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 Glyoxal tropospheric column retrievals from TROPOMI – multi-satellite intercomparison and ground-based validation

2021 ◽  
Vol 14 (12) ◽  
pp. 7775-7807
Author(s):  
Christophe Lerot ◽  
François Hendrick ◽  
Michel Van Roozendael ◽  
Leonardo M. A. Alvarado ◽  
Andreas Richter ◽  
...  
Keyword(s):  
Large Scale ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
High Signal ◽  
Data Sets ◽  
Systematic Bias ◽  
Monitoring Instrument ◽  
Error Characterization ◽  
Ozone Monitoring ◽  
The Impact

Abstract. We present the first global glyoxal (CHOCHO) tropospheric column product derived from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite. Atmospheric glyoxal results from the oxidation of other non-methane volatile organic compounds (NMVOCs) and from direct emissions caused by combustion processes. Therefore, this product is a useful indicator of VOC emissions. It is generated with an improved version of the BIRA-IASB scientific retrieval algorithm relying on the differential optical absorption spectroscopy (DOAS) approach. Among the algorithmic updates, the DOAS fit now includes corrections to mitigate the impact of spectral misfits caused by scene brightness inhomogeneity and strong NO2 absorption. The product comes along with a full error characterization, which allows for providing random and systematic error estimates for every observation. Systematic errors are typically in the range of 1 ×1014–3 ×1014 molec. cm−2 (∼30 %–70 % in emission regimes) and originate mostly from a priori data uncertainties and spectral interferences with other absorbing species. The latter may be at the origin, at least partly, of an enhanced glyoxal signal over equatorial oceans, and further investigation is needed to mitigate them. Random errors are large (>6×1014 molec. cm−2) but can be reduced by averaging observations in space and/or time. Benefiting from a high signal-to-noise ratio and a large number of small-size observations, TROPOMI provides glyoxal tropospheric column fields with an unprecedented level of detail. Using the same retrieval algorithmic baseline, glyoxal column data sets are also generated from the Ozone Monitoring Instrument (OMI) on Aura and from the Global Ozone Monitoring Experiment-2 (GOME-2) on board Metop-A and Metop-B. Those four data sets are intercompared over large-scale regions worldwide and show a high level of consistency. The satellite glyoxal columns are also compared to glyoxal columns retrieved from ground-based Multi-AXis DOAS (MAX-DOAS) instruments at nine stations in Asia and Europe. In general, the satellite and MAX-DOAS instruments provide consistent glyoxal columns both in terms of absolute values and variability. Correlation coefficients between TROPOMI and MAX-DOAS glyoxal columns range between 0.61 and 0.87. The correlation is only poorer at one mid-latitude station, where satellite data appear to be biased low during wintertime. The mean absolute glyoxal columns from satellite and MAX-DOAS generally agree well for low/moderate columns with differences of less than 1×1014 molec. cm−2. A larger bias is identified at two sites where the MAX-DOAS columns are very large. Despite this systematic bias, the consistency of the satellite and MAX-DOAS glyoxal seasonal variability is high.

TROPOMI NO2 retrieval: December 2020 (v1.4) and April 2021 (v2.2) upgrades, and comparisons with OMI and ground-based remote sensing

2021 ◽  
Author(s):  
Jos van Geffen ◽  
Henk Eskes ◽  
Maarten Sneep ◽  
Gaia Pinardi ◽  
Tijl Verhoelst ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Power Plants ◽  
Remote Sensing Data ◽  
High Signal ◽  
Cloud Height ◽  
Industrial Complexes ◽  
Global Coverage ◽  
The Impact ◽  
Small Cloud ◽  
Level 2

&lt;p&gt;The Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite is a unique instrument, combining daily global coverage, very high signal-to-noise, a broad spectral range and very small pixels up to 3.5 x 5.5 km&lt;sup&gt;2&lt;/sup&gt;. Retrievals are available for a large number of species, including NO&lt;sub&gt;2&lt;/sub&gt;. Due to the very small pixels and daily revisit, TROPOMI provides detailed information on individual sources and source sectors like individual power plants, industrial complexes, cities and suburbs, highways, and even individual ships. The TROPOMI Level-2 NO&lt;sub&gt;2&lt;/sub&gt; product is available from 30 April 2018 onwards.&lt;/p&gt;&lt;p&gt;Validation exercises of TROPOMI v1.2 &amp; v1.3 data (2018-2020) with OMI and ground-based remote sensing observations have shown that TROPOMI's tropospheric NO&lt;sub&gt;2&lt;/sub&gt; column are low by up to 50% over highly polluted areas compared to independent data. In contrast, the underlying slant columns of TROPOMI agree well with OMI and independent SAOZ observations. Differences between OMI and TROPOMI have been mainly attributed to the different cloud height retrieval, using the O&lt;sub&gt;2&lt;/sub&gt;-O&lt;sub&gt;2&lt;/sub&gt; versus O&lt;sub&gt;2&lt;/sub&gt;-A bands respectively.&lt;/p&gt;&lt;p&gt;In our presentation we discuss recent improvements in the TROPOMI NO&lt;sub&gt;2&lt;/sub&gt; retrieval and the impact these have on the tropospheric columns and on the comparisons with OMI and ground-based remote-sensing data.&lt;/p&gt;&lt;p&gt;Version v1.4, which became operational on 2 December 2020, entails a major improvement in the cloud height retrieval, based on a modification of the FRESCO-S cloud retrieval using the O&lt;sub&gt;2&lt;/sub&gt;-A band observations. In particular the cloud height over scenes with a small cloud coverage have increased, resulting in larger tropospheric columns in the retrievals over polluted areas.&lt;/p&gt;&lt;p&gt;Version v2.2, to become operational in April/May 2021, includes similar cloud retrieval modifications. Furthermore, it provides a better treatment of saturation issues and transients, is using improved (ir)radiance measurements (level-1b v2 spectra) including degradation corrections, and includes a new albedo treatment.&lt;/p&gt;&lt;p&gt;The TROPOMI NO&lt;sub&gt;2&lt;/sub&gt; retrievals are compared with OMI retrievals (from the QA4ECV product) and to ground-based observations with MAXDOAS and PANDORA instruments.&lt;/p&gt;

scholarly journals Glyoxal tropospheric column retrievals from TROPOMI, multi-satellite intercomparison and ground-based validation

2021 ◽  
Author(s):  
Christophe Lerot ◽  
François Hendrick ◽  
Michel Van Roozendael ◽  
Leonardo M. A. Alvarado ◽  
Andreas Richter ◽  
...  
Keyword(s):  
Large Scale ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
High Signal ◽  
Data Sets ◽  
Systematic Bias ◽  
Monitoring Instrument ◽  
Error Characterization ◽  
Ozone Monitoring ◽  
The Impact

Abstract. We present the first global glyoxal (CHOCHO) tropospheric column product derived from the TROPOspheric Monitoring Instrument (TROPOMI) on board of the Sentinel-5 Precursor satellite. Atmospheric glyoxal results from the oxidation of other non-methane volatile organic compounds (NMVOCs) and from direct emissions caused by combustion processes. Therefore, this product is a useful indicator of VOC emissions. It is generated with an improved version of the BIRA-IASB scientific retrieval algorithm relying on the Differential Optical Absorption Spectroscopy (DOAS) approach. Among the algorithmic updates, the DOAS fit now includes corrections to mitigate the impact of spectral misfits caused by scene brightness inhomogeneity and strong NO2 absorption. The product comes along with a full error characterization, which allows providing random and systematic error estimates for every observation. Systematic errors are typically in the range of 1–3 × 1014 molec/cm2 (~30–70 % in emission regimes). Random errors are larger (> 6 × 1014 molec/cm2) but can be reduced by averaging observations in space and/or time. Benefiting from a high signal-to-noise ratio and a large number of small-size observations, TROPOMI provides glyoxal tropospheric column fields with an unprecedented level of details. Using the same retrieval algorithmic baseline, glyoxal column data sets are also generated from the Ozone Monitoring Instrument (OMI) on Aura and from the Global Ozone Monitoring Experiment-2 (GOME-2) on board of Metop-A and Metop-B. Those four data sets are intercompared over large-scale regions worldwide and show a high level of consistency. The satellite glyoxal columns are also compared to glyoxal columns retrieved from ground-based Multi-Axis (MAX-) DOAS instruments at nine stations in Asia and Europe. In general, the satellite and MAX-DOAS instruments provide consistent glyoxal columns both in terms of absolute values and variability. Correlation coefficients between TROPOMI and MAX-DOAS glyoxal columns range between 0.61 and 0.87. The correlation is only poorer at one mid-latitude station, where satellite data appears low biased during wintertime. The mean absolute glyoxal columns from satellite and MAX-DOAS generally agree well for low/moderate columns with differences less than 1 × 1014 molec/cm2. A larger bias is identified at two sites where the MAX-DOAS columns are very large. Despite this systematic bias, the consistency of the satellite and MAX-DOAS glyoxal seasonal variability is excellent.

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.

Changes in tree species abundance in a Neotropical forest: impact of climate change

1996 ◽  
Vol 12 (2) ◽  
pp. 231-256 ◽  
Author(s):  
Richard Condit ◽  
Stephen P. Hubbell ◽  
Robin B. Foster
Keyword(s):  
Large Scale ◽  
Forest Canopy ◽  
Tree Mortality ◽  
Population Change ◽  
Species Abundance ◽  
Severe Drought ◽  
Colonizing Species ◽  
Rate Of Increase ◽  
Drying Trend ◽  
The Impact

ABSTRACTThe abundance of all tree and shrub species has been monitored for eight years in a 50 ha census plot in tropical moist forest in central Panama. Here we examine population trends of the 219 most numerous species in the plot, assessing the impact of a long-term drying trend. Population change was calculated as the mean rate of increase (or decrease) over eight years, considering either all stems ≥10 mm diameter at breast height (dbh) or just stems ≥100 mm dbh. For stems ≥10 mm, 40% of the species had mean growth rates <1% per year (either increasing or decreasing) and 12% had changes ≥5% per year. For stems ≥100 mm, the figures were 38% and 8%.Species that specialize on the slopes of the plot, a moist microhabitat relative to the plateau, suffered significantly more declines in abundance than species that did not prefer slopes (stems ≥10 mm dbh). This pattern was due entirely to species of small stature: 91% of treelets and shrubs that were slope-specialists declined in abundance, but just 19% of non-slope treelets and shrubs declined. Among larger trees, slope and non-slope species fared equally. For stems ≥100 mm dbh, the slope effect vanished because there were few shrubs and treelets with stems ≥100 mm dbh. Another edaphic guild of species, those occurring preferentially in a small swamp in the centre of the plot, were no more likely to decline in abundance than non-swamp species, regardless of growth form. Species that preferentially colonize canopy gaps in the plot were slightly more likely to decrease in abundance than non-colonizing species (only for stems ≥10 mm dbh, not ≥100 mm). Despite this overall trend, however, several colonizing species had the most rapidly increasing populations in the plot.The impact of a 25-year drying trend and an associated increase in the severity of the 4-month dry season is having an obvious impact on the BCI forest. At least 16 species of shrubs and treelets with affinities for moist microhabitats are headed for extinction in the plot. Presumably, these species invaded the forest during a wetter period prior to 1966. A severe drought of 1983 that caused unusually high tree mortality contributed to this trend, and may also have been responsible for sharp increases in abundance of a few gap-colonizers because it temporarily opened the forest canopy. The BCI forest is remarkably sensitive to a subtle climatic shift, yet we do not know whether this is typical for tropical forests because no other large-scale censuses exist for comparison.

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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