scholarly journals River inflow and retention time affecting spatial heterogeneity of chlorophyll and water–air CO<sub>2</sub> fluxes in a tropical hydropower reservoir

2014 ◽  
Vol 11 (6) ◽  
pp. 8531-8568
Author(s):  
F. S. Pacheco ◽  
M. C. S. Soares ◽  
A. T. Assireu ◽  
M. P. Curtarelli ◽  
F. Roland ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Retention Time ◽  
Spatial Data ◽  
Spatial Information ◽  
Chlorophyll Concentration ◽  
Ghg Emissions ◽  
Co2 Fluxes ◽  
Tropical Reservoir ◽  
River Temperature ◽  
River Inflow

Abstract. Much research has been devoted to understanding the complexity of biogeochemical and physical processes responsible for the greenhouse gas (GHG) emissions from hydropower reservoirs. Spatial complexity and heterogeneity of GHG emission may be observed in these systems because it is dependent on flooded biomass, river inflow, primary production and dam operation. In this study, we investigate the relationships between water–air CO2 fluxes and phytoplanktonic biomass in Funil Reservoir, an old and stratified tropical reservoir, where intense phytoplankton blooms and low partial pressure of CO2 (pCO2) are observed. Our results showed that Funil Reservoir seasonal and spatial variability of chlorophyll concentration (Chl) and pCO2 is more related to changes in river inflow over the year than environmental factor such as air temperature and solar radiation. Field data and hydrodynamic simulations reveal that the river inflow contributes to increased heterogeneity in dry season due to the variation of reservoir retention time and river temperature. Contradictory conclusion can be drawn if temporal data collected only near the dam is considered instead of spatial data to represent CO2 fluxes in whole reservoir. The average CO2 fluxes was −17.6 and 22.1 mmol m−2d−2 considering data collected near the dam and spatial data, respectively, in periods of low retention time. In this case, the lack of spatial information can change completely the role of Funil Reservoir regarding GHG emissions. Our results support the idea that Funil Reservoir is a dynamic system where the hydrodynamics represented by changes in river inflow and retention time is potentially more important force driving both Chl and pCO2 spatial variability than in-system ecological factors.

scholarly journals The effects of river inflow and retention time on the spatial heterogeneity of chlorophyll and water–air CO<sub>2</sub> fluxes in a tropical hydropower reservoir

2015 ◽  
Vol 12 (1) ◽  
pp. 147-162 ◽  
Author(s):  
F. S. Pacheco ◽  
M. C. S. Soares ◽  
A. T. Assireu ◽  
M. P. Curtarelli ◽  
F. Roland ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Retention Time ◽  
Spatial Data ◽  
Ghg Emissions ◽  
Temporal Data ◽  
Co2 Fluxes ◽  
Tropical Reservoir ◽  
Dynamic Simulations ◽  
River Temperature ◽  
River Inflow

Abstract. Abundant research has been devoted to understanding the complexity of the biogeochemical and physical processes that are responsible for greenhouse gas (GHG) emissions from hydropower reservoirs. These systems may have spatially complex and heterogeneous GHG emissions due to flooded biomass, river inflows, primary production and dam operation. In this study, we investigated the relationships between the water–air CO2 fluxes and the phytoplanktonic biomass in the Funil Reservoir, which is an old, stratified tropical reservoir that exhibits intense phytoplankton blooms and a low partial pressure of CO2 (pCO2). Our results indicated that the seasonal and spatial variability of chlorophyll concentrations (Chl) and pCO2 in the Funil Reservoir are related more to changes in the river inflow over the year than to environmental factors such as air temperature and solar radiation. Field data and hydro\\-dynamic simulations revealed that river inflow contributes to increased heterogeneity during the dry season due to variations in the reservoir retention time and river temperature. Contradictory conclusions could be drawn if only temporal data collected near the dam were considered without spatial data to represent CO2 fluxes throughout the reservoir. During periods of high retention, the average CO2 fluxes were 10.3 mmol m−2 d−1 based on temporal data near the dam versus −7.2 mmol m−2 d−1 with spatial data from along the reservoir surface. In this case, the use of solely temporal data to calculate CO2 fluxes results in the reservoir acting as a CO2 source rather than a sink. This finding suggests that the lack of spatial data in reservoir C budget calculations can affect regional and global estimates. Our results support the idea that the Funil Reservoir is a dynamic system where the hydrodynamics represented by changes in the river inflow and retention time are potentially a more important force driving both the Chl and pCO2 spatial variability than the in-system ecological factors.

DARCY RIBEIRO UNIVERSITY CAMPUS MAP – 2017- TECHNICAL NOTE

2018 ◽  
Vol 7 (2) ◽  
Author(s):  
Rafael Sanzio Araújo dos Anjos ◽  
Jose Leandro de Araujo Conceição ◽  
Jõao Emanuel ◽  
Matheus Nunes
Keyword(s):  
Spatial Data ◽  
Spatial Information ◽  
Technical Note ◽  
University Campus ◽  
Communication Tool ◽  
Sources Of Information ◽  
Data Manipulation ◽  
Geographic Space ◽  
The Many ◽  
New Generation

The spatial information regarding the use of territory is one of the many strategies used to answer and to inform about what happened, what is happening and what may happen in geographic space. Therefore, the mapping of land use as a communication tool for the spatial data made significant progress in improving sources of information, especially over the last few decades, with new generation remote sensing products for data manipulation.

An Optimal Framework for Spatial Query Optimization Using Hadoop in Big Data Analytics

2019 ◽  
Vol 12 ◽  
Author(s):  
Pankaj Dadheech ◽  
Dinesh Goyal ◽  
Sumit Srivastava ◽  
Ankit Kumar
Keyword(s):  
Big Data ◽  
Query Optimization ◽  
Spatial Data ◽  
Spatial Information ◽  
Big Data Analytics ◽  
Spatial Query ◽  
Data Process ◽  
Boolean Queries ◽  
Spatial Query Optimization ◽  
Hadoop System

Spatial queries frequently used in Hadoop for significant data process. However, vast and massive size of spatial information makes it difficult to process the spatial inquiries proficiently, so they utilized the Hadoop system for process Big Data. We have used Boolean Queries & Geometry Boolean Spatial Data for Query Optimization using Hadoop System. In this paper, we show a lightweight and adaptable spatial data index for big data which will process in Hadoop frameworks. Results demonstrate the proficiency and adequacy of our spatial ordering system for various spatial inquiries.

scholarly journals Development of Multilayer-Based Map Matching to Enhance Performance in Large Truck Fleet Dispatching

2021 ◽  
Vol 10 (2) ◽  
pp. 79
Author(s):  
Ching-Yun Mu ◽  
Tien-Yin Chou ◽  
Thanh Van Hoang ◽  
Pin Kung ◽  
Yao-Min Fang ◽  
...  
Keyword(s):  
Data Structures ◽  
Spatial Data ◽  
Spatial Information ◽  
Data Retrieval ◽  
Spatial Data Mining ◽  
Map Matching ◽  
Matching Algorithm ◽  
Table Method ◽  
Look Up Table ◽  
The Look

Spatial information technology has been widely used for vehicles in general and for fleet management. Many studies have focused on improving vehicle positioning accuracy, although few studies have focused on efficiency improvements for managing large truck fleets in the context of the current complex network of roads. Therefore, this paper proposes a multilayer-based map matching algorithm with different spatial data structures to deal rapidly with large amounts of coordinate data. Using the dimension reduction technique, the geodesic coordinates can be transformed into plane coordinates. This study provides multiple layer grouping combinations to deal with complex road networks. We integrated these techniques and employed a puncture method to process the geometric computation with spatial data-mining approaches. We constructed a spatial division index and combined this with the puncture method, which improves the efficiency of the system and can enhance data retrieval efficiency for large truck fleet dispatching. This paper also used a multilayer-based map matching algorithm with raster data structures. Comparing the results revealed that the look-up table method offers the best outcome. The proposed multilayer-based map matching algorithm using the look-up table method is suited to obtaining competitive performance in identifying efficiency improvements for large truck fleet dispatching.

scholarly journals SPATIAL DATA: FROM CARTOGRAPHY TO GEODATABASE

2017 ◽  
Vol 43 (4) ◽  
pp. 142-146 ◽  
Author(s):  
Ugo FALCHI
Keyword(s):  
Spatial Data ◽  
Common Good ◽  
Spatial Information ◽  
Geographic Information ◽  
The Public ◽  
Significant Acceleration ◽  
Management And Development ◽  
The Status ◽  
History Of ◽  
Essential Knowledge

The final goal of this paper was to fix a brief summary on the status of geographic information in Italy due to the technological steps and national regulations. The acquisition, processing and sharing of spatial data has experienced a significant acceleration thanks to the development of computer technology and the acknowledgment of the need for standardization and homogenization of information held by pub­lic authorities and individuals. The spatial data represents the essential knowledge in the management and development of a territory both in terms of planning for safety and environmental prevention. In Italy there is an enormous heritage of spatial information which is historically affected by a problem of consistency and uniformity, in order to make it often contradictory in its use by the public decision-maker and private par­ties. The recent history of geographic information is characterized by a significant effort aimed at optimiz­ing this decisive technical and cultural heritage allowing the use of it to all citizens in a logic of sharing and re-use and may finally represent a common good available to all.

Influence of Reservoir Transit on Riverine Algal Transport and Abundance

1984 ◽  
Vol 41 (12) ◽  
pp. 1803-1813 ◽  
Author(s):  
D. M. Søballe ◽  
R. W. Bachmann
Keyword(s):  
Water Temperature ◽  
Retention Time ◽  
Standing Crop ◽  
Algal Biomass ◽  
Chlorophyll Concentration ◽  
Light Limitation ◽  
Des Moines ◽  
Flow Dependence ◽  
Major Loss ◽  
Chlorophyll Concentrations

The Des Moines River lost 65–75% of its algal standing crop (chlorophyll a) in passing through each of two impoundments (mean retention times 11 and 16 d), and chlorophyll concentrations within both impoundments were 50–90% below the predictions of empirical chlorophyll–nutrient models. Sedimentation of river-borne algae and light limitation within the impoundments were identified as major loss processes. A reduction in algal size from upstream to downstream in one reservoir paralleled the loss of algal biomass. Algal losses in each impoundment increased with both increasing retention time and water temperature so that chlorophyll concentration below the dams was uncoupled from the temperature and flow dependence seen in river reaches not influenced by impoundments. The reduction in riverine algal transport associated with reservoir transit was cumulative over the two-reservoir series; this reduction can be interpreted as a "reset" to river headwater conditions.

scholarly journals PREFACE – ISPRS Geospatial Week 2019

2019 ◽  
Vol IV-2/W5 ◽  
pp. 1-1
Author(s):  
G. Vosselman ◽  
S. J. Oude Elberink ◽  
M. Y. Yang
Keyword(s):  
Remote Sensing ◽  
Data Quality ◽  
Spatial Data ◽  
Laser Scanning ◽  
Spatial Information ◽  
Smart Cities ◽  
International Workshop ◽  
Crowd Sourcing ◽  
Spatial Data Quality ◽  
Information Sciences

<p><strong>Abstract.</strong> The ISPRS Geospatial Week 2019 is a combination of 13 workshops organised by 30 ISPRS Working Groups active in areas of interest of ISPRS. The Geospatial Week 2019 is held from 10–14 June 2019, and is convened by the University of Twente acting as local organiser. The Geospatial Week 2019 is the fourth edition, after Antalya Turkey in 2013, La Grande Motte France in 2015 and Wuhan China in 2017.</p><p>The following 13 workshops provide excellent opportunities to discuss the latest developments in the fields of sensors, photogrammetry, remote sensing, and spatial information sciences:</p> <ul> <li>C3M&amp;amp;GBD – Collaborative Crowdsourced Cloud Mapping and Geospatial Big Data</li> <li>CHGCS – Cryosphere and Hydrosphere for Global Change Studies</li> <li>EuroCow-M3DMaN – Joint European Calibration and Orientation Workshop and Workshop onMulti-sensor systems for 3D Mapping and Navigation</li> <li>HyperMLPA – Hyperspectral Sensing meets Machine Learning and Pattern Analysis</li> <li>Indoor3D</li> <li>ISSDQ – International Symposium on Spatial Data Quality</li> <li>IWIDF – International Workshop on Image and Data Fusion</li> <li>Laser Scanning</li> <li>PRSM – Planetary Remote Sensing and Mapping</li> <li>SarCon – Advances in SAR: Constellations, Signal processing, and Applications</li> <li>Semantics3D – Semantic Scene Analysis and 3D Reconstruction from Images and ImageSequences</li> <li>SmartGeoApps – Advanced Geospatial Applications for Smart Cities and Regions</li> <li>UAV-g – Unmanned Aerial Vehicles in Geomatics</li> </ul> <p>Many of the workshops are part of well-established series of workshops convened in the past. They cover topics like UAV photogrammetry, laser scanning, spatial data quality, scene understanding, hyperspectral imaging, and crowd sourcing and collaborative mapping with applications ranging from indoor mapping and smart cities to global cryosphere and hydrosphere studies and planetary mapping.</p><p>In total 143 full papers and 357 extended abstracts were submitted by authors from 63 countries. 1250 reviews have been delivered by 295 reviewers. A total of 81 full papers have been accepted for the volume IV-2/W5 of the International Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences. Another 289 papers are published in volume XLII-2/W13 of the International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences.</p><p>The editors would like to thank all contributing authors, reviewers and all workshop organizers for their role in preparing and organizing the Geospatial Week 2019. Thanks to their contributions, we can offer an excessive and varying collection in the Annals and the Archives.</p><p>We hope you enjoy reading the proceedings.</p><p>George Vosselman, Geospatial Week Director 2019, General Chair<br /> Sander Oude Elberink, Programme Chair<br /> Michael Ying Yang, Programme Chair</p>

scholarly journals Cartographic Presentation as the Central Theme for Geospatial Education

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Rakesh Malhotra ◽  
Terry McNeill ◽  
Carrie Francis ◽  
Tim Mulrooney
Keyword(s):  
North Carolina ◽  
Spatial Data ◽  
Spatial Information ◽  
Essential Element ◽  
Learning And Teaching ◽  
Conflicting Information ◽  
Spatial Understanding ◽  
Written Information ◽  
Text Information ◽  
The 1960S

<p><strong>Abstract.</strong> North Carolina Central University is committed to student education and training in cartography and geospatial sciences. This paper demonstrates the importance of applying cartographic principles to train students to convert historical deed records into geospatial data. Students were required to take text information from the 1960s and input this information it into a spatial database. The historical information was recorded on typed deeds in COGO (direction-distance) and the historic coordinate system of 1927 in the 1960s. Students applied cartographic principles that were used to identify contextual and spatial variations and anomalies to flag areas and records that didn’t meet project specifications and to trouble shoot conflicting information.</p><p>This paper demonstrates the usefulness of using cartography as a tool to educate students in allied aspects of geospatial sciences such as creating and managing spatial data. For example, students used tools such as markers and color coding to identify areas of overlap and areas of mismatched records (Figure 1). The authors found that using cartography helped enhance the spatial understanding of the project for students.</p><p>Education is the foundation of projects at North Carolina Central University and cartography has demonstrated appeal at the university level. Various geospatial aspects such as datums and projections, overlays, gaps, overlaps, and converting written information to spatial (geometric) information lend themselves well to cartographic principles. Cartography is an essential element that supports learning and teaching of spatial information as demonstrated by this project. Students were in a better position to understand and detect spatial anomalies with help from cartography than they were without using cartography and relying solely of written information. This enhanced their understanding and use of spatial data.</p>

scholarly journals 3D MODELLING OF AN INDOOR SPACE USING A ROTATING STEREO FRAME CAMERA SYSTEM

2016 ◽  
Vol XLI-B4 ◽  
pp. 303-308 ◽  
Author(s):  
J. Kang ◽  
I. Lee
Keyword(s):  
Spatial Data ◽  
Input Data ◽  
3D Model ◽  
Spatial Information ◽  
Low Cost ◽  
Test Site ◽  
Location Awareness ◽  
Camera System ◽  
Cost System ◽  
Indoor Spaces

Sophisticated indoor design and growing development in urban architecture make indoor spaces more complex. And the indoor spaces are easily connected to public transportations such as subway and train stations. These phenomena allow to transfer outdoor activities to the indoor spaces. Constant development of technology has a significant impact on people knowledge about services such as location awareness services in the indoor spaces. Thus, it is required to develop the low-cost system to create the 3D model of the indoor spaces for services based on the indoor models. In this paper, we thus introduce the rotating stereo frame camera system that has two cameras and generate the indoor 3D model using the system. First, select a test site and acquired images eight times during one day with different positions and heights of the system. Measurements were complemented by object control points obtained from a total station. As the data were obtained from the different positions and heights of the system, it was possible to make various combinations of data and choose several suitable combinations for input data. Next, we generated the 3D model of the test site using commercial software with previously chosen input data. The last part of the processes will be to evaluate the accuracy of the generated indoor model from selected input data. In summary, this paper introduces the low-cost system to acquire indoor spatial data and generate the 3D model using images acquired by the system. Through this experiments, we ensure that the introduced system is suitable for generating indoor spatial information. The proposed low-cost system will be applied to indoor services based on the indoor spatial information.

scholarly journals MINING CO-LOCATION PATTERNS FROM SPATIAL DATA

2016 ◽  
Vol III-2 ◽  
pp. 85-90
Author(s):  
C. Zhou ◽  
W. D. Xiao ◽  
D. Q. Tang
Keyword(s):  
Spatial Data ◽  
Spatial Information ◽  
Spatial Proximity ◽  
Widespread Application ◽  
Location Pattern ◽  
Gps Technology ◽  
Location Patterns ◽  
Mining Algorithms ◽  
The City ◽  
Research Domains

Due to the widespread application of geographic information systems (GIS) and GPS technology and the increasingly mature infrastructure for data collection, sharing, and integration, more and more research domains have gained access to high-quality geographic data and created new ways to incorporate spatial information and analysis in various studies. There is an urgent need for effective and efficient methods to extract unknown and unexpected information, e.g., co-location patterns, from spatial datasets of high dimensionality and complexity. A co-location pattern is defined as a subset of spatial items whose instances are often located together in spatial proximity. Current co-location mining algorithms are unable to quantify the spatial proximity of a co-location pattern. We propose a co-location pattern miner aiming to discover co-location patterns in a multidimensional spatial data by measuring the cohesion of a pattern. We present a model to measure the cohesion in an attempt to improve the efficiency of existing methods. The usefulness of our method is demonstrated by applying them on the publicly available spatial data of the city of Antwerp in Belgium. The experimental results show that our method is more efficient than existing methods.

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