Environmental Assessment For Mapping Land Degradation and Lands Changes Using Remotely Sensed Data with Geospatial Analysis
DOI:
https://doi.org/10.22399/ijcesen.1045Keywords:
Land Use, Land Cover, Satellite Image, Remote SensingAbstract
Lands degradation is one of the problems that facing the humanity throughout the world as well as the abandonment of farming on their lands by farmers, in addition to the fragmentation of most orchards and agricultural fields and their conversion into residential areas, has a negative impact on the Economic, Environmental and Social (Reduced Agricultural Productivity, Economic Loss, Soil Degradation, Agricultural productivity. Water Scarcity, Biodiversity Loss, Rural-Urban Migration, Food Security, Conflict and Instability). However, in Karbala Province, Iraq, most the Agriculture lands are facing this dilemma since 2003. Therefore, in order to start solving this problem and, it is important to detect all the changes throughout the study area and then put recommendations for overcoming this dilemma. The aim of this study to monitor and detect the changes in LCLU the study area and detect the lands degradation and the reasons behind that. For that, Authors employed pixel based classification techniques (Maximum Likelihood Method) on four Landsat satellite (9 ,7 ETM+, TM5, TM4) images acquired at intervals (1990, 2000, 2010, and 2023). The first step in this research is applied the pre-processing stages (radiometric and geometric corrections) to correct the images, secondly, processing stage (layer stacking, and study area sub-setting) to all satellite images, then the corrected images classified using supervise classification to six regions. The results show that the desertification has markedly intensified in the city of Karbala since the last three decades. In 2023, the water volume, decreased by 14.21%, and both Urban area and dark soil increased by 3.05%, and 8.63% respectively, and that give a negative indicator about what happen in research area, it evidences of land degradation processes was seen, mostly due to Human activities such as urban expansion and unsustainable land use practices. The confusion matrix was applied to evaluate the results. The overall accuracy and kappa statistic were above the 90% and 0.90 respectively.
References
Abdel Rahman, M.A.E. (2023). An Overview of Land Degradation, Desertification and Sustainable Land Management Using GIS and Remote Sensing Applications. Rendiconti Lincei. Scienze Fisiche e Naturali. 34, 767–808. https://doi.org/10.1007/s12210-023-01155-3
UNCCD. (1994). Elaboration of an International Convention to Combat Desertification in Countries Experiencing Serious Drought and/or Desertification, Particularly in Africa; UN: Bonn, Germany,. https://digitallibrary.un.org/record/174569?v=pdf
Rivera-Marin, D., Dash, J., Ogutu, B. (2022). The Use of Remote Sensing for Desertification Studies: A Review. Journal of Arid Environments. 206, 104829. https://doi.org/10.1016/j.jaridenv.2022.104829
Krasilnikov, P., Makarov, O., Alyabina, I., Nachtergaele, F. (2016). Assessing Soil Degradation in Northern Eurasia. Geoderma Regional. 7(1);1–10. https://doi.org/10.1016/j.geodrs.2015.11.002.
Montanarella, L., Badraoui, M., Chude, V., Costa, I.d.S.B., Mamo, T., Yemefack, M.,et al. (2015). Status of the World’s Soil Resources: Main Report. FAO: Rome, Italy. ISBN 978-92-5-109004-6.
Reid, W.V., Mooney, H.A., Cropper, A., Capistrano, D., Carpenter, S.R., Chopra, K., et al. (2005). Ecosystems and Human Well-Being: Synthesis, Millennium Ecosystem Assessment (Program), Ed., Island Press: Washington, DC, USA, 2005, ISBN 978-1-59726-040-4.
Sengani, D., Ramoelo, A., Archer, E. (2023). A Review of Fusion Framework Using Optical Sensors and Synthetic Aperture Radar Imagery to Detect and Map Land Degradation and Sustainable Land Management in the Semi-Arid Regions. Geocarto International. 38(1). https://doi.org/10.1080/10106049.2023.2278325
Hermans, K., McLeman, R. (2021). Climate Change, Drought, Land Degradation and Migration: Exploring the Linkages. Current Opinion in Environmental Sustainability. 50;236–244. https://doi.org/10.1016/j.cosust.2021.04.013
S. Leelavathy, S. Balakrishnan, M. Manikandan, J. Palanimeera, K. Mohana Prabha, & R. Vidhya. (2024). Deep Learning Algorithm Design for Discovery and Dysfunction of Landmines. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.686
Ferrari, G., Ai, P., Alengebawy, A., Marinello, F., Pezzuolo, A. (2021). An assessment of nitrogen loading and biogas production from Italian livestock: A multilevel and spatial analysis. Journal of Cleaner Production. 317, 128388. https://doi.org/10.1016/j.jclepro.2021.128388
SDG Indicator Metadata. (2024). Available online: https://unstats.un.org/Sdgs/Metadata/Files/Metadata-15-03-01.Pdf (accessed on 18 June 2024).
Mbow, C., Brandt, M., Ouedraogo, I., De Leeuw, J., Marshall, M. (2015). What Four Decades of Earth Observation Tell Us about Land Degradation in the Sahel? Remote Sensing. 7(4);4048–4067. https://doi.org/10.3390/rs70404048
Dubovyk, O. (2017). The Role of Remote Sensing in Land Degradation Assessments: Opportunities and Challenges. European Journal of Remote Sensing. 50(1);601–613. https://doi.org/10.1080/22797254.2017.1378926
Xie, H., Zhang, Y., Wu, Z., Lv, T. (2020). A Bibliometric Analysis on Land Degradation: Current Status, Development, and Future Directions. Land. 9(1), 28. https://doi.org/10.3390/land9010028
Costa, D.P., Herrmann, S.M., Vasconcelos, R.N., Duverger, S.G., Franca Rocha, W.J.S., Cambuí, E.C.B., et al. (2023). Bibliometric Analysis of Land Degradation Studies in Drylands Using Remote Sensing Data: A 40-Year Review. Land. 12(9), 1721. https://doi.org/10.3390/land12091721
Erdanaev, E., Kappas, M.W., Pulatov, A., Klinge, M. (2015). Short Review of Climate and Land Use Change Impact on Land Degradation in Tashkent Province. International Journal of Geoinformatics, 11(4);39–48. https://journals.sfu.ca/ijg/index.php/journal/article/view/909
Al-bukhari, A., Hallett, S., Brewer, T. (2018). A Review of Potential Methods for Monitoring Rangeland Degradation in Libya. Pastoralism. 8(1). https://doi.org/10.1186/s13570-018-0118-4
Wang, Z., Ma, Y., Zhang, Y., Shang, J. (2022). Review of Remote Sensing Applications in Grassland Monitoring. Remote Sensing. 14(12), 2903. https://doi.org/10.3390/rs14122903
Yuan, F., Sawaya, K. E., Loeffelholz, B., & Bauer, M. E. (2005). Land cover classification and change analysis of the Twin Cities (Minnesota) metropolitan area by multi temporal Landsat remote sensing. Remote Sensing of Environment. 98(2–3), 317–328. https://doi.org/10.1016/j.rse.2005.08.006
Jensen , J. R. , & Lulla, K. (1987). Introductory digital image processing: A remote sensing perspective. Geocarto International. 2(1);65–65. https://doi.org/10.1080/10106048709354084
Wulder, M. A., White, J. C., Goward, S. N., Masek, J. G., Irons, J. R., Herold, M., et al. (2008). Landsat continuity: Issues and opportunities for land cover monitoring. Remote Sensing of Environment. 112(3);955–969. https://doi.org/10.1016/j.rse.2007.07.004
Shalaby, A., & Tateishi, R. (2007). Remote sensing and GIS for mapping and moni toring land cover and land-use changes in the Northwestern coastal zone of Egypt. Applied Geography. 27(1);28–41. https://doi.org/10.1016/j.apgeog.2006.09.004
Kheiralipour, K., Al-Ansari, N., Dibs, H. (2024). Monitoring air quality using ground and remote sensing based imaging techniques. The Future of Imaging Technology. 21–40. https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-110047
Al-Janabi, A. M. S., Dibs, H., Sammen, S. Sh., Yusuf, B., Ikram, R. M. A., Alzuhairy, S. H., & Kisi, O. (2024). Comparison Analysis of Seepage Through Homogenous Embankment Dams Using Physical, Mathematical and Numerical Models. Arabian Journal for Science and Engineering. https://doi.org/10.1007/s13369-024-09224-x
Hashim, F., Dibs, H., & Sabah Jaber, H. (2022). Adopting Gram-Schmidt and Brovey Methods for Estimating Land Use and Land Cover Using Remote Sensing and Satellite Images. Nature Environment and Pollution Technology. 21(2);867–881. https://doi.org/10.46488/nept.2022.v21i02.050
Hashim, F., Dibs, H., & Jaber, H. S. (2021). Applying Support Vector Machine Algorithm on Multispectral Remotely sensed satellite image for Geospatial Analysis. Journal of Physics: Conference Series. 1963(1), 012110. https://doi.org/10.1088/1742-6596/1963/1/012110
E. Selvamanju, & V. Baby Shalini. (2024). 5G Network needs estimation & Deployment Plan Using Geospatial Analysis for efficient data usage, Revenue Generation. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.692
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