Skip to main content

Advertisement

Springer Nature Link
Log in

Geospatial assessment of agroforestry land use systems using very high-resolution satellite images and artificial intelligence

  • Published:
Agroforestry Systems Aims and scope Submit manuscript

Abstract

In a vast agrarian country like India, multifunctional agroforestry land use systems play significant role. Reliable scientific database on agroforestry systems are essential to implement National Agroforestry Policy of Government of India. This study is a flagship initiative to develop a scalable geospatial approach for assessment of agroforestry resources using sub-meter satellite images and Deep Learning (DL) techniques. A reliable methodology was developed to quantify tree components in Indian agroforestry systems, using U-Net based DL architecture pre-trained on ResNet 34 backbone. 12,628 labelled training samples and in-season ground truth information from 1,767 locations, representing diverse agro-ecological regimes from 6 study areas were utilised for DL model development. Model accuracy was estimated as 93.72 percent, underscoring its robustness to extract major tree components like individual trees on farmland, linear and block plantations. DL outputs were integrated with harmonized Land Use Land Cover maps at 1:10,000 scale, to arrive at integrated agroforestry land use map with 10–15 classes, with an overall accuracy of 86.5 percent and kappa coefficient of 0.847. This is the first detailed study in India, adopting AI based technique for classifying tree components using sub-meter images within large geographic extent of 25,501 km2. It is a major step towards establishing improved geospatial procedure for scientific assessment of agroforestry systems to meet India’s commitments to UNFCCC and Intended Nationally Determined Contributions at COP21 in Paris on climate and environment under International Charters. This robust, DL based approach offers potential applications in ecological research, upliftment of agrarian community, sustainability and environmental policy planning.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

This study utilizes very high resolution satellite images of 0.6 to 0.7 m. Satellite images of 1 m or better spatial resolution cannot be shared in public domain, as per National Geospatial Policy of India.

References

  • Ahmad F, Goparaju L, Qayum A (2017) Agroforestry suitability analysis based upon nutrient availability mapping: a GIS based suitability mapping. AIMS Agric Food 2(2):201–220. https://doi.org/10.3934/agrfood.2017.2.201

    Article  Google Scholar 

  • Ahmad F, Uddin MM, Goparaju L, Rizvi J, Biradar C (2020) Quantification of the land potential for scaling agroforestry in South Asia. J Cartogr Geogr Inf 70:71–89. https://doi.org/10.1007/s42489-020-00045-0

    Article  Google Scholar 

  • Basu JP (2014) Agroforestry, climate change mitigation and livelihood security in India. N Z J For Sci 44(Suppl. 1):S11. https://doi.org/10.1186/1179-5395-44-S1-S11

    Article  Google Scholar 

  • Bhaskar V, Rao NS, Reddy BG, Vedavyasa K, Ravishankar HM, Venkatesh R (1991) Comparative studies of the associated soil moisture regimes and their productivity in an agroforestry system. In proceedings: International symposium on ‘Growth and water use of forest plantations’ held in Bangalore, India during 04–07 Feb.1991, https://www.osti.gov/biblio/140050

  • Bhoonidhi (2023) National Remote Sensing Centre (NRSC), Indian Space Research Organisation (ISRO). https://bhoonidhi.nrsc.gov.in/bhoonidhi/index.html#

  • Bhuvan Panchayat (2023) Bhuvan Panchayat 3.0, National Remote Sensing Centre (NRSC), Indian Space Research Organisation (ISRO). https://bhuvan-panchayat3.nrsc.gov.in/index.html

  • Brandt M, Tucker CJ, Kariryaa A et al (2020) An unexpectedly large count of trees in the West African Sahara and Sahel. Nature 587:78–82. https://doi.org/10.1038/s41586-020-2824-5

    Article  CAS  PubMed  Google Scholar 

  • Brandt J, Fred Stolle (2020) A global method to identify trees inside and outside of forests with medium resolution satellite imagery. Remote Sens Environ. https://arxiv.org/abs/2005.08702

  • Cannell MGR (1987) Agroforestry -a decade of development. International Center for Research in Agroforestry, ICRAF, 24, 393.

  • Chaudhari K, Nishant N, Upadhyay G, More R, Singh N, Vyas SP, Bhattacharya BK (2019) Crop Inventory of Orchard Crops in India using Remotely Sensed Data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3/W6, 2019 ISPRS-GEOGLAM-ISRS Joint Int. Workshop on “Earth Observations for Agricultural Monitoring”, 18–20 February 2019, New Delhi, India. isprs-archives-XLII-3-W6-269-2019.pdf (copernicus.org)

  • Dhyani SK (2014) National Agroforestry Policy 2014 and the need for area estimation under agroforestry. Curr Sci 107(1):9–10

    Google Scholar 

  • Dhyani S, Murthy IK, Kadaverugu R, Dasgupta R, Kumar M, Adesh Gadpayle K (2021) Agroforestry to achieve global climate adaptation and mitigation targets: are South Asian countries sufficiently prepared? Forests 12:303. https://doi.org/10.3390/f12030303

    Article  Google Scholar 

  • Dwivedi AP (2022) Agroforestry: principles and practices. Oxford & IBH Publishing Company, pp. 1–355.

  • FAO (2004) Improvement of rural livelihoods: the role of agroforestry. https://www.fao.org/forestry/7459-03b98002736c1812f203700d24b933c91.pdf

  • FAO (2005) Land cover classification system (LCCS), Version 2: Classification Concepts and User Manual, By A. Di Gregorio, Louisa J.M. Jansen, FAO Environment and Natural Resources Service Series, No. 8, Food and Agriculture Organisation (United Nations), Rome. Land Cover Classification System - Classification concepts and user manual (fao.org)

  • FAO (2013) Towards the assessment of trees outside forests. Forest Resources Assessment, Working Paper 183. A Thematic Report prepared in the framework of The Global Forest Resources Assessment 2010, Rome, 335. https://www.fao.org/3/aq071e/aq071e00.htm

  • FAO, ICRAF (2019) Agroforestry and tenure. Forestry Working Paper no. 8, Rome, 40. Agroforestry and tenure (fao.org)

  • FSI (2015) India State of forest report. Forest Survey of India, Dehradun

    Google Scholar 

  • FSI (2020) Trees outside forests resources in India. For Surv India Tech Inf Ser 2(1):1–30

    Google Scholar 

  • FSI (2021) India State of forest report, vol 17. Forest Survey of India, Dehradun

    Google Scholar 

  • FSI (2019) India State of forest report. Forest Survey of India, Dehradun 16: Vol. 1 & 2.

  • Gholz HL (1988) Agroforestry - introduction. In: Gholz HL (ed) Agroforestry: realities, possibilities and potentials. Martinus Nijhoff Publishers, Dordrecht

    Google Scholar 

  • Hebbar R, Ravishankar HM, Shivam Trivedi, Rama Subramoniam S, Uday Raj, Dadhwal VK (2014) Object-oriented classification of high-resolution data for inventory of horticultural crops. In: The international archives of the photogrammetry, remote sensing and spatial information sciences. XL-8, 2014 ISPRS Technical Commission VIII Symposium, 09–12 December 2014. isprsarchives-XL-8-745-2014.pdf (copernicus.org)

  • Hebbar R, Ravishankar HM, Shivam Trivedi, Manjula VB, Kumar NM, Mukharib DS, Mote JK, Sudheesh S, Uday Raj, Raghuramulu Y, Ganesh Raj K (2019) National level inventory of coffee plantations using high resolution satellite data. ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3/W6: 293–298. https://doi.org/10.5194/isprs-archives-XLII-3-W6-293-2019

  • Hudson WD, Ramm CW (1987) Correct formulation of the Kappa coefficient of agreement. Photogramm Eng Remote Sens 53(4):421–422

    Google Scholar 

  • ICAR-NBSS & LUP (2015) Serving science and society. National Bureau of Soil Survey & Land Use Planning, Indian Council of Agriculture Research, Nagpur, India

  • ICRAF (1993) A global agenda by Helen Van Houten. Annual report 1993. International Centre for Research in Agroforestry, 1–208.

  • Kapnias D, Milenov P, Kay S (2008) Guidelines for best practice and quality checking of ortho imagery. Issue 3.0, JRC Scientific and technical reports, European Commission 36. First issued: Ispra, 20th December 2008 (core.ac.uk)

  • Lasco RD, Delfino RJ, Catacutan DC, Simelton ES, Wilson DM (2014) Climate risk adaptation by smallholder farmers: the roles of trees and agroforestry. Curr Opin Environ Sustain 6:83–88. https://doi.org/10.1016/j.cosust.2013.11.013

    Article  Google Scholar 

  • Liknes GC, Perry CH, Meneguzzo DM (2010) Assessing tree cover in agricultural landscapes using high-resolution aerial imagery. J Terr Obs 2(1): Winter 2010

  • Liu T, Chen W (2015) A scalable deep convolutional neural network for high-resolution imagery scene classification. IEEE Geosci Remote Sens Lett 12(8):1621–1625

    Google Scholar 

  • Maa L, Liuc Y, Zhanga X, Yuanxin Y, Gaofei Y, Alan Johnson B (2019) Deep learning in remote sensing applications: a meta-analysis and review. ISPRS J Photogramm Remote Sens 152:166–177. https://doi.org/10.1016/j.isprsjprs.2019.04.015

    Article  Google Scholar 

  • Mercer DE, Miller RP (1997) Socioeconomic research in agroforestry: progress, prospects, priorities. Agrofor Syst 38(1):177–193. https://doi.org/10.1023/A:1005964830133

    Article  Google Scholar 

  • MoEFCC (2023) India: Third National communication and initial adaptation communication to the United Nations framework convention on climate change. Ministry of Environment, Forest and Climate Change, Government of India, New Delhi, pp 1–613

    Google Scholar 

  • MoEFCC (2021) India: third biennial update report to the United Nations framework convention on climate change. Ministry of Environment, Forest and Climate Change, Government of India, p.1–479. https://moef.gov.in/wp-content/uploads/2024/01/INDIA_BUR-3.pdf

  • Muralikrishnan S, Pillai A, Narender B, Reddy S, Raghu Venkataraman V, Dadhwal VK (2013) Validation of Indian national DEM from Cartosat-1 data. J Indian Soc Remote Sens 41:1–13. https://doi.org/10.1007/s12524-012-0212-9

    Article  Google Scholar 

  • Nair PKR (1985) Classification of agroforestry systems. Agrofor Syst 3:97–128

    Article  Google Scholar 

  • Nair PKR, Mohan Kumar B, Nair V (2021) An introduction to agroforestry: four decades of scientific developments. Second Edition, ISBN: 978-3-030-75357-3, Springer, 1–666. https://doi.org/10.1007/978-3-030-75358-0

  • National Agroforestry Policy (NAP), India (2014) National Agroforestry Policy 2014.pdf (agricoop.nic.in)

  • NRSC (2020a) Technical guidelines under SIS-DP Update Project, National Remote Sensing Centre, ISRO, NRSC-RC-DELHI-OCT2020-TR0001686-V1.0., 1–110.

  • NRSC (2020b) Revised legend for LULC thematic map under ‘SIS-DP Update’ project, National Remote Sensing Centre, ISRO, NRSC-NR-DIR-AUG-2020-TR-1371-V2.0.

  • Pandey A, Pati UC (2019) Image mosaicking: a deeper insight. Image Vis Comput 89:236–257. https://doi.org/10.1016/j.imavis.2019.07.002

    Article  Google Scholar 

  • Pujar GS, Dadhwal VK, Murthy MSR, Trivedi S, Reddy PM, Swapna D, Jha CS (2016) Geospatial approach for national level TOF assessment using IRS high resolution imaging: early results. J Indian Soc Remote Sens. https://doi.org/10.1007/s12524-015-0476-y

    Article  Google Scholar 

  • Puri S, Nair PKR (2004) Agroforestry research for development in India: 25 years of experiences of a national program. Agrofor Syst 61:437–452. https://doi.org/10.1023/B:AGFO.0000029014.66729.e0

    Article  Google Scholar 

  • Ravishankar HM, Trivedi S, Ramasubramoniam S, Mohammed Ahamed J, Nagashree TR, Manjula VB, Hebbar R, Jha CS, Dadhwal VK (2022) Geospatial applications in inventory of horticulture plantations. In: Jha CS, Pandey A, Chowdary V, Singh V (eds) Geospatial technologies for resources planning and management. Water Science and Technology Library, vol 115. Springer, Cham. https://doi.org/10.1007/978-3-030-98981-1_12

  • Ray SS, Mamatha S, Manjunath KR, Uday Raj, Seshasai MVR, Singh KK, Kimothi MM, Parihar JS, Mamta Saxena (2016) CHAMAN- a national level programme for horticulture assessment and development. NNRMS e-Bulletin, Department of Space, Government of India, Bengaluru, May 2016, 1–6. nnrms_bulletin_may_2016.pdf (isro.gov.in)

  • Rizvi RH, Handa AK, Sridhar KB et al. (2020) Mapping agroforestry and trees outside forest. Jointly published by the ICAR, Central Agroforestry Research Institute (CAFRI), Jhansi and World Agroforestry (ICRAF), South Asia Regional Programme, New Delhi. B20033.pdf (worldagroforestry.org)

  • Ronneberger O, Fisher P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. Compu Vis Pattern Recognit. https://arxiv.org/abs/1505.04597

  • Shin S, Soe KT, Lee H, Kim TH, Lee S, Park MS (2020) A systematic map of agroforestry research focusing on ecosystem services in the Asia-Pacific region. Forests 11(4):368. https://doi.org/10.3390/f11040368

    Article  Google Scholar 

  • Singh SK, Chatterji S, Chattaraj S, Butte PS (2018) ICAR-NBSS&LUP Technologies, ICAR- The National Bureau of Soil Survey and Land Use Planning (NBSS & LUP). Technical report, NBSS Publ. no. 176, p 90. https://doi.org/10.13140/RG.2.2.23495.11687

  • Smith LN (2018) A disciplined approach to neural network hyperparameters: part 1-learning rate, batch size, momentum, and weight decay. https://doi.org/10.48550/arXiv.1803.09820

  • SOI (2023) Survey of India open series maps, Survey of India, Ministry of Science and Technology, Government of India. Survey of India

  • Uthappa AR, Chavan SB, Handa AK, Ram Newaj, Dhiraj Kumar, Sridhar KB, Chaturvedi OP (2016) Agroforestry- a sustainable solution to address climate change challenges. In: Gupta SK, Panwar P, Kaushal R (eds) Agroforestry for increased production and livelihood security. New India Publishing Agency, 1–22.

  • van Noordwijk M (2021) Agroforestry-based ecosystem services. Land 10:770. https://doi.org/10.3390/land10080770

    Article  Google Scholar 

  • Vinod PV, Trivedi S, Hebbar R, Jha CS (2023) Assessment of Trees Outside Forest (TOF) in urban landscape using high-resolution satellite images and Deep Learning techniques. J Indian Soc Remote Sens 51(3):549–564. https://doi.org/10.1007/s12524-022-01646-0

    Article  Google Scholar 

  • Waldron A, Garrity D, Malhi Y, Girardin C, Miller DC, Seddon N (2017) Agroforestry can enhance food security while meeting other sustainable development goals. Trop Conserv Sci. https://doi.org/10.1177/1940082917720667

    Article  Google Scholar 

  • Zomer R, Trabucco A, Coe R, Place F, van Noordwijk M, Xu J (2014) Trees on farms: an update and reanalysis of agroforestry’s global extent and socio-ecological characteristics. Working Paper 179, World Agroforestry Center: Bogor, Indonesia, 1–33. WP14064.pdf (worldagroforestry.org)

Download references

Acknowledgements

Authors express their sincere gratitude to Indian Space Research Organisation (ISRO) Headquarters, Bengaluru and various wings of Ministries of Government of India for the facilitation and approval of Letter of Agreement for this study; FAO team members- Mr. Tomio Shichiri (FAO Representative in India), Mr. Illias Animon (Lead Technical Officer, FAO-RAP, Bangkok), Dr. Konda Reddy Chavva (Assistant FAO Representative in India, Programme) and Ms. Athira Sobhana (Project Associate, FAO-India); NRAA team members- Dr. Ashok Dalwai (CEO) & Mr. B. Rath (Technical Expert, WM) for their involvement and overall contribution in the study. The support received from State Forest Departments of Indian states, namely Karnataka, Uttar Pradesh, Haryana, Rajasthan and Assam for in-season field data collection is gratefully acknowledged. We are also thankful to Ms. D. Rama Devi, Scientist, NRSC Data Centre (NDC), NRSC for providing large number of satellite imageries in a timely manner. The contribution of scientists from RRSCs / NRSC, namely Mr. H.M. Ravishankar, Dr. D. Chakraborty, Mr. Y.K. Srivastava, Mr. Ashish Shrivastava, Mr. T.P. Girish Kumar, Mr. Naresh Nagamalle and the support provided by other technical & administrative staff of NRSC and Regional Centres during project execution is thankfully acknowledged.

Funding

This study was funded by Food and Agriculture Organisation (United Nations), New Delhi under Technical Cooperation Programme (TCP/IND/3710) with National Remote Sensing Centre, Indian Space Research Organisation, Government of India.

Author information

Authors and Affiliations

  1. Regional Remote Sensing Centre-South, National Remote Sensing Centre (NRSC), Indian Space Research Organisation (ISRO), Karthik Nagar, Marathahalli, Bengaluru, Karnataka, 560037, India

    Shivam Trivedi, P. V. Vinod, B. Chandrasekaran, M. K. Nagashree, S. Rama Subramoniam, V. B. Manjula, K. Nagajothi & R. Hebbar

  2. Regional Remote Sensing Centre-Central, NRSC, ISRO, Amravati Road, Nagpur, Maharashtra, 440033, India

    Amrita Singh, J. K. Mani, Arun S. Suryavanshi, Sushilkumar B. Rehpade & A. O. Varghese

  3. Regional Centres, NRSC, ISRO, Antariksh Bhavan, Plot No.7, Sadiq Nagar, New Delhi, 110049, India

    Akash Goyal, N. R. Shankar Ram & S. K. Srivastav

  4. Regional Remote Sensing Centre-East, NRSC, ISRO, New Town, Kolkata, West Bengal, 700156, India

    P. K. Das, Tanumi Kumar & Arati Paul

  5. Regional Remote Sensing Centre-West, NRSC, ISRO, New ISRO Complex, 9 Sector, Kuri Bhagtasani Housing Board, Jodhpur, Rajasthan, 342005, India

    M. K. Verma, Shashikant Sharma & A. K. Bera

  6. National Remote Sensing Centre, ISRO, Balanagar, Hyderabad, Telangana, 500037, India

    S. H. Rao, P. Aravinda Kumar, K. Chandrasekar, C. S. Jha & Prakash Chauhan

  7. Food and Agriculture Organisation of United Nations, C-293, Block C, Defense Colony, New Delhi, 110024, India

    Divya Shah & R. B. Sinha

Authors
  1. Shivam Trivedi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. V. Vinod
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. B. Chandrasekaran
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. K. Nagashree
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. S. Rama Subramoniam
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. V. B. Manjula
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Amrita Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. J. K. Mani
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Arun S. Suryavanshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Sushilkumar B. Rehpade
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Akash Goyal
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. N. R. Shankar Ram
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. P. K. Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Tanumi Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Arati Paul
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. M. K. Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Shashikant Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. A. O. Varghese
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. S. H. Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. P. Aravinda Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Divya Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. K. Chandrasekar
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. K. Nagajothi
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. A. K. Bera
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. R. Hebbar
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. C. S. Jha
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. S. K. Srivastav
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. R. B. Sinha
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Prakash Chauhan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization- JCS, HR, ST, VPV; Methodology- HR, VPV, ST; Software- VPV, CB; Validation- NMK, RSS, MVB, NK; Formal analysis- RSS, NK, ST, VPV, CB; Investigation- ST, CB, NMK, MVB, JKM, ASS, AG, SRNR, PKD, TK, AP, MKV, SS; Data curation- AKP, HRS, ST, MVB, NMK, RSS, NK, VPV, CB, AS, JKM, ASS, SKR, AG, SRNR, PKD, TK, AP, MKV, SS; Writing (original draft)- ST, VPV; Writing (review & editing)—HR, JCS; Visualization- ST, VPV, RSS, MVB, HR; Supervision- HR, JCS, SKS, SRB, PC, VAO, BAK; Project administration- HR, JCS, CK; Funding acquisition- SRB, JCS, DS.

Corresponding author

Correspondence to Shivam Trivedi.

Ethics declarations

Competing interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Trivedi, S., Vinod, P.V., Chandrasekaran, B. et al. Geospatial assessment of agroforestry land use systems using very high-resolution satellite images and artificial intelligence. Agroforest Syst (2024). https://doi.org/10.1007/s10457-024-01042-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10457-024-01042-2

Keywords

Search

Navigation