Remote Sensing Prospecting Deposit Model Using Aster Data: A Case Study from Southern Part of Taxkorgan Tajik Autonomous County, China

2015 ◽  
Vol 733 ◽  
pp. 124-129
Author(s):  
Hui Zhi Wu ◽  
Qi Gang Jiang ◽  
Chao Jun Bai
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Mineral Exploration ◽  
Information Source ◽  
Remote Sensing Data ◽  
Screening Methods ◽  
Data Sets ◽  
Sensing Data ◽  
Work Area ◽  
Geological Conditions

This work uses multiple types of remote sensing data to develop a model-based mineral exploration method. Data used include Worldview-2 satellite data as the main information source supplemented by QuickBird satellite data to assist in geological interpretations and ASTER satellite data to extract remote sensing anomalies. We have enhanced the spectral and spatial resolution of the remote sensing data using ENVI software. Human-computer interaction methods have been used to confirm the geological conditions. We have interpreted 24 distinct lithologic units, including various types of metamorphic and sedimentary rocks. A total of 471 remote sensing anomalies were delineated, consisting of 173 hydroxyl anomalies and 298 iron-staining anomalies. Geological background screening methods were applied to identify 98 remote sensing anomalies, of which 29 were recommended for further study. Based on the interpretation of anomalies extracted from the ASTER and other geological remote sensing data sets, we have established a typical-deposit prospecting model. In the model, we delineated remote sensing prospecting targets by considering: remote sensing anomalies, geologic bodies and structures, geophysical anomalies and geochemical anomalies. Using this model, we divided the work area into two zones based on types of mineral generation. Seven prospecting targets (one A class, three B class and three C class) were identified. Trenching and block sorting methods were conducted for field verification, and resulted in the discovery of two copper and two iron occurrences with commercial potential.

scholarly journals Suitability of satellite remote sensing data for yield estimation in northeast Germany

2021 ◽  
Author(s):  
Claudia Vallentin ◽  
Katharina Harfenmeister ◽  
Sibylle Itzerott ◽  
Birgit Kleinschmit ◽  
Christopher Conrad ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Precision Agriculture ◽  
Satellite Images ◽  
Spectral Band ◽  
Remote Sensing Data ◽  
Data Sets ◽  
Yield Estimation ◽  
Yield Data ◽  
Sensing Data

AbstractInformation provided by satellite data is becoming increasingly important in the field of agriculture. Estimating biomass, nitrogen content or crop yield can improve farm management and optimize precision agriculture applications. A vast amount of data is made available both as map material and from space. However, it is up to the user to select the appropriate data for a particular problem. Without the appropriate knowledge, this may even entail an economic risk. This study therefore investigates the direct relationship between satellite data from six different optical sensors as well as different soil and relief parameters and yield data from cereal and canola recorded by the thresher in the field. A time series of 13 years is considered, with 947 yield data sets consisting of dense point data sets and 755 satellite images. To answer the question of how well the relationship between remote sensing data and yield is, the correlation coefficient r per field is calculated and interpreted in terms of crop type, phenology, and sensor characteristics. The correlation value r is particularly high when a field and its crop are spatially heterogeneous and when the correct phenological time of the crop is reached at the time of satellite imaging. Satellite images with higher resolution, such as RapidEye and Sentinel-2 performed better in comparison with lower resolution sensors of the Landsat series. The additional Red Edge spectral band also has advantage, especially for cereal yield estimation. The study concludes that there are high correlation values between yield data and satellite data, but several conditions must be met which are presented and discussed here.

Evaluation of Capacity of Salandi Reservoir Using Remote Sensing Data Along With Qgis Software

2018 ◽  
Vol 6 (8) ◽  
pp. 339
Author(s):  
Rupali Dhal ◽  
D. P. Satapathy
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Remote Sensing Data ◽  
Project Work ◽  
Space Technology ◽  
Reservoir Water ◽  
Sediment Distribution ◽  
Sensing Data ◽  
Spread Area ◽  
Water Spread

The dynamic aspects of the reservoir which are water spread, suspended sediment distribution and concentration requires regular and periodical mapping and monitoring. Sedimentation in a reservoir affects the capacity of the reservoir by affecting both life and dead storages. The life of a reservoir depends on the rate of siltation. The various aspects and behavior of the reservoir sedimentation, like the process of sedimentation in the reservoir, sources of sediments, measures to check the sediment and limitations of space technology have been discussed in this report. Multi satellite remote sensing data provide information on elevation contours in the form of water spread area. Any reduction in reservoir water spread area at a specified elevation corresponding to the date of satellite data is an indication of sediment deposition. Thus the quality of sediment load that is settled down over a period of time can be determined by evaluating the change in the aerial spread of the reservoir at various elevations. Salandi reservoir project work was completed in 1982 and the same is taken as the year of first impounding. The original gross and live storages capacities were 565 MCM& 556.50 MCM respectively. In SRS CWC (2009), they found that live storage capacity of the Salandi reservoir is 518.61 MCM witnessing a loss of 37.89 MCM (i.e. 6.81%) in a period of 27 years.The data obtained through satellite enables us to study the aspects on various scales and at different stages. This report comprises of the use of satellite to obtain data for the years 2009-2013 through remote sensing in the sedimentation study of Salandi reservoir. After analysis of the satellite data in the present study(2017), it is found that live capacity of the reservoir of the Salandi reservoir in 2017 is 524.19MCM witnessing a loss of 32.31 MCM (i.e. 5.80%)in a period of 35 years. This accounts for live capacity loss of 0.16 % per annum since 1982. The trap efficiencies of this reservoir evaluated by using Brown’s, Brune’s and Gill’s methods are 94.03%, 98.01and 99.94% respectively. Thus, the average trap efficiency of the Salandi Reservoir is obtained as 97.32%.

Application of geoinformation technologies for arranging a satellite monitoring system

2019 ◽  
Vol 943 (1) ◽  
pp. 110-118
Author(s):  
A.A. Kadochnikov
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Remote Sensing Data ◽  
Web Gis ◽  
Nature Management ◽  
Satellite Monitoring ◽  
Technological Support ◽  
Sensing Data ◽  
Territorial Planning ◽  
Operational Information

Today, remote sensing data are an important source of operational information about the environment for thematic GIS, this data can be used for the development of water, forestry and agriculture management, in the ecology and nature management, with territorial planning, etc. To solve the problem of ensuring the effective use of the space activities’results in the Krasnoyarsk Territory a United Regional Remote Sensing Center was created. On the basis of the Center, a new satellite receiving complex of FRC KSC SB RAS was put into operation. It is currently receiving satellite data from TERRA, AQUA, Suomi NPP and FENG-YUN satellites. Within the framework in cooperation with the Siberian Regional Center for Remote Sensing the Earth, an archive of satellite data from domestic Resource-P and Meteor-M2 satellites was created. The work considers some features of softwaredevelopment and technological support tools for loading, processing and publishing remote sensing data. The product is created in the service-oriented paradigm based on geoportal technologies and interactive web-cartography. The focus in this article is paid to the peculiarities of implementing the software components of the web GIS, the efficient processing and presentation of geospatial data.

scholarly journals The predictability of reported drought events and impacts in the Ebro Basin using six different remote sensing data sets

2017 ◽  
Vol 21 (9) ◽  
pp. 4747-4765 ◽  
Author(s):  
Clara Linés ◽  
Micha Werner ◽  
Wim Bastiaanssen
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Data Sets ◽  
Drought Management ◽  
Ebro Basin ◽  
Sensing Data ◽  
Drought Impacts ◽  
Management Plans ◽  
Drought Indicators ◽  
Selection Of

Abstract. The implementation of drought management plans contributes to reduce the wide range of adverse impacts caused by water shortage. A crucial element of the development of drought management plans is the selection of appropriate indicators and their associated thresholds to detect drought events and monitor the evolution. Drought indicators should be able to detect emerging drought processes that will lead to impacts with sufficient anticipation to allow measures to be undertaken effectively. However, in the selection of appropriate drought indicators, the connection to the final impacts is often disregarded. This paper explores the utility of remotely sensed data sets to detect early stages of drought at the river basin scale and determine how much time can be gained to inform operational land and water management practices. Six different remote sensing data sets with different spectral origins and measurement frequencies are considered, complemented by a group of classical in situ hydrologic indicators. Their predictive power to detect past drought events is tested in the Ebro Basin. Qualitative (binary information based on media records) and quantitative (crop yields) data of drought events and impacts spanning a period of 12 years are used as a benchmark in the analysis. Results show that early signs of drought impacts can be detected up to 6 months before impacts are reported in newspapers, with the best correlation–anticipation relationships for the standard precipitation index (SPI), the normalised difference vegetation index (NDVI) and evapotranspiration (ET). Soil moisture (SM) and land surface temperature (LST) offer also good anticipation but with weaker correlations, while gross primary production (GPP) presents moderate positive correlations only for some of the rain-fed areas. Although classical hydrological information from water levels and water flows provided better anticipation than remote sensing indicators in most of the areas, correlations were found to be weaker. The indicators show a consistent behaviour with respect to the different levels of crop yield in rain-fed areas among the analysed years, with SPI, NDVI and ET providing again the stronger correlations. Overall, the results confirm remote sensing products' ability to anticipate reported drought impacts and therefore appear as a useful source of information to support drought management decisions.

scholarly journals Risk management support through India Remote Sensing Satellites

2014 ◽  
Vol XL-8 ◽  
pp. 1-6
Author(s):  
N. Aparna ◽  
A. V. Ramani ◽  
R. Nagaraja
Keyword(s):  
Remote Sensing ◽  
Risk Management ◽  
Satellite Data ◽  
Global Climate ◽  
Remote Sensing Data ◽  
Temporal Variations ◽  
Geographical Information ◽  
Indian Remote Sensing ◽  
Management Support ◽  
Sensing Data

Remote Sensing along with Geographical Information System (GIS) has been proven as a very important tools for the monitoring of the Earth resources and the detection of its temporal variations. A variety of operational National applications in the fields of Crop yield estimation , flood monitoring, forest fire detection, landslide and land cover variations were shown in the last 25 years using the Remote Sensing data. The technology has proven very useful for risk management like by mapping of flood inundated areas identifying of escape routes and for identifying the locations of temporary housing or a-posteriori evaluation of damaged areas etc. The demand and need for Remote Sensing satellite data for such applications has increased tremendously. This can be attributed to the technology adaptation and also the happening of disasters due to the global climate changes or the urbanization. However, the real-time utilization of remote sensing data for emergency situations is still a difficult task because of the lack of a dedicated system (constellation) of satellites providing a day-to-day revisit of any area on the globe. The need of the day is to provide satellite data with the shortest delay. Tasking the satellite to product dissemination to the user is to be done in few hours. Indian Remote Sensing satellites with a range of resolutions from 1 km to 1 m has been supporting disasters both National & International. In this paper, an attempt has been made to describe the expected performance and limitations of the Indian Remote Sensing Satellites available for risk management applications, as well as an analysis of future systems Cartosat-2D, 2E ,Resourcesat-2R &RISAT-1A. This paper also attempts to describe the criteria of satellite selection for programming for the purpose of risk management with a special emphasis on planning RISAT-1(SAR sensor).

scholarly journals Giovanni: The Bridge Between Data and Science

Eos ◽  
2017 ◽  
Author(s):  
Zhong Liu ◽  
James Acker
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Earth Science ◽  
Remote Sensing Data ◽  
Data Sets ◽  
Science Data ◽  
Web Based ◽  
Sensing Data ◽  
Daunting Task ◽  
Earth Science Data

Using satellite remote sensing data sets can be a daunting task. Giovanni, a Web-based tool, facilitates access, visualization, and exploration for many of NASA’s Earth science data sets.

scholarly journals A review of machine learning in processing remote sensing data for mineral exploration

2022 ◽  
Vol 268 ◽  
pp. 112750
Author(s):  
Hojat Shirmard ◽  
Ehsan Farahbakhsh ◽  
R. Dietmar Müller ◽  
Rohitash Chandra
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Mineral Exploration ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Analyses of the Prądnik riverbed Shape Based on Archival and Contemporary Data Sets—Old Maps, LiDAR, DTMs, Orthophotomaps and Cross-Sectional Profile Measurements

2020 ◽  
Vol 12 (14) ◽  
pp. 2208 ◽  
Author(s):  
Stanisław Szombara ◽  
Paulina Lewińska ◽  
Anna Żądło ◽  
Marta Róg ◽  
Kamil Maciuk
Keyword(s):  
Remote Sensing ◽  
Survey Methods ◽  
Remote Sensing Data ◽  
Sufficient Information ◽  
Data Sets ◽  
Time Step ◽  
Mountain Areas ◽  
Cross Sectional ◽  
Sensing Data ◽  
Old Maps

Analyses of riverbed shape evolution are crucial for environmental protection and local water management. For narrow rivers located in forested, mountain areas, it is difficult to use remote sensing data used for large river regions. We performed a study of the Prądnik River, located in the Ojców National Park (ONP), Poland. A multitemporal analysis of various data sets was performed. Light detection and ranging (LiDAR)-based data and orthophotomaps were compared with classical survey methods, and 78 cross-sectional profiles were done via GNSS and tachymetry. In order to add an extra time step, the old maps of this region were gathered, and their content was compared with contemporary data. The analysis of remote sensing data suggests that they do not provide sufficient information on the state and changes of riverbanks, river course or river depth. LiDAR data sets do not show river bottoms, and, due to plant life, do not document riverbanks. The orthophotomaps, due to tree coverage and shades, cannot be used for tracking the whole river course. The quality of old maps allows only for general shape analysis over time. This paper shows that traditional survey methods provide sufficient accuracy for such analysis, and the resulted cross-sectional profiles can and should be used to validate other, remote sensing, data sets. We diagnosed problems with the inventory and monitoring of such objects and proposed methods to refine the data acquisition.

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