AI techniques for feature extraction, simplification and aggregation geospatial data
DOI:
https://doi.org/10.35208/ert.1685945Keywords:
Remote sensing, artificial intelligence, machine learning, random forest, cartographyAbstract
This study proposes an Artificial Intelligence (AI) application to cartographic workflow with the purpose of environmental mapping. The Random Forest (RF) framework of Machine Learning (ML) was applied to satellite image analysis for detection and robust attribution of biodiversity changes in Africa. Changes in land cover types were investigated on Landsat using comparison of several algorithms integrated in data processing and cartographic scripts using Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS). The analysis of time series highlights biodiversity changes in north Namibian landscapes in relation to presumed impacts of multiple potential environmental drivers: climate-related factors in desert setting (high fluctuations in temperature, salt crust in the saline lake and low precipitation) and anthropogenic forces (land use, abandoned areas and agriculture). The computational results demonstrated following outcomes. According to the calculated number of pixels corresponding to the cells in the raster images covering the Etosha landscapes, the land use types demonstrated the following changes: 1. Mosaic vegetation and cropland (8.42%), Aquatic or regularly flooded vegetation decreased on 13,59% due to seasonal fluctuations, areas of artificial surfaces declined insignificantly (2.40%), Mixed broadleaved deciduous forests experienced slight decreasd (1.18%), and the class of open grasslands increased to 5.32 %. Moreover, rainfed croplands demonstrated stable development with 4.58% of changes, while salt hardpans changed significantly to 12, 73%. Finally, water bodies including Etosha basin decreased significantly due to the seasonal effects (21.73%), Herbaceous vegetation, shrubland and thickets developed relatively stable with minor changes not exceeding 3.25 %, and sparse savannah grasslands occupied similar areas with fluctuations not exceeding 4,72%. The accuracy of image classification was evaluated by kappa statistics and chi-square against the ground truth data obtained from the land cover and soil data inventory. The original data with Land Use Land Cover (LULC) thematic classes were reclassified into 10 categories. The results show variations in land cover change due to arid desert climate in northern Namibia. This provides evidence of biodiversity changes in Africa detected using AI-driven data analysis using multispectral satellite images.
Downloads
References
[1]. V. Patil, Z. S. N., R. Bhosale, N. Shetty and V. Shah, "Land Cover Mapping Using Semantic Segmentation Models," 2023 7th International Conference On Computing, Communication, Control And Automation (ICCUBEA), Pune, India, 2023, pp. 1-5, doi: 10.1109/ICCUBEA58933.2023.10392136.
[2]. M. Klaučo, B. Gregorová, U. Stankov, V. Marković, P. Lemenkova, “Determination of ecological significance based on geostatistical assessment: a case study from the Slovak Natura 2000 protected area”, Central European Journal of Geography, Vol. 5, 28–42, 2013.
[3]. P. Lemenkova (2022). Evapotranspiration, vapour pressure and climatic water deficit in Ethiopia mapped using GMT and TerraClimate dataset. Journal of Water and Land Development, 54(7-9), 201--209. 10.24425/jwld.2022.141573
[4]. S. Xiang, Q. Xie and M. Wang, "Semantic Segmentation for Remote Sensing Images Based on Adaptive Feature Selection Network," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 8006705, doi: 10.1109/LGRS.2021.3049125
[5]. M. Klaučo, B. Gregorová, P. Koleda, U. Stankov, V. Marković, P. Lemenkova, “Land Planning as a Support for Sustainable Development Based on Tourism: A Case Study of Slovak Rural Region”, Environmental Engineering and Management Journal, Vol. 16(2), 449–458, 2017.
[6]. Lemenkova, P. (2023). A GRASS GIS Scripting Framework for Monitoring Changes in the Ephemeral Salt Lakes of Chotts Melrhir and Merouane, Algeria. Applied System Innovation, 6(4), 61. https://doi.org/10.3390/asi6040061
[7]. P. Lemenkova, 2025. Machine Learning Methods of Remote Sensing Data Processing for Mapping Salt Pan Crust Dynamics in Sebkha de Ndrhamcha, Mauritania, Artificial Satellites, 60(2), 37--69. doi: 10.2478/arsa-2025-0003
[8]. G. Thiyagarajan and V. Vijayalakshmi, "Classification of Land Cover using Machine Learning Models in Landsat Satellite Data," 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kamand, India, 2024, pp. 1-6, doi: 10.1109/ICCCNT61001.2024.10725490.
[9]. F. Yasmin, A. H. M. Sarowar Sattar and M. Kumar Paul, "Water Bodies Identification in Landsat 8 OLI Image Using Machine Learning," 2019 22nd International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, 2019, pp. 1-6, doi: 10.1109/ICCIT48885.2019.9038562.
[10]. A. V. Kakade, S. Rajkumar, K. Suganthi, L. Ramanathan, "Object Detection in Satellite Images Using Modified Pyramid Scene Parsing Networks," in Sensor Data Analysis and Management: The Role of Deep Learning, IEEE, 2021, pp.147-160, doi: 10.1002/9781119682806.ch9.
[11]. Q. Safder, H. Zhang and Z. Zheng, "Burnt Area Segmentation with Densely Layered Capsules," IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022, pp. 2199-2202, doi: 10.1109/IGARSS46834.2022.9884323.
[12]. D. R. Rao, S. Noorjahan and S. A. Fathima, "Classification of Land Cover Usage from Satellite Images using Deep Learning Algorithms," 2022 International Conference on Electronics and Renewable Systems (ICEARS), Tuticorin, India, 2022, pp. 1302-1308, doi: 10.1109/ICEARS53579.2022.9752282.
[13]. Z. Benbahria, M. F. Smiej, I. Sebari and H. Hajji, "Land cover intelligent mapping using transfer learning and semantic segmentation," 2019 7th Mediterranean Congress of Telecommunications (CMT), Fez, Morocco, 2019, pp. 1-5, doi: 10.1109/CMT.2019.8931403.
[14]. Z. Liu, X. Liu, M. Yu and X. Yang, "DMAM-UNET:An Improved Unet Semantic Segmentation for Water Body Extraction from Remotely Sensed Image," IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 2023, pp. 3726-3729, doi: 10.1109/IGARSS52108.2023.10283313.
[15]. V. Poliyapram, N. Imamoglu and R. Nakamura, "Deep Learning Model for Water/Ice/Land Classification Using Large-Scale Medium Resolution Satellite Images," IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 3884-3887, doi: 10.1109/IGARSS.2019.8900323.
[16]. A. Shalan, K. Tarmissi and N. A. Walee, "Vulnerability Analysis and Quality Improvement of Early Wildfire Detection Datasets for Machine-Learning Applications," 2024 IEEE 13th International Conference on Communication Systems and Network Technologies (CSNT), Jabalpur, India, 2024, pp. 754-760, doi: 10.1109/CSNT60213.2024.10546223.
[17]. Monali Gulhane; Sandeep Kumar, "Oriental Method to Predict Land Cover and Land Usage Using Keras with VGG16 for Image Recognition," in Advances in Aerial Sensing and Imaging, Wiley, 2024, pp.33-46, doi: 10.1002/9781394175512.ch2.
[18]. Z. Tang, X. Luo, Z. Yan, S. Li, S. Xiao and H. Li, "An Automatic Sample Augmentation Method for Paddy Rice Mapping Based on Segment Anything Model and Phenological Features—A Case Study in Southwest China," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, doi: 10.1109/JSTARS.2025.3606650.
[19]. P. Lemenkova, (2025). Machine Learning Algorithms of Remote Sensing Data Processing for Mapping Changes in Land Cover Types over Central Apennines, Italy, Journal of Imaging, 11(5), 153. 10.3390/jimaging11050153
[20]. Lemenkova, P. (2025). Improving Bimonthly Landscape Monitoring in Morocco, North Africa, by Integrating Machine Learning with GRASS GIS. Geomatics, 5(1), 5. https://doi.org/10.3390/geomatics5010005
[21]. A. Cuartero, M. E. Paoletti, A. R. Presas and J. M. Haut, "Bi-Dimensional Vector Data Analysis of Positional Accuracy of Landsat-8 Image with Pycircularstats," IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022, pp. 2442-2445, doi: 10.1109/IGARSS46834.2022.9883588
[22]. P. Lemenkova, 2025. Mapping Woodlands in Angola, Tropical Africa: Calculation of Vegetation Indices From Remote Sensing Data. Agriculture and Forestry, 70(3), 185–202. 10.17707/AgricultForest.70.3.13
[23]. N. Dinh Duong, "Decomposition of Landsat 8 OLI Images by Simplified Spectral Patterns for Land Cover Mapping," 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS), Beijing, China, 2018, pp. 1-13, doi: 10.1109/PRRS.2018.8486245.
[24]. Chunhong Pan, Gang Wu, V. Prinet, Qing Yang and Songde Ma, "A band-weighted landuse classification method for multispectral images," 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), San Diego, CA, USA, 2005, pp. 96-102 vol. 1, doi: 10.1109/CVPR.2005.14.
[25]. M. Clements, and R. Washington, “Summer Dust Emissions From the Etosha Pan, Namibia: The Role of the Namib Anabatic-Sea Breeze System”, Journal of Geophysical Research: Atmospheres, Vol. 128, e2022JD036815, 2023.
[26]. R. I. Matichuk, P. R. Colarco, J. A. Smith, O. B. Toon, “Modeling the transport and optical properties of smoke aerosols from African savanna fires during the Southern African Regional Science Initiative campaign (SAFARI 2000)”, Journal of Geophysical Research: Atmospheres, Vol. 112, 2007.
[27]. G. F. S. Wiggs, M. C. Baddock, D. S. G. Thomas, R. Washington, J. M. Nield, S. Engelstaedter, R. G. Bryant, F. D. Eckardt, J. R. C. von Holdt, S. Kötting, “Quantifying Mechanisms of Aeolian Dust Emission: Field Measurements at Etosha Pan, Namibia”, Journal of Geophysical Research: Earth Surface, Vol. 127, e2022JF006675, 2022.
[28]. M. R. Jury, E. Brunke, and M. Schormann, “Aircraft section measurements of meteorology and ozone in northern Namibia during SAFARI-92”, Journal of Geophysical Research: Atmospheres, Vol. 101, 23713–23720, 1996.
[29]. N. M. Mahowald, R. G. Bryant, J. del Corral, L. Steinberger, “Ephemeral lakes and desert dust sources”, Geophysical Research Letters, Vol. 30, 2003.
[30]. M. Clements, R. Washington, Atmospheric Controls on Mineral Dust Emission From the Etosha Pan, Namibia: Observations From the CLARIFY-2016 Field Campaign. Journal of Geophysical Research: Atmospheres, Vol. 126, e2021JD034746, 2021.
[31]. V. E. Fox, P. M. Lindeque, R. E. Simmons, H. H. Berry, C. Brain, R. Brabys, “Flamingo ‘rescue’ in Etosha National Park, 1994: technical, conservation and economic considerations”, Ostrich, Vol. 68, 72–76, 1997.
[32]. B. Parasharya, D. Rank, D. M. Harper, G. Crosa, S. Zaccara, N. Patel, C. Joshi, “Long-distance dispersal capability of Lesser Flamingo Phoeniconaias minor between India and Africa: genetic inferences for future conservation plans”, Ostrich, Vol. 86(3), 221–229, 2015.
[33]. F. H. Neumann, M. K. Bamford, “Shaping of modern southern African biomes: Neogene vegetation and climate changes”, Transactions of the Royal Society of South Africa, Vol. 70(3), 195–212, 2015.
[34]. B. E. Morgan, D. T., Bolger, J. W. Chipman, J. T. Dietrich, “Lateral and longitudinal distribution of riparian vegetation along an ephemeral river in Namibia using remote sensing techniques”, Journal of Arid Environments, Vol. 181, 104220, 2020.
[35]. R. Van Aarde, Y. De Beer, R. Guldemond, “Elephants, drought and the woody vegetation in Namibia’s Etosha National Park”, Transactions of the Royal Society of South Africa, Vol. 59(2), 123, 2004.
[36]. D. H. M. Cumming, M. B. Fenton, I. L. Rautenbach, R. D. Taylor, G. S. Cumming, M. S. Cumming, J. M. Dunlop, A. G. Ford, M. D. Hovorka, D. S. Johnston, M. Kalcounis, Z. Mahlangu, and C. V. R. Portfors, “Elephants, woodlands and biodiversity in southern Africa”, South African Journal of Science, Vol. 93, 231–236, 1997.
[37]. D. Western, D. Maitumo, “Woodland loss and restoration in a savanna park: a 20-year experiment”, African Journal of Ecology, Vol. 42, 111–121, 2004.
[38]. E. Gargallo, “Community Conservation and Land Use in Namibia: Visions, Expectations and Realities”, Journal of Southern African Studies, Vol. 46(1), 129–147, 2020.
[39]. P. Lemenkova, “Monitoring Seasonal Fluctuations in Saline Lakes of Tunisia Using Earth Observation Data Processed by GRASS GIS”, Land, Vol. 12(11), 1995, 2023.
[40]. J. M. Melack, “Ecological dynamics in saline lakes”, SIL Proceedings, Vol. 1922-2010, 28(1), 29–40, 2002.
[41]. P. Lemenkova, 2022. Mapping Climate Parameters over the Territory of Botswana Using GMT and Gridded Surface Data from TerraClimate. ISPRS International Journal of Geo-Information, 11(9), 473, 10.3390/ijgi11090473
[42]. L. Krienitz, C. Bock, K. Kotut, W. Luo, “Picocystis salinarum (Chlorophyta) in saline lakes and hot springs of East Africa”, Phycologia, Vol. 51(1), 22–32, 2012.
[43]. S. Trigg, S. Flasse, “Characterizing the spectral-temporal response of burned savannah using in situ spectroradiometry and infrared thermometry”, International Journal of Remote Sensing, Vol. 21(16), 3161–3168, 2000.
[44]. H. Wagenseil, C. Samimi, “Assessing spatio-temporal variations in plant phenology using Fourier analysis on NDVI time series: results from a dry savannah environment in Namibia”, International Journal of Remote Sensing, Vol. 27(16), 3455–3471, 2006.
[45]. P. Lemenkova, “Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh”, Water, Vol. 16(8), 1141, 2024a.
[46]. Z. Huang, B. Lees, “Representing and reducing error in natural resource classification using model combination”, International Journal of Geographical Information Science, Vol. 19(5), 603–621, 2005.
[47]. P. Lemenkova, “Artificial Intelligence for Computational Remote Sensing: Quantifying Patterns of Land Cover Types around Cheetham Wetlands, Port Phillip Bay, Australia”, Journal of Marine Science and Engineering, Vol. 12(8), 1279, 2024b.
[48]. D. P. Roy, J. Ju, C. Mbow, P. Frost, T. Loveland, “Accessing free Landsat data via the Internet: Africa’s challenge”, Remote Sensing Letters, Vol. 1(2), 111–117, 2010.
[49]. P. Lemenkova, “Image Segmentation of the Sudd Wetlands in South Sudan for Environmental Analytics by GRASS GIS Scripts”, Analytics, Vol. 2(3), 745-780, 2023a.
[50]. P. Lemenkova, “Using open-source software GRASS GIS for analysis of the environmental patterns in Lake Chad, Central Africa”, Die Bodenkultur Journal of Land Management Food and Environment, Vol. 74(1), 49-64, 2023b.
[51]. T. S. Shahfahad, M. W Naikoo, A. Rahman, A. S. Gagnon, A. R. M. T. Islam, and A. Mosavi, “Comparative evaluation of operational land imager sensor on board Landsat 8 and Landsat 9 for land use land cover mapping over a heterogeneous landscape”, Geocarto International, Vol. 38(1), 2023.
[52]. P. Lemenkova, “Console-Based Mapping of Mongolia Using GMT Cartographic Scripting Toolset for Processing TerraClimate Data”, Geosciences, Vol. 12(3), 140, 2022a.
[53]. P. Lemenkova, “GRASS GIS Scripts for Satellite Image Analysis by Raster Calculations Using Modules r.mapcalc, d.rgb, r.slope.aspect”, Tehnicki Vjesnik, Vol. 29(6), 1956-1963, 2022b.
[54]. P. Lindh, and P. Lemenkova, “Evaluation of Different Binder Combinations of Cement, Slag and CKD for S/S Treatment of TBT Contaminated Sediments”, Acta Mechanica et Automatica, Vol. 15(4), 236-248, 2021.
[55]. P. Lemenkova, “Random Forest Classifier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique”, Coasts, Vol. 4(1), 127-149, 2024c.
[56]. P. Lemenkova, “Automation of image processing through ML algorithms of GRASS GIS using embedded Scikit-Learn library of Python”, Examples and Counterexamples, Vol. 7, 100180, 2025.
[57]. R. Li, L. Wang, G. Ou, W. Xu and Q. Dai, "Mapping Forest Type with Multi-Seasonal Landsat Data and Multiple Environmental Factors in Yunnan Province Based on Google Earth Engine," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, pp. 6468-6471, doi: 10.1109/IGARSS47720.2021.9554563.
[58]. J. Amorós-López, L. Gómez-Chova, L. Guanter, L. Alonso, J. Moreno and G. Camps-Valls, "Multitemporal fusion of Landsat and MERIS images," 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp), Trento, Italy, 2011, pp. 81-84, doi: 10.1109/Multi-Temp.2011.6005053.
[59]. K. B, R. V, G. S and R. J, "Advanced Pansharpening Techniques for Satellite Image Fusion," 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), Chennai, India, 2024, pp. 1-6, doi: 10.1109/ICPECTS62210.2024.10780411
[60]. Y. Gao, W. Zhang, J. Wang and C. Liu, "LULC classification of Landsat −7 ETM+ image from rugged terrain using TC, CA and SOFM neural network," 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain, 2007, pp. 3490-3493, doi: 10.1109/IGARSS.2007.4423598
[61]. Y. S. Goh, W. Q. Chua, S. Yean and B. S. Lee, "Lessons from applying SRGAN on Sentinel-2 images for LULC classification," 2023 17th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Bangkok, Thailand, 2023, pp. 107-114, doi: 10.1109/SITIS61268.2023.00025.
[62]. Y. Zheng, S. Liu, Z. Song, X. Tong and H. Xie, "Analyzing spatio-temporal characteristics of urban LULC and LST over Shanghai during 2009-2019," 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), Shanghai, China, 2019, pp. 1-4, doi: 10.1109/Multi-Temp.2019.8866897
[63]. A. Santangeli, O. Spiegel, P. Bridgeford, M. Girardello, “Synergistic effect of land-use and vegetation greenness on vulture nestling body condition in arid ecosystems”, Scientific Reports 8, 13027 (2018). https://doi.org/10.1038/s41598-018-31344-2
[64]. W. Thuiller, G.F. Midgley, G.O. Hughes, B. Bomhard, G. Drew, M.C. Rutherford, F.I. Woodward, “Endemic species and ecosystem sensitivity to climate change in Namibia”, Global Change Biology, 12, 2006, pp. 759-776. https://doi.org/10.1111/j.1365-2486.2006.01140.x
[65]. U. Dieckmann, “Thinking with relations in nature conservation? A case study of the Etosha National Park and Haiǁom”, J R Anthropol Inst, 29, 2023, pp. 859-879. https://doi.org/10.1111/1467-9655.14008
[66]. A. Vushe, “Proposed Research, Science, Technology, and Innovation to Address Current and Future Challenges of Climate Change and Water Resource Management in Africa”, 2021. In: Diop, S., Scheren, P., Niang, A. (eds) Climate Change and Water Resources in Africa. Springer, Cham. https://doi.org/10.1007/978-3-030-61225-2_21
[67]. R.G. Bryant, “Monitoring hydrological controls on dust emissions: preliminary observations from Etosha Pan, Namibia”, Geographical Journal, 169, (2003, pp. 131-141. https://doi.org/10.1111/1475-4959.04977
[68]. S. van Asselen, P.H. Verburg, “Land cover change or land-use intensification: simulating land system change with a global-scale land change model”, Global Change Biology, 19, 2013, pp. 3648-3667. https://doi.org/10.1111/gcb.12331
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Environmental Research and Technology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles in Environmental Research and Technology (ERT) are published under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
This license allows others to copy, distribute, and adapt the work for non-commercial purposes only, provided that proper credit is given to the original authors and to the journal.
