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Jena, P.
- Space technology support for development of agriculture in the North Eastern Region of India – scope and challenges
Abstract Views :143 |
PDF Views:79
Authors
B. K. Handique
1,
C. Goswami
1,
P. T. Das
1,
J. Goswami
1,
P. Jena
1,
F. Dutta
1,
D. K. Jha
1,
S. P. Aggarwal
1
Affiliations
1 North Eastern Space Applications Centre, Umiam 793 103, India, IN
1 North Eastern Space Applications Centre, Umiam 793 103, India, IN
Source
Current Science, Vol 123, No 8 (2022), Pagination: 975-986Abstract
The North Eastern Region of India (NER) has tremendous scope for accelerating its growth in agriculture and allied areas through advanced data acquisition, interpretation and dissemination methods with geospatial technology. For several thematic applications, geospatial tools and techniques are being used to provide synoptic, cost-efficient and timely information for effective crop planning and monitoring in the region. A review of space applications in agriculture, horticulture, sericulture, land-use suitability, shifting cultivation, groundwater prospecting, soil resources management, etc. has been made, highlighting the scope and limitation of using these advanced technologies. Satellite remote sensing has several limitations in NER, viz. small and fragmented farmlands, persistent clouds during monsoon, mixed farming, steep hills, etc. Considering these facts, unmanned aerial vehicles (UAVs) are used as an alternative for satellite remote sensing applications in agriculture. The increased availability of very high resolution satellite and UAV data will offer opportunities for innovative solutions to fulfil specific user needs of agriculture and allied sectors in NERFull Text
References
- Roy, A., Dhar, D. S., Tripathi, A. K., Singh, N. U., Kumar, D., Das, S. K. and Debnath, A., Growth performance of agriculture and allied sectors in the North East India. Econ. Affairs, 2014, 59, 783–795.
- Seitinthang, L., Cropping pattern of North East India: an appraisal. Am. Res. Thoughts, 2014, 1, 488–498.
- Dikshit, K. R. and Dikshit, J. K., Agriculture in North-East India: past and present. In North-East India: Land People and Economy, Springer Nature, 2014, pp. 587–637.
- Punitha, P. et al., Shifting cultivation in North East India: social dimension, cross cultural reflection and strategies for improvement. Indian J. Agric. Sci., 2018, 88, 811–819.
- Sahoo, P. M., Rai, A., Singh, R., Handique, B. K. and Rao, C. S., Integrated approach based on remote sensing and GIS for estimation of area under paddy crop in North-Eastern hilly region. J. Indian Soc. Agric. Stat., 2005, 59, 151–160.
- Justice, C. O. et al., An overview of MODIS land data processing and product status. Remote Sensing Environ., 2002, 83, 3–15.
- Navalgund, R., Jayaraman, V. and Roy, P., Remote sensing applications: an overview. Curr. Sci., 2007, 93(12), 1747–1766.
- Navalgund, R., Parihar, J. S., Ajai and Rao, P. P. N., Crop inventory using remotely sensed data. Curr. Sci., 1991, 61(3 and 4), 162–171.
- Dadhwal, V. K., Singh, R. P., Dutta, S. and Parihar, J. S., Remote sensing based crop inventory: a review of Indian experience. Trop. Ecol., 2002, 43, 107–122.
- Heupel, K., Spengler, D. and Itzerott, S., A progressive crop-type classification using multitemporal remote sensing data and phenological information. PFG-J. Photogramm. Remote Sensing Geoinf. Sci., 2018, 86, 53–69.
- Croft, H. and Chen, J. M., Leaf pigment content. In Comprehensive Remote Sensing (ed. Liang, S.), Elsevier, 2018, pp. 117– 142; https://doi.org/10.1016/b978-0-12-409548-9.10547-0.
- NASA, Reflected near-infrared waves, National Aeronautics and Space, Administration, 2010; https://science.nasa.gov/ems/08_ nearinfraredwaves (accessed on 10 December 2021).
- DeRiggi, J., Identify healthy vegetation from space. DAI, 2017; https://dai-globaldigital.com/about/
- Richards, J., Remote Sensing Digital Image Analysis, SpringerVerlag, Berlin, Germany, 1999, p. 240.
- Salah, M., A survey of modern classification techniques in remote sensing for improved image classification, J. Geomat., 2017, 11.
- Neetu and Ray, S. S., Exploring machine learning classification algorithms for crop classification using Sentinel 2 data. Int. Arch. Photogram, Remote Sensing Spat. Inf. Sci., 2019, XLII-3/W6, 573– 578; https://doi.org/10.5194/isprs-archives-XLII-3-W6-573-2019
- Singha, M., Dong, J., Zhang, G. and Xiao, X., High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data. Sci. Data, 2019, 6, 1–10.
- George, A., Crop discrimination and mapping using Sentinel-1 data in North East India. M.Sc. (Remote Sensing and GIS) thesis, Kerala University of Fisheries and Ocean Studies, Panangad, Kerala, 2018.
- Sahoo, P. M., Rai, A., Krishnamoorthy, S., Handique, B. K., Rao, P. P. N., Oza, M. P. and Parihar, J. S., Sampling approach for estimation of crop acreage under cloud cover satellite data in hilly regions. Proc. SPIE, 2006, 64, 1–9.
- Ghazaryan, G., Dubovyk, O., Löw, F., Lavreniuk, M., Kolotii, A., Schellberg, J. and Kussul, N., A rule-based approach for crop identification using multi-temporal and multi-sensor phenological metrics. Eur. J. Remote Sensing, 2018, 51, 511–524.
- Son, N. T., Chen, C. F., Chen, C. R., Duc, H. N. and Chang, L. Y., A phenology-based classification of time-series MODIS data for rice crop monitoring in Mekong Delta, Vietnam. Remote Sensing, 2013, 6, 135–156.
- Turner, M. D. and Congalton, R. G., Classification of multi-temporal SPOT-XS satellite data for mapping rice fields on a West African floodplain. Int. J. Remote Sensing, 1998, 19, 21–41.
- Wang, J., Huang, J., Zhang, K., Ki, X., She, B., Wei, C., Gao, J. and Song, X., Rice fields mapping in fragmented area using multitemporal HJ-1A/B CCD images. Remote Sensing, 2015, 7, 3467– 3488.
- Wang, J. et al., Mapping paddy rice planting area in wheat–rice double-cropped areas through integration of Landsat-8 OLI, MODIS, and PALSAR images. Sci. Rep., 2015, 5, 1–11.
- Singha, M., Wu, B. and Zhang, M., An object-based paddy rice classification using multi-spectral data and crop phenology in Assam, Northeast India. Remote Sensing, 2016, 8, 1–20.
- Ahmed, R. and Sajjad, H., Crop acreage estimation of Boro paddy using remote sensing and GIS techniques: a case from Nagaon district, Assam, India. Adv. Appl. Agric. Sci., 2015, 3, 16–25.
- Rajpoot, S. et al., Jute crop production estimation in major states of India: a comparative study of last 6 years’ FASAL and DES estimates. Int. Arch. Photogramm. Remote Sensing Spat. Inf. Sci., 2019, XLII-3/W6, 129–136.
- Parihar, J. S. and Oza, M. P., FASAL: an integrated approach for crop assessment and production forecasting. In Agriculture and Hydrology Applications of Remote Sensing (eds Kuligowski, R. J. et al.), 2006, pp. 1–13; https://doi.org/10.1117/12.713157.
- Shanmugapriya, P., Rathika, S., Ramesh, T. and Janaki, P., Applications of remote sensing in agriculture – a review. Int. J. Curr. Microbiol. Appl. Sci., 2019, 8, 2270–2283.
- Patel, N. R. and Yadav, K., Monitoring of spatio-temporal pattern of drought stress by use of integrated drought index over Bundelkhand region, India. Nat. Hazard, 2015, 77, 663–677.
- Rao, P. P. N., Shobha, V., Ramesh, K. and Somashekhar, R., Satellite-based assessment of agricultural drought in Karnataka State. J. Indian Soc. Remote Sensing, 2005, 33, 429–434.
- Dadhwal, V. K. and Ray, S. S., Crop assessment using remote sensing – Part II: Crop condition and yield assessment. In Proceedings of the National Seminar on Remote Sensing and Agricultural Statistics: Rationale, Scope and Aims, Ahmedabad, 1998.
- Miedema, P., The effects of low temperature on Zea mays. Adv. Agron., 1982, 2113, 60322–60323.
- Brun, K., Diedrichs, A. L., Chaar, J. E., Dujovne, D., Taffernaberry, C., Mercado, G. and Watteyne, T., A demo of the PEACH IoTbased frost event prediction system for precision agriculture. In 13th Annual IEEE International Conference on Sensing, Communication and Networking (SECON), London, UK, 2016.
- Choudhury, B., Webster, R., Sharma, V., Goswami, J., Meetei, T., Krishnappa, R. and Raju, P. L. N., Frost damage to maize in North East India: assessment and estimated loss of yield by hyperspectral proximal remote sensing. J. Appl. Remote Sensing, 2019, 13, 044527.
- Goswami, J., Sharma, V., Chaudhury, B. U. and Raju, P. L. N., Rapid identification of abiotic stress (frost) in in-filed maize crop using UAV remote sensing. In Proceedings of International Workshop on Earth Observations for Agricultural Monitoring, New Delhi, India, 1978, pp. 467–471; https://doi.org/10.5194/ isprs-archives-XLII-3-W6-467-2019.
- Bhanage, V., Latha, R. and Murthy, B. S., Estimation of water stress over Assam using remote sensing data. In Conference paper, NIRD, Assam, 2018; https://www.researchgate.net/publication/ 322635575
- Banerjee, S. and Pandey, A. C., Crop insurance model to consolidate academia–industry cooperation: a case study over Assam, India. Spat. Inf. Res., 2019, 27, 719–731.
- Sharma, A., Ojha, N., Pozzer, A., Beig, G. and Gunthe, S. S., Revisiting the crop yield loss in India attributable to ozone. Atmos. Environ., 2019, X(1), 100008.
- Handique, B. K., Goswami, J., Qadir, A., Gupta, C. and Raju, P. L. N., Rapid assessment of boro paddy infestation by brown plant hopper in Morigaon district, Assam, India using unmanned aerial vehicle. Curr. Sci., 2016, 111(10), 1604–1606.
- NITI Aayog, Annual Report 2016–17, National Institute for Transforming India, New Delhi, India, 2018, pp. 27–31.
- Raju, P. L. N., Handique, B. K. and Goswami, C., Remote sensing data for horticulture development in NE. Smart Agripost., 2019, 36–40.
- Das, P. T. and Sudhakar, S., Land suitability analysis for orange and pineapple: a multi criteria decision making approach using geo spatial technology. J. Geogr. Inf. Syst., 2014, 6, 40–44.
- Das, A., Ghosh, P., Choudhury, B., Patel, D., Munda, G., Ngachan, S. and Chowdhury, P., Climate change in North East India: recent facts and events – worry for agricultural management. In Proceedings of the Workshop on Impact of Climate Change on Agriculture, Ahmedabad, India, ISPRS XXXVIII-8/W3, 2009, pp. 32–37.
- Sen, T. K., Pande, L. M., Sehgal, J. L., Maji, A. K. and Chamuah, G. S., Satellite remote sensing in soil resource inventory of Dibrugarh district (part), Assam. J. Indian Soc. Remote Sensing, 1992, 20, 95–104.
- Ahuja, R. L., Manchanda, M. L., Sangwan, B. S., Goyal, P. V. and Agarwal, R. P., Utilization of remotely sensed data for soil resource mapping and its interpretation for land use planning of Bhiwani district, Haryana. J. Indian Soc. Remote Sensing, 1992, 20, 105–120.
- Sehgal, J. L., Sharma, P. K. and Karale, R. L., Soil resource inventory of Punjab using remote sensing technique. J. Indian Soc. Remote Sensing, 1988, 16, 39–47.
- Kudrat, M., Tiwari, A. K., Saha, S. K. and Bhan, S. K., Soil resource mapping using IRS-1A-LISS II digital data – a case study of Kandi area adjacent to Chandigarh, India. Int. J. Remote Sensing, 1992, 13, 3287–3302.
- Sharma, E., Rai, S. C. and Sharma, R., Soil, water and nutrient conservation in mountain farming systems: case-study from the Sikkim Himalaya. J. Environ. Manage., 2001, 61, 123–135.
- Choudhury, B. U. et al., Spatial variability in distribution of organic carbon stocks in the soils of North East India. Curr. Sci., 2013, 104(5), 604–614.
- Abdelrahman, M. A. E., Natarajan, A. and Hegde, R., Assessment of land suitability and capability by integrating remote sensing and GIS for agriculture in Chamarajanagar district, Karnataka, India. Egyp. J. Remote Sensing Space Sci., 2016, 19, 125–141.
- Sys, C., Van Ranst, E. and Debaveye, I. J., Land evaluation. Part I: Principles in land evaluation and crop production calculations. General Administration for Development Cooperation, Brussels, Belgium, 1991.
- Parthasarathy, U., Johny, A. K., Jayarajan, K. and Parthasarathy, V. A., Site suitability of turmeric production in India – a GIS interpretation. Nat. Prod. Rad., 2007, 6, 142–147.
- Moshia, M., Mashatola, M., Shaker, P., Fouché, P. and Boshomane, M., Land suitability assessment and precision farming prospects for irrigated maize–soybean intercropping in Syferkuil experimental farm using geospatial information technology. J. Agric. Soc. Res., 2009, 8, 1–12.
- Jafari, S. and Zaredar, N., Land suitability analysis using multi attribute decision making approach. Int. J. Environ. Sci. Dev., 2010, 1, 441–445.
- Bhaskar, B. P., Baruah, U., Vadivelu, S., Raja, P. and Sarkar, D., Remote sensing and GIS in the management of wetland resources of Majuli Island, Assam, India. Trop. Ecol., 2010, 51, 31–40.
- Lallianthanga, R. K., Sailo, R. L. and Colney, L., Identification of potential wet rice cultivation areas in Mizoram, India: a remote sensing and GIS approach. Int. J. Geol. Earth Environ. Sci., 2013, 3, 49–56.
- Sarmah, K., Deka, C. R. and Konwar, R., Land suitability analysis for identification of summer paddy cultivation sites based on multi criteria evaluation through GIS. Eur. Acad. Res., 2015, 2, 13584– 13606.
- Anilkumar, R., Chutia, D., Goswami, J., Sharma, V. and Raju, P. L. N., Evaluation of the performance of the fused product of Hyperion and RapidEye red edge bands in the context of classification accuracy. J. Geomat., 2018, 12, 35–46.
- Karthik, T., Veeraswami, G. G. and Samal, P. K., Forest recovery following shifting cultivation: an overview of existing research. Trop. Conserv. Sci., 2009, 2, 374–387.
- Coomes, O. T., Grimard, F. and Burt, G. J., Tropical forests and shifting cultivation: secondary forest fallow dynamics among traditional farmers of the Peruvian Amazon. Ecol. Econ., 2000, 32, 109–124.
- Pebam, R., A novel approach to understand the spatial and temporal pattern of shifting cultivation fields using GIS techniques in Longding division of Arunachal Pradesh, India. Int. J. Eng. Res. Appl., 2018, 8, 61–67.
- Kurien, A. J., Lele, S. and Nagendra, H., Farms or forests? Understanding and mapping shifting cultivation using the case study of West Garo hills, India. Land, 2019, 8, 1–26.
- Thong, P., Sahoo, U. K., Pebam, R. and Thangjam, U., Spatial and temporal dynamics of shifting cultivation in Manipur, Northeast India based on time-series satellite data. Remote Sensing Appl. Soc. Environ., 2019, 14, 126–137.
- Pasha, S. V., Behera, M. D., Mahawar, S. K., Barik, S. K. and Joshi, S. R., Assessment of shifting cultivation fallows in Northeastern India using Landsat imageries. Trop. Ecol., 2020, 61, 65–75.
- Das, P., Mudi, S., Behera, M. D., Barik, S. K., Mishra, D. R. and Roy, P. S., Automated mapping for long-term analysis of shifting cultivation in Northeast India. Remote Sensing, 2021, 13, 1066.
- NeSDR, North Eastern Spatial Data Repository, https://www. nesdr.gov.in/ (accessed on 10 September 2021).
- Sharma, R., Xu, J. and Sharma, G., Traditional agroforestry in the Eastern Himalayan Region: land management system supporting ecosystem services. Trop. Ecol., 2007, 48, 1–12.
- Giri, K., Mishra, G., Jayaraj, R. S. C. and Kumar, R., Agrobio-cultural diversity of alder based shifting cultivation practiced by Angami tribe in Khonoma village, Kohima, Nagaland. Curr. Sci., 2018, 115(4), 598–599.
- Rizvi, R. H., Dhyani, S. K., Newaj, R., Karmakar, P. S. and Saxena, A., Mapping agroforestry area in India through remote sensing and preliminary estimates. Indian Farm., 2014, 63, 62–64.
- Press Information Bureau, India will restore 26 million hectares of degraded land by 2030 (press release), Government of India. https://pib.gov.in/Pressreleaseshare.aspx?PRID=1584542 (accessed on 9 September 2019).
- Ramos, N. C., Gastauer, M. and Cordeiro, A. A. C., Environmental filtering of agroforestry systems reduce the risk of biological invasion. Agrofor. Syst., 2015, 89, 279–289.
- Hernandez, R. R. et al., The native shrub, Piliostigma reticulatum, as an ecological ‘resource island’ for mango trees in the Sahel. Agric. Ecosyst. Environ., 2015, 204, 51–61.
- Asbjornsen, H., Hernandez-Santana, V., Liebman, M., Bayala, J., Chen, J., Helmers, M. and Schulte, L., Targeting perennial vegetation in agricultural landscapes for enhancing ecosystem services. Renew. Agric. Food Syst., 2014, 29, 101–125.
- NRCAF, The Vision-2050, National Research Centre for Agroforestry, Ministry of Agriculture and Farmers Welfare, Governmnet of India, New Delhi, India, 2013.
- MEA, Millennium Ecosystem Assessment, Ecosystems and human well-being: biodiversity synthesis. World Resources Institute, Washington, DC, USA, 2005.
- Rizvi, R. H., Newaj, R., Handa, A. K., Sridhar, K. B. and Kumar, A., Agroforestry mapping in India through geospatial technologies: present status and way forward. Technical Bulletin 1/2019, ICARCentral Agroforestry Research Institute, Jhansi, 2019, pp. 1–35.
- Nath, A. J. et al., Agroforestry land suitability analysis in the Eastern Indian Himalayan region. Environ. Challeng., 2021, 100199, 1–22.
- Lakhankar, T., Krakauer, N. and Khanbilvardi, R., Applications of microwave remote sensing of soil moisture for agricultural applications. Int. J. Terraspace Sci. Eng., 2009, 2, 81–91.
- Prakash, A., Thermal remote sensing: concepts, issues and applications. Int. Arch. Photogramm. Remote Sensing, 2000, 33, 239–243.
- Aryalekshmi, B. N., Biradar, R. C. and Ahamed, J. M., Thermal imaging techniques in agricultural applications. Int. J. Innov. Technol. Explor. Eng., 2019, 8, 2162–2168.
- Mahlein, A. K., Oerke, E. C., Steiner, U. and Dehne, H. W., Recent advances in sensing plant diseases for precision crop protection. Eur. J. Plant Pathol., 2012, 133, 197–209.
- Sahoo, R. N., Ray, S. S. and Manjunath, K. R., Hyperspectral remote sensing of agriculture. Curr. Sci., 2015, 108(5), 848–859.
- Goswami, J., Das, R., Sarma, K. K. and Raju, P. L. N.. Red edge position (REP), an indicator for crop stress detection: implication on rice (Oryza sativa L). Int. J. Environ. Climate Change, 2021, 11, 88–96.
- Handique, B. K. et al., Hierarchical classification for assessment of horticultural crops in mixed cropping pattern using UAV-borne multi-spectral sensor. Int. Arch. Photogramm., Remote Sensing Spat. Inf. Sci., 2020, XLIII-B3-2020, 67–74, https://doi.org/10. 5194/isprs-archives-XLIII-B3-2020-67-2020
- Dubovik, O., Schuster, G. L., Xu, F., Hu, Y., Bösch, H., Landgraf, J. and Li, Z., Grand challenges in satellite remote sensing. Front. Remote Sensing, 2021, 2, 1–10; https://doi.org/10.3389/frsen.2021. 619818
- Kim, J., Jeong, U., Ahn, M.-H., Kim, J. H., Park, R. J. and Lee, H., New era of air quality monitoring from space geostationary environment monitoring spectrometer (GEMS). Bull. Am. Meteorol. Soc., 2020, 101, E1–E22; doi:10.1175/BAMS-D-18-0013.
- Gao, F., Masek, J., Schwaller, M. and Hall, F., On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Trans. Geosci. Remote Sensing, 2006, 44, 2207–2218.
- Pape, A. D. and Franklin, S. E., MODIS-based change detection for Grizzly Bear habitat mapping in Alberta. Photogramm. Eng. Remote Sensing, 2008, 74, 973–985.
- Simpson, J. J. and Stitt, J. R., A procedure for the detection and removal of cloud shadow from AVHRR data over land. IEEE Trans. Geosci. Remote Sensing, 1998, 36, 880–897.
- Liu, C. A., Chen, Z. X., Shao, Y., Chen, J. S., Hasi, T. and Pan, H., Research advances of SAR remote sensing for agriculture applications: a review. J. Integr. Agric., 2019, 18, 506–525.
- Conway, D. and Donnelly, S., Remote sensing, GIS and ground truthing. In Doing Development Research (eds Desai, V. and Potter, R. B.), SAGE Publications Ltd, 2006, pp. 251–261; https:// dx.doi.org/10.4135/9781849208925.
- Raju, P. L. N. et al., Training and capacity building initiatives in space technology applications for North Eastern Region – role of NESAC in expanding the outreach. In Asian Conference on Remote Sensing, New Delhi, 2017.
- Parks, S., The importance of calibrating your remote sensing imagery, 2020; https://www.materials-talks.com/author/susan-parks/ (accessed on 24 January 2021).
- Gupta, C. et al., Applications of unmanned aerial vehicle (UAV) based remote sensing in North Eastern Region of India. ISG Newsl., 2018, 23 & 24.
- Khanal, S., KC, K., Fulton, J. P., Shearer, S. and Ozkan, E., Remote sensing in agriculture – accomplishments, limitations, and opportunities. Remote Sensing, 2020, 12, 3783–3785.
- Çoban, S. and Oktay, T., Legal and ethical issues of unmanned aerial vehicles. J. Aviat., 2018, 2, 31–35.
- Roseman, C. A. and Argrow, B. M., Weather hazard risk quantification for sUAS safety risk management. J. Atmos. Ocean. Technol., 2020, 37, 1251–1268.
- Singha, M., Dong, J., Zhang, G. and Xiao, X., High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data. Sci. Data, 2019, 6, 1–10.
- Goswami, J., Sarma, K. K., Handique, B. K., Das, R., Rahman, N. and Raju, P. L. N., Study of cropping system in Morigaon district of Assam using geospatial technique. Int. J. Adv. Remote Sensing GIS Geogr., 2017, 5, 53–59.
- Hiloidhari, M., Das, D. and Baruah, D. C., Bioenergy potential from crop residue biomass in India. Renew. Sustain. Energy Rev., 2014, 32, 504–505.
- Hiloidhari, M. and Baruah, D. C., GIS mapping of rice straw residue for bioenergy purpose in a rural area of Assam, India. Biomass Bioenerg., 2014, 71, 125–133.
- Goswami, J., Chutia, D. and Sudhakar, S., A geospatial approach to climatic zone specific effective horticultural planning in East Khasi Hills district of Meghalaya, India. J. Geogr. Inf. Syst., 2012, 4, 267–272.
- Das, P. T., Handique, B. K. and Raju, P. L. N., Expansion of boro rice in Meghalaya using space technology. Curr. Sci., 2018, 115(10), 1865–1870.
- Negi, A., Adhikari, T., Goswami, C., Handique, B. K. and Raju, P. L. N., Site suitability analysis for turmeric in Jaintia Hills of Meghalaya, India using analytical hierarchical process and weighted overlay analysis: a comparative approach. Curr. Sci., 2020, 118(8), 1246–1254.
- Handique, B. K., Khan, A. Q., Goswami, C., Prashnani, M., Gupta, C. and Raju, P. L. N., Crop discrimination using multispectral sensor onboard unmanned aerial vehicle. Proc. Natl. Acad. Sci., India, Sect. A, 2017, 87, 713–719.
- Lallianthanga, R. K. and Sailo, R. L., Geospatial planning for improved land use system in Saiha District, Mizoram, India. Sci. Vis., 2013, 13, 120–132.
- Lallianthanga, R. K. and Sailo, R. L., A remote sensing & GIS approach for land use planning in Champhai district, Mizoram, India. Int. J. Eng. Sci. Res. Technol., 2013, 2, 3156–3163.
- Lallianthanga, R. K. and Hmingthanpuii, Integrated land use planning of Aizawl district, Mizoram, India using geospatial techniques. Int. J. Adv. Remote Sensing GIS, 2013, 2, 341–350.
- Lallianthanga, R. K., Sailo, R. L., Hmingthanpuii and Lalhmachhuana, H., Land use planning for Lawngtlai district, Mizoram, India: a remote sensing and GIS perspective. Int. J. Curr. Res. Acad. Rev., 2014, 2, 42–53.
- Sarma, P. K., Al, E. H., Baruah, B., Mipun, B. S. and Talukdar, B. K., Assessment of changing trends of shifting cultivation in Garo Hills landscape of Meghalaya – a geospatial approach. Int. Res. J. Environ. Sci., 2015, 4, 1–7.
- Chakraborty, K., Sarma, K. K., Kundu, S. S. and Das, A. K., Shifting cultivation dynamics in Barak basin of North East India – a geospatial approach. Int. J. Adv. Earth Environ. Sci., 2015, 3, 21–29.
- Nongkynrih, J. M., Pohshna, C. and Sarma, K. K., Dynamics of shifting cultivation in relation to slope and elevation in parts. Curr. Sci., 2018, 114(5), 1094–1099.
- Lalbiakmawia, F., Ground water quality mapping of Kolasib district, Mizoram, India using geo-spatial technology. SSRG Int. J. Geoinf. Geol. Sci., 2015, 2, 1–7.
- Lalbiakmawia, F. and Vanthangliana, V., Application of geo-spatial technologies for groundwater quality mapping of Aizawl district, Mizoram, India. Sci. Vis., 2015, 15, 115–123.
- Lalbiakmawia, F. and Kumar, S., Assessment of groundwater conditions in Bilkhawthlir rural development block, Kolasib district, Mizoram, India. Adv. Eng. Res., 2018, 178, 74–86; https://doi.org/10.2991/msc-18.2018.13.
- Maji, A. K., Nayak, D. C., Krishna, N. D. R., Srinivas, C. V., Kamble, K., Reddy, O. G. P. and Velayutham, M., Soil information system of Arunachal Pradesh in a GIS environment for land use planning. JAG, 2001, 3, 69–77.
- Tao, D. L., Singh, N. J. and Goswami, C., Spatial variability of soil organic carbon and available nutrients under different topography and land uses. IJPSS, 2018, 21, 1–16.
- Raj, U., Hebbar, R., Ravishankar, H. M., Jacob, J., Ray, D., Meti, S., Shebin, S. M. and Pradeep, B., Geospatial technology for acreage estimation of natural rubber and identification of potential areas for its cultivation in Tripura, National Remote Sensing Centre, Hyderabad and Rubber Research Institute of India, Kerala, 2012.
- Chakraborty, K., Sudhakar, S., Sarma, K. K., Raju, P. L. N. and Das, A. K., Recognizing the rapid expansion of rubber plantation – a threat to native forest in parts of Northeast India. Curr. Sci., 2018, 114(1), 207–213.
- Kalita, P., Identification of potential sites for mulberry cultivation in West Garo Hills of Meghalaya using geospatial techniques. M.Sc. (Applied Geography and Geoinformatics) thesis, Central University of Karnataka, Kalaburagi, India, 2017.