The spatiotemporal evolution of the snow cover may help to obtain conclusions on the variability of the atmospheric agents in high mountain areas. That evolution is difficult to analyze due to the heterogeneity of the snow distribution on the ground. The use of terrestrial images which are treated to obtain snow detection, is an inexpensive and promising technique more capable of solving the drawbacks that other techniques presented. This work analyzes the spatiotemporal variability of the snow by using terrestrial photography and the effects of scale on its modelling in the river Trevélez valley, in southern Spain. Temporal series of images of the area were employed from September, 2011 up to May, 2013. By georeferencing the images, the snow pixels were identified, and the temporal variations in the snow cover with respect to its spatial distribution were determined. The maps obtained were used as a direct source of data assimilation in that model. Finally, the improvement in the global simulation of the snow model when this data source was incorporated was assessed by making a comparative study between the temporal series of the snow flow measured at the gauging point band the flow obtained in the simulation. As a result, a temporal series of snow maps of the area was made. In turn, the assimilation of the data improved the simulation by up to 9.74% for the equivalent of water. At a watershed scale, the simulation of the flow at the control point reproduced the trend observed. These results permit one to conclude that the methodology used is precise enough to find out the exact position of snow cover and to improve the efficiency of the model used.
The dehesa, the most widespread agroforest ecosystem in Europe (≈ 3 million ha), is recognized as an example of
sustainable land use and for its importance in rural economy. It is characterized by widely-spaced oak trees (mostly
Quercus Ilex L.), combined with crops, pasture and shrubs in the sub-canopy region. The estimation of the ecosystem
evapotranspiration (ET) using remote sensing may assist the monitoring of its state from local to regional scales,
improving the management and the conservation of the ecosystem. Thermal-based energy balance techniques which
distinguish soil/substrate and vegetation contributions to the radiative temperature and radiation/turbulent fluxes have
proven to be reliable in the estimation of the energy surface fluxes, and therefore in the estimation of ET. In particular,
the two-source energy balance (TSEB) model of Norman et al. and Kustas and Norman has shown to be robust for
semi-arid sparse canopy-cover landscapes. With the objective of evaluating the model over this environment, an energy
flux measurement system has been used. It was installed in a dehesa located in Southern Spain (38°12′ N; 4°17′ W, 736m a.s.l) with 1 km homogeneous fetch in wind direction. The quality of the measured data fluxes has been tested with the energy-balance closure criterion yielding an average closure of 86% which is within the error range found in similar studies. The TSEB model was evaluated in the area for 2012 summer season, using images from MODIS (Moderate
Resolution Imaging Spectroradiometer) sensor and ground measured meteorological data. The half-hourly estimates
were compared with the flux tower measurements, obtaining a RMSD between modeled and measured energy fluxes
within the closure balance error.
Estuarine water in Mediterranean basins has high concentrations of suspended sediment. In order to study the temporal
and spatial distribution of turbidity, a monitoring network with sufficient temporal and spatial resolution is needed to
monitor water quality, and this is not always available. Thus, over the last few years, satellite images have been used as
an alternative way to estimate water quality parameters, such as turbidity. The Guadalquivir River estuary in south-west
Spain extends for 105 km and is one of the world’s most turbid estuaries. The sediments present are of a very fine texture
due to the great length of the river but, mainly, to the extreme trapping efficiency of the dense reservoir system upstream.
This work shows the relationship between turbidity patterns along the Guadalquivir river estuary and the data from
Landsat ETM+ images from August 2008 to 2010, and the suitability of the algorithms previously used in this estuary
environment, with the ultimate goal of obtaining turbidity maps. The results of this study show that the use of previously
developed algorithms underestimate turbidity values measured by the monitoring network used, which proves that one
single algorithm for the entire period of study does not provide a reliable reproduction of the real situation. The wide
variability in turbidity data along the estuary has enabled us to develop specific expressions for each day, which allow us
to obtain turbidity maps.
Spatio-temporal changes in vegetation at the basin scale are difficult to characterize, and remote sensing is a major
source of data for this purpose. These sensors may provide distributed series of spectral properties of the vegetation with
different spatial and temporal resolutions, but they do not always satisfy the requirements of some of the applications.
These limitations can be overcome with the use of image integration techniques, which allow for the combination of
sensors with different characteristics. This work presents the monitoring of the vegetation cover in the Guadalfeo River
Basin (Spain), with a view to its hydrological modeling, by using Landsat-TM and MODIS data, analyzing the
implications of the scale differences in an heterogeneous area. A preliminary study is carried out into the deviations of
NDVI and ground cover fraction (fv) between the concurrent data of both sensors. Thereafter, the STARFM integration
algorithm is applied and evaluated to obtain synthetic NDVI images at the spatial resolution of Landsat-TM data with
MODIS time steps. The comparison between Landsat-TM and MODIS parameters revealed deviations on average
between 2-5% for NDVI and 3-5% for fv. No direct relationship was found between these deviations and basin
topography. However, higher deviations corresponded with the vegetation types with higher ground cover fractions and
heterogeneous landuses (fv relative deviations of 10% and 6% for conifers and quercus-scrub, respectively) The
STARFM algorithm improved the NDVI estimations when compared to the previous Landsat-TM date, with reductions
in the average NDVI differences of around 0.02 on average for the six simulated dates, with the accuracy of the
predictions depending on data input for the model and vegetation cover types.
The albedo of snow affects the shortwave radiative flux at the land-atmosphere interface, so that it therefore plays an
important role in the snow mass and energy balance. In semiarid areas, their particular climate conditions enhance the
spatiotemporal variability of the snow albedo during the snow cover periods, increasing its difficulty in being measured
and monitored. Satellite remote sensing is a powerful tool for measuring snow albedo evolution. Ten years of Landsat-5
and Landsat-7 Thematic Mapper images were analysed to determine a trend in the albedo evolution throughout the year
in a Mediterranean site, Sierra Nevada Mountain (Southern Spain). A pattern in snow albedo from all the snow pixel in
each Landsat scene was obtained. It ranges from 0.8 in new snow to 0.4 in old dirty snow, with a decreasing rate of 0.003
albedo per day. This trend was validated with 5 specific location, where the snow is more persistent while the pixel
remains well-illuminated.
Evapotranspiration (ET) is a critical variable in hydrological processes and an accurate estimation of the rate of
evapotranspiration is required if we wish to apply integrated management procedures to water resources. This study
offers new insights into remote sensing-based models that estimate ET at basin scale, evaluating the combination of a
surface energy balance based on thermal remote sensing and the use of the crop coefficient (Kc), a simple operational method that is widely used in irrigated agriculture. The study area is the Guadalfeo river basin in southern Spain, a large watershed with major topographical and landscape contrasts. Reference evapotranspiration (ETo) surfaces were generated by applying the FAO56-PM [1] equation, and real ET surfaces were estimated following a two-source energy balance model [2] [3]. Crop and vegetation coefficients were obtained as the ratio between ET and ETo. Kc maps were analysed in terms of vegetation type and development. The resulting coefficients generally ranged between 0.1 and 1.5, and could be directly related to vegetation ground cover for the main vegetation types, including natural vegetation and crops, with the determination coefficient (r2) lying between 0.77 and 0.97 in both humid and dry seasons. Relationships based on these coefficients are proposed as a simple proxy to monitor the water use of the basin on a regular basis by means of optical remote sensors alone, providing data with higher frequency and spatial resolution than can be obtained by thermal measurements; data that could complement thermal sensors whenever these were available.
In Mediterranean regions, where the water shortage is a serious and recurrent problem, it is essential to know the
behaviour and evolution of the snow. Satellite remote sensing is widely used to find out the evolution of the snow cover
extension at medium-large scales. But these techniques pose some constraints if snow is heterogeneously distributed, as
they do not correctly represent the physical processes that occur on a smaller scale than the satellite image. In such cases,
terrestrial photographs, whose resolution can be more easily adapted to the required resolution for these study cases, are
an economic and also efficient alternative. This work presents a methodology for the georeferencing and automatic
detection of snow in terrestrial photography, as an alternative to the use of satellite images for the study of the snow
cover evolution in small areas. This methodology has been evaluated during the snowmelt period in the spring of 2009 at
a study site in Sierra Nevada Natural Park (Southern Spain). The resulting snow maps have been compared with the
results available for that period obtained from the analyses of four Landsat images. The results show that the use of
Landsat generally overestimated the extension of the snow cover in the study area.
This work shows the sensitivity of NDVI as an indicator of the global hydrological regime of the year. The annual
water balance in the area was simulated through a physically-based distributed hydrological model previously calibrated
and validated in the area from 2001 till 2010. NDVI was obtained from Landsat TM at the end of the dry season in 1000
points randomly distributed over a pine cover in a mountainous Mediterranean area. The influence of different
hydrological processes related to the water balance in the soil on the NDVI values was analyzed through Pearson
correlation matrices and Principal Components Analyses. Results showed that the NDVI was particularly sensitive to the
regime of annual variables related to the snow layer dynamics, especially to snowmelt. These relationships were
quantified, with the best fit being obtained between NDVI and the dimensionless index snowmelt divided by
precipitation (R2 around 0.7). The adjustments obtained could, in the future, constitute a tool for the estimation of hydrological variables from satellite data in data-poor situations conditioned by the commonly steep slopes and difficult
access in mountainous areas.
The integrated water resource management required to face the water scarcity situation in semiarid regions relies on the
ability to obtain accurate information about the use of water by crops and natural vegetation. Thermal remote sensing
provides key data about the vegetation water status. The integration of this remotely sensed data into water and energy
balance models help to better estimate evapotranspiration under heterogeneous cropping and natural vegetation patterns,
extending the field of application of these models from point to basin and regional scales.
In this work, we present an approach to estimate spatially distributed surface energy fluxes using a series of Landsat TM
satellite images combined with simulation modeling and ground-based measurements. A physically-based method for the
energy budget partitioning following the Two Source Model [1, 2] has been applied over an heterogeneous agricultural
area located in southern Spain. The study was performed during 2009 crop growing season and the results were validated
with field data collected with an eddy covariance system installed over a corn field during the season. The instantaneous
and daily estimations were compared to the measured data, obtaining a general good adjustment at both scales and
setting the basis for a larger scale application that may assist a decision - making tool for water resources planning in the
region.
Sierra Nevada Mountains are the highest continental altitude in Spain. Located in the South, facing the Mediterranean
Sea in a distance of less than 40 km, the high level of solar energy income throughout the year, together with the
extremely variable character of climate in such latitudes, make it necessary to use energy balance approaches to
characterize the snow cover evolution. Wind and relative humidity become decisive factors in the evolution of the snow
cover due to the high evaporation rates that can arise under favourable meteorological conditions. This work presents the
enhanced capability of the combination of Landsat TM data with the simulation of an energy balance model to produce
sequences of hourly high resolution maps of snow cover and depth distribution under variable meteorological conditions
such as those found in Mediterranean mountainous watersheds. Despite the good agreement found between observed and predicted snow pixels, different examples of disagreement arose in the boundaries, most of them related to the temperature and wind speed spatial pattern simulation together with the discrimination between rain and snowfall occurrence.
Dams are common structures in order to guarantee water supply and control flash floods in Mediterranean mountainous
watersheds. Even though they are known to modify in space and time the natural regimen of natural flows, little has been
said about local effects on the ecosystem along the river banks upstream the dam. In 2002, Rules dam (southern Spain)
started to function. This work presents the effects of the dam filling on the water balance in flood plains. The influence
of the enhanced soil moisture in the surroundings of the free surface of the reservoir on the vegetation cover status was
analyzed and related to meteorological agents and topographic features, before and after the construction of the dam.
Meteorological, topographic, soil and land use data were analyzed in the contributing area of the dam, together with
Landsat TM images during the period 1984-2010 to derive NDVI values. Results showed higher NDVI values (close to
20-30%) once the dam was filled and NDVI values in very dry years similar to the ones obtained in medium-wet years
prior to the construction. Besides, NDVI values after the filling of the dam proved to be highly related to meteorological
variables. Principal Component Analysis (PCA) was carried out in order to identify individual and combined interactions
of meteorological and dam-derived effects. 85% of the total variance can be explained with the combination of three Principal Components (PC) in which the first one includes the combination of NDVI, meteorological (rainfall) and hydrological variables (interception, infiltration, evapotranspiration from the soil), whilst the second and third PC mainly include topographic features. These results quantify the dam influence along the river banks and the superficial recharge effects in dry years.
Rainfall interception by the vegetation may constitute a significant fraction in the water budget at local and watershed
scales, especially in Mediterranean areas. Different approaches can be found to model locally the interception fraction,
but a distributed analysis requires time series of vegetation along the watershed for the study period, which includes both
type of vegetation and ground cover fraction. In heterogeneous watersheds, remote sensing is usually the only viable
alternative to characterize medium to large size areas, but the high number of scenes necessary to capture the temporal
variability during long periods, together with the sometimes extreme scarcity of data during the wet season, make it
necessary to deal with a limited number of images and interpolate vegetation maps between consecutive dates.
This work presents an interception model for heterogeneous watersheds which combines an interception continuous
simulation derived from Gash model and their derivations, and a time series of vegetation cover fraction and type from
Landsat TM data and vegetation inventories. A mountainous watershed in Southern Spain where a physical hydrological
modelling had been previously calibrated was selected for this study. The dominant species distribution and their
relevant characteristics regarding the interception process were analyzed from literature and digital cartography; the
evolution of the vegetation cover fraction along the watershed during the study period (2002-2005) was produced by the
application of a NDVI analysis on the available scenes of Landsat TM images. This model was further calibrated by field
data collected in selected areas in the watershed.
Many marsh areas in Southern Spain were dewatered during the 1950's for agricultural purposes. These actions were not
successful due to the high salinity trend exhibited in such soils, especially notable during the long dry summers in these
locations. Recently, many attempts to restore the marshes have been made to try to return the original flooding cycles to
the dewatered areas, and promote the development of spontaneous vegetation suitable to salty environments. This work
deals with the monitoring of the increase of the flooding area in the San Pedro River marshes (Cádiz) in Spain after the
demolition of a dam near the mouth, from the analysis of Landsat TM images with a linear mixture spectral model.
Three different components (vegetation, dry soil and wet soil) were quantified in the area over the two years following
the destruction of the dam and the increase in tidal entry to the marsh and compared to the results from a previous date.
The results were calibrated with field data measured directly on the terrain surface. The model used was capable of
discriminating such components with satisfactory accuracy, providing data on the evolution of the flooding area
throughout the year and the increase in vegetation distribution one year after the dam break. Differences in the tidal
advance along tidal creeks in the main reach of the river before and after the demolition were successfully identified. The
impact of the dam action on the development of vegetation was also quantified; the results showed the potential to
restore this degraded marsh land.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.