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 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 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 Long‐duration fully autonomous operation of rotorcraft unmanned aerial systems for remote‐sensing data acquisition

2019 ◽  
Vol 37 (1) ◽  
pp. 137-157 ◽  
Author(s):  
Danylo Malyuta ◽  
Christian Brommer ◽  
Daniel Hentzen ◽  
Thomas Stastny ◽  
Roland Siegwart ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Acquisition ◽  
Remote Sensing Data ◽  
Unmanned Aerial Systems ◽  
Sensing Data ◽  
Autonomous Operation ◽  
Long Duration ◽  
Aerial Systems

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 Data Analysis for the Ecological Stability Purposes

2021 ◽  
Vol 906 (1) ◽  
pp. 012068
Author(s):  
Jakub Chromcak ◽  
Matus Farbak ◽  
Alexander Ivannikov ◽  
Robert Sasik ◽  
Jana Dibdiakova
Keyword(s):  
Remote Sensing ◽  
Data Acquisition ◽  
Remote Sensing Data ◽  
Environmental Issues ◽  
Point Of View ◽  
Environment Monitoring ◽  
Ecological Stability ◽  
Nature Protection ◽  
Stability Calculation ◽  
Sensing Data

Abstract The remote sensing offers the opportunity of miscellaneous data acquisition with various ways of their consequent analysis and application. The processed remote sensing data in the form of georeferenced orthophotoimages or orthophotomaps enable the study of the examined locality from the chosen observed feature point of view. According to periodical data acquisition, it is possible to monitor the ongoing and emerging actions in time and then prevent and predict the upcoming actions. With the increasing interest in environmental issues and nature protection, the natural environment monitoring, preservation, protection and remediation present the number one priority. From the ecological point of view, the analysis of orthophotos/orthophotomaps present the up-to-date way of ecological stability calculation and monitoring.

Study on Disaster Monitoring Satellite Remote Sensing Data Fast Acquisition Programming Technology

2011 ◽  
Vol 356-360 ◽  
pp. 2864-2869
Author(s):  
Guang Bin Ma ◽  
Wen Yi Zhang ◽  
Peng Huang
Keyword(s):  
Remote Sensing ◽  
Data Acquisition ◽  
Satellite Data ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Programming System ◽  
Sensing Data ◽  
Fast Acquisition ◽  
Disaster Area ◽  
Disaster Monitoring

This paper studies the multi-satellites data fast acquisition programming technology for disaster area, and in this paper a disaster monitoring satellite data fast acquisition programming system is established. After the disaster, the system can program the multi-satellites observation schedule for the disaster area quickly and accurately, it can provide important technical support for the satellite data acquisition of the disaster area.

scholarly journals Monitoring Invasive Plant Species Using Hyperspectral Remote Sensing Data

Land ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Levente Papp ◽  
Boudewijn van Leeuwen ◽  
Péter Szilassi ◽  
Zalán Tobak ◽  
József Szatmári ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Plant Species ◽  
Invasive Plant ◽  
Reference Data ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Support Vector ◽  
Invasive Plant Species ◽  
Natural Habitats ◽  
Sensing Data

The species richness and biodiversity of vegetation in Hungary are increasingly threatened by invasive plant species brought in from other continents and foreign ecosystems. These invasive plant species have spread aggressively in the natural and semi-natural habitats of Europe. Common milkweed (Asclepias syriaca) is one of the species that pose the greatest ecological menace. Therefore, the primary purpose of the present study is to map and monitor the spread of common milkweed, the most common invasive plant species in Europe. Furthermore, the possibilities to detect and validate this special invasive plant by analyzing hyperspectral remote sensing data were investigated. In combination with field reference data, high-resolution hyperspectral aerial images acquired by an unmanned aerial vehicle (UAV) platform in 138 spectral bands in areas infected by common milkweed were examined. Then, support vector machine (SVM) and artificial neural network (ANN) classification algorithms were applied to the highly accurate field reference data. As a result, common milkweed individuals were distinguished in hyperspectral images, achieving an overall accuracy of 92.95% in the case of supervised SVM classification. Using the ANN model, an overall accuracy of 99.61% was achieved. To evaluate the proposed approach, two experimental tests were conducted, and in both cases, we managed to distinguish the individual specimens within the large variety of spreading invasive species in a study area of 2 ha, based on centimeter spatial resolution hyperspectral UAV imagery.

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