scholarly journals The Methodology for Identifying Secondary Succession in Non-Forest Natura 2000 Habitats Using Multi-Source Airborne Remote Sensing Data

2021 ◽  
Vol 13 (14) ◽  
pp. 2803
Author(s):  
Katarzyna Osińska-Skotak ◽  
Aleksandra Radecka ◽  
Wojciech Ostrowski ◽  
Dorota Michalska-Hejduk ◽  
Jakub Charyton ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Conservation Status ◽  
Voronoi Tessellation ◽  
Remote Sensing Data ◽  
Natura 2000 ◽  
Aerial Photographs ◽  
Recursive Feature Elimination ◽  
Species Differentiation ◽  
Sensing Data ◽  
Succession Process

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.

scholarly journals Mapping Succession in Non-Forest Habitats by Means of Remote Sensing: Is the Data Acquisition Time Critical for Species Discrimination?

2019 ◽  
Vol 11 (22) ◽  
pp. 2629 ◽  
Author(s):  
Katarzyna Osińska-Skotak ◽  
Aleksandra Radecka ◽  
Hubert Piórkowski ◽  
Dorota Michalska-Hejduk ◽  
Dominik Kopeć ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Acquisition ◽  
Remote Sensing Data ◽  
Natura 2000 ◽  
Recursive Feature Elimination ◽  
Acquisition Time ◽  
Species Discrimination ◽  
Natural Habitats ◽  
Sensing Data ◽  
Data Acquisition Time

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.

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 Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management

2020 ◽  
Vol 12 (7) ◽  
pp. 2854 ◽  
Author(s):  
Boudewijn van Leeuwen ◽  
Zalán Tobak ◽  
Ferenc Kovács
Keyword(s):  
Water Management ◽  
Remote Sensing ◽  
Remote Sensing Data ◽  
Weather Conditions ◽  
Human Interaction ◽  
Aerial Photographs ◽  
Sensing Data ◽  
Weather Patterns ◽  
Excess Water ◽  
Frequent Monitoring

Changing climate is expected to cause more extreme weather patterns in many parts of the world. In the Carpathian Basin, it is expected that the frequency of intensive precipitation will increase causing inland excess water (IEW) in parts of the plains more frequently, while currently the phenomenon already causes great damage. This research presents and validates a new methodology to determine the extent of these floods using a combination of passive and active remote sensing data. The method can be used to monitor IEW over large areas in a fully automated way based on freely available Sentinel-1 and Sentinel-2 remote sensing imagery. The method is validated for two IEW periods in 2016 and 2018 using high-resolution optical satellite data and aerial photographs. Compared to earlier remote sensing data-based methods, our method can be applied under unfavorite weather conditions, does not need human interaction and gives accurate results for inundations larger than 1000 m2. The overall accuracy of the classification exceeds 99%; however, smaller IEW patches are underestimated due to the spatial resolution of the input data. Knowledge on the location and duration of the inundations helps to take operational measures against the water but is also required to determine the possibilities for storage of water for dry periods. The frequent monitoring of the floods supports sustainable water management in the area better than the methods currently employed.

scholarly journals Methods of calculating landslide volume using remote sensing data

2020 ◽  
Vol 149 ◽  
pp. 02009
Author(s):  
Maira Razakova ◽  
Alexandr Kuzmin ◽  
Igor Fedorov ◽  
Rustam Yergaliev ◽  
Zharas Ainakulov
Keyword(s):  
Remote Sensing ◽  
Sea Level ◽  
Remote Sensing Data ◽  
Field Studies ◽  
Aerial Photographs ◽  
Field Observations ◽  
Sensing Data ◽  
Quantitative Estimates ◽  
Landslide Volume ◽  
Engineering Protection

The paper considers the issues of calculating the volume of the landslide from remote sensing data. The main methods of obtaining information during research are field observations. The most important results of field studies are quantitative estimates, such as the volume of the embankment resulting from a landslide, morphometric indicators, etc. The study of a remote and remote object was carried out by remote methods using aerial photographs in the Ile Alatau foothills at 1,600 meters above sea level. The obtained materials from the mudflow survey will be useful in developing solutions to mitigate the effects of disasters and in the design of measures for engineering protection from landslides.

Application of multisensoral remote sensing data in the mapping of alkaline fens Natura 2000 habitat

2016 ◽  
Vol 70 ◽  
pp. 196-208 ◽  
Author(s):  
Dominik KopeĿ ◽  
Dorota Michalska-Hejduk ◽  
ſukasz Sſawik ◽  
Tomasz Berezowski ◽  
Marcin Borowski ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Natura 2000 ◽  
Sensing Data

scholarly journals Remote sensing and the future of landscape ecology

2009 ◽  
Vol 33 (4) ◽  
pp. 528-546 ◽  
Author(s):  
Adrian C. Newton ◽  
Ross A. Hill ◽  
Cristian Echeverría ◽  
Duncan Golicher ◽  
José M. Rey Benayas ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Landscape Ecology ◽  
Remote Sensing Data ◽  
Scientific Discipline ◽  
Aerial Photographs ◽  
Ecological Data ◽  
Ecological Processes ◽  
Sensing Data ◽  
Landsat Satellite ◽  
The Uk

Landscape ecology focuses on the analysis of spatial pattern and its relationship to ecological processes. As a scientific discipline, landscape ecology has grown rapidly in recent years, supported by developments in GIS and spatial analysis techniques. Although remote sensing data are widely employed in landscape ecology research, their current and potential roles have not been evaluated critically. To provide an overview of current practice, 438 research papers published in the journal Landscape Ecology for the years 2004—2008 were examined for information about use of remote sensing. Results indicated that only 36% of studies explicitly mentioned remote sensing. Of those that did so, aerial photographs and Landsat satellite sensor images were most commonly used, accounting for 46% and 42% of studies, respectively. The predominant application of remote sensing data across these studies was for thematic mapping purposes. This suggests that landscape ecologists have been relatively slow to recognize the potential value of recent developments in remote sensing technologies and methods. The review also provided evidence of a frequent lack of key detail in studies recently published in Landscape Ecology , with 75% failing to provide any assessment of uncertainty or error relating to image classification and mapping. It is suggested that the role of remote sensing in landscape ecology might be strengthened by closer collaboration between researchers in the two disciplines, by greater integration of diverse remote sensing data with ecological data, and by increased recognition of the value of remote sensing beyond land-cover mapping and pattern description. This is illustrated by case studies drawn from Latin America (focusing on forest loss and fragmentation) and the UK (focusing on habitat quality for woodland birds). Such approaches might improve the analytical and theoretical rigour of landscape ecology, and be applied usefully to issues of outstanding societal interest, such as the impacts of environmental change on biodiversity and ecosystem services.

scholarly journals Visual characteristics of landscapes and methods for their assessment on GIS (Belogradchik rocks (Bulgaria) as an example)

2021 ◽  
Vol 27 (2) ◽  
pp. 191-204
Author(s):  
Elina Sheremet ◽  
Natalia Kalutskova ◽  
Vladimir Dekhnich
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Visual Quality ◽  
Aerial Photographs ◽  
Main Task ◽  
Field Observations ◽  
North West ◽  
Sensing Data ◽  
Visual Characteristics

Visual characteristics of landscapes are important factors for the assessment of tourist and recreational potential of territories. At present, a number of methodological approaches are applied to assess the visual characteristics of landscapes. They can be divided into traditional, associated exclusively with field research, and innovative, which is based on remote sensing data (RSD) of high spatial resolution and GIS technologies. Field assessment of the visual quality of landscapes utilizes a system of numerous elementary indicators to minimize subjectivity of assessment. They are conducted within separate areas or touristic routes. In its turn, modern GIS and high quality of remote sensing data allow assessing of most indicators of the visual quality of landscapes for any observation point on the entire territory. The main task of our research is to verify the results of automated processing of ultra-high resolution aerial photographs obtained from unmanned aerial vehicles (UAV) by field observations on a touristic route. The research was carried out on the territory of the “Belogradchik Rocks” Geopark (North-West Bulgaria). In our study, we estimated 4 out of 28 aesthetic indicators—the amount of mountain peaks visible from a site, the amount of mountain peaks on the skyline, the percentage of the forest-covered area, and the amount of open spaces in the wooded landscape. The obtained results confirmed that our approach allows calculating these aesthetic indicators at an accuracy level comparable to field observations.

scholarly journals Visual characteristics of landscapes and methods for their assessment on GIS (Belogradchik rocks (Bulgaria) as an example)

2021 ◽  
Vol 27 (2) ◽  
pp. 191-204
Author(s):  
Elina Sheremet ◽  
Natalia Kalutskova ◽  
Vladimir Dekhnich
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Visual Quality ◽  
Aerial Photographs ◽  
Main Task ◽  
Field Observations ◽  
North West ◽  
Sensing Data ◽  
Visual Characteristics

Visual characteristics of landscapes are important factors for the assessment of tourist and recreational potential of territories. At present, a number of methodological approaches are applied to assess the visual characteristics of landscapes. They can be divided into traditional, associated exclusively with field research, and innovative, which is based on remote sensing data (RSD) of high spatial resolution and GIS technologies. Field assessment of the visual quality of landscapes utilizes a system of numerous elementary indicators to minimize subjectivity of assessment. They are conducted within separate areas or touristic routes. In its turn, modern GIS and high quality of remote sensing data allow assessing of most indicators of the visual quality of landscapes for any observation point on the entire territory. The main task of our research is to verify the results of automated processing of ultra-high resolution aerial photographs obtained from unmanned aerial vehicles (UAV) by field observations on a touristic route. The research was carried out on the territory of the “Belogradchik Rocks” Geopark (North-West Bulgaria). In our study, we estimated 4 out of 28 aesthetic indicators—the amount of mountain peaks visible from a site, the amount of mountain peaks on the skyline, the percentage of the forest-covered area, and the amount of open spaces in the wooded landscape. The obtained results confirmed that our approach allows calculating these aesthetic indicators at an accuracy level comparable to field observations.

scholarly journals Mapping Mature Post-Agricultural Forests in the Polish Eastern Carpathians with Archival Remote Sensing Data

2021 ◽  
Vol 13 (10) ◽  
pp. 2018
Author(s):  
Zofia Jabs-Sobocińska ◽  
Andrzej N. Affek ◽  
Ireneusz Ewiak ◽  
Mihai Daniel Nita
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Management Practices ◽  
Forest Cover ◽  
Remote Sensing Data ◽  
Aerial Photographs ◽  
Secondary Forests ◽  
Agricultural Lands ◽  
Sensing Data ◽  
Eastern Carpathians

Post-WWII displacements in the Polish Carpathians resulted in widespread land abandonment. Most of the pre-war agricultural areas are now covered with secondary forests, which will soon reach the felling age. Mapping their exact cover is crucial to investigate succession–regeneration processes and to determine their role in the landscape, before making management decisions. Our goal was to map post-agricultural forests in the Polish Eastern Carpathians using archival remote sensing data, and to assess their connectivity with pre-displacement forests. We used German Flown Aerial Photography from 1944 to map agricultural lands and forests from before displacements, and Corona satellite images to map agricultural lands which converted into the forest as a result of this event. We classified archival images using Object-Based Image Analysis (OBIA) and compared the output with the current forest cover derived from Sentinel-2. Our results showed that mature (60–70 years old) post-agricultural forests comprise 27.6% of the total forest area, while younger post-agricultural forests comprise 9%. We also demonstrated that the secondary forests fill forest gaps more often than form isolated patches: 77.5% of patches are connected with the old-woods (forests that most likely have never been cleared for agriculture). Orthorectification and OBIA classification of German Flown Aerial Photographs and Corona satellite images made it possible to accurately determine the spatial extent of post-agricultural forest. This, in turn, paves the way for the implementation of site-specific forest management practices to support the regeneration of secondary forests and their biodiversity.

scholarly journals Features of Visual Decoding of Water Erosion by Remote Sensing Data

2020 ◽  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Water Erosion ◽  
Object Identification ◽  
Aerial Photographs ◽  
Linear Forms ◽  
Sensing Data ◽  
Erosion Processes ◽  
Sheet Erosion

Purpose: analysis of the features of visual decoding of eroded soils and erosion processes according to remote sensing data. Methods. Remote sensing, field, comparative geographical, historical, cartographic, GIS analysis. Results. The main attention in the article is paid to the features of visual decoding of linear forms of erosion. Comparative analysis of aerial photographs of 1943 and modern satellite imagery for the Kharkov region shown that in the second half of the 20th century the growth of gullies was almost stopped due to large-scale anti-erosion measures carried out at that time. Currently the main erosion losses occur in sheet erosion and small gully erosion. The article provides a list of decoding features that determine linear forms of erosion in the images. It is shown problems that can arise during automatic decoding. As an example of artifact formations requiring the participation of a human analyst in the decryption process, the so-called "Turkish Wall" is shown, the traces of which can be erroneously diagnosed as a manifestation of linear erosion Conclusions. Automatic decoding of water erosion processes and an inventory of erosion landforms requires the obligatory monitoring of a qualified analyst to eliminate object identification errors.

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