Peer-Reviewed Articles
361 Canopy Reflectance Model Inversion in Multiple
Forward Mode: Forest Structural Information Retrieval
from Solution Set Distributions
S. A. Soenen, D. R. Peddle, C. A. Coburn, R. J. Hall, and F.G.
Hall
Abstract Download
Full Article
Remote estimation of canopy structure is important in
forestry and a variety of environmental applications.
Multiple Forward Mode (MFM) look-up table (LUT) inversion
of canopy reflectance models is one approach for obtaining
forest canopy biophysical-structural information (BSI). MFM
provides inversion results from models that are not invertible
directly, and has advantages in terms of software
requirements, model complexity, computational demands,
and provision of physically-based BSI output. Proper
handling of MFM-LUT parameterization and inherent uncertainty
in the inversion procedure at the critical final BSI
retrieval stage is essential, and is the theme of this paper.
Three approaches are presented for deriving BSI from MFMLUT
multiple solution sets: reflectance equality (REQ),
nearest spectral distance (NSD), and spectral range domain
(SRD). These approaches were validated at a Rocky Mountain
test site, for which SRD corresponded best with field
data, with RMSE 0.4 m and 0.8 m obtained for horizontal
and vertical crown radius, respectively. Recommendations
for selecting MFM inversion approaches are provided for
future applications.
375 Hemispheric Image Modeling and Analysis Techniques
for Solar Radiation Determination in Forest Ecosystems
Ellen Schwalbe, Hans-Gerd Maas, Manuela Kenter, and Sven
Wagner
Abstract Download
Full Article
Hemispheric image processing with the goal of solar radiation
determination from ground-based fisheye images is a valuable
tool for silvicultural analysis in forest ecosystems. The basic
idea of the technique is taking a hemispheric crown image
with a camera equipped with a 180° fisheye lens, segmenting
the image in order to identify solar radiation relevant open
sky areas, and then merging the open sky area with a radiation
and sun-path model in order to compute the total annual
or seasonal solar radiation for a plant. The results of hemispheric
image processing can be used to quantitatively
evaluate the growth chances of ground vegetation (e.g., tree
regeneration) in forest ecosystems.
This paper shows steps towards the operationalization and optimization of the method. As a prerequisite to support geometric handling and georeferencing of hemispheric images, an equi-angular camera model is shown to describe the imaging geometry of fisheye lenses. The model is extended by a set of additional parameters to handle deviations from the ideal model. In practical tests, a precision potential of 0.1 pixels could be obtained with off-the-shelf fisheye lenses. In addition, a method for handling the effects of chromatic aberration, which may amount to several pixels in fisheye lens systems, is discussed. The central topic of the paper is the development of a versatile method for segmenting hemispheric forest crown images. The method is based on linear segmentoriented classification on radial profiles. It combines global thresholding techniques with local image analysis to ensure a reliable segmentation in different types of forest under various cloud conditions. Sub-pixel classification is incorporated to optimize the accuracy of the method. The performance of the developed method is validated in a number of practical tests.
385 Application of Association Rule Mining for Exploring
the Relationship between Urban Land Surface
Temperature and Biophysical/Social Parameters
Umamaheshwaran Rajasekar and Qihao Weng
Abstract Download
Full Article
Abstract
This paper explores the relationship between remote
sensing measurements of land surface temperature and
biophysical/socioeconomic data by utilizing the association
rule mining technique. The surfaces associated with urban
uses typically radiate more heat as compared to its rural
counterparts. There is a need to quantitatively analyze this
contrast in temperature and the biophysical and social
characteristics which influence it. Furthermore, in order to
consider the urban heat island (UHI) effect, a parameterization
is required to account for the urban surface characteristics
impacts on the magnitude of land surface temperature
(LST). The association rule mining model has demonstrated
to bring in additional quantitative information concerning
the relationships among urban parameters. The ASTER data
from 2000 was used for the selection of appropriate variables
to be used in the model. This information was then used for
generating association rules between land-use land-cover
(LULC) and LST information from 2000, 2001, and 2004. The
results thus obtained quantitatively described the relationships
between various urban parameters. It was found that
there was little change in the percentage area of the LULC
types from 2000 to 2004. This made the comparison of the
results possible. In the case of the 2000 data, it was found
that forest and impervious surfaces had strong association
with temperature and scaled normalized difference vegetation
index (SNDVI). Specific zones such as hospitals and
universities had negative association with water. The comparison
of data from 2000, 2001, and 2004 suggests that
impervious surface and the zoning category of airport had a
strong association. Nevertheless, the information extracted
needs to be analyzed in greater detail in order to arrive at
robust decision rules. Overall, the model so developed has
demonstrated to be effective in predicting associations
between urban LST and pertinent factors. This model could
be useful for urban planners and environmental managers in
quantifying rules that characterize a particular urban
landscape.
397 An Assessment of Geometric Activity Features for
Per-pixel Classification of Urban Man-made Objects
using Very High Resolution Satellite Imagery
Jonathan Cheung-Wai Chan, Rik Bellens, Frank Canters, and
Sidharta Gautama
Abstract Download
Full Article
In this paper, we propose the use of Geometric Activity (GA)
features for detecting man-made objects in urban areas
using VHR satellite imagery. These features describe the
geometric context of a pixel without the necessity of segmentation
and can be integrated as extra bands in a per-pixel
classification. Two main types of GA features were investigated:
ridge features based on the well-known facet model
and morphological features obtained by applying closing
transforms with structuring elements of different size and
shape. Our findings show a substantial increase in classification
accuracy for the man-made object classes “roads and
buildings with dark roof” after inclusion of GA features. Next
to GA features, the use of object-based features derived from
eCognition®, containing both geometric and textural information,
was also investigated for per-pixel classification.
Accuracies obtained with object-based features are comparable
to the accuracies obtained with GA features. The inclusion
of both GA features and object-based features further
improves the overall accuracy. GA features and object-based
features thus contain complementary information.
413 Agro-ecological Interpretation of Rice Cropping
Systems in Flood-prone Areas using MODIS Imagery
Toshihiro Sakamoto, Cao Van Phung, Nhan Van, Akihiko
Kotera, and Masayuki Yokozawa
Abstract Download
Full Article
This study attempts a new approach using Moderate
Resolution Imaging Spectroradiometer (MODIS) time-series
imagery to evaluate the agro-ecological interpretation of
rice-cropping systems in flood-prone areas. A series of
wavelet-based methodologies were applied to reveal the
dynamic relationships among annual flood inundation,
rice phenology, and land-use change in the Vietnamese
Mekong Delta (VMD). The rice-heading dates of multicropping
areas were estimated by detecting the local
maximal points in smoothed Enhanced Vegetation Index
(EVI) profiles, using the Wavelet-based Filter for determining
Crop Phenology (WFCP) and Wavelet-based Filter for
evaluating the spatial distribution of Cropping Systems
(WFCS) methods. The temporal information for annual
flood intensity was determined for the six annual flood
seasons over the period from 2000 to 2005 by the Waveletbased
Filter for detecting spatio-temporal changes in the
Flood Inundation (WFFI) method. Analysis using remote
sensing techniques revealed an interaction between the
regional environment and agricultural activity in the VMD.
First, comparing the estimated heading date of the winterspring
rice with the end date of flood inundation showed
that the cropping season for the winter-spring rice in the
flood-prone area fluctuates depending on the annual
change in flood scale. This result implied that the onset of
winter-spring rice is spatially and temporally linked to the
variable flood-recession season, and hence the annual
change in flood scale. Secondly, the field survey study of
the yearly change in the rice-cropping system in the An
Giang province from 2000 to 2006 showed that the triple
rice-cropped area in the An Giang province expanded from 2000 to 2005, because the construction of a ringdyke
system and water-resource infrastructure allowed
an additional rice crop to be sustained during the flood
season. However, the area of the third rice crop in the An
Giang province decreased drastically in 2006 as a result of
the management of pest outbreaks. Although the regional
water-resource environment was gradually transformed
by the construction of water-resource infrastructure, in
order to achieve favorable conditions for a third rice crop
during the flood season, the rapid change in land-use for
agricultural activity may complicate the spatio-temporal
configuration of the agricultural environment in the VMD.
This case study used MODIS time-series imagery to help
understand the functions of the macro-scale ecosystem,
including annual flood regimes and human activity.
425 Evaluating AISA+ Hyperspectral Imagery for Mapping
Black Mangrove
along the South Texas Gulf Coast
Chenghai Yang, James H. Everitt, Reginald S. Fletcher, R yan R.
Jensen, and Paul W. Mausel
Abstract Download
Full Article
Mangrove wetlands are economically and ecologically
important ecosystems and accurate assessment of these
wetlands with remote sensing can assist in their management
and conservation. This study was conducted to
evaluate airborne AISA+ hyperspectral imagery and image
transformation and classification techniques for mapping
black mangrove populations on the south Texas Gulf
coast. AISA+ hyperspectral imagery was acquired from two
study sites and both minimum noise fraction (MNF) and
inverse MNF transforms were performed. Four classification
methods, including minimum distance, Mahalanobis
distance, maximum likelihood, and spectral angle mapper
(SAM), were applied to the noise-reduced hyperspectral
imagery and to the band-reduced MNF imagery for distinguishing
black mangrove from associated plant species
and other cover types. Accuracy assessment showed that
overall accuracy varied from 84 percent to 95 percent for
site 1 and from 69 percent to 91 percent for site 2 among
the eight classifications for each site. The MNF images
provided similar or better classification results compared
with the hyperspectral images among the four classifiers.
Kappa analysis showed that there were no significant
differences among the four classifiers with the MNF
imagery, though maximum likelihood provided excellent
overall and class accuracies for both sites. Producer’s and
user’s accuracies for black mangrove were 91 percent and
94 percent, respectively, for site 1 and both 91 percent
for site 2 based on maximum likelihood applied to the
MNF imagery. These results indicate that airborne hyperspectral
imagery combined with image transformation and
classification techniques can be a useful tool for monitoring
and mapping black mangrove distributions in coastal
environments.
437 Morphology-based Building Detection from Airborne
Lidar Data
Xuelian Meng, Le Wang, and Nate Currit
Abstract Download
Full Article
The advent of Light Detection and Ranging (lidar) technique
provides a promising resource for three-dimensional building
detection. Due to the difficulty of removing vegetation,
most building detection methods fuse lidar data with multispectral
images for vegetation indices and relatively few
approaches use only lidar data. However, the fusing process
may cause errors introduced by resolution and time difference,
shadow and high-rise building displacement problems,
and the geo-referencing process. This research presents a
morphological building detecting method to identify buildings
by gradually removing non-building pixels. First, a
ground-filtering algorithm separates ground pixels with
buildings, trees, and other objects. Then, an analytical
approach removes the remaining non-building pixels using
size, shape, height, building element structure, and the
height difference between the first and last returns. The
experimental results show that this method provides a
comparative performance with an overall accuracy of
95.46 percent as in a study site in Austin urban area.