scholarly journals Use of Satellite Imagery for Pastoral Resources Monitoring in Kossi Province (Burkina Faso)

2019 ◽  
Vol 7 (2) ◽  
pp. 1
Author(s):  
Sieza Yssouf ◽  
Gomgnimbou P. K Alain ◽  
Belem Adama ◽  
Serme Idriss
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Burkina Faso ◽  
Satellite Imagery ◽  
Field Data ◽  
Satellite Images ◽  
Temporal Evolution ◽  
Livestock Farming ◽  
Important Place ◽  
Livestock Sector

In Burkina Faso, livestock sector has an important place in the country's economy. Essentially extensive, this livestock farming is characterized by transhumance system, which consists of leading livestock sometimes over long distances in search of good pastures and water.Satellite images from different periods can be used to monitor the evolution of pastoral resources (pasture areas and surface water points) in a given area. Field data, coupled with satellite images, provide a better understanding of livestock transhumance movements in the study area. The objective of this study was to monitor the spatial and temporal evolution of pastoral resources using remote sensing tools in Kossi province. Field data, coupled with satellite images, provide a better understanding of livestock transhumance movements in the study area.

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 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”.

Fire regimes and post-fire evolution of burned areas in selected plant biomes of the planet studying the phenology of the landscape with time series of satellite images

2021 ◽  
Author(s):  
Nikos Koutsias ◽  
Anastasia Karamitsou ◽  
Foula Nioti ◽  
Frank Coutelieris
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Fire History ◽  
Fire Regime ◽  
Fire Regimes ◽  
Burned Areas ◽  
History Of ◽  
Sentinel 2

&lt;p&gt;Plant biomes and climatic zones are characterized by a specific type of fire regime which can be determined from the history of fires in the area and it is a synergy mainly of the climatic conditions and the functional characteristics of the types of vegetation. They correspond also to specific phenology types, a feature that can be useful for various applications related to vegetation monitoring, especially when remote sensing methods are used. Both the assessment of fire regime from the reconstruction of fire history and the monitoring of post-fire evolution of the burned areas can be studied with satellite remote sensing based on satellite time series images. The free availability of (i) Landsat satellite imagery by US Geological Survey (USGS, (ii) Sentinel-2 satellite imagery by ESA and (iii) MODIS satellite imagery by NASA / USGS allow low-cost data acquisition and processing (eg 1984-present) which otherwise would require very high costs. The purpose of this work is to determine the fire regime as well as the patterns of post-fire evolution of burned areas in selected vegetation/climate zones for the entire planet by studying the phenology of the landscape with time series of satellite images. More specifically, the three research questions we are negotiating are: (i) the reconstruction of the history of fires in the period 1984-2017 and the determination of fire regimes with&amp;#160;Landsat and Sentinel-2 satellite data , (ii) the assessment of pre-fire phenological pattern of vegetation and (iii) the monitoring and comparative evaluation of post-fire evolution patterns of the burned areas.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Acknowledgements&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;This research has been co-financed by the Operational Program &quot;Human Resources Development, Education and Lifelong Learning&quot; and is co-financed by the European Union (European Social Fund) and Greek national funds.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

SOME RESULTS OF CROP STRESS MONITORING BY REMOTE SENSING IN NORTHERN MONGOLIA

2018 ◽  
Vol 21 (02) ◽  
pp. 59-63 ◽  
Author(s):  
Tuvshinbayar D ◽  
Erdenetuya B ◽  
Erkhembayar E ◽  
Batbileg B ◽  
Sarangerel J
Keyword(s):  
Remote Sensing ◽  
Stress Field ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Satellite Image ◽  
Stress Monitoring ◽  
Wheat Growth ◽  
Northern Mongolia ◽  
Middle Period ◽  
Farming Industry

This paper presents the spatiotemporal monitoring crop stress in the first period of wheat phenology by satellite image in northern Mongolia. We used 2 satellite images Landsat8 that are dated June 23rd and July 12th of this year. Also calculated are same ratio-based indices such as NDVI, LAI and GNDVI of 2 images in the middle period of wheat phrenology, which are indicated crop stress field reports. NDVI and LAI, derived from satellite imagery are the most important characteristics of wheat stress monitoring. According to our result, as shown satellite image, wheat growth is critical and fuzzily, which is predicted necessary some management for farming. Our results show the ability of pre-processing image to analyze and visualize agricultural environments and workflows has proven to be beneficial to those involved in the farming industry.

scholarly journals Surface Water Quality Assessment Using Remote Sensing, Gis and Artificial Intelligence

2019 ◽  
Vol 1 (1) ◽  
pp. 113-122
Author(s):  
A. KC ◽  
A. Chalise ◽  
D. Parajuli ◽  
N. Dhital ◽  
S. Shrestha ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Water Quality ◽  
Surface Water ◽  
Quality Assessment ◽  
Satellite Imagery ◽  
Surface Water Quality ◽  
Water Quality Assessment ◽  
The Relationship ◽  
The City

The deterioration of surface water quality occurs due to the presence of various types of pollutants from human activities such as agriculture, industry, construction, deforestation, etc. Thus, the presence of various pollutants in water bodies can lead to deterioration of both surface water quality and aquatic life. Conventional surface water quality assessment methods are widely performed using laboratory analysis, which are labour intensive, costly, and time consuming. Moreover, these methods can only provide individual concentration of surface water quality parameters (SWQPs), measured at monitoring stations and shown in a discrete point format, which are difficult for decision-makers to understand without providing the overall patterns of surface water quality. To such problem, Remote Sensing has been a blessing because of its low cost, spatial continuity and temporal consistency. The relationship between SWQPs and satellite data is complex to be modelled accurately by using regression-based methods. Therefore, our study attempts to develop an artificial intelligence modelling method for mapping concentrations of both optical and non-optical SWQPs. This study aims to develop techniques for estimating the concentration of both optical and non-optical SWQPs from Satellite Imagery (Landsat8) which supports coastal studies and mapping the complex relationship between satellite multi-spectral signature and concentration of SWQPs. It will also focus on classifying the most significant SWQPs that contribute to both spatial and temporal surface water quality. In contrast to traditionally performed surface water quality assessment methods, this research project will be focused on identifying such parameters incorporating the new and evolving machine intelligence that is Artificial Intelligence (AI). Significant number of samples have to be collected along with the GPS data which is used to model the relationship. In this context, a remote-sensing framework based on the back-propagation neural network (BPNN) will be developed to quantify concentrations of different SWQPs from the Landsat8 satellite imagery. The study area chosen for this research is Bijayapur River of distance approximately 10 km flowing above, through and down the Pokhara city. The sole purpose of this research is to examine the water quality before it flows through the city and analysing after it passes through the city.

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.

scholarly journals MONITORING OF URBAN AREA WITH SATELLITE IMAGERY

2019 ◽  
Vol 6 (1) ◽  
pp. 86-93
Author(s):  
Marina Plotnikova ◽  
Elena Khlebnikova
Keyword(s):  
Remote Sensing ◽  
Urban Area ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Satellite Images ◽  
Automated Detection ◽  
The Earth ◽  
Erdas Imagine ◽  
New Facilities

The problem of identifying changes occurring in the territory of an urban area due to construction of new facilities, renovations and reconstructions using remote sensing of the Earth was considered. Various algorithms for automated detection of changes from different-time satellite images in the ERDAS IMAGINE 2010 program are analyzed in practice. Factors that must be considered when monitoring urban areas are identified.

scholarly journals Detecting shrub recovery in sagebrush steppe: comparing Landsat-derived maps with field data on historical wildfires

Fire Ecology ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Cara Applestein ◽  
Matthew J. Germino
Keyword(s):  
Remote Sensing ◽  
Great Basin ◽  
Satellite Imagery ◽  
Field Data ◽  
Burned Area ◽  
Sagebrush Steppe ◽  
Pixel Resolution ◽  
Sensing Applications ◽  
Steppe Ecosystems ◽  
Landsat Satellite

Abstract Background The need for basic information on spatial distribution and abundance of plant species for research and management in semiarid ecosystems is frequently unmet. This need is particularly acute in the large areas impacted by megafires in sagebrush steppe ecosystems, which require frequently updated information about increases in exotic annual invaders or recovery of desirable perennials. Remote sensing provides one avenue for obtaining this information. We considered how a vegetation model based on Landsat satellite imagery (30 m pixel resolution; annual images from 1985 to 2018) known as the National Land Cover Database (NLCD) “Back-in-Time” fractional component time-series, compared with field-based vegetation measurements. The comparisons focused on detection thresholds of post-fire emergence of fire-intolerant Artemisia L. species, primarily A. tridentata Nutt. (big sagebrush). Sagebrushes are scarce after fire and their paucity over vast burn areas creates challenges for detection by remote sensing. Measurements were made extensively across the Great Basin, USA, on eight burn scars encompassing ~500 000 ha with 80 plots sampled, and intensively on a single 113 000 ha burned area where we sampled 1454 plots. Results Estimates of sagebrush cover from the NLCD were, as a mean, 6.5% greater than field-based estimates, and variance around this mean was high. The contrast between sagebrush cover measurements in field data and NLCD data in burned landscapes was considerable given that maximum cover values of sagebrush were ~35% in the field. It took approximately four to six years after the fire for NLCD to detect consistent, reliable signs of sagebrush recovery, and sagebrush cover estimated by NLCD ranged from 3 to 13% (equating to 0 to 7% in field estimates) at these times. The stabilization of cover and presence four to six years after fire contrasted with previous field-based studies that observed fluctuations over longer time periods. Conclusions While results of this study indicated that further improvement of remote sensing applications would be necessary to assess initial sagebrush recovery patterns, they also showed that Landsat satellite imagery detects the influence of burns and that the NLCD data tend to show faster rates of recovery relative to field observations.

scholarly journals A new tool for the remote sensing of groundwater tables: satellite images of pastoral wells

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Bernard Collignon
Keyword(s):  
Remote Sensing ◽  
Water Table ◽  
Satellite Images ◽  
Open Data ◽  
Google Earth ◽  
Livestock Farming ◽  
The Sustainable Development ◽  
Remote Sensing Technique ◽  
High Resolution Satellite Images ◽  
Sahel Region

Abstract In the Sahara and the Sahel, groundwater is a limited and indispensable resource for pastoral livestock farming. The daily life and work of the herders are organised around the location of the wells and the depth of the water table. To ensure the sustainable development of these regions, it is therefore essential to develop accurate piezometric maps, even in the areas that are most difficult to access. Thanks to high-resolution satellite images, the tracks made by cattle, goats and camels in the Sahara and Sahel could become a key indicator of the depth of the water table. In the northern Sahel, pastoralists water their livestock from deep wells. To draw water, they hitch oxen or camels to a rope whose length is an accurate measure of the depth of the piezometric surface of the water table. When pulling on this rope, the animals leave deep tracks on the ground that can be observed and measured on satellite images. We have developed a remote sensing technique that allows us to (a) identify pastoral wells, (b) isolate the tracks left by the animals used to draw water, and (c) use these animal tracks to estimate the water depth. After carefully calibrating the method, we were able to use open data (Landsat) and satellites images freely accessible data thanks to Google Earth Pro (SPOT and Worldview) to draw up, in just a few weeks, the piezometric map of a large aquifer (200,000km2) that is not easily accessible by other means due to the prevailing insecurity that has persisted in this part of the Sahel region for several years. This same method was then subsequently tested and validated on two other aquifers, one in Nigeria and one in Niger.

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”.

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