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Structure of the GIS technology for spatial analysis and visualisation of high-resolution GHG emissions data
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The geoinformation technology for spatial analysis and visualisation of greenhouse gas (GHG) emissions is proposed using Google Earth Engine cloud technology as a key component of interaction with high-resolution spatial data. This technology includes a website for spatial analysis and visualisation of vector data, as well as an interactive site fo...
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... The displacement in ODIAC/DMSP raster data was also found when comparing this data with the other high-resolution greenhouse gas data for Poland, which have been calculated using a socalled "bottom-up" approach Geoinformation technologies, spatio-temporal approaches, and full carbon account for improving the accuracy of GHG inventories (GESAPU) [18,20,21]. Even if the geolocation error is small (∼ 1.7 km), it can significantly distort the emission values within cities or urban areas [15,22,23]. It is also important to note that the magnitude of geolocation biases is on the same order of the size as the satellite footprints of the recent carbon observing satellites, such as OCO-2 [24] and OCO-3 [25]. ...
Accurate geospatial modeling of greenhouse gas (GHG) emissions is an essential part of the future of global GHG monitoring systems. Our previous work found a systematic displacement in the high-resolution carbon dioxide (CO2) emission raster data of the Open-source Data Inventory for Anthropogenic CO2 (ODAIC) emission product. It turns out this displacement is due to geolocation bias in the Defense Meteorological Satellite Program (DMSP) nighttime lights (NTL) data products, which are used as a spatial emission proxy for estimating non-point source emissions distributions in ODIAC. Mitigating such geolocation error (~1.7 km), which is on the same order of the size of the carbon observing satellites’ field of view, is especially critical for the spatial analysis of emissions from cities. In this paper, we propose a method to mitigate the geolocation bias in DMSP NTL data that can be applied to DMSP NTL-based geospatial products, such as ODIAC. To identify and characterize the geolocation bias, we used the OpenStreetMap repository to define city boundaries for a large number of global cities. We assumed that the total emissions within the city boundaries are at the maximum if there is no displacement (geolocation bias) in NTL data. Therefore, we searched for an optimal vector (distance and angle) that maximizes the ODIAC total emissions within cities by shifting the emission fields. In the process of preparing annual composites of the nighttime stable lights data, some pixels of the DMSP data corresponding to water bodies were zeroed, which due to the geolocation bias unreasonably distorted the ODIAC emission fields. Therefore, we proposed an original approach for restoring data in such pixels; thus, we eliminated the factor that distorted the ODIAC emission fields. We also propose a bias correction method for shifted high-resolution emission fields in ODIAC. The bias correction was applied to multiple cities from different continents. We show that the bias correction to the emission data (elimination of geolocation error in non-point emission source fields) increases the total CO2 emissions within city boundaries by 4.76% on average, due to reduced emissions from non-urban areas to which these emissions were likely to be erroneously attributed.
... More generally, this bias is observed for urban areas (for example, [17]), especially when comparing these data with other high-resolution greenhouse gas emission data (for example, for Poland [18,19]), which are obtained from the inventory of these gases by the so-called bottom-up approach (Geoinformation technologies, spatio-temporal approaches, and full carbon account for improving the accuracy of GHG inventories -GESAPU [20][21][22]), or when comparing with high-quality NTL data have become available recently, such as NTL data from the Visible Infrared Imaging Radiometer Suite (VIIRS) [23][24][25] (Fig. 2). The spatial mismatch seems to be small and insignificant; however, even small shifts by a few pixels could significantly skew urban emission estimates because emission intensity is often larger than other areas. ...
This study examined the potential geolocation biases/errors in the Defense Meteorological Satellite Program (DMSP) nightlight data that has been used in carbon emission models, such as the global high-resolution emission inventory Open-source Data Inventory for Anthropogenic CO2 (ODIAC). Quantifying and mitigating the bias has become an urgent, critical task in obtaining robust emission estimates for urban areas, the major sources of the global carbon emissions, from nightlight-based emission estimates. We first attempted to characterize the bias by changing the location of urban cores identified by the DMSP data and their orientation using the city boundary data from the Open Street Map (OMS) as a reference. We hypothesized that a geolocation bias-free emission map should maximize the total emission within the administrative boundaries, and developed an iterative optimization algorithm to obtain correction vectors (distance and angle) for shifting emission data (or DMSP data). We implemented the proposed algorithms for many global cities from different continents, and discovered the latitudinal dependence of the bias. The paper is focused on the methodological aspects of the bias correction, and the implementation and applications to global cities.
This study develops an approach, algorithms and software to analyse the bias of nightlights remote sensing satellite data and corresponding bias calculated on the basis of global data on greenhouse gas emissions. We used city boundary data from Open Street Map, as well as high-resolution Open-Source Data Inventory for Anthropogenic CO2 (ODIAC) data on carbon dioxide emissions. We based the algorithms of the analysis on the iterative calculation of correction vectors for shifting emission data that provide the maximum magnitude of carbon dioxide emissions within the administrative boundaries of cities. We demonstrate the main stages of the proposed algorithm implementation for a number of cities from different continents.
Emission Inventories (EIs) are the fundamental tool to monitor compliance with greenhouse gas (GHGs) emissions and emission-reduction commitments. Inventory accounting guidelines provide the best practices to help EI compilers across different countries and regions make comparable, national emission estimates regardless of differences in data availability. However, there are a variety of sources of error and uncertainty that originate beyond what the inventory guidelines can define. Spatially-explicit EIs, which are a key product for atmospheric modeling applications, are often developed for research purposes and there are no specific guidelines to achieve spatial emission estimates. The errors and uncertainties associated with the spatial estimates are unique to the approaches employed and are often difficult to assess. This study compares the global, high-resolution (1 km), fossil-fuel, carbon dioxide (CO2), gridded EI Open-source Data Inventory for Anthropogenic CO2 (ODIAC) to the multi-resolution, spatially-explicit bottom-up EI Geoinformation technologies, spatio-temporal approaches, and full carbon account for improving the accuracy of GHG inventories (GESAPU) over the domain of Poland. By taking full advantage of the data granularity that bottom-up EI offers, this study characterized the potential biases in spatial disaggregation by emission sector (point and non-point emissions) across different scales (national, subnational/regional, and urban policy-relevant scales) and identified the root causes. While two EIs are in agreement in total and sectoral emissions (2.2% for the total emissions), the emission spatial patterns showed large differences (10~100% relative differences at 1km) especially at the urban-rural transitioning areas (90-100%). We however found that the agreement of emissions over urban areas is surprisingly good compared to the estimates previously reported for U.S. cities. This paper also discusses the use of spatially-explicit EIs for climate mitigation applications beyond the common use in atmospheric modeling. We conclude with discussion of current and future challenges of EIs in support of successful implementation of GHG emission monitoring and mitigation activity under the Paris Climate Agreement from the United Nations Framework Convention on Climate Change (UNFCCC) 21st Conference of the Parties (COP21). We highlight the importance of capacity building for EI development and coordinated research efforts of EI, atmospheric observations, and modeling to overcome the challenges.
