Random Forest-Based LULC Mapping in and around Itanagar using IRS LISS-IV Multispectral Data

International Journal of Civil Engineering

© 2025 by SSRG - IJCE Journal

Volume 12 Issue 6

Year of Publication : 2025

Authors : Konthoujam James Singh, Ajay Bharti, Salam Shantikumar Singh

10.14445/23488352/IJCE-V12I6P109

How to Cite?

Konthoujam James Singh, Ajay Bharti, Salam Shantikumar Singh, "Random Forest-Based LULC Mapping in and around Itanagar using IRS LISS-IV Multispectral Data," SSRG International Journal of Civil Engineering, vol. 12,  no. 6, pp. 91-104, 2025. Crossref, https://doi.org/10.14445/23488352/IJCE-V12I6P109

Abstract:

Land Use Land Cover information is an indispensable asset in sustainable resources management. The current study investigates the yield of Land Use Land Cover (LULC) information in and around Itanagar, capital of Arunachal Pradesh, India. The area under research consists of five administrative circles, namely Itanagar, Naharlagun, Doimukh, Gumto, and Banderdewa circle, which are studied individually. In this study, the Indian Remote Sensing (IRS) Linear Imaging Self-Scanning Sensor (LISS)-IV multispectral image of 2022 is used in producing the LULC information. The Random Forest classifier in QGIS is employed for image classification, producing five classes in the study area: Dense forest, Degraded forest, Fallow land, Builtup and Water body. Accuracy assessment uses an equal stratified random method to produce the confusion matrix. The results obtained are encouraging with high overall accuracy, 85%, 83%, 78%, 85%, and 79% for Itanagar, Naharlagun, Doimukh, Gumto and Banderdewa circle, respectively and kappa coefficient ranging from 0.81, 0.79, 0.73, 0.82 and 0.74 for Itanagar, Naharlagun, Doimukh, Gumto and Banderdewa circle, respectively. Finally, the LULC map is prepared in ArcGIS.

Keywords:

Random forest, LULC, Machine Learning, Confusion matrix, Kappa coefficient.

References:

[1] M. Palaniyandi, and V. Nagarathinam, “Land Use/Land Cover Mapping and Change Detection using Space Borne Data,” Journal of the Indian Society of Remote Sensing, vol. 25, pp. 27-33, 1997.
[CrossRef] [Google Scholar] [Publisher Link]
[2] D.G. Brown, B.C. Pijanowski, and J.D. Duh, “Modeling the Relationships between Land Use and Land Cover on Private Lands in the Upper Midwest, USA,” Journal of Environmental Management, vol. 59, no. 4, pp. 247-263, 2000.
[CrossRef] [Google Scholar] [Publisher Link]
[3] T.P. Singh et al., “Vegetation Mapping and Characterization in West Siang District of Arunachal Pradesh, India - A Satellite Remote Sensing-based Approach,” Current Science, vol. 83, no. 10, pp. 1221-1230, 2002.
[Google Scholar] [Publisher Link]
[4] Santosh Babar, and H. Ramesh, “Streamflow Response to Land Use–Land Cover Change over the Nethravathi River Basin, India,” Journal of Hydrologic Engineering, vol. 20, no. 10, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[5] S. Setti et al., “Attribution of Hydrologic Changes in a Tropical River Basin to Rainfall Variability and Land-Use Change: Case Study from India,” Journal of Hydrologic Engineering, vol. 25, no. 8, pp. 1-15, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Ji Han et al., “Evaluating Land-Use Change in Rapidly Urbanizing China: Case Study of Shanghai,” Journal of Urban Planning and Development, vol. 135, no. 4, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Parth Sarathi Roy et al., “Anthropogenic Land Use and Land Cover Changes—A Review on Its Environmental Consequences and Climate Change,” Journal of the Indian Society of Remote Sensing, vol. 50, pp. 1615-1640, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Jawan Singh Rawat, and Ramesh Chandra Joshi, “Remote-Sensing and GIS-based Landslide-Susceptibility Zonation using the Landslide Index Method in Igo River Basin, Eastern Himalaya, India,” International Journal of Remote Sensing, vol. 33, no. 12, pp. 3751-3767, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Gopala Areendran et al., “A Geospatial Study to Assess the Land Use Land Cover of Mehao Wildlife Sanctuary in Arunachal Pradesh, India,” Asian Journal of Geographical Research, vol. 1, no. 2, pp. 1-8, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Yomto Mayi, Joanica Delicia Jyrwa, and Santanu K Patnaik, “Monitoring of Land Use/ Land Cover Changes of Daying Ering Wildlife Sanctuary, Arunachal Pradesh, India, Using Remote Sensing and Geographic Information System,” Journal of Applied and Natural Science, vol. 15, no. 2, pp. 713-719, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Zhongqi Wang, Qi Han, and Bauke de Vries, “Land Use/Land Cover and Accessibility: Implications of the Correlations for Land Use and Transport Planning,” Applied Spatial Analysis and Policy, vol. 12, pp. 923-940, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Amritpal Digra et al., “Temporal Land-Use/Land-Cover Change Analysis in Kotla Sub-Watershed of Rupnagar District (Punjab) Using Remote Sensing and GIS,” Journal of the Indian Society of Remote Sensing, vol. 50, pp. 1371-1391, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Nova D. Doyog, “Urban Forest Cover and LST Change Monitoring through Optical and Thermal Remote Sensing Approach,” Journal of the Indian Society of Remote Sensing, vol. 51, pp. 2467-2480, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Heng Sun, Wayne Forsythe, and Nigel Waters, “Modeling Urban Land Use Change and Urban Sprawl: Calgary, Alberta, Canada,” Networks and Spatial Economics, vol. 7, pp. 353-376, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[15] A.S. Antonarakis, K.S. Richards, and J. Brasington, “Object-based Land Cover Classification using Airborne LiDAR,” Remote Sensing of Environment, vol. 112, no. 6, pp. 2988-2998, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Dilong Li et al., “AGFP-Net: Attentive Geometric Feature Pyramid Network for Land Cover Classification using Airborne Multispectral LiDAR Data,” International Journal of Applied Earth Observation and Geoinformation, vol. 108, pp. 1-12, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Andrew Clark, Stuart Phinn, and Peter Scarth, “Optimised U-Net for Land Use–Land Cover Classification Using Aerial Photography,” PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, vol. 91, pp. 125-147, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[18] N.C. Gautam, “Aerial Photo-Interpretation Techniques for Classifying Urban Land Use,” Photogrammetric Engineering & Remote Sensing, vol. 42, no. 6, pp. 815-822, 1976.
[Google Scholar] [Publisher Link]
[19] Jonathan R.B. Fisher et al., “Impact of Satellite Imagery Spatial Resolution on Land Use Classification Accuracy and Modeled Water Quality,” Remote Sensing in Ecology and Conservation, vol. 4, no. 2, pp. 137-149, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Rongjun Qin, and Tao Liu, “A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images—Analysis Unit, Model Scalability and Transferability,” Remote Sensing, vol. 14, no. 3, pp. 1-28, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] V. Jayaraman et al., “Derivation of Enhanced Radiometric Information from IRS LISS-2 Data,” Acta Astronautica, vol. 35, no. 7, pp. 483-487, 1995.
[CrossRef] [Google Scholar] [Publisher Link]
[22] A.O. Varghese et al., “Habitat Suitability Assessment of Ardeotis Nigriceps (Vigors) in Great Indian Bustard Sanctuary, Maharashtra (India) Using Remote Sensing and GIS,” Journal of the Indian Society of Remote Sensing, vol. 44, pp. 49-57, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Mingting Zhou et al., “Large-scale Road Extraction from High-Resolution Remote Sensing Images based on a Weakly-Supervised Structural and Orientational Consistency Constraint Network,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 193, pp. 234-251, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Hao Wang et al., “Urban Boundary Extraction and Urban Sprawl Measurement Using High-Resolution Remote Sensing Images: A Case Study of China’s Provincial,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 42, pp. 1713-1719, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Mengmeng Li, and Alfred Stein, “Mapping Land Use from High Resolution Satellite Images by Exploiting the Spatial Arrangement of Land Cover Objects,” Remote Sensing, vol. 12, no. 24, pp. 1-21, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[26] A. Şekertekin et al., “Analysing the Effects of Different Land Cover Types on Land Surface Temperature Using Satellite Data,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 40, pp. 665-667, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Markand Oza, “Choosing Optimal Spatial Resolution —Study of Two Agriculture Dominated Areas,” Journal of the Indian Society of Remote Sensing, vol. 21, pp. 179-183, 1993.
[CrossRef] [Google Scholar] [Publisher Link]
[28] James Thomas Steven Savage et al., “Quantifying the Importance of Spatial Resolution and other Factors through Global Sensitivity Analysis of a Flood Inundation Model,” Water Resources Research, vol. 52, no. 11, pp. 9146-9163, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Pooja Sonde, Sanjay Balamwar, and Rohini S. Ochawar, “Urban Sprawl Detection and Analysis Using Unsupervised Classification of High Resolution Image Data of Jawaharlal Nehru Port Trust Area in India,” Remote Sensing Applications: Society and Environment, vol. 17, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Katalin Varga et al., “Improved Land Cover Mapping using Aerial Photographs and Satellite Images,” Open Geosciences, vol. 7, no. 1, 15-26, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Leo Breiman, “Random Forests,” Machine Learning, vol. 45, pp. 5-32, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Hamid Ebrahimy et al., “Per-Pixel Land Cover Accuracy Prediction: A Random Forest-based Method with Limited Reference Sample Data,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 172, pp. 17-27, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[33] V.F. Rodriguez-Galiano et al., “An Assessment of the Effectiveness of a Random Forest Classifier for Land-cover Classification,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 67, pp. 93-104, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Weiqi Zhou et al., “Object-based Land Cover Classification of Shaded Areas in High Spatial Resolution Imagery of Urban Areas: A Comparison Study,” Remote Sensing of Environment, vol. 113, no. 8, pp. 1769-1777, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[35] S. Rajesh et al., “Land Cover/Land Use Mapping of LISS IV Imagery Using Object-Based Convolutional Neural Network with Deep Features,” Journal of the Indian Society of Remote Sensing, vol. 48, pp. 145-154, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Foivos I. Diakogiannis et al., “ResUNet-a: A Deep Learning Framework for Semantic Segmentation of Remotely Sensed Data,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 162, pp. 94-114, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[37] Victor Alhassan et al., “A Deep Learning Framework for Land-use/Land-Cover Mapping and Analysis using Multispectral Satellite Imagery,” Neural Computing and Applications, vol. 32, pp. 8529-8544, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Wei Han et al., “A Semi-Supervised Generative Framework with Deep Learning Features for High-Resolution Remote Sensing Image Scene Classification,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 145, pp. 23-43, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[39] Diksha, and Amit Kumar, “Analysing Urban Sprawl and Land Consumption Patterns in Major Capital Cities in the Himalayan Region using Geoinformatics,” Applied Geography, vol. 89, pp. 112-123, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[40] Soumik Saha et al., “Modelling and Predicting of Landslide in Western Arunachal Himalaya, India,” Geosystems and Geoenvironment, vol. 2, no. 2, pp. 1-16, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[41] Prashant Patil et al., “Monitoring Land Use Land Cover Change and Its Impact on Climatic Parameters Using Remote Sensing and GIS: A Case Study of Lower Dibang Valley, Arunachal Pradesh, India,” Geoinformatica Polonica, vol. 23, pp. 59-75, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[42] Ranjit Mahato et al., “AHP and GIS-based Delineation of Groundwater Potential of Papum Pare District of Arunachal Pradesh, India,” Journal of the Geological Society of India, vol. 98, no. 1, pp. 102-112, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[43] Jyotishman Deka et al., “Study on Land-use and Land-cover Change Dynamics in Eastern Arunachal Pradesh, N.E. India using Remote Sensing and GIS,” Tropical Ecology, vol. 60, pp. 199-208, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[44] Preeti Barsha Borah, “Assessment of Land use Land Cover Change Detection and Future Prediction: A GIS based Study of Pasighat, Arunachal Pradesh, India,” Ecology Environment and Conservation, vol. 30, no. 3, pp. 1037-1047, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[45] M. Vese et al., “Decadal Variations in Area under Different Soil Erosion Classes using RUSLE and GIS: Case Studies of River Basins from Western and Eastern Arunachal Pradesh,” Journal of the Geological Society of India, vol. 99, pp. 1725-1737, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[46] Trilochan Singh, and Prakash Tewari, “Impact of Earthquake Disasters on New Urbanization Pattern in North Eastern Region of India,” Journal of Environmental Systems, vol. 32, no. 1, pp. 69-90, 2005.
[CrossRef] [Google Scholar] [Publisher Link]
[47] Ram Krishna Mandal, “Urbanization in Arunachal Pradesh: Trends and Patterns 1961-2000,” Journal of Global Economy, vol. 5, no. 4, pp. 320-340, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[48] Swapna Acharjee, “Urban Land use and Geohazards in Itanagar, Arunachal Pradesh, India: the Need for Geotechnical Intervention and Geoethical Policies in Urban Disaster Resilience Programmes in a Changing Climate,” Geological Society, London, Special Publications, vol. 419, no. 1, pp. 63-68, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[49] Leo Breiman, “Bagging Predictors,” Machine Learning, vol. 24, pp. 123-140, 1996.
[CrossRef] [Google Scholar] [Publisher Link]
[50] Tin Kam Ho, “Random Decision Forests,” Proceedings of 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada, pp. 278-282, 1995.
[CrossRef] [Google Scholar] [Publisher Link]
[51] Tin Kam Ho, “The Random Subspace Method for Constructing Decision Forests,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 832-844, 1998.
[CrossRef] [Google Scholar] [Publisher Link]
[52] Yali Amit, and Donald Geman, “Shape Quantization and Recognition with Randomized Trees,” Neural Computation, vol. 9, no. 7, pp. 1545-1588, 1997.
[CrossRef] [Google Scholar] [Publisher Link]
[53] Khaled Fawagreh, Mohamed Medhat Gaber, and Eyad Elyan, “Random Forests: From Early Developments to Recent Advancements,” Systems Science & Control Engineering, vol. 2, no. 1, pp. 602-609, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[54] Charlotte Pelletier et al., “Assessing the Robustness of Random Forests to Map Land Cover with High Resolution Satellite Image Time Series Over Large Areas,” Remote Sensing of Environment, vol. 187, pp. 156-168, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[55] Maruf Billah et al., “Random Forest Classifications for Landuse Mapping to Assess Rapid Flood Damage using Sentinel-1 and Sentinel-2 Data,” Remote Sensing Applications: Society and Environment, vol. 30, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[56] Di Shi, and Xiaojun Yang, “An Assessment of Algorithmic Parameters Affecting Image Classification Accuracy by Random Forests,” Photogrammetric Engineering & Remote Sensing, vol. 82, no. 6, pp. 407-417, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[57] Hsiao-Chien Shih et al., “From Land Cover to Land Use: Applying Random Forest Classifier to Landsat Imagery for Urban Land-use Change Mapping,” Geocarto International, vol. 37, no. 19, pp. 5523-5546, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[58] William Gemmell Cochran, Sampling Techniques, 3rd ed., Wiley, pp. 1-428, 1977.
[Google Scholar] [Publisher Link]
[59] Pontus Olofsson et al., “Good Practices for Estimating Area and Assessing Accuracy of Land Change,” Remote Sensing of Environment, vol. 148, pp. 42-57, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[60] Seyed Kazem Alavipanah, “The Shadow Effect on Surface Biophysical Variables Derived from Remote Sensing: A Review,” Land, vol. 11, no. 11, pp. 1-30, 2022.
[CrossRef] [Google Scholar] [Publisher Link]