Prediction of Canopy Cover for Agricultural Land Classification in Land Parcel Identification System (LPIS) Data Using Planet-Scope Multispectral Images: A Case Study of Gelendost District

Year 2024, , 407 - 417, 15.07.2024

Sinan Demir

https://doi.org/10.47115/bsagriculture.1490400

Abstract

Determining canopy cover (CC) temporal variation is critical for sustainable management of natural resources and environmental protection efforts. Data analysis and interpretation methods for remote sensing are important for understanding these changes and adapting to natural systems. In this study used the Parcel Identification System (LPIS) database physical blocks as field ground data. In the study area, agricultural areas were determined from LPIS data, including classes A0, A1, A3, A4, S1, T0, and T1, and a total of 8424 physical blocks and an area of 14651.9 hectares were evaluated. CC estimates were made using 3-m spatial resolution Planet Scope multispectral satellite images of July and August 2023, and it was determined that there were significant differences in parcel-based distinctions, especially in parcels A0, A1, T0, and T1 (P<0.05). According to the study results, it was determined that using the estimated CC data, the A0 (69.27%) and T0 (30.43%) land cover types could be successfully used to determine the changes in the phenological period caused by environmental impact assessment such as climate change. At the same time, this study contributes to the rapid monitoring of agricultural production areas caused by climate change by using physical blocks of agricultural land classes within the LPIS data, the rapid determination of agricultural land management, and support payments with remote sensing data. In this regard, the use of modern technologies and data analysis methods will contribute to increasing agricultural sustainability.

Keywords

Land use/land cover, Canopy cover, NDVI, LPIS, Climate change, Remote sensing

References

• Akgün İ, Altındal D, Kara B. 2011. Determination of suitable sowing dates for some bread and durum wheat cultivars under Isparta ecological conditions. JAS, 17(4): 300-309.
• Aljanabi F, Dedeoğlu M, Şeker C. 2024. Environmental monitoring of Land Use/Land Cover by integrating remote sensing and machine learning algorithms. J Eng Sustain Devel, 28(4): 455-466.
• Anonymous. 2024. Republic of Türkiye Ministry of Agriculture and Forestry Agricultural Production Planning Group Work Document. URL: https://cdniys.tarimorman.gov.tr/api/File/GetGaleriFile/330/DosyaGaleri/956/16%20Tar%C4%B1msal%20%C3%9Cretim%20%20Planlamas%C4%B1%20Grubu%20%C3%87al%C4%B1%C5%9Fma%20Belgesi.pdf (accessed date: May 19, 2024).
• ArcGIS. 2024. ArcGIS Pro geoprocessing tool reference. URL: https://pro.arcgis.com/en/pro-app/latest/tool-reference/main/arcgis-pro-tool-reference.htm (accessed date: May 19, 2024).
• Baret F. Guyot G. 1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sens Environ, 35(2-3): 161-173.
• Basso B, Cammarano D, De Vita P. 2004. Remotely sensed vegetation indices: Theory and applications for crop management. Rivista Italiana di Agrometeorologia, 1(5): 36-53.
• Berger K, Machwitz M, Kycko M, Kefauver SC, Van Wittenberghe S, Gerhards M, Verrelst J, Atzberger C, van der Tol C, Damm A, Rascher U, Herrmann I, Sobejano Paz V, Fahrner S, Pieruschka R, Prikaziuk E, Buchaillot ML, Halabuk A, Celesti M, Koren G, Tunc Gormus E, Rossini M, Foerster M, Siegmann B, Abdelbaki A, Tagliabue G, Hank T, Darvishzadeh R, Aasen H, Garcia M, Poças I, Bandopadhyay S, Sulis M, Tomelleri E, Rozenstein O, Filchev L, Stancile G, Schlerf M. 2022. Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review. Remote Sens Environ, 280: 113198.
• Çakır M, Yıldırım A, Çelik C, Esen M. 2021. The effect of different plant growth regulators on the quality and biochemical content of Jeromine apple cultivar. Anadolu J Agri Sci, 36(3): 478-487.
• Carella A, Bulacio Fischer PT, Massenti R, Lo Bianco R. 2024. Continuous plant-based and remote sensing for determination of fruit tree water status. Horticulturae, 10(5): 516.
• Clevers JGPW. 1989. Application of a weighted infrared-red vegetation index for estimating leaf area index by correcting for soil moisture. Remote Sens Environ, 29(1): 25-37.
• Damm A, Paul-Limoges E, Haghighi E, Simmer C, Morsdorf F, Schneider FD, van der Tol C, Migliavacca M, Rascher U. 2018. Remote sensing of plant-water relations: An overview and future perspectives. J Plant Physiol, 227: 3-19.
• Demir S, Başayiğit L. 2024. Digital mapping burn severity in agricultural and forestry land over a half-decade using sentinel satellite images on the Google earth engine platform: A case study in Isparta province. Trees for People, 16: 100520.
• Demir S, Dedeoğlu M, Başayiğit L. 2024. Yield prediction models of organic oil rose farming with agricultural unmanned aerial vehicles (UAVs) images and machine learnaing algorithms. Remote Sens Appl, 33: 1-25.
• Demir S. 2023. Determination of burned areas at different threshold values using Sentinel-2 satellite images on Google Earth Engine. Turk J Remote Sens GIS, 4(2): 262-275.
• Demir S. 2024. Determination of suitable agricultural areas and current land use in Isparta Province, Türkiye, through a linear combination technique and geographic information systems. Environ Dev Sustain, 26: 13455-13493.
• Erdas. 2024. How to create an NDVI image using Erdas Imagine. URL: https://supportsi.hexagon.com/help/s/article/How-to-Create-an-NDVI-Image-using-ERDAS-IMAGINE?language=en_US (accessed date: May 19, 2024).
• Esetlili M T, Serbeş Z A, Çolak Esetlili B, Kurucu Y, Delibacak S. 2022. Determination of water footprint for the cotton and maize production in the Küçük Menderes Basin. Water, 14(21): 3427.
• Eskimez İ, Polat M, Mertoğlu K. 2020. Phenological and physico-chemical characteristics of Arapkızı, Jonagold and Fuji Kiku Apple (Malus domestica Bork.) varieties grafted on M9 rootstock in Isparta ecological conditions. Uluslar Tarim Yaban Hayati Bil Derg, 6(2): 152-159.
• Ghiat I, Mackey HR, Al-Ansari T. 2021. A review of evapotranspiration measurement models. techniques and methods for open and closed agricultural field applications. Water, 13(18): 2523.
• Ghuffar S. 2018. DEM generation from multi-satellite PlanetScope imagery. Remote Sens, 10(9): 1462.
• Gitelson AA. 2013. Remote estimation of crop fractional vegetation cover: the use of noise equivalent as an indicator of performance of vegetation indices. Int J Remote Sens, 34(17): 6054-6066.
• Gong H, Cheng Q, Jin H, Ren Y. 2023. Effects of temporal, spatial, and elevational variation in bioclimatic indices on the NDVI of different vegetation types in Southwest China. Ecol Indic, 154: 110499.
• Hassan MA, Yang M, Rasheed A, Jin X, Xia X, Xiao Y, He Z. 2018. Time-series multispectral indices from unmanned aerial vehicle imagery reveal senescence rate in bread wheat. Remote Sens, 10(6): 809.
• Hatfield JL, Prueger JH, Sauer TJ, Dold C, O’Brien P, Wacha K. 2019. Applications of vegetative indices from remote sensing to agriculture: Past and future. Inventions, 4(4): 71.
• Huete AR. 1988. A soil-adjusted vegetation index (SAVI). Remote Sens Environ, 25(3): 295-309.
• Kaufman YJ, Tanre D. 1992. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans Geosci Remote Sens, 30(2): 261-270.
• Kazemi Garajeh M, Salmani B, Zare Naghadehi S, Valipoori Goodarzi H, Khasraei A. 2023. An integrated approach of remote sensing and geospatial analysis for modeling and predicting the impacts of climate change on food security. Sci Rep, 13(1): 1057.
• Küçükyumuk Z, Erdal İ. 2022. Effect of calcium on and mineral nutrient concentrations and fruit quality in different apple tree varieties. J Elem, 27(1): 75-85.
• Küçükyumuk Z. 2021. Foliarly applied osmotic preservative contributes to pear (Pyrus comminus) leaf and root nutritional status under drought stress. Appl Ecol Environ Res, 19(4): 3019-3028.
• Kumar V, Sharma A, Bhardwaj R, Thukral AK. 2018. Comparison of different reflectance indices for vegetation analysis using Landsat-TM data. Remote Sens Appl, 12: 70-77.
• Kumar V, Sharma KV, Pham QB, Srivastava AK, Bogireddy C, Yadav SM. 2024. Advancements in drought using remote sensing: assessing progress, overcoming challenges, and exploring future opportunities. Theor Appl Climatol, 2024: 1-38.
• Le TS, Harper R, Dell B. 2023. Application of remote sensing in detecting and monitoring water stress in forests. Remote Sens, 15(13):3360.
• Ličina V, Krogstad T, Fotirić Akšić M, Meland M. 2024. Apple growing in Norway-ecologic factors, current fertilization practices and fruit quality: a case study. Horticulturae, 10(3): 233.
• MGM. 2024. General Directorate of Meteorology, Turkish State Meteorological Service. URL: https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?m=ISPARTA (accessed date: May 19, 2024).
• Minitab. 2024. Minitab statistical software. URL: https://www.minitab.com/en-us/products/minitab/free-trial/ (accessed date: May 19, 2024).
• Nhemachena C, Nhamo L, Matchaya G, Nhemachena CR, Muchara B, Karuaihe ST, Mpandeli S. 2020. Climate change impacts on water and agriculture sectors in Southern Africa: Threats and opportunities for sustainable development. Water, 12(10): 2673.
• Oliveira S, Cunha J, Nóbrega RL, Gash JH, Valente F. 2024. Enhancing global rainfall interception loss estimation through vegetation structure modeling. J Hydrol, 631: 130672.
• Pei F, Wu C, Liu X, Li X, Yang K, Zhou Y, Wang K, Xu L, Xia G. 2018. Monitoring the vegetation activity in China using vegetation health indices. Agric For Meteorol, 248: 215-227.
• Pinty B, Verstraete MM. 1992. GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio, 101:15-20.
• Planet. 2024. Planet imagery product specifications. URL: https://assets.planet.com/docs/Planet_Combined_Imagery_Product_Specs_letter_screen.pdf (accessed date: May 19, 2024).
• Qin S, Li S, Cheng L, Zhang L, Qiu R, Liu P, Xi H. 2023. Partitioning evapotranspiration in partially mulched interplanted croplands by improving the Shuttleworth-Wallace model. Agric For Meteorol, 276: 108040.
• Rezvi HUA, Tahjib-Ul-Arif M, Azim MA, Tumpa TA, Hasan Tipu MM, Najnine F, Dawood MFA, Skalicky M, Brestič M. 2023. Rice and food security: Climate change implications and the future prospects for nutritional security. Food Energy Secur, 12(1): e430.
• Rondeaux G, Steven M, Baret F. 1996. Optimization of soil-adjusted vegetation indices. Remote Sens Environ, 55(2): 95-107.
• Rouse JW, Haas RH, Schell JA, Deering DW. 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ., New York, US, 351(1): 309-330.
• Selim S, Sönmez NK, Çoşlu M. 2022. The effect of temporal variation in land surface temperature on land cover classes and agricultural areas: Recent Studies in Planning and Design. İKSAD Publishing House, Ankara, Türkiye, 1th ed., pp: 183-207.
• Selim S, Sönmez NK. 2015. Determination of sweetgum (Liquidambar orientalis Miller) populations distribution with geographic information systems and evaluation of landscape metrics by using habitat quality assessment; a case study of Mugla Koycegiz. Tekirdag Zir Fak Derg, 12(1): 30-38.
• Seong S, Chang A, Mo J, Na S, Ahn H, Oh J, Choi J. 2024. Crop classification in South Korea for multitemporal PlanetScope imagery using SFC-DenseNet-AM. Int J Appl Earth Obs Geoinf, 126: 103619.
• Şimşek FF 2023. Land cover and land use classification at national scale using Land Parcel Identification System Data (LPIS). Turk J Remote Sens GIS, 4(2): 276-288.
• Şimşek FF, Durduran SS. 2022. Land cover classification using Land Parcel Identification System (LPIS) data and open source Eo-Learn library. Geocarto Int, 38: 1-18.
• Talsma CJ, Good SP, Jimenez C, Martens B, Fisher JB, Miralles DG, McCabe MF, Purdy AJ. 2018. Partitioning of evapotranspiration in remote sensing-based models. Agric For Meteorol, 260: 131-143.
• Team P. 2017. Planet application program interface: In space for life on Earth. San Francisco, US, pp: 67.
• Tenreiro TR, García-Vila M, Gómez JA, Jiménez-Berni JA, Fereres E. 2021. Using NDVI for the assessment of canopy cover in agricultural crops within modelling research. Comput Electron Agri, 182: 106038.
• Thieme A, Prabhakara K, Jennewein J, Lamb BT, McCarty GW, Hively WD. 2024. Intercomparison of same-day remote sensing data for measuring winter cover crop biophysical traits. Sensors, 24(7): 1-25.
• Trout TJ, Johnson LF, Gartung J. 2008. Remote sensing of canopy cover in horticultural crops. Hort Sci, 43(2): 333-337.
• Trout TJ, Johnson LF. 2007. Estimating crop water use from remotely sensed NDVI. crop models. and reference ET. USCID Fourth International Conference on Irrigation and Drainage, October 3-6, Sacramento, California, US, pp: 275-285.
• Tsakmakis ID, Gikas GD, Sylaios GK 2021. Integration of Sentinel-derived NDVI to reduce uncertainties in the operational field monitoring of maize. Agri Water Manag, 255: 106998.
• Tucker CJ. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ, 8(2): 127-150.
• Uçgun K, Gezgin S. 2017. Interpretation of leaf analysis performed in early vegetation in apple orchards. Commun Soil Sci Plant Anal, 48(14): 1719-1725.
• Vos K, Harley MD, Splinter KD, Simmons JA, Turner IL. 2019. Sub-annual to multi-decadal shoreline variability from publicly available satellite imagery. Coast Eng, 150: 160-174.
• Yalçın B, Yıldırım A, Yıldırım F, Çelik C. 2021. Biochemical and mineral contents of some walnut cultivars in Isparta ecology. ADÜ Zir Derg, 18(2): 285-291.
• Yılmaz R, Yıldırım A, Çelik C, Karakurt Y. 2021. Determination of nut characteristics and biochemical components of some pecan nut cultivars. YYU J Agri Sci, 31(4): 906-914.