Estimation of vegetation biophysical parameters in grasslands and crops in Chile through hemispheric digital photography by a GoPro camera

D. Uribe, C. Mattar, F. Camacho


The estimation of the biophysical parameters of vegetation such as LAI (Leaf Area Index), FAPAR (Fraction of Absorbed Photosynthetically Active Radiation) and FCOVER (Fraction of Green Vegetation) have many climatic, hydrologic, ecosystem and silvo-agricultural applications. Despite the various satellite products that estimate these parameters continuously and globally, it’s necessary to continue generating in situ estimations to validate these remote data. It’s in this context where Digital Hemispheric Photography (DHP) technique stands out as being one of the most accurate an adaptable to operate continuously with diverse photographic equipment and field scenarios. The objective of this paper is to estimate effective LAI (LAIeff), true LAI (LAItrue), FAPAR and FCOVER through the DHP method on several agricultural land covers in Chile, between the years 2015 and 2016 using a GoPro camera and the CAN-EYE software to process hemispheric photographs. The results obtained were initially compared with those provided by a CANON EOS 6D camera mounted together with a SIGMA 8mm F3.5-EX DG fisheye lens and subsequently with satellite products provided by the Copernicus Global Land service, derived from PROBA-V mission at 333 m2 spatial resolution. The comparison between the CANON and GoPro shows similar values and R2 over 0,72 for all parameters. The comparison with PROBA-V resulted in values over 0,52 of R2 for the parameters, and similar multitemporal patterns. It’s concluded that it’s possible to estimates LAIeff, FAPAR and FCOVER like other fish eyes cameras. Concerning PROBA-V, except for FAPAR, the estimates with the GoPro do not show much correlation. In both campaigns significant discrepancies were observed in the LAItrue, which could be related to the calculation of CAN-EYE canopy clumping with the characteristics of the camera itself.


GoPro; Vegetation Biophysical Parameters; DHP

Full Text:



Baret, F., Camacho, F., Cernicharo, J., Lacaze, R., Weiss, M. 2013. GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production. Remote Sensing of Environment, 137, 310-329.

Baret, F., Hagolle, O., Geiger, B., Bicheron, P., Miras, B., Huc, M., Berthelot, B., Niño, F.,Weiss, M., Samain, O., Roujean, J.L., Leroy, M. 2007. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION. Part 1: Principles of the algorithm. Remote Sensing of Environment, 110(3), 275-286.

Baret, F., Weiss, M. 2018. Gio Global Land Component - Lot I “Operation of the Global Land Component” Algorithm Theoretical Basis Document, 1-41.

Baret, F., Weiss, M., Allard, D., Garrigues, S., Leroy, M., Jeanjean, H., et al., 2005. VALERI: a network of sites and a methodology for the validation of medium spatial resolution land satellite products. Remote Sensing of Environment, 76(3), 36-39.

Baret, F., Weiss, M., Verger, A., Smets, B. 2016. Gio Global Land Component - ATBD. Bréda, N.J.J. 2003. Ground-based measurements of leaf area index: A review of methods, instruments and current controversies. Journal of Experimental Botany, 54(392), 2403-2417.

Bréda, N.J.J. 2003. Ground-based measurements of leaf area index: A review of methods, instruments and current controversies. Journal of Experimental Botany, 54(392), 2403-2417.

Casanova, M., Salazar, O., Seguel, O., Luzio, W. 2013. The Soils of Chile, Springer.

Cescatti, A. 2007. Indirect estimates of canopy gap fraction based on the linear conversion of hemispherical photographs. Methodology and comparison with standard thresholding techniques. Agricultural and Forest Meteorology, 143(1-2), 1-12.

Chen, J.M., Black, T.A. 1992. Defining leaf area index for non-flat leaves. Plant, Cell & Environment, 15(4), 421-429.

Confalonieri, R., Foi, M., Casa, R., Aquaro, S., Tona, E., Peterle, M., et al. 2013. Development of an app for estimating leaf area index using a smartphone. Trueness and precision determination and comparison with other indirect methods. Computers and Electronics in Agriculture, 96, 67- 74.

De Bei, R., Fuentes, S., Gilliham, M., Tyerman, S., Edwards, E., Bianchini, N., Smith, J., Collins, C. 2016. Viticanopy: A free computer app to estimate canopy vigor and porosity for grapevine. Sensors, 16(4).

Demarez, V., Duthoit, S., Baret, F., Weiss, M., Dedieu, G. 2008. Estimation of leaf area and clumping indexes of crops with hemispherical photographs. Agricultural and Forest Meteorology, 148(4), 644-655.

Fang, H., Liang, S., Hoogenboom, G. 2011. Integration of MODIS LAI and vegetation index products with the CSM-CERES-Maize model for corn yield estimation. International Journal of Remote Sensing, 32(4), 1039-1065.

Fournier, R.A., Landry, R., August, N.M., Fedosejevs, G., Gauthier, R.P. 1996. Modelling light obstruction in three conifer forests using hemispherical photography and fine tree architecture. Agricultural and Forest Meteorology, 82(1-4), 47-72.

Garrigues, S., Shabanov, N. V, Swanson, K., Morisette, J.T., Baret, F., Myneni, R.B. 2008. Intercomparison and sensitivity analysis of Leaf Area Index retrievals from LAI-2000, AccuPAR, and digital hemispherical photography over croplands. Agricultural and Forest Meteorology, 148(8-9), 1193-1209.

Gower, S.T., Kucharik, C.J., Norman, J.M. 1999. Direct and indirect estimation of leaf area index, fAPAR, and net primary production of terrestrial ecosystems. Remote Sensing of Environment, 70(1), 29-51.

Jonckheere, I., Fleck, S., Nackaerts, K., Muys, B., Coppin, P., Weiss, M., Baret, F. 2004. Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography. Agricultural and Forest Meteorology, 121(1-2), 19- 35.

Kross, A., McNairn, H., Lapen, D., Sunohara, M., Champagne, C. 2015. Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. International Journal of Applied Earth Observation and Geoinformation, 34(1), 235-248.

Lang, M., Kuusk, A., Mõttus, M., Rautiainen, M., Nilson, T. 2010. Canopy gap fraction estimation from digital hemispherical images using sky radiance models and a linear conversion method. Agricultural and Forest Meteorology, 150(1), 20-29.

Latorre, C., Camacho, F., Mattar, C., Santamaría-Artigas, A., Leiva-Büchi, N., Lacaze, R. 2016. Obtención de mapas verdad-terreno de LAI, FAPAR y cobertura vegetal a partir de imágenes del satélite chileno FASat-C y medidas in-situ en la zona agrícola de Chimbarongo, Chile, para la validación de productos de satélite. Revista de Teledeteccion, 2016(47), 51- 64.

Li, W., Weiss, M., Waldner, F., Defourny, P., Demarez, V., Morin, D., Hagolle, O., Baret, F. 2015. A generic algorithm to estimate LAI, FAPAR and FCOVER variables from SPOT4_HRVIR and landsat sensors: Evaluation of the consistency and comparison with ground measurements. Remote Sensing, 7(11), 15494- 15516.

López-Lozano, R., Baret, F., García de Cortázar-Atauri, I., Bertrand, N., Casterad, M.A. 2009. Optimal geometric configuration and algorithms for LAI indirect estimates under row canopies: The case of vineyards. Agricultural and Forest Meteorology, 149(8), 1307- 1316.

Martínez, B., Camacho-de Coca, F., García-Haro, F. 2005a. Estimación de parámetros biofísicos de la cubierta vegetal a alta resolución a partir de medidas in-situ obtenidas en SPARC’03. XI Congreso Nacional de Teledetección, 21-23.

Martínez, B., García-haro, F., Camacho-de Coca, F. 2005b. Estimación de parámetros biofísicos de vegetación utilizando el método de la cámara hemisférica. Revista de Teledetección, 23, 13-26.

Mattar, C., Hernández, J., Santamaría-Artigas, A., Durán-Alarcón, C., Olivera-Guerra, L., Inzunza, M., Tapia, D., Escobar-lavín, E. 2014. A first in-flight absolute calibration of the Chilean Earth Observation Satellite. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 16-25.

Mougin, E., Demarez, V., Diawara, M., Hiernaux, P., Soumaguel, N., Berg, A. 2014. Estimation of LAI, fAPAR and fCover of Sahel rangelands (Gourma, Mali). Agricultural and Forest Meteorology, 198, 155- 167.

Nestola, E., Sánchez-Zapero, J., Latorre, C., Mazzenga, F., Matteucci, G., Calfapietra, C., Camacho, F. 2017. Validation of PROBA-V GEOV1 and MODIS C5 & C6 fAPAR Products in a Deciduous Beech Forest Site in Italy. Remote Sensing, 9(2), 126.

Olivera-Guerra, L., Mattar, C., Galleguillos, M. 2014. Estimation of real evapotranspiration and its variation in Mediterranean landscapes of central-southern Chile. International Journal of Applied Earth Observation and Geoinformation, 28(1), 160-169.

Olivera-Guerra, L., Merlin, O., Mattar, C., Duran-Alarcon, C., Santamaria-Artigas, A., Stefan, V. 2015. Combining meteorological and lysimeter data to evaluate energy and water fluxes over a row crop for remote sensing applications. International Geoscience and Remote Sensing Symposium (IGARSS), 2015-Novem, 4649- 4651.

Paul M. Rich, 1990. Characterizing Plant Canopies with Hemispherical Photograph s. Remote Sensing Reviews, 5(November 2012), 37-41.

Rigon, J.P.G., Capuani, S., Fernandes, D.M., Guimarães, T.M. 2016. A novel method for the estimation of soybean chlorophyll content using a smartphone and image analysis. Photosynthetica, 54(4), 559-566.

Sarricolea, P., Herrera-Ossandon, M., MeseguerRuiz, Ó. 2017. Climatic regionalisation of continental Chile. Journal of Maps, 13(2), 66-73.

Sellers, P.J., Dickinson, R.E., Randall, D.A., Betts, A.K., Hall, F.G., Berry, J.A., et al. 1997. Modeling the Exchanges of Energy, Water, and Carbon between Continents and the Atmosphere. Science , 275(5299), 502-509.

Tarnavsky, E., Garrigues, S., Brown, M.E. 2008. Multiscale geostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products. Remote Sensing of Environment, 112(2), 535-549.

Verger, A., Baret, F., Camacho, F. 2011. Optimal modalities for radiative transfer-neural network estimation of canopy biophysical characteristics: Evaluation over an agricultural area with CHRIS/PROBA observations. Remote Sensing of Environment, 115(2), 415-426.

Weiss, M., Baret, F. 2016. Can Eye User Manual.

Weiss, M., Baret, F., Smith, G.J., Jonckheere, I., Coppin, P. 2004. Review of methods for in situ leaf area index (LAI) determination Part II. Estimation of LAI, errors and sampling. Agricultural and Forest Meteorology, 121(1-2), 37-53.

Zheng, G., Moskal, L.MX009. Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors. Sensors, 9(4), 2719-2745.

Abstract Views

Metrics Loading ...

Metrics powered by PLOS ALM


Cited-By (articles included in Crossref)

This journal is a Crossref Cited-by Linking member. This list shows the references that citing the article automatically, if there are. For more information about the system please visit Crossref site

1. Low-cost unmanned aerial vehicle-based digital hemispherical photography for estimating leaf area index: a feasibility assessment
Luke A. Brown, David H. Sutherland, Jadunandan Dash
International Journal of Remote Sensing  vol: 41  issue: 23  first page: 9064  year: 2020  
doi: 10.1080/2150704X.2020.1802527


This journal is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International

Universitat Politècnica de València

Official Journal of the Spanish Association of Remote Sensing

e-ISSN: 1988-8740    ISSN: 1133-0953