Integration of aerial photo and LiDAR data for determining the position and height of oil palm trees using object-based analysis and canopy height model algorithm
DOI: https://doi.org/10.3846/gac.2025.21974Abstract
The monitoring of oil palm trees using various technologies, methods, and software is conducted to replace the traditional techniques that are less effective. In this study, an analysis was conducted on the automatic detection results of oil palm trees to determine the estimated height of the trees. The trees were automatically extracted and calculated using eCognition Developer and eCognition Oil Palm Application (OPA) with the Object-Based Image Analysis (OBIA) algorithm on three sample areas: homogeneous, semi- homogeneous, and heterogeneous. The performance test of the two software on the three samples showed that the detection accuracy reached more than 80%. The automatic detection results were used to calculate the tree height using the Canopy Height Model (CHM). The Root Mean Square error (RMSe) was calculated for all centroid samples to evaluate the accuracy of the tree position detection and as a basis for determin- ing the height. The RMSe position result of eCognition OPA was lower than that of eCognition Developer. The RMS values for the homogeneous; semi-homogeneous; and heterogeneous areas were 0.8149; 0.7772; and 0.02118 for eCognition OPA, respectively, which are lower than the values of 0.7718; 0.9044; and 1.0517 for eCognition Developer, this indicates better estimated tree height results.
Keywords:
palm oil, aerial photo, LiDAR, tree count, position, tree heightHow to Cite
Share
License
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Abidin, Z. H., Andreas, H., Maulana, D., Hendrasto, M., Gamal, M., & Suganda, O. K. (2004). Penentuan tinggi orthometrik Gunung Semeru berdasarkan data survei GPS dan Model Geoid EGM 1996 [Determination of the orthometric height of Mount Semeru based on GPS survey data and the 1996 EGM Geoid Model]. Journal of Mathematical and Fundamental Sciences, 36(2), 145–157. https://doi.org/10.5614/itbj.sci.2004.36.2.4
Alexander, C., Korstjens, A. H., & Hill, R. A. (2018). Influence of micro-topography and crown characteristics on tree height estimations in tropical forests based on LiDAR canopy height models. International Journal of Applied Earth Observation and Geoinformation, 65, 105–113. https://doi.org/10.1016/j.jag.2017.10.009
Badan Pusat Statistik. (2017). Statistik kelapa sawit Indonesia 2017 [Indonesian palm oil statistics 2017]. https://www.bps.go.id/id/publication/2018/11/13/b73ff9a5dc9f8d694d74635f/statistik-kelapa-sawit-indonesia-2017.html
Ernawa, Y. G. (2020). Perhitungan pohon pada perkebunan kelapa sawit menggunakan software trimble ecognition developer dari citra foto udara (Studi kasus: Muara Bengkal, Kutai Timur, Kutai Kartanegara, Kalimantan Timur) [Tree calculation on oil palm plantations using trimble ecognition developer software from aerial photographic images (Case study: Muara Bengkal, East Kutai, Kutai Kartanegara, East Kalimantan] [Doctoral dissertation, Institut Teknologi Nasional Malang].
Featherstone, W. E., & Kuhn, M. (2006). Height systems and vertical datums: A review in the Australian context. Journal of Spatial Science, 51(1), 21–41. https://doi.org/10.1080/14498596.2006.9635062
Federal Geographic Data Committee. (1998). Geospatial positioning accuracy standards part 3: National Standard for spatial data accuracy (FGDC Standard No. FGDC-STD-007.3-1998). https://www.fgdc.gov/standards/projects/accuracy/part3/index_html
Hamdani, H., Septiarini, A., Sunyoto, A., Suyanto, S., & Utaminingrum, F. (2021). Detection of oil palm leaf disease based on color histogram and supervised classifier. Optik, 245, Article 167753. https://doi.org/10.1016/j.ijleo.2021.167753
Hao, J., Li, X., Wu, H., Yang, K., Zeng, Y., Wang, Y., & Pan, Y. (2023). Extraction and analysis of tree canopy height information in high-voltage transmission-line corridors by using integrated optical remote sensing and LiDAR. Geodesy and Geodynamics, 14(3), 292–303. https://doi.org/10.1016/j.geog.2022.11.008
Hao, Y., Zhen, Z., Li, F., & Zhao, Y. (2019). A graph-based progressive morphological filtering (GPMF) method for generating canopy height models using ALS data. International Journal of Applied Earth Observation and Geoinformation, 79, 84–96. https://doi.org/10.1016/j.jag.2019.03.008
Hashim, S. A., Daliman, S., Rodi, I. N. M., Aziz, N. A., Amaludin, N. A., & Rak, A. E. (2020). Analysis of oil palm tree recognition using drone-based remote sensing images. IOP Conference Series: Earth and Environmental Science, 596, Article 012070. https://doi.org/10.1088/1755-1315/596/1/012070
Held, A., Ticehurst, C., Lymburner, L., & Williams, N. (2003). High resolution mapping of tropical mangrove ecosystems using hyperspectral and radar remote sensing. International Journal of Remote Sensing, 24(13), 2739–2759. https://doi.org/10.1080/0143116031000066323
Irsanti, D., Sasmito, B., & Bashit, N. (2019). Kajian pengaruh penajaman citra untuk perhitungan jumlah pohon kelapa sawit secara otomatis menggunakan foto udara (Studi kasus: KHG Bentayan Sumatra Selatan) [Stydy of the effect of image sharpening on automation calculation of the number of oil palm trees using aerial photographs (Case study: KHG Bentayan, South Sumatra)]. Jurnal Geodesi Undip, 8(1), 428–434.
Khosravipour, A., Skidmore, A. K., Wang, T., Isenburg, M., & Khoshelham, K. (2015). Effect of slope on treetop detection using a LiDAR Canopy Height Model. ISPRS Journal of Photogrammetry and Remote Sensing, 104, 44–52. https://doi.org/10.1016/j.isprsjprs.2015.02.013
Kipli, K., Osman, S., Joseph, A., Zen, H., Awang Salleh, D. N. S. D., Lit, A., & Chin, K. L. (2023). Deep learning applications for oil palm tree detection and counting. Smart Agricultural Technology, 5, Article 100241. https://doi.org/10.1016/j.atech.2023.100241
Li, F., Zhu, H., Luo, Z., Shen, H., & Li, L. (2021). An adaptive surface interpolation filter using cloth simulation and relief amplitude for airborne laser scanning data. Remote Sensing, 13(15), Article 2938. https://doi.org/10.3390/rs13152938
Lourenço, P., Teodoro, A. C., Gonçalves, J. A., Honrado, J. P., Cunha, M., & Sillero, N. (2021). Assessing the performance of different OBIA software approaches for mapping invasive alien plants along roads with remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 95, Article 102263. https://doi.org/10.1016/j.jag.2020.102263
Münzinger, M., Prechtel, N., & Behnisch, M. (2022). Mapping the urban forest in detail: From LiDAR point clouds to 3D tree models. Urban Forestry & Urban Greening, 74, Article 127637. https://doi.org/10.1016/j.ufug.2022.127637
Norzaki, N., & Tahar, K. N. (2019). A comparative study of template matching, ISO cluster segmentation, and tree canopy segmentation for homogeneous tree counting. International Journal of Remote Sensing, 40(19), 7477–7499. https://doi.org/10.1080/01431161.2018.1524182
Nauthika, T. A., Suprayogi, A., & Sudarsono, B. (2017). Identifikasi dan estimasi tingkat produktivitas kelapa sawit menggunakan teknologi LiDAR (Studi kasus: Air Upas, Kabupaten Ketapang) [Identification and estimation of palm oil productivity level using LiDAR technology (Case study: Air Upas, Ketapang Regency)]. Jurnal Geodesi Undip, 6(4), 254–262.
Nurmasari, Y., & Wijayanto, A. W. (2021). Oil palm plantation detection in indonesia using Sentinel-2 and Landsat-8 optical satellite imagery (Case study: Rokan Hulu Regency, Riau province). International Journal of Remote Sensing and Earth Sciences (IJReSES), 18(1), 1–18. https://doi.org/10.30536/j.ijreses.2021.v18.a3537
Nyland, R. D. (2016). Silviculture: Concepts and applications (3rd ed.). Waveland Press.
Okojie, J. A., Okojie, L. O., Effiom, A. E., & Odia, B. E. (2020). Relative canopy height modelling precision from UAV and ALS datasets for forest tree height estimation. Remote Sensing Applications: Society and Environment, 17, Article 100284. https://doi.org/10.1016/j.rsase.2019.100284
Oya, N., Fazlina, Y. D., & Rusdi, M. (2022). Analisis citra resolusi menengah untuk menghitung tanaman kelapa sawit [Medium resoliution image analysis for counting oil palm plants]. Jurnal Ilmiah Mahasiswa Pertanian, 7(3), 463–466.
Peynaud, E., & Momo Takoudjou, S. (2024). Terrestrial LiDAR point cloud dataset of cocoa trees grown in agroforestry systems in Cameroon. Data in Brief, 53, Article 110108. https://doi.org/10.1016/j.dib.2024.110108
Priambodo, D. A., Tjahjadi, M. E., & Suhari, K. T. (2022). Pembuatan model 3D jalan raya Bayat Untuk keperluan existing menggunakan metode foto udara (UAV) di Klaten [Creation of a 3D model of Bayat Highway for existing purposes using aerial photography (UAV) method in Klaten]. Teras Jurnal: Jurnal Teknik Sipil, 12(1), 177–190. https://doi.org/10.29103/tj.v12i1.654
Purnamasayangsukasih, P. R., Norizah, K., Ismail, A. A. M., & Shamsudin, I. (2016). A review of uses of satellite imagery in monitoring mangrove forests. IOP Conference Series: Earth and Environmental Science, 37, Article 012034. https://doi.org/10.1088/1755-1315/37/1/012034
Putra, Y. C., & Wijayanto, A. W. (2023). Automatic detection and counting of oil palm trees using remote sensing and object-based deep learning. Remote Sensing Applications: Society and Environment, 29, Article 100914. https://doi.org/10.1016/j.rsase.2022.100914
Rizeei, H. M., Shafri, H. Z. M., Mohamoud, M. A., Pradhan, B., & Kalantar, B. (2018). Oil palm counting and age estimation from WorldView-3 imagery and LiDAR data using an integrated OBIA height model and regression analysis. Journal of Sensors, Article 2536327. https://doi.org/10.1155/2018/2536327
Sastrosayono, S. (2003). Budi daya kelapa sawit [Oil palm cultivation]. AgroMedia.
Smith, A. M. S., Strand, E. K., Steele, C. M., Hann, D. B., Garrity, S. R., Falkowski, M. J., & Evans, J. S. (2008). Production of vegetation spatial-structure maps by per-object analysis of juniper encroachment in multitemporal aerial photographs. Canadian Journal of Remote Sensing, 34(sup2), S268–S285. https://doi.org/10.5589/m08-048
Susanti, A., & Maryudi, A. (2016). Development narratives, notions of forest crisis, and boom of oil palm plantations in Indonesia. Forest Policy and Economics, 73, 130–139. https://doi.org/10.1016/j.forpol.2016.09.009
Syetiawan, A., & Haidar, M. (2019). Pemetaan perkebunan sawit rakyat dari foto udara nonmetrik menggunakan analisis berbasis objek [Mapping of smallholder oil palm plantations from non-metric aerial photographs using-object based analysis]. Majalah Ilmiah Globë, 21(1), 53–62.
Wang, H., Seaborn, T., Wang, Z., Caudill, C. C., & Link, T. E. (2021). Modeling tree canopy height using machine learning over mixed vegetation landscapes. International Journal of Applied Earth Observation and Geoinformation, 101, Article 102353. https://doi.org/10.1016/j.jag.2021.102353
Webster, C., Essery, R., Mazzotti, G., & Jonas, T. (2023). Using just a canopy height model to obtain lidar-level accuracy in 3D forest canopy shortwave transmissivity estimates. Agricultural and Forest Meteorology, 338, Article 109429. https://doi.org/10.1016/j.agrformet.2023.109429
Wolf, P. R. (1978). [Review of the book Elements of photogrammetry (with air photo interpretation and remote sensing]. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 15(1), A22. https://doi.org/10.1016/0148-9062(78)90936-1
Yun, T., Jiang, K., Li, G., Eichhorn, M. P., Fan, J., Liu, F., Chen, B., An, F., & Cao, L. (2021). Individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization-based approach. Remote Sensing of Environment, 256, Article 112307. https://doi.org/10.1016/j.rse.2021.112307
Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., & Yan, G. (2016). An easy-to-use airborne LiDAR data filtering method based on cloth simulation. Remote Sensing, 8(6), Article 501. https://doi.org/10.3390/rs8060501
Zhang, Z., Gerke, M., Vosselman, G., & Yang, M. Y. (2018). Filtering photogrammetric point clouds using standard LiDAR filters towards DTM generation. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV–2, 319–326. https://doi.org/10.5194/isprs-annals-IV-2-319-2018
Zhou, L., Meng, R., Tan, Y., Lv, Z., Zhao, Y., Xu, B., & Zhao, F. (2022). Comparison of UAV-based LiDAR and digital aerial photogrammetry for measuring crown-level canopy height in the urban environment. Urban Forestry & Urban Greening, 69, Article 127489. https://doi.org/10.1016/j.ufug.2022.127489
View article in other formats
CrossMark check
Published
Issue
Section
Copyright
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.