Prediction of regional vegetation cover using spatial image features and semantic segmentation

Ganesh Chirkhare; Ramchand Hablani; Sanjay Balamwar

doi:10.53730/ijhs.v6nS4.9980

Authors

Ganesh Chirkhare
ganeshc.edu@gmail.com
Dept. of Computer Science and Engg., Shri Ramdeobaba College of Engineering & Management, Nagpur, India
Ramchand Hablani Dept. of Computer Science and Engg., Shri Ramdeobaba College of Engineering & Management, Nagpur, India
Sanjay Balamwar Maharashtra Remote Sensing Application Centre Nagpur, India

Keywords:

multispectral, atmospherically resistant vegetation index (ARVI), normalized difference vegetation index (NDVI), near infrared (NIR), maharashtra remote sensing application centre (MRSAC), U-Net, remote sensing

Abstract

Vegetation is an essential part of our ecosystem; it also determines health of our planet. According to “The State of the World’s Forests 2020” only 31% of the global land area is covered with vegetation. This paper presents a superior way of representing vegetation cover in a region by utilizing Atmospherically Resistant Vegetation Index (ARVI) as vital features out of multispectral satellite images. These images comprise Green, Blue, Red and Near Infrared bands data which was further utilized by U-Net to efficiently segment satellite images. In remote sensing greenery of environment is traditionally determined using NDVI, one of the critical imperfections with this method is that it is liable to compute inaccurate values as a consequence of variations in soil, air moisture and shadowing affected by varying incidence angle of sunlight. On the other hand, ARVI is immune to such flaws and integration with deep learning provides a better solution for segmenting regional vegetation and predicting its coverage up to some extent. This method can be employed for predicting vegetation in any region along with assisting in events such as repopulating trees and urban planning while conserving beauty of our nature.

Downloads

Download data is not yet available.

References

Anusha, C. (2016). Image Enhancement Approach for Digital Applications Based on Generalized Histogram Equalization Model. International Journal of Engineering and Computer Science.

Arora, V., Rao, S. S., Amminedu, E., & Rao, P. J. (2019). Incorporating Spectral Indices and Textural Features for Improved Classification Accuracy Towards Semi-Automated River Sand Mapping Using High-Resolution Multispectral Satellite Imagery. Journal of Global Resources, 06(01), 8–15. DOI: 10.46587/jgr.2019.v06i01.002.

Ayhan, B., Kwan, C., Budavari, B., Kwan, L., Lu, Y., Perez, D., Li, J., Skarlatos, D., & Vlachos, M. (2020). Vegetation Detection Using Deep Learning and Conventional Methods. Remote Sensing, 12(15), 2502. DOI: 10.3390/rs12152502.

Bhandari, A., Kumar, A., & Singh, G. (2012). Feature Extraction using Normalized Difference Vegetation Index (NDVI): A Case Study of Jabalpur City. Procedia Technology, 6, 612–621.

Bhatnagar, S., Gill, L., & Ghosh, B. (2020). Drone Image Segmentation Using Machine and Deep Learning for Mapping Raised Bog Vegetation Communities. Remote Sensing, 12(16), 2602.

Dafalla, M. S. (2022). New Remote Sensing Vegetation Index for Recognition of Sparse Vegetation in Arid land. Case Study: Algezira State, Sudan. Academia Letters. DOI: 10.20935/al4807

DOI: 10.1016/j.jag.2019.101897.

DOI: 10.1016/j.protcy.2012.10.074.

DOI: 10.1016/j.rsase.2021.100582.

DOI: 10.11648/j.com.20210901.11.

DOI: 10.14704/web/v19i1/web19100.

DOI: 10.1556/606.2019.14.3.19.

DOI: 10.18535/ijecs/v5i9.13.

DOI: 10.2174/1574362415666191227154656

DOI: 10.3390/rs10050790.

DOI: 10.3390/rs12162602.

Dutta, P. K. (2019). Image Segmentation Based Approach for the Purpose of Developing Satellite Image Spatial Information Extraction for Forestation and River Bed Analysis. International Journal of Image and Graphics, 19(01), 1950002. DOI: 10.1142/s0219467819500025.

Exposto, L. A. S., & Januraga, P. P. (2021). Domestic waste characteristics and the management systematic review. International Journal of Health & Medical Sciences, 4(2), 253-259. https://doi.org/10.31295/ijhms.v4n2.1731

Flood, N., Watson, F., & Collett, L. (2019). Using a U-net convolutional neural network to map woody vegetation extent from high resolution satellite imagery across Queensland, Australia. International Journal of Applied Earth Observation and Geoinformation, 82, 101897.

Gahlot, N., Prusty, G., & Dhara, M. (2022). Potential of Multi-resolution Satellite Imagery Products for Scale Variant Topographic Mapping. Journal of the Indian Society of Remote Sensing, 50(1), 175–187. DOI: 10.1007/s12524-021-01449-9.

Goni, I., Sandra Ahmadu, A., & Musa Malgwi, Y. (2021). Multi-class Object Detection Model in Satellite Images Using Convolutional Neural Network. Communications, 9(1), 1.

Ibrahim, D. A. W. S., & Atya, D. B. A. K. (2022). Detection of Diseases in Rice Leaf Using Deep Learning and Machine Learning Techniques. Webology, 19(1), 1493–1503.

Jin, X., Song, K., Du, J., Liu, H., & Wen, Z. (2017). Comparison of different satellite bands and vegetation indices for estimation of soil organic matter based on simulated spectral configuration. Agricultural and Forest Meteorology, 244–245, 57–71. DOI: 10.1016/j.agrformet.2017.05.018.

Kaufman, Y., & Tanre, D. (1992). Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing, 30(2), 261–270. DOI: 10.1109/36.134076.

Kouda, R. (2017). GIS and Satellite Image Analysis Using QGIS（No.1 Introduction）. Geoinformatics, 28(3), 111–137. DOI: 10.6010/geoinformatics.28.3_111.

Kumar, V. S., Sivaprakasam, S. A., Naganathan, R., & Kavitha, S. (2019). Fast K-Means technique for hyper-spectral image segmentation by multiband reduction. Pollack Periodica, 14(3), 201–212.

Lee, H. S., & in Cho, S. (2022). Spatial color histogram-based image segmentation using texture-aware region merging. Multimedia Tools and Applications. DOI: 10.1007/s11042-022-11983-4.

Lees, T., Tseng, G., Atzberger, C., Reece, S., & Dadson, S. (2022). Deep Learning for Vegetation Health Forecasting: A Case Study in Kenya. Remote Sensing, 14(3), 698. DOI: 10.3390/rs14030698.

Li, H., Jing, L., Tang, Y., & Wang, L. (2018). An Image Fusion Method Based on Image Segmentation for High-Resolution Remotely-Sensed Imagery. Remote Sensing, 10(5), 790.

Manju, S., & Helenprabha K. (2019). Analysis Of Vegetation Classification Algorithms On Satellite Images And Medical Images. Current Signal Transduction Therapy, 15.

Pan, Z., Xu, J., Guo, Y., Hu, Y., & Wang, G. (2020). Deep Learning Segmentation and Classification for Urban Village Using a Worldview Satellite Image Based on U-Net. Remote Sensing, 12(10), 1574. DOI: 10.3390/rs12101574.

Phornphatcharaphong, W., & Eua-Anant, N. (2020). Edge-Based Color Image Segmentation Using Particle Motion in a Vector Image Field Derived from Local Color Distance Images. Journal of Imaging, 6(7), 72. DOI: 10.3390/jimaging6070072.

Raju, A., Singh, A., Kumar, S., & Pati, P. (2016). Temporal monitoring of coal fires in Jharia Coalfield, India. Environmental Earth Sciences, 75(12). DOI: 10.1007/s12665-016-5799-7.

Rinartha, K., & Suryasa, W. (2017). Comparative study for better result on query suggestion of article searching with MySQL pattern matching and Jaccard similarity. In 2017 5th International Conference on Cyber and IT Service Management (CITSM) (pp. 1-4). IEEE.

Rinartha, K., Suryasa, W., & Kartika, L. G. S. (2018). Comparative Analysis of String Similarity on Dynamic Query Suggestions. In 2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS) (pp. 399-404). IEEE.

Roy, B. (2021). Optimum machine learning algorithm selection for forecasting vegetation indices: MODIS NDVI & EVI. Remote Sensing Applications: Society and Environment, 23, 100582.

Singh, N. J., & Nongmeikapam, K. (2022). Semantic Segmentation of Satellite Images Using Deep-Unet. Arabian Journal for Science and Engineering. DOI: 10.1007/s13369-022-06734-4.

Somvanshi, S. S., & Kumari, M. (2020). Comparative analysis of different vegetation indices with respect to atmospheric particulate pollution using sentinel data. Applied Computing and Geosciences, 7, 100032. DOI: 10.1016/j.acags.2020.100032.

Sowmya, V., Govind, D., & Soman, K. P. (2016). Significance of incorporating chrominance information for effective color-to-grayscale image conversion. Signal, Image and Video Processing, 11(1), 129–136. DOI: 10.1007/s11760-016-0911-8.

Wu, M., Zhang, C., Liu, J., Zhou, L., & Li, X. (2019). Towards Accurate High Resolution Satellite Image Semantic Segmentation. IEEE Access, 7, 55609–55619. DOI: 10.1109/access.2019.2913442.

Zhang, L., Qiao, N., Baig, M. H. A., Huang, C., Lv, X., Sun, X., & Zhang, Z. (2019). Monitoring vegetation dynamics using the universal normalized vegetation index (UNVI): An optimized vegetation index-VIUPD. Remote Sensing Letters, 10(7), 629–638. DOI: 1080/2150704x.2019.1597298.