Author Details

Scroll

Refine your search

Collections

Basic & Applied Science Collection

Co-Authors

Journals

Current Science

Year

Authors

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All

Gowtham, R.

The Impacts of Climate Change on Tamil Nadu Rainfed Maize Production:A Multi-Model Approach to Identify Sensitivities and Uncertainties

Abstract Views :239 | PDF Views:84

Authors

Sonali McDermid ¹, R. Gowtham ², K. Bhuvaneswari ², Geethalakshmi Vellingiri ², Lakshmanan Arunachalam ²

Affiliations
1 Department of Environmental Studies, New York University, New York, US
2 Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore 641 003, IN

Source

Current Science, Vol 110, No 7 (2016), Pagination: 1257-1271

Abstract

This study evaluates the impacts of climate change on maize yields in Tamil Nadu, and assesses the efficacy of adaptation strategies, using a novel multi-climate, multi-crop model approach based on AgMIP Protocols (www.agmip.org). While the climate models displayed consistent changes to rainfall and temperature, substantial uncertainty exists between the different climate-crop model responses that warrant further study. Adaptation strategies proved beneficial under a current climate context, but showed diminished efficacy under future climate conditions. We recommend that future work focus on identifying the main sources of climate-crop model uncertainty, and that additional work may focus on more transformative adaptation measures.

Keywords

Adaptation, Climate Change, Crop Model, Climate Model, Maize.

Full Text

Inundation Mapping of Kerala Flood Event in 2018 using ALOS-2 and Temporal Sentinel-1 SAR Images

Abstract Views :163 | PDF Views:94

Authors

V. S. K. Vanama ¹, Mohamed Musthafa ², Unmesh Khati ², R. Gowtham ², Gulab Singh ², Y. S. Rao ²

Affiliations
1 Centre for Urban Science and Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, IN
2 Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, IN

Source

Current Science, Vol 120, No 5 (2021), Pagination: 915-925

Abstract

In August 2018, the southern Indian state of Kerala received unusually heavy rainfall leading to largescale flooding and destruction. Reliable flood inundation maps derived from remote sensing techniques help in flood disaster management activities. The freely available Sentinel-1A/B SAR data have the potential for flood inundation mapping due to its all-weather imaging capability. In this study, temporal dual-pol Sentinel-1 SAR data have been utilized. Single-date ALOS-2/PALSAR-2 commercial SAR data were also used to fill the gap between Sentinel-1 acquisitions during the peak flood-period. Two flood-mapping approaches, viz. rule-based classification in case of temporal SAR data and histogram-based thresholding approach in case of single-date imagery, were utilized in the study. Also, flood inundation mapping with different data constraints, i.e. availability of single-date and multi-date imagery has been analysed and discussed. The obtained results were validated with multiple data sources like survey data and secondary data from government agencies. An overall accuracy of 90.6% and a critical success index of 81.6% were achieved with the proposed rule-based classification approach. This study highlights the potential of the combination of Sentinel-1 and ALOS-2/PALSAR-2 data for flood inundation mapping.

Keywords

Disaster Management, Floods, Inundation Mapping, Remote Sensing, Rule-based Classification.

Full Text

References

Huang, X. et al., Flood hazard in hunan province of China: an economic loss analysis. Nat. Hazards, 2008, 47, 65–73.

Rijal, S., Rimal, B. and Sloan, S., Flood hazard mapping of a rapidly urbanizing city in the foothills (Birendranagar, Surkhet) of Nepal. Land, 2018, 7, 60.

Ologunorisa, T. and Abawua, M., Flood risk assessment: a review.J. Appl. Sci. Environ. Manage., 2005, 9, 57–63.

Panhalkar, S. and Jarag, A. P., Flood risk assessment of Panchganga River (Kolhapur district, Maharashtra) using GIS-based multicriteria decision technique. Curr. Sci., 2017, 112, 785–793.

Cleve, C., Kelly, M., Kearns, F. R. and Moritz, M., Classification of the wildland–urban interface: a comparison of pixel-and objectbased classifications using high-resolution aerial photography. Comput. Environ. Urban Syst., 2008, 32, 317–326.

Horritt, M. S., Mason, D. C. and Luckman, A. J., Flood boundary delineation from Synthetic Aperture Radar imagery using a statistical active contour model. Int. J. Remote Sensing, 2001, 22, 2489–2507.

Horritt, M., Waterline mapping in flooded vegetation from airborne SAR imagery. Remote Sensing Environ., 2003, 85, 271–281.

Zhou, C., Luo, J., Yang, C., Li, B. and Wang, S., Flood monitoring using multi-temporal AVHRR and RADARSAT imagery. Photogrammetric Eng. Remote Sensing, 2000, 66(5), 633–638.

Pierdicca, N., Pulvirenti, L., Boni, G., Squicciarino, G. and Chini, M., Mapping flooded vegetation using COSMO-SkyMed: comparison with polarimetric and optical data over rice fields. IEEE J. Selected Top. Appl. Earth Observ. Remote Sensing, 2017, 10, 2650–2662.

Hess, L. L., Melack, J. M. and Simonett, D. S., Radar detection of flooding beneath the forest canopy: a review. Int. J. Remote Sensing, 1990, 11, 1313–1325.

Hess, L. L., Melack, J. M. and Davis, F. W., Mapping of floodplain inundation with multi-frequency polarimetric SAR: use of a tree-based model. In Proceedings of IGARSS ’94 – IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 1994, pp. 1072–1073.

Hess, L. L., Melack, J. M., Filoso, S. and Wang, Y., Delineation of inundated area and vegetation along the Amazon floodplain with the SIR-C synthetic aperture radar. IEEE Trans. Geosci. Remote Sensing, 1995, 33, 896–904.

Hess, L. L. and Malack, J. M., Mapping floodplain vegetation in the central Amazon basin with multi-temporal SIR-C data. In IGARSS 98 – Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings (Cat. No.98CH36174), IEEE, Seattle, WA, USA, 1998, vol. 4, p. 2115.

Hess, L., Dual-season mapping of wetland inundation and vegetation for the central Amazon basin. Remote Sensing Environ., 2003, 87, 404–428.

Arnesen, A. S. et al., Monitoring flood extent in the lower Amazon River floodplain using ALOS/PALSAR ScanSAR images. Remote Sensing Environ., 2013, 130, 51–61.

Alahacoon, N., Matheswaran, K., Pani, P. and Amarnath, G., A decadal historical satellite data and rainfall trend analysis (2001– 2016) for flood hazard mapping in Sri Lanka. Remote Sensing, 2018, 10, 448.

Martinis, S. and Rieke, C., Backscatter analysis using multitemporal and multi-frequency SAR data in the context of flood mapping at River Saale, Germany. Remote Sensing, 2015, 7, 7732–7752.

Pierdicca, N., Chini, M., Pulvirenti, L. and Macina, F., Integrating physical and topographic information into a Fuzzy scheme to map flooded area by SAR. Sensors, 2008, 8, 4151–4164.

Pulvirenti, L., Pierdicca, N., Boni, G., Fiorini, M. and Rudari, R., Flood damage assessment through multitemporal COSMOSkyMed data and hydrodynamic models: the Albania 2010 case study. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sensing, 2014, 7, 2848–2855.

Pulvirenti, L., Marzano, F. S., Pierdicca, N., Mori, S. and Chini, M., Discrimination of water surfaces, heavy rainfall and wet snow using COSMO-SkyMed observations of severe weather events. IEEE Trans. Geosci. Remote Sensing, 2014, 52, 858–869.

Voormansik, K., Praks, J., Antropov, O., Jagomagi, J. and Zalite, K., Flood mapping with TerraSAR-X in forested regions in estonia. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sensing, 2014, 7, 562–577.

Cao, H., Zhang, H., Wang, C. and Zhang, B., Operational flood detection using Sentinel-1 SAR data over large areas. Water, 2019, 11, 786.

Anusha, N. and Bharathi, B., Flood detection and flood mapping using multi-temporal synthetic aperture radar and optical data. Egypt. J. Remote Sensing Space Sci., 2020, 23, 207–219.

Kundu, S., Aggarwal, S., Kingma, N., Mondal, A. and Khare, D., Flood monitoring using microwave remote sensing in a part of Nuna river basin, Odisha, India. Natural Hazards, 2015, 76, 123– 138.

Baghdadi, N., Bernier, M., Gauthier, R. and Neeson, I., Evaluation of C-band SAR data for wetlands mapping. Int. J. Remote Sensing, 2001, 22, 71–88.

Greifeneder, F., Wagner, W., Sabel, D. and Naeimi, V., Suitability of SAR imagery for automatic flood mapping in the Lower Mekong basin. Int. J. Remote Sensing, 2014, 35, 2857–2874.

Yulianto, F., Sofan, P., Zubaidah, A., Sukowati, K. A. D., Pasaribu, J. M. and Khomarudin, M. R., Detecting areas affected by flood using multi-temporal ALOS PALSAR remotely sensed data in Karawang, West Java, Indonesia. Nat. Hazards, 2015, 77, 959–985.

Chini, M., Hostache, R., Giustarini, L. and Matgen, P., A hierarchical split-based approach for parametric thresholding of SAR images: flood inundation as a test case. IEEE Trans. Geosci. Remote Sensing, 2017, 55, 6975–6988.

Giustarini, L., Hostache, R., Matgen, P., Schumann, G. J.-P., Bates, P. D. and Mason, D. C., A change detection approach to flood mapping in urban areas using TerraSAR-X. IEEE Trans. Geosci. Remote Sensing, 2013, 51, 2417–2430.

Refice, A. et al., SAR and InSAR for flood monitoring: examples with COSMO-SkyMed data. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sensing, 2014, 7, 2711–2722.

Pulvirenti, L., Chini, M., Pierdicca, N. and Boni, G., Use of SAR data for detecting floodwater in urban and agricultural areas: the role of the interferometric coherence. IEEE Trans. Geosci. Remote Sensing, 2016, 54, 1532–1544.

Amitrano, D., Di Martino, G., Iodice, A., Riccio, D. and Ruello, G., Unsupervised rapid flood mapping using Sentinel-1 GRD SAR images. IEEE Trans. Geosci. Remote Sensing, 2018, 56, 3290– 3299.

Martinis, S., Plank, S. and Ćwik, K., The use of Sentinel-1 timeseries data to improve flood monitoring in arid areas. Remote Sensing, 2018, 10, 583.

Jo, M.-J., Osmanoglu, B., Zhang, B. and Wdowinski, S., Flood extent mapping using dual-polarimetric Sentinel-1 synthetic aperture radar imagery. ISPRS – Int. Arch. Photogramm., Remote Sensing Spat. Inf. Sci., 2018, XLII–3, 711–713.

Tsyganskaya, V., Martinis, S., Marzahn, P. and Ludwig, R., Detection of temporary flooded vegetation using Sentinel-1 time series data. Remote Sensing, 2018, 10, 1286.

Plank, S., Jüssi, M., Martinis, S. and Twele, A., Mapping of flooded vegetation by means of polarimetric Sentinel-1 and ALOS2/PALSAR-2 imagery. Int. J. Remote Sensing, 2017, 38, 3831–3850.

Ramsar, Annotated list of wetlands of international importance, 2018; http://saconenvis.nic.in/publication/Ramsar-Sites-annotatedsummaryIndia.pdf

India Meteorological Department, Performance of South West Monsoon 2018 over Kerala. Meteorological Centre, Thiruvananthapuram, 2018, pp. 1–16.

Bhatt, C. M., Rao, G. S., Diwakar, P. G. and Dadhwal, V. K., Development of flood inundation extent libraries over a range of potential flood levels: a practical framework for quick flood response. Geomat., Nat. Hazards Risk, 2017, 8(2), 384–401.

Chung, H.-W., Liu, C.-C., Cheng, I.-F., Lee, Y.-R. and Shieh, M.-C., Rapid response to a typhoon-induced flood with an SARderived map of inundated areas: case study and validation. Remote Sensing, 2015, 7, 11954–11973.

Stephens, E., Schumann, G. and Bates, P., Problems with binary pattern measures for flood model evaluation. Hydrol. Process., 2014, 28, 4928–4937.

Chaabani, C., Chini, M., Abdelfattah, R., Hostache, R. and Chokmani, K., Flood mapping in a complex environment using bistatic TanDEM-X/TerraSAR-X InSAR coherence. Remote Sensing, 2018, 10(12), 1873.

Feng, Q., Liu, J. and Gong, J., Urban flood mapping based on unmanned aerial vehicle remote sensing and random forest classifier – a case of Yuyao, China. Water, 2015, 7, 1437–1455.

Feng, Q., Gong, J., Liu, J. and Li, Y., Flood mapping based on multiple endmember spectral mixture analysis and random forest classifier – the case of Yuyao, China. Remote Sensing, 2015, 7, 12539–12562.

CWC, Study Report: Kerala Flood of August 2018. Hydrological Studies Organization, Central Water Commission, Government of India, 2018, pp. 1–46.

Drought intensity and frequency analysis using SPI for Tamil Nadu, India

Abstract Views :184 | PDF Views:88

Authors

S. Kokilavani ¹, S. P. Ramanathan ¹, Ga. Dheebakaran ¹, N. K. Sathyamoorthy ¹, N. K. Maragatham ¹, R. Gowtham ¹

Affiliations
1 Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore 641 003, IN

Source

Current Science, Vol 121, No 6 (2021), Pagination: 781-788

Abstract

To assess the drought hazard for different agro-climatic zones of Tamil Nadu (TN), India, the present study deals with temporal trend and spatial pattern of drought over the period 1981–2019. Standardized Precipitation Index (SPI) has been used to detail the geographical variations of drought intensity, duration and frequency at multiple time steps. The spatial rainfall variability of the Southwest monsoon (SWM) ranged from 69.3 mm (Tuticorin) to 772.8 mm (the Nilgiris), and that for the Northeast monsoon (NEM) ranged from 277.8 mm (Krishnagiri) to 825.9 mm (Nagapattinam), while annual rainfall variability ranged from 558.8 mm (Tuticorin) to 1466.8 mm (the Nilgiris) for TN. Irrespective of all the regions, the frequency of moderate drought occurrence was higher compared to other drought nomenclature. The NEM season recorded on par and higher number of drought occurrences with respect to SWM season. Out of 39 years, TN experienced severely dry to extremely dry climate during 2002. The result underlines the potential of SPI in drought identification and also revealed that the rainfall is strongly linked to drought policies and measures implemented for the state.

Keywords

Northeast monsoon, rainfall, southwest monsoon, spatial variability, standardized precipitation index

Full Text

References

IPCC, Working Group, I Contribution to the IPCC Fifth Assessment Report on Climate Change 2013: The Physical Science Basis – Summary for Policymakers, Intergovernmental Panel on Climate Change, Stockholm, 2013.

Lesk, C., Rowhani, P. and Ramankutty, N., Influence of extreme weather disasters on global crop production. Nature, 2016, 529, 84–87.

NRC, Adapting to the Impacts of Climate Change: America’s Climate Choices, National Academies Press, Washington, DC, USA, 2010.

Agha Kouchak, A., Feldman, D., Hoerling, M., Huxman, T. and Lund, J., Recognize anthropogenic drought. Nature, 2015, 524, 409–411.

Dai, A., Increasing drought under global warming in observations and models. Nature Climate Change, 2013, 3, 52–58.

Trenberth, K. E., Dai, A., Schrier, G., Jones, P. D., Barichivich, J., Briffa, K. R. and Sheffield, J., Global warming and changes in drought. Nature Climate Change, 2014, 4, 17–22.

Dai, A., Drought under global warming: a review. Climate Change, 2011, 2(1), 45–65.

Kamble, M. V., Ghosh, K., Rajeevan, M. and Samui, R. P., Drought monitoring over India through normalized difference vegetation index (NDVI). Mausam, 2010, 61, 537–546.

CRED, Country profile of natural disasters, EM-DAT: The International Disaster Database, Centre for Research on the Epidemiology of Disasters, 2016.

Thomas, T., Nayak, P. C. and Ghosh, N. C., Spatiotemporal analysis of drought characteristics in the Bundelkhand region of Central India using the standardized precipitation index. J. Hydrol. Eng., 2015, 20(11), 05015004-1–05015004-12.

Mishra, A. K. and Singh, V. P., Drought modelling – a review. J. Hydrol., 2011, 403(1–2), 157–175.

Hosseinizadeh, A., Seyed Kaboli, H., Zareie, H., Akhondalim, A. and Farjad, B., Impact of climate change on the severity, duration, and frequency of drought in a semi-arid agricultural basin. Geoenviron. Disasters, 2015, 2, 23.

Hayes, M. J., Wilhelmi, O. V. and Knutson, C. L., Reducing drought risk: bridging theory and practice. Nat. Hazards Rev., 2004, 5(2), 106–113.

Guttman, N. B., Comparing the Palmer drought index and the standardized precipitation index. J. Am. Water Resour. Assoc., 1998, 34(1), 113–121.

Pramudya, Y. and Onishi, T., Assessment of the standardized precipitation index (SPI) in Tegal city, Central Java, Indonesia. IOP Conf. Series: Earth Environ. Sci., 2018, 129, 012019.

Kogan, F. N., Contribution of remote sensing to drought early warning. In Early Warning Systems for Drought Preparedness and Drought Management (eds Wilnite, D. A. and Wood, D A.), World Meteorological Organization, Geneva, 2000, pp. 75–87.

Hayes, M. J., Svoboda, M., Wall, N. and Widhalm, M., The Lincoln Declaration on drought indices: universal meteorological drought index recommended. Bull. Am. Meteorol. Soc., 2011, 92(4), 485–488.

McKee, T. B., Doesken, N. J. and Kleist, J., The relationship of drought frequency and duration to time scales. In 8th Conference on Applied Climatology, Anaheim, CA, USA, 1993, pp. 179–184.

Morid, S., Smakhtin, V. and Moghaddasi, M., Comparison of seven meteorological indices for drought monitoring in Iran. Int. J. Climatol., 2006, 26, 971–985.

Kokilavani, S., Panneerselvam, S. and Dheebakaran, Ga, Centurial rainfall analysis for drought in Coimbatore city of Tamil Nadu, India. Madras Agric. J., 2019, 106(7–9), 484–487.

Ramaraj, A. P., Kokilavani, S., Manikandan, N., Arthirani, B. and Rajalakshmi, D., Rainfall stability and drought valuation (using SPI) over southern zone of Tamil Nadu. Curr. World Environ., 2015, 10(3), 928–933.

Effect of temperature on brown planthopper Infestation in rice using hyperspectral remote Sensing

Abstract Views :93 | PDF Views:61

Authors

S. Sivaranjani ¹, V. Geethalakshmi ¹, S. Pazhanivelan ¹, J. S. Kennedy ¹, S. P. Ramanathan ¹, R. Gowtham ², K. Pugazenthi ¹

Affiliations
1 Tamil Nadu Agricultural University, Coimbatore 641 003, India., IN
2 Indian Farmers Fertilizers Cooperative Limited, Coimbatore 641 003, India., IN

Source

Current Science, Vol 124, No 10 (2023), Pagination: 1194-1200

Abstract

Hyperspectral remote sensing captures images in multiple wavelengths and is widely used to detect plant stress in agriculture. A study was conducted on brown planthopper (BPH) infestation in rice at various temperature regimes (15°C, 20°C, 25°C, 30°C and 35°C). The experimentation was done in the Environmental Control Chamber, Tamil Nadu Agricultural University, Coimbatore, India. The field spectroradiometer and vegetation indices were used to study the early and late infestations of BPH in rice. The results reveal that reflectance at certain wavelengths (550, 670 and 700 nm) indicates plant stress. Among the vegetation indices, MCARI performed better than NDVI, PRI, NDRE and SR for the detection of early and late infestation of BPH. Hence, hyperspectral reflectance from rice has been used to detect pest damage and improve management policies.

Keywords

Brown planthopper, hyperspectral sensor, Plant stress, rice, vegetation indices.

Full Text

References

Oghaz, M. M. D., Razaak, M., Kerdegari, H., Argyriou, V. and Remagnino, P., Scene and environment monitoring using aerial im-agery and deep learning. IEEE, 2019.

Childs, N. and LeBeau, B., Rice Outlook, Report, FAO, 2022.

Saravanakumar, V., Lohano, H. D. and Balasubramanian, R., A dis-trict-level analysis for measuring the effects of climate change on production of rice: evidence from southern India. Theor. Appl. Cli-matol., 2022, 150(3–4), 941–953.

Min, S., Lee, S. W., Choi, B.-R., Lee, S. H. and Kwon, D. H., In-secticide resistance monitoring and correlation analysis to select appropriate insecticides against Nilaparvata lugens (Stål), a migra-tory pest in Korea. J. Asia-Pac. Entomol., 2014, 17(4), 711–716.

Cabauatan, P. Q., Cabunagan, R. C. and Choi, I.-R., Rice viruses transmitted by the brown planthopper Nilaparvata lugens. In Planthoppers: New Threats to the Sustainability of Intensive Rice Production Systems in Asia, IRRI Books, International Rice Res-earch Institute, 2009, pp. 357–368.

Mohapatra, S. D. et al., Eco-smart pest management in rice farming: prospects and challenges. Oryza, 2019, 56(Special Issue), 143–155.

Muller, A., Prakash, A., Lazutkaite, E. M. D., Amdihun, A. and Ouma, J., Scientific linkages between climate change and (trans-boundary) crop pest and disease outbreaks. In TMG Working Paper, 2022, p. 29.

Abd El-Ghany, N. M., Abd El-Aziz, S. E. and Marei, S. S., A review: application of remote sensing as a promising strategy for insect pests and diseases management. Environ. Sci. Pollut. Res., 2020, 27, 33503–33515.

Wang, F. M., Huang, J. F. and Wang, X. Z., Identification of optimal hyperspectral bands for estimation of rice biophysical parameters. J. Integr. Plant Biol., 2008, 50(3), 291–299.

Katsoulas, N., Elvanidi, A., Ferentinos, K. P., Kacira, M., Bartzanas, T. and Kittas, C., Crop reflectance monitoring as a tool for water stress detection in greenhouses: a review. Biosyst. Eng., 2016, 151, 374–398.

Curran, P., Principles of Remote Sensing, Longman, London, UK, 1985.

Prasannakumar, N. R., Chander, S., Sahoo, R. N. and Gupta, V. K., Assessment of brown planthopper (Nilaparvata lugens) damage in rice using hyperspectral remote sensing. Int. J. Pest Manage., 2013, 59(3), 180–188.

Seager, S., Turner, E. L., Schafer, J. and Ford, E. B., Vegetation’s red edge: a possible spectroscopic biosignature of extraterrestrial plants. Astrobiology, 2005, 5(3), 372–390.

Yang, C. M. and Chen, R. K., Differences in growth estimation and yield prediction of rice crop using satellites data simulated from near ground hyperspectral reflectance. J. Photogramm. Remote Sensing, 2007, 12(1), 93–105.

Liu, X. D. and Sun, Q. H., Early assessment of the yield loss in rice due to the brown planthopper using a hyperspectral remote sensing method. Int. J. Pest Manage., 2016, 62(3), 205–213.

Liu, J., Han, J., Chen, X., Shi, L. and Zhang, L., Nondestructive de-tection of rape leaf chlorophyll level based on vis–NIR spectroscopy. Spectrochim. Acta Part A, 2019, 222, 117202.

Huang, J., Liao, H., Zhu, Y., Sun, J., Sun, Q. and Liu, X., Hyper-spectral detection of rice damaged by rice leaf folder (Cnaphalo-crocis medinalis). Comput. Electron. Agric., 2012, 82, 100–107.

Abdel-Rahman, E. M., Ahmed, F. B., van den Berg, M. and Way, M. J., Potential of spectroscopic data sets for sugarcane thrips (Fulmekiola serrata Kobus) damage detection. Int. J. Remote Sens-ing, 2010, 31(15), 4199–4216.

Madasamy, B., Balasubramaniam, P. and Dutta, R., Microclimate-based pest and disease management through a forewarning system for sustainable cotton production. Agriculture, 2020, 10(12), 641.

Penuelas, J., Gamon, J. A., Griffin, K. L. and Field, C. B., Assessing community type, plant biomass, pigment composition, and photo-synthetic efficiency of aquatic vegetation from spectral reflectance. Remote Sensing Environ., 1993, 46(2), 110–118.

Daughtry, C. S. T., Walthall, C. L., Kim, M. S., DeColstoun, E. B. and McMurtrey Iii, J. E., Estimating corn leaf chlorophyll concen-tration from leaf and canopy reflectance. Remote Sensing Environ., 2000, 74(2), 229–239.

Rouse, J. W., Haas, R. H., Schell, J. A. and Deering, D. W., Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ., 1974, 351(1), 309.

Gates, D. M., Keegan, H. J., Schleter, J. C. and Weidner, V. R., Spectral properties of plants. Appl. Opt., 1965, 4(1), 11–20.

Fitzgerald, G. J., Rodriguez, D., Christensen, L. K., Belford, R., Sadras, V. O. and Clarke, T. R., Spectral and thermal sensing for nitrogen and water status in rainfed and irrigated wheat environ-ments. Precis. Agric., 2006, 7, 233–248.

Carter, G. A., Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. Remote Sensing, 1994, 15(3), 697–703.

Yang, C. M., Cheng, C. H. and Chen, R. K., Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder. Crop Sci., 2007, 47(1), 329–335.

Sahoo, R. N., Ray, S. S. and Manjunath, K. R., Hyperspectral re-mote sensing of agriculture. Curr. Sci., 2015, 108(5), 848–859.

Hunt Jr, E. R. and Rock, B. N., Detection of changes in leaf water content using near-and middle-infrared reflectances. Remote Sens-ing Environ., 1989, 30(1), 43–54.

Clarke, A. and Fraser, K. P. P., Why does metabolism scale with temperature? Funct. Ecol., 2004, 18(2), 243–251.

Taylor, R. A. J., Herms, D. A., Cardina, J. and Moore, R. H., Climate change and pest management: unanticipated consequences of trophic dislocation. Agronomy, 2018, 8(1), 7.

Hannigan, S., Nendel, C. and Krull, M., Effects of temperature on the movement and feeding behaviour of the large lupine beetle, Sitona gressorius. J. Pest Sci., 2022, 1–14.

Priyadarshini, S., Ghosh, S. K. and Nayak, A. K., Field screening of different chilli cultivars against important sucking pests of chilli in West Bengal. Bull. Environ., Pharmacol. Life Sci., 2019, 8(7), 134–140.

Yan, T., Xu, W., Lin, J., Duan, L., Gao, P., Zhang, C. and Lv, X., Com-bining multi-dimensional convolutional neural network (CNN) with visualization method for detection of Aphis gossypii Glover infec-tion in cotton leaves using hyperspectral imaging. Front. Plant Sci., 2021, 12, 604.

Polivova, M. and Brook, A., Detailed investigation of spectral vegeta-tion indices for fine field-scale phenotyping. Vegetation Index Dyna-mics, 2021.

Huang, J. R., Sun, J. Y., Liao, H. J. and Liu, X.-D., Detection of brown planthopper infestation based on SPAD and spectral data from rice under different rates of nitrogen fertilizer. Precis. Agric., 2015, 16, 148–163.

Vanegas, F., Bratanov, D., Powell, K., Weiss, J. and Gonzalez, F., A novel methodology for improving plant pest surveillance in vine-yards and crops using UAV-based hyperspectral and spatial data. Sensors, 2018, 18(1), 260.

Pinter Jr, P. J., Hatfield, J. L., Schepers, J. S., Barnes, E. M., Moran, M. S., Daughtry, C. S. T. and Upchurch, D. R., Remote sensing for crop management, 2003.

Broge, N. H. and Leblanc, E., Comparing prediction power and sta-bility of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing Environ., 2001, 76(2), 156–172.

de Lima, I. P., Jorge, R. G. and de Lima, J. L. M. P., Remote sensing monitoring of rice fields: Towards assessing water saving irrigation management practices. Front. Remote Sensing, 2021, 2, 762093.

Kurbanov, R. and Zakharova, N., Justification and selection of vegetation indices to determine the early soybeans readiness for harvesting. EDP Sciences, 2021.

Luo, J., Huang, W., Zhao, J., Zhang, J., Zhao, C. and Ma, R., Detecting aphid density of winter wheat leaf using hyperspectral measure-ments. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sensing, 2013, 6(2), 690–698.

Prabhakar, M., Prasad, Y., Desai, S. and Thirupathi, M., Spectral and spatial properties of rice brown plant hopper and groundnut late leaf spot disease infestation under field conditions. J. Agrome-teorol., 2013, 15, 57–62.

Sogawa, K., The rice brown planthopper: feeding physiology and host plant interactions. Ann. Rev. Entomol., 1982, 27(1), 49–73.

Watanabe, T. and Kitagawa, H., Photosynthesis and translocation of assimilates in rice plants following phloem feeding by the planthopper Nilaparvata lugens (Homoptera: Delphacidae). J. Econ. Entomol., 2000, 93(4), 1192–1198.

Liu, J. L., Yu, J. F., Wu, J. C., Yin, J. L. and Gu, H. N., Physiological responses to Nilaparvata lugens in susceptible and resistant rice varie-ties: allocation of assimilates between shoots and roots. J. Econ. Entomol., 2008, 101(2), 384–390.

Vanitha, K., Suresh, S. and Gunathilagaraj, K., Influence of brown planthopper Nilaparvava lugens feeding on nutritional biochemis-try of rice plant. ORYZA – Int. J. Rice, 2011, 48(2), 142–146.

Username
Password
Remember me