This paper presents the study of modern pollen analogs from the Balearic Islands. While similar studies have been largely applied to mainland areas, research focused on modern vegetation dynamics on ...Mediterranean islands remains very rare. In this research, we combine vegetation surveys, pollen analysis and multivariate statistics to understand landscape composition. The main objectives of are: (1) to examine pollen-vegetation relationships in relation to environmental and land-use variables; (2) to understand modern pollen representation in a mosaic landscape structure; and (3) to propose pollen indicators that characterize the primary vegetation types from the Balearic Islands to better interpret past pollen records in Mediterranean island environments. Pollen results and Redundancy Analysis (RDA) distinguish three major groups: (a) Holm oak and box formations; (b) maquis and garrigues; and (c) anthropogenic and open habitats. Landscape form, mean decadal rainfall, mean decadal temperature, fire activity, trampling, slope percentage, wet/flooded soil, saline soil, distance to agropastoral cells, gHM index, domestic herbivory presence, agropastoral use, and soil type are the major variables explaining modern pollen assemblage variation in our research. Poaceae undiff., Plantago sp., Apiaceae undiff., Cerealia-t, and Cichorieae are highly correlated to human activities but should be interpreted cautiously when occurring in low values. Quercus ilex-t, Hypericum, and Buxus are correlated to humid locations while Pistacia, Pinus, Juniperus-t, and Olea to high mean decadal temperatures. Our study indicates how pollen analysis and multivariate analysis are powerful tools for characterizing the mosaic landscape, with special focus on the main vegetation types of the Balearic Islands.
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This open access volume is the first comprehensive assessment of the Hindu Kush Himalaya (HKH) region. It comprises important scientific research on the social, economic, and environmental pillars of ...sustainable mountain development and will serve as a basis for evidence-based decision-making to safeguard the environment and advance people’s well-being. The compiled content is based on the collective knowledge of over 300 leading researchers, experts and policymakers, brought together by the Hindu Kush Himalayan Monitoring and Assessment Programme (HIMAP) under the coordination of the International Centre for Integrated Mountain Development (ICIMOD). This assessment was conducted between 2013 and 2017 as the first of a series of monitoring and assessment reports, under the guidance of the HIMAP Steering Committee: Eklabya Sharma (ICIMOD), Atiq Raman (Bangladesh), Yuba Raj Khatiwada (Nepal), Linxiu Zhang (China), Surendra Pratap Singh (India), Tandong Yao (China) and David Molden (ICIMOD and Chair of the HIMAP SC). This First HKH Assessment Report consists of 16 chapters, which comprehensively assess the current state of knowledge of the HKH region, increase the understanding of various drivers of change and their impacts, address critical data gaps and develop a set of evidence-based and actionable policy solutions and recommendations. These are linked to nine mountain priorities for the mountains and people of the HKH consistent with the Sustainable Development Goals. This book is a must-read for policy makers, academics and students interested in this important region and an essentially important resource for contributors to global assessments such as the IPCC reports. ; Constitutes the first comprehensive assessment of the Hindu Kush Himalaya region, providing an authoritative overview of the region Assembles the collective knowledge of over 300 leading researchers, practitioners, experts, and policymakers Combines the current state of knowledge of the Hindu Kush Himalaya region in one volume Offers Open Access to a set of practically oriented policy recommendations
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Coseismic landslides pose immediate and prolonged hazards to mountainous communities, and provide a rare opportunity to study the effect of large earthquakes on erosion and sediment budgets. By ...mapping landslides using high-resolution satellite imagery, we find that the 25 April 2015 Mw7.8 Gorkha earthquake and aftershock sequence produced at least 25,000 landslides throughout the steep Himalayan Mountains in central Nepal. Despite early reports claiming lower than expected landslide activity, our results show that the total number, area, and volume of landslides associated with the Gorkha event are consistent with expectations, when compared to prior landslide-triggering earthquakes around the world. The extent of landsliding mimics the extent of fault rupture along the east-west trace of the Main Himalayan Thrust and increases eastward following the progression of rupture. In this event, maximum modeled Peak Ground Acceleration (PGA) and the steepest topographic slopes of the High Himalaya are not spatially coincident, so it is not surprising that landslide density correlates neither with PGA nor steepest slopes on their own. Instead, we find that the highest landslide density is located at the confluence of steep slopes, high mean annual precipitation, and proximity to the deepest part of the fault rupture from which 0.5–2Hz seismic energy originated. We suggest that landslide density was determined by a combination of earthquake source characteristics, slope distributions, and the influence of precipitation on rock strength via weathering and changes in vegetation cover. Determining the relative contribution of each factor will require further modeling and better constrained seismic parameters, both of which are likely to be developed in the coming few years as post-event studies evolve. Landslide mobility, in terms of the ratio of runout distance to fall height, is comparable to small volume landslides in other settings, and landslide volume-runout scaling is consistent with compilations of data on larger slope failures. In general, the size ratios of landslide source area to full landslide area are smaller than global averages, and hillslope length seems to largely control runout distance, which we propose reflects a topographic control on landslide mobility in this setting. We find that landslide size dictates runout distance and that more than half of the landslide debris was deposited in direct connection with stream channels. Connectivity, which is defined as the spatial proximity of landslides to fluvial channels, is greatest for larger landslides in the high-relief part of the High Himalaya. Although these failures are less abundant than those at lower elevations, they may have a disproportionate impact on sediment dynamics and cascading hazards, such as landslide reactivation by monsoon rainfall and landslide dams that lead to outburst floods. The overall high fluvial connectivity of coseismic landsliding in the Gorkha event suggests coupling between the earthquake cycle and sediment/geochemical budgets of fluvial systems in the Himalaya.
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Accurate modeling of cryospheric surface albedo is essential for ourunderstanding of climate change as snow and ice surfaces regulate the globalradiative budget and sea-level through their albedo and ...massbalance. Although significant progress has been made using physicalprinciples to represent the dynamic albedo of snow, models of glacier icealbedo tend to be heavily parameterized and not explicitly connected withphysical properties that govern albedo, such as the number and size of airbubbles, specific surface area (SSA), presence of abiotic and biotic lightabsorbing constituents (LACs), and characteristics of any overlyingsnow. Here, we introduce SNICAR-ADv4, an extension of the multi-layertwo-stream delta-Eddington radiative transfer model with theadding–doubling solver that has been previously applied to represent snowand sea-ice spectral albedo. SNICAR-ADv4 treats spectrally resolved Fresnelreflectance and transmittance between overlying snow and higher-densityglacier ice, scattering by air bubbles of varying sizes, and numerous typesof LACs. SNICAR-ADv4 simulates a wide range of clean snow and ice broadbandalbedo (BBA), ranging from 0.88 for (30 µm) fine-grain snow to 0.03for bare and bubble-free ice under direct light. Our results indicate thatrepresenting ice with a density of 650 kg m-3 as snow with norefractive Fresnel layer, as done previously, generally overestimates theBBA by an average of 0.058. However, because mostnaturally occurring ice surfaces are roughened “white ice”, we recommendmodeling a thin snow layer over bare ice simulations. We find optimalagreement with measurements by representing cryospheric media with densitiesless than 650 kg m-3 as snow and larger-density media as bubbly icewith a Fresnel layer. SNICAR-ADv4 also simulates the non-linear albedoimpacts from LACs with changing ice SSA, with peak impact per unit mass ofLACs near SSAs of 0.1–0.01 m2 kg-1. For bare, bubble-free ice, LACsactually increase the albedo. SNICAR-ADv4 represents smooth transitionsbetween snow, firn, and ice surfaces and accurately reproduces measuredspectral albedos of a variety of glacier surfaces. This work paves the wayfor adapting SNICAR-ADv4 to be used in land ice model components of Earthsystem models.
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The 2016 National Land Cover Database (NLCD) product suite (available on www.mrlc.gov), includes Landsat-based, 30 m resolution products over the conterminous (CONUS) United States ...(U.S.) for land cover, urban imperviousness, and tree, shrub, herbaceous and bare ground fractional percentages. The release of NLCD 2016 provides important new information on land change patterns across CONUS from 2001 to 2016. For land cover, seven epochs were concurrently generated for years 2001, 2004, 2006, 2008, 2011, 2013, and 2016. Products reveal that land cover change is significant across most land cover classes and time periods. The land cover product was validated using existing reference data from the legacy NLCD 2011 accuracy assessment, applied to the 2011 epoch of the NLCD 2016 product line. The legacy and new NLCD 2011 overall accuracies were 82% and 83%, respectively, (standard error (SE) was 0.5%), demonstrating a small but significant increase in overall accuracy. Between 2001 and 2016, the CONUS landscape experienced significant change, with almost 8% of the landscape having experienced a land cover change at least once during this period. Nearly 50% of that change involves forest, driven by change agents of harvest, fire, disease and pests that resulted in an overall forest decline, including increasing fragmentation and loss of interior forest. Agricultural change represented 15.9% of the change, with total agricultural spatial extent showing only a slight increase of 4778 km2, however there was a substantial decline (7.94%) in pasture/hay during this time, transitioning mostly to cultivated crop. Water and wetland change comprised 15.2% of change and represent highly dynamic land cover classes from epoch to epoch, heavily influenced by precipitation. Grass and shrub change comprise 14.5% of the total change, with most change resulting from fire. Developed change was the most persistent and permanent land change increase adding almost 29,000 km2 over 15 years (5.6% of total CONUS change), with southern states exhibiting expansion much faster than most of the northern states. Temporal rates of developed change increased in 2001–2006 at twice the rate of 2011–2016, reflecting a slowdown in CONUS economic activity. Future NLCD plans include increasing monitoring frequency, reducing latency time between satellite imaging and product delivery, improving accuracy and expanding the variety of products available in an integrated database.
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Determining the rates, mechanisms, and geographic variability of relative sea-level (RSL) change following the Last Glacial Maximum (LGM) provides insight into the sensitivity of ice sheets to ...climate change, the response of the solid Earth and gravity field to ice-mass redistribution, and constrains statistical and physical models used to project future sea-level rise. To do so in a scientifically robust way requires standardized datasets that enable broad spatial comparisons that minimize bias. As part of a larger goal to develop a unified, spatially-comprehensive post-LGM global RSL database, in this special issue we provide a standardized global synthesis of regional RSL data that resulted from the first ‘Geographic variability of HOLocene relative SEA level (HOLSEA)’ meetings in Mt Hood, Oregon (2016) and St Lucia, South Africa (2017). The HOLSEA meetings brought together sea-level researchers to agree upon a consistent protocol to standardize, interpret, and incorporate realistic uncertainties of RSL data. This special issue provides RSL data from ten geographical regions including new databases from Atlantic Europe and the Russian Arctic and revised/expanded databases from Atlantic Canada, the British Isles, the Netherlands, the western Mediterranean, the Adriatic, Israel, Peninsular Malaysia, Southeast Asia, and the Indian Ocean. In total, the database derived from this special issue includes 5634 (5290 validated) index (n = 3202) and limiting points (n = 2088) that span from ∼20,000 years ago to present. Progress in improving the standardization of sea-level databases has also been accompanied by advancements in statistical and analytical methods used to infer spatial patterns and rates of RSL change from geological data that have a spatially and temporally sparse distribution and geochronological and elevational uncertainties. This special issue marks the inception of a unified, spatially-comprehensive post-LGM global RSL database.
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Identifying and characterizing vascular plants in time and space is required in various disciplines, e.g. in forestry, conservation and agriculture. Remote sensing emerged as a key technology ...revealing both spatial and temporal vegetation patterns. Harnessing the ever growing streams of remote sensing data for the increasing demands on vegetation assessments and monitoring requires efficient, accurate and flexible methods for data analysis. In this respect, the use of deep learning methods is trend-setting, enabling high predictive accuracy, while learning the relevant data features independently in an end-to-end fashion. Very recently, a series of studies have demonstrated that the deep learning method of Convolutional Neural Networks (CNN) is very effective to represent spatial patterns enabling to extract a wide array of vegetation properties from remote sensing imagery. This review introduces the principles of CNN and distils why they are particularly suitable for vegetation remote sensing. The main part synthesizes current trends and developments, including considerations about spectral resolution, spatial grain, different sensors types, modes of reference data generation, sources of existing reference data, as well as CNN approaches and architectures. The literature review showed that CNN can be applied to various problems, including the detection of individual plants or the pixel-wise segmentation of vegetation classes, while numerous studies have evinced that CNN outperform shallow machine learning methods. Several studies suggest that the ability of CNN to exploit spatial patterns particularly facilitates the value of very high spatial resolution data. The modularity in the common deep learning frameworks allows a high flexibility for the adaptation of architectures, whereby especially multi-modal or multi-temporal applications can benefit. An increasing availability of techniques for visualizing features learned by CNNs will not only contribute to interpret but to learn from such models and improve our understanding of remotely sensed signals of vegetation. Although CNN has not been around for long, it seems obvious that they will usher in a new era of vegetation remote sensing.
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The free and open access to all archived Landsat images in 2008 has completely changed the way of using Landsat data. Many novel change detection algorithms based on Landsat time series have been ...developed We present a comprehensive review of four important aspects of change detection studies based on Landsat time series, including frequencies, preprocessing, algorithms, and applications. We observed the trend that the more recent the study, the higher the frequency of Landsat time series used. We reviewed a series of image preprocessing steps, including atmospheric correction, cloud and cloud shadow detection, and composite/fusion/metrics techniques. We divided all change detection algorithms into six categories, including thresholding, differencing, segmentation, trajectory classification, statistical boundary, and regression. Within each category, six major characteristics of different algorithms, such as frequency, change index, univariate/multivariate, online/offline, abrupt/gradual change, and sub-pixel/pixel/spatial were analyzed. Moreover, some of the widely-used change detection algorithms were also discussed. Finally, we reviewed different change detection applications by dividing these applications into two categories, change target and change agent detection.
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We evaluate modelled Antarctic ice sheet (AIS) near-surface climate, surface
mass balance (SMB) and surface energy balance (SEB) from the updated polar
version of the regional atmospheric climate ...model, RACMO2 (1979–2016). The
updated model, referred to as RACMO2.3p2, incorporates upper-air relaxation,
a revised topography, tuned parameters in the cloud scheme to generate more
precipitation towards the AIS interior and modified snow properties reducing
drifting snow sublimation and increasing surface snowmelt. Comparisons of RACMO2 model output with several independent observational
data show that the existing biases in AIS temperature, radiative fluxes and
SMB components are further reduced with respect to the previous model
version. The model-integrated annual average SMB for the ice sheet including
ice shelves (minus the Antarctic Peninsula, AP) now amounts to
2229 Gt y−1, with an interannual variability of 109 Gt y−1. The
largest improvement is found in modelled surface snowmelt, which now compares
well with satellite and weather station observations. For the high-resolution
(∼ 5.5 km) AP simulation, results remain comparable to earlier
studies. The updated model provides a new, high-resolution data set of the contemporary
near-surface climate and SMB of the AIS; this model version will be used for
future climate scenario projections in a forthcoming study.
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