Publications related to Land Cover - Land Use Change | Geographical Sciences Department

Tian, Z., D. Ramsbottom, L. Sun*, Y. Huang, H. Zou, J. Liu. 2023. Dynamic adaptive engineering pathways for mitigating flood risks in Shanghai with regret theory. Nature Water, Vol.1, issue 2, pp. 198–208. https://doi.org/10.1038/s44221-022-00017-w. Dynamic adaptive engineering pathways for mitigating flood risks in Shanghai with regret theory_NatureWater2023.pdf1.36 MB
Caraballo-Vega, J. A., Carroll, M. L., Neigh, C. S. R., Wooten, M., Lee, B., Weis, A., ... & Williams, Z. (2023). Optimizing WorldView-2,-3 cloud masking using machine learning approaches. Remote Sensing of Environment, 284, 113332.
Liang, M., Duncanson, L., Silva, J. A., & Sedano, F. (2023). Quantifying aboveground biomass dynamics from charcoal degradation in Mozambique using Gedi Lidar and landsat. Remote Sensing of Environment, 284, 113367. https://doi.org/10.1016/j.rse.2022.
Vadrevu, K.P., 2023. Fractal analysis revealed persistent correlations in long-term vegetation fire data in most South and Southeast Asian countries. Environmental Research Communications. DOI 10.1088/2515-7620/acb041
Vadrevu, K., Eaturu, A., Casadaban, E., Lasko, K., Schroeder, W., Biswas, S., Giglio, L. and Justice, C. 2022.Spatial variations in vegetation fires and emissions in South and Southeast Asia during COVID-19 and pre-pandemic. Scientific Reports, 12(1)1-2112(1), pp.1-21.
Song, X.-P., Li, H., Potapov, P., & Hansen, M.C. (2022). Annual 30 m soybean yield mapping in Brazil using long-term satellite observations, climate data and machine learning. Agricultural and Forest Meteorology, 326, 109186View the full article: https://authors.elsevier.com/a/1fqeQcFXJcsCK
Abys, C., Skakun, S., & Becker-Reshef, I. (2022). The Rise and Volatility of Russian Winter Wheat Production. Environmental Research Communications, 4(10), art. num. 101003.
Qi, Xinxian, Kuishuang Feng, Laixiang Sun*, Dandan Zhao, Xianjin Huang. 2022. Drivers of rising agriculture water scarcity in China. One Earth (2021 IF: 14.944), Vol. 5, pp. 1139–1152. https://doi.org/10.1016/j.oneear.2022.09.008. Rising agricultural water scarcity in China_OneEarth2022.pdf3.85 MB
Eun, J., & Skakun, S. (2022). Characterizing land use with night-time imagery: the war in Eastern Ukraine (2012–2016). Environmental Research Letters, 17, art. num. 095006.
Prudente, V.H.R., Skakun, S., Oldoni, L.V., Xaud, H.A., Xaud, M.R., Adami, M., & Sanches, I.D.A. (2022). Multisensor approach to land use and land cover mapping in Brazilian Amazon. ISPRS Journal of Photogrammetry and Remote Sensing, 189, 95–109.
Franch, B., Cintas, J., Becker-Reshef, I., Sanchez-Torres, M. J., Roger, J., Skakun, S., Sobrino, J. A., Van Tricht, K., Degerickx, J., Gilliams, S., Koetz, B., Szantoi, Z., & Whitcraft, A. (2022).Global crop calendars of maize and wheat in the framework of the WorldCereal project. GIScience & Remote Sensing, 59(1), 885–913.
Funes, J., L. Sun, T. Benson, F. Sedano, G. Baiocchi. 2022. Social Interaction and Geographic Diffusion of Iron Biofortified Beans in Rwanda. Agricultural Economics (2020 IF: 2.585). Published online 14 June 2022. https://doi.org/10.1111/agec.12722. Agricultural Economics-2022-Social interaction and geographic diffusion of iron‐biofortified beans in Rwanda.pdf1.12 MB
Kerner, H. R., Sahajpal, R., Pai, D. B., Skakun, S., Puricelli, E., Hosseini, M., ... & Becker-Reshef, I. (2022). Phenological normalization can improve in-season classification of maize and soybean: A case study in the central US Corn Belt.Science of Remote Sensing, 6, art. num. 100059.
Khan. I.M.P., K. Hubacek, K. L. Brubaker, L. Sun, G. Moglen. 2022. Stormwater management adaptation pathways under climate change and urbanization. Journal of Sustainable Water in the Built Environment (2020 IF: 1.78). 8(4): 04022009. DOI: 10.1061/JSWBAY.0000992 Khan et al-Stormwater Management Adaptation Pathways-2022.pdf2.26 MB
Kumar, I., K. Feng, L. Sun, V. Bandaru. 2022. Adoption of biomass for electricity generation in Thailand: Implications for energy security, employment, environment, and land use change. Renewable Energy (2020 IF: 8.001). Available online 5 June 2022. https://doi.org/10.1016/j.renene.2022.05.162.
Lu, J., Huang, C., Tao, X., Gong, W., & Schleeweis, K. (2022). Annual forest disturbance intensity mapped using Landsat time series and field inventory data for the conterminous United States (1986–2015). Remote Sensing of Environment, 275, 113003.
Tian, Z., X. Lyu, H. Zou, H. Yang, L. Sun3, M. S. Pinya, Q. Chao, A. Feng, B. Smith. 2022. Advancing index-based climate risk assessment to facilitate adaptation planning: Application in Shanghai and Shenzhen, China. Advances in Climate Change Research (2020 IF: 4.13). Vol. 13, Issue 3, pp. 432-442. https://doi.org/10.1016/j.accre.2022.02.003. Advancing index-based climate risk assessment to facilitate adaptation planning_AdvanceClimateChangeResearch2022.pdf1.29 MB
Zhang, Y., Skakun, S., Adegbenro, M.O., & Ying, Q. (2022). Leveraging the use of labeled benchmark datasets for urban area change mapping and area estimation: a case study of the Washington DC–Baltimore region. International Journal of Digital Earth.https://doi.org/10.1080/17538947.2022.2094001
Nakalembe, C., Zubkova, M., Hall, J. V., Argueta, F., & Giglio, L. (2022). Impacts of large-scale refugee resettlement on LCLUC: Bidi Bidi refugee settlement, Uganda case study. Environmental Research Letters, 17(6), 064019. Nakalembe_2022_Environ._Res._Lett._17_064019.pdf9.23 MB
Vadrevu, K.P., Le Toan, T., Ray, S.S., and Justice, C. 2022. Remote Sensing of Agriculture and Land Cover/Land Use Changes in South and Southeast Asian Countries. Springer Book. https://doi.org/10.1007/978-3-030-92365-5
Huang, X., Fu, Y., Wang, J., Dong, J., Zheng, Y., Pan, B., Skakun, S., & Yuan, W. (2022). High-Resolution Mapping of Winter Cereals in Europe by Time Series Landsat and Sentinel Images for 2016–2020. Remote Sensing, 14(9), art. num. 2120.
Kussul, Nataliia, Klaus Deininger, Leonid Shumilo, Mykola Lavreniuk, Daniel Ayalew Ali, and Oleg Nivievskyi. "Biophysical Impact of Sunflower Crop Rotation on Agricultural Fields." Sustainability 14, no. 7 (2022): 3965.
Cao, Y., S. Liang, L. Sun, J. Liu, X. Cheng, D. Wang, Y. Chen, M. Yu, K. Feng. 2022. Trans-Arctic shipping routes expanding faster than the model projections. Global Environmental Change (2020 IF: 9.523). 73, article no. 102488. https://doi.org/10.1016/j.gloenvcha.2022.102488
Li, X.-Y., Li, X., Fan, Z., Mi, L., Kandakji, T., Song, Z., Li, D., & Song, X.-P. (2022). Civil war hinders crop production and threatens food security in Syria. Nature Food, 3, 38-46Link to the article: https://www.nature.com/articles/s43016-021-00432-4
Gutman, G., Skakun, S., Gitelson, A. (2021). Revisiting the Use of Red and Near-Infrared Reflectances in Vegetation Studies and Numerical Climate Models. Science of Remote Sensing, 4, art. num. 100025