The work is concerned with assessing the health status of trees of the Norway spruce species using airborne hyperspectral (HS) data (HyMap). The study was conducted in the Sokolov basin in the western part of the Czech Republic. First, statistics were employed to assess and validate diverse empirical models based on spectral information using the ground truth data (biochemically determined chlorophyll content). The model attaining the greatest accuracy (D718/D704∶RMSE = 0.2055 mg/g, R2 = 0.9370) was selected to produce a map of foliar chlorophyll concentrations (Cab). The Cab values retrieved from the HS data were tested together with other nonquantitative vegetation indicators derived from the HyMap image reflectance to create a statistical method allowing assessment of the condition of Norway spruce. As a result, we integrated the following HyMap derived parameters (Cab, REP, and SIPI) to assess the subtle changes in physiological status of the macroscopically undamaged foliage of Norway spruce within the four studied test sites. Our classification results and the previously published studies dealing with assessing the condition of Norway spruce using chlorophyll contents are in a good agreement and indicate that this method is potentially useful for general applicability after further testing and validation.
The quality of the environment has a great impact on public health while air quality is a major factor that is
especially relevant for respiratory diseases. PM10 (particulate matter below 10 μ) particles are among the
most dangerous pollutants, which enter the lower respiratory tract and cause serious health problems.
Obtaining reliable air pollution data is limited to a number of ground measuring stations and their spatial
location. We used an alternative approach and created statistical models that employed remotely sensed
imageries. To establish empirical relationships, we used multi-temporal (2006-2009) MODIS aerosol optical
thickness data (product MOD04, Level 2) and the PM10 ground mass concentrations. The north-western part
of the Czech Republic (namely the Karlovarský and the Ustecký regions) was chosen as a test site, as all the
different types of cultural landscape (forest-economical, agricultural, mining, and urban) can be found within
one MODIS scene. This study was focused on the various aspects as follows (i) analysis of MODIS AOT /
stationary PM10 time-series trend between 2006-2009, (ii) establishing a linear relationship between PM10
and AOT values for each station and (iii) evaluation of a spatial relationship of the annual mean AE
(Ångstrom Exponent) and PM10 values.
This study focused on testing the feasibility of up-scaling ground-spectra-derived parameters to HyMap
spectral and spatial resolution and whether they could be further used for a quantitative determination of the
following geochemical parameters: As, pH and Clignite content. The study was carried on the Sokolov lignite
mine as it represents a site with extreme material heterogeneity and high heavy-metal gradients. A new
segmentation method based on the unique spectral properties of acid materials was developed and applied to
the multi-line HyMap image data corrected for BRDF and atmospheric effects. The quantitative parameters
were calculated for multiple absorption features identified within the VIS/VNIR/SWIR regions (simple band
ratios, absorption band depth and quantitative spectral feature parameters calculated dynamically for each
spectral measurement (centre of the absorption band (λ), depth of the absorption band (D), width of the
absorption band (Width), and asymmetry of the absorption band (S)). The degree of spectral similarity
between the ground and image spectra was assessed. The linear models for pH, As and the Clignite content of
the whole and segmented images were cross-validated on the selected homogenous areas defined in the HS
images using ground truth. For the segmented images, reliable results were achieved as follows: As: R2=0.84,
Clignite: R2=0.88 and R2 pH: R2= 0.57.
S. Adar, G. Notesco, A. Brook, I. Livne, P. Rojik, V. Kopacková, K. Zelenkova, J. Misurec, A. Bourguignon, S. Chevrel, C. Ehrler, C. Fisher, J. Hanus, Y. Shkolnisky, E. Ben Dor
Two HyMap images acquired over the same lignite open-pit mining site in Sokolov, Czech Republic, during the
summers of 2009 and 2010 (12 months apart), were investigated in this study. The site selected for this research is one of
three test sites (the others being in South Africa and Kyrgyzstan) within the framework of the EO-MINERS FP7 Project
(http://www.eo-miners.eu). The goal of EO-MINERS is to "integrate new and existing Earth Observation tools to
improve best practice in mining activities and to reduce the mining related environmental and societal footprint".
Accordingly, the main objective of the current study was to develop hyperspectral-based means for the detection of small
spectral changes and to relate these changes to possible degradation or reclamation indicators of the area under
investigation. To ensure significant detection of small spectral changes, the temporal domain was investigated along with
careful generation of reflectance information. Thus, intensive spectroradiometric ground measurements were carried out
to ensure calibration and validation aspects during both overflights. The performance of these corrections was assessed
using the Quality Indicators setup developed under a different FP7 project-EUFAR (http://www.eufar.net), which
helped select the highest quality data for further work. This approach allows direct distinction of the real information
from noise. The reflectance images were used as input for the application of spectral-based change-detection algorithms
and indices to account for small and reliable changes. The related algorithms were then developed and applied on a
pixel-by-pixel basis to map spectral changes over the space of a year. Using field spectroscopy and ground truth
measurements on both overpass dates, it was possible to explain the results and allocate spatial kinetic processes of the
environmental changes during the time elapsed between the flights. It was found, for instance, that significant spectral
changes are capable of revealing mineral processes, vegetation status and soil formation long before these are apparent to
the naked eye. Further study is being conducted under the above initiative to extend this approach to other mining areas
worldwide and to improve the robustness of the developed algorithm.
The present study deals with evaluation of landslide prone zones in the northern part of El Salvador. The study area falls
onto a tectonically and seismically active zone of Central America with on-going neo-tectonic activities. Focus has been
put on applying the technique that allows a fast assessment of large regions. The analysis was based on digital data sets
including various derivatives of digital elevation models (DEMs) as well as Landsat-based information such as micro-lineament
density and landcover; seismic database, geological and morphological maps. Spatial multi-layered
information has been used for landslide susceptibility analysis. Here, an inventory map of 363 landslides induced in 1998
by hurricane Mitch were used to produce a dependent variable, the statistical hazard analysis has been carried out while
the zonal statistics was used to assign the weights for individual classes of the studied factors. Thus, all the relevant
thematic layers representing various independent factors (slope, aspect, relative relief, lithology, drainage density, micro-lineament
density and land cover) were relatively weighted and classified due to its disposition to cause landslides.
Principle Component Analyses (PCA) was used as a multivariate statistical method that allowed decorrelation of the
individual hazard triggers. It has been observed that the high potential zones were found to have very high lineament
density, high relative relief and drainage density areas. On the young volcanic pyroclastic deposits, heavy rainfall and
sparse vegetation cover cause persistent recurrence of landslides along this region. As result, a landslide susceptibility
map integrating morphological, lithological and hydrological information was computed. Delineated hazard zones were
again validated with the landslide inventory map and both, the model and terrain mapping, showed a good agreement as
the highest class occupied the 64% of the landslide areas and the two highest classes together occupied 90% of the
landslide areas, on the other hand none of the landslides fell into the lowest class.
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