International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
ESTIMATION OF VEGETATION HEIGHT THROUGH SATELLITE IMAGE TEXTURE
ANALYSIS
Z. I. Petrou?^", C. Tarantino^, M. Adamo“, P. Blonda®, M. Petrou®
aDepartment of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ,
United Kingdom - z.petroul0 @imperial.ac.uk
Information Technologies Institute, P.O.Box 60361, 6th km Harilaou - Thermi, 57001, Thessaloniki, Greece - (zpetrou, petrou) @iti.gr
‘Institute for Studies on Intelligent System for Automation (ISSIA), National Research Council (CNR), Via Amendola 122/D-O
70126, Bari, Italy. - (tarantino, adamo, blonda) @ba.issa.cnr.it
KEY WORDS: Texture analysis, land cover, vegetation classification, Quickbird, mapping
ABSTRACT:
Vegetation height plays a crucial role in various ecological and environmental applications, such as biodiversity assessment and
monitoring, landscape characterization, conservation planning and disaster management. Its estimation is traditionally based on in situ
measurements or airborne Light Detection And Ranging (LiDAR) sensors. However, such methods are often proven insufficient in
covering large area landscapes due to high demands in cost, labor and time. Considering a multispectral image from a passive satellite
sensor as the only available source of information, we propose and evaluate new ways of discriminating vegetated habitat species
according to their height, through calculation of texture analysis measures, based on local variance, entropy and local binary patterns.
The methodology is applied in a Quickbird image of Le Cesine protected site, Italy. The proposed methods are proven particularly
effective in discriminating low and mid phanerophytes from tall phanerophytes, having a height of less and more than 2 meters,
respectively. The results indicate a promising alternative in vegetation height estimation when in situ or LiDAR data are not available
or affordable, thus facilitating and reducing the cost of ecological monitoring and environmental sustainability planning tasks.
1 INTRODUCTION
Estimation of canopy structure and vegetation height is funda-
mental for a series of ecological studies, including biodiversity
monitoring, conservation planning, fire modeling and biomass es-
timation (Hyde et al., 2006, Dong and Wu, 2008). In addition, in
various landscape mapping applications, certain land cover and
habitat categories are discriminated based on their height, thus its
measurement is of fundamental importance. Characteristic ex-
amples of such categories are included in the Land Cover Clas-
sification System (LCCS), proposed by the Food and Agricul-
ture Organization of the United Nations (Di Gregorio and Jansen,
1998), and the General Habitat Categories (Bunce et al., 2008),
for land cover and habitat mapping, respectively, both adopted
by the BIO_SOS (BIOdiversity Multi-Source Monitoring System:
from Space To Species) European project, concerned with biodi-
versity monitoring.
Numerous studies have been proposed in the literature on vegeta-
tion height measurement through field campaigns with hand-held
devices (Payero et al., 2004, Weltz et al., 1994, Buckley et al.,
1999), considered as the most accurate approach. In cases where
in situ measurements were not available or possible, LIDAR data,
mainly from airborne sensors, have been recorded as the most ef-
ficient alternative (Nilsson, 1996, Kwak et al., 2007, Dubayah
et al., 2010, Lefsky et al., 2005). Airborne Synthetic Aperture
Radar (SAR) data have also been used to a lesser extent (Praks
et al., 2009). However, such methods provide coverage to a re-
stricted spatial extent and can be particularly expensive and time
and labor demanding.
Satellite data, on the other hand, mainly from passive sensors,
providing a large area coverage often at a reasonable cost, seem to
constitute a rational potential alternative. Different studies have
been conducted recently, trying to investigate potential correla-
tion of the spectral characteristics of areas captured in satellite
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"Corresponding author. E-mail: z.petrou10 Q imperial.ac.uk.
images with their vegetation height, usually in a synergy with
airborne LiDAR data. Stojanova et al. (2010) calculated statistic
measures in the segments of Landsat imagery and, together with
LiDAR, tried to extract vegetation height in a Slovenian forested
area. Various vegetation indices were calculated from Landsat
data by Dong and Wu (2008) and, in combination with LiDAR
satellite data, were used to estimate vegetation height in a moun-
tainous protected region in China. Similar vegetation indices
from Landsat were used by Yanhong ef al. (2010) to approxi-
mate height in a Chinese inland river basin. Hyde ef al. (2006)
used airborne LiDAR and SAR and satellite Landsat and Quick-
bird data and compared their potential in height estimation in a
forested site in USA. LiDAR clearly outperformed all other sin-
gle sensors in height estimation accuracy; when data from other
sensors, especially Landsat, were combined with LiDAR, the re-
sults were further improved. Data from the Moderate Resolution
Imaging Spectroradiometer (MODIS) sensor have also been used
recently for large area vegetation height estimation in forests of
USA and Costa Rica (Wang et al., 2011).
In all the aforementioned studies, where data from passive sen-
sors were used, only reflectance-based characteristics were em-
ployed. In this paper, we introduce the use of textural charac-
teristics in vegetation height estimation through passive satellite
sensors. Textural characteristics are expected to reveal spatial
structural properties of the studied areas. The main idea behind
this approach is the fact that in areas with short and shrubby vege-
tation the texture of the image appears more homogeneous than in
areas with high vegetation, where vegetation canopy, tree trunks
and bare ground alternate, making the texture variant and inho-
mogeneous. Different texture measures are proposed and evalu-
ated as far as their efficiency in discriminating habitat types based
on their vegetation height, is regarded. In particular, the discrimi-
nation of low and mid from tall phanerophyte habitats, according
to the GHC scheme, is regarded.
In addition, extended in situ measurements and LiDAR data are
