and economic system, using input-output analysis method quantitatively describes the Tarim River basin of social water cycle paths and analysis of water resources management in Tarim River basin sustainable development process of the key issues. The results show that the main sectors of virtual water export in the Tarim River Basin are agriculture, petroleum, natural gas and food industry. Agricultural water accounts for more than 98% of total water consumption, most of which is transferred to the food and textile industries, and the food industry export water from the agricultural sector. Shandong Province is the largest virtual water transport area in the Tarim River Basin. The main sector of virtual water input in the Tarim River Basin is the metallurgical industry. Finally, in view of the problems arising from the inter-industrial and inter-regional social water circulation in the Tarim River basin, the paper puts forward the ways and strategies of regulating agricultural and industrial water use in the Tarim River Basin.     

Intraseasonal oscillation of the rainfall variability over Rwanda and evaluation of its subseasonal forecasting skill

非洲中东部地区的经济主要依靠自给农业支撑,该地区农业经济对降水的变化尤为敏感.本文以卢旺达为例,观测分析指出卢旺达的次季节降雨主要集中在10-25天;根据次季节尺度降水变率的单点相关方法,发现卢旺达的次季节降水变率和周围区域变化一致;进一步合成结果显示该地区次季节降水变率与异常西风有关,这可追溯到赤道地区西传的赤道Rossby波.最后,本文评估了当前动力模式ECMWF对 卢旺达地区(即非洲中东部)次季节降水变率的预报能力,发现EC模式在对该区域降水和相关风场指数的预报技巧都在18天左右,且预报技巧表现出一定的年际差异,这可能与热带太平洋的背景海温信号有关.该工作增进了当 前对非洲中东部地区的次季节降水变率和预测水平的认知,并且对该地区国家粮食安全和防灾减灾具有启示性意义.     

Intraseasonal oscillation of the southwest monsoon over Sri Lanka and evaluation of its subseasonal forecast skill

斯里兰卡的雨季发生于5-9月间,主要受西南季风的控制.本文发现该地区的西南季风降水存在很强的次季节变率,主导周期为10-35天.降水的季节内变化与西传的异常气旋有关.进一步,利用S2S比较计划中欧洲中心的数值预报模式(ECMWF)提供的回报试验数据,评估了当今动力模式对斯里兰卡西南季风次季节变化的预报技巧.结果显示,对季风指数的预测技巧超过30天,而对降水指数的预测技巧大约两周,且模式的预报技巧具有明显的年际差异.分析表明,能否正确模拟出大尺度环流对热带对流的响应是影响斯里兰卡降水预测的重要因子.     

A virtual globe-based time-critical adaptive visualization method for 3D city models

3D city models, which are important items of content on the virtual globe, are characterized by complicated structures and large amounts of data. These factors make the visualization of 3D city models highly dependent upon the performance of computer hardware. Thus, achieving the efficient rendering of 3D city models using different hardware performance levels represents one of the key problems currently facing researchers. This paper proposes a time-critical adaptive visualization method that first estimates the possible rendering time for each model according to the data structure of the model in addition to the CPU/GPU performance of the computer. It then dynamically adjusts the rendering level for each model based on the results of an estimation of the rendering time to ensure that the final scene can be completed within a given time. To verify the effectiveness and flexibility of this method, it is applied using different computers. The results show that the adaptive visualization method presented in this paper not only can adapt to computers with different levels of performances but also demonstrates an obvious improvement in the time estimation precision, visual effects, and optimization speed relative to existing adaptive visualization methods.     

2-D delineation of individual citrus trees from UAV-based dense photogrammetric surface models

One of the challenges of remote sensing and computer vision lies in the three-dimensional (3-D) reconstruction of individual trees by using automated methods through very high-resolution (VHR) data sets. However, a successful and complete 3-D reconstruction relies on precise delineation of the trees in two dimensions. In this paper, we present an original approach to detect and delineate citrus trees using unmanned aerial vehicles based on photogrammetric digital surface models (DSMs). The symmetry of the citrus trees in a DSM is handled by an orientation-based radial symmetry transform which is computed in a unique way. Next, we propose an efficient strategy to accurately build influence regions of each tree, and then we delineate individual citrus trees through active contours by taking into account the influence region of each canopy. We also present two efficient strategies to filter out erroneously detected canopy regions without having any height thresholds. Experiments are carried out on eight test DSMs composed of different types of citrus orchards with varying densities and canopy sizes. Extensive comparisons to the state-of-the-art approaches reveal that our proposed approach provides superior detection and delineation performances through supporting a nice balance between precision and recall measures.     

Evaluation of effectiveness of three fuzzy systems and three texture extraction methods for building damage detection from post-event LiDAR data

Building damage maps after disasters can help us to better manage the rescue operations. Researchers have used Light Detection and Ranging (LiDAR) data for extracting the building damage maps. For producing building damage maps from LiDAR data in a rapid manner, it is necessary to understand the effectiveness of features and classifiers. However, there is no comprehensive study on the performance of features and classifiers in identifying damaged areas. In this study, the effectiveness of three texture extraction methods and three fuzzy systems for producing the building damage maps was investigated. In the proposed method, at first, a pre-processing stage was utilized to apply essential processes on post-event LiDAR data. Second, textural features were extracted from the pre-processed LiDAR data. Third, fuzzy inference systems were generated to make a relation between the extracted textural features of buildings and their damage extents. The proposed method was tested across three areas over the 2010 Haiti earthquake. Three building damage maps with overall accuracies of 75.0%, 78.1% and 61.4% were achieved. Based on outcomes, the fuzzy inference systems were stronger than random forest, bagging, boosting and support vector machine classifiers for detecting damaged buildings.     

Geometric-constrained multi-view image matching method based on semi-global optimization

Targeting at a reliable image matching of multiple remote sensing images for the generation of digital surface models, this paper presents a geometric-constrained multi-view image matching method, based on an energy minimization framework. By employing a geometrical constraint, the cost value of the energy function was calculated from multiple images, and the cost value was aggregated in an image space using a semi-global optimization approach. A homography transform parameter calculation method is proposed for fast calculation of projection pixel on each image when calculating cost values. It is based on the known interior orientation parameters, exterior orientation parameters, and a given elevation value. For an efficient and reliable processing of multiple remote sensing images, the proposed matching method was performed via a coarse-to-fine strategy through image pyramid. Three sets of airborne remote sensing images were used to evaluate the performance of the proposed method. Results reveal that the multi-view image matching can improve matching reliability. Moreover, the experimental results show that the proposed method performs better than traditional methods.     

Modelling coastal land use change by incorporating spatial autocorrelation into cellular automata models

This paper presents a spatial autoregressive (SAR) method-based cellular automata (termed SAR-CA) model to simulate coastal land use change, by incorporating spatial autocorrelation into transition rules. The model captures the spatial relationships between explained and explanatory variables and then integrates them into CA transition rules. A conventional CA model (LogCA) based on logistic regression (LR) was studied as a comparison. These two CA models were applied to simulate urban land use change of coastal regions in Ningbo of China from 2000 to 2015. Compared to the LR method, the SAR model yielded smaller accumulated residuals that showed a random distribution in fitting the CA transition rules. The better-fitting SAR model performed well in simulating urban land use change and scored an overall accuracy of 85.3%, improving on the LogCA model by 3.6%. Landscape metrics showed that the pattern generated by the SAR-CA model has less difference with the observed pattern.     

Novel fusion approach on automatic object extraction from spatial data: case study Worldview-2 and TOPO5000

The automatic extraction of information content from remotely sensed data is always challenging. We suggest a novel fusion approach to improve the extraction of this information from mono-satellite images. A Worldview-2 (WV-2) pan-sharpened image and a 1/5000-scaled topographic vector map (TOPO5000) were used as the sample data. Firstly, the buildings and roads were manually extracted from WV-2 to point out the maximum extractable information content. Subsequently, object-based automatic extractions were performed. After achieving two-dimensional results, a normalized digital surface model (nDSM) was generated from the underlying digital aerial photos of TOPO5000, and the automatic extraction was repeated by fusion with the nDSM to include individual object heights as an additional band for classification. The contribution was tested by precision, completeness and overall quality. Novel fusion technique increased the success of automatic extraction by 7% for the number of buildings and by 23% for the length of roads.