Estimation of canopy attributes in beech forests using true colour digital images from a small fixed-wing UAV

Abstract Accurate estimates of forest canopy are essential for the characterization of forest ecosystems. Remotely-sensed techniques provide a unique way to obtain estimates over spatially extensive areas, but their application is limited by the spectral and temporal resolution available from these systems, which is often not suited to meet regional or local objectives. The use of unmanned aerial vehicles (UAV) as remote sensing platforms has recently gained increasing attention, but their applications in forestry are still at an experimental stage. In this study we described a methodology to obtain rapid and reliable estimates of forest canopy from a small UAV equipped with a commercial RGB camera. The red, green and blue digital numbers were converted to the green leaf algorithm (GLA) and to the CIE L*a*b* colour space to obtain estimates of canopy cover, foliage clumping and leaf area index (L) from aerial images. Canopy attributes were compared with in situ estimates obtained from two digital canopy photographic techniques (cover and fisheye photography). The method was tested in beech forests. UAV images accurately quantified canopy cover even in very dense stand conditions, despite a tendency to not detecting small within-crown gaps in aerial images, leading to a measurement of a quantity much closer to crown cover estimated from in situ cover photography. Estimates of L from UAV images significantly agreed with that obtained from fisheye images, but the accuracy of UAV estimates is influenced by the appropriate assumption of leaf angle distribution. We concluded that true colour UAV images can be effectively used to obtain rapid, cheap and meaningful estimates of forest canopy attributes at medium-large scales. UAV can combine the advantage of high resolution imagery with quick turnaround series, being therefore suitable for routine forest stand monitoring and real-time applications.

[1]  Ugo Chiavetta,et al.  The estimation of canopy attributes from digital cover photography by two different image analysis methods , 2014 .

[2]  R. McMurtrie,et al.  Estimation of leaf area index in eucalypt forest using digital photography , 2007 .

[3]  R. Houborg,et al.  Mapping leaf chlorophyll and leaf area index using inverse and forward canopy reflectance modeling and SPOT reflectance data , 2008 .

[4]  A. Lang Estimation of leaf area index from transmission of direct sunlight in discontinuous canopies , 1986 .

[5]  Xihan Mu,et al.  A novel method for extracting green fractional vegetation cover from digital images , 2012 .

[6]  Pavel Propastin,et al.  Retrieval of remotely sensed LAI using Landsat ETM+ data and ground measurements of solar radiation and vegetation structure: Implication of leaf inclination angle , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[7]  Jan Pisek,et al.  Estimation of foliage clumping from the LAI-2000 Plant Canopy Analyzer: effect of view caps , 2014, Trees.

[8]  Shannon R. Clemens,et al.  PROCEDURES FOR CORRECTING DIGITAL CAMERA IMAGERY ACQUIRED BY THE AGGIEAIR REMOTE SENSING PLATFORM , 2012 .

[9]  K. Swain,et al.  Adoption of an unmanned helicopter for low-altitude remote sensing to estimate yield and total biomass of a rice crop. , 2010 .

[10]  Jan Pisek,et al.  Is the spherical leaf inclination angle distribution a valid assumption for temperate and boreal broadleaf tree species , 2013 .

[11]  Karen Anderson,et al.  Lightweight unmanned aerial vehicles will revolutionize spatial ecology , 2013 .

[12]  I. R. Cowan,et al.  Maintenance of Leaf Temperature and the Optimisation of Carbon Gain in Relation to Water Loss in a Tropical Mangrove Forest , 1988 .

[13]  N. Breda,et al.  Climate-tree-growth relationships of European beech (Fagus sylvatica L.) in the French Permanent Plot Network (RENECOFOR) , 2005, Trees.

[14]  C. Daughtry,et al.  Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and Nitrogen Status , 2005, Precision Agriculture.

[15]  Luc Lens,et al.  Airborne remote sensing of spatiotemporal change (1955-2004) in indigenous and exotic forest cover in the Taita Hills, Kenya , 2009, Int. J. Appl. Earth Obs. Geoinformation.

[16]  C. Daughtry,et al.  REMOTE SENSING OF CROP LEAF AREA INDEX USING UNMANNED AIRBORNE VEHICLES , 2008 .

[17]  G. Campbell Extinction coefficients for radiation in plant canopies calculated using an ellipsoidal inclination angle distribution , 1986 .

[18]  P. Zarco-Tejada,et al.  Mapping radiation interception in row-structured orchards using 3D simulation and high-resolution airborne imagery acquired from a UAV , 2012, Precision Agriculture.

[19]  J. Norman,et al.  Instrument for Indirect Measurement of Canopy Architecture , 1991 .

[20]  Frédéric Baret,et al.  Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography , 2004 .

[21]  Guangjian Yan,et al.  Extracting the Green Fractional Vegetation Cover from Digital Images Using a Shadow-Resistant Algorithm (SHAR-LABFVC) , 2015, Remote. Sens..

[22]  V. Demarez,et al.  A Modeling Approach for Studying Forest Chlorophyll Content , 2000 .

[23]  Piermaria Corona,et al.  Structural attributes of stand overstory and light under the canopy , 2015 .

[24]  Jb Miller,et al.  A formula for average foliage density , 1967 .

[25]  D. Diner,et al.  Analysis of the MISR LAI/FPAR product for spatial and temporal coverage, accuracy and consistency , 2007 .

[26]  J. Pisek,et al.  Variations of leaf inclination angle distribution with height over the growing season and light exposure for eight broadleaf tree species , 2015 .

[27]  Stefan Fleck,et al.  Three-dimensional lamina architecture alters light-harvesting efficiency in Fagus: a leaf-scale analysis. , 2003, Tree physiology.

[28]  S. T. Gower,et al.  Direct and Indirect Estimation of Leaf Area Index, fAPAR, and Net Primary Production of Terrestrial Ecosystems , 1999 .

[29]  K. Soudani,et al.  Estimation of forest leaf area index from SPOT imagery using NDVI distribution over forest stands , 2006 .

[30]  Craig S. T. Daughtry,et al.  Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring , 2010, Remote. Sens..

[31]  Jing M. Chen,et al.  Quantifying the effect of canopy architecture on optical measurements of leaf area index using two gap size analysis methods , 1995, IEEE Trans. Geosci. Remote. Sens..

[32]  Karin S. Fassnacht,et al.  Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites , 1999 .

[33]  Piermaria Corona,et al.  Exploring forest structural complexity by multi-scale segmentation of VHR imagery , 2008 .

[34]  Craig Macfarlane,et al.  Automated estimation of foliage cover in forest understorey from digital nadir images , 2012 .

[35]  N. Breda Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. , 2003, Journal of experimental botany.

[36]  Piermaria Corona,et al.  Estimation of leaf area index in understory deciduous trees using digital photography , 2014 .

[37]  J. M. Norman,et al.  THE ARCHITECTURE OF A DECIDUOUS FOREST CANOPY IN EASTERN TENNESSEE, U.S.A. , 1986 .

[38]  T. Nilson A theoretical analysis of the frequency of gaps in plant stands , 1971 .

[39]  Andrea Cutini,et al.  Estimation of canopy properties in deciduous forests with digital hemispherical and cover photography , 2013 .

[40]  Ina C. Meier,et al.  Leaf Size and Leaf Area Index in Fagus sylvatica Forests: Competing Effects of Precipitation, Temperature, and Nitrogen Availability , 2008, Ecosystems.

[41]  A. Formaggio,et al.  Influence of data acquisition geometry on soybean spectral response simulated by the prosail model , 2013 .

[42]  Katja Brinkmann,et al.  Monitoring of crop biomass using true colour aerial photographs taken from a remote controlled hexacopter , 2015 .

[43]  E. D. Ford,et al.  The Leaf Canopy of a Coppiced Deciduous Woodland: I. Development and Structure , 1971 .

[44]  S. Leblanc Correction to the plant canopy gap-size analysis theory used by the Tracing Radiation and Architecture of Canopies instrument. , 2002, Applied optics.

[45]  Hideki Kobayashi,et al.  How to quantify tree leaf area index in an open savanna ecosystem: A multi-instrument and multi-model approach , 2010 .