scholarly journals Estimating mass-wasting processes in active earth slides – earth flows with time-series of High-Resolution DEMs from photogrammetry and airborne LiDAR

2009 ◽  
Vol 9 (2) ◽  
pp. 433-439 ◽  
Author(s):  
A. Corsini ◽  
L. Borgatti ◽  
F. Cervi ◽  
A. Dahne ◽  
F. Ronchetti ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Large Scale ◽  
Airborne Lidar ◽  
Mitigation Measures ◽  
Mass Wasting ◽  
Aerial Photos ◽  
Use Of Time ◽  
Elevation Changes ◽  
Earth Flows

Abstract. This paper deals with the use of time-series of High-Resolution Digital Elevation Models (HR DEMs) obtained from photogrammetry and airborne LiDAR coupled with aerial photos, to analyse the magnitude of recently reactivated large scale earth slides – earth flows located in the northern Apennines of Italy. The landslides underwent complete reactivation between 2001 and 2006, causing civil protection emergencies. With the final aim to support hazard assessment and the planning of mitigation measures, high-resolution DEMs are used to identify, quantify and visualize depletion and accumulation in the slope resulting from the reactivation of the mass movements. This information allows to quantify mass wasting, i.e. the amount of landslide material that is wasted during reactivation events due to stream erosion along the slope and at its bottom, resulting in sediment discharge into the local fluvial system, and to assess the total volumetric magnitude of the events. By quantifying and visualising elevation changes at the slope scale, results are also a valuable support for the comprehension of geomorphological processes acting behind the evolution of the analysed landslides.

scholarly journals Post-AGB binaries as tracers of stellar evolution.

2016 ◽  
Vol 12 (S323) ◽  
pp. 231-234 ◽  
Author(s):  
Hans Van Winckel
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Stellar Evolution ◽  
Accretion Disc ◽  
Monitoring Programme ◽  
Use Of Time ◽  
Central Stars

AbstractIn this contribution the focus is on post-AGB binaries. It is now well established that these are often surrounded by stable long-lived circumbinary discs of gas and dust. Here we introduce our monitoring programme with our high-resolution spectrograph HERMES mounted on the 1.2m Mercator telescope. We illustrate the use of time-series high-resolution spectra and show that jets observed in many systems are launched at the location of the companion. The jet is likely originating from a circum-companion accretion disc. The link of these systems to some PNe relies on the detection of similar orbits and hence wide spectroscopic orbits among central stars of PNe. The conclusion is that Keplerian discs as well as circum-companion discs are fundamental to understanding the properties and evolution of these interacting evolved binaries.

scholarly journals High-resolution Mapping of Forest Carbon Stocks in the Colombian Amazon

2012 ◽  
Vol 9 (3) ◽  
pp. 2445-2479 ◽  
Author(s):  
G. P. Asner ◽  
J. K. Clark ◽  
J. Mascaro ◽  
G. A. Galindo García ◽  
K. D. Chadwick ◽  
...  
Keyword(s):  
High Resolution ◽  
Tropical Forest ◽  
Large Scale ◽  
Carbon Stocks ◽  
Forest Carbon ◽  
Airborne Lidar ◽  
High Resolution Mapping ◽  
Resolution Mapping ◽  
Carbon Mapping ◽  
Colombian Amazon

Abstract. High-resolution mapping of tropical forest carbon stocks can assist forest management and improve implementation of large-scale carbon retention and enhancement programs. Previous high-resolution approaches have relied on field plot and/or Light Detection and Ranging (LiDAR) samples of aboveground carbon density, which are typically upscaled to larger geographic areas using stratification maps. Such efforts often rely on detailed vegetation maps to stratify the region for sampling, but existing tropical forest maps are often too coarse and field plots too sparse for high resolution carbon assessments. We developed a top-down approach for high-resolution carbon mapping in a 16.5 million ha region (>40 %) of the Colombian Amazon – a remote landscape seldom documented. We report on three advances for large-scale carbon mapping: (i) employing a universal approach to airborne LiDAR-calibration with limited field data; (ii) quantifying environmental controls over carbon densities; and (iii) developing stratification- and regression-based approaches for scaling up to regions outside of LiDAR coverage. We found that carbon stocks are predicted by a combination of satellite-derived elevation, fractional canopy cover and terrain ruggedness, allowing upscaling of the LiDAR samples to the full 16.5 million ha region. LiDAR-derived carbon mapping samples had 14.6 % uncertainty at 1 ha resolution, and regional maps based on stratification and regression approaches had 25.6 % and 29.6 % uncertainty, respectively, in any given hectare. High-resolution approaches with reported local-scale uncertainties will provide the most confidence for monitoring changes in tropical forest carbon stocks. Improved confidence will allow resource managers and decision-makers to more rapidly and effectively implement actions that better conserve and utilize forests in tropical regions.

scholarly journals Automated High-Resolution Time Series Mapping of Mangrove Forests Damaged by Hurricane Irma in Southwest Florida

2020 ◽  
Vol 12 (11) ◽  
pp. 1740
Author(s):  
Matthew J. McCarthy ◽  
Brita Jessen ◽  
Michael J. Barry ◽  
Marissa Figueroa ◽  
Jessica McIntosh ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Land Cover ◽  
Satellite Imagery ◽  
Large Scale ◽  
Detection Methods ◽  
Mangrove Forests ◽  
Wetland Ecosystems ◽  
Southwest Florida ◽  
Hurricane Irma

In September of 2017, Hurricane Irma made landfall within the Rookery Bay National Estuarine Research Reserve of southwest Florida (USA) as a category 3 storm with winds in excess of 200 km h−1. We mapped the extent of the hurricane’s impact on coastal land cover with a seasonal time series of satellite imagery. Very high-resolution (i.e., <5 m pixel) satellite imagery has proven effective to map wetland ecosystems, but challenges in data acquisition and storage, algorithm training, and image processing have prevented large-scale and time-series mapping of these data. We describe our approach to address these issues to evaluate Rookery Bay ecosystem damage and recovery using 91 WorldView-2 satellite images collected between 2010 and 2018 mapped using automated techniques and validated with a field campaign. Land cover was classified seasonally at 2 m resolution (i.e., healthy mangrove, degraded mangrove, upland, soil, and water) with an overall accuracy of 82%. Digital change detection methods show that hurricane-related degradation was 17% of mangrove forest (~5 km2). Approximately 35% (1.7 km2) of this loss recovered one year after Hurricane Irma. The approach completed the mapping approximately 200 times faster than existing methods, illustrating the ease with which regional high-resolution mapping may be accomplished efficiently.

scholarly journals A New Statistical–Dynamical Downscaling Procedure Based on EOF Analysis for Regional Time Series Generation

2013 ◽  
Vol 52 (4) ◽  
pp. 935-952 ◽  
Author(s):  
Yosvany Martinez ◽  
Wei Yu ◽  
Hai Lin
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Large Scale ◽  
Dynamical Downscaling ◽  
Resource Assessment ◽  
Computational Effort ◽  
Limited Area ◽  
Eof Analysis ◽  
Large Scale Data ◽  
Wind Resource

AbstractA new statistical–dynamical downscaling procedure is developed and then applied to high-resolution (regional) time series generation and wind resource assessment. The statistical module of the new procedure uses empirical orthogonal function (EOF) analysis for the generation of large-scale atmospheric component patterns. The dominant atmospheric patterns (associated with the EOF modes explaining most of the statistical variance) are then dynamically downscaled or adjusted to high-resolution terrain and surface roughness by using the Global Environmental Multiscale–Limited Area Model (GEM-LAM). Regional time series are constructed using the model outputs. The new method is applied to the Gaspé region of Québec in Canada. The dataset used is the NCEP–NCAR reanalysis of wind, temperature, humidity, and geopotential height during the period 1958–2004. Regional time series of wind speed and temperature are constructed, and a numerical wind atlas of the Gaspé region is generated. The generated time series and the numerical wind atlas are compared with observations at different masts located in the Gaspé Peninsula and are also compared with a numerical wind atlas for the same region generated in Yu et al. The results suggest that the newly developed procedure can be useful to generate regional time series and reasonably accurate numerical wind atlases using large-scale data with much less computational effort than previous techniques.

scholarly journals CHARACTERISTICS OF TEXTURE INDEX OF DAMAGED BUILDINGS USING TIME-SERIES HIGH-RESOLUTION OPTICAL SATELLITE IMAGES

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1709-1714
Author(s):  
M. Sonobe
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Texture Analysis ◽  
Large Scale ◽  
Satellite Images ◽  
Image Interpretation ◽  
High Resolution Satellite Images ◽  
Survey Results ◽  
Occurrence Matrix ◽  
Optical Satellite Images

Abstract. A large-scale disaster has occurred due to the earthquake. In particular, 20% of the world's earthquakes with a magnitude of 6 or more occur near Japan. Damage analysis of buildings by image analysis have been effectively carried out using optical high-resolution satellite images and aerial photograph with spatial resolution of about 2 m or less. In this study, the damaged buildings caused by large-scale and continuous earthquakes in Kumamoto, Japan that occurred in April 2016 was selected as a typical example of damaged buildings. For these earthquake event, the applicability of damage distribution of buildings and recovery/restoration status by texture analysis was examined. The applicability of the representative in the dissimilarity texture analysis methods Gray- Level Co-occurrence Matrix (GLCM) method by image interpretation in the case of a large number of collapsed and wrecked buildings in a wide area was assessed. These results suggest that dissimilarity was applicable to the extraction of damaged and removed buildings in the event of such an earthquake. In addition, the analysis results were appropriately evaluated by comparing the field survey results with the image interpretation results of the pan-sharpened image. From these results, we confirmed the effectiveness of texture analysis using time-series high-resolution satellite images in grasping the damaged buildings before and immediately after the disaster and in the restoration situation 1 year after the disaster.

scholarly journals Par@Graph – a parallel toolbox for the construction and analysis of large complex climate networks

2015 ◽  
Vol 8 (1) ◽  
pp. 319-349 ◽  
Author(s):  
H. Ihshaish ◽  
A. Tantet ◽  
J. C. M. Dijkzeul ◽  
H. A. Dijkstra
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Network Analysis ◽  
Large Scale ◽  
Ocean Model ◽  
Clustering Coefficient ◽  
Connected Components ◽  
Eigenvector Centrality ◽  
Key Innovation ◽  
Climate Networks

Abstract. In this paper, we present Par@Graph, a software toolbox to reconstruct and analyze complex climate networks having a large number of nodes (up to at least O (106)) and of edges (up to at least O (1012)). The key innovation is an efficient set of parallel software tools designed to leverage the inherited hybrid parallelism in distributed-memory clusters of multi-core machines. The performance of the toolbox is illustrated through networks derived from sea surface height (SSH) data of a global high-resolution ocean model. Less than 8 min are needed on 90 Intel Xeon E5-4650 processors to construct a climate network including the preprocessing and the correlation of 3 × 105 SSH time series, resulting in a weighted graph with the same number of vertices and about 3 × 106 edges. In less than 5 min on 30 processors, the resulted graph's degree centrality, strength, connected components, eigenvector centrality, entropy and clustering coefficient metrics were obtained. These results indicate that a complete cycle to construct and analyze a large-scale climate network is available under 13 min. Par@Graph therefore facilitates the application of climate network analysis on high-resolution observations and model results, by enabling fast network construction from the calculation of statistical similarities between climate time series. It also enables network analysis at unprecedented scales on a variety of different sizes of input data sets.

scholarly journals VERY HIGH RESOLUTION LAND USE AND LAND COVER MAPPING USING PLEIADES-1 STEREO IMAGERY AND MACHINE LEARNING

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 675-682
Author(s):  
D. James ◽  
A. Collin ◽  
A. Mury ◽  
S. Costa
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Large Scale ◽  
Near Infrared ◽  
Airborne Lidar ◽  
Support Vector ◽  
Surface Model ◽  
Global Changes ◽  
Very High

Abstract. Anthropocene is featured with increasing human population and global changes that strongly affect landscapes at an unprecedented pace. As a flagship, the coastal fringe is subject to an accelerated conversion of natural areas into agricultural ones, in turn, into urban ones, generating hazardous soil artificialization. Very high resolution (VHR) technologies such as airborne LiDAR or UAV imageries are good assets to model the topography and classify the land use/land cover (LULC), helping local management. Even if their spatial resolution suits with the management scale, their extent covers a few km2, making large-scale monitoring complex and time-consuming. VHR spaceborne imagery has a great potential to address this spatial challenge given its regional acquisition. This research proposes to evaluate the capabilities of a Pleiades-1 stereo-satellite multispectral imagery (blue, green, red, BGR, and near-infrared, NIR) to both model the surface topography and classify LULC. Horizontal and vertical accuracies of the photogrammetry-driven digital surface model (DSM) attain 0.53 m and 0.65 m, respectively. Nine LULC generic classes are studied using the maximum likelihood (ML) and support vector machine (SVM) algorithms. The classification accuracy of the basic BGR (reaching 84.64 % and 76.13 % with ML and SVM, respectively) is improved by the DSM contribution (5.49 % and 2.91 % for ML and SVM, respectively), and the NIR contribution (6.78 % and 3.89 % for ML and SVM, respectively). The gain of the DSM-NIR combination totals 8.91 % and 8.40 % for ML and SVM, respectively, making the ML-based full combination the best performance (93.55 %).

scholarly journals Resource availability and disturbance shape maximum tree height across the Amazon

2020 ◽  
Author(s):  
Eric Gorgens ◽  
Matheus Henrique Nunes ◽  
Tobias Jackson ◽  
David Coomes ◽  
Michael Keller ◽  
...  
Keyword(s):  
High Resolution ◽  
Environmental Variables ◽  
Evolutionary Biology ◽  
Large Scale ◽  
Brazilian Amazon ◽  
Airborne Lidar ◽  
Maximum Height ◽  
Amazon Forest ◽  
Secondary Forests ◽  
Growth And Survival

AbstractThe factors shaping the distribution of giant tropical trees are poorly understood, despite its importance as a link between evolutionary biology and ecosystem biogeochemistry. The recent discovery of clusters of trees over 80 metres tall in the Guiana Shield region of the Amazon rainforest challenges the current understanding of the factors controlling the growth and survival of giant trees. The new discovery led us to revisit the question: what determines the distribution of the tallest trees of the Amazon?Here, we used high-resolution airborne LiDAR (Light Detection and Ranging) surveys to measure canopy height across 282,750 ha of primary old-growth and secondary forests throughout the entire Brazilian Amazon to investigate the relationship between the occurrence of giant trees and the environmental factors that influence their growth and survival. Our results suggest that the factors controlling where trees grow extremely tall are distinct from those controlling their longevity. Trees grow taller in areas with high soil clay content (> 42%), lower radiation (< 130 clear days per year) and wind speeds, avoiding alluvial areas (elevations higher than 40 m a.s.l), and with an optimal precipitation range of 1,500 to 2,500 mm yr-1. We then used an envelope model to determine the environmental conditions that support the very tallest trees (i.e. over 70 m height). We found that, as opposed to the myriad of interacting factors that control the maximum height at a large scale, wind speed had by far the largest influence on the distribution of these sentinel trees, and explained 67% of the probability of finding trees over 70 m in the Brazilian Amazon forest.The high-resolution pan-Amazon LiDAR data showed that environmental variables that drive growth in height are fundamentally different from environmental variables that support their survival. While precipitation and temperature seem to have lower importance for their survival than expected from previous studies, changes in wind and radiation regimes could reshape our forested biomes. This should be carefully considered by policy-makers when identifying important hotspots for the conservation of biodiversity in the Amazon.

scholarly journals Par@Graph – a parallel toolbox for the construction and analysis of large complex climate networks

2015 ◽  
Vol 8 (10) ◽  
pp. 3321-3331 ◽  
Author(s):  
H. Ihshaish ◽  
A. Tantet ◽  
J. C. M. Dijkzeul ◽  
H. A. Dijkstra
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Network Analysis ◽  
Large Scale ◽  
Ocean Model ◽  
Clustering Coefficient ◽  
Connected Components ◽  
Eigenvector Centrality ◽  
Key Innovation ◽  
Climate Networks

Abstract. In this paper, we present Par@Graph, a software toolbox to reconstruct and analyze complex climate networks having a large number of nodes (up to at least 106) and edges (up to at least 1012). The key innovation is an efficient set of parallel software tools designed to leverage the inherited hybrid parallelism in distributed-memory clusters of multi-core machines. The performance of the toolbox is illustrated through networks derived from sea surface height (SSH) data of a global high-resolution ocean model. Less than 8 min are needed on 90 Intel Xeon E5-4650 processors to reconstruct a climate network including the preprocessing and the correlation of 3 × 105 SSH time series, resulting in a weighted graph with the same number of vertices and about 3.2 × 108 edges. In less than 14 min on 30 processors, the resulted graph's degree centrality, strength, connected components, eigenvector centrality, entropy and clustering coefficient metrics were obtained. These results indicate that a complete cycle to construct and analyze a large-scale climate network is available under 22 min Par@Graph therefore facilitates the application of climate network analysis on high-resolution observations and model results, by enabling fast network reconstruct from the calculation of statistical similarities between climate time series. It also enables network analysis at unprecedented scales on a variety of different sizes of input data sets.

scholarly journals Methane as a diagnostic tracer of changes in the net circulation of the middle atmosphere

2014 ◽  
Vol 14 (17) ◽  
pp. 24183-24220
Author(s):  
E. E. Remsberg
Keyword(s):  
Time Series ◽  
Northern Hemisphere ◽  
Large Scale ◽  
Middle Atmosphere ◽  
Chemical Conversion ◽  
Global Scale ◽  
Positive Trend ◽  
Residual Circulation ◽  
Use Of Time ◽  
Extratropical Latitude

Abstract. This study makes use of time series of methane (CH4) data from the Halogen Occultation Experiment (HALOE) to determine whether there were any statistically significant changes of the net circulation within the stratosphere and lower mesosphere during 1992–2005. HALOE CH4 profiles in terms of mixing ratio vs. pressure-altitude are binned into subtropical and extratropical latitude zones of the southern and of the Northern Hemisphere, and their separate time series are then analyzed using multiple linear regression (MLR) techniques. A positive trend in the subtropics and a negative trend in the extratropics is interpreted as indicating an acceleration of the net circulation. A significant acceleration is found in the Northern Hemisphere from 20 hPa to 7 hPa, a likely indication of changes from the effects of wave activity during those years. No similar acceleration is found in the Southern Hemisphere. The trends from HALOE H2O are analyzed and compared with those from CH4 for consistency because H2O is a primary product in the upper stratosphere of the chemical conversion of CH4. The CH4 and H2O trends have a ratio of nearly 2 : 1, and they are anti-correlated most clearly near the stratopause in the southern extratropics. Seasonal anomalies are found in the HALOE CH4 time series of the lower mesosphere, and they are ascribed to wave-driven, secondary residual circulation cells associated with the descent of the SAO westerlies. The time series residuals for CH4 of the lower mesosphere also exhibit aperiodic structure, and it is anti-correlated with that of the tracer-like species HCl. Such structure indicates the effects of variations in the wave forcing. It is concluded that multi-year, global-scale distributions of CH4 are very useful for diagnosing large-scale changes of the net transport within the middle atmosphere.

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