scholarly journals Improving Forest Baseline Maps in Tropical Wetlands Using GEDI-Based Forest Height Information and Sentinel-1

Forests ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1374
Author(s):  
Kamiel Verhelst ◽  
Yaqing Gou ◽  
Martin Herold ◽  
Johannes Reiche
Keyword(s):  
Random Forest ◽  
Radar Data ◽  
Tropical Wetlands ◽  
Trade Off ◽  
Forest Mapping ◽  
Forest Height ◽  
Probability Threshold ◽  
The Impact ◽  
Moisture Dynamics ◽  
Texture Metrics

Remote Sensing-based global Forest/Non-Forest (FNF) masks have shown large inaccuracies in tropical wetland areas. This limits their applications for deforestation monitoring and alerting in which they are used as a baseline for mapping new deforestation. In radar-based deforestation monitoring, for example, moisture dynamics in unmasked non-forest areas can lead to false detections. We combined a GEDI Forest Height product and Sentinel-1 radar data to improve FNF masks in wetland areas in Gabon using a Random Forest model. The GEDI Forest Height, together with texture metrics derived from Sentinel-1 mean backscatter values, were the most important contributors to the classification. Quantitatively, our mask outperformed existing global FNF masks by increasing the Producer’s Accuracy for the non-forest class by 14%. The GEDI Forest Height product by itself also showed high accuracies but contained Landsat artifacts. Qualitatively, our model was best able to cleanly uncover non-forest areas and mitigate the impact of Landsat artifacts in the GEDI Forest Height product. An advantage of the methodology presented here is that it can be adapted for different application needs by varying the probability threshold of the Random Forest output. This study stresses that, in any application of the suggested methodology, it is important to consider the UA/PA trade-off and the effect it has on the classification. The targeted improvements for wetland forest mapping presented in this paper can help raise the accuracy of tropical deforestation monitoring.

The Random Forest Method in Research of Impact of Macroeconomic Indicators of Regional Development on Informal Employment Rate

2020 ◽  
Vol 27 (6) ◽  
pp. 37-55
Author(s):  
E. V. Zarova ◽  
E. I. Dubravskaya
Keyword(s):  
Random Forest ◽  
Regional Development ◽  
Russian Federation ◽  
Regional Level ◽  
Informal Employment ◽  
Macroeconomic Factors ◽  
Random Forest Method ◽  
Macroeconomic Indicators ◽  
The Russian Federation ◽  
The Impact

The topic of quantitative research on informal employment has a consistently high relevance both in the Russian Federation and in other countries due to its high dependence on cyclicality and crisis stages in economic dynamics of countries with any level of economic development. Developing effective government policy measures to overcome the negative impact of informal employment requires special attention in theoretical and applied research to assessing the factors and conditions of informal employment in the Russian Federation including at the regional level. Such effects of informal employment as a shortfall in taxes, potential losses in production efficiency, and negative social consequences are a concern for the authorities of the federal and regional levels. Development of quantitative indicators to determine the level of informal employment in the regions, taking into account their specifics in the general spatial and economic system of Russia are necessary to overcome these negative effects. The article proposes and tests methods for solving the problem of assessing the impact of hierarchical relationships on macroeconomic factors at the regional level of informal employment in constituent entities of the Russian Federation. Majority of the works on the study of informal employment are based on basic statistical methods of spatial-dynamic analysis, as well as on the now «traditional» methods of cluster and correlation-regression analysis. Without diminishing the merits of these methods, it should be noted that they are somewhat limited in identifying hidden structural connections and interdependencies in such a complex multidimensional phenomenon as informal employment. In order to substantiate the possibility of overcoming these limitations, the article proposes indicators of regional statistics that directly and indirectly characterize informal employment and also presents the possibilities of using the «random forest» method to identify groups of constituent entities of the Russian Federation that have similar macroeconomic factors of informal employment. The novelty of this method in terms of research objectives is that it allows one to assess the impact of macroeconomic indicators of regional development on the level of informal employment, taking into account the implicit, not predetermined by the initial hypotheses, hierarchical relationships of factor indicators. Based on the generalization of the studies presented in the literature, as well as the authors’ statistical calculations using Rosstat data, the authors came to the conclusion about the high importance of macroeconomic parameters of regional development and systemic relationships of macroeconomic indicators in substantiating the differentiation of the informal level across the constituent entities of the Russian Federation.

scholarly journals Soil Moisture Dynamics in Response to Precipitation and Thinning in a Semi-Dry Forest in Northern Mexico

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 105
Author(s):  
Argelia E. Rascón-Ramos ◽  
Martín Martínez-Salvador ◽  
Gabriel Sosa-Pérez ◽  
Federico Villarreal-Guerrero ◽  
Alfredo Pinedo-Alvarez ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Forest Management ◽  
Water Availability ◽  
Dry Forest ◽  
Rainfall Events ◽  
Rainfall Characteristics ◽  
Northern Mexico ◽  
The Difference ◽  
The Impact ◽  
Moisture Dynamics

Understanding soil moisture behavior in semi-dry forests is essential for evaluating the impact of forest management on water availability. The objective of the study was to analyze soil moisture based in storm observations in three micro-catchments (0.19, 0.20, and 0.27 ha) with similar tree densities, and subject to different thinning intensities in a semi-dry forest in Chihuahua, Mexico. Vegetation, soil characteristics, precipitation, and volumetric water content were measured before thinning (2018), and after 0%, 40%, and 80% thinning for each micro-catchment (2019). Soil moisture was low and relatively similar among the three micro-catchments in 2018 (mean = 8.5%), and only large rainfall events (>30 mm) increased soil moisture significantly (29–52%). After thinning, soil moisture was higher and significantly different among the micro-catchments only during small rainfall events (<10 mm), while a difference was not noted during large events. The difference before–after during small rainfall events was not significant for the control (0% thinning); whereas 40% and 80% thinning increased soil moisture significantly by 40% and 53%, respectively. Knowledge of the response of soil moisture as a result of thinning and rainfall characteristics has important implications, especially for evaluating the impact of forest management on water availability.

scholarly journals Market buoyancy, information transparency and pricing strategy in the Scottish housing market

Urban Studies ◽  
2021 ◽  
pp. 004209802199178
Author(s):  
Nan Liu
Keyword(s):  
Pricing Strategy ◽  
Selling Price ◽  
Trade Off ◽  
Information Transparency ◽  
North East ◽  
Sealed Bid ◽  
Strategy Changes ◽  
The Impact ◽  
Role Of Information

In housing markets there is a trade-off between selling time and selling price, with pricing strategy being the balancing act between the two. Motivated by the Home Report scheme in Scotland, this paper investigates the role of information symmetry played in such a trade-off. Empirically, this study tests if sellers’ pricing strategy changes when more information becomes available and whether this, in turn, affects the trade-off between the selling price and selling time. Using housing transaction data of North-East Scotland between 1998Q2 and 2018Q2, the findings show that asking price has converged to the predicted price of the property since the introduction of the Home Report. While information transparency reduces the effect of ‘overpricing’ on selling time, there is little evidence to show that it reduces the impact of pricing strategy on the final selling price in the sealed-bid context.

Interrelationship Among Competition, Diversification and Liquidity Creation: Evidence from Indian Banks

2021 ◽  
Vol 15 (2) ◽  
pp. 183-204
Author(s):  
Pankaj Sinha ◽  
Naina Grover
Keyword(s):  
Inverse Relationship ◽  
Consumer Welfare ◽  
Adverse Impact ◽  
Public And Private ◽  
Jel Codes ◽  
Trade Off ◽  
Private Banks ◽  
Indian Banks ◽  
The Impact

This study analyses the impact of competition on liquidity creation by banks and investigates the dynamics between diversification, liquidity creation and competition for banks operating in India during the period from 2005 to 2018. Using the broad and narrow measures of liquidity creation, an inverse relationship is determined between liquidity creation and competition. The study also indicates a trade-off between pro-competitive policies to improve consumer welfare and the liquidity-destroying effects of competition, and it highlights how diversification affects liquidity creation. Highly diversified banks in India create less liquidity compared with less-diversified banks, both public and private. The liquidity-destroying effects of competition is intensified among highly diversified private banks, which suggest that diversification has not moderated the adverse impact of competition. JEL Codes: G01, G18, G21, G28

scholarly journals Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10

2019 ◽  
Vol 12 (3) ◽  
pp. 1209-1225 ◽  
Author(s):  
Christoph A. Keller ◽  
Mat J. Evans
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Gas Phase ◽  
Atmospheric Chemistry ◽  
Random Forest Regression ◽  
Data Set ◽  
Gas Phase Chemistry ◽  
Chemical Conditions ◽  
Phase Chemistry ◽  
The Impact

Abstract. Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models are numerically intense, and previous attempts to reduce the numerical cost of chemistry solvers have not delivered transformative change. We show here the potential of a machine learning (in this case random forest regression) replacement for the gas-phase chemistry in atmospheric chemistry transport models. Our training data consist of 1 month (July 2013) of output of chemical conditions together with the model physical state, produced from the GEOS-Chem chemistry model v10. From this data set we train random forest regression models to predict the concentration of each transported species after the integrator, based on the physical and chemical conditions before the integrator. The choice of prediction type has a strong impact on the skill of the regression model. We find best results from predicting the change in concentration for long-lived species and the absolute concentration for short-lived species. We also find improvements from a simple implementation of chemical families (NOx = NO + NO2). We then implement the trained random forest predictors back into GEOS-Chem to replace the numerical integrator. The machine-learning-driven GEOS-Chem model compares well to the standard simulation. For ozone (O3), errors from using the random forests (compared to the reference simulation) grow slowly and after 5 days the normalized mean bias (NMB), root mean square error (RMSE) and R2 are 4.2 %, 35 % and 0.9, respectively; after 30 days the errors increase to 13 %, 67 % and 0.75, respectively. The biases become largest in remote areas such as the tropical Pacific where errors in the chemistry can accumulate with little balancing influence from emissions or deposition. Over polluted regions the model error is less than 10 % and has significant fidelity in following the time series of the full model. Modelled NOx shows similar features, with the most significant errors occurring in remote locations far from recent emissions. For other species such as inorganic bromine species and short-lived nitrogen species, errors become large, with NMB, RMSE and R2 reaching >2100 % >400 % and <0.1, respectively. This proof-of-concept implementation takes 1.8 times more time than the direct integration of the differential equations, but optimization and software engineering should allow substantial increases in speed. We discuss potential improvements in the implementation, some of its advantages from both a software and hardware perspective, its limitations, and its applicability to operational air quality activities.

Anticompetitive Effects in EU Competition Law

2020 ◽  
Author(s):  
Pablo Ibáñez Colomo
Keyword(s):  
Competition Law ◽  
Case Law ◽  
Time Variable ◽  
Court Of Justice ◽  
Open Questions ◽  
Eu Competition Law ◽  
Competitive Disadvantage ◽  
Probability Threshold ◽  
The Impact ◽  
Freedom Of Action

Abstract This article examines the meaning and scope of the notion of anticompetitive effects in EU competition law. It does so by bringing together several strands of the case law (and this across all provisions, namely Articles 101 and 102 TFEU and merger control). The analysis is structured around a framework that considers the main variables that shape the notion in practice: the time variable (actual or potential effects); the dimensions of competition and the counterfactual; the meaning of effects and the probability threshold (plausibility, likelihood, certainty). The exercise shows that it is possible to discern a concrete meaning to the notion of anticompetitive effects. Some central questions, including the role and operation of the counterfactual and the threshold of effects, have already been answered by the Court of Justice. In particular, it has long been clear that anticompetitive effects amount to more than a mere competitive disadvantage and/or a limitation of a firm’s freedom of action. The impact on equally efficient firms’ ability and/or incentive to compete would need to be established. At the same time, some open questions and some potential areas of friction (relating, inter alia, to stakeholders’ tendency to conflate appreciability and effects) remain. These are also discussed.

scholarly journals Predictability of Deep Convection in Idealized and Operational Forecasts: Effects of Radar Data Assimilation, Orography, and Synoptic Weather Regime

2019 ◽  
Vol 148 (1) ◽  
pp. 63-81 ◽  
Author(s):  
Kevin Bachmann ◽  
Christian Keil ◽  
George C. Craig ◽  
Martin Weissmann ◽  
Christian A. Welzbacher
Keyword(s):  
Data Assimilation ◽  
Deep Convection ◽  
Radar Data ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Model Errors ◽  
Ensemble Prediction System ◽  
Beneficial Impact ◽  
The Impact ◽  
Radar Data Assimilation

Abstract We investigate the practical predictability limits of deep convection in a state-of-the-art, high-resolution, limited-area ensemble prediction system. A combination of sophisticated predictability measures, namely, believable and decorrelation scale, are applied to determine the predictable scales of short-term forecasts in a hierarchy of model configurations. First, we consider an idealized perfect model setup that includes both small-scale and synoptic-scale perturbations. We find increased predictability in the presence of orography and a strongly beneficial impact of radar data assimilation, which extends the forecast horizon by up to 6 h. Second, we examine realistic COSMO-KENDA simulations, including assimilation of radar and conventional data and a representation of model errors, for a convectively active two-week summer period over Germany. The results confirm increased predictability in orographic regions. We find that both latent heat nudging and ensemble Kalman filter assimilation of radar data lead to increased forecast skill, but the impact is smaller than in the idealized experiments. This highlights the need to assimilate spatially and temporally dense data, but also indicates room for further improvement. Finally, the examination of operational COSMO-DE-EPS ensemble forecasts for three summer periods confirms the beneficial impact of orography in a statistical sense and also reveals increased predictability in weather regimes controlled by synoptic forcing, as defined by the convective adjustment time scale.

scholarly journals Simulation of X-Band Rainfall Observations from S-Band Radar Data

2006 ◽  
Vol 23 (9) ◽  
pp. 1195-1205 ◽  
Author(s):  
V. Chandrasekar ◽  
S. Lim ◽  
E. Gorgucci
Keyword(s):  
Doppler Radar ◽  
Theoretical Models ◽  
Radar Data ◽  
Shape Distribution ◽  
Microphysical Properties ◽  
Rain Medium ◽  
Microphysical Parameters ◽  
X Band ◽  
State Water ◽  
The Impact

Abstract To design X-band radar systems as well as evaluate algorithm development, it is useful to have simultaneous X-band observation with and without the impact of path attenuation. One way to develop that dataset is through theoretical models. This paper presents a methodology to generate realistic range profiles of radar variables at attenuating frequencies, such as X band, for rain medium. Fundamental microphysical properties of precipitation, namely, size and shape distribution information, are used to generate realistic profiles of X band starting with S-band observation. Conditioning the simulation from S band maintains the natural distribution of rainfall microphysical parameters. Data from the Colorado State University’s University of Chicago–Illinois State Water Survey (CHILL) radar and the National Center for Atmospheric Research S-band dual-polarization Doppler radar (S-POL) are used to simulate X-band radar variables. Three procedures to simulate the radar variables and sample applications are presented.

scholarly journals Assessing the evidence on case management

2002 ◽  
Vol 181 (1) ◽  
pp. 17-21 ◽  
Author(s):  
S. J. Ziguras ◽  
G. W. Stuart ◽  
A. C. Jackson
Keyword(s):  
Mental Health ◽  
Case Management ◽  
Statistical Power ◽  
Cochrane Collaboration ◽  
Randomised Trials ◽  
Inclusion Criteria ◽  
Management Effectiveness ◽  
Trade Off ◽  
The Cochrane Collaboration ◽  
The Impact

BackgroundEvidence on the impact of case management is contradictory.AimsTo discuss two different systematic reviews (one conducted by the authors and one conducted through the Cochrane collaboration) that came to contradictory conclusions about the impact of case management in mental health services.MethodWe summarised the findings of the two reviews with respect to case management effectiveness, examined key methodological differences between the two approaches and discuss the impact of these on the validity of the results.ResultsThe differences in conclusions between the two reviews result from the differences in inclusion criteria, namely non-randomised trials, data from unpublished scales and data from variables with skewed distributions. The theoretical and empirical effects of these are discussed.ConclusionsSystematic reviewers may face a trade-off between the application of strict criteria for the inclusion of studies and the amount of data available for analysis and hence statistical power. The available research suggests that case management is generally effective.

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