Implementation of Land Degradation Cloud Mapping with Earth Observation Data Cubes

Authors

  • Sergey Stankevich
  • Mykhailo Popov
  • Anna Kozlova
  • Anna Khyzhniak
  • Elena Zaitseva
  • Vitaly Levashenko
  • Peter Sedláček
  • Sergiy Lyubchyk
  • Svitlana Lyubchyk
  • Sergii Maltsev
  • Yuliia Lypska

DOI:

https://doi.org/10.47839/ijc.25.2.4647

Keywords:

land degradation, Earth observation, data cube, probabilistic model, cloud mapping, software implementation, online toolbox

Abstract

The software implementation of land degradation cloud mapping with Earth observation data cubes is described. The software implementation was performed on the ArcGIS Online platform as an external geospatial toolbox. The toolbox was developed in the Python programming language using ArcGIS-compatible Python libraries. The toolbox runs a previously proposed probabilistic model. This probabilistic model leverages the synergy between observed distributions of biogeophysical characteristics of the Earth’s surface, i.e., indicators, and existing knowledge of the causes and drivers of land degradation. Land degradation indicators are Earth observation (EO) data products that are aggregated into geospatial EO data cubes and analyzed jointly. The developed geospatial toolbox was applied to the long-term mapping of land degradation over the Kryvyi Rih (Ukraine) test region with EO data cubes of seven selected indicators.

References

I. Salii, V. Zaselskyi, O. Kryvoruchkina, D. Popolov, N. Suslo, D. Sahalai, V. Fortuna, “Analysis and research of the state of soils and hydrosphere of Kryvbas,” Ecological Sciences, vol. 4, no. 31, pp. 20-26, 2020. https://doi.org/10.32846/2306-9716/2020.eco.4-31.3.

O. Demchenko, “Ecological characteristics of rural areas in Ukraine: current realities,” Economy and Society, vol. 66, a. 20, 2024. https://doi.org/10.32782/2524-0072/2024-66-20.

P. Saik, I. Koshkalda, L. Bezuhla, N. Stoiko, A. Riasnianska, “Achieving land degradation neutrality: land-use planning and ecosystem approach,” Frontiers in Environmental Science, vol. 12, a. 1446056, 2024. https://doi.org/10.3389/fenvs.2024.1446056.

M. Solokha, O. Demyanyuk, L. Symochko, S. Mazur, N. Vynokurova, K. Sementsova, R. Mariychuk, “Soil degradation and contamination due to armed conflict in Ukraine,” Land, vol. 13, no. 10, a. 1614, 2024. https://doi.org/10.3390/land13101614.

F. Gianoli, M. Weynants, M. Cherlet, “Land degradation in the European Union – where does the evidence converge?” Land Degradation & Development, vol. 34, no. 8, pp. 2256-2275, 2023. https://doi.org/10.1002/ldr.4606.

L. Yelistratova, A. Apostolov, A. Khodorovskyi, O. Tomchenko, M. Tymchyshyn, “Satellite monitoring of anthropogenic processes and factors of land degradation in Ukraine,” Proceedings of 24th International Multidisciplinary Scientific GeoConference (SGEM 2024), vol. 24, no. 2.1, Albena, Bulgaria, 1-7 July 2024, pp. 295-304. https://doi.org/10.5593/sgem2024/2.1/s10.32.

R. Kaiss, Z. Benjouid, N. Snoussi, E.K. El Mountassir, N. Nabil, A. Halim, S.S. Zarouali, “Impact of climate change on water resources and ecological sustainability in Morocco: a 1990–2022 analysis,” Research in Ecology, vol. 7, no. 2, pp. 53-70, 2025. https://doi.org/10.30564/re.v7i2.9205.

D. Montero, G. Kraemer, A. Anghelea, C. Aybar, G. Brandt, G. Camps-Valls, F. Cremer, I. Flik, F. Gans, S. Habershon, C. Ji, T. Kattenborn, L. Martínez-Ferrer, F. Martinuzzi, M. Reinhardt, M. Söchting, Khalil Teber, M.D. Mahecha, “Earth system data cubes: avenues for advancing Earth system research,” Environmental Data Science, vol. 3, a. e27, 2024. https://doi.org/10.1017/eds.2024.22.

A. Munteanu, “Data cubes and cloud-native environments for Earth observation: an overview,” Scalable Computing: Practice and Experience, vol. 25, no. 6, pp. 5745-5759, 2024. https://doi.org/10.12694/scpe.v25i6.4999.

B. Han, S. Zhang, X. Shi, M. Reichstein, “Bridging remote sensors with multisensor geospatial foundation models,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVRP 2024), Seattle, USA, June 17-21, 2024, pp. 27852–2786. https://doi.org/10.1109/CVPR52733.2024.02631.

F. D’Acunto, F. Marinello, A. Pezzuolo, “Rural land degradation assessment through remote sensing: current technologies, models, and applications,” Remote Sensing, vol. 16, no. 16, a. 3059, 2024. https://doi.org/10.3390/rs16163059.

N. Farhat, M. Ayoub, A. Kobeissi, H. Makki, B. Fadel, “Remote sensing and GIS to assess land degradation: a case study from Lebanon,” Plants and Environment, vol. 4, no. 1, pp. 10-15, 2022. https://doi.org/10.22271/2582-3744.2022.mar.10.

T. Akin, S. Berberoglu, “Modeling land degradation using remote sensing data: the case of Seyhan basin,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLVIII-M-1-2023, pp. 449-454, 2023. https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-449-2023.

L. Sabljić, T. Lukić, D. Bajić, Marković S.B., D. Delić, “Application of remote sensing in monitoring land degradation: A case study of Stanari municipality (Bosnia and Herzegovina),” Open Geosciences, vol. 16, no. 1, a. 20220671, 2024. https://doi.org/10.1515/geo-2022-0671.

J. Reith, G. Ghazaryan, F. Muthoni, O. Dubovyk, “Assessment of land degradation in semiarid Tanzania – using multiscale remote sensing datasets to support sustainable development goal 15.3,” Remote Sensing, vol. 13, no. 9, a. 1754, 2021. https://doi.org/10.3390/rs13091754.

C. Schillaci, A. Jones, D. Vieira, M. Munafò, L. Montanarella, “Evaluation of the United Nations Sustainable Development Goal 15.3.1 indicator of land degradation in the European Union,” Land Degradation & Development, vol. 34, no. 1, pp. 250-268, 2023. https://doi.org/10.1002/ldr.4457.

M. Gonzalez-Roglich, A. Zvoleff, M. Noon, H. Liniger, R. Fleiner, N. Harari, C. Garcia, “Synergizing global tools to monitor progress towards land degradation neutrality: Trends.Earth and the world overview of conservation approaches and technologies sustainable land management database,” Environmental Science & Policy, vol. 93, pp. 34-42, 2019. https://doi.org/10.1016/j.envsci.2018.12.019.

P. Manna, A. Agrillo, M. Bancheri, M. Di Leginio, G. Ferraro, G. Langella, F.A. Mileti, N. Riitano, M. Munafò, “A geospatial decision support system for supporting the assessment of land degradation in Europe,” Land, vol. 13, no. 1, a. 89, 2024. https://doi.org/10.3390/land13010089.

J. Ahmad, S.A. Mahmood, M. Haseeb, “Comprehensive assessment of soil erosion using cloud-based geospatial analysis: insights from the RUSLE model,” Soil and Environment, vol. 44, no.2, pp. 191-207, 2025. https://doi.org/10.25252/SE/2025/253773.

I. Teich, N. Harari, P. Caza, J.P. Henao-Henao, J.C. Lopez, E. Raviolo, A.M. Díaz-González, H. González, S. Bastidas, C. Morales-Opazo, C.L. García, “An interactive system to map land degradation and inform decision-making to achieve land degradation neutrality via convergence of evidence across scales: a case-study in Ecuador,” Land Degradation & Development, vol. 34, no. 15, pp. 4475-4487, 2023. https://doi.org/10.1002/ldr.4645.

G. Quaranta, R. Salvia, P. Cudlin, L. Salvati, “Evaluating the spatial coherence of composite indexes of land degradation at small administrative units,” Environmental Impact Assessment Review, vol. 103, a. 107226, 2023. https://doi.org/10.1016/j.eiar.2023.107226.

W. Shao, Z. Zhang, Q. Guan, Y. Yan, J. Zhang, “Comprehensive assessment of land degradation in the arid and semiarid area based on the optimal land degradation index model,” Catena, vol. 234, a. 107563, 2024. https://doi.org/10.1016/j.catena.2023.107563.

A. Temenos, N. Temenos, I.N. Tzortzis, I. Rallis, A. Doulamis, N. Doulamis, “C2A-DC: a context-aware adaptive data cube framework for environmental monitoring and climate change crisis management,” Remote Sensing Applications: Society and Environment, vol. 34, a. 101171, 2024. https://doi.org/10.1016/j.rsase.2024.101171.

E. Zaitseva, S. Stankevich, A. Kozlova, I. Piestova, V. Levashenko, P. Rusnak, “Assessment of the risk of disturbance impact on primeval and managed forests based on Earth observation data using the example of Slovak Eastern Carpathians,” IEEE Access, vol. 9, pp. 162847-162856, 2021. https://doi.org/10.1109/ACCESS.2021.3134375.

E. Fink, A. Sarin and J.G. Carbonell, “Analysis of uncertain data: smoothing of histograms,” Proceedings of the International Conference on Systems, Man and Cybernetics (SMC’09), San Antonio, USA, October 11-14, 2009, pp. 2549-2555. https://doi.org/10.1109/ICSMC.2009.5346324.

D.W. Scott. Multivariate Density Estimation: Theory, Practice, and Visualization, second ed., John Wiley, Hoboken, 2015, 384 p. https://doi.org/10.1002/9781118575574.

S. Stankevich, A. Kozlova, E. Zaitseva, V. Levashenko, “Multivariate risk assessment of land degradation by remotely-sensed data,” Proceedings of the International Conference on Information and Digital Technologies (IDT 2023), Žilina, Slovakia, June 20-22, 2023, pp. 45-50. https://doi.org/10.1109/IDT59031.2023.10194398.

P. Fu. Getting to Know Web GIS, fifth ed., ESRI Press, Redlands, 2022, 448 p. ISBN: 9781589487277.

D. Crawford, D. Yaw Jr. ESRI Advanced Guide to Python in ArcGIS, ESRI Press, Redlands, 2025, 268 p. ISBN: 9781589488236.

M. Popov, S. Stankevich, A. Kozlova, I. Piestova, A. Khyzhnyak, E. Zaitseva, V. Levashenko, E. Seredinin, S. Maltsev, Y. Lypska, A. Kukharuk, V. Rashchuk, A. Smitiukh, “The architecture of land degradation early warning based on Earth observation,” Proceedings of the International Conference on Information and Digital Technologies (IDT 2023), Žilina, Slovakia, June 20-22, 2023, pp. 125-132. https://doi.org/10.1109/IDT59031.2023.10194406.

30 Best GIS Software Applications [Rankings], https://gisgeography.com/best-gis-software/.

T.E. Parece, J.A. McGee III. Remote Sensing with ArcGIS Pro, second ed., Pressbooks, Montreal, 2023, 526 p. ISBN:‎ 9798854833301.

J. Unpingco. Python for Probability, Statistics, and Machine Learning, second ed., Springer, Cham, 2019, 398 p. https://doi.org/10.1007/978-3-030-18545-9.

M. Gravey, “Python scripting in Google Earth engine code editor with the open Earth engine extension,” Proceedings of the EGU General Assembly, Vienna, Austria, April 14–19, 2024, EGU24-15855. https://doi.org/10.5194/egusphere-egu24-15855.

E. Zio, An introduction to the basic of reliability and risk analysis, World Scientific Publishing, Singapore, 2007, 236 p. https://doi.org/10.1142/6442.

C. Zhou, “Exploring future GIS visions in the era of the scientific and technological revolution,” Information Geography, vol. 1, no. 1, a. 100007, 2025. https://doi.org/10.1016/j.infgeo.2025.100007.

Downloads

Published

2026-06-30

How to Cite

Stankevich, S., Popov, M., Kozlova, A., Khyzhniak, A., Zaitseva, E., Levashenko, V., Sedláček, P., Lyubchyk, S., Lyubchyk, S., Maltsev, S., & Lypska, Y. (2026). Implementation of Land Degradation Cloud Mapping with Earth Observation Data Cubes. International Journal of Computing, 25(2), 201-209. https://doi.org/10.47839/ijc.25.2.4647

Issue

2026, Volume 25, Issue 2

Section

Articles

License

International Journal of Computing is an open access journal. Authors who publish with this journal agree to the following terms:
•    Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
•    Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
•    Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.