共查询到20条相似文献，搜索用时 609 毫秒
1.
基于2DSTMON(2-Dimensional spatio-temporal indexfor moving objects in network)二维时空数据模型,提出了一种新的二维网络中移动对象的时空索引2DSTI及其时空查询算法。这种二维时空索引机制简单且易于实现,支持当前轨迹数据和历史轨迹数据的大量时空查询操作。在此基础上,通过实验实现并验证了二维时空索引机制及其时空查询算法。 相似文献
2.
Handling Grammatical Errors, Ambiguity and Impreciseness in GIS Natural Language Queries 总被引：1，自引：0，他引：1
F Wang 《Transactions in GIS》2003,7(1):103-121
A natural language query interface is very desirable for a geographic information system (GIS). It converts natural language queries (sentences) into a formal query language. A key task in building a natural language query interface is handling uncertainty and impreciseness. Grammatical errors and parsing ambiguity cause uncertainty. A natural language query may contain imprecise terms that have to be processed before an executable query can be generated. In this paper, a three–step technique is presented that can be used to correct grammatically incomplete natural language queries, solve parsing ambiguity, and translate imprecise conditions into executable selection conditions. 相似文献
3.
针对三维虚拟地球中实时GIS数据组织、管理与动态可视化的要求,设计了三维虚拟地球中移动对象的时空数据组织方法。提出了基于HR-tree扩展的时空索引方法,实现了对虚拟地球中移动对象的检索与查询,并基于三维虚拟地球平台对本文方法进行了实验,结果验证了本文方法的有效性和可行性。 相似文献
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Soil respiration (Rs) is of great importance to the global carbon balance. Remote sensing of Rs is challenging because of (1) the lack of long-term Rs data for model development and (2) limited knowledge of using satellite-based products to estimate Rs. Using 8-years (2002–2009) of continuous Rs measurements with nonsteady-state automated chamber systems at a Canadian boreal black spruce stand (SK-OBS), we found that Rs was strongly correlated with the product of the normalized difference vegetation index (NDVI) and the nighttime land surface temperature (LSTn) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The coefficients of the linear regression equation of this correlation between Rs and NDVI × LSTn could be further calibrated using the MODIS leaf area index (LAI) product, resulting in an algorithm that is driven solely by remote sensing observations. Modeled Rs closely tracked the seasonal patterns of measured Rs and explained 74–92% of the variance in Rs with a root mean square error (RMSE) less than 1.0 g C/m2/d. Further validation of the model from SK-OBS site at another two independent sites (SK-OA and SK-OJP, old aspen and old jack pine, respectively) showed that the algorithm can produce good estimates of Rs with an overall R2 of 0.78 (p < 0.001) for data of these two sites. Consequently, we mapped Rs of forest landscapes of Saskatchewan using entirely MODIS observations for 2003 and spatial and temporal patterns of Rs were well modeled. These results point to a strong relationship between the soil respiratory process and canopy photosynthesis as indicated from the greenness index (i.e., NDVI), thereby implying the potential of remote sensing data for detecting variations in Rs. A combination of both biological and environmental variables estimated from remote sensing in this analysis may be valuable in future investigations of spatial and temporal characteristics of Rs. 相似文献
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Thaumastocoris peregrinus (T. peregrinus) is a sap sucking insect that feeds on Eucalyptus leaves. It poses a threat to the forest industry by reducing the photosynthetic ability of the tree, resulting in stunted growth and even death of severely infested trees. Remote sensing techniques offer the potential to detect and map T. peregrinus infestations in plantation forests using current operational hyperspectral scanners. This study resampled field spectral data measured from a field spectrometer to the band settings of the Hyperion sensor in order to assess its potential in predicting T. peregrinus damage. Normalized indices based on NDVI ratios were calculated using the resampled visible and near-infrared bands of the Hyperion sensor to assess its utility in predicting T. peregrinus damage using Partial Least Squares (PLS) regression. The top 20 normalized indices were based on specific biochemical absorption features that predicted T. peregrinus damage with a mean bootstrapped R2 value of 0.63 on an independent test dataset. The top 20 indices were located in the near-infrared region between 803.3 nm and 894.9 nm. Twenty three previously published hyperspectral indices which have been used to assess stress in vegetation were also used to predict T. peregrinus damage and resulted in a mean bootstrapped R2 value of 0.59 on an independent test dataset. The datasets were combined to assess its collective strength in predicting T. peregrinus damage and significant indices were chosen based on variable importance scores (VIP) and were then entered into a PLS model. The indices chosen by VIP predicted T. peregrinus damage with a mean bootstrapped R2 value of 0.71 on an independent test dataset. A greedy backward variable selection model was further tested on the VIP selected indices in order to find the best subset of indices with the best predictive accuracy. The greedy backward variable selection model identified 3 indices and performed the best by predicting damage with an R2 value of 0.74 with the lowest RMSE of 1.30% on an independent test dataset. The best three indices identified include the anthocyanin reflectance index, carotenoid reflectance index and the normalized index calculated at 864.4 and 884.7 nm. Individual relationships between these indices and T. peregrinus damage indicate that high correlations are obtained with the inclusion of a few severely infested trees in the sample size. When the severely infested trees were removed from the study, the normalized index (864.4 and 884.7 nm) and the anthocyanin reflectance index still yielded significant correlations at the 99% confidence interval. This study indicates the significance of normalized indices and spectral indices calculated from the visible and near-infrared bands in hyperspectral data for the prediction of T. peregrinus damage. 相似文献
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The uncertainties involved in remote sensing inversion of CDOM (Colored Dissolved Organic Matter) were analyzed in estuarine and coastal regions of three North American rivers: Mississippi, Hudson, and Neponset. Water optical and biogeochemical properties, including CDOM absorption and above-surface spectra, were collected in very high resolution. CDOM’s concentrations (ag(440), absorption coefficient at 440 nm) were inverted from EO-1 Hyperion images, using a quasi-analytical algorithm for CDOM (QAA-CDOM). Uncertainties are classified to five levels, in which the underwater measurement uncertainty (level 1), image preprocessing uncertainty (level 4) and inverse model uncertainty (level 5) were evaluated. Results indicate that at level 1, in situ CDOM measurement is significant with 0.1 in the unit of QSU and 0.01 in the unit of ag(440) (m−1). At level 4, surface wave is a potential uncertainty source for high-resolution images in estuarine and coastal regions. The remote sensing reflectance of wavy water is about 10 times of the truth. At level 5, the overall uncertainty of QAA-CDOM inversion is 0.006 m−1, with accuracy R2 = 0.77, k = 1.1 and RMSElog = 0.33 m−1. The correlations between uncertainties and other water properties indicate that the large uncertainty in some rivers, such as the Neponset and Atchafalaya, might be caused by high-concentration chlorophyll or sediments. The relationships among the three level uncertainties show that the level 1 uncertainty generally does not propagate into level 4 and 5, but the large uncertainty at level 4 usually introduce large uncertainty at level 5. 相似文献
8.
Hui Sheng 《Journal of the Indian Society of Remote Sensing》2014,42(1):139-147
In this study, a MDC3A algorithm (Multi-Data Crossing Algorithm for Accuracy Accessing) was developed for accessing the accuracy of chl-a (chlorophyll-a) retrieval model in case of no sufficient available in situ measurements. Three simple estimation algorithms of chl-a concentration, i.e., two-band algorithm, three-band algorithm and four-band algorithm, were used as input dataset of MDC3A algorithm to illuminate its performance. These three simple algorithms were calibrated and validated by calibration and validation dataset collected on October 27–28, 2003. According to model calibration and validation results, it was found that the four-band algorithm (R 2?=?0.8676) had a superior performance to the two-band (R 2?=?0.5061) and three-band (R 2?=?0.5142) algorithm. The uncertainties in modeling prediction of these three simple algorithms were underestimated as 0.07 %, 0.04 % and 8.07 % for calibration dataset and 8.38 %, 9.33 % and 9.37 % for validation dataset by MDC3A algorithm through comparison with in situ measurements. Because the MDC3A algorithm was able to detect the random errors from measured values, but had an inadequate ability to detect systematical errors and gross errors from measured values. The uncertainty estimated by MDC3A algorithm was usually lower than that estimated by in situ measurements. 相似文献
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Arsenic stress induces in subtle changes in the canopy chlorophyll content (CCC). Therefore, the establishment of a spectral index that is sensitive to subtle changes in the CCC is important for monitoring crop arsenic contamination in large areas by remote sensing. Experimental sites with three contamination levels were selected and were located in Chang Chun City, Jilin City, Jilin Province, China. Arsenic stress can induce small changes in the CCC, reflecting in the crop spectrum. This study created a new index to monitor the CCC. Then, the results from the index were compared with these from other indices and the random forest model, respectively. The final purpose of this study is to find an optimal index, which is sensitive to small changes in the CCC under arsenic stress for monitoring regional CCC in rice. The results indicate that the distribution of the CCC is aligned with the distribution of the arsenic stress level and that NVI (R640, R732, and R752) is the best index for monitoring CCC. The correlation coefficient R2 between the predicated values using NVI and the measured values of canopy chlorophyll content is 0.898, which performs better than the random forest model and other indices. 相似文献
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Fei Hu Yongyao Jiang Yun Li Weiwei Song Daniel Q. Duffy 《International Journal of Digital Earth》2020,13(3):410-428
ABSTRACTEarth observations and model simulations are generating big multidimensional array-based raster data. However, it is difficult to efficiently query these big raster data due to the inconsistency among the geospatial raster data model, distributed physical data storage model, and the data pipeline in distributed computing frameworks. To efficiently process big geospatial data, this paper proposes a three-layer hierarchical indexing strategy to optimize Apache Spark with Hadoop Distributed File System (HDFS) from the following aspects: (1) improve I/O efficiency by adopting the chunking data structure; (2) keep the workload balance and high data locality by building the global index (k-d tree); (3) enable Spark and HDFS to natively support geospatial raster data formats (e.g., HDF4, NetCDF4, GeoTiff) by building the local index (hash table); (4) index the in-memory data to further improve geospatial data queries; (5) develop a data repartition strategy to tune the query parallelism while keeping high data locality. The above strategies are implemented by developing the customized RDDs, and evaluated by comparing the performance with that of Spark SQL and SciSpark. The proposed indexing strategy can be applied to other distributed frameworks or cloud-based computing systems to natively support big geospatial data query with high efficiency. 相似文献
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轨迹数据处理与分析是目前空间信息和数据库等相关领域的研究热点之一。本文从Stop-Move轨迹模型出发,通过集成地理空间上下文信息来建模轨迹数据,并研究轨迹时空模式的查询处理技术。首先分析Stop/Move对象与点/线/面地理空间要素之间的时空关联关系,据此提出显式表达该关联语义的地理关联轨迹模型,在此基础上利用关系-对象数据库技术,为地理关联轨迹模型设计独立于应用的关系模式,接着定义轨迹时空模式查询,并提出基于地理关联轨迹关系模式的SQL处理框架,最后以典型性检索请求为例,讨论分析位置-时间、位置-顺序和位置-关系等三类轨迹时空模式查询的纯SQL处理技术,并以样例轨迹数据验证了本文方法的可行性。 相似文献
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《ISPRS Journal of Photogrammetry and Remote Sensing》1999,54(4):263-272
In this paper, we address the problem of content-based image retrieval using queries on shape and topology. We focus on the particularities of image databases encountered in typical topographic applications and present the development of a spatial data management system that enables such queries. The query requires user-provided sketches of the shape and spatial configuration of the object (or objects) which should appear in the images to be retrieved. The objective of the search is to retrieve images that contain a configuration of objects sufficiently similar to the one specified in the query. Our approach combines the design of an integrated database with the development of a feature library and the necessary matching tools. In this paper, we present our overall scheme, introduce some individual database components, and provide some implementation results. 相似文献
13.
机载LiDAR数据估算样地和单木尺度森林地上生物量 总被引：2，自引：0，他引：2
利用机载激光雷达点云数据,结合大量实测单木结构信息,分别从样地和单木尺度估算了森林地上生物量AGB。首先,利用局部最大值单木提取算法提取了每个样地内的单木结构参数,并针对样地和单木尺度分别计算了一组激光雷达变量。然后,利用激光雷达变量和地上生物量及其两者的对数形式,从样地和单木尺度分别构建了估算模型。最后,针对两种尺度估算过程中存在的不确定性进行了详细讨论。结果表明:(1)样地和单木尺度模型估算的森林地上生物量与地面实测值都具有明显的相关性,且对数模型估算效果要优于非对数模型;(2)样地尺度模型估算效果(R2=0.84,rRMSE=0.23)明显优于单木尺度模型(R2=0.61,rRMSE=0.46);(3)按树木类型分别进行估算可以提高单木地上生物量的估算精度;(4)不论是样地还是单木尺度地上生物量估算都存在一定的不确定性,与样地尺度相比,单木尺度估算过程的不确定性更大,这种不确定性主要来自单木识别过程。 相似文献
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A. Shamshad C.S. LeowA. Ramlah W.M.A. Wan HussinS.A. Mohd. Sanusi 《International Journal of Applied Earth Observation and Geoinformation》2008
The study evaluated the performance and suitability of AnnAGNPS model in assessing runoff, sediment loading and nutrient loading under Malaysian conditions. The watershed of River Kuala Tasik in Malaysia, a combination of two sub-watersheds, was selected as the area of study. The data for the year 2004 was used to calibrate the model and the data for the year 2005 was used for validation purposes. Several input parameters were computed using methods suggested by other researchers and studies carried out in Malaysia. The study shows that runoff was predicted well with an overall R2 value of 0.90 and E value of 0.70. Sediment loading was able to produce a moderate result of R2 = 0.66 and E = 0.49, nitrogen loading predictions were slightly better with R2 = 0.68 and E = 0.53, and phosphorus loading performance was slightly poor with an R2 = 0.63 and E = 0.33. The erosion map developed was in agreement with the erosion risk map produced by the Department of Agriculture, Malaysia. Rubber estates and urban areas were found to be the main contributors to soil erosion. The simulation results showed that AnnAGNPS has the potential to be used as a valuable tool for planning and management of watersheds under Malaysian conditions. 相似文献
15.
Xia Yao Yan ZhuYongChao Tian Wei FengWeiXing Cao 《International Journal of Applied Earth Observation and Geoinformation》2010
Hyperspectral sensing can provide an effective means for fast and non-destructive estimation of leaf nitrogen (N) status in crop plants. The objectives of this study were to design a new method to extract hyperspectral spectrum information, to explore sensitive spectral bands, suitable bandwidth and best vegetation indices based on precise analysis of ground-based hyperspectral information, and to develop regression models for estimating leaf N accumulation per unit soil area (LNA, g N m−2) in winter wheat (Triticum aestivum L.). Three field experiments were conducted with different N rates and cultivar types in three consecutive growing seasons, and time-course measurements were taken on canopy hyperspectral reflectance and LNA under the various treatments. Then, normalized difference spectral indices (NDSI) and ratio spectral indices (RSI) based on the original spectrum and the first derivative spectrum were constructed within the range of 350–2500 nm, and their relationships with LNA were quantified. The results showed that both LNA and canopy hyperspectral reflectance in wheat changed with varied N rates, with consistent patterns across different cultivars and seasons. The sensitive spectral bands for LNA existed mainly within visible and near infrared regions. The best spectral indices for estimating LNA in wheat were found to be NDSI (R860, R720), RSI (R990, R720), NDSI (FD736, FD526) and RSI (FD725, FD516), and the regression models based on the above four spectral indices were formulated as Y = 26.34x1.887, Y = 5.095x − 6.040, Y = 0.609 e3.008x and Y = 0.388x1.260, respectively, with R2 greater than 0.81. Furthermore, expanding the bandwidth of NDSI (R860, R720) and RSI (R990, R720) from 1 nm to 100 nm at 1 nm interval produced the LNA monitoring models with similar performance within about 33 nm and 23 nm bandwidth, respectively, over which the statistical parameters of the models became less stable. From testing of the derived equations, the model for LNA estimation on NDSI (R860, R720), RSI (R990, R720), NDSI (FD736, FD526) and RSI (FD725, FD516) gave R2 over 0.79 with more satisfactory performance than previously reported models and physical models in wheat. It can be concluded that the present hyperspectral parameters of NDSI (R860, R720), RSI (R990, R720), NDSI (FD736, FD526) and RSI (FD725, FD516) can be reliably used for estimating LNA in winter wheat. 相似文献
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Cyanobacterial blooms in water supply sources in both central Indiana USA (CIN) and South Australia (SA) are a cause of great concerns for toxin production and water quality deterioration. Remote sensing provides an effective approach for quick assessment of cyanobacteria through quantification of phycocyanin (PC) concentration. In total, 363 samples spanning a large variation of optically active constituents (OACs) in CIN and SA waters were collected during 24 field surveys. Concurrently, remote sensing reflectance spectra (Rrs) were measured. A partial least squares–artificial neural network (PLS–ANN) model, artificial neural network (ANN) and three-band model (TBM) were developed or tuned by relating the Rrs with PC concentration. Our results indicate that the PLS–ANN model outperformed the ANN and TBM with both the original spectra and simulated ESA/Sentinel-3/Ocean and Land Color Instrument (OLCI) and EO-1/Hyperion spectra. The PLS–ANN model resulted in a high coefficient of determination (R2) for CIN dataset (R2 = 0.92, R: 0.3–220.7 μg/L) and SA (R2 = 0.98, R: 0.2–13.2 μg/L). In comparison, the TBM model yielded an R2 = 0.77 and 0.94 for the CIN and SA datasets, respectively; while the ANN obtained an intermediate modeling accuracy (CIN: R2 = 0.86; SA: R2 = 0.95). Applying the simulated OLCI and Hyperion aggregated datasets, the PLS–ANN model still achieved good performance (OLCI: R2 = 0.84; Hyperion: R2 = 0.90); the TBM also presented acceptable performance for PC estimations (OLCI: R2 = 0.65, Hyperion: R2 = 0.70). Based on the results, the PLS–ANN is an effective modeling approach for the quantification of PC in productive water supplies based on its effectiveness in solving the non-linearity of PC with other OACs. Furthermore, our investigation indicates that the ratio of inorganic suspended matter (ISM) to PC concentration has close relationship to modeling relative errors (CIN: R2 = 0.81; SA: R2 = 0.92), indicating that ISM concentration exert significant impact on PC estimation accuracy. 相似文献
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Chaoyang Wu Xiuzhen Han Jinsheng Ni Zheng Niu Wenjiang Huang 《International Journal of Applied Earth Observation and Geoinformation》2010
Gross primary production (GPP) is a parameter of significant importance for carbon cycle and climate change research. Remote sensing combined with other climate and meteorological data offers a convenient tool for large scale GPP estimation. This paper presents a study of GPP estimation using three methods with in situ measurements of canopy reflectance, LAI, and the photosynthetically active radiation (PAR). First, because LAI is considered as an indicator of the factor of absorbed PAR (fAPAR), it provides reasonable estimates of GPP for all types of wheat with coefficient of determination R2 of 0.7353. The second method uses four kinds of vegetation indices (VIs) to estimate GPP because these indices are suggested to be reliable candidates in the estimation of light use efficiency (LUE). Good determination coefficients were acquired in estimating GPP with R2 ranging from the lowest of 0.7604 for NDVI to the highest of 0.8505 for EVI. A new method was proposed for the estimation of GPP following the Monteith logic, which considering GPP as a product of VI × VI × PAR. Results indicated that this method can provide the best estimates of GPP as determination coefficient R2 increased largely compared to the other two methods. EVI × EVI × PAR was demonstrated to be the most suitable for the estimation of GPP with the highest R2 of 0.9207, which was about 10% larger as compared to GPP estimated from the single EVI. These results will be helpful for the development of new models of GPP estimation with all remote sensing inputs. 相似文献
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