688 research outputs found
The DV-Xα molecular-orbital calculation method
No full textThis multi-author contributed volume contains chapters featuring the development of the DV-Xα method and its application to a variety of problems in Materials Science and Spectroscopy written by leaders of the respective fields. The volume contains a Foreword written by the Chairs of Japanese and Korea DV-X alpha Societies. This book is aimed at individuals working in Quantum Chemistry
Synergistic Use of Sentinel-1 and Sentinel-2 to Map Natural Forest and Acacia Plantation and Stand Ages in North-Central Vietnam
No full textMany remote sensing studies do not distinguish between natural and planted forests. We combine C-Band Synthetic Aperture Radar (Sentinel-1, S-1) and optical satellite imagery (Sentinel-2, S-2) and examine Random Forest (RF) classification of acacia plantations and natural forest in North-Central Vietnam. We demonstrate an ability to distinguish plantation from natural forest, with overall classification accuracies of 87% for S-1, and 92.5% and 92.3% for S-2 and for S-1 and S-2 combined respectively. We found that the ratio of the Short-Wave Infrared Band to the Red Band proved most effective in distinguishing acacia from natural forest. We used RF on S-2 imagery to classify acacia plantations into 6 age classes with an overall accuracy of 70%, with young plantation consistently separated from older. However, accuracy was lower at distinguishing between the older age classes. For both distinguishing plantation and natural forest, and determining plantation age, a combination of radar and optical imagery did nothing to improve classification accuracy
Old-Growth Forest Disturbance in the Ukrainian Carpathians
No full textHuman activity has greatly reduced the area of old-growth forest in Europe, with some of the largest remaining fragments in the Carpathian Mountains of south-western Ukraine. We used satellite image analysis to calculate old-growth forest disturbance in this region from 2010 to 2019. Over this period, we identified 1335 ha of disturbance in old-growth forest, equivalent to 1.8% of old-growth forest in the region. During 2015 to 2019, the average annual disturbance rate was 0.34%, varying with altitude, distance to settlements and location within the region. Disturbance rates were 7–8 times lower in protected areas compared to outside of protected areas. Only one third of old-growth forest is currently within protected areas; expansion of the protected area system to include more old-growth forests would reduce future loss. A 2017 law that gave protection to all old-growth forest in Ukraine had no significant impact on disturbance rates in 2018, but in 2019 disturbance rates reduced to 0.19%. Our analysis is the first indication that this new legislation may be reducing loss of old-growth forest in Ukraine
Identifying European Old-Growth Forests using Remote Sensing: A Study in the Ukrainian Carpathians
No full textOld-growth forests are an important, rare and endangered habitat in Europe. The ability to identify old-growth forests through remote sensing would be helpful for both conservation and forest management. We used data on beech, Norway spruce and mountain pine old-growth forests in the Ukrainian Carpathians to test whether Sentinel-2 satellite images could be used to correctly identify these forests. We used summer and autumn 2017 Sentinel-2 satellite images comprising 10 and 20 m resolution bands to create 6 vegetation indices and 9 textural features. We used a Random Forest classification model to discriminate between dominant tree species within old-growth forests and between old-growth and other forest types. Beech and Norway spruce were identified with an overall accuracy of around 90%, with a lower performance for mountain pine (70%) and mixed forest (40%). Old-growth forests were identified with an overall classification accuracy of 85%. Adding textural features, band standard deviations and elevation data improved accuracies by 3.3%, 2.1% and 1.8% respectively, while using combined summer and autumn images increased accuracy by 1.2%. We conclude that Random Forest classification combined with Sentinel-2 images can provide an effective option for identifying old-growth forests in Europe
Determination of Structural Characteristics of Old-Growth Forest in Ukraine Using Spaceborne LiDAR
No full textA forest’s structure changes as it progresses through developmental stages from establishment to old-growth forest. Therefore, the vertical structure of old-growth forests will differ from that of younger, managed forests. Free, publicly available spaceborne Laser Range and Detection (LiDAR) data designed for the determination of forest structure has recently become available through NASA’s General Ecosystem and Development Investigation (GEDI). We use this data to investigate the structure of some of the largest remaining old-growth forests in Europe in the Ukrainian Carpathian Mountains. We downloaded 18489 cloud-free shots in the old-growth forest (OGF) and 20398 shots in adjacent non-OGF areas during leaf-on, snow-free conditions. We found significant differences between OGF and non-OGF over a wide range of structural metrics. OGF was significantly more open, with a more complex vertical structure and thicker ground-layer vegetation. We used Random Forest classification on a range of GEDI-derived metrics to classify OGF shapefiles with an accuracy of 73%. Our work demonstrates the use of spaceborne LiDAR for the identification of old-growth forests
Decline of Late Spring and Summer Snow Cover in the Scottish Highlands from 1984 to 2022: A Landsat Time Series
No full textLate spring and summer snow cover, the remnants of winter and early spring snowfall, not only possess an intrinsic importance for montane flora and fauna, but also act as a sensitive indicator for climate change. The variability and potential trends in late spring and summer (snowmelt season) snow cover in mountain regions are often poorly documented. May to mid-September Landsat imagery from 1984 to 2022 was used to quantify changes in the snow-covered area of upland regions in the Scottish Highlands. There was substantial annual variability in the area of May to mid-September snow cover combined with a significant decline over the 39-year study period (p = 0.02). Long-term climate data used to show variability in May to mid-September snow cover was positively related to winter snowfall and negatively related to winter and April temperatures. The results from a long-running field survey counting the number of snow patches that survive until the following winter were used to check the veracity of the study. Further, accuracy was estimated through comparison with higher resolution Sentinel-2 imagery, giving a user and producer accuracy rate of 99.8% and 87%, respectively. Projected future warming will further diminish this scarce, valuable habitat, along with its associated plant communities, thus threatening the biodiversity and scenic value of the Scottish Highlands
The Improvement Based on the DV-Hop Localization Algorithm for Wireless Sensor Networks
No full textAs the problems of lower localization accuracy appeared in the traditional DV-Hop algorithm，the
author analyzed three main factors that influence the localization accuracy of original DV-Hop algorithm which
started from the calculation of the average jump distance in algorithm and the calculation of node coordinates. The
author then proposed the calculation method of average jump distance based on the weighted correction of the ＲSSI
( Ｒeceive Signal Strength Indicator ) and the calculation method of unknown node coordinates based on the
correction of the total least squares ( TLS) . The simulation result shows that compared to the traditional DV-Hop
algorithm，the accuracy of the improved algorithm increased by about 30% in distance measurement as well as rose
approximately by 35% in relative localization precision
Climate benefits of intact Amazon forests and the biophysical consequences of disturbance
No full textTropical forests have an important regulating influence on local and regional climate, through modulating the exchange of moisture and energy between the land and the atmosphere. Deforestation disrupts this exchange, though the climatic consequences of progressive, patch-scale deforestation of formerly intact forested landscapes have not previously been assessed. Remote sensing datasets of land surface and atmospheric variables were used to compare the climate responses of Amazon evergreen broadleaf forests that lost their intact status between 2000 and 2013. Clear gradients in environmental change with increasing disturbance were observed. Leaf area index (LAI) showed progressively stronger reductions as forest loss increased, with evapotranspiration (ET) showing a comparative decline. These changes in LAI and ET were related to changes in temperature (T), with increased warming as deforestation increased. Severe deforestation of intact Amazon forest, defined as areas where canopy cover was reduced below 70 %, was shown to have increased daytime land surface T by 0.44 °C over the study period. Differences between intact and disturbed forests were most pronounced during the dry season, with severely deforested areas warming as much as 1.5 °C. Maintenance of canopy cover was identified as an important factor in minimising the impacts of disturbance. Overall, the results highlight the climate benefits provided by intact tropical forests, providing further evidence that protecting intact forests is of utmost importance
Natural aerosol–climate feedbacks suppressed by anthropogenic aerosol
No full textThe natural environment is an important source of atmospheric aerosol such as dust, sea spray, and wildfire smoke. Climate controls many of these natural aerosol sources, which, in turn, can alter climate through changing the properties of clouds and the Earth's radiative balance. However, the Earth's atmosphere is now heavily modified by anthropogenic pollution aerosol, but how this pollution may alter these natural aerosol–climate feedbacks has not been previously explored. Here we use a global aerosol microphysics model to analyze how anthropogenic aerosol alters one link within these feedbacks, namely, the sensitivity of cloud albedo to changes in natural aerosol. We demonstrate that anthropogenic aerosol in the Northern Hemisphere has halved the hemispheric mean cloud albedo radiative effect that occurs due to changes in natural aerosol emissions. Such a suppression has not occurred in the more pristine Southern Hemisphere
Observed reductions in rainfall due to tropical deforestation
No full textTropical deforestation affects local and regional precipitation, but the effects are uncertain and have not been determined using observations. Satellite data sets were used to show reductions in precipitation over areas of tropical forest loss, with stronger reductions seen as the deforested area expands
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