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Optimizing segmented trajectory data storage with HBase for improved spatio-temporal query efficiency

International Journal of Digital Earth 2023年第1期16卷 1124-1143页

作者： Yi Bao Zhou Huang Xuri Gong Yuyang Zhang Ganmin Yin Han Wang Institute of Remote Sensing and Geographical Information Systems Peking UniversityBeiingPeople's Republic of China Beijing Key Lab of Spatial Information Integration&Its Applications Peking UniversityBejjingPeople's Republic of China

The surging accumulation of trajectory data has yielded invaluable insights into urban systems,but it has also presented challenges for data storage and management *** response,specialized storage systems based on non-relational databases have been developed to support large data quantities in distributed ***,these systems often utilize storage by point or storage by trajectory methods,both of which have *** this study,we evaluate the effectiveness of segmented trajectory data storage with HBase optimizations for spatio-temporal *** develop a prototype system that includes trajectory segmentation,serialization,and spatio-temporal indexing and apply it to taxi trajectory data in *** indicate that the segmented system provides enhanced query speed and reduced memory usage compared to the Geomesa system.

关键词： Trajectory storage HBase trajectory segmentation spatio-temporal query

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Ecosystem Service Classification of Vegetation in North Tianshan Mountain, Xinjiang, China

Ecosystem Service Classification of Vegetation in North Tian...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Dechao Zhai Zhicheng Huang Naijie Peng Qunchao He Siqi Yang Wenjie Fan Institute of RS and GIS School of Earth and Space Sciences Peking University Beijing China Beijing Key Lab of Spatial Information Integration and Its Application Peking University Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Ecosystem services (ESs) indicate that species comprising an ecosystem can provide services that are significant to human beings. ES classification of vegetation is significant for accelerating ecological restoration and achieving sustainable development. However, current vegetation classification product cannot effectively reflect their ESs. This study focused on the north Tianshan Mountain, Xinjiang, China, and tried to classify ESs of vegetation based on machine learning and remote sensing techniques. This study utilized multiband Landsat-8 surface reflectance data from Google Earth Engine (GEE) together with meteorological data and Digital Elevation Model data. The classification map accurately portrayed the spatial distribution of seven types of vegetation ES, including agricultural production (AP) cropland, sandstorm prevention (SP) and water retention (WR) forest, grassland, and shrub. The classification results indicated that, the two types of grassland occupy the largest area besides desert, followed by AP cropland, whereas SP forest and WR shrub have the smallest area. The accuracy of ES classification for vegetation is very high, with an impressive overall accuracy of 89.61% and a kappa coefficient of 0.874. This study provides valuable insights into ES classification of vegetation, which can serve as a reference for formulating an ES development plan in north Tianshan Mountain, Xinjiang.

关键词： Earth Grasslands Accuracy Graphical models Vegetation mapping Machine learning Forestry

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An Angle-Dependent Non-Linear Split-Window Algorithm for Estimating Sea Surface Temperature from Chinese HY-1D Satellite

An Angle-Dependent Non-Linear Split-Window Algorithm for Est...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Fengguang Li Huazhong Ren Baozhen Wang Jinshun Zhu Songyi Lin Wenjie Fan Zian Wang Qiming Qin Institute of Remote Sensing and Geographic Information System School of Earth and Space Sciences Peking University Beijing China Beijing Key Lab of Spatial Information Integration and Its Application Peking University Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The estimation of Sea Surface Temperature (SST) from ocean satellites with large observation angles must account for the angular effects on SST. This study developed an angle-dependent non-linear split-window algorithm (A-NLSW) to retrieve SST from Chinese ocean satellite HY-1D thermal infrared data. The algorithm coefficients were obtained based on the simulated dataset and grouped by initial SSTs, total atmospheric column water vapor content (TCWV), and satellite zenith angle (SZA). The A-NLSW algorithm is validated and re-calibrated using the bulk temperature collected by the iQuam in-situ dataset. After re-calibration, the accuracy of the SST was improved from 1.53 K to 0.87 K for SZA ranging from 0 to 70.5 ° . Nearly 60% of the validation points achieved an accuracy of 0.5 K and over 90% achieved an accuracy of 1.0 K. These findings highlight the robustness of the A-NLSW algorithm in reliably retrieving SST from HY-1D satellite images, even when observations are made at large SZA.

关键词： Accuracy Satellites Systematics Indian Ocean Sea measurements Geoscience and remote sensing Estimation

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Validation of Lunar Surface Emissivity Retrieval from Diviner Image Using Apollo Samples Spectra Data

Validation of Lunar Surface Emissivity Retrieval from Divine...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Zian Wang Huazhong Ren Institute of Remote Sensing and Geographic Information System School of Earth and Space Science Peking University Beijing China Beijing Key Lab of Spatial Information Integration and Its Application Peking University Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The Diviner sensor provides global lunar surface observation at total nine channels from solar reflective wavelength (0.35-2.8 μm) to thermal infrared wavelength (7.55–400 μm Using the three 8-μm thermal channels (7.55–8.05, 8.10–8.40, and 8.38–8.68 μm), the lunar surface temperature and emissivity can be retrieved using physical temperature-emissivity separation (TES) algorithm and the Empirical regression (ER) algorithm. However, up to now there is no report about the ground validation of the accuracy about the retrieval results. This paper performs the ground validation on the lunar surface emissivity using the samples spectra collected by Apollo missions from 14 to 17, and results showed that the emissivity RMSEs of the three channels of the TES algorithm are respectively 0.025, 0.030 and 0.021 with the total RMSE of 0.026, while those of the ER algorithm are respectively 0.043, 0.038, and 0.030 with the total RMSE of 0.038. The TES algorithm got a better accuracy than the ER algorithm.

关键词： Temperature sensors Accuracy Uncertainty Surface waves Moon Measurement uncertainty Time measurement

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Ecological Service Function-Based Forest Classification in North Tianshan Mountain, Xinjiang, China

Ecological Service Function-Based Forest Classification in N...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Dechao Zhai Zhihao Wei Siqi Yang Naijie Peng Yunzhu Tao Wenjie Fan Institute of RS and GIS School of Earth and Space Sciences Peking University Beijing China Beijing Key Lab of Spatial Information Integration and Its Application Peking University Beijing China

Forests are essential for stabilizing the biosphere and providing ecological service functions that are significant to human beings. However, current forest landcover products cannot effectively reflect their ecological service functions. This study focused on the north Tianshan Mountain, China and proposed an ecological service function-based forest classification method using multi-source landcover products and Digital Elevation Model (DEM) data. The proposed method included spatial superposition, clustering, and window sliding techniques. The resulting forest classification map accurately portrayed the spatial distribution of three ecological service function-based forests: water and soil conservation (WSC), farmland shelter (FS), and windbreak and sand-fixation (WSF) forests. Results indicated that WSC forests dominate the forest distribution in the north Tianshan Mountain, constituting over half of the total forest area, while FS forests are mostly spread across the western part of the region, accounting for 34.1% of the entire forest area. WSF forests are relatively small and mainly distributed in the central part of the region. This study provides valuable insights into forest classification based on their ecological service functions, which is critical for understanding the spatial distribution and dynamic changes of forests, promoting sustainable development and preserving their ecological service values.

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Atmospheric Correction for Night-Time Light Data Using Radiative Transfer Model

Atmospheric Correction for Night-Time Light Data Using Radia...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Hongqin Zhang Huazhong Ren Chenchen Jiang Jinshun Zhu Baozhen Wang Songyi Lin Hanlin Ye Institute of Remote Sensing and Geographic Information System School of Earth and Space Sciences Peking University Beijing China Beijing Key Lab of Spatial Information Integration and Its Application Peking University Beijing China Institute of Atmospheric Physics CAS Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Night-time light remote sensing has been widely used in various fields, including human activity analysis, urbanization studies, and economic research. However, Earth’s nighttime environment is very complex so that nighttime light images are seriously affected by atmospheric elements and moonlight. To address this issue, this paper proposed an atmospheric correction algorithm on basis of a newly developed radiative transfer model (RTM) that aims to derive the ground radiance of artificial lights from satellite nighttime light images. The SDGSAT-1 night-time light image of beijing is used as a case study to demonstrate the effectiveness of the proposed algorithm. Results show that the new algorithm can effectively removes the atmospheric effects and improves the data quality of nighttime light images.

关键词： Energy consumption Satellites Image color analysis Atmospheric modeling Urban areas Estimation Artificial light

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An Improved Canopy Brightness Temperature Model Based On Spectral Invariants

An Improved Canopy Brightness Temperature Model Based On Spe...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Qunchao He Biao Cao Siqi Yang Naijie Peng Dechao Zhai Wenjie Fan Institute of Remote Sensing and Geographic Information System School of Earth and Space Sciences Peking University Beijing China Beijing Key Lab of Spatial Information Integration and Its Application Peking University Beijing China Innovation Research Center of Satellite Application Faculty of Geographical Science Beijing Normal University Beijing

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

As one of the most important spectral invariants, the recollision probability (p) is a crucial physical parameter that describes the interaction process between the photon and the canopy. This study discusses the applicability of the recollision probability in the thermal infrared (TIR) domain by Monte Carlo simulation. By extending the recollision probability from the VNIR to the TIR, an Improved Canopy Brightness Temperature model based on the p theory (ICBT-P) is introduced to simulate the canopy thermal radiation directionality. A cross-validation between the analytical ICBT-P model and 3D DART model was conducted, yielding R 2 values over 0.99 in two homogeneous scenarios where the leaf and soil components exhibit a significant temperature difference. In other two homogeneous scenarios where the leaf and soil components have the same temperature, the R 2 values are over 0.98. The average brightness temperature difference across the four typical scenes is 0.08K. This new model (i.e., ICBT-P) has the potential to be used for the canopy temperature estimation and the component temperature separation of leaves and soil.

关键词： Temperature sensors Analytical models Solid modeling Temperature distribution Three-dimensional displays Vegetation mapping Scattering

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A Two-Step Method for Winter Wheat Leaf Chlorophyll Estimation from UAV Hyperspectral Imagery

A Two-Step Method for Winter Wheat Leaf Chlorophyll Estimati...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Siqi Yang Naijie Peng Dechao Zhai Yunzhu Tao Haobo Wu Huazhong Ren Wenjie Fan Institute of RS and GIS School of Earth and Space Sciences Peking University China Beijing Key Lab of Spatial Information Integration and Its Application Peking University China

Leaf chlorophyll content (LCC) is a critical indicator for precision agriculture. Accurately estimating winter wheat LCC based on remote sensing at high spatial resolution is of great significance for agricultural management and decision. In this study, a two-step method was used to retrieve wheat LCC from UAV hyperspectral imagery. The first step is converting canopy reflectance to leaf reflectance using Look-up tables (LUTs) generated from the unified model of bidirectional reflectance distribution function (BRDF). The second step is retrieving wheat LCC from derived leaf reflectance using the PROSPECT-PRO model. Retrieved wheat LAI, leaf reflectance, and LCC were validated against field measurements. The results indicate a good agreement between retrieved and measured LAI with RMSE of 0.092 and R 2 of 0.605. Leaf reflectance retrieved from UAV canopy reflectance exhibit good consistency with measured leaf reflectance with RMSE of 0.017 and R 2 of 0.962. Retrieved wheat LCC is of good quality compared to measured LCC, with R 2 of 0.7675 and RMSE of 4.33 μg/cm 2 . In conclusion, this study holds potential in estimating wheat LCC from UAV hyperspectral imagery.

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iTourSPOT:a context-aware framework for next POI recommendation in location-based social networks

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International Journal of Digital Earth 2022年第1期15卷 1614-1636页

作者： Lin Wan Han Wang Yuming Hong Ran Li Wei Chen Zhou Huang School of Computer Science China University of GeosciencesWuhanPeople's Republic of China Institute of Remote Sensing and Geographical Information Systems Peking UniversityBeijingPeople's Republic of China Beijing Key Lab of Spatial Information Integration & Its Applications Peking UniversityBeijingPeople's Republic of China Information Center Hubei Geological Environment StationWuhanPeople's Republic of China College of Geoscience and Surveying Engineering China University of Mining & TechnologyBeijingPeople's Republic of China

The rising prosperity of Location-based Social Networks(LBSNs)witnessed an explosion in the availability of geo-tagged social media data,which enables tremendous location-aware online services,especially next point of interest(POI)***,previous next POI recommendation studies usually adopt fix-length time windows for user check-in sequence modeling,leading to a limited capacity in capturing fine-grained user temporal preferences that easily change over ***,existing methods often directly leverage multi-modal contexts as auxiliary to alleviate the data sparsity issue,which fails to fully exploit the sequential patterns of contextual information for inferring user interest *** address the above challenges,we propose a novel framework named iTourSPOT which extends traditional collaborative filtering methods with a context-aware POI embedding *** enhancing temporal interests modeling capacity,we associate the context feature extraction with varying-length sessions and incorporate check-in frequencies of POIs as prior knowledge to instruct the session representation learning of our ***,a collaborative sequence transduction model is designed for joint context sequence modeling and session-based POI *** results on a real-world geo-tagged photo dataset clearly demonstrate the effectiveness of the proposed framework when compared with state-of-the-art baseline methods,especially in both sparse and cold-start scenarios.

关键词： Next POI recommendation context-aware recommendation location-based social network geo-tagged photos

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Simultaneous Retrieval of Land Surface Temperature and Emissivity from Chinese Geostationary Satellite Fengyun-4B Image

Simultaneous Retrieval of Land Surface Temperature and Emiss...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Baozhen Wang Huazhong Ren Rongyuan Liu Wenjie Fan Qiming Qin Songyi Lin Yunzhu Tao Siqi Yang Institute of Remote Sensing and Geographic Information System School of Earth and Space Sciences Peking University Beijing China Beijing Key Lab of Spatial Information Integration and Its Application Peking University Beijing China China Aero Geophysical Survey and Remote Sensing Center for Natural Resources Beijing China

The Advanced Geostationary Radiation Imager (AGRI) on board of the Chinese geostationary satellite FengYun-4B (FY4B) designs four thermal infrared channels, which has the characteristics of wide observation range, high observation frequency and fixed point observation, with a spatial resolution of 4 km at nadir and a full-disk observation every 15 minutes. Therefore, it can monitor the surface temperature changes on a large time scale, providing important data support for agricultural drought monitoring and climate change. However, there is currently no algorithm for land surface temperature retrieval with this sensor. This paper proposed a three-channel temperature–emissivity separation (TES) algorithm that estimates the LST and emissivity from three thermal-infrared (TIR) images. The analysis shows that the algorithm can theoretically retrieve the LST and emissivity with errors less than 0.8 K and 0.016, respectively.

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