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High-Impact Hot Spots of Land Cover Land Use Change: Ukraine and Neighboring Countries
Project Start Date
01/01/2021
Project End Date
12/31/2023
Grant Number
80NSSC21K0314
Solicitation
default

Team Members:

Person Name Person role on project Affiliation
Sergii Skakun Principal Investigator University of Maryland, College Park, USA
Jean-Claude Roger Co-Investigator University of Maryland, College Park, United States
Joanne Hall Co-Investigator University of Maryland, College Park, College Park, United States
Natacha Kalecinski Co-Investigator University of Maryland, College Park MD 20740 USA, USA
Nataliia Kussul Collaborator Space Research Institute NAS Ukraine & SSA Ukraine, Kyiv, Ukraine
Abstract

Since the breakup of the Soviet Union in 1991, Ukraine has been experiencing major changes in land cover and land use (LCLUC). The major drivers for these changes have been continuous economical and policy changes as well as climate variability. In the past 5-7 years, these changes particularly magnified due to the military conflict in the Eastern Ukraine and annexation of Crimea, preparation of the policy to open the land market, conversion to double cropping due to temperature increase and a sharp increase in the production of industrial crops, and continuous practise of burning agricultural fields. All these have led to the LCLUC “hotspots” throughout the country spanning several sectors (agriculture, urban and forestry) and having considerable socio-economic impacts.

Therefore, Ukraine represents a perfect testbed with multiple LCLUC “hotspots” of national and regional importance that have a significant socio-economic impact and are policy relevant. The overall objective of the proposal is to analyze and quantify the impact of LCLUC in Ukraine targeting agricultural, forestry, and urban sectors. We will analyze and quantify on a yearly basis (2013–2022) changes due to the military conflict in Eastern Ukraine. We will develop a methodological framework for crop rotation violation identification and area estimation with a focus on rapeseed and sunflower, and assess the potential environmental impact of not respecting optimal rotations with a focus on SDG goals related to sustainable agriculture and land degradation. We will quantify the area of unregistered (not in the StateGeoCadaster system) agricultural and forestry lands in Ukraine on a yearly basis. We will generate improved crop residue emissions estimates by leveraging both local and satellite-based datasets in combination with expert knowledge of the burning and agricultural practices in Ukraine. The developed methods will be thoroughly validated in Ukraine, and its applicability and robustness will be tested for other regions (Poland and Russia).

The obtained results are expected to have a significant impact on policy, as generated maps and LCLUC quantifications would allow decision-makers and local authorities to gather objective information on processes occurring in conflict-plagued regions in Ukraine and project future relief effort expenses; obtain objective information on land, including those unregistered in the official GeoCadaster system, which is essential for future land markets; enact and improve existing policies related to crop rotation violation and open field burnings.

The activities within this proposal are as follows: (1) data preparation and collection (satellite, in situ, official statistics); (2) development of methods for fusing multi-source satellite data for LCLUC identification and quantification; (3) generation and validation of LCLUC maps for “hotspots” at moderate and very high spatial resolution; (4) analysis and quantification of LCLUC and its impact within a number of use-cases (military conflict, crop rotations, unregistered lands, open burning); (5) demonstration of the robustness of the developed methods for other areas. The main source of remote sensing information will be Landsat-8 (NASA/USGS), Sentinel-2 and Sentinel-1 (EU/Copernicus), MODIS (NASA), VIIRS (NASA/NOAA), Planet/Dove (Planet Labs) and WorldView-3 (DigitalGlobe).

The proposed project will be built on a successful long-term collaboration between US and Ukrainian partners and will leverage results from previous and ongoing projects, including “Crop yield assessment and mapping by a combined use of Landsat-8, Sentinel-2 and Sentinel-1 images” (NASA/LCLUC), “Methodology for SDGs indicators assessment” (GEO-AWS Earth Observation Cloud Credits Program) and “Supporting Transparent Land Governance in Ukraine” (WorldBank).

Project Research Area

Project Documents

Year Authors Type Title
2024 Sergii Skakun Publications He, E., Xie, Y., Chen, W., Skakun, S., Bao, H., Ghosh, R., ... & Jia, X. (2024). Learning With Location-Based Fairness: A Statistically-Robust Framework and Acceleration. IEEE Transactions on Knowledge and Data Engineering, https://doi.org/10.1109/TKDE.2024.3371460.
2024 Sergii Skakun Publications Qadir, A., Skakun, S., Kussul, N., Shelestov, A., & Becker-Reshef, I. (2024). A generalized model for mapping sunflower areas using Sentinel-1 SAR data. Remote Sensing of Environment, 306, 114132. https://doi.org/10.1016/j.rse.2024.114132.
2024 Sergii Skakun Publications Abys, C., Skakun, S., & Becker-Reshef, I. (2024). Two decades of winter wheat expansion and intensification in Russia. Remote Sensing Applications: Society and Environment, 33, 101097.
2024 Sergii Skakun Publications Shumilo, L., & Skakun, S. (2024). Optical Flow of Temperature Reveals Climate Change Patterns for Agriculture and Forestry. Remote Sensing Applications: Society and Environment, 101198. https://doi.org/10.1016/j.rsase.2024.101198.
2023 Sergii Skakun Publications A. Qadir, S. Skakun, J. Eun, M. Prashnani, L. Shumilo, Sentinel-1 time series data for sunflower (Helianthus annuus) phenology monitoring, Remote Sensing of Environment, Volume 295, 2023, 113689, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2023.113689.
2023 Sergii Skakun Publications Xie, Y., Li, Z., Bao, H., Jia, X., Xu, D., Zhou, X., & Skakun, S. (2023, June). Auto-CM: Unsupervised deep learning for satellite imagery composition and cloud masking using spatio-temporal dynamics. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 12, pp. 14575-14583).
2023 Sergii Skakun Publications Erik C. Duncan, Sergii Skakun, Ankit Kariryaa, Alexander V. Prishchepov, Detection and mapping of artillery craters with very high spatial resolution satellite imagery and deep learning, Science of Remote Sensing, Volume 7, 2023, 100092, ISSN 2666-0172, https://doi.org/10.1016/j.srs.2023.100092.
2023 Sergii Skakun Publications Li, Z., Xie, Y., Jia, X., Stuart, K., Delaire, C., & Skakun, S. (2023, June). Point-to-region co-learning for poverty mapping at high resolution using satellite imagery. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 12, pp. 14321-14328).
2023 Sergii Skakun Publications Doxani, G., Vermote, E. F., Roger, J. C., Skakun, S., Gascon, F., Collison, A., ... & Yin, F. (2023). Atmospheric Correction Inter-comparison eXercise, ACIX-II Land: An assessment of atmospheric correction processors for Landsat 8 and Sentinel-2 over land. Remote Sensing of Environment, 285, 113412. https://doi.org/10.1016/j.rse.2022.113412
2023 Sergii Skakun Publications Shumilo, L., Skakun, S., Gore, M. L., Shelestov, A., Kussul, N., Hurtt, G., & Yarotskiy, V. (2023). Conservation policies and management in the Ukrainian Emerald Network have maintained reforestation rate despite the war. Communications Earth & Environment, 4(1), 443.
2022 Sergii Skakun Publications 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. https://doi.org/10.1016/j.isprsjprs.2022.04.025
2022 Sergii Skakun Publications Xie, Y., He, E., Jia, X., Chen, W., Skakun, S., Bao, H., Jiang, Z., Ghosh, R., & Ravirathinam, P. (2022). Fairness by “Where”: A Statistically-Robust and Model-Agnostic Bi-Level Learning Framework. Proc. of The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI’22), vol. 36, no. 11, pp. 12208-12216.
2022 Sergii Skakun Publications Skakun, S., Wevers, J., Brockmann, C., Doxani, G., Aleksandrov, M., Batič, M., Frantz, D., Gascon, F., Gómez-Chova, L., Hagolle, O., López-Puigdollers, D., Louis, J., Lubej, M., Mateo-García, G., Osman, J., Peressutti, D., Pflug, B., Puc, J., Richter, R., Roger, J.-C., Scaramuzza, P., Vermote, E., Vesel, N., Zupanc, A., Žust, L. (2022). Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2. Remote Sensing of Environment, 274, art. num. 112990. https://doi.org/10.1016/j.rse.2022.112990
2022 Sergii Skakun Publications 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, 15(1), 1169-1186. https://doi.org/10.1080/17538947.2022.2094001
2022 Sergii Skakun Publications 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. https://doi.org/10.1088/1748-9326/ac8b23
2022 Publications Abys, C. J., Skakun, S., & Becker-Reshef, I. (2022). The Rise and Volatility of Russian Winter Wheat Production. Environmental Research Communications, 4, art. num. 101003. https://doi.org/10.1088/2515-7620/ac97d2
2022 Sergii Skakun Publications 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. https://doi.org/10.1016/j.srs.2022.100059