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Co-Authors
- S. Sudhakar
- P. T. Das
- J. Goswami
- C. Goswami
- P. S. Singh
- C. J. Prabhakar
- C. M. Bajpeyi
- P. L. N. Raju
- Jonali Goswami
- Abdul Qadir
- Chirag Gupta
- Pratibha T. Das
- Ashu Negi
- Tapan Adhikari
- Chandan Goswami
- D. K. Gogoi
- N. Rasid
- G. Subrahmanyam
- P. P. Bora
- R. Das
- P. Jena
- F. Dutta
- D. K. Jha
- S. P. Aggarwal
- Bullo Yami
- N. J. Singh
- Sanjay Swami
Journals
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Handique, B. K.
- Timber Volume Estimation by Double Sampling Using High Resolution Satellite Data
Abstract Views :245 |
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Authors
Affiliations
1 North Eastern Space Applications Centre Dept. of Space, Umiam 793103 Meghalaya, IN
1 North Eastern Space Applications Centre Dept. of Space, Umiam 793103 Meghalaya, IN
Source
Indian Forester, Vol 140, No 2 (2014), Pagination: 154-161Abstract
Double sampling or two-phase sampling design offers a variety of possibilities for effective use of auxiliary information such as those from high resolution remote sensing data. This study has been made to examine the possibilities of different forms of auxiliary information derived from remote sensing data in double sampling design and suggest an appropriate estimator for forest timber volume estimation in the context of preparing forest working plan inputs. A regression-cum-ratio estimator has been derived for double sampling using information on two auxiliary variables derived from high resolution satellite data. The estimator has been adopted for forest timber volume estimation utilizing tree crown diameter (x) and NDVI (z) as auxiliary variables. Relationship between timber volume (y) and 2 2 average crown diameter (x) has a higher value of R (0.733) as compared to that with NDVI (z), where R =0.528. It is shown that combination of these two auxiliary variables under doubling sampling design has significantly reduce the standard error of the timber volume estimate (CV of SE=11.49%) against estimates brought out by common sampling design (31% for simple random sampling, 22.34% for systematic sampling and 18.69% for stratified random sampling). This indicates that the estimator can be employed in a variety of conditions where there is good correlation of satellite derived auxiliary information with sample based ground measurements and when the cost of ground measurements is relatively high.Keywords
Double Sampling, Regression-cum-ratio Estimator, High Resolution Satellite Data, Timber Volume Estimation, Mean Square Error.
References
- Bhusan, S., Pandey, S. and Katara, S. (2008). A class of estimators in double sampling using two auxiliary variables, Journal of Reliability and Statistical Studies,1(1),67-73.
- Cochran, W.G. (2002). Sampling Techniques, John Wiley and Sons Rem. Sensing (Asia) Pvt. Ltd., Singapore.
- FSI, (1996). Volume Equations for Forests of India, Nepal and Bhutan, Forest survey of India, Dehradun.
- Frayer, W.E. and Furnival, G.M. (1999). Forest sampling designs: a history, Journal of Forestry, 97: 4-8.
- Gregoire, T.G., Valentine, H.T. and Furnival, G.M. (1987). Sampling methods for estimating stem volume and volume increment, Forest Ecology and Management, 21: 311-323.
- Gunawardena, A. R., Nissanka, S.P. and Dayawansa, N.D.K.( 2008). Development of Merchantable Timber Volume Estimation of Pinus caribaea Plantations using Multi-Spectral Satellite Images, Engineer, XXXXI (5), 68-73.
- Hidiroglou, M.A. and Sarndal, C.E. (1998). Use of auxiliary variable for two phase sampling, Survey Methodology, 24(1):11-20.
- Jensen, J.R. (1999). Introductory Digital Image processing, a Remotesensing Perspective, Prentice Hall, New Jersey.
- Jhajj, H.S. and Walia, G.S. (2011). A generalized Difference-cum-Ratio type estimator for the population mean in double sampling, Proceedings of the Statistics 2011 Canada: 5th Canadian Conference in Applied Statistics, July 1-4, 2011, Concordia University, Montreal, Canada.
- Johnson, E. W. (2000). Forest sampling desk reference, CRC Press, New York.
- Kadilar, C and Cingi, H. (2003). A study on the chain ratio-type estimator, Hacettepe Journal of Mathematics and Statistics. 32: 105-108.
- Katsch, C.H.R. and Van Laar, A. (2002). The estimation of the growing stock of eucalypt plantation forests, based on spectral signatures of satellite imagery, in South Africa. South African Forestry Journal, 194: 65-70.
- Khoshnevisan, M., Singh, H.P., Singh, S. and Smarandache, F. (2002). A general class of estimators of population median using two auxiliary variables in double sampling, In : Randomness and optimal estimation in data sampling ( 2nd edition), American Research Press, 26- 43.
- Kiregyera, B. (1984). Regression-type estimators using two auxiliary variables and the model of double sampling from finite populations, Metrika, 31: 215-226.
- Kohl, M. and Kushwaha, S.P.S. (1994). A four-phase sampling method for assessing standing volume using Landsat-TM data, aerial photography and field assessments, Commonwealth Forestry Review, 73(1): 35-42.
- Korhonen, L., Korhonen, K.T., Rautiainen, M. and Stenberg, P. (2006). Estimation of forest canopy cover:a comparison of field measurement techniques, Silva Fennica, 40(4): 577–588.
- Lillesand, T.M. and Keifer, R.W. (2003). RemoteSensing and Image Interpretation, 5th edition, John Wiley and Sons, New York.
- Magnussen, S. and Boudewyn, P. (1998). Derivations of stand heights from airborne laser scanner data with canopy-based quantile estimators, Canadian Journal of Forest Reserach, 28: 1016-1031.
- Mahto, B. (2001). Quantification of forest growing stock using remote sensing data for planning and management: a case study of Tikauli forest in Chitawan district, Nepal, Proc. 22ndAsian Conference on remote sensing,Nov 5-9, 2001, Singapore.
- McRoberts, R.E., Wendt, D.G. and Liknes, G.C. (2005). Stratified estimation of forest inventory variables using spatially summarized stratifications, Silva Fennica 39(4): 559–571.
- Mutanga, O. and Skidmore, A.K.(2004). Merging double sampling with remote sensing for a rapid estimation of fuelwood. Geocarto International,19 (4): 49-55.
- NESAC (2007). RemoteSensing and GIS inputs for forest working plan inputs for North Cachar Hills, Project Report (NESAC-SR- 50-2007).
- Roy, P.S. Sharma, K.P. and Jain, A., (1996). Stratification of density in dry deciduous forest using satellite remote sensing digital data—An approach based on spectral indices, Journal of Biosciences,. 21(5): 723–734.
- Sahoo, L.N., Senapati, S.C. and Dalabehera, M. (2004). On two methods of unbiased estimation in two-phase sampling using two auxiliary variables, Bulletin of the Malaysian Mathematical Society (Second Series), 27, 73-75.
- Sarma, K.K., Handique, B.K., Devi, H. Suchitra and Chakraborty, K. (2009). Remote Sensing and GIS based Forest Working Plan inputs for Hills Circle, North Cachar Hills District, Assam, NNRMS Bulletin, 33: 71-78.
- Scheer, L., Akca, A., and Feldkotter, C. (1997). Efficient growing stock estimation from satellite data employing two-phased sampling with regression. Geo-Informations-Systeme, 3/97: 22-25.
- Schreuder, H. T., Gregoire, T. G. and Wood, G. B. (1993). Sampling methods for multiresource forest inventory, John Wiley and Sons, New York.
- Senapati, S.C. and Sahoo, L.N. (2006). An alternate class of estimators in double sampling, Bull. Malay. Math. Soc. (Second series), 29: 89-94.
- Shiver, B.D. and Borders, B.D. (1996). Sampling Techniques for Forest Resource Inventory, John Wiley and Sons, Singapore.
- Singh, R., Chauhan, P. and Sawan, N. (2007). A family of estimators for estimating population mean using known correlation coefficient in two phase sampling, Statistics In Transition-new series, 8(1): 89-96.
- Sukhatme, B.V. (1962). Some ratio type estimators in two phase sampling, Journal of American Statistical Association, 57.
- Thompson, D.N.(2000).Stratified sampling based on photo interpreted forestty pesreduces the cost of strategic forest inventory, Tas forest, 12:77-82.
- Udaya Lakshmi, V., Murthy, M.S.R. and Dutt, C.B.S. (1998). Efficient forest resources management through GIS and remote sensing, Current Science, 75: 271-282.
- Vries, P.G.de (1986). Sampling Theory for Forest Inventory, Springer& Verlag.
- Expansion of Sericulture in India Using Geospatial Tools and Web Technology
Abstract Views :238 |
PDF Views:90
Authors
B. K. Handique
1,
P. T. Das
1,
J. Goswami
1,
C. Goswami
1,
P. S. Singh
1,
C. J. Prabhakar
2,
C. M. Bajpeyi
2,
P. L. N. Raju
1
Affiliations
1 North Eastern Space Applications Centre, Department of Space, Shillong 793 103, IN
2 Central Silk Board, Ministry of Textiles, B.T.M. Layout, Madivala, Bengaluru 560 068, IN
1 North Eastern Space Applications Centre, Department of Space, Shillong 793 103, IN
2 Central Silk Board, Ministry of Textiles, B.T.M. Layout, Madivala, Bengaluru 560 068, IN
Source
Current Science, Vol 111, No 8 (2016), Pagination: 1312-1318Abstract
Potential areas for expansion of sericulture in 108 selected districts covering 24 states in the country were mapped using remote sensing, GIS and GPS tools. Special emphasis was given to northeastern (NE) region, where 41 districts out of a total of 108 districts were selected. Potential area identification for sericulture development was based on land evaluation, water resources and climatic requirements for growing silkworm food plants as well as rearing silkworms. Among NE states, Mizoram has maximum highly suitable area (4.7% of total geographical area) followed by Meghalaya (2.8%), that can be brought under mulberry sericulture. Among non-traditional sericulture states, Himachal Pradesh has the highest suitable area (0.9% highly suitable and 6.2% moderately suitable areas) in the selected districts. Among the five traditional sericulture states, Tamil Nadu has the highest area under highly suitable category, which is about 4% of the total geographical area in the selected districts. To provide information on sericulture and spatial information on potential areas for the selected 108 districts, a geoportal titled 'Sericulture Information Linkages and Knowledge System' (SILKS) was conceptualized and developed using open source GIS, and put in the public domain (http://silks.csb.gov.in). Within three years, the portal could make a significant impact in the country particularly in NE states and a number of sericulture expansion activities have been taken up based on the study.Keywords
Geoportal, Geospatial Tools, Open Source GIS, Sericulture, Web Technology.Full Text
References
- Rama Rao, N., Protection for Indian Sericulture. Curr. Sci., 1938, 7(6), 263–266.
- Navalgund, R. R., Parihar, J. S., Ajai and Nageswara Rao, P. P., Crop inventory using remotely sensed data. Curr. Sci., 1991, 61(3&4), 162–171.
- Nageswara Rao, P. P., Ranganath, B. K. and Chandrashekhar, M. G., Remote sensing applications in sericulture. Indian Silk, 1991, 30, 7–15.
- CSB, Manual of Satellite Remote Sensing Applications for Sericulture Development, Central Silk Board, Bangalore, 1994.
- Sys, C., Land Evaluation: Part I, II& III. 1985, State University Ghent Publication, Belgium.
- Sys, C., Ranst, V., Debaveye, J. and Beernaert, F., Land evaluation Part III, crop requirements. Agric. Pub. 1993, No. 7, ITC, Ghent.
- NRSC, Manual–National Land Use Land Cover Mapping using Multi-temporal Satellite Data, Land Use Division, National Remote Sensing Agency, Hyderabad, 2006.
- NRSC, Wasteland Atlas of India, National Remote Sensing Agency, Hyderabad, 2011, pp. 4–14.
- FAO, Manual of Sericulture, United Nations, Rome, Italy, 1990.
- Patel, N. R., Mandal, U. K. and Pande, L. M., Agro-ecological zoning system. A remote sensing and GIS perspective. J. Agrometeorol., 2000, 2(1), 1–13.
- Goovaerts, P., Performance comparison of geostatistical algorithms for incorporating elevation into the mapping of precipitation. Geocomputation, 1999, 99, 1–17.
- Thornthwaite, An approach for a rational classification of climate. Geog. Rev., 1948, 38, 55–94.
- FAO, A framework for land evaluation. Soil Bulletin, Food and Agriculture Organization. United Nations, Rome, Italy, 1976, No. 32.
- Rapid Assessment of Boro Paddy Infestation by Brown Planthopper in Morigaon District, Assam, India Using Unmanned Aerial Vehicle
Abstract Views :244 |
PDF Views:91
Authors
Affiliations
1 North Eastern Space Applications Centre, Department of Space, Government of India, Umiam 793 103, IN
1 North Eastern Space Applications Centre, Department of Space, Government of India, Umiam 793 103, IN
Source
Current Science, Vol 111, No 10 (2016), Pagination: 1604-1606Abstract
In April 2016, farmers from Morigaon and Nagaon districts of Assam, India encountered severe pest infestation in their boro paddy (summer paddy) areas, which was unusual. Morigaon district with an area of 1550 sq. km having a population of about 9.6 lakhs (as of 2011), was the worst affected with four out of the five revenue circles, viz. Mayong, Bhuragaon, Laharighat, Morigaon and Mikirbheta being affected. Investigations by the District Agricultural Department and Regional Agricultural Research Station, Nagaon confirmed it to be infestation by brown planthopper (BPH), Nilaparvata lugens (Stal). Planthoppers are a problem in rainfed and irrigated wetland environments.Full Text
References
- Preap, V., Zalucki, M. P. and Jahn, G. C., Cambodian J. Agric., 2006, 7(1), 17–25.
- Holt, J., Chancellor, T. C. B., Reynolds, D. R. and Tiongco, E., Crop Prot., 1996, 14(4), 359–368.
- Prasannakumar, N. R., Chander, S. and Pal, M., Curr. Sci., 2012, 103(10), 1201–1205.
- Dubey, R. C., Maske, S. J. and Ronghe, J. A., Mausam, 1985, 36(4), 509–512.
- Nageswara Rao, P. P. and Rao, V. R., Proceedings of the third Asian Conference of Remote Sensing, Dhaka, Bangladesh, 4–7 December 1982, pp. 1–12.
- Ranganath, B. K., Pradeep, N., Manjula V. B., Gowda, B., Ranjana, M. D., Shettgar, D. and Nageswara Rao, P. P., J. Indian Soc. Remote Sensing, 2004, 32(10), 49–58.
- Everaerts, J., The Int. Arch. Photogramm., Remote Sensing Spatial Inf. Sci., 2008, XXXVII(part B1), 1187–1191.
- Colomina, I. and Molina, P., ISPRS J. Photogramm. Remote Sensing, 2014, 92, 79–97.
- Expansion of Boro Rice in Meghalaya using Space Technology
Abstract Views :232 |
PDF Views:80
Authors
Affiliations
1 North Eastern Space Applications Centre, Department of Space, Govt of India, Umiam 793 103, IN
1 North Eastern Space Applications Centre, Department of Space, Govt of India, Umiam 793 103, IN
Source
Current Science, Vol 115, No 10 (2018), Pagination: 1865-1870Abstract
Suitable areas for boro rice expansion in Meghalaya were identified using geospatial technology based on land evaluation using information on soil, slope, elevation, rainfall and temperature. The study showed that 635 ha area is highly suitable followed by 581.74 sq. km and 219.07 sq. km area is marginally and moderately suitable respectively. The suitable areas are distributed in 20 blocks of 8 districts. More than 50% of suitable areas are distributed in West Garo hills. The highest suitable areas are found in Selsella and Dadenggre block. The findings of this study are being used by the user department for expanding boro rice cultivation in the state.Keywords
Boro Rice, Geospatial Tools, Meghalaya, Site Suitability.Full Text
References
- http://www.megagriculture.gov.in
- http://www.meghalaya.gov.in
- FAO, A framework for land evaluation. Soil Bulletin, Food and Agriculture Organisation, United Nations, Rome, Italy, 1976, no. 32.
- Rao, D. P., Gautam, N. C., Nagaraja, R. and Ram Mohan, P., IRSIC application in land use mapping and planning. Curr. Sci., 1996, 70, 575–578.
- Panigrahy, S., Manjunath, K. R. and Ray, S. S., Deriving cropping system performance indices using remote sensing data and GIS. Int. J. Remote Sensing, 2006. 26, 2595–2606.
- FAO, Guidelines: land evaluation for rainfed agriculture, Soil Bulletin, Food and Agriculture Organisation, United Nations, Rome, Italy, 1983, no. 52.
- FAO, Guidelines: land evaluation for irrigated agriculture. Soil Bulletin, Food and Agriculture Organization, United Nations, Rome, Italy, 1985, no. 55.
- FAO, A framework for land evaluation, Soil Bulletin, Food and Agriculture Organization, United Nations. Rome, Italy, 2007, no. 6.
- Bandyopadhyay, S., Jaiswal, R. K., Hegde, V. S. and Jayaraman, V., Assessment of land suitability potentials for agriculture using a remote sensing and GIS based approach. Int. J. Remote Sensing, 2009, 30(4), 879–895.
- Sys, C., Ranst, V., Debaveye, J. and Beernaert, F., Land evaluation. Part III, Crop requirements. Agric. Pub. ITC, Ghent, 1993, no. 7.
- Site-suitability Analysis for Turmeric in Jaintia Hills of Meghalaya, India, using Analytical Hierarchical Process and Weighted Overlay Analysis:A Comparative Approach
Abstract Views :168 |
PDF Views:81
Authors
Affiliations
1 North Eastern Space Applications Centre, Umiam 793 103, IN
1 North Eastern Space Applications Centre, Umiam 793 103, IN
Source
Current Science, Vol 118, No 8 (2020), Pagination: 1246-1254Abstract
India is the largest producer, consumer and exporter of turmeric (Curcuma longa L.). The Lakadong variety of turmeric is endemic to Jaintia Hills of Meghalaya, India. It is considered as the best quality turmeric containing 7.5% curcumin, which is about three times higher than the other varieties (2–3%). This study identifies potential sites for turmeric cultivation in Jaintia Hills using geospatial techniques, viz. analytical hierarchical process (AHP) and weighted overlay analysis (WOA). WOA identified a total of 162,263.70 ha suitable for the expansion of Lakadong variety of turmeric in Jaintia Hills, of which 18% was highly suitable, 31% moderately suitable and 32% was marginally suitable. In the case of AHP, 21% area was found to be highly suitable, 25% moderately suitable and 45% marginally suitable.Keywords
Analytical Hierarchical Process, Site Suitability Analysis, Turmeric, Weighted Overlay Analysis.Full Text
References
- Moorthi, C. and Kathiresan, K., Curcumin–piperine/curcumin– quercetin/curcumin–silibinin dualdrug-loaded nanoparticulate combination therapy: a novel approach to target and treat multi-drug-resistant cancers. J. Med. Hypotheses Ideas, 2013, 7(1), 15–20.
- Mandal, D. K., Khule, S., Mandal, C., Lal, S., Hajare, N. and Prasad, J., Soil suitability evaluation for turmeric in Wardha district of Maharashtra. Agropedology, 2008, 18(2), 83–92.
- Rahman, S. A., Baldauf, C., Mollee, E. M., Abdullah-Al-Pavel, M., Abdullah-Al-Mamun, M., Toy, M. M. and Sunderland, T., Cultivated plants in the diversified homegardens of local communities in Ganges Valley, Bangladesh. Sci. J. Agric. Res. Manage., 2013.
- Benzie, I. F. and Wachtel, G. S., Herbal Medicine: Biomolecular and Clinical Aspects,2nd edn, CRC Press, Florida, USA, 2011.
- Asati, B. S. and Yadav, D. S., Diversity of horticultural crops in north eastern region. ENVIS Bull.: Himalayan Ecol., 2004, 12(1), 1.
- Deshmukh, N. A. Jha, A. K., Verma, V. K., Rymbai, H., Deka, B. C. and Ngachan, S. V., Megha Turmeric-1: popularization through farmers’ participatory mode in Meghalaya: a success story, ICAR Research Complex for NEH Region, Meghalaya, 2017.
- Saaty, T. L. and Vargas, G. L., Models Methods, Concepts and Applications of the Analytical Heirarchy Process, Kluwer, Boston, USA, 2001.
- Semlani, I., Ouadif, L. and Bahi, L., Landslide susceptibility mapping using the analytical heirarchy process and GIS. Curr. Sci., 2019, 116(5), 773–779.
- Saaty, T. L., The Analytical Heirarchy Process, McGraw Hill, New York, USA, 1980.
- Nandy, S., Kushwaha, S. P. S. and Gaur, P., Identification of swamp deer (Cervus duvauceli Cuvier) potential habitat in Jhilmil Jheel Conservation Reserve, Uttarakhand, India using multi-criteria analysis. Environ. Manage., 2012, 49(4), 902–914.
- Bhalerao, A. K., Kumar, B., Singha, A. K., Jat, P. C., Bordoloi, R. and Deka, B. C., Cachar District Inventory of agriculture, ICAR-Agricultural Technology Application Research Institute, Umiam, Meghalaya, 2015.
- Congalton, R. G., Accuracy assessment and validation of remotely sensed and other spatial information. Int. J. Wildland Fire, 2001, 10(4), 321–328.
- Mohan, S. C., Samra, J. S. Srivastava, A. K. and Sharda, V. N., Training Manual for Soil Conversation and Watershed Management, Soil, Agronomy and Socio-Economic Aspects, Central Soil and Water Conversation Research and Training Institute, Dehradun,2007, vol. 1, p. 93.
- Naidu, L. G. K., Ramamurthy, V., Challa, O., Hegde, R. and Krishnan, P., Soil-Site Suitability for Major Crops, NBSS Publication No. 129, 2006.
- Oliver, M. A. and Webster, R., Kriging: a method of interpolation for geographical information systems. Int. J. Geogr. Inform. Syst., 1990, 4(3), 313–332.
- Triantaphyllou, E., Multi-criteria decision making methods. In Multi-criteria Decision Making Methods: A Comparative Study, Springer, Boston, MA, USA, 2000, pp. 5–21.
- Saaty, T. L., Decision making with the analytic hierarchy process. Sci. Iran., 2002, 9(3), 215–229.
- Feizizadeh, B. and Blaschke, T., Land suitability analysis for Tabriz County, Iran: a multi- criteria evaluation approach using GIS. J. Environ. Plan. Manage., 2013, 56(1), 1–23.
- Magesh, N. S., Chandrasekar, N. and Soundranayagam, J. P., Delineation of groundwater potential zones in Theni district, Tamil Nadu, using remote sensing, GIS and MIF techniques. Geosci. Front., 2012, 3(2), 189–196.
- Arkoprovo, B., Adarsa, J. and Prakash, S. S., Delineation of groundwater potential zones using satellite remote sensing and geographic information system techniques: a case study from Ganjam district, Orissa, India. Res. J. Recent Sci., 2012, 1(9), 59– 66.
- Development of a Muga Disease Early Warning System – A Mobile-Based Service for Seri Farmers
Abstract Views :176 |
PDF Views:74
Authors
J. Goswami
1,
D. K. Gogoi
2,
N. Rasid
1,
B. K. Handique
1,
G. Subrahmanyam
2,
P. P. Bora
2,
R. Das
2,
P. L. N. Raju
1
Affiliations
1 North Eastern Space Applications Centre, Department of Space, Umiam 793 103, IN
2 Central Muga Eri Research and Training Institute, Central Silk Board, Lahdoigarh, Jorhat 785 700, IN
1 North Eastern Space Applications Centre, Department of Space, Umiam 793 103, IN
2 Central Muga Eri Research and Training Institute, Central Silk Board, Lahdoigarh, Jorhat 785 700, IN
Source
Current Science, Vol 121, No 10 (2021), Pagination: 1328-1334Abstract
Flacherie is a major bacterial disease causing >40% loss during Muga summer crops. For finding the ischolar_main causes of the diseases, relationships were established between rearing and production data corresponding to land use/land cover, land surface temperature and meteorological parameters. Adverse affects were found in farms associated with anthropogenic activities, in contrast to forest cover which shows a negative trend. Muga disease early warning system, a mobile-based application and dashboard has been developed to predict rate of flacherie infestation at least 5–10 days in advance, for proper precautionary measures by the farmers to avoid disease outbreak and crop lossKeywords
Crop Loss, Early Warning System, Flacherie Disease, Mobile-Based Service, Muga Silkworm, Remote Sensing. Muga Silkworm.Full Text
References
- Tikader, A., Vijayan, K. and Saratchandra, B., Muga silkworm, Antheraea assamensis (Lepidoptera: Saturniidae) – an overview of distribution, biology and breeding. Eur. J. Entomol., 2013, 110(2).
- Kumar, R. and Rajkhowa, G., Muga silkworm, Antheraea assamensis (Insecta: Lepidoptera: Saturniidae): rearing and insect. Hartmann and Weipert. Proceedings: Biodiversität und Naturausstattung im Himalaya IV.–Erfurt, Germany, 2012, pp. 187–190.
- FAO, Manuals of Sericulture, Food and Agriculture Organization, Rome, 1976.
- Madhusudhan, K. N. et al., Impact of varying different abiotic factors on the survivability of tasar silkworm in outdoor rearing fields. J. Entomol. Zool. Stud., 2017, 5(6), 957–963.
- Chakravorty, C., Das, R., Neog, R., Das, K and Sahu, M., A Diagnostic Manual for Diseases and Pests of Muga Silkworms and their Host Plants, Central Muga Eri Research and Training Institute, CSB Publication, 2007, 1st edn.
- Subrahmanyam, G. et al., Isolation and molecular identification of microsporidian pathogen causing nosemosis in Muga silkworm, Antheraea assamensis Helfer (Lepidoptera: Saturniidae). Indian J. Microbiol., 2019, 59(4), 525–529.
- Ueda, S., Kimura, R. and Suzuki, K., Studies on the growth of the silkworm, Bombyx mori L., 4: Mutual relationships between the growth in the fifth instar larvae and the productivity of silk substance. Bull. Sericult. Exp. Station, 1975.
- Benjamin, K. V. and Jolly, M. S., Principles of silkworm rearing.
- In Proceedings of Seminar on Problems and Prospects of Sericulture (ed. Mahalingam, S.), Vellore, 1986, pp. 63–108.
- Sys, C. et al., Land Evaluation. Part I, II & III: Crop Requirements, Agricultural Publications N° 7, GADC, Brussels, Belgium, 1993, p. 191.
- Sys, C., Van Ranst, E., Debaveye, J. and Beenaert, F., Land evaluation part III. Crop Requirements. Agriculture Publication, 1993, vol. 7, p. 166.
- Handique, B. K. et al., Expansion of sericulture in India using geospatial tools and web technology. Curr. Sci., 2016, 111(8), 1312–1318.
- National Remote Sensing Agency, Manual of National Land Use/Land Cover Mapping using Multi-Temporal Satellite Data, Hyderabad, 2006.
- Malik, B., The problem of shifting cultivation in the Garo Hills of North-East India, 1860–1970. Conserv. Soc., 2003, 287–315.
- Willmer, C. W., Stone, G. and Johnston, I., Environmental Physiology of Animals, Blackwell Science, Oxford, UK, 2009, 2dn edn, pp. 175–183.
- Shirota, T., Selection of healthy silkworm strain through high temperature rearing of fifth instar larvae. Rep. Silk Sci. Res. Inst. (Jpn), 1992.
- Tajima, Y. and Ohnuma, A., Preliminary experiments on the breeding procedure for synthesizing a high temperature resistant commercial strain of the silkworm, Bombyx mori. Rep. Silk Sci. Res. Inst. (Jpn), 1995, 1–16.
- 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.
- Mapping and Monitoring of Soil Organic Carbon Using Regression Analysis of Spectral Indices
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1 College of Post Graduate Studies in Agricultural Sciences, Central Agricultural University, Imphal–Umroi Road, Umiam 793 103, IN
2 North Eastern Space Applications Centre, Department of Space, Government of India, Umiam 793 103, IN
1 College of Post Graduate Studies in Agricultural Sciences, Central Agricultural University, Imphal–Umroi Road, Umiam 793 103, IN
2 North Eastern Space Applications Centre, Department of Space, Government of India, Umiam 793 103, IN
Source
Current Science, Vol 124, No 12 (2023), Pagination: 1431-1444Abstract
The soil carbon sinking ability is dominantly controlled by local topographical settings, soil–crop management and traditional farming practices on which the food demand of the major population is dependent. The degradation of natural resources causing poor soil health is likely to strain the hilly and mountain ecosystem. This study aims to map soil organic carbon (SOC) of rice–fallow system under varying slopes and its changes during the past 20 years under traditional management practice using geospatial tools and techniques. Regression models of SOC were derived from remote sensing (RS)-based indices using multiple linear regression-stepwise (MLR-stepwise), partial least square regression (PLSR) and principal component analysis-regression (PCA-R). The MLR-stepwise model was found to be superior in performance with high R2 (0.87) and least RMSE (0.026) compared to PLSR (R2 = 0.71 and RMSE = 0.05) and PCA-R (R2 = 0.27 and RMSE = 0.11) models for SOC prediction.Keywords
Regression Models, Remote Sensing, Rice–Fallow System, Soil Organic Carbon, Spectral Indices.Full Text
References
- Rajasekaran, B. and Whiteford, M. B., Rice–crop production system: The role of indigenous knowledge in designing food security policies. Food Policy, 1992, 18(3), 237–247.
- Gadgil, M., Berkes, F. and Folke, C., Indigenous knowledge for biodiversity conservation. Ambio, 1993, 22, 151–156.
- Kala, C. P., Traditional ecological knowledge on characteristics, conservation and management of soil in tribal communities of Pachmarhi Biosphere Reserve, India. J. Soil Sci. Plant Nutr., 2013, 13(1), 201–214.
- Sanders, D., Soil conservation. In Land Use, Land Cover and Soil Sciences (ed. Willy, H. V.), UNESCO, Eolss Publishers, Oxford, UK, 2004, 4, 1–21.
- Arunrat, N., Kongsurakan, P., Sereenonchai, S. and Hatano, R., Soil organic carbon in sandy paddy fields of northeast Thailand: a review. Agronomy, 2020, 10(8), 1061.
- Zhang, Y. et al., Prediction of soil organic carbon based on Landsat 8 monthly NDVI data for the Jianghan Plain in Hubei Province, China. Remote Sensing, 2019, 11, 1683; doi:10.3390/rs11141683.
- Meetei, T. T., Kundu, M. C. and Devi, Y. B., Long-term effect of rice-based cropping systems on pools of soil organic carbon in farmer’s field in hilly agroecosystem of Manipur, India. Environ. Monit. Assess., 2020, 192(4), 1–17.
- Wu, J. S. and Xiao, H. A., Measuring the gross turnover time of soil microbial biomass C under incubation. Acta Pedol. Sin., 2004, 41(3), 401–407.
- Lui, D. et al., Spatial distribution of soil organic carbon and analysis of related factors in cropland of the black soil region, Northeast China. Agric. Ecosyst. Environ., 2006, 113, 73–81.
- Li, Z. W., Nie, X. D., Chen, X. L., Lu, Y. M., Jiang, W. G. and Zeng, G. M., The effects of land use and landscape position on labile organic carbon and carbon management index in red soil hilly region, sourthern China. J. Mt. Sci., 2015, 12(3); doi:10.1007/s11629-013-2964-2.
- Bayer, C. and Mielniczuk, J., Capítulo 2: Dinâmica da matéria orgânica. In Fundamentos da Matéria orgânica no solo: Ecossistemas Tropicais e Subtropicais (eds Santos, G. A. et al.), 07–18, Porto Alegre, Metropole, Brazil, 2008.
- Viscarra Rossel, R. A., Walvoort, D. J. J., McBratney, A. B., Janik, L. J. and Skjemstad, J. O., Visible, near infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma, 2006, 131, 59–75.
- Shi, P. et al., Land-use types and slope topography affect the soil labile carbon fractions in the loess hilly–gully area of Shaanxi, China. Arch. Agron. Soil Sci., 2020, 66(5), 638–650.
- Zhang, X. et al., Which slope aspect and gradient provides the best afforestation-driven soil carbon sequestration on the China’s Loess plateau? Ecol. Eng., 2020, 147, 105782.
- Jendoubi, D., Liniger, H. and Ifejika Speranza, C., Impacts of land use and topography on soil organic carbon in a Mediterranean landscape (north-western Tunisia). Soil, 2019, 5(2), 239–251.
- Rengel, Z., The role of crop residues in improving soil fertility. In Nutrient Cycling in Terrestrial Ecosystems, Springer, Berlin, Germany, 2007, pp. 183–214.
- Jiang, D., Zhuang, D. and Huang, Y., Crop residues as an energy feedstock: availability and sustainability. In Sustainable Bioenergy Production, CRC Press, Boca Raton, Florida, USA, 2014, pp. 236–249.
- Zhang, L. et al., Soil labile organic carbon fractions and soil enzyme activities after 10 years of continuous fertilization and wheat residue incorporation. Sci. Rep., 2020, 10, 11318; https://doi.org/10.1038/s41598-020-68163-3.
- The Meghalayan, Dispatches from the farmlands of-Ri-bhoi; https://themeghalayan.com/dispatches-from-the-farmlands-of-RiBhoi (accessed on 6 April 2022).
- Singh, P. and Benbi, D. K., Soil organic carbon pool changes in relation to slope position and land-use in Indian lower Himalayas. Catena, 2018, 166, 171–180.
- Angelopoulou, T., Tziolas, N., Balafoutis, A., Zalidis, G. and Bochtis, D., Remote sensing techniques for soil organic carbon estimation: a review. Remote Sensing, 2019, 11(6), 676; https://doi.org/10.3390/rs11060676.
- Vohland, M., Ludwig, B., Seidel, M. and Hutengs, C., Quantification of soil organic carbon at regional scale: benefits of fusing Vis-NIR and MIR diffuse reflectance data are greater for in situ than for laboratory-based modelling approaches. Geoderma, 2022, 405, 115426.
- Bhunia, G. S., Shit, P. K. and Pourghasemi, H. R., Soil organic carbon mapping using remote sensing techniques and multivariate regression model. Geocarto Int., 2019, 34(2), 215–226.
- Partyka, T. and Hamkalo, Z., Estimation of oxidizing ability of organic matter of forest and arable soil. Zemdirbyste-Agric., 2010, 97, 33–40.
- Wu, H. Y., Zeng, F. P., Song, T. Q., Peng, W. X., Li, X. H. and OuYang, Z. W., Spatial variations of soil organic carbon and nitrogen in peak-cluster depression areas of Karst region. Plant Nutr. Fert. Sci., 2009, 15, 1029–1036.
- Zhang, L., Gao, P., Wang, C., Liu, S. and Li, X., Spatial distribution of soil organic carbon in the forestland of the Yaoxiang small watershed in central and southern Shandong Province. Sci. Soil Water Conserv., 2015, 13, 83–89.
- Kumar, S., Lal, R. and Liu, D., A geographically weighted regression kriging approach for mapping soil organic carbon stock. Geoderma, 2012, 189, 627–634.
- Chabala, L. M., Mulolwa, A. and Lungu, O., Application of ordinary kriging in mapping soil organic carbon in Zambia. Pedosphere, 2017, 27, 338–343.
- Lu, F., Zhao, Y., Huang, B. and Wang, J., Comparison of predicting methods for mapping the spatial distribution of topsoil organic matter content in cropland of hailun. J. Soil Sci., 2012, 43, 662–667.
- Xu, E. and Zhang, H., Multi-scale analysis of kriging interpolation and conditional simulation for soil organic matters in newly reclaimed area in Yili. Soils, 2013, 45, 91–98.
- Zhao, D., Zhao, H., Rao, J. and Gao, X., Analysis of the spatial distribution pattern of cultivated land quality and the influential factors based on trend-surface. Res. Soil Water Conserv., 2015, 22, 219–223.
- Martín, J. A. R. et al., Soil organic carbon stock on the Majorca Island: temporal change in agricultural soil over the last 10 years. Catena, 2019, 181, 104087.
- Zhao, M. S., Qiu, S. Q., Wang, S. H., Li, D. C. and Zhang, G. L., Spatial–temporal change of soil organic carbon in Anhui Province of East China. Geoderma Reg., 2021, 26, e00415.
- Guo, L., Linderman, M., Shi, T. Z., Chen, Y. Y., Duan, L. J. and Zhang, H. T., Exploring the sensitivity of sampling density in digital mapping of soil organic carbon and its application in soil sampling. Remote Sensing, 2018, 10, 27.
- Lin, Y., Zhu, A., Qin, C., Li, B. and Pei, T., A soil sampling method based on representativeness grade of sampling points. Acta Pedol. Sin., 2011, 48, 938–946.
- Liu, Y., Guo, L., Jiang, Q., Zhang, H. and Chen, Y., Comparing geospatial techniques to predict soc stocks. Soil Till. Res., 2015, 148, 46–58.
- Malone, B. P., Jha, S. K., Minasny, B. and McBratney, A. B., Comparing regression-based digital soil mapping and multiple-point geostatistics for the spatial extrapolation of soil data. Geoderma, 2016, 262, 243–253.
- Žížala, D., Minařík, R. and Zádorová, T., Soil organic carbon mapping using multispectral remote sensing data: prediction ability of data with different spatial and spectral resolutions. Remote Sensing, 2019, 11(24), 2947.
- Yu, H., Zha, T., Zhang, X., Nie, L., Ma, L. and Pan, Y., Spatial distribution of soil organic carbon may be predominantly regulated by topography in a small revegetated watershed. Catena, 2020, 188, 104459.
- Zhong, Z. et al., Relationship between soil organic carbon stocks and clay content under different climatic conditions in Central China. Forests, 2018, 9(10), 598.
- Churchman, G. J., Singh, M., Schapel, A., Sarkar, B. and Bolan, N., Clay minerals as the key to the sequestration of carbon in soils. Clays Clay Miner., 2020, 68(2), 135–143.
- Zhang, H., Kang, J., Xu, X. and Zhang, L., Accessing the temporal and spectral features in crop type mapping using multi-temporal Sentinel-2 imagery: a case study of Yi’an County, Heilongjiang Province, China. Comput. Electron. Agric., 2020, 176, 105618.
- Ferugoson, B., Lukens, W. E., El Masri, B. and Stinchcomb, G. E., Alluvial landform and the occurrence of paleosols in a humid–subtropical climate have an effect on long-term soil organic carbon storage. Geoderma, 2020, 371, 114388.
- Department of Agriculture, Agriculture contingency plan for district RiBhoi. Directorate of Agriculture, Government of Meghalaya. 2014, pp. 1–3.
- Sehgal, J. L., Sys, C., Stoops, G. and Tavernier, R., Morphology, genesis and classification of two dominant soils of the warm temperate and humid region of the central Himalayas. J. Indian Soc. Soil Sci., 1985, 33(4), 846–857.
- Chen, J. and Zhu, W., Comparing Landsat-8 and Sentinel-2 top of atmosphere and surface reflectance in high latitude regions: case study in Alaska. Geocarto Int., 2021, 37(9), 1–20; doi:10.1080/10106049.2021.1924295.
- Mondal, A., Khare, D., Kundu, S., Mondal, S., Mukherjee, S. and Mukhopadhyay, A., Spatial soil organic carbon (SOC) prediction by regression kriging using remote sensing data. Egypt. J. Remote Sensing Space Sci., 2017, 20(1), 61–70.
- Young, N. E., Anderson, R. S., Chignell, S. M., Vorster, A. G., Lawrence, R. and Evangelista, P. H., A survival guide to Landsat preprocessing. Ecology, 2017, 98(4), 920–932.
- Thaler, E. A., Larsen, I. J. and Yu, Q., A new index for remote sensing of soil organic carbon based solely on visible wavelengths. Soil Sci. Soc. Am. J., 2019, 83(5), 1443–1450.
- Singh, R. P. et al., Retrieval of wheat leaf area index using price approach based on inversion of canopy reflectance model. J. Indian Soc. Remote Sensing, 2005, 33(2), 307–313.
- Ray, S. S., Sood, A., Panigrahy, S. and Parihar, J. S., Derivation of indices using remote sensing data to evaluate cropping systems. J. Indian Soc. Remote Sensing, 2005, 33(4), 475.
- Kumari, M. and Sarma, K., Changing trends of land surface temperature in relation to land use/cover around thermal power plant in Singrauli district, Madhya Pradesh, India. Spat. Inf. Res., 2017, 25(6), 769–777.
- Nageswara, P. P. R., Shobha, S. V., Ramesh, K. S. and Somashekhar, R. K., Satellite-based assessment of agricultural drought in Karnataka state. J. Indian Soc. Remote Sensing, 2005. 33(3), 429–434.
- Gbolo, P., Gerla, P. J. and Vandeberg, G. S., Using high resolution, multispectral imagery to assess the effect of soil properties on vegetation reflectance at an abandoned feedlot. Geocarto Int., 2015, 30(7), 1–17.
- Rouse, J. W., Haas, R. H., Schelle, J. A., Deering, D. W. and Harlan, J. C., Monitoring the vernal advancement or retrogradation of natural vegetation. NASA/GSFC, Type III, Final Report, NASA, Greenbelt, MD, USA, 1974, p. 371.
- Tucker, C. J., Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing Environ., 1979, 8(2), 127–150.
- Lillesaeter, E., Spectral reflectance of partly transmitting leaves: laboratory measurements and mathematical modeling. Remote Sensing Environ., 1982, 12, 247–254.
- Holben, B. N., Kaufman, Y. J. and Kendall, J. D., NOAA-11 AVHRR visible and near-IR in flight calibration. Int. J. Remote Sensing, 1990, 11(8), 1511–1519.
- Holben, B. and Fraser, R. S., Red and near-infrared sensor response to off-nadiir viewing. Int. J. Remote Sensing, 1984, 5(1), 145–160.
- Huete, A. R., A soil-adjusted vegetation index (SAVI). Remote Sensing Environ., 1988, 25, 295–309.
- Pye, K. and Tsoar, H., Aeolian Sand and Sand Dunes, Springer, Berlin, Germany, 2009, p. 458.
- Mayhew, S. and Penny, A., The Concise Oxford Dictionary of Geography, University Press, Oxford, UK, 1992, p. 250.
- Geo University, Spectral indices with multispectral satellite data; https://www.geo.university›pages›blo (accessed on 18 December 2022).
- Bartholomeus, H., Schaepman, M., Kooistra, L., Stevens, A., Hoogmoed, W. and Spaargaren, O., Spectral reflectance based indices for soil organic carbon quantification. Geoderma, 2008, 145, 28–36; doi:10.1016/j.geoderma.2008.01.010.
- Zhou, W., Han, G., Liu, M. and Li, X., Effects of soil pH and texture on soil carbon and nitrogen in soil profiles under different land uses in Mun River Basin, Northeast Thailand. Peer J., 2019, 7, e7880.
- Page, K. L., Dang, Y. P. and Dalal, R. C., The ability of conservation agriculture to conserve soil organic carbon and the subsequent impact on soil physical, chemical, and biological properties and yield. Front. Sustain. Food Syst., 2020, 4, 31.
- Nelson, D. W. and Sommers, L. E., Total carbon, organic carbon, and organic matter. In Methods of Soil Analysis: Part 3 – Chemical Methods, SSA Book Series, Madison, 1996, vol. 5, pp. 961–1010.
- He, T., Wang, J., Lin, Z. and Cheng, Y., Spectral features of soil organic matter. Geo-spat. Inf. Sci., 2009, 12(1), 33–40.
- Lin, L., Wang, Y., Teng, J. and Wang, X., Hyperspectral analysis of soil organic matter in coal mining regions using wavelets, correlations, and partial least squares regression. Environ. Monit. Assess., 2016, 188(2), 1–11.
- Jaber, S. M., Lant, C. L. and Al-Qinna, M. I., Estimating spatial variations in soil organic carbon using satellite hyperspectral data and map algebra. Int. J. Remote Sensing, 2012, 32(18), 5077–5103.
- Somvanshi, S. S., Kunwar, P., Tomar, S. and Singh, M., Comparative statistical analysis of the quality of image enhancement techniques. Int. J. Image Data Fusion, 2018, 9(2), 131–151.
- Rasool, S. N., Gaikwad, S. W. and Talat, M. A., Relationship between soil properties and slope segments of Sallar Wullarhama watershed in the Liddar catchment of Jammu and Kashmir. Asian J. Eng. Res., 2014, 2(2), 1–10.
- Jaksic, S., Ninkov, J., Milic, S., Vasin, J., Zivanov, M., Jaksic, D. and Komlen, V., Influence of slope gradient and aspect on soil organic carbon content in the region of Nis, Serbia. Sustainability, 2021, 13(15), 8332.
- Wilding, L. P., Spatial variability: its documentation, accommodation and implication to soil surveys. In Soil Spatial Variability. Proceedings of a Workshop of the ISSS and the SSA (eds Nielsen, D. R. and Bouma, J.), Las Vegas NV, Wageningen, USA, 30 November–1 December 1985, pp. 166–187.
- Hamzehpour, N., Shafizadeh-Moghadam, H. and Valavi, R., Exploring the driving forces and digital mapping of soil organic carbon using remote sensing and soil texture. Catena, 2019, 182, 104141.
- Wang, S., Zhuang, Q., Jin, X., Yang, Z. and Liu, H., Predicting soil organic carbon and soil nitrogen stocks in topsoil of forest ecosystems in Northeastern China using remote sensing data. Remote Sensing, 2020, 12(7), 1115.
- Singh, N. J., Kudrat, M. and Jain, K., Effect of land use and topography on spatial distribution of soil organic carbon in semi-arid sub-tropical ecosystems in Uttar Pradesh, India. Int. J. Ecol. Environ. Sci., 2014, 40, 189–197.
- Challam, L. L., Singh, N. J., Debbarma, K., Ray, L. I. and Swami, S., Effect of topographical settings on distribution of soil organic carbon fractions in rice ecosystem of North East India. Int. J. Ecol. Environ. Sci., 2016, 42(5), 129–135.
- Viscarra Rossel, R. V. and Behrens, T., Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma, 2010, 158(1–2), 46–54.
- Castaldi, F., Palombo, A., Santini, F., Pascucci, S., Pignatti, S. and Casa, R., Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon. Remote Sensing Environ., 2016, 179, 54–65.
- Son, N. T., Chen, C. F., Chen, C. R., Minh, V. Q. and Trung, N. H., A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation. Agric. For. Meteorol., 2014, 197, 52–64.
- Nuarsa, I. W., Nishio, F. and Hongo, C., Relationship between rice spectral and rice yield using MODIS data. J. Agric. Sci., 2011, 3(2), 80.
- Soil and Water Conservation Department, Integrated Watershed Management Project, Detailed project report, Govt of Meghalaya, Umsning C & RD block, 2009; https://megsoil.gov.in›iwmp›2009-10
- IMP, Rainfall statistics of India, India Meteorological Department, Report no. MoES/IMD/HS/RAINFALL REPORT/30, 2019.
- Pulak, G., Bhagwat, P. P., Satpute, U. S., Menon P., Prasad, A. K., Sable, S. T. and Advani, S. C., Observed rainfall variability and changes over Meghalaya state. India Meteorological Department, Pune.
- Arunrat, N., Pumijumnong, N. and Hatano, R., Predicting local-scale impact of climate change on rice yield and soil organic carbon sequestration: a case study in RoiEt Province, Northeast Thailand. Agric. Syst., 2018, 164, 58–70.
- Laxminarayana, K. and Bharali, S., Distribution of inorganic N fractions and N availability indices in the rice soils of Meghalaya. ORYZA – Int. J. Rice, 2010, 47(2), 128–135.
- Gabarrón-Galeote, M. A., Trigalet, S. and van Wesemael, B., Effect of land abandonment on soil organic carbon fractions along a Mediterranean precipitation gradient. Geoderma, 2015, 249, 69–78.
- Rouse Jr, J. W., Haas, R. H., Schell, J. A. and Deering, D. W., Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publication, Greenbelt, MD, USA, NASA Goddard Space Flight Center, 1974, vol. 351, p. 309.
- Escadafal, R., Belghith, A. and Ben-Moussa, H., Indices spectraux pour la télédétction de la dégradation des milieux naturels en Tunisie aride. In Actes du 6eme Symp. Int. sur les mesures physiques et signatures en teledetection, Val d’Isère (France), 17–24 January 1994, pp. 253–259.
- Roujean, J. L. and Breon, F. M., Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing Environ., 1995, 51(3), 375–384.
- Mokarram, M., Roshan, G. and Negahban, S., Landform classification using topography position index (case study: salt dome of Korsia-Darab plain, Iran). Model. Earth Syst. Environ., 2015, 1(4), 1–7.
- Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H. and Sorooshian, S., A modified soil adjusted vegetation index. Remote Sensing Environ., 1994, 48(2), 119–126.
- Jiang, Z., Huete, A. R., Kim, Y. and Didan, K., 2-band enhanced vegetation index without a blue band and its application to AVHRR data. In Remote Sensing and Modeling of Ecosystems for Sustainability IV, SPIE, 2007, vol. 6679, pp. 45–53.
- Madeira, J., Bedidi, A., Cervelle, B., Pouget, M. and Flay, N., Visible spectrometric indices of hematite (Hm) and goethite (Gt) content in lateritic soils: the application of a Thematic Mapper (TM) image for soil-mapping in Brasilia, Brazil. Int. J. Remote Sensing, 1997, 18(13), 2835–2852.
- Pouget, J. P., Jozefowicz, M. E., Epstein, A., Tang, X. and Mac-Diarmid, A. G., X-ray structure of polyaniline. Macromolecules, 1991, 24(3), 779–789.
- Jamalabad, M. and Abkar, A., Forest canopy density monitoring using satellite images. In 20th ISPRS Congress on International Society for Photogrammetry and Remote Sensing, Istanbul, 2004, Turkey, pp. 12–23.
- Deng, Y., Wu, C., Li, M. and Chen, R., RNDSI: a ratio normalized difference soil index for remote sensing of urban/suburban environments. Int. J. Appl. Earth Obs. Geoinf., 2015, 39, 40–48.
- Rogers, A. S. and Kearney, M. S., Reducing signature variability in unmixing coastal marsh Thematic Mapper scenes using spectral indices. Int. J. Remote Sensing, 2004, 25(12), 2317–2335.
- McFeeters, S. K., The use of the normalized difference water index (NDWI) in the delineation of open water features. Int. J. Remote Sensing, 1996, 17(7), 1425–1432.