scholarly journals Recognition of indicative landscape objects within protected areas

2020 ◽  
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Satellite Imagery ◽  
Remote Sensing Data ◽  
Practical Significance ◽  
Conservation Areas ◽  
Field Methods ◽  
Sensing Data ◽  
High Resolution Satellite Imagery ◽  
Visual Changes

Formulation of the problem. In this article the author describes monitoring of landscape objects within protected area. We created 'image of landscape' from remote sensing data. The developed methodology allows to obtain remotely information about visual changes, to analyze and predict the further development of landscapes of the facies level. It is difficult to investigate nature conservation areas at the facies level in areas with plant diversity. Field methods are time-consuming and labor-intensive, but changes can occur frequently. We offer a methodology for identifying indicative landscape objects by creating an image and its visualization using high-resolution satellite imagery decoding Sentinel-2 (resolution 10 m) and Planet Scope (resolution 3 m). This method with using satellite imagery of study makes it possible to gain access to the terrain that is accessible in hard-to-reach places, namely in swampy areas, in dense forest impassable territories and others. The purpose of the article. The main goal is creating methodic for recognition indicative objects of landscape within protected territories through the appearance of visual changes by the cameral method. Materials and methods. We have improved the method of processing satellite images to identify indicative objects of changes in landscapes at the facies level. We used the method of controlled classification to obtain "a picture" of the landscape in office conditions, carried out an analysis of comparison on the ground and identified objects of interest. Based on experiments we chosen supervised classification and methods for different resolution of remote sensing data. Results and scientific novelty. We have changed the traditional landscape study process and approach in our work. We created a landscape rendering model and then carried out work directly on the ground, comparing the characteristics. this allows you to explore the territory at a distance, in hard-to-reach places and in protected areas, which allows a person to analyze information at a distance, predict and take further measures to preserve landscapes and individual objects. Practical significance. Identification of indicative objects within protected areas allows monitoring changes in landscapes, analyzing and taking measures to preserve them. Systematization of the entire analysis during processing allows you to identify changes in time even in hard-to-reach regions and quickly receive information remotely. The analyzed data allow designing a successful combination of the normal functioning of nature and human activity.

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 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.

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 Analytical review of remote sensing data sources suitable for mapping of land resource in Chernivtsi region

2013 ◽  
pp. 331-338
Author(s):  
J. Smirnov
Keyword(s):  
Remote Sensing ◽  
Key Words ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Remote Sensing Data ◽  
Data Sources ◽  
Land Resource ◽  
Land Resources ◽  
Sensing Data ◽  
Analytical Review

In the article described the sources of remote sensing data and analyzed their suitability for involvement in the process Chernivtsi region land resources mapping. Taken into account space surveying systems of different spatial resolution and aerial photographic surveys. As a result, have been identified the best sources of data that can be used in the Chernivtsi region land resources mapping. Key words: land resources, remote sensing, satellite imagery, mapping of land resources, sources of remote sensing data.

scholarly journals Geoinformation Modeling of Flooded Areas in Settlements (in the Example of Lutsk)

2018 ◽  
Author(s):  
Anna Shostak ◽  
Volodymyr Voloshyn ◽  
Oleksandr Melnyk ◽  
Pavlo Manko
Keyword(s):  
Remote Sensing ◽  
Water Level ◽  
Remote Sensing Data ◽  
Water Levels ◽  
Practical Significance ◽  
Natural Phenomenon ◽  
Short Term ◽  
Sensing Data ◽  
Geoinformation Modeling ◽  
Flooded Areas

Object. Flooding in Ukraine is a common natural phenomenon that repeats periodically and in some cases it becomes disastrous. In an average year floods on the rivers of Volyn region take place from one to three times which extend beyond the limits of the floodplain. The floodplain of Styr river is located in the historical center of Lutsk city, that`s why issues of research and forecasting of floods are very important for a given city. Methodology. Using modern technologies of geodesy and remote sensing allows to quickly determine and predict the floodplain area of settlements. Based on the statistical data of the Volyn Regional Center for Hydrometeorology during the 7 year period 2011-2017 about water levels of the river Styr. We conducted mathematical modeling of fluctuations of water levels within the territory of Lutsk, based on creating a partial Fourier series for discrete values of middle-ten-day water levels values. The post hydrological measurements of Styr river water levels in the territory of Lutsk located on the Shevchenko Street comply with an altitude 172.87 meters. Based on the data of short-term flood forecasting in February and March, and relief data from the Department of Architecture and Urban Development of Volyn State Administration, we conducted visualization of the results using geographic information system QGIS. Results. The results of mathematical processing were the basis for geoinformation simulation of flooded areas using remote sensing data that are publicly available. Use of statistical and geospatial data in this article has great potential for further application in modeling the processes of natural and technogenic origin. Scientific novelty. The mathematical model of short-term forecasting of water levels during the flood period on the river Styr with implementation of geoinformation modeling of flooded areas using remote sensing data is proposed. Practical significance. The research results of water level changes on the Styr River and flood zones within the limits of Lutsk is proposed. The spring flood in February-March 2018, with the maximum water level 5.33 m, corresponds to an absolute mark of 178.20 m, which is forecasted in this article.

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 Deforestation pattern dynamics in protected areas of the Brazilian Legal Amazon using remote sensing data

2018 ◽  
Vol 100 ◽  
pp. 101-115 ◽  
Author(s):  
Ana I.R. Cabral ◽  
Carlos Saito ◽  
Henrique Pereira ◽  
Anne Elisabeth Laques
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Pattern Dynamics

scholarly journals A new look at the Butic Canal, Egypt

2021 ◽  
Vol 70 (1) ◽  
pp. 29-38
Author(s):  
Robert Schiestl
Keyword(s):  
Remote Sensing ◽  
Roman Empire ◽  
Digital Elevation Model ◽  
Satellite Imagery ◽  
Nile Delta ◽  
Remote Sensing Data ◽  
Historical Sources ◽  
Sensing Data ◽  
Digital Elevation ◽  
Elevation Model

Abstract. The Butic Canal – a Roman period transversal route across the northern Nile Delta – was the longest artificial watercourse in the Nile Delta, yet it remains very poorly understood. To date, the canal has not yet been verified by archeological excavations. The route of the eastern section of the canal has been indirectly identified based on a linear elevated feature most likely representing earth from the excavation of the canal. This study combines the analysis of historical sources and remote sensing data, such as satellite imagery and the TanDEM-X digital elevation model, in order to discuss its date of construction, route, and functions. Based on the data of the digital elevation model, new constructional features are visible in the eastern delta providing the first detailed route of a Roman-era artificial watercourse in Egypt. It is suggested that the canal's construction is placed in the context of imperial investments in the infrastructure of the eastern part of the Roman empire.

scholarly journals Gudalur Spectral Target Detection (GST-D): A New Benchmark Dataset and Engineered Material Target Detection in Multi-Platform Remote Sensing Data

2020 ◽  
Vol 12 (13) ◽  
pp. 2145 ◽  
Author(s):  
Sudhanshu Shekhar Jha ◽  
Rama Rao Nidamanuri
Keyword(s):  
Remote Sensing ◽  
Target Detection ◽  
Satellite Imagery ◽  
Hyperspectral Imagery ◽  
Remote Sensing Data ◽  
Benchmark Dataset ◽  
Remote Sensing Imagery ◽  
Sensing Data ◽  
Detection Algorithms ◽  
Reference Target

Target detection in remote sensing imagery, mapping of sparsely distributed materials, has vital applications in defense security and surveillance, mineral exploration, agriculture, environmental monitoring, etc. The detection probability and the quality of retrievals are functions of various parameters of the sensor, platform, target–background dynamics, targets’ spectral contrast, and atmospheric influence. Generally, target detection in remote sensing imagery has been approached using various statistical detection algorithms with an assumption of linearity in the image formation process. Knowledge on the image acquisition geometry, and spectral features and their stability across different imaging platforms is vital for designing a spectral target detection system. We carried out an integrated target detection experiment for the detection of various artificial target materials. As part of this work, we acquired a benchmark multi-platform hyperspectral and multispectral remote sensing dataset named as ‘Gudalur Spectral Target Detection (GST-D)’ dataset. Positioning artificial targets on different surface backgrounds, we acquired remote sensing data by terrestrial, airborne, and space-borne sensors on 20th March 2018. Various statistical and subspace detection algorithms were applied on the benchmark dataset for the detection of targets, considering the different sources of reference target spectra, background, and the spectral continuity across the platforms. We validated the detection results using the receiver operation curve (ROC) for different cases of detection algorithms and imaging platforms. Results indicate, for some combinations of algorithms and imaging platforms, consistent detection of specific material targets with a detection rate of about 80% at a false alarm rate between 10−2 to 10−3. Target detection in satellite imagery using reference target spectra from airborne hyperspectral imagery match closely with the satellite imagery derived reference spectra. The ground-based in-situ reference spectra offer a quantifiable detection in airborne or satellite imagery. However, ground-based hyperspectral imagery has also provided an equivalent target detection in the airborne and satellite imagery paving the way for rapid acquisition of reference target spectra. The benchmark dataset generated in this work is a valuable resourcefor addressing intriguing questions in target detection using hyperspectral imagery from a realistic landscape perspective.

scholarly journals Remote Sensing Applications for Monitoring Terrestrial Protected Areas: Progress in the Last Decade

2020 ◽  
Vol 12 (12) ◽  
pp. 5016
Author(s):  
Lijun Mao ◽  
Mingshi Li ◽  
Wenjuan Shen
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Protected Areas ◽  
Driving Forces ◽  
Remote Sensing Data ◽  
Natural Disturbance ◽  
Vegetation Dynamic ◽  
Sensing Data ◽  
Sensing Applications

Terrestrial protected areas (PAs) play an essential role in maintaining biodiversity and ecological processes worldwide, and the monitoring of PAs is a useful tool in assessing the effectiveness of PA management. Advanced remote sensing technologies have been increasingly used for mapping and monitoring the dynamics of PAs. We review the advances in remote sensing-based approaches for monitoring terrestrial PAs in the last decade and identify four types of studies in this field: land use & land cover and vegetation community classification, vegetation structure quantification, natural disturbance monitoring, and land use & land cover and vegetation dynamic analysis. We systematically discuss the satellite data and methods used for monitoring PAs for the four research objectives. Moreover, we summarize the approaches used in the different types of studies. The following suggestions are provided for future studies: (1) development of remote sensing frameworks for local PA monitoring worldwide; (2) comprehensive utilization of multisource remote sensing data; (3) improving methods to investigate the details of PA dynamics; (4) discovering the driving forces and providing measures for PA management. Overall, the integration of remote sensing data and advanced processing methods can support PA management and decision-making procedures.

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