scholarly journals RDCRMG: A Raster Dataset Clean & Reconstitution Multi-Grid Architecture for Remote Sensing Monitoring of Vegetation Dryness

2018 ◽  
Vol 10 (9) ◽  
pp. 1376 ◽  
Author(s):  
Sijing Ye ◽  
Diyou Liu ◽  
Xiaochuang Yao ◽  
Huaizhi Tang ◽  
Quan Xiong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Storage ◽  
Large Scale ◽  
High Efficiency ◽  
Data Extraction ◽  
Automatic Monitoring ◽  
Information Distortion ◽  
Remote Sensing Monitoring ◽  
Data Inversion ◽  
Analysis Models

In recent years, remote sensing (RS) research on crop growth status monitoring has gradually turned from static spectrum information retrieval in large-scale to meso-scale or micro-scale, timely multi-source data cooperative analysis; this change has presented higher requirements for RS data acquisition and analysis efficiency. How to implement rapid and stable massive RS data extraction and analysis becomes a serious problem. This paper reports on a Raster Dataset Clean & Reconstitution Multi-Grid (RDCRMG) architecture for remote sensing monitoring of vegetation dryness in which different types of raster datasets have been partitioned, organized and systematically applied. First, raster images have been subdivided into several independent blocks and distributed for storage in different data nodes by using the multi-grid as a consistent partition unit. Second, the “no metadata model” ideology has been referenced so that targets raster data can be speedily extracted by directly calculating the data storage path without retrieving metadata records; third, grids that cover the query range can be easily assessed. This assessment allows the query task to be easily split into several sub-tasks and executed in parallel by grouping these grids. Our RDCRMG-based change detection of the spectral reflectance information test and the data extraction efficiency comparative test shows that the RDCRMG is reliable for vegetation dryness monitoring with a slight reflectance information distortion and consistent percentage histograms. Furthermore, the RDCGMG-based data extraction in parallel circumstances has the advantages of high efficiency and excellent stability compared to that of the RDCGMG-based data extraction in serial circumstances and traditional data extraction. At last, an RDCRMG-based vegetation dryness monitoring platform (VDMP) has been constructed to apply RS data inversion in vegetation dryness monitoring. Through actual applications, the RDCRMG architecture is proven to be appropriate for timely vegetation dryness RS automatic monitoring with better performance, more reliability and higher extensibility. Our future works will focus on integrating more kinds of continuously updated RS data into the RDCRMG-based VDMP and integrating more multi-source datasets based collaborative analysis models for agricultural monitoring.

scholarly journals Cartographic Remote-Sensing Monitoring of Glaciological Systems (Example, Mount El‛ Brus, U.S.S.R.) (Abstract)

1987 ◽  
Vol 9 ◽  
pp. 247-248
Author(s):  
Yu. F. Knizhnikov ◽  
V.I. Kravtsova ◽  
I.A. Labutina
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
Quantitative Information ◽  
Snow Accumulation ◽  
Aerial Photographs ◽  
Small Scale ◽  
Medium Scale ◽  
Remote Sensing Monitoring ◽  
Periglacial Zone

Remote-sensing methods in monitoring the glacierization of Mount EI‛ brus are used to produce base and dynamic maps, and to obtain quantitative information (dynamic indices) about the rate, intensity, and variations of the process. The monitoring system is divided, according to scope and territory covered, into small-scale for total glacierization and the periglacial zone, medium-scale for separate glaciers, and large-scale (detailed) for part of the glaciers or sectors of the adjoining slopes. The approximate relationship of even scales is 1 : 4. Small-scale monitoring remote-sensing systems are important for making maps showing the complex characteristics of the glaciological system. A series of maps was produced including geographical, those of high-altitude zones, slope and exposure angles, geological, glaciomorphological, climatic (temperature, precipitation, and winds), distribution of direct solar radiation, hydrological (source of streams), seats of avalanches, and landslides. All these data serve as a cartographical basis in monitoring the glacierization of Mount EI‛ brus. They are compiled from remotely sensed and Earth-based data. Current monitoring on a small scale includes observations of the conditions which determine the existence of the glacial system - this includes data on winter snowfall and the period of snow cover. These observations were obtained from meteorological and resource satellites, and from scanner data of medium and high resolution. Also important are observations of changes in the outline of glaciers, times of snowfall and character of the distribution of snow, and its redistribution due to avalanches and snowstorms. High-resolution space photographs, small-scale aerial photographs, and aerovisual observations provide the data for these observations. It has been determined that the area of the glaciers of Mount El‛ brus has been reduced by 1 % in the last 25 years, i.e. the rate of its deglacierization dropped sharply as compared to preceding decades. The role of quantitative information gains importance in the medium-scale level of monitoring. Topographical maps of separate glaciers compiled from aerial photographs or data from ground stereo-photogrammetric surveys constitute the base maps at this level. The main method used in monitoring were large-scale surveys from aircraft, perspective surveys from helicopters, and phototheodolite surveys. Multi-date surveys of the glaciers provide data about the changes in their outlines and height, the character of their relief, their moraines, the amount of snow accumulation and ablation in separate years, the surface rates of ice flow and their fluctuations. The techniques by which quantitative information is obtained about changes in the glaciers are derived from processing the data of multi-date surveys. The organization and techniques of phototheodolite surveys have been improved. A theory evolved for determining the surface-ice movement by stereo-photogrammetric means and the technique for it has also improved; algorithms and programs for machine processing of the data of multi-date surveys (ground and from aircraft) have been produced At this level of monitoring, it has been found that the retreat rate of most glaciers has slowed down and several glaciers are now in equilibrium. Several glaciers became active at the beginning of the 1970s and 1980s; this was accompanied by an increase in their height and forward movement. For example, activation of Kyukyurtlyu Glacier has been recorded (higher surface and increasing flow rate) which has caused the glacier to move forward 100 m. Surveys at an interval of 2 years recorded the beginning of the process of retreat of this glacier. Detailed monitoring is used to detect the mechanism of the dynamic processes and to study it on local representative sectors. On a glacier it may take the form of annual surveys of its tongue, which makes it possible to observe the processes of formation of moraines and glacio-fluvial relief. Studies may also be made of the mechanism of the movement of avalanches and landslides, deducing their quantitative characteristics and appraising the results of avalanches and landslides. Multi-date surveys of sectors of the slopes provide information about processes in the periglacial zone. At this level, regularly repeated ground stereo-photogrammetric surveys are the main means of observation. Glaciological remote-sensing monitoring provides a wealth of data for theoretical development in the field of glaciology. It makes it possible to forecast and produce warnings about hazardous processes and phenomena.

Automatic Monitoring of Mines Mining based on Multitemporal Remote Sensing Image Change Detection

2020 ◽  
Author(s):  
Chengyi Li
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Large Scale ◽  
Meaningful Work ◽  
Automatic Monitoring ◽  
Human Society ◽  
Monitoring Method ◽  
Beijing Area ◽  
Illegal Mining ◽  
Multitemporal Remote Sensing

<p>For the country and human society, it is a very important and meaningful work to make the mines mining controlled and rationally. Otherwise, illegal mining and unreasonable abandonment will cause waste and loss of resources. With the features of convenient, cheap, and instantaneous, remote sensing technology makes it possible to automatic monitoring the mines mining in large-scale.</p><p>We proposed a mine mining change detection framework based on multitemporal remote sensing images. In this framework, the status of mine mining is divided into mining in progress and stopped mining. Based on the multitemporal GF-2 satellite data and the mines mining data from Beijing, China, we have built a mines mining change dataset(BJMMC dataset), which includes two types, from mining to mining, and from mining to discontinued mining. And then we implement a new type of semantic change detection based on convolutional neural networks (CNNs), which involves intuitively inserting semantics into the detected change regions.</p><p>We applied our method to the mining monitoring of the Beijing area in another year, and combined with GIS data and field work, the results show that our proposed monitoring method has outstanding performance on the BJMMC dataset.</p>

scholarly journals DESIGN AND VERIFICATION OF REMOTE SENSING IMAGE DATA CENTER STORAGE ARCHITECTURE BASED ON HADOOP

2018 ◽  
Vol XLII-3 ◽  
pp. 1639-1642
Author(s):  
D. Tang ◽  
X. Zhou ◽  
Y. Jing ◽  
W. Cong ◽  
C. Li
Keyword(s):  
Remote Sensing ◽  
Data Storage ◽  
Data Center ◽  
High Speed ◽  
Large Scale ◽  
Distributed Storage ◽  
Image Data ◽  
Remotely Sensed Data ◽  
Processing Technologies ◽  
Data Parallel

The data center is a new concept of data processing and application proposed in recent years. It is a new method of processing technologies based on data, parallel computing, and compatibility with different hardware clusters. While optimizing the data storage management structure, it fully utilizes cluster resource computing nodes and improves the efficiency of data parallel application. This paper used mature Hadoop technology to build a large-scale distributed image management architecture for remote sensing imagery. Using MapReduce parallel processing technology, it called many computing nodes to process image storage blocks and pyramids in the background to improve the efficiency of image reading and application and sovled the need for concurrent multi-user high-speed access to remotely sensed data. It verified the rationality, reliability and superiority of the system design by testing the storage efficiency of different image data and multi-users and analyzing the distributed storage architecture to improve the application efficiency of remote sensing images through building an actual Hadoop service system.

Research on Improved Method of Storage and Query of Large-Scale Remote Sensing Images

2018 ◽  
Vol 29 (3) ◽  
pp. 1-16 ◽  
Author(s):  
Jing Weipeng ◽  
Tian Dongxue ◽  
Chen Guangsheng ◽  
Li Yiyuan
Keyword(s):  
Remote Sensing ◽  
Data Storage ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Index Structure ◽  
Hilbert Curve ◽  
Remote Sensing Images ◽  
Storage Model ◽  
Sensing Data ◽  
Low Efficiency

The traditional method is used to deal with massive remote sensing data stored in low efficiency and poor scalability. This article presents a parallel processing method based on MapReduce and HBase. The filling of remote sensing images by the Hilbert curve makes the MapReduce method construct pyramids in parallel to reduce network communication between nodes. Then, the authors design a massive remote sensing data storage model composed of metadata storage model, index structure and filter column family. Finally, this article uses MapReduce frameworks to realize pyramid construction, storage and query of remote sensing data. The experimental results show that this method can effectively improve the speed of data writing and querying, and has good scalability.

scholarly journals Density Estimates as Representations of Agricultural Fields for Remote Sensing-Based Monitoring of Tillage and Vegetation Cover

2022 ◽  
Vol 12 (2) ◽  
pp. 679
Author(s):  
Markku Luotamo ◽  
Maria Yli-Heikkilä ◽  
Arto Klami
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Large Scale ◽  
Agricultural Land ◽  
Automatic Monitoring ◽  
Integrated Analysis ◽  
Density Estimates ◽  
Remote Imaging ◽  
Multiple Imaging ◽  
Tillage Method

We consider the use of remote sensing for large-scale monitoring of agricultural land use, focusing on classification of tillage and vegetation cover for individual field parcels across large spatial areas. From the perspective of remote sensing and modelling, field parcels are challenging as objects of interest due to highly varying shape and size but relatively uniform pixel content and texture. To model such areas we need representations that can be reliably estimated already for small parcels and that are invariant to the size of the parcel. We propose representing the parcels using density estimates of remote imaging pixels and provide a computational pipeline that combines the representation with arbitrary supervised learning algorithms, while allowing easy integration of multiple imaging sources. We demonstrate the method in the task of the automatic monitoring of autumn tillage method and vegetation cover of Finnish crop fields, based on the integrated analysis of intensity of Synthetic Aperture Radar (SAR) polarity bands of the Sentinel-1 satellite and spectral indices calculated from Sentinel-2 multispectral image data. We use a collection of 127,757 field parcels monitored in April 2018 and annotated to six tillage method and vegetation cover classes, reaching 70% classification accuracy for test parcels when using both SAR and multispectral data. Besides this task, the method could also directly be applied for other agricultural monitoring tasks, such as crop yield prediction.

scholarly journals Cartographic Remote-Sensing Monitoring of Glaciological Systems (Example, Mount El‛ Brus, U.S.S.R.) (Abstract)

1987 ◽  
Vol 9 ◽  
pp. 247-248
Author(s):  
Yu. F. Knizhnikov ◽  
V.I. Kravtsova ◽  
I.A. Labutina
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
Quantitative Information ◽  
Snow Accumulation ◽  
Aerial Photographs ◽  
Small Scale ◽  
Medium Scale ◽  
Remote Sensing Monitoring ◽  
Periglacial Zone

Remote-sensing methods in monitoring the glacierization of Mount EI‛ brus are used to produce base and dynamic maps, and to obtain quantitative information (dynamic indices) about the rate, intensity, and variations of the process. The monitoring system is divided, according to scope and territory covered, into small-scale for total glacierization and the periglacial zone, medium-scale for separate glaciers, and large-scale (detailed) for part of the glaciers or sectors of the adjoining slopes. The approximate relationship of even scales is 1 : 4.Small-scale monitoring remote-sensing systems are important for making maps showing the complex characteristics of the glaciological system. A series of maps was produced including geographical, those of high-altitude zones, slope and exposure angles, geological, glaciomorphological, climatic (temperature, precipitation, and winds), distribution of direct solar radiation, hydrological (source of streams), seats of avalanches, and landslides. All these data serve as a cartographical basis in monitoring the glacierization of Mount EI‛ brus. They are compiled from remotely sensed and Earth-based data.Current monitoring on a small scale includes observations of the conditions which determine the existence of the glacial system - this includes data on winter snowfall and the period of snow cover. These observations were obtained from meteorological and resource satellites, and from scanner data of medium and high resolution. Also important are observations of changes in the outline of glaciers, times of snowfall and character of the distribution of snow, and its redistribution due to avalanches and snowstorms. High-resolution space photographs, small-scale aerial photographs, and aerovisual observations provide the data for these observations. It has been determined that the area of the glaciers of Mount El‛ brus has been reduced by 1 % in the last 25 years, i.e. the rate of its deglacierization dropped sharply as compared to preceding decades.The role of quantitative information gains importance in the medium-scale level of monitoring. Topographical maps of separate glaciers compiled from aerial photographs or data from ground stereo-photogrammetric surveys constitute the base maps at this level. The main method used in monitoring were large-scale surveys from aircraft, perspective surveys from helicopters, and phototheodolite surveys. Multi-date surveys of the glaciers provide data about the changes in their outlines and height, the character of their relief, their moraines, the amount of snow accumulation and ablation in separate years, the surface rates of ice flow and their fluctuations. The techniques by which quantitative information is obtained about changes in the glaciers are derived from processing the data of multi-date surveys. The organization and techniques of phototheodolite surveys have been improved. A theory evolved for determining the surface-ice movement by stereo-photogrammetric means and the technique for it has also improved; algorithms and programs for machine processing of the data of multi-date surveys (ground and from aircraft) have been producedAt this level of monitoring, it has been found that the retreat rate of most glaciers has slowed down and several glaciers are now in equilibrium. Several glaciers became active at the beginning of the 1970s and 1980s; this was accompanied by an increase in their height and forward movement. For example, activation of Kyukyurtlyu Glacier has been recorded (higher surface and increasing flow rate) which has caused the glacier to move forward 100 m. Surveys at an interval of 2 years recorded the beginning of the process of retreat of this glacier.Detailed monitoring is used to detect the mechanism of the dynamic processes and to study it on local representative sectors. On a glacier it may take the form of annual surveys of its tongue, which makes it possible to observe the processes of formation of moraines and glacio-fluvial relief. Studies may also be made of the mechanism of the movement of avalanches and landslides, deducing their quantitative characteristics and appraising the results of avalanches and landslides. Multi-date surveys of sectors of the slopes provide information about processes in the periglacial zone. At this level, regularly repeated ground stereo-photogrammetric surveys are the main means of observation.Glaciological remote-sensing monitoring provides a wealth of data for theoretical development in the field of glaciology. It makes it possible to forecast and produce warnings about hazardous processes and phenomena.

scholarly journals RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System

2021 ◽  
Vol 13 (9) ◽  
pp. 1815
Author(s):  
Xiaohua Zhou ◽  
Xuezhi Wang ◽  
Yuanchun Zhou ◽  
Qinghui Lin ◽  
Jianghua Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Storage ◽  
Management System ◽  
Large Scale ◽  
Distributed Storage ◽  
Storage System ◽  
Remote Sensing Data ◽  
Data Access ◽  
Remote Sensing Images ◽  
Sensing Data

With the remarkable development and progress of earth-observation techniques, remote sensing data keep growing rapidly and their volume has reached exabyte scale. However, it's still a big challenge to manage and process such huge amounts of remote sensing data with complex and diverse structures. This paper designs and realizes a distributed storage system for large-scale remote sensing data storage, access, and retrieval, called RSIMS (remote sensing images management system), which is composed of three sub-modules: RSIAPI, RSIMeta, RSIData. Structured text metadata of different remote sensing images are all stored in RSIMeta based on a set of uniform models, and then indexed by the distributed multi-level Hilbert grids for high spatiotemporal retrieval performance. Unstructured binary image files are stored in RSIData, which provides large scalable storage capacity and efficient GDAL (Geospatial Data Abstraction Library) compatible I/O interfaces. Popular GIS software and tools (e.g., QGIS, ArcGIS, rasterio) can access data stored in RSIData directly. RSIAPI provides users a set of uniform interfaces for data access and retrieval, hiding the complex inner structures of RSIMS. The test results show that RSIMS can store and manage large amounts of remote sensing images from various sources with high and stable performance, and is easy to deploy and use.

scholarly journals RESEARCH ON THE CONSTRUCTION OF REMOTE SENSING AUTOMATIC INTERPRETATION SYMBOL BIG DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 381-384
Author(s):  
Y. Gao ◽  
R. Liu ◽  
J. Liu ◽  
T. Cheng
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Data Storage ◽  
Large Scale ◽  
Image Interpretation ◽  
Good Method ◽  
Learning Technology ◽  
Fast Method ◽  
Online Data ◽  
Automatic Interpretation

Remote sensing automatic interpretation symbol (RSAIS) is an inexpensive and fast method in providing precise in-situ information for image interpretation and accuracy. This study designed a scientific and precise RSAIS data characterization method, as well as a distributed and cloud architecture massive data storage method. Additionally, it introduced an offline and online data update mode and a dynamic data evaluation mechanism, with the aim to create an efficient approach for RSAIS big data construction. Finally, a national RSAIS database with more than 3 million samples covering 86 land types was constructed during 2013–2015 based on the National Geographic Conditions Monitoring Project of China and then annually updated since the 2016 period. The RSAIS big data has proven to be a good method for large scale image interpretation and field validation. It is also notable that it has the potential to solve image automatic interpretation with the assistance of deep learning technology in the remote sensing big data era.

The Potential of Photoelectrochemical Carbon Sinks

2018 ◽  
Author(s):  
Matthias May ◽  
Kira Rehfeld
Keyword(s):  
Global Warming ◽  
High Temperature ◽  
Greenhouse Gas ◽  
Large Scale ◽  
High Efficiency ◽  
Carbon Sink ◽  
Solar Fuels ◽  
Negative Emissions ◽  
Viable Approach ◽  
Low Sensitivity

Greenhouse gas emissions must be cut to limit global warming to 1.5-2C above preindustrial levels. Yet the rate of decarbonisation is currently too low to achieve this. Policy-relevant scenarios therefore rely on the permanent removal of CO<sub>2</sub> from the atmosphere. However, none of the envisaged technologies has demonstrated scalability to the decarbonization targets for the year 2050. In this analysis, we show that artificial photosynthesis for CO<sub>2</sub> reduction may deliver an efficient large-scale carbon sink. This technology is mainly developed towards solar fuels and its potential for negative emissions has been largely overlooked. With high efficiency and low sensitivity to high temperature and illumination conditions, it could, if developed towards a mature technology, present a viable approach to fill the gap in the negative emissions budget.<br>

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