Multi-Scale Mapping of Energy Consumption Carbon Emission Spatiotemporal Characteristics: A Case Study of the Yangtze River Delta Region

Land, Journal Year: 2025, Volume and Issue: 14(1), P. 95 - 95

Published: Jan. 6, 2025

Climate issues significantly impact people’s lives, prompting governments worldwide to implement energy-saving and emission-reducing measures. However, many areas lack carbon emission data at the lower administrative divisions. Additionally, inconsistency in standards, scope, accuracy of dioxide statistics across different regions makes mapping spatial patterns complex. Nighttime light (NTL) combined with land use enable detailed temporal disaggregation a finer level, facilitating scientifically informed policy formulation by government. Differentiating sector will help us further identify efficiency sectors environmental regulators most cost-effective emission-reduction strategy. This study uses integrated remote-sensing estimate emissions from fossil fuels (CEFs). Experimental results indicate (1) that regional CEF can be calculated combining NTL Landuse has good fit; (2) high-intensity area is mainly concentrated Shanghai its surrounding areas, showing concentric circle structure; (3) there are obvious differences distribution characteristics among departments; (4) hot spot analysis reveals three-tiered Yangtze River Delta, increasing west east distinct characteristics.

Language: Английский

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Random Forest-Based Retrieval of XCO2 Concentration from Satellite-Borne Shortwave Infrared Hyperspectral

Atmosphere, Journal Year: 2025, Volume and Issue: 16(3), P. 238 - 238

Published: Feb. 20, 2025

As carbon dioxide (CO2) concentrations continue to rise, climate change, characterized by global warming, presents a significant challenge sustainable development. Currently, most shortwave infrared CO2 retrievals rely on fully physical retrieval algorithms, for which complex calculations are necessary. This paper proposes method predict the concentration of column-averaged (XCO2) from hyperspectral satellite data, using machine learning avoid iterative computations method. The training dataset is constructed Orbiting Carbon Observatory-2 (OCO-2) spectral XCO2 OCO-2, surface albedo and aerosol optical depth (AOD) measurements 2019. study employed variety including Random Forest, XGBoost, LightGBM, analysis. results showed that Forest outperforms other models, achieving correlation 0.933 with products, mean absolute error (MAE) 0.713 ppm, root square (RMSE) 1.147 ppm. model was then applied retrieve column 2020. 0.760 Total Column Observing Network (TCCON) measurements, higher than 0.739 product verifying effectiveness

Language: Английский

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A High Resolution Spatially Consistent Global Dataset for CO2 Monitoring

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(9), P. 1617 - 1617

Published: May 2, 2025

Climate change poses a global threat, affecting both biodiversity and human populations. To implement efficient mitigating strategies, the consistency accuracy of our monitoring greenhouse gases at local level must be improved. We can achieve this with more advanced instruments or an enhancement processing techniques, which will in turn improve data attributes such as spatial temporal resolutions accuracy. This paper presents daily high resolution XCO2 dataset aiming to help monitor atmospheric CO2 concentration on scale greater detail compared existing datasets. Using super deep learning model, we increase OCO-2-derived from 0.5° × 0.625° 0.03° 0.04° show that product maintains quality original while consistently improving pollution field. conduct benchmark highlights how outperforms similar products present use case regional level. In conclusion, work provides complementary approach area continuous reconstruction focuses adjacent problem specific features

Language: Английский

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Forest Fire Burn Scar Mapping Based on Modified Image Super-Resolution Reconstruction via Sparse Representation

Forests, Journal Year: 2024, Volume and Issue: 15(11), P. 1959 - 1959

Published: Nov. 7, 2024

It is of great significance to map forest fire burn scars for post-disaster management and assessment fires. Satellites can be utilized acquire imagery even in primitive forests with steep mountainous terrain. However, scar mapping extracted by the Burned Area Index (BAI), differenced Normalized Burn Ratio (dNBR), Feature Extraction Rule-Based (FERB) approaches directly at pixel level limited satellite spatial resolution. To further improve resolution mapping, we improved image super-resolution reconstruction via sparse representation (SCSR) named it modified (MSCSR). was compared Subpixel Mapping–Feature (BASM-FERB) method screen a better approach. Based on Sentinel-2 imagery, MSCSR BASM-FERB were used subpixel level, extraction result validated using actual data. The results show that obtained has higher resolution; particular, approach more effectively reduce noise effect level. Five accuracy indexes, Overall Accuracy (OA), User’s (UA), Producer’s (PA), Intersection over Union (IoU), Kappa Coefficient (Kappa), are assess pixel/subpixel based BAI, dNBR, FERB, approaches. average values OA, UA, PA, IoU, superior dNBR FERB In detected 98.49%, 99.13%, 92.31%, 95.83%, 92.81%, respectively, which 1.48%, 10.93%, 2.47%, 15.55%, 5.90%, than concluded extracts

Language: Английский

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