Evaluating co-relationship between OCO-2 XCO2 and in situ CO2 measured with portable equipment in Seoul

Hwang, Young Seok; Um, Jung‐Sup

doi:10.1007/s41324-016-0053-7

Cited by 13 publications

(8 citation statements)

References 21 publications

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“…The GOSAT fossil-fuel CO 2 flux provides CO 2 emissions in terms of locations of CO 2 sources and provides a measure of the direct exchange of CO 2 between in situ CO 2 sources and the atmosphere over CO 2 sources located within an area. [13,14]. Thereby, the satellite-based fossil-fuel CO 2 flux has the advantage of monitoring and comparing the average flux from the CO 2 sources located in heterogeneous countries because the satellite measures the CO 2 flux all over the world with the same standardized method and unit.…”

Section: Characteristics Of the Gosat Fossil-fuel Co 2 Fluxmentioning

confidence: 99%

Evaluating the Mutual Relationship between IPAT/Kaya Identity Index and ODIAC-Based GOSAT Fossil-Fuel CO2 Flux: Potential and Constraints in Utilizing Decomposed Variables

Hwang

Schlüter

2020

IJERPH

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The IPAT/Kaya identity is the most popular index used to analyze the driving forces of individual factors on CO2 emissions. It represents the CO2 emissions as a product of factors, such as the population, gross domestic product (GDP) per capita, energy intensity of the GDP, and carbon footprint of energy. In this study, we evaluated the mutual relationship of the factors of the IPAT/Kaya identity and their decomposed variables with the fossil-fuel CO2 flux, as measured by the Greenhouse Gases Observing Satellite (GOSAT). We built two regression models to explain this flux; one using the IPAT/Kaya identity factors as the explanatory variables and the other one using their decomposed factors. The factors of the IPAT/Kaya identity have less explanatory power than their decomposed variables and comparably low correlation with the fossil-fuel CO2 flux. However, the model using the decomposed variables shows significant multicollinearity. We performed a multivariate cluster analysis for further investigating the benefits of using the decomposed variables instead of the original factors. The results of the cluster analysis showed that except for the M factor, the IPAT/Kaya identity factors are inadequate for explaining the variations in the fossil-fuel CO2 flux, whereas the decomposed variables produce reasonable clusters that can help identify the relevant drivers of this flux.

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Section: Characteristics Of the Gosat Fossil-fuel Co 2 Fluxmentioning

confidence: 99%

Evaluating the Mutual Relationship between IPAT/Kaya Identity Index and ODIAC-Based GOSAT Fossil-Fuel CO2 Flux: Potential and Constraints in Utilizing Decomposed Variables

Hwang

Schlüter

2020

IJERPH

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“…An in situ CO 2 density survey was carried out at the specific time (11-13 h) and season (08.01-09.30.2018), during which photosynthetic activity and plant growth are vigorous and the atmosphere is stable after the rainy season. (2,10) Wind velocity strongly affects the accuracy of CO 2 measurement. (12) It is essential to validate the CO 2 measurement accuracy by comparing the wind speed measurements between portable instruments and standardized wind measurements at national meteorological telemetry stations.…”

Section: Methodsmentioning

confidence: 99%

“…The difference in velocity between the in situ data and the national meteorological telemetry station data was 1.3 m/s, showing a deviation similar to those in previous studies. (2,13) We selected the measurement points (182 points) ( Fig. 3) that show different normalized difference vegetation indexes (NDVIs) to explore the topographical above-ground biomass in terms of the CO 2 uptake and the capability of the above-ground biomass.…”

Section: Methodsmentioning

confidence: 99%

“…(1) To overcome the limitations of the current MRV system for the PDD, it is necessary to use the CO 2 concentration data measured at project sites as fundamental evidence for MRV. (2) If a portable carbon measurement device can be used to correlate the CO 2 data measured from the ground with the carbon uptake documented in the PDD, it can be an alternative tool to overcome the limitations of MRV based on carbon emission factors and to assess carbon footprints based on land cover at specific points.…”

Section: Introductionmentioning

confidence: 99%

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Exploring Explainable Range of In-situ Portable CO2 Sensor Signatures for Carbon Stock Estimated in Forestry Carbon Project

Hwang¹,

Lee²,

Park³

et al. 2019

Sensors and Materials

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“…It is quite difficult to obtain the spatial resolution required to evaluate cool roofing performance of individual buildings occupying approximately 5 to 10 square meters by utilizing traditional satellite images (e.g., LANDSAT and MODIS) taken at an altitude of 700 km or more [23] and the authors had written extensively about these elsewhere [24][25][26][27][28][29][30][31]. In the case of buildings in urban areas, using aerial photography to investigate cool roofing performance is virtually impossible because of the densely populated area, where there is legal regulation prohibiting low-altitude flight of manned aircraft.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Operational Potential of LRV Signatures Derived from UAV Imagery in Performance Evaluation of Cool Roofs

et al. 2019

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It is quite difficult to find studies regarding area-wide data from UAV (Unmanned Aerial Vehicle) remote sensing in evaluating the energy saving performance of a cool roof. Acknowledging these constraints, we investigated whether LRV (Light Reflectance Value) signatures derived from UAV imagery could be used effectively as an indicator of area-wide heating and cooling load that distinctively appears according to rooftop color. The case study provides some quantitative tangible evidence for two distinct colors: A whitish color roof appears near the edge of the highest LRV (91.36) and with a low temperature (rooftop surface temperature: (38.03 °C), while a blackish color roof shows the lowest LRV (18.14) with a very high temperature (65.03 °C) where solar radiation is extensively absorbed. A strong negative association (Pearson correlation coefficient, r = −0.76) was observed between the LRV and surface temperature, implying that a higher LRV (e.g., a white color) plays a decisive role in lowering the surface temperature. This research can be used as a valuable reference introducing LRV in evaluating the thermal performance of rooftop color as rooftops satisfying the requirement of a cool roof (reflecting 75% or more of incoming solar energy) are identified based on area-wide objective evidence from UAV imagery.

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Evaluating co-relationship between OCO-2 XCO2 and in situ CO2 measured with portable equipment in Seoul

Cited by 13 publications

References 21 publications

Evaluating the Mutual Relationship between IPAT/Kaya Identity Index and ODIAC-Based GOSAT Fossil-Fuel CO2 Flux: Potential and Constraints in Utilizing Decomposed Variables

Evaluating the Mutual Relationship between IPAT/Kaya Identity Index and ODIAC-Based GOSAT Fossil-Fuel CO2 Flux: Potential and Constraints in Utilizing Decomposed Variables

Exploring Explainable Range of In-situ Portable CO2 Sensor Signatures for Carbon Stock Estimated in Forestry Carbon Project

Evaluating the Operational Potential of LRV Signatures Derived from UAV Imagery in Performance Evaluation of Cool Roofs

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