基于多源数据的黑河流域日尺度蒸散发量模拟

植被蒸散发是地表水量平衡和热量平衡的重要参量,也是衡量植被生长状况和作物产量的重要指标,同时也是进行流域水资源优化配置的依据。遥感技术的快速发展使其成为区域尺度蒸散发模拟的重要手段。以黑河流域为例,构建遥感驱动的蒸散发模拟模型,结合多源遥感数据(MODIS、TRMM等)以及GLDAS全球陆面数据同化系统数据,对黑河流域2005、2010、2015年三个时期的潜在蒸散发和实际蒸散发进行了时间尺度为每日、空间尺度为1km的模拟。研究结果表明:潜在蒸散发月际变化明显,从5月开始增长,于7月达到峰值,然后逐渐减少;实际蒸散发在月份之间变化趋势明显,在2015年均达到了最高值;精度检验结果表明本研究采用的两个模型均达到了很好的效果,Kristensen-Jensen模型更适合用于黑河流域。本研究为黑河流域地表特征数据集提供了重要的日尺度蒸散发数据。     

Automatic detection of forest fire disturbance based on dynamic modelling from MODIS time-series observations

Forest disturbances provide an important reference and a basis for studying the carbon cycle, biodiversity, and eco-social development. Remote sensing is a promising data source for monitoring forest ecosystem dynamics and detecting disturbance areas. This research used a seasonal trend method to model Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series from 2007 to 2011 recursively with a fixed-size temporal sliding window and a step length of 1 (i.e. 16 days). Model parameter variations were monitored to detect changes in the structure of the time-series data. Significant changes in the time-series structure were captured as disturbance signals. The method was applied to the 2009 Minto Flats fire in Alaska, USA, and the forest-disturbance detection results obtained using the proposed method essentially agreed with the Monitoring Trends of Burned Severity data set. This result indicates that the proposed method can reliably reveal the occurrence of forest fire disturbances. Moreover, because the model parameter variations reflect the disturbance signal, and the modelling and detection process requires only MODIS NDVI time-series data without any other ancillary ground information, the disturbance area can be detected effectively and automatically.     

新疆轮台奎玉克协海尔古城空间考古综合研究

西域都护府是西汉政府在西域设置的管理机构,位于丝绸之路的战略要地,地处新疆轮台县境内,但城址具体的地理位置目前尚无定论。根据西部干旱区的环境特点,整合了遥感、地球物理、田野调查及空间分析方法,选取了可见光、雷达影像、历史航片,分析不同时相、不同尺度影像上的形状和光谱特征,提取了奎玉克协海尔古城的考古异常区域,发现了疑似壕沟、城墙和城门等遗迹;根据遥感异常区域采用地球物理和考古钻探进行了验证,确认考古目标,初步探明了遗址的内部结构。基于该遗址的地望、距西域各国城址的距离、城 址形制、规格、绝对年代测定等综合考证,提出了奎玉克协海尔古城就是西域都护府的治所所在地的假设。     

An fast simulation tool for fluid animation in VR application based on GPUs

Multimedia Tools and Applications - Realistic and real-time simulation of fluid animation is widely used to the application of virtual reality(VR) such as VR game, special effect in film, augmented...     

Evaluating the generalization ability of convolutional neural networks for built-up area extraction in different cities of China

The difficulty of build-up area extraction is due to complexity of remote sensing data in terms of heterogeneous appearance with large intra-class variations and lower inter-class variations. In order to extract the built-up area from Landsat 8-OLI images provided by Google earth engine(GEE), we propose a convolutional neural networks(CNN) utilizing spatial and spectral information synchronously, which is built in Google drive using Colaboratory-Keras. To train a CNN model with good generalization ability, we choose Beijing, Lanzhou, Chongqing, Suzhou and Guangzhou of China as the training sites, which are very different in term of natural environments. The Arc GIS-Model Builder is employed to automatically select 99 332 samples from the 38-m global built-up production of the European Space Agency(ESA) in 2014. The validate accuracy of the five experimental sites is higher than 90%. We compare the results with other existing building data products. The classification results of CNN can be very good for the details of the built-up areas, and greatly reduce the classification error and leakage error. We applied the well-trained CNN model to extract built-up areas of Chengdu, Xi’an, Zhengzhou, Harbin, Hefei, Wuhan, Kunming and Fuzhou, for the sake of evaluating the generalization ability of the CNN. The fine classification results of the eight sites indicate that the generalization ability of the well-trained CNN is pretty good. However, the extraction results of Xi’an, Zhengzhou and Hefei are poor. As for the training data, only Lanzhou is located in the northwest region, so the trained CNN has poor image classification ability in the northwest region of China.     

室内外一体化最优路径分析算法实现

为了弥补传统路径导航服务在室内立体空间方面的不足,提出了一种室内外一体化的网络数据模型和最优路径分析解决方案。以几何网络模型为基础,设计了一种楼层数据偏移策略,实现室内三维空间路径拓扑模型快速构建和二维可视化表达。对开源pgRouting库内置的高效Dijkstra路径查询函数进行扩展,实现了基于PostgreSQL库的任意两点之间最优路径和转弯方向语义信息查询。最后,利用GeoServer和OpenLayers等开源软件开发了室内外一体化路径查询原型系统,并采用大规模室内外一体化路径网络模型数据进行测试,定性与定量分析对比结果验证了该方法的正确性和高效性。该方法能够最大化兼容城市交通网络数据和成熟的最短路径分析算法,具有普适性与实用性。     

Estimation of Forest Aboveground Biomass and Canopy Heightin Jiangxi Province Using GLAS and Landsat TM Images

Aiming at the problem that optical remote sensing cannot estimate forest biomass exactly because it’s easily affected by the weather and hard to penetrate the canopy of the forest.Using Jiangxi forest as the study area,established forest canopy height and forest biomass model by GLAS waveform data,integrating multispectral data(TM) and filed survey data.The study results show:(1) using waveform feature parameter,terrain feature parameters and field survey data to build forest canopy height model can eliminate the terrain influence and obtain the discrete canopy height.(2) Combined with the NDVI and discrete canopy height can be carried out large scale continuous forest canopy height mapping.(3) Power function relationship between canopy height and forest biomass can be used to estimate forest biomass.In general,large\|footprint LiDAR combined with optical Landsat TM data can give full paly to the advantages of multi\|source remote sensing and improve the precision of forest biomass inversion.     

Panchromatic and multi-spectral image fusion for new satellites based on multi-channel deep model

With the launch and rapid development of new satellites such as WorldView-3, the bands number of multi-spectral images from new satellites is greatly increased. However, the spectral matching between the panchromatic image and multi-spectral images is deteriorated with the existing image fusion methods. In this paper, a novel method based on the multi-channel deep model is proposed to fuse images for new satellites. The deep model is implemented by convolutional neural networks and trained on each band to reduce the impact of spectral range mismatch. The proposed method also preserves the detailed information in multi-spectral images, which is ignored by the traditional methods. It also effectively alleviates the inconvenience for obtaining the remote sensing images by the data augmentation processing. In addition, it reduces the randomness of manual setting parameters using the parameter self-learning. Visual and quantitative assessments of fusion results show that the proposed method clearly improves the fusion quality compared to the state-of-the-art methods.     

地理信息检索关键技术研究综述

互联网上大部分的数字化信息都与地球上的地点和位置关联,信息检索查询中大量地包含地理信息,传统的基于关键字匹配方法没有考虑检索中的空间关系,无法满足此类检索需求。地理信息检索根据地理范围从文档中获取空间语义匹配的地理知识文档,成为国内外信息检索和GIS领域的热点研究方向。提出了一个地理信息检索的基本系统框架,依据该框架对地理信息知识库、地理信息抽取、地理信息检索模型、混合索引和检索可视化等关键性技术进行了分类概括总结。在对已有技术进行深入对比分析的基础上,指出了该领域未来的研究工作和面临的挑战,并提供了大量的参考文献。     

基于栈式自编码的上海地铁短时流量预测

城市公共交通网每时每刻都承载巨大的客流量,客流量的增多为公共交通网和交通智能调度带来了巨大的压力。地铁站点短时的客流预测是智能地铁调度系统中重要的决策基础与技术支持。利用历史刷卡数据,提出了一种基于深度学习的地铁短时客流量预测方法,基于栈式自编码器构建深度神经网络模型,采用自下而上逐层非监督预训练,在预训练结束之后,采用反向传播BP算法自上而下来微调整个网络的参数。利用上海一个月范围内的地铁刷卡记录数据进行实验测试,实验结果优于小波神经网络Wavelet NN与自回归移动平均模型ARIMA。