scholarly journals Recent Lateral Expansion of Sphagnum Bogs Over Central Fen Areas of Boreal Aapa Mire Complexes

Ecosystems ◽  
2021 ◽  
Author(s):  
Lars Granlund ◽  
Ville Vesakoski ◽  
Antti Sallinen ◽  
Tiina H. M. Kolari ◽  
Franziska Wolff ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Carbon Accumulation ◽  
Aerial Images ◽  
Ice Age ◽  
Spatial Patterning ◽  
Lateral Expansion ◽  
Peat Core ◽  
Sphagnum Bogs ◽  
Ams Dating ◽  
Pollen Density

AbstractWe investigated recent changes in spatial patterning of fen and bog zones in five boreal aapa mire complexes (mixed peatlands with patterned fen and bog parts) in a multiproxy study. Comparison of old (1940–1970s) and new aerial images revealed decrease of flarks (wet hollows) in patterned fens by 33–63% in middle boreal and 16–42% in northern boreal sites, as lawns of bog Sphagnum mosses expanded over fens. Peat core transects across transformed areas were used to verify the remote sensing inference with stratigraphic analyses of macrofossils, hyperspectral imaging, and age-depth profiles derived from 14C AMS dating and pine pollen density. The transect data revealed that the changes observed by remote sensing during past decades originated already from the end of the Little Ice Age (LIA) between 1700–1850 CE in bog zones and later in the flarks of fen zones. The average lateral expansion rate of bogs over fen zones was 0.77 m y−1 (range 0.19–1.66) as estimated by remote sensing, and 0.71 m y−1 (range 0.13–1.76) based on peat transects. The contemporary plant communities conformed to the macrofossil communities, and distinct vegetation zones were recognized as representing recently changed areas. The fen-bog transition increased the apparent carbon accumulation, but it can potentially threaten fen species and habitats. These observations indicate that rapid lateral bog expansion over aapa mires may be in progress, but more research is needed to reveal if ongoing fen-bog transitions are a commonplace phenomenon in northern mires.

Unexpected Problems in AMS 14C Dating of Fen Peat

Radiocarbon ◽  
2014 ◽  
Vol 56 (1) ◽  
pp. 95-108 ◽  
Author(s):  
Minna Väliranta ◽  
Markku Oinonen ◽  
Heikki Seppä ◽  
Sanna Korkonen ◽  
Sari Juutinen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Accelerator Mass Spectrometry ◽  
Time Window ◽  
Plant Macrofossils ◽  
Carbon Accumulation ◽  
14C Dating ◽  
Plant Macrofossil ◽  
Peat Core ◽  
Ams Dating ◽  
Ams 14C Dating

Four fen peat sequences in northern Finland were dated by the accelerator mass spectrometry (AMS) radiocarbon method in order to study past peatland dynamics and carbon accumulation patterns. Initially, plant macrofossils were used for dating. However, the dates were severely disordered, with marked inversions in all sequences. In one 140-cm peat core, for example, all ages fell within a ∼1000-yr time window. Following these unreliable results, a few bulk peat samples were dated to help assess if any of the plant macrofossil-derived dates were reliable. Bulk dates did not help to solve the problem. This study evaluates the possible sources of error but is unable to single out one clear cause. It is probable that many factors related to the fen environment, such as flooding and root intrusion, may have contributed to the errors. Peat plant macrofossils and bulk peat samples are considered to be reliable dating materials, but the examples given herein highlight the difficulties that can be associated with AMS dating of peat samples.

scholarly journals Climate changes, lead pollution and soil erosion in south Greenland over the past 700 years

2015 ◽  
Vol 84 (2) ◽  
pp. 159-173 ◽  
Author(s):  
Noemí Silva-Sánchez ◽  
J. Edward Schofield ◽  
Tim M. Mighall ◽  
Antonio Martínez Cortizas ◽  
Kevin J. Edwards ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Isotope Ratio Mass Spectrometry ◽  
Isotopic Analysis ◽  
Carbon Accumulation ◽  
Ice Age ◽  
Lead Pollution ◽  
Human Environment ◽  
Peat Core ◽  
Lead Source ◽  
Atmospheric Lead Pollution

A peat core from southern Greenland provided a rare opportunity to investigate human-environment interactions, climate change and atmospheric pollution over the last ~ 700 years. X-ray fluorescence, gas chromatography-combustion, isotope ratio mass spectrometry, peat humification and fourier-transform infrared spectroscopy were applied and combined with palynological and archaeological evidence. Variations in peat mineral content seem to be related to soil erosion linked with human activity during the late Norse period (13th–14thcenturies AD) and the modern era (20thcentury). Cooler conditions during the Little Ice Age (LIA) are reflected by both slow rates of peat growth and carbon accumulation, and by low bromine (Br) concentrations. Spörer and Maunder minima in solar activity may be indicated by further declines in Br and enrichment in easily degradable compounds such as polysaccharides. Peat organic matter composition was also influenced by vegetation changes at the end of the LIA when the expansion of oceanic heath was associated with polysaccharide enrichment. Atmospheric lead pollution was recorded in the peat after ~ AD 1845, and peak values occurred in the 1970s. There is indirect support for a predominantly North American lead source, but further Pb isotopic analysis would be needed to confirm this hypothesis.

Application of remote sensing methods for assessment of anthropogenic transformation of rangelands

2000 ◽  
pp. 16-25
Author(s):  
E. I. Rachkovskaya ◽  
S. S. Temirbekov ◽  
R. E. Sadvokasov
Keyword(s):  
Remote Sensing ◽  
New Technology ◽  
Field Studies ◽  
Digital Data ◽  
Aerial Images ◽  
Expert Assessment ◽  
Automated Classification ◽  
Anthropogenic Transformation ◽  
Initial Processing ◽  
Remote Sensing Methods

Capabilities of the remote sensing methods for making maps of actual and potential vegetation, and assessment of the extent of anthropogenic transformation of rangelands are presented in the paper. Study area is a large intermountain depression, which is under intensive agricultural use. Color photographs have been made by Aircraft camera Wild Heerburg RC-30 and multispectral scanner Daedalus (AMS) digital aerial data (6 bands, 3.5m resolution) have been used for analysis of distribution and assessment of the state of vegetation. Digital data were processed using specialized program ENVI 3.0. Main stages of the development of cartographic models have been described: initial processing of the aerial images and their visualization, preliminary pre-field interpretation (classification) of the images on the basis of unsupervised automated classification, field studies (geobotanical records and GPS measurements at the sites chosen at previous stage). Post-field stage had the following sub-stages: final geometric correction of the digital images, elaboration of the classification system for the main mapping subdivisions, final supervised automated classification on the basis of expert assessment. By systematizing clusters of the obtained classified image the cartographic models of the study area have been made. Application of the new technology of remote sensing allowed making qualitative and quantitative assessment of modern state of rangelands.

scholarly journals Archaeological Surveying of Subarctic and Arctic Landscapes: Comparing the Performance of Airborne Laser Scanning and Remote Sensing Image Data

2021 ◽  
Vol 13 (4) ◽  
pp. 1917
Author(s):  
Alma Elizabeth Thuestad ◽  
Ole Risbøl ◽  
Jan Ingolf Kleppe ◽  
Stine Barlindhaug ◽  
Elin Rose Myrvoll
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Digital Terrain Model ◽  
Image Data ◽  
Remote Sensing Image ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Detection Rates ◽  
Airborne Laser ◽  
Pros And Cons

What can remote sensing contribute to archaeological surveying in subarctic and arctic landscapes? The pros and cons of remote sensing data vary as do areas of utilization and methodological approaches. We assessed the applicability of remote sensing for archaeological surveying of northern landscapes using airborne laser scanning (LiDAR) and satellite and aerial images to map archaeological features as a basis for (a) assessing the pros and cons of the different approaches and (b) assessing the potential detection rate of remote sensing. Interpretation of images and a LiDAR-based bare-earth digital terrain model (DTM) was based on visual analyses aided by processing and visualizing techniques. 368 features were identified in the aerial images, 437 in the satellite images and 1186 in the DTM. LiDAR yielded the better result, especially for hunting pits. Image data proved suitable for dwellings and settlement sites. Feature characteristics proved a key factor for detectability, both in LiDAR and image data. This study has shown that LiDAR and remote sensing image data are highly applicable for archaeological surveying in northern landscapes. It showed that a multi-sensor approach contributes to high detection rates. Our results have improved the inventory of archaeological sites in a non-destructive and minimally invasive manner.

scholarly journals Remote sensing phenology of two Chinese northern Sphagnum bogs under climate drivers during 2001 and 2018

2021 ◽  
Vol 129 ◽  
pp. 107968
Author(s):  
Yuwen Pang ◽  
Yuxin Huang ◽  
Li He ◽  
Yinying Zhou ◽  
Jun Sui ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sphagnum Bogs

scholarly journals ANALYZING SPECTRAL CHARACTERISTICS OF SHADOW AREA FROM ADS-40 HIGH RADIOMETRIC RESOLUTION AERIAL IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 223-227 ◽  
Author(s):  
Yi-Ta Hsieh ◽  
Shou-Tsung Wu ◽  
Chaur-Tzuhn Chen ◽  
Jan-Chang Chen
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Vegetation Index ◽  
Spectral Characteristics ◽  
Aerial Images ◽  
Aerial Image ◽  
Optical Remote Sensing ◽  
Shadow Area ◽  
Vegetation Indexes ◽  
Research Findings

The shadows in optical remote sensing images are regarded as image nuisances in numerous applications. The classification and interpretation of shadow area in a remote sensing image are a challenge, because of the reduction or total loss of spectral information in those areas. In recent years, airborne multispectral aerial image devices have been developed 12-bit or higher radiometric resolution data, including Leica ADS-40, Intergraph DMC. The increased radiometric resolution of digital imagery provides more radiometric details of potential use in classification or interpretation of land cover of shadow areas. Therefore, the objectives of this study are to analyze the spectral properties of the land cover in the shadow areas by ADS-40 high radiometric resolution aerial images, and to investigate the spectral and vegetation index differences between the various shadow and non-shadow land covers. According to research findings of spectral analysis of ADS-40 image: (i) The DN values in shadow area are much lower than in nonshadow area; (ii) DN values received from shadowed areas that will also be affected by different land cover, and it shows the possibility of land cover property retrieval as in nonshadow area; (iii) The DN values received from shadowed regions decrease in the visible band from short to long wavelengths due to scattering; (iv) The shadow area NIR of vegetation category also shows a strong reflection; (v) Generally, vegetation indexes (NDVI) still have utility to classify the vegetation and non-vegetation in shadow area. The spectral data of high radiometric resolution images (ADS-40) is potential for the extract land cover information of shadow areas.

scholarly journals Remote Sensing for Property Valuation: A Data Source Comparison in Support of Fair Land Taxation in Rwanda

2021 ◽  
Vol 13 (18) ◽  
pp. 3563
Author(s):  
Mila Koeva ◽  
Oscar Gasuku ◽  
Monica Lengoiboni ◽  
Kwabena Asiama ◽  
Rohan Mark Bennett ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Aerial Images ◽  
Primary Data ◽  
Structured Interviews ◽  
Remotely Sensed Data ◽  
Quality Of Data ◽  
New Approach ◽  
Property Valuation ◽  
Land Administration

Remotely sensed data is increasingly applied across many domains, including fit-for-purpose land administration (FFPLA), where the focus is on fast, affordable, and accurate property information collection. Property valuation, as one of the main functions of land administration systems, is influenced by locational, physical, legal, and economic factors. Despite the importance of property valuation to economic development, there are often no standardized rules or strict data requirements for property valuation for taxation in developing contexts, such as Rwanda. This study aims at assessing different remote sensing data in support of developing a new approach for property valuation for taxation in Rwanda; one that aligns with the FFPLA philosophy. Three different remote sensing technologies, (i) aerial images acquired with a digital camera, (ii) WorldView2 satellite images, and (iii) unmanned aerial vehicle (UAV) images obtained with a DJI Phantom 2 Vision Plus quadcopter, are compared and analyzed in terms of their fitness to fulfil the requirements for valuation for taxation purposes. Quantitative and qualitative methods are applied for the comparative analysis. Prior to the field visit, the fundamental concepts of property valuation for taxation and remote sensing were reviewed. In the field, reference data using high precision GNSS (Leica) was collected and used for quantitative assessment. Primary data was further collected via semi-structured interviews and focus group discussions. The results show that UAVs have the highest potential for collecting data to support property valuation for taxation. The main reasons are the prime need for accurate-enough and up-to-date information. The comparison of the different remote sensing techniques and the provided new approach can support land valuers and professionals in the field in bottom-up activities following the FFPLA principles and maintaining the temporal quality of data needed for fair taxation.

scholarly journals Mapping the Unseen: Exploiting Super-Resolution for Semantic Segmentation in Low-Resolution Images

2020 ◽  
Author(s):  
Matheus B. Pereira ◽  
Jefersson Alex Dos Santos
Keyword(s):  
Remote Sensing ◽  
Pattern Recognition ◽  
Super Resolution ◽  
Remote Sensing Data ◽  
Semantic Segmentation ◽  
The Other ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Remote Sensing Images ◽  
Low Resolution

High-resolution aerial images are usually not accessible or affordable. On the other hand, low-resolution remote sensing data is easily found in public open repositories. The problem is that the low-resolution representation can compromise pattern recognition algorithms, especially semantic segmentation. In this M.Sc. dissertation1 , we design two frameworks in order to evaluate the effectiveness of super-resolution in the semantic segmentation of low-resolution remote sensing images. We carried out an extensive set of experiments on different remote sensing datasets. The results show that super-resolution is effective to improve semantic segmentation performance on low-resolution aerial imagery, outperforming unsupervised interpolation and achieving semantic segmentation results comparable to highresolution data.

scholarly journals Going Deep into Remote Sensing Spatial Feature Learning

2020 ◽  
Author(s):  
Keiller Nogueira ◽  
William Robson Schwartz ◽  
Jefersson Alex Dos Santos
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Feature Learning ◽  
Aerial Images ◽  
Morphological Operations ◽  
Pixel Classification ◽  
Specific Data ◽  
Convolutional Networks ◽  
Spatial Features ◽  
New Strategy

A lot of information may be extracted from the Earth’s surface through aerial images. This information may assist in myriad applications, such as urban planning, crop and forest management, disaster relief, etc. However, the process of distilling this information is strongly based on efficiently encoding the spatial features, a challenging task. Facing this, Deep Learning is able to learn specific data-driven features. This PhD thesis1 introduces deep learning into the remote sensing domain. Specifically, we tackled two main tasks, scene and pixel classification, using Deep Learning to encode spatial features over high-resolution remote sensing images. First, we proposed an architecture and analyze different strategies to exploit Convolutional Networks for image classification. Second, we introduced a network and proposed a new strategy to better exploit multi-context information in order to improve pixelwise classification. Finally, we proposed a new network based on morphological operations towards better learning of some relevant visual features.

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