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This PDF file contains the front matter associated with SPIE Proceedings Volume 6742, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.
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Opening Session: RS for Agriculture, Ecosystems, and Hydrology
The remote sensing and GIS communities are still separate worlds with their own tools and data formats. It is extremely
difficult to easily share data among scientists representing these communities without performing some cumbersome
conversions. This paper shows in a case study how these two worlds can benefit from each other by implementing online
satellite derived soil moisture in a GIS based operational flood early warning system. We obtained near real time satellite
data from the currently active satellite microwave sensor AQUA AMSR-E from the National Snow and Ice Data Center
data pool and converted the data to soil moisture maps with the Land Parameter Retrieval Model. The soil moisture
maps, with a spatial resolution of 0.1 degree and temporal resolution of approximately 1 day, were converted in a
gridded format and directly added to an operational Flood Early Warning System. The developed opportunity to directly
visualize soil moisture in such a system appears to be a powerful tool, because it creates the ability to study both the
spatial and temporal evolution of soil moisture within the river basin. Furthermore, near real time qualitative information
on soil moisture conditions prior to rainfall events, such as generated by our system, can even lead to more accurate
estimations for flood hazard conditions. Finally, the current and future role and value of remote sensing products in flood
forecasting systems are discussed.
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Simulating the main terms of forest carbon budget (GPP, NPP, NEE) is important for both scientific and practical
reasons. This operation was performed for a region of Central Italy (Tuscany) by the integrated processing of ground and
satellite data. Several data layers (meteorology, forest type, volume, etc.) were first collected in order to characterize the
eco-climatic and forest features of the region. FAPAR estimates with 1 km resolution were obtained by processing VGT
NDVI data. Relying on these data sets, monthly estimates of forest GPP were produced by means of a simplified, NDVIbased
parametric model, C-Fix. These GPP estimates were used to calibrate a well known bio-geochemical model,
BIOME-BGC, in order to find its best configurations for simulating all main functions (photosynthesis, respirations,
allocations, etc.) of the most widespread Tuscany forest types. The calibrated versions of BIOME-BGC were then
applied to produce respiration estimates for all regional forest surfaces during the study period. The obtained GPP and
respiration estimates, which were referred to equilibrium conditions, were converted into the values of actual forests by
applying a simplified approach which relies on the ratio of actual over potential tree volume as an indicator of forest
distance from climax. The C-Fix photosynthesis estimates of actual forests were finally integrated with relevant BIOMEBGC
simulated respirations in order to assess net forest carbon fluxes.
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Minnaert constants are calculated from the intensity of the light scattered from the surface of
objects. Focused on remote sensing of vegetation on the earth's surface, the light reflected
from leaves is measured. A remote sensing simulator is used in an experimental room with a
halogen lamp as an optical source and a Wratten gelatin filter No. 25 as a filter. Bidirectional
reflectance from two kinds of leaves with different degrees of roughness is measured and their
Minnaert constants are obtained by recurrent analysis of the results. The Minnaert constant for
a leaf with a smooth surface is larger than 1 (Lambertian surface) and that with a rough surface
is smaller than 1.
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Forest fires are one of the major environmental hazards in Mediterranean Europe. Biomass burning reduces
carbon fixation in terrestrial vegetation, while soil erosion increases in burned areas. For these reasons, more
sophisticated prevention tools are needed by local authorities to forecast fire danger, allowing a sound allocation
of intervention resources. Various factors contribute to the quantification of fire hazard, and among them
vegetation moisture is the one that dictates vegetation susceptibility to fire ignition and propagation. Many
authors have demonstrated the role of remote sensing in the assessment of vegetation equivalent water thickness
(EWT), which is defined as the weight of liquid water per unit of leaf surface. However, fire models rely on the
fuel moisture content (FMC) as a measure of vegetation moisture. FMC is defined as the ratio of the weight
of the liquid water in a leaf over the weight of dry matter, and its retrieval from remote sensing measurements
might be problematic, since it is calculated from two biophysical properties that independently affect vegetation
reflectance spectrum.
The aim of this research is to evaluate the potential of the Moderate Resolution Imaging Spectrometer
(MODIS) in retrieving both EWT and FMC from top of the canopy reflectance. The PROSPECT radiative
transfer code was used to simulate leaf reflectance and transmittance as a function of leaf properties, and the
SAILH model was adopted to simulate the top of the canopy reflectance. A number of moisture spectral indexes
have been calculated, based on MODIS bands, and their performance in predicting EWT and FMC has been
evaluated. Results showed that traditional moisture spectral indexes can accurately predict EWT but not FMC.
However, it has been found that it is possible to take advantage of the multiple MODIS short-wave infrared
(SWIR) channels to improve the retrieval accuracy of FMC (r2 = 0.73). The effects of canopy structural
properties on MODIS estimates of FMC have been evaluated, and it has been found that the limiting factor is
leaf area index (LAI). The best results are recorded for LAI>2 (r2 = 0.83), while acceptable results (r2 = 0.58)
can still be achieved for lower vegetation cover density.
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During the past few years, the European Space Agency has launched several projects related to forest fires from global to
local scales. The ATSR World Fire Atlas (WFA) project started in late 1995 and is still running today. It provides the
longest time series ever produced on the global distribution of active fires. The WFA consists of 12 years of coherent and
consistent data sets. The night time fire occurrences are derived from the 3.7 micron channel on-board the ATSR
instrument series hosted by the ERS-2 and ENVISAT satellites. The ATSR WFA products were validated first in 1998
with the support of IGBP and more recently by extensive comparisons with existing data sets on forest fires events. A
smooth transition from the ERS-2 ATSR-2 to the ENVISAT AATSR has been performed in January 2003 and the
quality of the WFA products continuity verified. The ATSR WFA products are available in near real time since May
2006. The distribution of the ATSR WFA products will be thoroughly analyzed in this paper and a synthesis of the work
performed by more than 900 registered users will be presented. The GlobCarbon project started in early 2003 with the
objective to develop a service for the production of multi-year / multi-sensors global level 3 Land products to be used as
input to carbon assimilation models. Understanding the spatial and temporal variation in carbon fluxes is essential to
constrain models that predict climate change. However our current knowledge of these spatial and temporal patterns is
uncertain, particularly over land. One of the bio-geophysical parameters that the GlobCarbon project aims to measure is
the fully calibrated estimate of the burned areas quasi-independent of the original satellite sensor. These burned areas
estimates are used in dynamic global vegetation models, a central component of the IGBP-IHDP-WCRP Global Carbon
Cycle Joint Project. The service will feature global estimates of burned areas amongst other variables from 1998 to 2007,
derived from Earth Observation sensors (ERS-2 ATSR-2, ENVISAT AATSR and SPOT VEGETATION). Finally the
RISK-EOS project started in 2003 under the framework of the Global Monitoring for Environment and Security
(GMES) initiative, with the objective to establish a network of European service providers for the provision of geoinformation
services in support to the risk management of meteorological hazards (floods and fires). The Fire component
of RISK-EOS feature two main services: the Burn Scar Mapping (BSM) service that provides some seasonal mapping of
forests and semi-natural burned areas at high spatial resolution (minimum mapping unit of 3 to 5 ha); and the Regional
Fire Monitoring (RFM) service that provides near real time observation of active fires, based on middle resolution
satellite data (AQUA/TERRA MODIS and MSG SEVIRI). The RISK-EOS BSM service builds on the achievements of
ITALSCAR, a demonstration project for the yearly mapping of burned areas in Italy, using the LANDSAT Thematic
Mapper sensor. The paper will provide a synthesis of the RISK-EOS products validation and utility reports collected
during the 2006 summer season.
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The dynamics of vegetation covers in burned and unburned areas can be monitored by using satellite data, which provide
a wide spatial coverage and internal consistency of data sets. Several indices based on satellite data can be used for this
aim. In particular, NDVI (Normalized Difference Vegetation Index) is the most widely used index for vegetation
monitoring based on remote sensing. This paper aims to perform a dynamical characterization of burned and unburned
vegetation covers, using time series of remotely sensed data of some fire-affected and fire-unaffected sites. For this
purpose, we used the Detrended Fluctuation Analysis (DFA), which permits the detection of persistent properties in
nonstationary signals. Our results point out that the persistence of vegetation dynamics is significantly increased by the
occurrence of fires.
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A thermal sharpening algorithm (TsHARP) providing fine resolution land surface temperature (LST) to the Two-Source-
Model (TSM) for mapping evapotranspiration (ET) was applied over two agricultural regions in the U.S. One site is a
rainfed corn and soybean production region in central Iowa, while the other is an irrigated agricultural area in the Texas
High Plains. Application of TsHARP to coarse (1 km) resolution thermal data over the rainfed agricultural area is found
to produce reliable fine/within-field (60 m) resolution ET estimates, while in contrast, the TsHARP algorithm applied to
the irrigated area does not perform as well, possibly due to significant sub-pixel moisture variations from irrigation. As a
result, there may be little benefit in applying TsHARP for generating TSM-derived 60 m ET maps for the irrigated
compared to the rainfed region. Consequently, reliable estimation of fine/within-field ET and crop stress still requires
fine native resolution thermal imagery in areas with significant sub-pixel moisture variations.
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Two common approaches for estimating crop evapotranspiration (ET) using satellite imagery are the reflectance-based
crop coefficient method and the energy balance method. The reflectance-based crop coefficient method
relates a reflectance-based vegetation index such as the soil adjusted vegetation index (SAVI) to ET basal crop
coefficients such as those described by Wright (1982) [1] and the FAO 56 manual [2]. A time-series of remotely
sensed inputs is then used to build the crop coefficient curve in each field being monitored. In order to obtain actual
ET, a water balance must be maintained in the root zone of the crop in order to make the appropriate adjustments due
to soil moisture deficits and wet soil surface from irrigation and/or rain. Ground meteorological data must be
provided by a weather station located in the modeled area for the estimation of reference ET. In the energy balance
approach, surface temperatures are used in the estimation of sensible heat fluxes and depending on the complexity of
the model, different methods are used to either handle the aerodynamic temperature term or deal with sparse
canopies (empirical approaches, two-source model, SEBAL model). Remotely sensed inputs are also used for the
estimation of net radiation and soil heat flux, with latent heat flux (ET) obtained as a residual from the energy
balance equation. The energy balance approach results in the actual ET being estimated directly. Instantaneous
values of ET must be extrapolated to the entire day and over time in between satellite overpass inputs. This paper
describes a hybrid approach that uses both methods in combination to monitor actual ET over a growing season for
irrigated and non-irrigated crops. The model has been coded in an ArcGIS environment, using visual basic for the
calculations. This paper describes the modeling environment and coded ET models within and presents some
application results.
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The determination of soil moisture content is often based on the measurements of the ratio of
the vertically and horizontally polarized cross sections for large angles of incidence, where
the cross sections could be significantly different. Using the high frequency, physical optics
model of the earth's surface, this ratio depends primarily on the Fresnel reflection
coefficients for the two polarizations, while the impact of surface roughness factors out of the
cross section ratio. Thus for highly conducting moisture saturated soils, this ratio approaches
one. Using the low frequency, small height-small slope perturbation model of the earth's
surface, the vertically and horizontally polarized cross sections are critically dependent on
polarization for large angles of incidence, even for the perfectly conducting rough surfaces.
However using the standard perturbation model, the ratios of the cross sections are also
independent of the surface roughness. Applying the small perturbation approach to highly
conducting rough surfaces, the ratio of the horizontally to vertically polarized cross sections
approaches zero for grazing angles of incidence, for which the two cross sections differ
significantly. There is ample experimental evidence that neither the physical optics nor the
small perturbation models are adequate.
The standard hybrid two scale physical optics-perturbation approach depends critically upon
the decomposition of the composite surface into smaller and larger scale surfaces. The
smaller scale surface is restricted to small Rayleigh roughness parameters, proportional to the
mean square height, and the larger scale surface is restricted by the large radii of curvature
criteria.
Using a two scale full wave approach, the cross section are expressed as a weighted sum of a
physical optics cross section for the larger scale surface, reduced by a factor equal to the
square of the small scale surface characteristic function, and a cross section for the smaller
scale surface that is modulated by the slopes of the larger scale surface. A variation technique
is used to decompose the surface height spectral density function in a continuous, smooth
manner into spectral density functions for the larger and smaller scale surfaces. It is shown
that the corresponding polarization dependent rough surface cross sections are stationary
over a wide range of the variation parameters. The ratio of the cross sections are dependent
of the surface roughness, since the horizontally polarized cross sections are significantly
dependent on modulation by the slopes of the larger scale surfaces, for large angles of
incidence.
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Ground-penetrating radar (GPR) is a high resolution surveying method applied to civil engineering, surface geology,
archaeology and other disciplines. Mainly it is used solving the direct problem and obtaining a model of the studied
medium. Otherwise, the study of the inverse problem could provide other valuable information: the electromagnetic
properties of the medium. These parameters are obtained from the changes of the velocity, attenuation and frequency of
the recorded wave. The physical properties of the medium related to those wave parameters are, mainly, the water
content and the porosity. Several lab experiences are performed in order to obtain these parameters from different soil
samples. Porosity and water content are measured and controlled. Velocity is obtained by measuring the two-way travel
time of the reflected wave and comparing wave reflected amplitudes on the surface of the samples. Attenuation
coefficients are determined from the analysis of the amplitude of the wave traveling in different thickness samples.
Frequencies velocities and wave attenuation are analyzed in the different cases in order to characterize those different
media and to relate its water content and its porosity with these measured parameters. The experimental results were also
compared with the complex refraction index model (CRIM).
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For a comprehensive vegetation monitoring and/or management, a good understanding of the distribution of the solar
radiation energy among components of this vegetation is needed. The energy received by the vegetation is measured by
spectroradiometers either at satellite elevations or near the ground (in situ measurements). In this study, in situ,
radiometric data and laser scanning techniques are combined, in order to evaluate the contribution of the vegetation
structure to the variability of canopy reflectance. Advanced processing laser techniques are not only an efficient tool for
the generation of physical models but also give information about the vertical structure of canopies (height, shape,
density) and their horizontal extension. To conduct this study, airborne multispectral radiation data and, laser pulse
returns are recorded from a low flying helicopter above the vegetation of a boreal forest. These measurements are used to
derive canopy optical and structural variables. The impact of the canopy 2-dimensional structural variability on the
distribution of the solar radiation reflected by plants of this area is discussed. The results obtained show that the laser
technology can be used for the selection of the most appropriate configuration of radiation measurements, and
optimization of canopy physical characteristics, in future airborne missions.
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The main goal of this research was to investigate the structural-spectral interactions that exist in managed, homogeneous,
even-aged Eucalyptus plantations through plot-level volume and basal area modelling in Kwazulu-Natal, South Africa.
Eucalyptus plantations used in this study range between four and ten years old. Small-footprint light detection and
ranging (lidar; ALTM 3033 two-return laser system; 0.2 mrad footprint, 33 kHz pulse rate) and IKONOS multispectral
data were collected during the spring season of 2006. Structural characterisation of 15 m radius inventory plots were
performed by derivation of independent model variables from plot-level distributions of a canopy height model, lidar
point heights, multispectral data, and all data sets combined. The multispectral data and lidar data were used to
characterise the structural differences across a gradient of plot volume and basal area values towards determination of
structural variability contribution to spectral responses. These aspects relate to the implementation of accepted remote
sensing data sources for forest structure assessment and how forest structure affects model outcomes. Results for plotlevel
volume and basal area were encouraging using structural (lidar) data, with adjusted R2 values of 0.94 and 0.82 for
volume and basal area, respectively. Values for multispectral data were distinctly lower at 0.60 and 0.55 for the same
dependent variables. Adjusted R2 values for all data sets combined were only marginally better than lidar data with
values of 0.95 and 0.88 for volume and basal area, respectively. Results show that lidar data are more amenable than a
multispectral approach to forest structure assessment, although integration of the two data sources should be further
investigated for scaling to larger areas.
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Earth Observation (E.O.) technologies provide a valuable data base for the monitoring of crop and soil characteristics on
a large scale, in a rapid, accurate and cost-effective way. The present work aims at evaluating different methods and
models for the estimation of the Leaf Area Index (LAI) by means of hyperspectral data acquired by the optical airborne
instrument CASI during the ESA AgriSAR 2006 campaign. Inversion of a physical model using an iterative optimization
technique (SQP) and a fast look-up-table (LUT) approach is performed and results are compared with an empirical
model based on the relationship between LAI and WDVI. Furthermore, the analyses carried out on the inversion of the
physical models provide the opportunity to test the spectral bands proposed for the upcoming E.O. satellite Sentinel-2
developed by ESA in the framework of GMES (Global Monitoring for Environment and Security). The Sentinel-2
spectral sampling is compared with the one proposed by an independent study determining the wavebands best
characterizing vegetation and crops. Accuracy of LAI estimation, evaluated with the AgriSAR 2006 field measurements,
is discussed in the context of operational agricultural monitoring.
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This research is intended to develop a model to monitor rice yields using the photosynthetic yield index, which integrates
solar radiation and air temperature effects on photosynthesis and grain-filling from heading to ripening. Monitoring crop
production using remotely sensed and daily meteorological data can provide an important early warning of poor crop
production to Asian countries, with their still-growing populations, and also to Japan, which produces insufficient grain
for its population. The author improved a photosynthesis-and-sterility-based crop production CPI index to crop yield
index CYI, which estimates rice yields, in place of the crop situation index CSI. The CSI gives a percentage of rice
yields compared to normal annual production. The model calculates photosynthesis rates including biomass effects, lowtemperature
sterility, and high-temperature injury by incorporating: solar radiation, effective air temperature, normalized
difference vegetation index NDVI, and the effect of temperature on photosynthesis by grain plant leaves. The method is
based on routine observation data, enabling automated monitoring of crop production at arbitrary regions without special
observations. The method aims to quantity grain production at an early stage to raise the alarm in Asian countries, which
are facing climate fluctuation through this century of global warming.
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We propose using geostatistical methods for the spatial analysis of data pertaining to the size of trees (in
terms of canopy surface area) obtained by means of remote sensing methods. Geostatistical methods are
suitable because the locations of the trees are at the nodes of an unstructured grid. More specifically, we
present a semivariogram analysis to detect correlations in the tree size spatial distribution, and we apply a
novel method of anisotropy analysis to search for possible anisotropy in the size distribution. We use a
combination of aerial photographs and satellite images in four snapshots covering 37 years to investigate the
temporal behavior in addition to the spatial distribution at a single time. The aerial photographs were taken in
1964, 1984, 1993, and the IKONOS satellite image in 2001. We follow a study area covering over 139 ha and
over 2,000 tree individuals. Our plots are located in the Southern Kalahari savanna near the city of Kimberley,
South Africa.
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Spatial variations in Southern Italy vegetation production were analyzed between 1995 and 2005. The analysis
was carried out using Normalized Difference Vegetation Index (NDVI) time series derived from NOAA AVHRR
images, retrieved from DLR/EOWEB archives and corrected using an ad-hoc method. The correction method
exploits more accurate MODIS NDVI maps which only became available on NASA archives in the last three years.
The changes occurred is "biomass production" in the last decade were analyzed by only considering vegetation
behaviour during the growing seasons. This approach improves the results by excluding from the analysis the
winter months, during which vegetation exhibits a stationary behaviour, NDVI data are less significant and the
biomass production is poor.
The correlation between vegetation relative change patterns and terrain altitude, as derived from SRTM
topographic data, was also investigated. This analysis shows the presence of different changing patterns such as:
i) a vegetation production degradation in several areas with variations up to -1% ; ii) a systematic vegetation
production increase over the Apennines up to 6% . These results, in agreement with temperature trends for
the winter months in the last years highlight climate change processes occurring in the Mediterranean areas:
temperature mitigation facilitates "robust" vegetation, like conifer stands, deciduous stands and in general
Mediterranean maquis, i.e, the kind of vegetation which can be found in upland and mountain zones. On the
contrary, particularly in the plains, plant foliation in the autumn-winter period is strongly affected by sudden
low temperature peaks and by human activities.
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Low resolution images from MODIS multispectral sensor are used for extracting indices correlated with major
parameters of productivity, for two deciduous forests (Fagus sylvatica, Quercus sp.) and one shrubland dominated by the
semi-deciduous Phlomis fruticosa. Ground ecophysiological measurements were conducted for three growing periods
(2005-2007) and are used for indices evaluation as well as input parameters for an ecosystem productivity model. The
results of the ground-based productivity model are compared to the 8day MODIS GPP product, showing that MODIS
algorithm underestimates productivity and does not closely follow ecosystem dynamics. In an attempt for a more precise
productivity product a new light-use efficiency model based on satellite and meteorological data is designed and
presented. Moreover, hyperspectral images from CHRIS/PROBA are used for a more detailed study of the semi-decidual
Phlomis fruticosa ecosystem. Ground ecophysiological measurements from two growing periods (2006-2007) are used
for evaluation purposes. Images are geometrically corrected and atmospherically adjusted. The reflectance spectra
obtained are used for extracting indices related to numerous plant physiological parameters. Fast responsive plant
processes, such as the function of the photosynthetic apparatus, the photoprotective response to stress factors (low or
high temperature, lack of precipitation) and the detailed pigment content of leaves (chlorophyll a, chlorophyll b,
carotenoids) may well be followed by such indices issued from hyperspetral data, offering great advantage over
multispectral images for ecosystem remote sensing.
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Satellite and Airborne Systems for Monitoring and Change Detection I
This study is focused on the use of Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data along
with high and very high resolution satellite images. The use of these data requires special attention and the elaboration of
novel preprocessing methods, which is presented through two case studies. In the first one, the aim is to assess subpixel-scale
changes associated to agricultural practice in an agricultural landscape. This requires the integration of temporal
information from MODIS daily time series and spatial information from high resolution satellite images by means of
subpixel unmixing. Our second case study is concentrated on the use of these images in direct radiometric rectification of
high-resolution optical imagery. Our results show that the "standard" preprocessing is not sufficient for carrying out
accurate subpixel analysis in a MODIS time series or for reliable radiometric rectification. The main reasons include
raster-based processing not taking into account the changes in observation dimensions throughout the scene, and pixel
mixing caused by the triangular point spread function (PSF). To resolve this, we propose a novel method based on the
vector data model, in which each MODIS pixel is replaced by a polygon with its real size and orientation. Our results
show this method yields a significant improvement in the radiometric fit of high-resolution and MODIS data.
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Numerous satellite sensor systems have been launched during the last twenty years and satellite data are increasingly
being used in regional or global vegetation monitoring. The observation of global vegetation from multiple satellites
requires much effort to ensure continuity and compatibility due to differences in sensor characteristics and product
generation algorithms. More recently the launch of hyperspectral sensor like Hyperion make the compatibility problem
even more difficult as the very narrow hyperspectral bands need to be simulated to the broader multispectral bands
before proceed to any further comparison. In this study we tried to compare multispectral (Landsat ETM+ and EO-1
Advanced Land Imager) data with hyperspectral (Hyperion) data for the vegetation cover mapping of Milos Island. All
the data were collected the same day within one-minute time. As a result the atmospheric conditions were exactly the
same and that make the data ideal for comparison. The performance of the EO-1 Hyperion imaging spectrometer with
the Advanced Land Imager (ALI) and the Landsat 7 ETM+ sensor was compared using a method that aggregated
portions of the Hyperion 10 nm bands to simulate the broader multispectral bands of ALI and ETM+. The general
process was to calculate a weighted sum of the Hyperion bands that covered each Landsat band. The weights used in the
sum were derived, by comparing the spectral response of the hyperspectral bands with the respective multispectral band.
The Normalized Difference Vegetation Index was used for the comparison of the three data sets and the results are
presented in this study.
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This study presents the application of LIDAR data to the evaluation and quantification of fluvial habitat in river systems,
coupling remote sensing techniques with hydrological modeling and ecohydraulics. Fish habitat studies depend on the
quality and continuity of the input topographic data. Conventional fish habitat studies are limited by the feasibility of
field survey in time and budget. This limitation results in differences between the level of river management and the
level of models. In order to facilitate upscaling processes from modeling to management units, meso-scale methods were
developed (Maddock & Bird, 1996; Parasiewicz, 2001). LIDAR data of regulated River Cinca (Ebro Basin, Spain) were
acquired in the low flow season, maximizing the recorded instream area. DTM meshes obtained from LIDAR were used
as the input for hydraulic simulation for a range of flows using GUAD2D software. Velocity and depth outputs were
combined with gradient data to produce maps reflecting the availability of each mesohabitat unit type for each modeled
flow. Fish habitat was then estimated and quantified according to the preferences of main target species as brown trout
(Salmo trutta). LIDAR data combined with hydraulic modeling allowed the analysis of fluvial habitat in long fluvial
segments which would be time-consuming with traditional survey. LIDAR habitat assessment at mesoscale level avoids
the problems of time efficiency and upscaling and is a recommended approach for large river basin management.
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Advanced technology in airborne detection of crop growth can help optimize the strategies of fertilization, and help
maximize the grain output by adjusting field inputs. In this study, Push-broom Hyperspectral Image sensor (PHI) was
used to investigate the influence of soil nitrogen supplied and variable-rate fertilization to the growth of winter wheat.
The objective was to determine to what extent the reflectance obtained in the 80 visible and near-infrared (NIR)
wavebands (from 410nm to 832nm) might be related to differences of variance of soil nitrogen and variable-rate
fertilization. Management plots were arranged at Beijing Precision Farming Experimental Station. Three flights were
made during the wheat growing season. Several field experiments, including the crop sampling, soil sampling and
variable-rate fertilization were carried out in the field. Data were analyzed for each flight and each band separately.
Some spectrum indices were derived from PHI images and statistical correlation analysis were carried out among the
spectrum indices and soil nitrogen, variable-rate fertilization amount. In addition, the spectrum indices difference
between elongation stage and grain filling stage are calculated and the correlation analysis was also carried out. The
analysis results indicated that the reflectance of winter wheat is significantly influenced at certain wavelength by the soil
nitrogen and the variable-rate fertilization. The soil nitrogen effect was detectable in all the three flights. Differences in
response due to soil nitrogen variance were most evident at spectrum indices, such as dλ red, INFLEX, Green/Red, NIRness,
DVI and RDVI. Furthermore, analysis results also indicated that the variable fertilization can reduce the growth
difference of winter wheat caused by spatial distribution difference of soil nitrogen.
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Satellite and Airborne Systems for Monitoring and Change Detection II
Land use/cover is an important watershed surface characteristic that affects surface runoff and erosion. Many of the
available hydrological models divide the watershed into Hydrological Response Units (HRU), which are spatial units
with expected similar hydrological behaviours. The division into HRU's requires good-quality spatial data on land
use/cover. This paper presents different approaches to attain an optimal land use/cover map based on remote sensing
imagery for a Himalayan watershed in northern India. First digital classifications using maximum likelihood classifier
(MLC) and a decision tree classifier were applied. The results obtained from the decision tree were better and even
improved after post classification sorting. But the obtained land use/cover map was not sufficient for the delineation of
HRUs, since the agricultural land use/cover class did not discriminate between the two major crops in the area i.e. paddy
and maize. Therefore we adopted a visual classification approach using optical data alone and also fused with ENVISAT
ASAR data. This second step with detailed classification system resulted into better classification accuracy within the
'agricultural land' class which will be further combined with topography and soil type to derive HRU's for physically-based
hydrological modelling.
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Surface albedo is one of the most important biophysical parameter responsible for energy balance control and the surface
temperature and boundary-layer structure of the atmosphere. Forest land surface albedo is also highly variable
temporally showing both diurnal as well as seasonal variations. In forest systems, albedo controls the microclimate
conditions which affects ecosystem physical, physiological, and biogeochemical processes such as energy balance,
evapotranspiration, photosynthesis. Due to anthropogenic and natural factors, land cover and land use changes result is
the land surfaces albedo change. The main aim of this paper is to investigate the albedo patterns due to the impact of
atmospheric pollution and climate variations of a forest ecosystem Branesti-Cernica, placed to the North-East of
Bucharest city, Romania based on satellite Landsat ETM+, IKONOS and MODIS data and climate station observations.
Our study focuses on 3 years of data (2003-2005), each of which had a different climatic regime. As the physical
climate system is very sensitive to surface albedo, forest ecosystems could significantly feedback to the projected climate
change modeling scenarios through albedo changes. The results of this research have a number of applications in
weather forecasting, climate change, and forest ecosystem studies.
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This paper combines a water balance model with satellite-based remote-sensing estimates of evapotranspiration (ET) to provide accurate irrigation scheduling guidelines for individual fields. The satellite-derived ET was used in the daily soil water balance model to improve accuracy of field-by-field ET demands and subsequent field-scale irrigation schedules. The combination of satellite-based ET with daily soil water balance incorporates the advantages of satellite remote-sensing and daily calculation time steps, namely, high spatial resolution and high temporal resolution. The procedure was applied to Genil - Cabra Irrigation Scheme in Spain, where irrigation water supply is often limited by regional drought. Compared with traditional applications of water balance models (i.e. without the satellite-based ET), the combined procedure provided significant improvements in irrigation schedules for both the average condition and when considering field-to-field variability. A 24% reduction in water use was estimated for cotton if the improved irrigation schedules were followed. Irrigation efficiency calculated using satellite-based ET and actual applied irrigation water helped to identify specific agricultural fields experiencing problems in water management, as well as to estimate general irrigation efficiencies of the scheme by irrigation and crop type. Estimation of field irrigation efficiency ranged from 0.72 for cotton to 0.90 for sugar beet.
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Vegetation moisture dynamics plays a key role in wildland fire risk assessment. While dead fuel moisture can be
considered only dependent on the dynamics of the meteorological variables, live fuel dynamics is also related to the
phenological state of the considered species as well as the soil water content. Minimal variations in live moisture content
can cause a great effect in wildfire risk condition, since the fine fuel load is mainly composed by live vegetation.
This work presents a model able to predict the moisture content of live fine fuel starting from the phenological principles
of leaf growth cycle. This approach assumes that the phenological state of a given group of vegetation at a given time
instant (day of the year) can be modelled as a function of the local meteorological conditions, the soil parameters, and
some vegetational parameters. The Vegetation Index products of MODIS sensor have been used to parameterize and
calibrate the model. To this end, experimental parcels, fully representative of the Mediterranean vegetation cover of
Liguria Region (Italy), were used as test areas. Such areas are equipped with a suite of meteorological sensors, and are
periodically subject to sampling campaign aiming at characterizing the phenological state and the moisture contents of
their different vegetation species. The data collected during the field campaigns were completed by the observations of
MODIS-NDVI from 2001 to 2006.
The paper provides a calibration procedure of phenological module carried on using the whole data sets (meteorological
data, phenological and physiological data, NDVI imagery), and formalized through a mathematical programming
approach. The phenological model was implemented with reference to five areas placed on the Italian territory.
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Multi-spectral and multi-temporal satellite imagery provide the most reliable technique of monitoring of different urban structures regarding the net radiation and heat fluxes associated with urbanization at the regional scale. Investigation of radiation properties, energy balance and heat fluxes is based on satellite data from various satellite sensors and in-situ monitoring data, linked to numerical models and quantitative biophysical information extracted from spatially distributed NDVI-data and net radiation. Based on Landsat TM, Landsat ETM and IKONOS satellite images were classified for Bucharest, Romania, urban land use/cover and analyzed surface biophysical parameters like urban surface temperature and NDVI for 1984 - 2005 period. Spatio-temporal changes of surface biophysical parameters were examined in association with landuse changes to illustrate how these parameters respond to rapid urban expansion in Bucharest and surrounding region. This study attempts to provide environmental awareness to urban planners in future urban development. The land cover information, properly classified, can provide a spatially and temporally explicit view of societal and environmental attributes and can be an important complement to in-situ measurements.
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The monitoring of vegetation in nearby urban regions is made difficult by the low spatial and temporal resolution image captures. Image fusion is one of the important techniques for spatial image resolution enhancing. In order to utilize respective information from different remote sensing images, we propose an image fusion method based on improved resolution method. Recent studies have successfully estimated NDVI using improved resolution method such as from the MODIS onboard EOS Terra satellite. Enhancement of MODIS NDVI image using LANDSAT TM image is performed on various images. The results demonstrate accurate spectral preservation on vegetated regions where MODIS image enhances the fusion product, which can be usefully applied for both visual analysis and classification purposes. Subjective visual effect and objective statistical results indicate that the performance of the improved resolution method is better than original MODIS images. It not only preserves spectral information of the original multi-spectral image well, but also enhances spatial detail information greatly. To provide a continuous monitoring capability for NDVI, in situ measurements of NDVI from paddy field was carried out in 2004 for comparison with remotely sensed MODIS data. We compare and discuss NDVI estimates from MODIS sensors and in-situ spectro-radiometer data over Ochang plain region. These results indicate that the MODIS NDVI is underestimated by approximately 50%.
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A reliable mapping of fuel types is very important for computing fire hazard and risk and simulating fire growth and
intensity across a landscape. Due to the complex nature of fuel characteristic a fuel map is considered one of the most
difficult thematic layers to build up especially for large areas. The advent of satellite sensors with increased spatial
resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate
the accuracy and utility of imagery from the Advanced Spaceborne Thermal Emission and Reflection Radiometer
(ASTER) satellite imagery. In order to ascertain how well ASTER data can provide an exhaustive classification of fuel
properties a sample area characterized by mixed vegetation covers was analysed. The selected sample areas has an
extension at around 60 km2 and is located inside the Sila plateau in the Calabria Region (South of Italy). Fieldwork fuel
type recognitions, performed before, after and during the acquisition of remote sensing ASTER data, were used as
ground-truth dataset to assess the results obtained for the considered test area. Results from our analysis showed that the
use ASTER data provided a valuable characterization and mapping of fuel types with a classification accuracy higher
than 78%.
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In this study, Hyperspectral data of two variety of rice (common rice and hybrid rice) in whole growing stage during 2002 and 2003 was measured using the ASD FieldSpec UV/VNIR Spectroradiometer with resolution of 3 nm, and the LAI and leaf chlorophyll content of rice agricultural parameter were obtained. Analyses of the correlation between rice agricultural parameter, and hyperspectal data, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and the red-edge position (REP) were studied. Results showed that a strong non-linear correlation was found between the rice LAI of two varieties and REP. The REP, EVI and NDVI were well related with LAI for the common rice, but the REP and EVI were more sensitive than the NDVI to rice LAI for the hybrid rice because of different body for two variety rice.
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The sampling protocol adopted during a field campaign at an Alpine meadow site (Shandan site), during July 2002 is
based on the so-called "Valeri" protocol (VALERI). The field campaign LAI measurements in Shandan are scaled up to
30×30 m2 raster maps based on Landsat ETM+ imagery. Regression analysis is applied to construct empirical transfer
functions for the determination of Leaf Area Index (LAI) raster imagery from ETM+ Normalized Difference Vegetation
Index (NDVI) and Simple Ratio (SR) data. Subsequently, the scaling up of the LAI raster maps is performed by the
aggregation of the 30x30 m2 data into 1×1 km2 pixels by calculating the average LAI values for the low resolution pixels.
The up-scaled data are used to validate the MODIS LAI product at the Shandan site. A power regression model
(LAI=2.3758*NDVI3.5216, R2=0.66, P<0.01), established between field measured LAI and ETM+ NDVI, elicits a high
statistical significance. A linear regression model (LAI=0.1798*SR-0.3574, R2=0.55, P<0.01) is established between
field measured LAI and ETM+ SR. The MODIS LAI product correlates best with the ETM+ LAI transfer function
obtained with NDVI data. Its R2 reaches 0.46, its slope 0.97, but the intercept is 0.7, which suggests that MODIS LAI is
systematically underestimated. The results illustrate that LAI measured with a LAI-2000 instrument at the VALERI
Shandan site leads to an underestimation of the MODIS LAI product. A plausible cause for the systematic
underestimation related with the LAI field measurements is discussed.
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Advanced site-specific determination of grain protein content by remote sensing can provide opportunities to optimize the strategies for purchasing and pricing grain, and to maximize the grain output by adjusting field inputs. Field experiments were performed to study the relationship between grain quality indicators and foliar nitrogen concentration. Foliar nitrogen concentration at the anthesis stage is suggested to be significantly correlated with grain protein content, while spectral vegetation index is significantly correlated to foliar nitrogen concentration around the anthesis stage. Based on the relationships among nitrogen reflectance index (NRI), foliar nitrogen concentration, and grain protein content, a statistical evaluation model of grain protein content was developed. NRI proved to be able to evaluate foliar nitrogen concentration with a coefficient of determination of R2= 0.7302 in year 2002. The relationship between measured and remote sensing derived foliar nitrogen concentration had a coefficient of determination of R2=0.7279 in year 2003. The results mentioned above indicate that the inversion of foliar nitrogen concentration and the evaluation of grain protein content by NRI are surprisingly good.
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The northwest China, typical arid and semi-arid regions, is the first or second-degree sensitivity zones for global change. Monitoring vegetation change is an important method to study the impacts of global climate change. Time-series satellite remote sensing data make it possible to monitor vegetation at different spatial and temporal resolutions globally. A long time series of Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data with 8x8 km2 spatial resolution and during 1982 to 2003 were used to monitor the vegetation cover in the northwest China. The monitoring results indicate an obvious greening trend exists. The precipitation and relative humidity have high correlations with the SINDVI. So the water condition is the most important factors for the spatial distribution of the SINDVI levels. The precipitation and temperature are the primary driving factors for inter-annual vegetation changes.
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For various reasons, aerial archaeologists use(d) film when studying their objects in the Near InfraRed (NIR). However, even the use of colour InfraRed (CIR) emulsions remained severely restricted till today due to some ignorance or a severe lack of knowledge about the subject and - not at least - the critical imaging process. This error-prone film-based workflow belongs now to the past, thanks to the advent of digital cameras. In this article, two new approaches will be outlined, both in an attempt to overcome the constraints on the common archaeological interpretation of varying visible colours in vegetation: the use of modified hand-held digital cameras to photograph the NIR spectrum on the one hand, as well as future plans to digitally capture both Red and NIR wavelengths simultaneously on the other. Besides additional technical background information on NIR photography, the paper treats the advantages (and disadvantages) of NIR to normal archaeological aerial imaging. In the end, an introduction of a new, remotely controlled system to support (aerial) archaeologists in their (NIR) photography is given together with several approaches to NIR image processing.
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The main aim of this study is to evaluate the feasibility of utilizing satellite remote sensing and GIS tools to assist the
technical flooding aspects of the catchment area of Agriakalamin River located in Kissonerga Village in Paphos-Cyprus.
The remote sensing technique has been used to quantify the actual increase in urban area over the past forty three years.
The Agriokalamin River area in Kissonerga Village in Paphos-Cyprus has been chosen as the study area. By analyzing
the temporal satellite imageries and the observed meteorological and hydrologic data of the catchment, the local
authorities can use the proposed methodology to investigate and assessing all the catchment areas versus the
urbanization. The results of this study will encourage decision makers or the local authorities to consider land use
planning as an effective non-structural measure for flood risk mitigation. Remotely sensed data such as aerial photos,
Landsat-5 TM and Quickbird image data have been used to track the urbanization i.e. building activities near the
catchment area (urbanization factor for the years 1963-2006: 0.9 % - 27.0 %). GPS measurements have been used to
locate in-situ the boundaries of the catchment area. Indicative flood risk assessment study was applied.
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