Publication

[1] Kang, J., “Accessible Remote Sensing: Interdisciplinary Approach and Applications”, 2024, CRC Press | Taylor & Francis (Textbook, forthcoming).New~

preorder is here: https://www.amazon.com/Accessible-Remote-Sensing-Interdisciplinary-Applications/dp/1032451831

[2] Kang, J., Zhang, B., & Dang, A. (2024). A novel geospatial machine learning approach to quantify non-linear effects of land use/land cover change (LULCC) on carbon dynamics. International Journal of Applied Earth Observation and Geoinformation, 128, 103712. (SCI, IF:8.6) https://doi.org/10.1016/j.jag.2024.103712 New~

[3] Zhang, B., Kang, J. * and Feng, T. “Global Disparities in CO2 Emissions from Mobility Sectors of Diverse Economies: A Macroscopic Exploration Across 188 Countries/Regions”. Environmental and Sustainability Indicators, 2024, 100455.(SCI, IF: 5.4) https://doi.org/10.1016/j.indic.2024.100455 New~

[4] Zhang, B., Kang, J.* “Quantitative Attribution Framework for Urban Air Pollutant: Insights of the Policies’ Impacts on NO2 Emissions from Megacities in China and Japan”. Sustainable Cities and Society. 2023, 104965. (SCI, IF: 11.8) https://doi.org/10.1016/j.scs.2023.104965

[5] Zhang, B., Kang, J.* and Feng. T. (2024). A Novel Approach to Evaluating the Accessibility of Electric Vehicle Charging Infrastructure via Dynamic Thresholding in Machine Learning. Environment and Planning B: Urban Analytics and City Science, 23998083241249322. https://doi.org/10.1177/2399808324124932 (SSCI, IF: 3.9) New~

[6] Kang, J., Zhang, B., Zhang, J., and Dang, A. “Quantifying the Effects of Different Containment Policies on Urban NO2 Decline: Evidence from Remote Sensing Integrated with Ground-Station Data”. Remote Sensing, 2023, 15(4), 1068. (SCI, IF:5) https://doi.org/10.3390/rs15041068

[7] Kang, J., Kong, H., Lin, Z., and Dang, A. “Mapping the Dynamics of Electric Vehicle Charging Demand Within Beijing's Spatial Structure”. Sustainable Cities and Society, 2022, 76, 103507. (SCI, IF: 11.8) https://doi.org/10.1016/j.scs.2021.103507

[8] Kang, J., Kan, C., and Lin, Z. “Are Electric Vehicles Reshaping the City? An Investigation of the Clustering of Electric Vehicle Owners’ Dwellings and Their Interaction with Urban Spaces”. ISPRS International Journal of Geo-Information, 2021, 10(5), 320. (SCI, IF:3.4) https://doi.org/10.3390/ijgi10050320

[9] Kang, J., Cheng, X., Hui, F., and Ci T. “An Accurate and Automated Method for Identifying and Mapping Exposed Rock Outcrop in Antarctica Using Landsat 8 Images”. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 1(11), 57-67. (SCI, IF: 5.5, Front Cover) https://doi.org/10.1109/JSTARS.2017.2755502

[10] Kang, J., Ci, T., Dang, A., et.al. “An Automatic Method for Water Extraction from High Spatial Resolution GF-1 Imagery Based on A Deep Learning Algorithm”. Computer Intelligent Systems and Network Remote Control, 2019, 555-562 (Peer-reviewed)

[11] Kang, J., Dang, A., Zhang, B., Wang, Y., Su, H., Su, F., Ci, T., and Wang, F. “An Improved Convolutional Neural Network for Monocular Depth Estimation”. Green, Smart and Connected Transportation Systems, 2020, Springer, 1229-1237. (Peer-reviewed)

[12] Kang, J., Zhang, J. “COVID-19 and big data technologies: Experience in China”. In Transportation Amid Pandemics: Lessons Learned from COVID-19 (Eds.). 2022, Elsevier, pp.359-370. (Peer-reviewed, book chapter)

[13] Kang, J., Cheng, X., Liu, Y., Hui, F., Ouyang, L., Li, Tian. “Shadow Detection Method of Landsat8 Satellite Imagery in Antarctic Region”. Journal of Beijing Normal University, 2017 (Peer-reviewed, in Chinese)

[14] Ma, Q., Gong, Z., Kang, J., and Dang, A. “Measuring Functional Urban Shrinkage with Multi-Source Geospatial Big Data: A Case Study of The Beijing-Tianjin-Hebei Megaregion”. Remote Sensing, 2020, 12(16), 2513. (SCI, IF:5.349)

[15] Zhang, J., Feng, T., Kang, J., Li, S., Liu, R., Ma, S., Zhai, B., Zhang, R., Ding, H., and Zhu, T. “What Should Be Computed for Supporting Post-Pandemic Recovery Policymaking? A Life-Oriented Perspective”. Computational Urban Science, 2021, 1(1), 1-16. (SCI)

[16] Hui F, Kang, J., Liu Y., Cheng, X., Gong, P., Wang, F., Li, Z., Ye, Y., and Guo, Z. “AntarcticaLC2000: The New Antarctic Land Cover Database for The Year 2000”. Science China Earth Sciences, 2017, 60(4), 686-696. (SCI, IF:5.492)

[17] He, H., Cheng, X., Li, X., Zhu, R., Hui, F., Wu, W., Zhao, T., Kang, J., and Tang, J. “Aerial Photography-Based Census of Adélie Penguin and its Application in CH4 and NO2 Budget Estimation in Victoria Land Antarctic”. Scientific Reports, 2017, 7(1), 1-11. (SCI, IF:4.379)

[18] He, H., Cheng, X., Li, X., Zhu, R., Tang, J., Hui, F., Wu, W., Zhao, T., and Kang, J. “Aerial Photography based Estimation of Greenhouse Gas Emissions from Penguins in Victoria Land, Antarctica”. Science Bulletin, 2016, 61(30), 3268-3277. (SCI, IF:18.9)

[19] Zhao, C., Cheng, X., Liu, Y., Hui, F., Kang, J., Wang, X., and Cheng, C. “The Slow-Growing Tooth of The Amery Ice Shelf From 2004 To 2012”. Journal of Glaciology, 2013, 59(215), 592-596. (SCI, IF:4.278)

[20] Zhao, C., Cheng, X., Hui, F., Kang, J., Liu, Y., Wang, X., and Zhai, M. “Monitoring the Amery Ice Shelf Front During 2004–2012 Using ENVISAT ASAR data”. Advances Polar Science, 2013, 24(2), 133-137. (SCI, IF: 0.64)

[21] Feng, Z., Cheng, X., Kang, J., Hui, F., Liu, Y., Cheng, C., Wang, F., Wang, X., Zhao, C., Zhao, S., and Chen T. “Review of the NASA Icebridge Mission: Progress and Prospects”. Journal of Remote Sensing, 2013, 17(2), 399-422. (Peer-reviewed)

[22] Hui, F., Cheng, X., Zhao, S., Chen, T., Wang, Z., Wang, F., Kang, J., Liu, Y., Wang, K. “Emergency monitoring of ice conditions on the route of Antarctic expedition snow dragon ship based on multi-temporal satellite remote sensing data”. The 28th Annual Meeting of China Meteorological Society—S6 Cryosphere and Polar Meteorology (Peer-reviewed)

[23] Lin, Z., and Kang, J., "Electric Vehicles in The City”. Policy Digest, University of Pennsylvania, kleinmanenergy.upenn.edu. 2020.

[24] Hui F, Cheng X., Liu Y., Kang J., Li X. High-Resolution Remote Sensing Mapping of Antarctica, 2022, China Ocean Press. (Book, co-author)

Human-Environment Interactions: Urban Society and Health Issue

Quantitative attribution framework for urban air pollutant:

Investigating policy impact on NO2 emissions of megacities in China and Japan

Highlights

A quantitative attribution framework to measure policy impacts was developed.
School commuting has a significant impact on NO2 emissions in megacities.
International Travel contributes to NO2 emissions more in coastal megacities.
A discernible linkage exists between policy interventions and environmental results.

Abstract

This comparison study deciphered the intricate dynamics of policy impacts on NO2 air pollution emissions across five populous megacities – Tokyo and Osaka in Japan, Shanghai, Beijing, and Tianjin in China. Utilizing spatiotemporal mapping and time series analysis, we traced pollution trends from 2015 to 2022, underlining the profound influence of rapidly enacted policy measures on these densely populated urban areas. Leveraging daily policy data coupled with NO2 emission records, we developed a quantitative attribution framework to measure policy impacts. The results showed that 'School Closure' emerged as a dominant factor in four out of five cities. Besides, 'International Travel Controls' played a pivotal role in coastal megacities like Tokyo, Osaka, and Tianjin, revealing their unique emission characteristics. The insights demonstrated the essential need for targeted, city-specific policy interventions, cautioning against the over-simplified approach of one-size-fits-all strategies.

Mapping of the spatial-temporal changes of the five cities, from top to bottom are Tokyo, Osaka, Shanghai, Beijing, and Tianjin; from left to right are the characteristics of the annual emissions from 2019 to 2022, respectively.

Quantifying the Effects of Different Containment Policies on Urban NO2 Decline:

Evidence from Remote Sensing and Ground-Station Data

Abstract

Cities exposed their vulnerabilities during the COVID-19 pandemic. Unprecedented policies restricted human activities but left a unique opportunity to quantify anthropogenic effects on urban air pollution. This study aimed to explore the underlying urban development issues behind these restrictions and support a sustainable transition. The data from ground stations and Sentinel-5P satellite were used to assess the temporal and spatial anomalies of NO2. Beijing China was selected for a case study because this mega city maintained a “dynamic zero-COVID” policy with adjusted restrictions, which allowed for better tracking of the effects. The time-series decomposition and prediction regression model were employed to estimate the normal NO2 levels in 2020. The deviation between the observations and predictions was identified and attributed to the policy interventions, and spatial stratified heterogeneity statistics were used to quantify the effects of different policies. Workplace closures (54.8%), restricted public transport usage (52.3%), and school closures (46.4%) were the top three restrictions that had the most significant impacts on NO2 anomalies. These restrictions were directly linked to mismatched employment and housing, educational inequality, and long-term road congestion issues. Promoting the transformation of urban spatial structures can effectively alleviate air pollution.

Interdisciplinary Studies Toward Urban and Regional Planning

New Energy Infrastructure Planning: GIS+Big Data+Machine Learning+Urban Planning

Mapping the dynamics of electric vehicle charging demand within Beijing's spatial structure

Highlights

Data mining from smartphone offers a new lens to study EV charging behaviors.
Location-based service big data reveals spatial heterogeneity of public charging demands in the large city.
Mapping demonstrates 45% of public charging stations in Beijing may be underutilized.
Spatial regression model confirms correlation between EV travel pattern and urban spatial structure.
EV charging behaviors vary significantly over weekdays and weekend.
High-density residential areas beyond the city center with imperfect public service facilities tend to be more reliant on PCS, which is often underestimated in infrastructure planning.

Mismatch patterns between supply and demand

Mapping the dynamics of electric vehicle charging demand within Beijing's spatial structure using Geospatial Big Data [6,7]: A novel GIS-based framework was developed, utilizing location-based service (LBS) data from smartphone, to accurately estimate the dynamic charging demands for EVs and quantified its interactions with 15 land use categories. We looked at how emerging technologies would impact the urban forms as well as human travel behaviors. This work was cited by the New York Times.

Toward Carbon Neutrality: New Energy Transition and Infrastructure Deployment

Assessing the Effects of Electric Vehicle Adoption on Urban Energy Structure Transition

Developed a Geospatial Machine Learning model to examine the nonlinear associations between the spatial penetration rate of EV owners and the urban environment factors, showing how can the relationship between EV users' residential locations and new energy infrastructure be best coordinated.

Are Electric Vehicles Reshaping the City?

An Investigation of the Clustering of Electric Vehicle Owners’ Dwellings and Their Interaction with Urban Spaces

Remote Sensing, Earth Observention, Monitoring, Environmental Change

Natural-based Mitigation to Climate Change

Land Use and Land Cover Change (LULCC) introduces considerable uncertainties in its relationship with carbon emissions, posing challenges to nature-based climate mitigation. The effective establishment and protection of carbon sinks through land use management lack clarity. To address this challenge, this study utilized top-down interannual grided CO2 data from satellite observations, revealing a 7 % decline in carbon sinks in mainland China from 2016 to 2019. Faced with this anomaly, we proposed a novel approach that combined machine learning with traditional regression analysis, to investigate the nonlinear relationship between annual spatiotemporal variations in net carbon exchange and LULCC. Sentinel-2 imagery was employed for high-resolution (10 m) LULC classification based on uniform rules. Particularly, time-series LULC class probabilities were considered and estimated using a deep learning framework via the Google Earth Engine (GEE) cloud platform, which allows us to access the effects of dynamic LULCC. GIS methods were applied to enhance machine learning interpretability, integrating multi-source remote sensing datasets, particularly for capturing nonlinear features in spatiotemporal aspects of LULCC and carbon emissions. Threshold effects revealed how LULCC transformed areas were associated with carbon sinks or sources. The results mapped carbon sink shrinkage locations, highlighting correlations with significant reductions in snow cover (−6.25 %), changing water patterns (−1.3 %), urban expansion (1 %), and mixed forest changes (regrowth of 4 % and deforestation of −1%). This research aims to advance understanding of carbon emissions through remote sensing, bridging different Earth observation data within a geostatistical context, and expects to provide a new validation method for the bottom-up approach.

Highlights

Spatial machine learning to explore nonlinear linkages between annual carbon exchange and LULCC is developed.
New perspective to integrate multi-source remote sensing datasets using GIS is proposed.
Exploring the potential of top-down carbon satellite monitoring inversion data.
Short-term CO2 rebound may be linked to carbon sink loss significantly affected by LULCC.
Deforestation exceeding 1% may require triple the effort of forest regrowth to achieve the compensation effect.