Integrating remote sensing in Natura 2000 habitat monitoring: Prospects on the way forward

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).

Download Full-text

Calibration and verification of remote sensing data for east Asia migratory plague locust reed habitat monitoring

IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2002.1026805 ◽

2003 ◽

Cited By ~ 1

Author(s):

Jianwen Ma ◽

H.X. Hasibagan ◽

T. Devision

Keyword(s):

Remote Sensing ◽

East Asia ◽

Remote Sensing Data ◽

Habitat Monitoring ◽

Sensing Data ◽

Calibration And Verification

Download Full-text

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

Ecological Indicators ◽

10.1016/j.ecolind.2016.06.001 ◽

2016 ◽

Vol 70 ◽

pp. 196-208 ◽

Cited By ~ 11

Author(s):

Dominik KopeĿ ◽

Dorota Michalska-Hejduk ◽

ſukasz Sſawik ◽

Tomasz Berezowski ◽

Marcin Borowski ◽

...

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Natura 2000 ◽

Sensing Data

Download Full-text

Classification of Expansive Grassland Species in Different Growth Stages Based on Hyperspectral and LiDAR Data

Remote Sensing ◽

10.3390/rs10122019 ◽

2018 ◽

Vol 10 (12) ◽

pp. 2019 ◽

Cited By ~ 11

Author(s):

Adriana Marcinkowska-Ochtyra ◽

Anna Jarocińska ◽

Katarzyna Bzdęga ◽

Barbara Tokarska-Guzik

Keyword(s):

Remote Sensing ◽

Vegetation Structure ◽

Natura 2000 ◽

Grass Species ◽

Lidar Data ◽

Growth Stages ◽

Natural Habitats ◽

Waveform Data ◽

Grassland Species ◽

Full Waveform

Expansive species classification with remote sensing techniques offers great support for botanical field works aimed at detection of their distribution within areas of conservation value and assessment of the threat caused to natural habitats. Large number of spectral bands and high spatial resolution allows for identification of particular species. LiDAR (Light Detection and Ranging) data provide information about areas such as vegetation structure. Because the species differ in terms of features during the growing season, it is important to know when their spectral responses are unique in the background of the surrounding vegetation. The aim of the study was to identify two expansive grass species: Molinia caerulea and Calamagrostis epigejos in the Natura 2000 area in Poland depending on the period and dataset used. Field work was carried out during late spring, summer and early autumn, in parallel with remote sensing data acquisition. Airborne 1-m resolution HySpex images and LiDAR data were used. HySpex images were corrected geometrically and atmospherically before Minimum Noise Fraction (MNF) transformation and vegetation indices calculation. Based on a LiDAR point cloud generated Canopy Height Model, vegetation structure from discrete and full-waveform data and topographic indexes were generated. Classifications were performed using a Random Forest algorithm. The results show post-classification maps and their accuracies: Kappa value and F1 score being the harmonic mean of producer (PA) and user (UA) accuracy, calculated iteratively. Based on these accuracies and botanical knowledge, it was possible to assess the best identification date and dataset used for analysing both species. For M. caerulea the highest median Kappa was 0.85 (F1 = 0.89) in August and for C. epigejos 0.65 (F1 = 0.73) in September. For both species, adding discrete or full-waveform LiDAR data improved the results. We conclude that hyperspectral (HS) and LiDAR airborne data could be useful to identify grassland species encroaching into Natura 2000 habitats and for supporting their monitoring.

Download Full-text

Remote sensing and GIS paves the way for depth of cover pipeline analysis

10.1117/12.2600110 ◽

2021 ◽

Author(s):

Ulrich Michel ◽

Simon Daniels ◽

Daniel Finley

Keyword(s):

Remote Sensing ◽

Remote Sensing And Gis ◽

The Way

Download Full-text

Visibility and Cloud Height

Meteorological Measurement Systems ◽

10.1093/oso/9780195134513.003.0013 ◽

2001 ◽

Author(s):

Fred V. Brock ◽

Scott J. Richardson

Keyword(s):

Remote Sensing ◽

Red Light ◽

Moderate Intensity ◽

Atmospheric Transparency ◽

Cloud Height ◽

Meteorological Systems ◽

Human Eyes ◽

The Way

Visibility measurement is the most human-oriented measurement discussed because the objective of such measurement is to determine the distance at which humans (pilots, seamen, etc.) can see objects. Thus we are concerned with light that can be seen by humans (0.4 to 0.7μm), the way human eyes perceive such light, and then with the transparency of the atmosphere. Throughout this chapter, in the discussion of atmospheric transparency or absorption, the range of wavelengths from 0.4 (violet) to 0.7μm (red light) will be assumed. Cloud height is a remote sensing measurement but is included here because airport meteorological systems usually include a cloud height sensor. According to the WMO, meteorological visibility by day is defined as the greatest distance that a black object of suitable dimensions, situated near the ground, can be seen and recognized when observed against a background of fog, sky, etc. Visibility at night is defined as the greatest distance at which lights of moderate intensity can be seen and identified.

Download Full-text