scholarly journals LIGHT POLLUTION IN SLOVENIA IN 2019 WITH SPECIAL REGARD TO NATURA 2000 AREAS

2020 ◽  
Vol 42 ◽  
pp. 69-81
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Remote Sensing Data ◽  
Natura 2000 ◽  
The State ◽  
Light Pollution ◽  
Sensing Data ◽  
Special Regard ◽  
Ljubljansko Barje

Light pollution in Slovenia in 2019 with special regard to Natura 2000 areas The article shows the state of light pollution in Slovenia. Remote sensing data from the Suomi satellite were analysed. Light pollution is shown by radiance expressed in nW/(sr cm2 ). In Slovenia, there are large differences in state of light polution. The most polluted areas are located in the area of larger settlements and in areas with higher levels of infrastructure. The spread of light does not stop at the borders of protected areas, so we also analyzed the state of light pollution in Natura 2000 sites in Slovenia. It turns out that the most lightpolluted areas are those that lie around larger settlements or suburbanised regions (Ljubljansko Barje, Šmarna gora, Drava).

scholarly journals Characterizing Light Pollution Trends across Protected Areas in China Using Nighttime Light Remote Sensing Data

2018 ◽  
Vol 7 (7) ◽  
pp. 243 ◽  
Author(s):  
Wei Jiang ◽  
Guojin He ◽  
Wanchun Leng ◽  
Tengfei Long ◽  
Guizhou Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Remote Sensing Data ◽  
Light Pollution ◽  
Sensing Data ◽  
Nighttime Light

scholarly journals Efficient land use based on remote sensing data

2021 ◽  
Vol 3 ◽  
pp. 180-185
Author(s):  
Y. M. Kenzhegaliyev ◽  
◽  
◽  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Remote Sensing Data ◽  
Crop Productivity ◽  
The State ◽  
Agricultural Crops ◽  
Earth Remote Sensing ◽  
Spectral Reflectivity ◽  
Sensing Data ◽  
Current Location

The goal -is to explore ways of using Earth remote sensing data for efficient land use. Methods - detailed information on current location of certain types of agricultural crops in the study areas has been summarized, which opens up opportunities for the effective use of cultivated areas. It was revealed that the basis of the principle of the method under consideration is the relationship between the state and structure of vegetation types with its reflective ability. It has been determined that information on the spectral reflective property of the vegetation cover in the future can help replace more laborious methods of laboratory analysis. For classification of farmland, satellite images of medium spatial resolution with a combination of channels in natural colors were selected. Results - a method for identifying agricultural plants by classification according to the maximum likelihood algorithm was considered. The commonly used complexes of geoinformation software products with modules for special image processing allow displaying indicators in the form of raster images. It is shown that the use of Earth remote sensing data is the most relevant solution in the field of crop recognition and makes it possible to simplify the implementation of such types of work as the analysis of the intensity of land use, the assessment of the degree of pollution with weeds and determination of crop productivity. Conclusions - the research results given in the article indicate that timely information on the current location of certain types of agricultural crops in the studied territories significantly simplifies the implementation of the tasks and increases the resource potential of agricultural lands. In turn, the timing of the survey and the state of environment affect the spectral reflectivity of vegetation.

scholarly journals Experience of the application of remote sensing data in the problems of geo-ecological monitoring of the coal mining area

2019 ◽  
Vol 75 ◽  
pp. 02001
Author(s):  
Olga Giniyatullina ◽  
Evgeniy Schastlivtsev ◽  
Vladimir Kovalev
Keyword(s):  
Remote Sensing ◽  
Coal Mining ◽  
Remote Sensing Data ◽  
Ecological Monitoring ◽  
Mining Area ◽  
The State ◽  
Sensing Data ◽  
Coal Mining Area ◽  
Mining Region ◽  
Dust Load

The experience of solving problems of geoecological monitoring of coal mining region with the use of remote sensing data is presented. The results of control over the boundaries of coal-mining enterprises, assessment of the degree of self-growth of dumps, monitoring of the state of vegetation near objects of coal mining and dust load of the area are shown.

Assessment of the state of urban ecosystems on the basis of remote sensing data

2013 ◽  
Vol 46 (4) ◽  
pp. 447-458
Author(s):  
I. N. Gorokhova ◽  
T. I. Borisochkina ◽  
E. A. Shishkonakova
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Urban Ecosystems ◽  
The State ◽  
Sensing Data

scholarly journals Statistical assessment of vegetation dynamics within protected areas using remote sensing data

2013 ◽  
Vol 43 (4) ◽  
pp. 5
Author(s):  
Maria Elena Menconi ◽  
David Grohmann
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Vegetation Dynamics ◽  
Vegetation Index ◽  
Photosynthetic Activity ◽  
Remote Sensing Data ◽  
Ratio Method ◽  
Vegetation Development ◽  
Sensing Data ◽  
Landscape Factors

This study aimed to test the effectiveness of protected areas to preserve vegetation. The first step was to identify vegetation suitable areas, designed as areas with optimal morphological terrain features for a good photosynthetic activity. These areas were defined according to the following landscape factors: slope, altitude, aspect and land use. Enhanced vegetation index (EVI) was chosen as vegetation dynamics indicator. This method is based on a statistical approach using remote sensing data in a geographic information system (GIS) environment. The correlation between EVI and landscape factor was evaluated using the frequency ratio method. Classes of landscape factors that show good correlation with a high EVI were combined to obtain vegetation suitable areas. Once identified, these areas and their vegetation dynamics were analysed by comparing the results obtained whenever these areas are included or not included in protected areas. A second EVI dataset was used to verify the accuracy in identifying vegetation suitable areas and the influence of each landscape factor considered in their identification. This validation process showed that vegetation suitable areas are significant in identifying areas with good photosynthetic activity. The effects analysis showed a positive influence of all landscape factors in determining suitability. This methodology, applied to central regions of Italy, shows that the vegetation suitable areas located inside protected areas are <em>greener</em> than those outside protected areas. This suggests that the protective measures established by the institution of the parks have proved to be effective, at least as far as the status of vegetation development is concerned.

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