scholarly journals Integrated Multiscale Method for Obtaining Accurate Forest Surface Area Statistics over Large Areas

2019 ◽  
Vol 8 (2) ◽  
pp. 58
Author(s):  
Shilun Kang ◽  
Xinqi Zheng ◽  
Yongqiang Lv
Keyword(s):  
Surface Area ◽  
Carbon Balance ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Multiscale Method ◽  
Projected Area ◽  
Forested Area ◽  
Calculation Results ◽  
The Impact ◽  
Scale Surface

Forest surface area is a fundamental input for forest-related research, such as carbon balance, biodiversity conservation, and ecosystem functioning and services. However, an accurate assessment of the area of forestland in China is not available because the forested area is usually calculated as a 2D projected area rather than a 3D surface area, and the impact of changes in the surface terrain on the area is ignored. In this study, we propose an integrated multiscale method that combines geomorphic regionalization and surface area algorithms to calculate the forest surface area in China. The results show that (1) China’s forested area is approximately 4.91% larger than the conventional estimates and corresponds to a carbon storage estimate that is approximately 383.72 million tons higher; (2) the integrated multiscale method exhibits good adaptability and high precision for large-scale surface area calculations; and (3) the calculation results of this method are superior to those of remote sensing data or single surface area algorithms, and the calculation efficiency is high.

scholarly journals Retrieving Forest Canopy Elements Clumping Index Using ICESat GLAS Lidar Data

2021 ◽  
Vol 13 (5) ◽  
pp. 948
Author(s):  
Lei Cui ◽  
Ziti Jiao ◽  
Kaiguang Zhao ◽  
Mei Sun ◽  
Yadong Dong ◽  
...  
Keyword(s):  
Large Scale ◽  
Forest Canopy ◽  
Remote Sensing Data ◽  
Alternative Methods ◽  
High Signal ◽  
Laser Altimeter ◽  
Waveform Data ◽  
Forest Park ◽  
Clumping Index ◽  
The Impact

Clumping index (CI) is a canopy structural variable important for modeling the terrestrial biosphere, but its retrieval from remote sensing data remains one of the least reliable. The majority of regional or global CI products available so far were generated from multiangle optical reflectance data. However, these reflectance-based estimates have well-known limitations, such as the mere use of a linear relationship between the normalized difference hotspot and darkspot (NDHD) and CI, uncertainties in bidirectional reflectance distribution function (BRDF) models used to calculate the NDHD, and coarse spatial resolutions (e.g., hundreds of meters to several kilometers). To remedy these limitations and develop alternative methods for large-scale CI mapping, here we explored the use of spaceborne lidar—the Geoscience Laser Altimeter System (GLAS)—and proposed a semi-physical algorithm to estimate CI at the footprint level. Our algorithm was formulated to leverage the full vertical canopy profile information of the GLAS full-waveform data; it converted raw waveforms to forest canopy gap distributions and gap fractions of random canopies, which was used to estimate CI based on the radiative transfer theory and a revised Beer–Lambert model. We tested our algorithm over two areas in China—the Saihanba National Forest Park and Heilongjiang Province—and assessed its relative accuracies against field-measured CI and MODIS CI products. We found that reliable estimation of CI was possible only for GLAS waveforms with high signal-to-noise ratios (e.g., >65) and at gentle slopes (e.g., <12°). Our GLAS-based CI estimates for high-quality waveforms compared well to field-based CI (i.e., R2 = 0.72, RMSE = 0.07, and bias = 0.02), but they showed less correlation to MODIS CI (e.g., R2 = 0.26, RMSE = 0.12, and bias = 0.04). The difference highlights the impact of the scale effect in conducting comparisons of products with huge differences resolution. Overall, our analyses represent the first attempt to use spaceborne lidar to retrieve high-resolution forest CI and our algorithm holds promise for mapping CI globally.

Understanding our capabilities in observing and modelling Coronal Mass Ejections

2020 ◽  
Author(s):  
Christine Verbeke ◽  
Marilena Mierla ◽  
M. Leila Mays ◽  
Christina Kay ◽  
Mateja Dumbovic ◽  
...  
Keyword(s):  
Coronal Mass Ejections ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Large Set ◽  
Common Procedure ◽  
Propagation Models ◽  
Weather Events ◽  
Prediction Time ◽  
First Results ◽  
The Impact

&lt;p&gt;Coronal Mass Ejections (CMEs) are large-scale eruptions of plasma and magnetic fields from the Sun. They are considered to be the main drivers of strong space weather events at Earth. Multiple models have been developed over the past decades to be able to predict the propagation of CMEs and their arrival time at Earth. Such models require input from observations, which can be used to fit the CME to an appropriate structure.&lt;/p&gt;&lt;p&gt;When determining input parameters for CME propagation models, it is common procedure to derive kinematic parameters from remote-sensing data. The resulting parameters can be used as inputs for the CME propagation models to obtain an arrival prediction time of the CME f.e. at Earth. However, when fitting the CME structure to obtain the needed parameters for simulations, different geometric structures and also different parts of the CME structure can be fitted. These aspects, together with the fact that 3D reconstructions strongly depend on the subjectivity and judgement of the scientist performing them, may lead to uncertainties in the fitted parameters. Up to now, no large study has tried to map these uncertainties and to evaluate how they affect the modelling of CMEs. &amp;#160;&lt;/p&gt;&lt;p&gt;Fitting a large set of CMEs within a selected period of time, we aim to investigate the uncertainties in the CME fittings in detail. Each event is fitted multiple times by different scientists. We discuss statistics on uncertainties of the fittings. We also present some first results of the impact of these uncertainties on CME propagation modelling.&lt;/p&gt;&lt;p&gt;Acknowledgements: This work has been partly supported by the International Space Science Institute (ISSI) in the framework of International Team 480 entitled: Understanding Our Capabilities In Observing And Modelling Coronal Mass Ejections'.&lt;/p&gt;

scholarly journals THE IMPACT OF MUNICIPAL MERGERS ON LOCAL PUBLIC SPENDING: EVIDENCE FROM REMOTE-SENSING DATA

2020 ◽  
Vol 20 (2) ◽  
pp. 243-266
Author(s):  
Steve Pickering ◽  
Seiki Tanaka ◽  
Kyohei Yamada
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Public Spending ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Before And After ◽  
Administrative Units ◽  
Political Units ◽  
Political Leverage ◽  
The Impact

AbstractHow are resources distributed when administrative units merge? We take advantage of recent, large-scale municipal mergers in Japan to systematically study the impact of municipal mergers within merged municipalities and, in particular, what politicians do when their districts and constituencies suddenly change. We argue that when rural and sparsely populated municipalities merge with more urban and densely populated municipalities, residents of the former are likely to see a reduced share of public spending because they lost political leverage through the merger. Our empirical analyses detect changes in public spending before and after the municipal mergers with remote sensing data, which allows for flexible units of analysis and enables us to proxy for spending within merged municipalities. Overall, our results show that politicians tend to reduce benefits allocated to areas where there are a small number of voters, while increasing the allocation to more populous areas. The micro-foundation of our argument is also corroborated by survey data. The finding suggests that, all things being equal, the quantity rather than quality of electorates matters for politicians immediately after political units change.

scholarly journals Analysis of Surface Water Areal changes using Remote Sensing Data

2020 ◽  
Vol 02 (03) ◽  
pp. 1-1
Author(s):  
Heath Murray ◽  
◽  
Mehdi Khaki ◽  
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Surface Area ◽  
Human Life ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Area Reduction ◽  
Water Surface Area ◽  
Inland Water Bodies ◽  
The Impact

Inland water bodies are crucial for supporting human life in various parts of the world. Therefore, it is essential to accurately monitor its spatiotemporal variations for better water management. The main objective of this study is to investigate the application of remote sensing data for quantifying the surface area changes and the impact of climatological variabilities over Lakes Mead and Chapala. Historical time series of monthly surface area dynamics were developed using Landsat 1-8 scenes and the climate variability was analysed using evaporation rate and precipitation. Results show that estimated surface water changes from satellite data agree well with independent data. A significant decline in surface area of about 40% since 2000 was found over the Lake Mead region. The relationship between surface area, precipitation and evaporation indicate that climatological factors have contributed to the lake surface area reduction. Lake Chapala’s surface area, on the other hand, has not fallen significantly despite negative trends in precipitation. It was found that human interactions with the lake are likely the main cause of surface area variations. The information about water surface area variation in this study is valuable for monitoring and characterising the predictability of water availability of the regions.

scholarly journals The impact of lateral carbon fluxes on the European carbon balance

2008 ◽  
Vol 5 (5) ◽  
pp. 1259-1271 ◽  
Author(s):  
P. Ciais ◽  
A. V. Borges ◽  
G. Abril ◽  
M. Meybeck ◽  
G. Folberth ◽  
...  
Keyword(s):  
Carbon Balance ◽  
Large Scale ◽  
Food Product ◽  
Carbon Fluxes ◽  
Carbon Accumulation ◽  
Main Process ◽  
Carbon Budgets ◽  
Lateral Transport ◽  
Gaseous Species ◽  
The Impact

Abstract. To date, little is known about the impact of processes which cause lateral carbon fluxes over continents, and from continents to oceans on the CO2 – and carbon budgets at local, regional and continental scales. Lateral carbon fluxes contribute to regional carbon budgets as follows: Ecosystem CO2 sink=Ecosystem carbon accumulation+Lateral carbon fluxes. We estimated the contribution of wood and food product trade, of emission and oxidation of reduced carbon species, and of river erosion and transport as lateral carbon fluxes to the carbon balance of Europe (EU-25). The analysis is completed by new estimates of the carbon fluxes of coastal seas. We estimated that lateral transport (all processes combined) is a flux of 165 Tg C yr−1 at the scale of EU-25. The magnitude of lateral transport is thus comparable to current estimates of carbon accumulation in European forests. The main process contributing to the total lateral flux out of Europe is the flux of reduced carbon compounds, corresponding to the sum of non-CO2 gaseous species (CH4, CO, hydrocarbons, ...) emitted by ecosystems and exported out of the European boundary layer by the large scale atmospheric circulation.

scholarly journals A derivation error that affects carbon balance models exists in the current implementation of the modified Arrhenius function

2020 ◽  
Author(s):  
Bridget Murphy ◽  
Joseph R. Stinziano
Keyword(s):  
Carbon Balance ◽  
Large Scale ◽  
Arrhenius Equation ◽  
Temperature Response ◽  
Carbon Fluxes ◽  
Current Approach ◽  
Carbon Gain ◽  
List Type ◽  
The Impact ◽  
Temperature Responses

SummaryUnderstanding biological temperature responses is crucial to predicting global carbon fluxes. The current approach to modelling temperature responses of photosynthetic capacity in large scale modelling efforts uses a modified Arrhenius equation.We rederived the modified Arrhenius equation from the source publication from 1942 and uncovered a missing term that was dropped by 2002. We compare fitted temperature response parameters between the correct and incorrect derivation of the modified Arrhenius equation.We find that most parameters are minimally affected, though activation energy is impacted quite substantially. We then scaled the impact of these small errors to whole plant carbon balance and found that the impact of the rederivation of the modified Arrhenius equation on modelled daily carbon gain causes a meaningful deviation of ~18% day−1.This suggests that the error in the derivation of the modified Arrhenius equation has impacted the accuracy of predictions of carbon fluxes at larger scales since >40% of Earth System Models contain the erroneous derivation. We recommend that the derivation error be corrected in modelling efforts moving forward.

scholarly journals Exploring the Impact of Land Cover and Topography on Rainfall Maxima in the Netherlands

2013 ◽  
Vol 14 (2) ◽  
pp. 524-542 ◽  
Author(s):  
H. W. ter Maat ◽  
E. J. Moors ◽  
R. W. A. Hutjes ◽  
A. A. M. Holtslag ◽  
A. J. Dolman
Keyword(s):  
Land Use ◽  
The Netherlands ◽  
Land Surface ◽  
Large Scale ◽  
Mesoscale Model ◽  
Atmospheric Modeling ◽  
Turbulence Parameterization ◽  
Forested Area ◽  
Synoptic Conditions ◽  
The Impact

Abstract The relative contribution of topography and land use on precipitation is analyzed in this paper for a forested area in the Netherlands. This area has an average yearly precipitation sum that can be 75–100 mm higher than the rest of the country. To analyze this contribution, different configurations of land use and topography are fed into a mesoscale model. The authors use the Regional Atmospheric Modeling System (RAMS) coupled with a land surface scheme simulating water vapor, heat, and momentum fluxes [Soil–Water–Atmosphere Plant System–Carbon (SWAPS-C)]. The model simulations are executed for two periods that cover varying large-scale synoptic conditions of summer and winter periods. The output of the experiments leads to the conclusion that the precipitation maximum at the Veluwe is forced by topography and land use. The effect of the forested area on the processes that influence precipitation is smaller in summertime conditions when the precipitation has a convective character. In frontal conditions, the forest has a more pronounced effect on local precipitation through the convergence of moisture. The effect of topography on monthly domain-averaged precipitation around the Veluwe is a 17% increase in the winter and a 10% increase in the summer, which is quite remarkable for topography with a maximum elevation of just above 100 m and moderate steepness. From this study, it appears that the version of RAMS using Mellor–Yamada turbulence parameterization simulates precipitation better in wintertime, but the configuration with the medium-range forecast (MRF) turbulence parameterization improves the simulation of precipitation in convective circumstances.

scholarly journals Estimating the Impact of COVID-19 on the PM2.5 Levels in China with a Satellite-Driven Machine Learning Model

2021 ◽  
Vol 13 (7) ◽  
pp. 1351
Author(s):  
Qiulun Li ◽  
Qingyang Zhu ◽  
Muwu Xu ◽  
Yu Zhao ◽  
K. M. Venkat Narayan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Yangtze River ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Yangtze River Delta ◽  
River Delta ◽  
The North ◽  
Spatial Coverage ◽  
Major Predictor ◽  
The Impact

China implemented an aggressive nationwide lockdown procedure immediately after the COVID-19 outbreak in January 2020. As China emerges from the impact of COVID-19 on national economic and industrial activities, it has become the site of a large-scale natural experiment to evaluate the impact of COVID-19 on regional air quality. However, ground measurements of fine particulate matters (PM2.5) concentrations do not offer comprehensive spatial coverage, especially in suburban and rural regions. In this study, we developed a machine learning method with satellite aerosol remote sensing data, meteorological fields and land use parameters as major predictor variables to estimate spatiotemporally resolved daily PM2.5 concentrations in China. Our study period consists of a reference semester (1 November 2018–30 April 2019) and a pandemic semester (1 November 2019–30 April 2020), with six modeling months in each semester. Each period was then divided into subperiod 1 (November and December), subperiod 2 (January and February) and subperiod 3 (March and April). The reference semester model obtained a 10-fold cross-validated R2 (RMSE) of 0.79 (17.55 μg/m3) and the pandemic semester model obtained a 10-fold cross-validated R2 (RMSE) of 0.83 (13.48 μg/m3) for daily PM2.5 predictions. Our prediction results showed high PM2.5 concentrations in the North China Plain, Yangtze River Delta, Sichuan Basin and Xinjiang Autonomous Region during the reference semester. PM2.5 levels were lowered by 4.8 μg/m3 during the pandemic semester compared to the reference semester and PM2.5 levels during subperiod 2 decreased most, by 18%. The southeast region was affected most by the COVID-19 outbreak with PM2.5 levels during subperiod 2 decreasing by 31%, followed by the Northern Yangtze River Delta (29%) and Pearl River Delta (24%).

Impact of COVID-19 on Nuclear Medicine in Germany, Austria and Switzerland: An International Survey in April 2020

2020 ◽  
Vol 59 (04) ◽  
pp. 294-299 ◽  
Author(s):  
Lutz S. Freudenberg ◽  
Ulf Dittmer ◽  
Ken Herrmann
Keyword(s):  
Nuclear Medicine ◽  
Health Systems ◽  
Personal Protective Equipment ◽  
Large Scale ◽  
Diagnostic Procedures ◽  
Protective Equipment ◽  
Current Crisis ◽  
Web Based ◽  
Public Versus Private ◽  
The Impact

Abstract Introduction Preparations of health systems to accommodate large number of severely ill COVID-19 patients in March/April 2020 has a significant impact on nuclear medicine departments. Materials and Methods A web-based questionnaire was designed to differentiate the impact of the pandemic on inpatient and outpatient nuclear medicine operations and on public versus private health systems, respectively. Questions were addressing the following issues: impact on nuclear medicine diagnostics and therapy, use of recommendations, personal protective equipment, and organizational adaptations. The survey was available for 6 days and closed on April 20, 2020. Results 113 complete responses were recorded. Nearly all participants (97 %) report a decline of nuclear medicine diagnostic procedures. The mean reduction in the last three weeks for PET/CT, scintigraphies of bone, myocardium, lung thyroid, sentinel lymph-node are –14.4 %, –47.2 %, –47.5 %, –40.7 %, –58.4 %, and –25.2 % respectively. Furthermore, 76 % of the participants report a reduction in therapies especially for benign thyroid disease (-41.8 %) and radiosynoviorthesis (–53.8 %) while tumor therapies remained mainly stable. 48 % of the participants report a shortage of personal protective equipment. Conclusions Nuclear medicine services are notably reduced 3 weeks after the SARS-CoV-2 pandemic reached Germany, Austria and Switzerland on a large scale. We must be aware that the current crisis will also have a significant economic impact on the healthcare system. As the survey cannot adapt to daily dynamic changes in priorities, it serves as a first snapshot requiring follow-up studies and comparisons with other countries and regions.

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