SiM: Satellite Image Mixed Pixel Deforestation Analysis in Optical Satellite for Land Use Land Cover Application

Authors

  • Priyanka Darbari

    Department of CEA, GLA University, Mathura 281406, India
  • Ankush Agarwal

    Department of CEA, GLA University, Mathura 281406, India
  • Manoj Kumar

    Department of CEA, GGV University, Guwahati 781021, India

DOI:

https://doi.org/10.30564/jees.v7i2.7737
Received: 10 November 2024 | Revised: 5 December 2024 | Accepted: 9 December 2024 | Published Online: 10 February 2025

Abstract

Brazil’s deforestation monitoring integrates accuracy and current monitoring for land use and land cover applications. Regular monitoring of deforestation and non-deforestation requires Sentinel-2 multispectral satellite images of several bands at various frequencies, the mix of high- and low-resolution images that make object classification difficult because of the mixed pixel problem. Accuracy is impacted by the mixed pixel problem, which occurs when pixels belong to different classes and makes detection challenging. To identify mixed pixels, Band Math is used to merge numerous bands to generate a new band NDVI. Thresholding is used to analyze the edges of deforested and non-deforested areas. Segmentation is then used to analyze the pixels which helps to identify the number of mixed pixels to compute the deforested and non-deforested areas. Segmented image pixels are used to categorize the deforestation of the Brazilian Amazon Forest between 2019 and 2023. Verify how many pixels are mixed to improve accuracy and identify mixed pixel issues; compare the mixed and pure pixels of fuzzy clustering with the subtracted morphological image pixels. With the help of segmentation and clustering researchers effectively validate mixed pixels in a specific area. The proposed methodology is easy to analyze and helpful for an appropriate calculation of deforested and non-deforested areas.

Keywords:

Amazon Forest; Mixed Pixel Problem; Band Math; Segmentation; Clustering

References

[1] Avtar, R., Tripathi, S., Aggarwal, A.K., et al., 2019. Population–urbanization–energy nexus: A review. Resources. 8(3), 136.

[2] Chamling, M., Bera, B., 2020. Spatio-temporal patterns of land use/land cover change in the Bhutan–Bengal foothill region between 1987 and 2019: Study towards geospatial applications and policy making. Earth Systems and Environment. 4(1), 117–130.

[3] Waleed, M., Sajjad, M., 2022. Leveraging cloud-based computing and spatial modeling approaches for land surface temperature disparities in response to land cover change: Evidence from Pakistan. Remote Sensing applications: Society and Environment. 25, 100665.

[4] Trujillo-Jiménez, M.A., Liberoff, A.L., Pessacg, N., et al., 2022. SatRed: New classification land use/land cover model based on multi-spectral satellite images and neural networks applied to a semiarid valley of Patagonia. Remote Sensing Applications: Society and Environment. 26, 100703.

[5] Atay Kaya, İ., Kut Görgün, E., 2020. Land use and land cover change monitoring in Bandırma (Turkey) using remote sensing and geographic information systems. Environmental Monitoring and Assessment. 192(7), 430.

[6] Roy, P.S., Ramachandran, R.M., Paul, O., et al., 2022. Anthropogenic land use and land cover changes—A review on its environmental consequences and climate change. Journal of the Indian Society of Remote Sensing. 50(8), 1615–1640.

[7] Baig, M.F., Mustafa, M.R.U., Baig, I., et al., 2022. Assessment of land use land cover changes and future predictions using CA-ANN simulation for selangor, Malaysia. Water. 14(3), 402.

[8] Gaur, S., Singh, R., 2023. A comprehensive review on land use/land cover (LULC) change modeling for urban development: current status and future prospects. Sustainability. 15(2), 903.

[9] Griffiths, P., Jakimow, B., Hostert, P., 2018. Reconstructing long term annual deforestation dynamics in Pará and Mato Grosso using the Landsat archive. Remote Sensing of Environment. 216, 497–513.

[10] Nepstad, D., Soares-Filho, B.S., Merry, F., et al., 2009. The end of deforestation in the Brazilian Amazon. Science. 326(5958), 1350–1351.

[11] Griffiths, P., Jakimow, B., Hostert, P., 2018. Reconstructing long term annual deforestation dynamics in Pará and Mato Grosso using the Landsat archive. Remote Sensing of Environment. 216, 497–513.

[12] Available from: https://www.globalforestwatch.org (cited 8 August 2024).

[13] European Space Agency, 2020. Sentinel-2: Resolution and Swath. Available from: https:// sentinel.esa.int/web/sentinel/missions/sentinel-2/instrument-payload/resolution-and-swath (cited 18 August 2024).

[14] Darbari, P., Kumar, M., 2022. Satellite image enhancement techniques: A comprehensive review. Proceedings of International Conference on Communication and Artificial Intelligence: ICCAI 2021; May 10, 2022; Singapore. pp. 431–447.

[15] Roy, D.P., Li, J., Zhang, H.K., et al., 2017. Examination of Sentinel-2A multi-spectral instrument (MSI) reflectance anisotropy and the suitability of a general method to normalize MSI reflectance to nadir BRDF adjusted reflectance. Remote Sensing of Environment. 199, 25–38.

[16] Sánchez-Espinosa, A., Schröder, C., 2019. Land use and land cover mapping in wetlands one step closer to the ground: Sentinel-2 versus Landsat 8. Journal of Environmental Management. 247, 484–498.

[17] Petrou, M., 1999. Mixed pixel classification:An overview. Information Processing for Remote Sensing. 69–83.

[18] Pandey, A.C., Kumari, N., Ahmad, S., et al., 2023. Evaluating biochemical and biophysical characteristics of tropical deciduous forests of eastern India using remote sensing and in-situ parameter estimation. Remote Sensing Applications: Society and Environment. 29, 100909.

[19] Kaur, S., Bansal, R.K., 2017. Novel approach for mixed pixel extraction from remote sensing images. International Journal of Applied Engineering Research. 12, 12860–12871

[20] Chen, X., Wang, D., Chen, J., et al., 2018. The mixed pixel effect in land surface phenology: A simulation study. Remote Sensing of Environment. 211, 338–344.

[21] Kaur, S., Bansal, R.K., Mittal, M., et al., 2019. Mixed pixel decomposition based on extended fuzzy clustering for single spectral value remote sensing images. Journal of the Indian Society of Remote Sensing. 47, 427–437. DOI: https://doi.org/10.1007/s12524-019-00946-2

[22] Sherwani, A.R., Ali, Q.M., 2022. Parametric classification using fuzzy approach for handling the problem of mixed pixels in ground truth data for a satellite image. Annals of Data. Science. 10, 1459–1472. DOI: https://doi.org/10.1007/s40745-022-00383-y

[23] Sherwani, A.R., Ali, Q.M., Ali, I., 2022. A modified fuzzy clustering approach in unsupervised classification for detecting the mixed pixels of satellite images. Journal of Mathmathical and Computational. Science. 12, 97.

[24] Nghiyalwa, H.S., Urban, M., Baade, J., et al., 2021. Spatiotemporal mixed pixel analysis of savanna ecosystems: A review. Remote Sensing. 13(19), 3870.

[25] Mantripragada, K., Qureshi, F. Z., 2024. Hyperspectral pixel unmixing with latent dirichlet variational autoencoder. IEEE Transactions on Geoscience and Remote Sensing. 62.

[26] Kodl, G., Streeter, R., Cutler, N., et al., 2024. Arctic tundra shrubification can obscure increasing levels of soil erosion in NDVI assessments of land cover derived from satellite imagery. Remote Sensing of Environment. 301, 113935.

[27] Pu, J., Yan, K., Roy, S., et al., 2024. Sensor-independent LAI/FPAR CDR: Reconstructing a global sensor-independent climate data record of MODIS and VIIRS LAI/FPAR from 2000 to 2022. Earth System Science Data Discussions. 16(1), 15–34.

[28] Zheng, L., Zhu, J., Shi, J., et al., 2024. Efficient mixed transformer for single image superresolution. Engineering Applications of Artificial Intelligence. 133, 108035.

[29] Meerdink, S., Hiatt, D., Flory, S.L., et al., 2024. Dealing with imperfect data for invasive species detection using multispectral imagery. Ecological Informatics. 79, 102432.

[30] Jiang, L., Zhou, C., Li, X., 2023. Sub-pixel surface water mapping for heterogeneous areas from Sentinel-2 images: A case study in the Jinshui Basin, China. Water. 15(8), 1446.

[31] Nagaraj, R., Kumar, L.S., 2024. Extraction of surface water bodies using optical remote sensing images: A review. Earth Science Informatics. 17, 893–956. DOI: https://doi.org/10.1007/s12145-023-01196-0

[32] Guo, J., Du, S., 2024. Generating fuzzy membership functions for modeling wetland ecosystems from multispectral remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 17, 7640–7654.

[33] Ertel, J., Goldman, L., Spore, J., 2023. Available from: https://www.globalforestwatch.org/blog/data-and-research/tree-cover-data-comparison/ (cited 6 June 2023).

[34] Ramsay, D., 2021. Available from: https://forestsnews.cifor.org/70481/how-one-brazilian-state-is-tackling-deforestationwithin-its-borders?fnl= (cited 12 January 2021).

[35] World Rainforest Movement, 2023. REDD and the Green Economy exacerbate oppression and deforestation in Pará, Brazil. Available from: https://www.wrm.org.uy/bulletin-articles/redd-and-the-green-economy-exacerbateoppression-and-deforestation-in-para-brazil (cited 12-07-2024).

[36] Phiri, D., Simwanda, M., Salekin, S., et al., 2020. Sentinel-2 data for land cover/use mapping: A review. Remote Sensing. 12(14), 2291.

[37] Gurjar, S.B., Padmanabhan, N., 2005. Study of various resampling techniques for high-resolution remote sensing imagery. Journal of the Indian Society of Remote Sensing. 33, 113–120. DOI: https://doi.org/10.1007/BF02989999

[38] Home | Sentinel Hub custom scripts. Available from: https://custom-scripts.sentinel-hub.com/custom-scripts/sentinel-2/bands/ (cited 8 August 2024).

[39] Darbari, P., Kumar, M., Agarwal, A., 2024. SIT. net: SAR deforestation classification of amazon forest for land use land cover application. Journal of Computer and Communications. 12(3), 68–83.

[40] Darbari, P., Kumar, M., Agarwal, A., 2024. Satellite image enhancement optical and sar image fusion for lulc application: Comprehensive review for state of art. TuijinJishu/Journal of Propulsion Technology. 45(1), 1891–1906.

Downloads

How to Cite

Darbari, P., Ankush Agarwal, & Manoj Kumar. (2025). SiM: Satellite Image Mixed Pixel Deforestation Analysis in Optical Satellite for Land Use Land Cover Application. Journal of Environmental & Earth Sciences, 7(2), 228–247. https://doi.org/10.30564/jees.v7i2.7737

Issue

Vol. 7 , Iss. 2 (February 2025)

Article Type

Article

License

Copyright © 2025 Priyanka Darbari, Ankush Agarwal, Manoj Kumar

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

Crossref
Scopus
Google Scholar
Europe PMC