Comparing canopy density measurement from UAV and hemispherical photography: an evaluation for medium resolution of remote sensing-based mapping

Deha Agus Umarhadi, Projo Danoedoro


UAV and hemispherical photography are common methods used in canopy density measurement. Those two methods have the opposite viewing angle where hemispherical photography measures canopy density upwardly, while UAV captures images downwardly. This study aims to analyze and compare both methods to be used as the input data for canopy density estimation when linked with a lower spatial resolution of remote sensing data i.e. Landsat image. We correlated the field data of canopy density with vegetation indices (NDVI, MSAVI, and AFRI) from Landsat-8. The canopy density values measured from UAV and hemispherical photography displayed a strong relationship with 0.706 coefficient of correlation. Further results showed that both measurements can be used in canopy density estimation using satellite imagery based on their high correlations with Landsat-based vegetation indices. The highest correlation from downward and upward measurement appeared when linked with NDVI with a correlation of 0.962 and 0.652, respectively. Downward measurement using UAV exhibited a higher relationship compared to hemispherical photography. The strong correlation between UAV data and Landsat data is because both are captured from the vertical direction, and 30 m pixel of Landsat is a downscaled image of the aerial photograph. Moreover, field data collection can be easily conducted by deploying drone to cover inaccessible sample plots.


image processing; canopy density; remote sensing; UAV; hemispherical photography; Landsat-8; vegetation index


C. Macfarlane, et al., “Estimation of Leaf Area Index in Eucalypt Forest Using Digital Photography,” Agricultural and Forest Meteorology, 143(3-4), 176-188, 2007.

Z. Azizi, et al., “Forest Canopy Density Estimating, Using Satellite Images,” in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B8. Beijing, 2008.

S. Arjasakusuma, et al., “Accuracy and Spatial Pattern Assessment of Forest Cover change Datasets in Central Kalimantan,” Indonesian Journal of Geography, Vol. 50, No.2, 222-227, 2018.

N. R. Tohir, et al., “Pemetaan Perubahan Kerapatan Kanopi di Hutan Rakyat Kabupaten Kuningan Jawa Barat [Mapping of Canopy Density Change in Community Forest, Kuningan District, West Java],” in Seminar Nasional Penginderaan Jauh: Deteksi Parameter Geobiofisik dan Diseminasi Penginderaan Jauh, 322–331, 2014.

F. Taureau, et al., “Mapping the Mangrove Forest Canopy Using Spectral Unmixing of Very High Spatial Resolution Satellite Images,” Remote Sensing, 11, 367, 2019.

M. Kamal, et al., “Assessment of Mangrove Forest Degradation Through Canopy Fractional Cover in Karimunjawa Island, Central Java, Indonesia,” Geoplanning: Journal of Geomatics and Planning, 3(2), 107-116, 2016.

S. Arjasakusuma, et al., “Local-Scale Accuracy Assessment of Vegetation Cover Change Maps Derived from Global Forest Change Data, ClasLite, and Supervised Classifications: Case Study at Part of Riau Province, Indonesia,” Applied Geomatics, 10(3), 205-217, 2018.

C. Li, et al., “Improving Estimation of Forest Aboveground Biomass Using Landsat 8 Imagery by Incorporating Forest Crown Density as a Dummy Variable,” Canadian Journal of Forest Research, 50(4), 390-398, 2020.

R. N. Khairiah, et al., “Agroforestry Tree Density Estimation Based on Hemispherical Photos & Landsat 8 OLI/TIRS Image: a Case Study at Cidanau Watershed, Banten-Indonesia,” in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XLII-3/W7, Kyoto, 12–14 March 2019.

J. C. Jiménez-Muñoz, et al., “Comparison Between Fractional Vegetation Cover Retrievals from Vegetation Indices and Spectral Mixture Analysis: Case Study of PROBA/CHRIS Data Over an Agricultural Area,” Sensors, 9, 768-793, 2014.

A. Abdollahnejad, et al., “Forest Canopy Density Assessment Using Different Approaches – Review,” Journal of Forest Science, 63, 106–115, 2017.

F. Chianucci, et al., “The Estimation of Canopy Attributes from Digital Cover Photography by Two Different Image Analysis Methods,” iForest, 7, 255-259, 2014.

F. Chianucci, “An Overview of In Situ Digital Canopy Photography in Forestry,” Canadian Journal of Forest Research, 50(3), 227-242, 2020.

F. Chianucci and A. Cutini, “Digital Hemispherical Photography for Estimating Forest Canopy Properties: Current Controversies and Opportunities,” iForest, 5, 290-295, 2012.

F. Ashaari, et al., “Comparison of Model Accuracy in Tree Canopy Density Estimation Using Single Band, Vegetation Indices and Forest Canopy Density (FCD) Based on Landsat-8 Imagery (Case Study: Peat Swamp Forest in Riau Province),” International Journal of Remote Sensing and Earth Sciences, 15, 81–92, 2018.

S. A. Suab and R. Avtar, “Unmanned Aerial Vehicle System (UAVS) Applications in Forestry and Plantation Operations: Experiences in Sabah and Sarawak, Malaysian Borneo,” in Unmanned Aerial Vehicle: Application in Environment and Agriculture, R. Avtar and T. Watanabe (Eds.), Cham: Springer International Publishing, 2020.

Y. Erfanifard and Z. Khodaee, “Canopy Density Mapping on Ultracam-D Aerial Imagery in Zagros Woodlands, Iran,” in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XL-1/W3, 2013 SMPR 2013, Tehran, Iran, 5-8 October 2013.

F. Chianucci, et al., “Estimation of Canopy Attributes in Beech Forests Using True Colour Digital Images from A Small Fixed Wing UAV,” International Journal of Applied Earth Observation and Geoinformation, 47, 60–68, 2016.

W. Khokthong, et al., “Drone-Based Assessment of Canopy Cover for Analyzing Tree Mortality in an Oil Palm Agroforest,” Frontiers in Forests and Global Change, 2:12, 2019.

C. Huang, et al., “A Strategy for Estimating Tree Canopy Density Using Landsat 7 ETM+ and High Resolution Images Over Large Area,” in Third International Conference on Geospatial Information in Agriculture and Forestry, Denver, Colorado, 5-7 November, 2001.

D. A. Umarhadi, et al., “The Comparison of Canopy Density Measurement Using UAV and Hemispherical Photography for Remote Sensing Based Mapping”, in 2018 4th International Conference on Science and Technology (ICST), Yogyakarta, pp. 1-5, 2018.

R. D. Dimyati, et al., “A Minimum Cloud Cover Mosaic Image Model of the Operational Land Imager Landsat-8 Multitemporal Data using Tile based,” International Journal of Electrical and Computer Engineering, Vol. 8, No. 1, pp. 360-371, 2018.

M. Palmolina, “Pengelolaan Hutan Rakyat pada Lahan Sempit [Community Forest Management in Narrow Land],” in Prosiding Seminar Nasional Masyarakat Biodiversitas Indonesia, 1, 732-737, 2015.

S. Dhingra and D. Kumar, “A Review of Remotely Sensed Satellite Image Classification,” International Journal of Electrical and Computer Engineering, Vol. 9, No. 3, pp. 1720-1731, 2019.

M. Xu, et al., “Automatic Cloud Removal for Landsat 8 OLI Images Using Cirrus Band,” in 2014 IEEE Geoscience and Remote Sensing Symposium, pp. 2511–2514, 2014.

D. A. Umarhadi and P. Danoedoro, "Correcting topographic effect on Landsat-8 images: an evaluation of using different DEMs in Indonesia", in Proc. SPIE 11311, Sixth Geoinformation Science Symposium, 113110L, 21 November 2019.

W. Rouse, et al., “Monitoring Vegetation Systems in the Great Plains with ERTS,” in Proc. Third Earth Resources Technology Satellite-1 Symposium, SP-351, 3010-3017, 1974.

J. Qi, et al., “A Modified Soil Adjusted Vegetation Index,” Remote Sensing of Environment, 48, 119-126, 1994.

A. Karnieli, et al., “AFRI – Aerosol Free Vegetation Index,” Remote Sensing of Environment, 77, 10-21, 2001.

R. M. McCoy, “Field Methods in Remote Sensing,” The Guilford Press A Division of Guilford Publications, Inc.72 Spring Street, New York, NY 10012, 2005.

H. Zheng, et al., “Evaluation of RGB, Color-Infrared and Multispectral Images Acquired from Unmanned Aerial Systems for the Estimation of Nitrogen Accumulation in Rice,” Remote Sensing, 10(6), 824, 2018.

M. Weiss and F. Baret, “Can Eye V6.4.91 User Manual,” INRA Science & Impact, 2017.

D. D. Gupita and S.H.M.B. Santosa, “Soil Erosion and Its Correlation with Vegetation Cover: An Assessment Using Multispectral Imagery and Pixel-Based Geographic Information System in Gesing Sub-Watershed, Central Java, Indonesia,” in IOP Conf. Series: Earth and Environmental Science, 54, 012047, 2017.

S. B. Jennings, et al., “Assessing Forest Canopies and Understorey Illumination: Canopy Closure, Canopy Cover and Other Measures,” Forestry, 72, 59-74, 1999.

M. Kamal, et al., “Characterizing the Spatial Structure of Mangrove Features for Optimizing Image-Based Mangrove Mapping,” Remote Sensing, 6, 984-1006, 2014.

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