The emerald ash borer (EAB) poses a significant economic and environmental threat to ash trees in southern Ontario, Canada, and the northern states of the USA. It is critical that effective technologies are urgently developed to detect, monitor, and control the spread of EAB. This paper presents a methodology using multisourced data to predict potential infestations of EAB in the town of Oakville, Ontario, Canada. The information combined in this study includes remotely sensed data, such as high spatial resolution aerial imagery, commercial ground and airborne hyperspectral data, and Google Earth imagery, in addition to nonremotely sensed data, such as archived paper maps and documents. This wide range of data provides extensive information that can be used for early detection of EAB, yet their effective employment and use remain a significant challenge. A prediction function was developed to estimate the EAB infestation states of individual ash trees using three major attributes: leaf chlorophyll content, tree crown spatial pattern, and prior knowledge. Comparison between these predicted values and a ground-based survey demonstrated an overall accuracy of 62.5%, with 22.5% omission and 18.5% commission errors.
KEYWORDS: LIDAR, Vegetation, 3D modeling, Solar radiation models, Instrument modeling, Transmittance, Atmospheric propagation, Optical filters, Sensors, Signal to noise ratio
Structural and biophysical parameters of vegetation canopies, such as tree heights, biomass, vertical and horizontal
heterogeneity are important factors affecting flows of energy, water, carbon and trace gases through terrestrial systems.
Knowing such parameters is required to model processes associated with photosynthesis, energy transfer, and
evapotranspiration at local and global scales. Monitoring vegetation canopies has long been one of the main tasks of
several proposed and launched space missions. Lidar instruments have demonstrated the best potential to provide
estimates of vegetation height, cover, and canopy vertical structural profiles. A spaceborne lidar would deliver such data
on global scale producing the total land biomass value with the accuracy demanded by carbon-cycle and global-change
modelers.
This paper presents the preliminary results of a numerical model simulating signal returns of a spaceborne lidar for the
assessment of land-vegetation canopy biomass. It is a part of work with the overall purpose to develop a trade-off
analysis tool for a spaceborne lidar system as a payload of a land-vegetation observation space mission. An end-to-end
propagation of a spaceborne lidar sensing pulse through vegetation canopies is considered by the model. It consists of
the modules characterizing the laser and the receiver optical systems, satellite's orbit, atmosphere, vegetation canopies,
optical filtering, and detectors. This tool can be used to evaluate the effects of instrument configurations on the retrieval
of vegetation structures, and to carry out trade-off studies in the instrument design.
In this study, classification was investigated based on seasonal variation of the state parameters of vegetation canopies as
inferred from visible and near-infrared spectral bands. This analysis was carried out on data collected over agricultural
fields with the hyperspectral CHRIS (Compact High Resolution Imaging Spectrometer) in May, June and July, 2004.
The singular spectrum analysis was used to remove noise in each reflectance spectrum of the whole image. Decision tree
classification was performed on different features, such as reflectance, vegetation indices, and principal components
acquired by PCA (Principal Component Analysis) and MNF (Minimum Noise Fraction). The results demonstrated
noise-removal using SSA increased classification accuracy by 3-6 percentages depending on the features used.
Classification using MNF components was shown to provide the highest accuracy followed by that using vegetation
indices.
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