Search

Satellite-borne multispectral data are suitable for regional-scale grassland community classification owing to comprehensive coverage. However, the spectral similarity of different communities makes it challenging to distinguish them based on a single multispectral data. To address this issue, we proposed a support vector machine (SVM)–based method integrating multispectral data, two-band enhanced vegetation index (EVI2) time-series, and phenological features extracted from Chinese GaoFen (GF)-1/6 satellite with ( 16m) spatial and ( 2 day) temporal resolution. To obtain cloud-free images, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) algorithm was employed in this study. By using the algorithm on the coarse cloudless images at the same or similar time as the fine images with cloud cover, the cloudless fine images were obtained, and the cloudless EVI2 time-series and phenological features were generated. The developed method was applied to identify grassland communities in Ordos, China. The results show that the Caragana pumila Pojark, Caragana davazamcii Sanchir and Salix schwerinii E. L. Wolf grassland, the Potaninia mongolica Maxim, Ammopiptanthus mongolicus S. H. Cheng and Tetraena mongolica Maxim grassland, the Caryopteris mongholica Bunge and Artemisia ordosica Krasch grassland, the Calligonum mongolicum Turcz grassland, and the Stipa breviflora Griseb and Stipa bungeana Trin grassland are distinguished with an overall accuracy of 87.25%. The results highlight that, compared to multispectral data only, the addition of EVI2 time-series and phenological features improves the classification accuracy by 9.63% and 14.7%, respectively, and even by 27.36% when these two features are combined together, and indicate the advantage of the fine images in this study, compared to 500m moderate-resolution imaging spectroradiometer (MODIS) data, which are commonly used for grassland classification at regional scale, while using 16m GF data suggests a 23.96% increase in classification accuracy with the same extracted features. This study indicates that the proposed method is suitable for regional-scale grassland community classification.

Inland waters, such as lakes, reservoirs and rivers, are important sources of climate forcing trace gases. A key parameter that regulates the gas exchange between water and the atmosphere is the gas transfer velocity, which itself is controlled by near-surface turbulence in the water. While in lakes and reservoirs, near-surface turbulence is mainly driven by atmospheric forcing, in shallow rivers and streams it is generated by bottom friction of gravity-forced flow. Large rivers represent a transition between these two cases. Near-surface turbulence has rarely been measured in rivers and the drivers of turbulence have not been quantified. We analyzed continuous measurements of flow velocity and quantified turbulence as the rate of dissipation of turbulent kinetic energy over the ice-free season in a large regulated river in Northern Finland. Measured dissipation rates agreed with predictions from bulk parameters, including mean flow velocity, wind speed, surface heat flux, and with a one-dimensional numerical turbulence model. Values ranged from to . Atmospheric forcing or gravity was the dominant driver of near-surface turbulence for similar fraction of the time. Large variability in near-surface dissipation rate occurred at diel time scales, when the flow velocity was strongly affected by downstream dam operation. By combining scaling relations for boundary-layer turbulence at the river bed and at the air-water interface, we derived a simple model for estimating the relative contributions of wind speed and bottom friction of river flow as a function of depth. Plain Language Summary Inland water bodies such as lakes, reservoirs and rivers are an important source of climate forcing trace gases to the atmosphere. Gas exchange between water and the atmosphere is regulated by the gas transfer velocity and the concentration difference between the water surface and the atmosphere. The gas transfer velocity depends on near-surface turbulence, but robust formulations have not been developed for river systems. Their surface area is sufficiently large for meteorological forcing to cause turbulence, as in lakes and reservoirs, but turbulence generated from bed and internal friction of gravity-driven flows is also expected to contribute. Here we quantify near-surface turbulence using data from continuous air and water side measurements conducted over the ice-free season in a large subarctic regulated river in Finland. We find that turbulence, quantified as the dissipation rate of turbulent kinetic energy, is well described using equations for predicting turbulence from meteorological data for sufficiently high wind speeds whereas the contribution from bottom shear dominated at higher flow velocities. A one-dimensional river model successfully captured these processes. We provide a fundamental model for estimating the relative contributions of atmospheric forcing and bottom friction as a function of depth.

In this paper, for the first time, a regional-scale 1:100,000 landslide-susceptibility map (LSM) is presented for Sisak-Moslavina County in Croatia. The spatial relationship between landslide occurrence and landslide predictive factors (engineering geological units, relief, roughness, and distance to streams) is assessed using the integration of a statistically based frequency ratio (FR) into the analytical hierarchy process (AHP). Due to the lack of landslide inventory for the county, LiDAR-based inventories are completed for an area of 132 km2. From 1238 landslides, 549 are chosen to calculate the LSM and 689 for its verification. Additionally, landslides digitized from available geological maps and reported via the web portal “Report a landslide” are used for verification. The county is classified into four susceptibility classes, covering 36% with very-high and high and 64% with moderate and low susceptibility zones. The presented approach, using limited LiDAR data and the extrapolation of the correlation results to the entire county, is encouraging for primary regional-level studies, justifying the cost-benefit ratio. Still, the positioning of LiDAR polygons prerequires a basic statistical analysis of predictive factors.

Air quality modelling can be a strong tool for air quality management. In the present study, the Danish Urban Background Model (UBM) and the USEPA AERMOD are applied for calculating NOx and total particulate matter (TPM) concentrations for Mumbai city of India for the years 2007 and 2012. In order to compare the results from the two models, two sets of simulations are performed using the same sets of input data for boundary conditions, emissions, and meteorology. The results showed that the NOx calculations from the UBM model were in better agreement with observed data when compared with similar results from the AERMOD model. However, the opposite was the case for TPM for which the results from the AERMOD model were in better agreement with observed data when compared with the results from the UBM. The concentration levels for 2012 were generally higher than for 2007, reflecting differences in meteorological conditions for the 2 years. When comparing the obtained model results with measurements, it should be noted, that the emission inventories have various shortcomings, and that the boundary conditions from the DEHM (Danish Eulerian Hemispheric Model) are obtained with a coarse resolution of 150 km × 150 km. One of the main shortcomings of the TPM emission inventories is that it is not accounted for all the sources. Moreover, for both TPM and NOx, the boundaries of model calculations of the UBM and AERMOD model domain are underestimating the actual concentrations due to the relatively coarse resolution. When the UBM model calculations are scaled to fit the level of the observed concentrations, it is evident that spatial and temporal variation reproduced better results when compared with the results obtained from AERMOD.

High spatial resolution (1–5 m) remotely sensed datasets are increasingly being used to map land covers over large geographic areas using supervised machine learning algorithms. Although many studies have compared machine learning classification methods, sample selection methods for acquiring training and validation data for machine learning, and cross-validation techniques for tuning classifier parameters are rarely investigated, particularly on large, high spatial resolution datasets. This work, therefore, examines four sample selection methods—simple random, proportional stratified random, disproportional stratified random, and deliberative sampling—as well as three cross-validation tuning approaches—k-fold, leave-one-out, and Monte Carlo methods. In addition, the effect on the accuracy of localizing sample selections to a small geographic subset of the entire area, an approach that is sometimes used to reduce costs associated with training data collection, is investigated. These methods are investigated in the context of support vector machines (SVM) classification and geographic object-based image analysis (GEOBIA), using high spatial resolution National Agricultural Imagery Program (NAIP) orthoimagery and LIDAR-derived rasters, covering a 2,609 km2 regional-scale area in northeastern West Virginia, USA. Stratified-statistical-based sampling methods were found to generate the highest classification accuracy. Using a small number of training samples collected from only a subset of the study area provided a similar level of overall accuracy to a sample of equivalent size collected in a dispersed manner across the entire regional-scale dataset. There were minimal differences in accuracy for the different cross-validation tuning methods. The processing time for Monte Carlo and leave-one-out cross-validation were high, especially with large training sets. For this reason, k-fold cross-validation appears to be a good choice. Classifications trained with samples collected deliberately (i.e., not randomly) were less accurate than classifiers trained from statistical-based samples. This may be due to the high positive spatial autocorrelation in the deliberative training set. Thus, if possible, samples for training should be selected randomly; deliberative samples should be avoided.

An increased awareness by river managers of the importance of river channel migration to sediment dynamics, habitat complexity and other ecosystem functions has led to an advance in the science and practice of identifying, protecting or restoring specific erodible corridors across which rivers are free to migrate. One current challenge is the application of these watershed-specific goals at the regional planning scales (e.g., the European Water Framework Directive). This study provides a GIS-based spatial analysis of the channel migration rates at the regional-scale. As a case study, 99 reaches were sampled in the French part of the Rhone Basin and nearby tributaries of the Mediterranean Sea (111,300 km2). We explored the spatial correlation between the channel migration rate and a set of simple variables (e.g., watershed area, channel slope, stream power, active channel width). We found that the spatial variability of the channel migration rates was primary explained by the gross stream power (R2 = 0.48) and more surprisingly by the active channel width scaled by the watershed area. The relationship between the absolute migration rate and the gross stream power is generally consistent with the published empirical models for freely meandering rivers, whereas it is less significant for the multi-thread reaches. The discussion focused on methodological constraints for a regional-scale modelling of the migration rates, and the interpretation of the empirical models. We hypothesize that the active channel width scaled by the watershed area is a surrogate for the sediment supply which may be a more critical factor than the bank resistance for explaining the regional-scale variability of the migration rates.

International audience; In a context of increased land and natural resources scarcity, the possibilities for local authorities and stakeholders of anticipating evolutions or testing the impact of envisaged developments through scenario simulation are new challenges. PRECOS's approach integrates data pertaining to the fields of water and soil resources, agronomy, urbanization, land use and infrastructure etc. It is complemented by a socio-economic and regulatory analysis of the territory illustrating its constraints and stakes. A modular architecture articulates modeling software and spatial and temporal representations tools. It produces indicators in three core domains: soil degradation, water and soil resources and agricultural production. As a territory representative of numerous situations of the Mediterranean Basin (urban pressures, overconsumption of spaces, degradation of the milieus), a demonstration in the Crau's area (Southeast of France) has allowed to validate a prototype of the approach and to test its feasibility in a real life situation. Results on the Crau area have shown that, since the beginning of the 16th century, irrigated grasslands are the cornerstones of the anthropic-system, illustrating how successfully men's multi secular efforts have maintained a balance between environment and local development. But today the ecosystem services are jeopardized firstly by urban sprawl and secondly by climate change. Pre-diagnosis in regions of Emilia-Romagna (Italy) and Valencia (Spain) show that local end-users and policy-makers are interested by this approach. The modularity of indicator calculations and the availability of geodatabases indicate that PRECOS may be up scaled in other socio-economic contexts. (C) 2016 Elsevier Ltd. All rights reserved.

We present an estimate of net ecosystem exchange (NEE) of CO2 in Europe for the years 2001-2007. It is derived with a data assimilation that uses a large set of atmospheric CO2 mole fraction observations (∼70 000) to guide relatively simple descriptions of terrestrial and oceanic net exchange, while fossil fuel and fire emissions are prescribed. Weekly terrestrial sources and sinks are optimized (i.e., a flux inversion) for a set of 18 large ecosystems across Europe in which prescribed climate, weather, and surface characteristics introduce finer scale gradients. We find that the terrestrial biosphere in Europe absorbed a net average of -165 Tg C yr-1 over the period considered. This uptake is predominantly in non-EU countries, and is found in the northern coniferous (-94 Tg C yr-1) and mixed forests (-30 Tg C yr-1) as well as the forest/field complexes of eastern Europe (-85 Tg C yr-1). An optimistic uncertainty estimate derived using three biosphere models suggests the uptake to be in a range of -122 to -258 Tg C yr-1, while a more conservative estimate derived from the a-posteriori covariance estimates is -165±437 Tg C yr-1. Note, however, that uncertainties are hard to estimate given the nature of the system and are likely to be significantly larger than this. Interannual variability in NEE includes a reduction in uptake due to the 2003 drought followed by 3 years of more than average uptake. The largest anomaly of NEE occurred in 2005 concurrent with increased seasonal cycles of observed CO2. We speculate these changes to result from the strong negative phase of the North Atlantic Oscillation in 2005 that lead to favorable summer growth conditions, and altered horizontal and vertical mixing in the atmosphere. All our results are available through http://www.carbontracker.eu. © 2009 Blackwell Publishing Ltd.

We adapt general statistical methods to estimate the optimal error covariance matrices in a regional inversion system inferring methane surface emissions from atmospheric concentrations. Using a minimal set of physical hypotheses on the patterns of errors, we compute a guess of the error statistics that is optimal in regard to objective statistical criteria for the specific inversion system. With this very general approach applied to a real-data case, we recover sources of errors in the observations and in the prior state of the system that are consistent with expert knowledge while inferred from objective criteria and with affordable computation costs. By not assuming any specific error patterns, our results depict the variability and the inter-dependency of errors induced by complex factors such as the misrepresentation of the observations in the transport model or the inability of the model to reproduce well the situations of steep gradients of concentrations. Situations with probable significant biases (e.g., during the night when vertical mixing is ill-represented by the transport model) can also be diagnosed by our methods in order to point at necessary improvement in a model. By additionally analysing the sensitivity of the inversion to each observation, guidelines to enhance data selection in regional inversions are also proposed. We applied our method to a recent significant accidental methane release from an offshore platform in the North Sea and found methane fluxes of the same magnitude than what was officially declared.

Among MODIS products (freely available to the scientific community from 2001), albedo data (MCD43B3) are 16 days composites at 1km spatial resolution, widely used for various applications in climate models, but which still remains difficult to validate. The objective of this study is to propose a method to validate these products with high spatial and temporal resolution data. 31 FORMOSAT-2 images acquired over a small region in the South-Eastern France at 8m for spatial resolution were aggregated at MODIS resolution using a Point Spread Function. The correlation coefficient resulting from comparisons between albedo MODIS and the 1-km FORMOSAT-2 albedos varied from 0.93 to 0.98, which show reasonably accurate results for this study area.

Because of its role in soil functioning, our ability to predict soil organic matter (SOM) dynamics, as influenced by natural and anthropogenic processes, is essential to mitigating soil degradation, ensuring food security, and improving the global environment. Numerous mathematical models have been developed to predict the response of SOM to agricultural practices at the soil-profile or small-plot scales. The same models, coupled with spatial databases, have been applied to larger spatial extents, especially in response to the demand for national inventories of soil C sequestration potential. Modeling SOM dynamics must also be developed at an intermediate integrative level to better investigate the relative importance of transfer and transformation processes in SOM dynamics in agricultural landscapes. Predictive models at the landscape scale will facilitate the assessment of the impact of SOM dynamics on the environment and provide management guidelines at the farm and watershed levels. We review the existing approaches and outline the various needs toward an integrated modeling of SOM at the landscape scale. Landscape-scale modeling involves specific land area representation and model requirements, which include: modeling SOM dynamics in the uncultivated elements of a landscape; simulating SOM distribution and dif f erential dynamics along the soil profile; modeling SOM vertical and lateral fluxes linked to erosion, dissolved organic matter fluxes, and litter transfer; and modeling the spatial distribution of organic matter input and management practices. Even though progress is being made toward all of these aspects, a fully integrated framework for SOM modeling at the landscape level has still to be developed. This will only be possible with the design of a flexible, three-dimensional, spatially explicit representation of the landscape system and with the integration of functional interactions and organic matter transfer functions into the classical SOM modeling frameworks.

The loss of biodiversity has become a matter of urgent concern and a better understanding of local drivers is crucial for conservation. Although environmental heterogeneity is recognized as an important determinant of biodiversity, this has rarely been tested using field data at management scale. We propose and provide evidence for the simple hypothesis that local species diversity is related to spatial environmental heterogeneity. Species partition the environment into habitats. Biodiversity is therefore expected to be influenced by two aspects of spatial heterogeneity: 1) the variability of environmental conditions, which will affect the number of types of habitat, and 2) the spatial configuration of habitats, which will affect the rates of ecological processes, such as dispersal or competition. Earlier, simulation experiments predicted that both aspects of heterogeneity will influence plant species richness at a particular site. For the first time, these predictions were tested for plant communities using field data, which we collected in a wooded pasture in the Swiss Jura mountains using a four-level hierarchical sampling design. Richness generally increased with increasing environmental variability and "roughness" (i.e. decreasing spatial aggregation). Effects occurred at all scales, but the nature of the effect changed with scale, suggesting a change in the underlying mechanisms, which will need to be taken into account if scaling up to larger landscapes. Although we found significant effects of environmental heterogeneity, other factors such as history could also be important determinants. If a relationship between environmental heterogeneity and species richness can be shown to be general, recently available high-resolution environmental data can be used to complement the assessment of patterns of local richness and improve the prediction of the effects of land use change based on mean site conditions or land use history.

16 pages, 16 figures, 6 tables, The SW Iberian continental margin is well recognized as a tectonically active area, where major canyons and mass wasting events are both present. Earthquake triggered submarine landslides may cause tsunami and result in catastrophic damage to bordering coastal areas. In this setting, submarine landslide susceptibility mapping represents a major step towards a regional risk mitigation strategy. Landslide susceptibility mapping in large offshore areas presents significant challenges as a result of the limited information on controlling variables, large uncertainties in triggering mechanisms and limited geotechnical data. In this study, a geotechnical model-based approach has been followed that narrows the range of controlling factors and, within a probabilistic framework, allows a systematic treatment of parameter uncertainties. This model-based analysis covers the whole SW Iberian margin increasing by three orders of magnitude the areal extent of precedent offshore slope stability susceptibility studies. This jump in spatial scale is facilitated by application of a systematic Bayesian updating procedure, to combine geotechnical information from global databases and that available from regional sites. Seismic shaking is estimated using an available regional database of seismogenic faults. These tools are implemented within a GIS to generate, via Montecarlo simulations, probabilistic landslide susceptibility maps based on two different analytical seismic infinite slope stability models. These models differ mainly in the form of their final results, either as distributions of slope stability safety factors or as distributions of seismic-triggered slope displacements. Receiving Operator Curves are used to assess the different landslide susceptibility predictions obtained against a comprehensive regional database of submarine landslides. It turns out that the models analyzed correctly predict 92% and 82% of the mapped landslide subset chosen for validation for pseudo-static and displacement-based method respectively. This suggests that, within the limits of the currently available databases, seismic events are the dominant factor at the origin of the submarine landslides mapped in the study area. An advantage of the framework presented is that it can quickly incorporate new regional geotechnical information or better regional landslide databases, as they become available, This research received funding from the European Union's Horizon 2020 research and innovating programme under the Marie Sklodowska-Curie grant agreement No 721403. The Spanish “Ministerio de Ciencia e Innovación” and the European Regional Development Fund through grant CTM2015-70155-R (project INSIGHT) are also acknowledged, With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)