Abstract
Canopy cover is an important structural trait that is frequently used in forest inventories to assess sustainability as well as many other important aspects of forest stands. Remote sensing data is more effective and suitable for canopy cover estimating than traditional field measurements such as sample plots, especially at broad scales. Measurement and mapping this attribute in fine-scale is a difficult task. Aerial imagery using unmanned aerial vehicle (UAV) has been recognized as an excellent tool to estimate canopy attributes. In this study, we compared the potential of using digital hemispherical photography (DHP), digital cover photography (DCP), UAV RGB data, and canopy height model (CHM) for estimation of canopy cover of mix broad-leaved forest on seven different stands. The canopy cover was measured from two digital canopy photographic methods, including DHP (as the reference method) and DCP. The stand orthophotos were segmented using a multi-resolution image segmentation method. Afterward, the classification in two classes of the canopy cover and the non-canopy cover was conducted using minimum distance classification to estimate canopy cover. The CHM layer was generated based on the SfM algorithm and utilized in the canopy cover estimation in each stand as auxiliary data. The results showed a slight improvement when we used the CHM as auxiliary data. Overall, the results showed that the efficiency of the ground digital canopy photographic methods (zenith view) in multi-storied and dense forests is the lowest. In return, our method for digital aerial canopy photography (object-based canopy segmentation and classification) is simple, quick, efficient, and cost-effective.
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References
Alivernini, A., Fares, S., Ferrara, C., & Chianucci, F. (2018). An objective image analysis method for estimation of canopy attributes from digital cover photography. Trees. https://doi.org/10.1007/s00468-018-1666-3
Brovkina, O., Cienciala, E., Surový, P., & Janata, P. (2018). Unmanned aerial vehicles (UAV) for assessment of qualitative classification of Norway spruce in temperate forest stands. Geo-Spatial Information Science, 21(1), 12–20. https://doi.org/10.1080/10095020.2017.1416994
Brovkina, O., Cienciala, E., Surový, P., Janata, P., Group, F. (2018). Geo-spatial Information Science Unmanned aerial vehicles (UAV) for assessment of qualitative classification of Norway spruce in temperate forest stands. Geo-Spatial Information Science, 5020, 1–9. https://doi.org/10.1080/10095020.2017.1416994
Chianucci, F. (2016). A note on estimating canopy cover from digital cover and hemispherical photography. Silva Fennica, 50(1), 1–10.
Chianucci, F., Chiavetta, U., & Cutini, A. (2014). The estimation of canopy attributes from digital cover photography by two different image analysis methods. Iforest, 7(4), 255–259. https://doi.org/10.3832/ifor0939-007
Chianucci, F., & Cutini, A. (2013). Estimation of canopy properties in deciduous forests with digital hemispherical and cover photography. Agricultural and Forest Meteorology, 168, 130–139.
Chianucci, F., Disperati, L., Guzzi, D., Bianchini, D., Nardino, V., Lastri, C., et al. (2016a). 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. https://doi.org/10.1016/j.jag.2015.12.005
Chianucci, F., Disperati, L., Guzzi, D., Bianchini, D., Nardino, V., Lastri, C., et al. (2016b). Estimation of canopy attributes in beech forests using true colour digital images from a small fixed-wing UAV. International Journal of Applied Earth Observations and Geoinformation, 47, 60–68. https://doi.org/10.1016/j.jag.2015.12.005
Chianucci, F., Ferrara, C., Pollastrini, M., & Corona, P. (2019). Development of digital photographic approaches to assess leaf traits in broadleaf tree species. Ecological Indicators, 106, 105547. https://doi.org/10.1016/j.ecolind.2019.105547
Daryaei, A., Sohrabi, H., Atzberger, C., & Immitzer, M. (2020). Fine-scale detection of vegetation in semi-arid mountainous areas with focus on riparian landscapes using Sentinel-2 and UAV data. Computers and Electronics in Agriculture, 177, 105686. https://doi.org/10.1016/j.compag.2020.105686
Fernandez-Gallego, J. A., Kefauver, S. C., Kerfal, S., & Araus, J. L. (2018). Comparative canopy cover estimation using RGB images from UAV and ground. In C. M. Neale & A. Maltese (Eds.), Remote Sensing for Agriculture, Ecosystems, and Hydrology XX, 10783, 20. SPIE. https://doi.org/10.1117/12.2501531
Godinho, S., Guiomar, N., & Gil, A. (2018). Estimating tree canopy cover percentage in a mediterranean silvopastoral systems using Sentinel-2A imagery and the stochastic gradient boosting algorithm. International Journal of Remote Sensing, 39(14), 4640–4662. https://doi.org/10.1080/01431161.2017.1399480
Gülci, S. (2019). The determination of some stand parameters using SfM-based spatial 3D point cloud in forestry studies: An analysis of data production in pure coniferous young forest stands. Environmental Monitoring and Assessment, 191(8). https://doi.org/10.1007/s10661-019-7628-4
Hojas-Gascón, L., Belward, A., Eva, H., Ceccherini, G., Hagolle, O., Garcia, J., & Cerutti, P. (2015). Potential improvement for forest cover and forest degradation mapping with the forthcoming Sentinel-2 program. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. International Society for Photogrammetry and Remote Sensing, 40, 417–423. https://doi.org/10.5194/isprsarchives-XL-7-W3-417-2015
Iiames, J. S., Pilant, A. N., Lewis, T. E., & Congalton, R. G. (2004). Leaf area index (LAI) change detection on loblolly pine forest stands with complete understory removal. ASPRS Annual Conference Proceedings, Denver, Colorado, 74(11), 11.
Khokthong, W., Zemp, D. C., Irawan, B., Sundawati, L., Kreft, H., & Hölscher, D. (2019). Drone-based assessment of canopy cover for analyzing tree mortality in an oil palm agroforest. Frontiers in Forests and Global Change, 2, 12. https://doi.org/10.3389/ffgc.2019.00012
Korhonen, L., Hadi, P., & P., & Rautiainen, M. (2017). Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index. Remote Sensing of Environment, 195, 259–274. https://doi.org/10.1016/j.rse.2017.03.021
Lisein, J., Michez, A., Claessens, H., & Lejeune, P. (2015). Discrimination of deciduous tree species from time series of unmanned aerial system imagery. PLoS ONE, 10(11), 1–20. https://doi.org/10.1371/journal.pone.0141006
Liu, X., & Wang, L. (2018). Feasibility of using consumer-grade unmanned aerial vehicles to estimate leaf area index in Mangrove forest. Remote Sensing Letters, 9(11), 1040–1049. https://doi.org/10.1080/2150704X.2018.1504339
Macfarlane, C., & Ogden, G. N. (2012). Automated estimation of foliage cover in forest understorey from digital nadir images. Methods in Ecology and Evolution, 3(2), 405–415. https://doi.org/10.1111/j.2041-210X.2011.00151.x
Miraki, M., Sohrabi, H., Fatehi, P., & Kneubuehler, M. (2021). Individual tree crown delineation from high-resolution UAV images in broadleaf forest. Ecological Informatics, 61, 101207. https://doi.org/10.1016/j.ecoinf.2020.101207
Otero, V., Van De Kerchove, R., Satyanarayana, B., Martínez-Espinosa, C., Fisol, M. A., Bin, I., Bin, M. R., et al. (2018). Managing mangrove forests from the sky: Forest inventory using field data and unmanned aerial vehicle (UAV) imagery in the Matang Mangrove Forest Reserve, peninsular Malaysia. Forest Ecology and Management, 411, 35–45. https://doi.org/10.1016/j.foreco.2017.12.049
Panagiotidis, D., Abdollahnejad, A., Surový, P., & Chiteculo, V. (2016). Determining tree height and crown diameter from high-resolution UAV imagery. International Journal of Remote Sensing, 38(8–10), 2392–2410. https://doi.org/10.1080/01431161.2016.1264028
Pekin, B., Macfarlane, C. (2009). Measurement of crown cover and leaf area index using digital cover photography and its application to remote sensing. Remote Sensing, 1298–1320. https://doi.org/10.3390/rs1041298
Poblete-echeverría, C., Fuentes, S., Ortega-farias, S. (2015). Digital cover photography for estimating leaf area index (LAI) in apple trees using a variable light extinction coefficient. Sensors, 2860–2872. https://doi.org/10.3390/s150202860
Puliti, S., Solberg, S., Granhus, A., Puliti, S., Solberg, S., & Granhus, A. (2019). Use of UAV photogrammetric data for estimation of biophysical properties in forest stands under regeneration. Remote Sensing, 11(3), 233. https://doi.org/10.3390/rs11030233
Shamsoddini, A., Turner, R., & Trinder, J. C. (2013). Improving lidar-based forest structure mapping with crown-level pit removal. Journal of Spatial Science, 58(1), 29–51. https://doi.org/10.1080/14498596.2012.759092
Tang, L., & Shao, G. (2015). Drone remote sensing for forestry research and practices. Journal of Forestry Research, 26(4), 791–797. https://doi.org/10.1007/s11676-015-0088-y
Tinkham, W. T., & Swayze, N. C. (2021). Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models. Forests, 12(2), 250. https://doi.org/10.3390/F12020250
Banu, T. P., Borlea, G. F., & Banu, C. (2016). The use of drones in forestry. Journal of Environmental Science and Engineering B, 5(11), 557–562. https://doi.org/10.17265/2162-5263/2016.11.007
Vauhkonen, J., Ene, L., Gupta, S., Heinzel, J., Holmgren, J., Pitkanen, J., et al. (2012). Comparative testing of single-tree detection algorithms under different types of forest. Forestry, 85(1), 27–40. https://doi.org/10.1093/forestry/cpr051
Wu, X., Shen, X., Cao, L., Wang, G., & Cao, F. (2019). Assessment of individual tree detection and canopy cover estimation using unmanned aerial vehicle based light detection and ranging (UAV-LiDAR) data in planted forests. Remote Sensing, 11(8), 908. https://doi.org/10.3390/rs11080908
Yang, J., Jones, T., Caspersen, J., & He, Y. (2015). Object-based canopy gap segmentation and classification: Quantifying the pros and cons of integrating optical and LiDAR data. Remote Sensing, 7(12), 15917–15932. https://doi.org/10.3390/rs71215811
Zhang, D., Liu, J., Ni, W., Sun, G., Zhang, Z., Liu, Q., & Wang, Q. (2019). Estimation of forest leaf area index using height and canopy cover information extracted from unmanned aerial vehicle stereo imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(2), 471–481. https://doi.org/10.1109/JSTARS.2019.2891519
Zhao, Q., Wang, F., Zhao, J., Zhou, J., Yu, S., & Zhao, Z. (2018). Estimating forest canopy cover in black locust (Robinia pseudoacacia L.) plantations on the Loess Plateau using random forest. Forests, 9(10), 623. https://doi.org/10.3390/f9100623
Zimudzi, E., Sanders, I., Rollings, N., & Omlin, C. W. (2019). Remote sensing of mangroves using unmanned aerial vehicles: Current state and future directions. Journal of Spatial Science. Mapping Sciences Institute Australia. https://doi.org/10.1080/14498596.2019.1627252
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We would like to express our thanks to the Tarbiat Modares University for the financial support.
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Miraki, M., Sohrabi, H. Using canopy height model derived from UAV imagery as an auxiliary for spectral data to estimate the canopy cover of mixed broadleaf forests. Environ Monit Assess 194, 45 (2022). https://doi.org/10.1007/s10661-021-09695-7
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DOI: https://doi.org/10.1007/s10661-021-09695-7