Balancing on the Borderline – a Low‐cost Approach to Visualize the Red‐edge Shift for the Benefit of Aerial Archaeology

Scientists from different research disciplines have provided essential information that relates the biophysical characteristics of plants to their spectral reflectance. This fundamental understanding has facilitated the development of various non-destructive sensing methods for detecting vegetation stresses, monitoring plant growth and calculating crop yield. Aerial archaeologists flying in small aeroplanes have only partially exploited this knowledge. Instead of basing archaeological interpretation on only direct visual inspection of the conventionally acquired colour photographs, this contribution briefly reviews the reflectance properties of plants and uses them to present a new low-cost imaging technique beneficial for the detection of (faint) archaeologically induced vegetation marks. The new approach consists of three simultaneously operated digital still cameras, each of them capturing information in a different spectral waveband: the visible, near-infrared and red-edge spectral region. The latter two bands are used in the calculation of a R700/R800 vegetation index. Besides a theoretical underpinning, real-world examples will assess the potential of this new approach in detection of vegetation marks and prove that this low-cost, multispectral method might be beneficial in identifying and enhancing weak crop stresses that are lost when taking only the broad visible spectrum into account. In the final discussion, some thoughts on future archaeological aerial research are given. Copyright © 2011 John Wiley & Sons, Ltd.

[1]  G. A. Blackburn,et al.  Hyperspectral remote sensing of plant pigments. , 2006, Journal of experimental botany.

[2]  Thomas F. Eck,et al.  Reflectance anisotropy for a spruce-hemlock forest canopy , 1994 .

[3]  D. Horler,et al.  The red edge of plant leaf reflectance , 1983 .

[4]  Geert Verhoeven,et al.  It's all about the format – unleashing the power of RAW aerial photography , 2010 .

[5]  G. Carter,et al.  Narrow-band Reflectance Imagery Compared with ThermalImagery for Early Detection of Plant Stress , 1996 .

[6]  Darrel L. Williams,et al.  Multispectral bidirectional reflectance of northern forest canopies with the advanced solid-state array spectroradiometer (ASAS)☆ , 1994 .

[7]  B. M. Fagan.  Cropmarks in Antiquity , 1959, Antiquity.

[8]  Nadine Gobron,et al.  Optical remote sensing of vegetation: Modeling, caveats, and algorithms , 1995 .

[9]  Dirk Poelman,et al.  Spectral Characterization of a Digital Still Camera's NIR Modification to Enhance Archaeological Observation , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[10]  J. Woolley.  Reflectance and transmittance of light by leaves. , 1971, Plant physiology.

[11]  J. Dungan,et al.  Exploring the relationship between reflectance red edge and chlorophyll content in slash pine. , 1990, Tree physiology.

[12]  A. Gitelson,et al.  Signature Analysis of Leaf Reflectance Spectra: Algorithm Development for Remote Sensing of Chlorophyll , 1996 .

[13]  T. Eck,et al.  Characterization of the reflectance anisotropy of three boreal forest canopies in spring-summer , 1999 .

[14]  G. Carter,et al.  Early detection of plant stress by digital imaging within narrow stress-sensitive wavebands , 1994 .

[15]  Yuri A. Gritz,et al.  Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. , 2003, Journal of plant physiology.

[16]  Geert Verhoeven,et al.  Near-Infrared Aerial Crop Mark Archaeology: From its Historical Use to Current Digital Implementations , 2012 .

[17]  B. Aminzadeh,et al.  Identifying the boundaries of the historical site of Persepolis using remote sensing , 2006 .

[18]  Jane Drummond,et al.  Finding archaeological cropmarks: a hyperspectral approach , 2007, SPIE Remote Sensing.

[19]  Sidney F. Ray,et al.  Applied Photographic Optics: Lenses and optical systems for photography, film, video, electronic and digital imaging , 2002 .

[20]  J. Colwell.  Vegetation canopy reflectance , 1974 .

[21]  Claus Buschmann,et al.  In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation , 1993 .

[22]  A. Gitelson,et al.  Quantitative estimation of chlorophyll-a using reflectance spectra : experiments with autumn chestnut and maple leaves , 1994 .

[23]  A. Gitelson,et al.  Application of Spectral Remote Sensing for Agronomic Decisions , 2008 .

[24]  M. Fowler.  Satellite remote sensing and archaeology: a comparative study of satellite imagery of the environs of Figsbury Ring, Wiltshire , 2002 .

[25]  O. Crawford.  Air survey and archæology , 1923 .

[26]  D. Lobell,et al.  View angle effects on canopy reflectance and spectral mixture analysis of coniferous forests using AVIRIS , 2002 .

[27]  G. Carter.  Reflectance Wavebands and Indices for Remote Estimation of Photosynthesis and Stomatal Conductance in Pine Canopies , 1998 .

[28]  Gregory A. Carter,et al.  Identification of a far-red reflectance response to ectomycorrhizae in slash pine , 1992 .

[29]  D. Kimes.  Dynamics of directional reflectance factor distributions for vegetation canopies. , 1983, Applied optics.

[30]  A. Gitelson,et al.  Three‐band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves , 2006 .

[31]  Gregory A. Carter,et al.  General Spectral Characteristics of Leaf Reflectance Responses to Plant Stress and Their Manifestation at the Landscape Scale , 2002 .

[32]  Stanley B. Brown,et al.  THE DEGRADATION OF CHLOROPHYLL - A BIOLOGICAL ENIGMA. , 1987, The New phytologist.

[33]  G. Guyot,et al.  2 – OPTICAL PROPERTIES OF VEGETATION CANOPIES , 1990 .

[34]  Moon S. Kim,et al.  Ratio analysis of reflectance spectra (RARS): An algorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B, and carotenoids in soybean leaves , 1992 .

[35]  G. Verhoeven,et al.  An attempt to push back frontiers – digital near-ultraviolet aerial archaeology , 2010 .

[36]  J. Qi,et al.  Identification of red and NIR spectral regions and vegetative indices for discrimination of cotton nitrogen stress and growth stage , 2005 .

[37]  Hartmut K. Lichtenthaler,et al.  The Chlorophyll Fluorescence Ratio F735/F700 as an Accurate Measure of the Chlorophyll Content in Plants , 1999 .

[38]  Anatoly A. Gitelson,et al.  Why and What for the Leaves Are Yellow in Autumn? On the Interpretation of Optical Spectra of Senescing Leaves (Acerplatanoides L.)* , 1995 .

[39]  A. Gitelson,et al.  Novel algorithms for remote estimation of vegetation fraction , 2002 .

[40]  G. Carter,et al.  Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. , 2001, American journal of botany.

[41]  Dong-Hwan Har,et al.  SLR digital camera for forensic photography , 2004, IS&T/SPIE Electronic Imaging.

[42]  Rosa Lasaponara,et al.  On the potential of QuickBird data for archaeological prospection , 2006 .

[43]  Geert Verhoeven.  Imaging the invisible using modified digital still cameras for straightforward and low-cost archaeological near-infrared photography , 2008 .

[44]  J. Norman,et al.  Contrasts among Bidirectional Reflectance of Leaves, Canopies, and Soils , 1985, IEEE Transactions on Geoscience and Remote Sensing.

[45]  F. Baret,et al.  Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .

[46]  G. Carter.  Ratios of leaf reflectances in narrow wavebands as indicators of plant stress , 1994 .

[47]  E. Hunt,et al.  Estimating near-infrared leaf reflectance from leaf structural characteristics. , 2001, American journal of botany.

[48]  Klaus I. Itten,et al.  A field goniometer system (FIGOS) for acquisition of hyperspectral BRDF data , 1999, IEEE Trans. Geosci. Remote. Sens..