scholarly journals AN ADAPTIVE WEB-BASED LEARNING ENVIRONMENT FOR THE APPLICATION OF REMOTE SENSING IN SCHOOLS

2016 ◽  
Vol XLI-B6 ◽  
pp. 53-56
Author(s):  
N. Wolf ◽  
V. Fuchsgruber ◽  
G. Riembauer ◽  
A. Siegmund
Keyword(s):  
Remote Sensing ◽  
Learning Environment ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Environmental Issues ◽  
Web Based ◽  
Learning Platform ◽  
Web Based Learning ◽  
School Teaching

Satellite images have great educational potential for teaching on environmental issues and can promote the motivation of young people to enter careers in natural science and technology. Due to the importance and ubiquity of remote sensing in science, industry and the public, the use of satellite imagery has been included into many school curricular in Germany. However, its implementation into school practice is still hesitant, mainly due to lack of teachers’ know-how and education materials that align with the curricula. In the project “Space<i>4</i>Geography” a web-based learning platform is developed with the aim to facilitate the application of satellite imagery in secondary school teaching and to foster effective student learning experiences in geography and other related subjects in an interdisciplinary way. The platform features ten learning modules demonstrating the exemplary application of original high spatial resolution remote sensing data (RapidEye and TerraSAR-X) to examine current environmental issues such as droughts, deforestation and urban sprawl. In this way, students will be introduced into the versatile applications of spaceborne earth observation and geospatial technologies. The integrated web-based remote sensing software “BLIF” equips the students with a toolset to explore, process and analyze the satellite images, thereby fostering the competence of students to work on geographical and environmental questions without requiring prior knowledge of remote sensing. This contribution presents the educational concept of the learning environment and its realization by the example of the learning module “Deforestation of the rainforest in Brasil”.

scholarly journals AN ADAPTIVE WEB-BASED LEARNING ENVIRONMENT FOR THE APPLICATION OF REMOTE SENSING IN SCHOOLS

2016 ◽  
Vol XLI-B6 ◽  
pp. 53-56 ◽  
Author(s):  
N. Wolf ◽  
V. Fuchsgruber ◽  
G. Riembauer ◽  
A. Siegmund
Keyword(s):  
Remote Sensing ◽  
Learning Environment ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Environmental Issues ◽  
Web Based ◽  
Learning Platform ◽  
Web Based Learning ◽  
School Teaching

Satellite images have great educational potential for teaching on environmental issues and can promote the motivation of young people to enter careers in natural science and technology. Due to the importance and ubiquity of remote sensing in science, industry and the public, the use of satellite imagery has been included into many school curricular in Germany. However, its implementation into school practice is still hesitant, mainly due to lack of teachers’ know-how and education materials that align with the curricula. In the project “Space&lt;i&gt;4&lt;/i&gt;Geography” a web-based learning platform is developed with the aim to facilitate the application of satellite imagery in secondary school teaching and to foster effective student learning experiences in geography and other related subjects in an interdisciplinary way. The platform features ten learning modules demonstrating the exemplary application of original high spatial resolution remote sensing data (RapidEye and TerraSAR-X) to examine current environmental issues such as droughts, deforestation and urban sprawl. In this way, students will be introduced into the versatile applications of spaceborne earth observation and geospatial technologies. The integrated web-based remote sensing software “BLIF” equips the students with a toolset to explore, process and analyze the satellite images, thereby fostering the competence of students to work on geographical and environmental questions without requiring prior knowledge of remote sensing. This contribution presents the educational concept of the learning environment and its realization by the example of the learning module “Deforestation of the rainforest in Brasil”.

scholarly journals A WEB-BASED LEARNING ENVIRONMENT OF REMOTE SENSING EXPERIMENTAL CLASS WITH PYTHON

2020 ◽  
Vol XLIII-B5-2020 ◽  
pp. 57-61
Author(s):  
J. Li ◽  
J. Sheng ◽  
Y. Chen ◽  
L. Ke ◽  
N. Yao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Learning Environment ◽  
Remote Sensing Data ◽  
Remote Sensing Image ◽  
Local Network ◽  
Web Based ◽  
Web Based Learning ◽  
Network Server ◽  
The Web

Abstract. Remote sensing course is a general disciplinary required course of human geography and urban-rural planning major. Its class hour is 48, including theoretical classes and experimental classes. Rapid technological developments is remote sensing area demand quick and steady changes in the education programme and its realization, especially in experimental classes. Experimental classes include: introduction to remote sensing software and basic operations, remote sensing data pre-processing (input, output, 2D and 3D terrain display, image cut, image mosaic, and projection transformation), remote sensing image enhancement, remote sensing image transformation, computer aided classification, image interpretation, and remote sensing image terrain analysis. There are two difficulties in the remote sensing experimental classes. First, it cost a lot of time to prepare the remote sensing software and the remote sensing images. Second, some students just want to use the remote sensing as a tool to investigate environment changing, some other students may want to study more remote sensing image processing technologies. A web-based learning environment of remote sensing is developed to facilitate the application of remote sensing experimental teaching. To make the learning more effective, there are eight modules including four optional modules. The Python programming language is chosen to implement the web-based remote sensing learning environment. The web-based learning environment is implemented in a local network server, including the remote sensing data processing algorithms and many satellite image data. Students can easily exercise the remote sensing experimental courses by connecting to the local network server. It is developed mainly for remote sensing experimental course, and also can be adopted by digital image processing or other courses. The feature of web-based learning may be very useful as the online education adopted because of Corona Virus Disease 2019. The results are encouraging and some recommendations will be extracted for the future.

scholarly journals Monitoring of specially protected natural territories of forest-steppe landscapes of the Stavropol upland by means of remote sensing data

2021 ◽  
Vol 27 (1) ◽  
pp. 364-376
Author(s):  
Tigran Shahbazyan
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Anthropogenic Factors ◽  
Forest Steppe ◽  
Landsat 8 ◽  
Sensing Data ◽  
Ndvi Index ◽  
The Impact

The article considers the methodology of monitoring specially protected natural areas using remote sensing data. The research materials are satellite images of the Landsat 5 and Landsat 8 satellites, obtained from the resource of the US Geological Survey. The key areas of the study were 3 specially protected areas located within the boundaries of the forest-steppe landscapes of the Stavropol upland, the reserves «Alexandrovskiy», «Russkiy Les», «Strizhament». The space survey materials were selected for the period 1991–2020, and the data from the summer seasons were used. The NDVI index is chosen as the method of processing the spectral channels of satellite imagery. To integrate long-term satellite imagery into a single raster image, the method of variance of the variation series for the NDVI index was used. The article describes an algorithm for processing satellite images, which allows us to identify the features of the dynamics of the vegetation state of the studied territory for the period 1991–2020. The bitmap image constructed by means of the variance of the NDVI index was classified by the quantile method, to translate numerical values into classes with qualitative characteristics. There were 4 classes of the territory according to the degree of dynamism of the vegetation state: “stable”, “slightly variable”, “moderately variable”, “highly variable”. The paper highlights the factors of landscape transformation, including natural and anthropogenic ones. In the course of the study, the determining influence of anthropogenic factors of transformation was noted. The greatest impact is on the reserve «Alexandrovskiy», the least on the reserve «Russkiy Les», in the reserve «Strizhament» the impact is expressed locally. The paper identifies the leading anthropogenic factors of vegetation transformation, based on their influence on vegetation.

scholarly journals An Integrated Model to Enhance Virtual Learning Environments with Current Social Networking Perspective

2018 ◽  
Vol 13 (09) ◽  
pp. 252 ◽  
Author(s):  
Kashif Laeeq ◽  
Zulfiqar Ali Memon
Keyword(s):  
Learning Environment ◽  
Social Networking ◽  
Social Networking Sites ◽  
Virtual Learning ◽  
Integrated Model ◽  
Virtual Learning Environment ◽  
Web Based ◽  
Learning Platform ◽  
Web Based Learning ◽  
E Learning

A Virtual Learning Environment (VLE) is an accumulation of incorporated soft-ware components, empowering the administration of web-based learning (online learning, e-learning). The quality of VLE is indispensable for the accomplishment of e-learning goals and responsible to engage students during web based learning. The attributes of existing VLES does not completely meet with the requirements of today’s e-learning. Consequently, decreases students’ interest, participation and engagement with web-based e-learning. In contrast, the learners’ engagement and participation are higher in social networking sites (SNS). For this reason, many researchers believe that the inclusion of features, similar to SNSs, in VLE may in-crease its effectivity. The goal of this research article is to investigate existing e-learning platform and make a room for SNSs and online tools for e-learning. The paper proposes an integrated model to enhance existing e-learning environment by incorporating the strength of SNSs and other potential tools with VLEs. This re-search article will provide a good direction and new thoughts for the researchers of technology supported learning-domain.

scholarly journals WATERMARKING METHOD OF REMOTE SENSING DATA USING STEGANOGRAPHY TECHNIQUE BASED ON LEAST SIGNIFICANT BIT HIDING

2018 ◽  
Vol 15 (1) ◽  
pp. 63
Author(s):  
Destri Yanti Hutapea ◽  
Octaviani Hutapea
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Original Image ◽  
Least Significant Bit ◽  
Confidential Information ◽  
Sensing Data ◽  
Remote Sensing Satellite

Remote sensing satellite imagery is currently needed to support the needs of information in various fields. Distribution of remote sensing data to users is done through electronic media. Therefore, it is necessary to make security and identity on remote sensing satellite images so that its function is not misused. This paper describes a method of adding confidential information to medium resolution remote sensing satellite images to identify the image using steganography technique. Steganography with the Least Significant Bit (LSB) method is chosen because the insertion of confidential information on the image is performed on the rightmost bits in each byte of data, where the rightmost bit has the smallest value. The experiment was performed on three Landsat 8 images with different area on each composite band 4,3,2 (true color) and 6,5,3 (false color). Visually the data that has been inserted information does not change with the original data. Visually, the image that has been inserted with confidential information (or stego image) is the same as the original image. Both images cannot be distinguished on histogram analysis.  The Mean Squared Error value of stego images of  all three data less than 0.053 compared with the original image.  This means that information security with steganographic techniques using the ideal LSB method is used on remote sensing satellite imagery.

A Case Study on Web-based Learning Environment Evaluation in Higher Education: Learner Perspectives in a Private University in Malaysia.

2020 ◽  
Vol 1 (2) ◽  
pp. 18-33
Author(s):  
Zarina Che Imbi ◽  
Tse-Kian Neo ◽  
Mai Neo
Keyword(s):  
Higher Education ◽  
Learning Environment ◽  
Private University ◽  
Systematic Evaluation ◽  
Digital Learning ◽  
Mixed Method Research ◽  
Web Based ◽  
Web Based Learning ◽  
Kirkpatrick’S Model

In the era of digital learning, multimedia-based classroom has been commonly used in higher education including Malaysian higher education institutions. A case study has been performed to evaluate web-based learning using Level 1 to 3 of Kirkpatrick's model in a multi-disciplinary course at Multimedia University, Malaysia. In this study, mixed method research was employed in which triangulation was performed from multiple sources of data collection to give deeper understanding. Students perceived that learning with multimedia was enjoyable. They were also motivated in learning and engaged through the use of web module as multimedia was perceived to motivate them and make learning fun. Students showed significant improvements in their knowledge based on the pre-test and post-test results on learning evaluation. Students were perceived to transfer the learning from web-based learning into the learning outcome. The systematic evaluation can provide the feedback that educators and institution as a whole need to improve the learning environment and programme quality. This study contributes to the research field by adding another perspective in evaluations of web-based learning. It also provides empirical evidence on student perspectives, learning and behaviour in a private university. It demonstrated that the Kirkpatrick's model is useful as an evaluation tool to be used in higher education.

Cuckoo Search Based Decision Fusion Techniques for Natural Terrain Understanding

2014 ◽  
Vol 5 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Arpita Sharma ◽  
Samiksha Goel
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Cuckoo Search ◽  
Decision Fusion ◽  
Majority Voting ◽  
Natural Terrain ◽  
Terrain Features ◽  
Level Fusion ◽  
Better Than

This paper proposes two novel nature inspired decision level fusion techniques, Cuckoo Search Decision Fusion (CSDF) and Improved Cuckoo Search Decision Fusion (ICSDF) for enhanced and refined extraction of terrain features from remote sensing data. The developed techniques derive their basis from a recently introduced bio-inspired meta-heuristic Cuckoo Search and modify it suitably to be used as a fusion technique. The algorithms are validated on remote sensing satellite images acquired by multispectral sensors namely LISS3 Sensor image of Alwar region in Rajasthan, India and LANDSAT Sensor image of Delhi region, India. Overall accuracies obtained are substantially better than those of the four individual terrain classifiers used for fusion. Results are also compared with majority voting and average weighing policy fusion strategies. A notable achievement of the proposed fusion techniques is that the two difficult to identify terrains namely barren and urban are identified with similar high accuracies as other well identified land cover types, which was not possible by single analyzers.

Study of Ancient Land Boundaries at Tauric Chersonesos Using Satellite Images

2019 ◽  
Vol 25 (1) ◽  
pp. 44-58 ◽  
Author(s):  
Edgar A. Terekhin ◽  
Tatiana N. Smekalova
Keyword(s):  
Remote Sensing ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Satellite Images ◽  
Agricultural Land ◽  
Remote Sensing Data ◽  
Field Surveys ◽  
The 1960S ◽  
Better Than ◽  
Made In

Abstract The near chora (agricultural land) of Tauric Chersonesos was investigated using multiyear remote sensing data and field surveys. The boundaries of the land plots were studied with GIS (Geographic Information Systems) technology and an analysis of satellite images. Reliable reconstruction of the borders has been done for 231 plots (from a total of about 380), which is approximately 53% of the Chersonesean chora. During the last 50 years, most of the ancient land plots have been destroyed by modern buildings, roads, or forests. However, in the 1960s, a significant part of the chora was still preserved. Changes in preservation with time were studied with the aid of satellite images that were made in 1966 and 2015. During that period, it was found that the number of plots with almost-complete preservation decreased from 47 to 0. Those land plots whose preservation was better than 50% dropped from 104 to 4. A temporal map shows this decline in preservation. It was found that the areas of land plots could be determined accurately with satellite images; compared to field surveys, this accuracy was about 99%.

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