Analysis of human impact within Natura 2000 protected areas using remote sensing data

Author(s):  
Marinela Adriana Chetan ◽  
Andrei Dornik
2020 ◽  
Vol 42 ◽  
pp. 69-81

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).


2013 ◽  
Vol 43 (4) ◽  
pp. 5
Author(s):  
Maria Elena Menconi ◽  
David Grohmann

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.


2016 ◽  
Vol 70 ◽  
pp. 196-208 ◽  
Author(s):  
Dominik KopeĿ ◽  
Dorota Michalska-Hejduk ◽  
ſukasz Sſawik ◽  
Tomasz Berezowski ◽  
Marcin Borowski ◽  
...  

2018 ◽  
Vol 7 (7) ◽  
pp. 243 ◽  
Author(s):  
Wei Jiang ◽  
Guojin He ◽  
Wanchun Leng ◽  
Tengfei Long ◽  
Guizhou Wang ◽  
...  

2018 ◽  
Vol 100 ◽  
pp. 101-115 ◽  
Author(s):  
Ana I.R. Cabral ◽  
Carlos Saito ◽  
Henrique Pereira ◽  
Anne Elisabeth Laques

2020 ◽  
Vol 12 (12) ◽  
pp. 5016
Author(s):  
Lijun Mao ◽  
Mingshi Li ◽  
Wenjuan Shen

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.


2021 ◽  
Vol 13 (14) ◽  
pp. 2803
Author(s):  
Katarzyna Osińska-Skotak ◽  
Aleksandra Radecka ◽  
Wojciech Ostrowski ◽  
Dorota Michalska-Hejduk ◽  
Jakub Charyton ◽  
...  

The succession process of trees and shrubs is considered as one of the threats to non-forest Natura 2000 habitats. Poland, as a member of the European Union, is obliged to monitor these habitats and preserve them in the best possible condition. If threats are identified, it is necessary to take action—as part of the so-called active protection—that will ensure the preservation of habitats in a non-deteriorated condition. At present, monitoring of Natura 2000 habitats is carried out in expert terms, i.e., the habitat conservation status is determined during field visits. This process is time- and cost-intensive, and it is subject to the subjectivism of the person performing the assessment. As a result of the research, a methodology for the identification and monitoring of the succession process in non-forest Natura 2000 habitats was developed, in which multi-sensor remote sensing data are used—airborne laser scanner (ALS) and hyperspectral (HS) data. The methodology also includes steps required to analyse the dynamics of the succession process in the past, which is done using archival photogrammetric data (aerial photographs and ALS data). The algorithms implemented within the methodology include structure from motion and dense image matching for processing the archival images, segmentation and Voronoi tessellation for delineating the spatial extent of succession, machine learning random forest classifier, recursive feature elimination and t-distributed stochastic neighbour embedding algorithms for succession species differentiation, as well as landscape metrics used for threat level analysis. The proposed methodology has been automated and enables a rapid assessment of the level of threat for a whole given area, as well as in relation to individual Natura 2000 habitats. The prepared methodology was successfully tested on seven research areas located in Poland.


2019 ◽  
Vol 11 (22) ◽  
pp. 2629 ◽  
Author(s):  
Katarzyna Osińska-Skotak ◽  
Aleksandra Radecka ◽  
Hubert Piórkowski ◽  
Dorota Michalska-Hejduk ◽  
Dominik Kopeć ◽  
...  

The process of secondary succession is one of the most significant threats to non-forest (natural and semi-natural open) Natura 2000 habitats in Poland; shrub and tree encroachment taking place on abandoned, low productive agricultural areas, historically used as pastures or meadows, leads to changes to the composition of species and biodiversity loss, and results in landscape transformations. There is a perceived need to create a methodology for the monitoring of vegetation succession by airborne remote sensing, both from quantitative (area, volume) and qualitative (plant species) perspectives. This is likely to become a very important issue for the effective protection of natural and semi-natural habitats and to advance conservation planning. A key variable to be established when implementing a qualitative approach is the remote sensing data acquisition date, which determines the developmental stage of trees and shrubs forming the succession process. It is essential to choose the optimal date on which the spectral and geometrical characteristics of the species are as different from each other as possible. As part of the research presented here, we compare classifications based on remote sensing data acquired during three different parts of the growing season (spring, summer and autumn) for five study areas. The remote sensing data used include high-resolution hyperspectral imagery and LiDAR (Light Detection and Ranging) data acquired simultaneously from a common aerial platform. Classifications are done using the random forest algorithm, and the set of features to be classified is determined by a recursive feature elimination procedure. The results show that the time of remote sensing data acquisition influences the possibility of differentiating succession species. This was demonstrated by significant differences in the spatial extent of species, which ranged from 33.2% to 56.2% when comparing pairs of maps, and differences in classification accuracies, which when expressed in values of Cohen’s Kappa reached ~0.2. For most of the analysed species, the spring and autumn dates turned out to be slightly more favourable than the summer one. However, the final recommendation for the data acquisition time should take into consideration the phenological cycle of deciduous species present within the research area and the abiotic conditions.


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.


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