scholarly journals Integration of Earth Observation Data and Spatial Approach to Delineate and Manage Aeolian Sand-Affected Wasteland in Highly Productive Lands of Haryana, India

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Kishan Singh Rawat ◽  
Shashi Vind Mishra ◽  
Sudhir Kumar Singh
Keyword(s):  
Accuracy Assessment ◽  
Satellite Image ◽  
Geographical Area ◽  
Integrated Approach ◽  
Observation Data ◽  
Visual Interpretation ◽  
Aeolian Sand ◽  
Spatial Approach ◽  
Total Geographical Area ◽  
Earth Observation Data

The western part of the country India is surrounded by Thar desert. Due to climate change, many regions in the world are facing different challenges. The objective of the study was to quantify the aeolian sand-affected land through integrated approach. The LANDSAT-ETM+ satellite image of 2009 has been used to distinguish recently affected areas by aeolian sand. A combined approach of digital classification backed with visual interpretation and ground verification was adopted. In addition to classification accuracy assessment was performed using field observations. Evidence based results of aeolian sand-affected areas have suggested that wasteland area has increased up to 4,427.55 ha (6.79%) of total geographical area. Two types of aeolian sands areas have been detected, namely, moderately affected (3,881.77 ha) and severely affected (545.79 ha). Moderately and severely affected aeolian soil lands have been more accurately mapped with reasonably good accuracy whereas smaller aeolian affected areas within croplands are mapped with low accuracy. The present study provides easy methodology for delineation, classification, and characterization of aeolian affected sands.

scholarly journals Signature capture of red soil patches and their acidity-A case study of Banka district, Bihar, India

2016 ◽  
Vol 8 (2) ◽  
pp. 874-878
Author(s):  
Binod K. Vimal ◽  
Rajkishore Kumar ◽  
C. D. Choudhary ◽  
Sunil Kumar ◽  
Rakesh Kumar ◽  
...  
Keyword(s):  
Spectral Band ◽  
Satellite Image ◽  
Geographical Area ◽  
Principal Component ◽  
Red Soil ◽  
Visual Interpretation ◽  
Spectral Bands ◽  
Total Geographical Area ◽  
Colour Composite ◽  
Soil Patch

Colour in soils as well as other object is the visual perceptual property which is perceived by human eye. They are governed by spectrum of light corresponding to wavelength or reflected energy of the material. Developed model for soil acidity is based on visual interpretation, principal component and spectral enhancement techniques by using of the satellite image (IRS LISS III, 2014). In this context, red soil patch is much sensitive in red spectral band comparison to green and blue spectral bands and perceived as red tone by human eyes but same soil patch appears green in false colour composite (FCC) image of NIR (0.70-0.80μm), Red (0.60-0.70 μm) and Green (0.50-0.60μm) bands. The maximum coverage of red soil patches having low pH < 6.5 (1:2.5) was recognized in 44.07 per cent of the total geographical area (3019.56 sq.km) under Banka district. Maximum red soil patches having their acidity were recognised in Katoria (18.56%), Chanan (15.15%), Bounsi (10.44%) and Banka (9.92%) blocks. Overall results indicated that variation of tone in different bands helps for the separation of red soil patches.

scholarly journals GEE TIMESERIES EXPLORER FOR QGIS – INSTANT ACCESS TO PETABYTES OF EARTH OBSERVATION DATA

2021 ◽  
Vol XLVI-4/W2-2021 ◽  
pp. 155-158
Author(s):  
P. Rufin ◽  
A. Rabe ◽  
L. Nill ◽  
P. Hostert
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Satellite Image ◽  
Google Earth ◽  
Earth Observation ◽  
Series Data ◽  
Observation Data ◽  
Local Data ◽  
Small Areas ◽  
Earth Observation Data

Abstract. Earth observation analysis workflows commonly require mass processing of time series data, with data volumes easily exceeding terabyte magnitude, even for relatively small areas of interest. Cloud processing platforms such as Google Earth Engine (GEE) leverage accessibility to satellite image archives and thus facilitate time series analysis workflows. Instant visualization of time series data and integration with local data sources is, however, currently not implemented or requires coding customized scripts or applications. Here, we present the GEE Timeseries Explorer plugin which grants instant access to GEE from within QGIS. It seamlessly integrates the QGIS user interface with a compact widget for visualizing time series from any predefined or customized GEE image collection. Users can visualize time series profiles for a given coordinate as an interactive plot or visualize images with customized band rendering within the QGIS map canvas. The plugin is available through the QGIS plugin repository and detailed documentation is available online (https://geetimeseriesexplorer.readthedocs.io/).

Integrative and Comprehensive Understanding on Polar Environments (iCUPE) – concept, results and outlook

2021 ◽  
Author(s):  
Tuukka Petäjä ◽  
Keyword(s):  
Integrated Approach ◽  
The Arctic ◽  
Observation Data ◽  
Polar Regions ◽  
Comprehensive Understanding ◽  
Natural Resource Extraction ◽  
Pollutant Concentrations ◽  
Earth Observation Data ◽  
Art Research ◽  
Set Up

&lt;p&gt;The world is changing. The polar regions are critical component in the Earth system and influenced by on-going megatrends, such as globalization and demographical changes. The extensive use of Arctic natural resources will have effects on regional pollutant concentrations in the Arctic. We set up the ERA-PLANET Strand 4 project &amp;#8220;iCUPE &amp;#8211; integrative and Comprehensive Understanding on Polar Environments&amp;#8221; to provide novel insights and observational data on global grand challenges with a polar focus. We deploy an integrated approach with in-situ observations, satellite remote sensing and multi-scale modeling to synthesize data from a suite of comprehensive long-term measurements, intensive campaigns, and satellites. This enabled us to deliver novel data and indicators descriptive of the polar environment. The iCUPE framework includes thematic state-of-the-art research and the provision of novel data in atmospheric pollution, local sources and transboundary transport, characterization of arctic surfaces and their changes, an assessment of the concentrations and impacts of heavy metals and persistent organic pollutants and their cycling, the quantification of emissions from natural resource extraction, and the validation and optimization of satellite Earth observation data streams. Here we summarize the project results and provide novel insights into continuation of the work.&amp;#160;&lt;/p&gt;

An integrated approach to the mapping of Canada's north

Polar Record ◽  
2007 ◽  
Vol 43 (2) ◽  
pp. 165-167
Author(s):  
Daniel Clavet ◽  
Alexandre Beaulieu
Keyword(s):  
Integrated Approach ◽  
Earth Observation ◽  
Observation Data ◽  
Northern Canada ◽  
Topographic Information ◽  
Space Agency ◽  
Earth Observation Data ◽  
Base Mapping

Between 2003 and 2006, the Centre for Topographic Information in Sherbrooke (CTI), Québec, under the Canadian Space Agency (CSA) Government Related Initiatives Programme (GRIP), conducted a project (Cartonord project) aimed at new base mapping at a scale of 1:50,000 for unmapped areas of northern Canada using earth observation data.

scholarly journals On-the-Fly Fusion of Remotely-Sensed Big Data Using an Elastic Computing Paradigm with a Containerized Spark Engine on Kubernetes

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2971
Author(s):  
Wei Huang ◽  
Jianzhong Zhou ◽  
Dongying Zhang
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Satellite Image ◽  
Remotely Sensed ◽  
Observation Data ◽  
Fusion Model ◽  
Computing Paradigm ◽  
Big Picture ◽  
Elastic Computing ◽  
Earth Observation Data

Remotely-sensed satellite image fusion is indispensable for the generation of long-term gap-free Earth observation data. While cloud computing (CC) provides the big picture for RS big data (RSBD), the fundamental question of the efficient fusion of RSBD on CC platforms has not yet been settled. To this end, we propose a lightweight cloud-native framework for the elastic processing of RSBD in this study. With the scaling mechanisms provided by both the Infrastructure as a Service (IaaS) and Platform as a Services (PaaS) of CC, the Spark-on-Kubernetes operator model running in the framework can enhance the efficiency of Spark-based algorithms without considering bottlenecks such as task latency caused by an unbalanced workload, and can ease the burden to tune the performance parameters for their parallel algorithms. Internally, we propose a task scheduling mechanism (TSM) to dynamically change the Spark executor pods’ affinities to the computing hosts. The TSM learns the workload of a computing host. Learning from the ratio between the number of completed and failed tasks on a computing host, the TSM dispatches Spark executor pods to newer and less-overwhelmed computing hosts. In order to illustrate the advantage, we implement a parallel enhanced spatial and temporal adaptive reflectance fusion model (PESTARFM) to enable the efficient fusion of big RS images with a Spark aggregation function. We construct an OpenStack cloud computing environment to test the usability of the framework. According to the experiments, TSM can improve the performance of the PESTARFM using only PaaS scaling to about 11.7%. When using both the IaaS and PaaS scaling, the maximum performance gain with the TSM can be even greater than 13.6%. The fusion of such big Sentinel and PlanetScope images requires less than 4 min in the experimental environment.

scholarly journals Open Data Cube for Natural Resources Mapping in Mexico

10.29007/d19p ◽  
2019 ◽  
Author(s):  
José Luis Ornelas De Anda ◽  
Juan Carlos Camacho Pérez ◽  
Hugo Alfredo Sánchez Miranda
Keyword(s):  
Natural Resources ◽  
Spatial Data ◽  
Landsat Imagery ◽  
Open Data ◽  
Temporal Analysis ◽  
Data Cube ◽  
Full Potential ◽  
Observation Data ◽  
Visual Interpretation ◽  
Earth Observation Data

In recent years, the efforts to enhance the analysis of Earth’s surface with satellite imagery have forced the scientific community to develop different techniques and methodologies. The Open Data Cube aims to provide tools to execute multi-temporal analysis and get accurate products, excluding low-quality pixels in small or large areas of study with an accuracy subject to the resolution of the data used for the analysis. This means that we can make use of the full potential of Earth observation data available from satellite data providers, in this document we take a closer look at Landsat Imagery and its applications. The beginning of the implementation of the Open Data Cube platform began in 2018, positioning itself as a valuable source of spatial data for Natural Resources projects in INEGI and seeks to support the decision-making process based on territorial analyzes with great certainty. The use of this technological solution represents a great leap between the traditional visual interpretation of raster data and the automated processing of data in time series.

scholarly journals Satellite Image Time Series Analysis for Big Earth Observation Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2428
Author(s):  
Rolf Simoes ◽  
Gilberto Camara ◽  
Gilberto Queiroz ◽  
Felipe Souza ◽  
Pedro R. Andrade ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Data Analysis ◽  
Time Series Analysis ◽  
Satellite Image ◽  
Earth Observation ◽  
Observation Data ◽  
Accuracy Measurement ◽  
Series Analysis ◽  
Earth Observation Data

The development of analytical software for big Earth observation data faces several challenges. Designers need to balance between conflicting factors. Solutions that are efficient for specific hardware architectures can not be used in other environments. Packages that work on generic hardware and open standards will not have the same performance as dedicated solutions. Software that assumes that its users are computer programmers are flexible but may be difficult to learn for a wide audience. This paper describes sits, an open-source R package for satellite image time series analysis using machine learning. To allow experts to use satellite imagery to the fullest extent, sits adopts a time-first, space-later approach. It supports the complete cycle of data analysis for land classification. Its API provides a simple but powerful set of functions. The software works in different cloud computing environments. Satellite image time series are input to machine learning classifiers, and the results are post-processed using spatial smoothing. Since machine learning methods need accurate training data, sits includes methods for quality assessment of training samples. The software also provides methods for validation and accuracy measurement. The package thus comprises a production environment for big EO data analysis. We show that this approach produces high accuracy for land use and land cover maps through a case study in the Cerrado biome, one of the world’s fast moving agricultural frontiers for the year 2018.

Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

GIS Business ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 12-14
Author(s):  
Eicher, A
Keyword(s):  
Earth Observation ◽  
Observation Data ◽  
Business World ◽  
The Earth ◽  
Earth Observation Data

Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

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