This PDF file contains the front matter associated with SPIE Proceedings Volume 8887, including the Title Page, Copyright information, Table of Contents, Invited Panel Discussion, and Conference Committee listing.

The use of imaging spectroscopy for florisic mapping of forests is complicated by the spectral similarity among coexisting species. Here we evaluated an alternative spectral unmixing strategy combining a time series of EO-1 Hyperion images and an automated feature selection strategy in MESMA. Instead of using the same spectral subset to unmix each image pixel, our modified approach allowed the spectral subsets to vary on a per pixel basis such that each pixel is evaluated using a spectral subset tuned towards maximal separability of its specific endmember class combination or species mixture. The potential of the new approach for floristic mapping of tree species in Hawaiian rainforests was quantitatively demonstrated using both simulated and actual hyperspectral image time-series. With a Cohen’s Kappa coefficient of 0.65, our approach provided a more accurate tree species map compared to MESMA (Kappa = 0.54). In addition, by the selection of spectral subsets our approach was about 90% faster than MESMA. The flexible or adaptive use of band sets in spectral unmixing as such provides an interesting avenue to address spectral similarities in complex vegetation canopies.

Optimization principles of accounting for the most informative spectral channels in hyperspectral remote sensing data processing serve to enhance the efficiency of the employed high-productive computers. The problem of pattern recognition of the remotely sensed land surface objects with the accent on the forests is outlined from the point of view of the spectral channels optimization on the processed hyperspectral images. The relevant computational procedures are tested using the images obtained by the produced in Russia hyperspectral camera that was installed on a gyro-stabilized platform to conduct the airborne flight campaigns. The Bayesian classifier is used for the pattern recognition of the forests with different tree species and age. The probabilistically optimal algorithm constructed on the basis of the maximum likelihood principle is described to minimize the probability of misclassification given by this classifier. The classification error is the major category to estimate the accuracy of the applied algorithm by the known holdout cross-validation method. Details of the related techniques are presented. Results are shown of selecting the spectral channels of the camera while processing the images having in mind radiometric distortions that diminish the classification accuracy. The spectral channels are selected of the obtained subclasses extracted from the proposed validation techniques and the confusion matrices are constructed that characterize the age composition of the classified pine species as well as the broad age-class recognition for the pine and birch species with the fully illuminated parts of their crowns.

Alternating Least Squares (ALS) is a blind source separation method commonly used in Chemometrics to simultaneously estimate the absorption spectrum and concentration of the different components in a chemical sample. In this study, the transferability of ALS from Chemometrics to agricultural Remote Sensing is evaluated. Due to the subpixel contribution of background components, spectral unmixing has become an indispensable processing step in the spectral analysis of agricultural areas. Yet, traditional unmixing techniques only allow estimating the sub-pixel cover distribution of the different components, but fail to provide an estimate of the pure spectral signature of the crop. This info is, however, highly valuable as this pure crop signature could be used to monitor the health status of the trees. Here, we anticipate that ALS can provide a solution. ALS estimates both the concentration and the absorption spectra of the different components in a chemical sample and this can easily be translated into estimating both the subpixel cover fraction and spectral signature of the different components in a mixed image pixel. The ALS model was tested on simulated hyperspectral images of Citrus orchards in which ray-tracing software was used to realistically incorporate spectral variability, multiple scattering and shadowing effects. Both the accuracy of the extracted cover fractions and the pure spectral signatures of the crop were assessed, as well as the accuracy with which the biophysical parameters of the trees (i.e. chlorophyll content, leaf water content and Leaf Area Index) could be derived from the extracted crop signature. ALS indeed allowed to simultaneously estimate the subpixel cover distribution (RMSE = 0.05), as well as the pure spectral signatures of the different endmembers (RRMSE < 0.12), and considerably improved the extraction of biophysical parameters (ΔR2 up to 0.43). ALS thus provides a promising new image analysis tool for agricultural remote sensing.

As urbanization continues to grow, retrieval of aerosols in higher urbanized areas becomes more important and successful unbiased retrievals in urban areas will become more important both for air quality and in climate applications. However, retrieval of AOD by satellite remote sensing measurements over land is complicated by the fact that the Top of Atmosphere (TOA) reflectance is a combination of the desired atmospheric path reflectance as well as the ground reflectance. To avoid this problem, AOD retrieval with the MODIS instrument attempts to isolate “dark” pixels such as dense dark vegetation. To account for land surface reflection properties, the latest MODIS retrieval algorithm over land tries to improve on surface albedo modeling using collocated MODIS and AERONET sky radiometer data to improve the VIS - SWIR ratios. However, the matchup data taken on a global scale still was heavily concentrated over vegetated areas since there are very few urban AERONET sites and the resulting surface models are incapable of describing the urban surface reflection ratios properly. In addition, as we move to higher spatial resolution such as Collect 6 3km retrievals, the ability to isolate dark pixels is significantly reduced and the ability to independently characterize the surface using MODIS land classification data is crucial to avoid biases and improve on retrievals. The purpose of this presentation is to demonstrate that the C005 surface models trained on a global scale and used by the MODIS algorithm to estimate the ground reflectance is not appropriate for urban areas and that the use of Urban based surface models together with a land classification flag can remove biases and improve retrieval.

With global food demand on course to double in the next 50 years the pressures of agricultural intensification on ecosystem services in highly managed landscapes are increasing. Within an agricultural landscape non-cropped areas are a key component of ecological heterogeneity and the sustainability of ecosystem services. Management of the landscape for both production of food and ecosystem services requires configuring the non-cropped areas in an optimal way, which, in turn requires large scale information on the distribution of non-cropped areas. In this study the Canny edge detection algorithm was used to delineate 93% of all boundaries within 422 ha of agricultural land in south east England. The resulting image was used in conjunction with vegetation indices derived from Color Infra Red (CIR) aerial photography and auxiliary landuse data in an Object Orientated (OO) Knowledge Based Classifier (KBC) to identify non-cropped areas. An overall accuracy of 94.27% (Kappa 0.91) for the KBC compared favorably with 63.04% (Kappa 0.55) for a pixel based hybrid classifier of the same area.

The objective of this study was to evaluate the potential for monitoring forest change using Landsat ETM data and Aster data for two periods (2000 - 2003 and 2003 - 2006). This was accomplished by performing three widely used vegetation indices: Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Transformed Difference Vegetation Index (TDVI). An RGB-NDVI change detection strategy to detect major decreases or increases in forest vegetation was developed as well. These indices were applied to a case study in El Rawashda forest reserve, Gedaref State, Sudan, and their results and accuracy were discussed. Results showed that the vegetation index maps obtained by NDVI and SAVI transformations within each computational group were similar in terms of spatial distribution pattern and statistical characteristics. As far as the degree of greenness of vegetation was concerned, the TDVI appeared to be the most sensitive. For the first period, the highest accuracy was obtained by SAVI (62.5%); however, the poorest accuracy was achieved by TDVI (59.5%). For the second period, TDVI revealed the highest accuracy (60.1%), whereas both NDVI and SAVI counted accuracy of 59.2%. Generally, the study proved that all vegetation indices produced reasonable approaches to map land cover changes over time and help to pinpoint deforestation and regrowth in the study area.

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.

The semi-arid Northeast of Brazil is characterized by distinct rainy and dry seasons. The water supply for the local population is based on surface reservoirs in which precipitation is collected. There are more than 150 reservoirs in the 933 km² Benguê catchment, however, little is known about the temporal dynamics of the water storage in the reservoirs. In this study, we use TerraSAR-X imagery for a year-long monitoring of reservoir surface areas and their seasonal changes. The precise extraction of the reservoir surface areas forms the basis of the monitoring. Therefore, we evaluated the results of a pixel-based threshold classification and a feature-based segmentation (mean shift). The evaluation was based on in-situ GPS measurements and manual digitization. The results of the manual digitization and threshold classification were similar as both tended to underestimate the water surface area in comparison to GPS in-situ data. The mean shift segmentation, however, tended to spread over the shorelines into the surrounding areas. We used the threshold classification for the analysis of 47 TerraSAR-X images. The viewing direction of the TerraSAR-X sensor was also important for the distinction of the entire area of the reservoirs, since geometric effects at the shorelines shaded parts of the water surface area. For the monitoring of the reservoir area with only one viewing direction we derived an empirical geometry correction factor.

Several biogeographic theories propose that the species richness depends on the structure and ecosystems diversity. The habitat productivity, a surrogate for these variables, can be evaluated through satellite imagery, namely using vegetation indexes (e.g. NDVI). We analyzed the correlation between species richness (from the Portuguese Atlas of Amphibians and Reptiles) and NDVI (from Landsat, MODIS, and Vegetation images). The species richness database contains more than 80000 records, collected from bibliographic sources (at 1 or 10 km of spatial resolution) and fieldwork sampling stations (recorded with GPS devices). Several study areas were chosen for Landsat images (three subsets), and all Portugal for MODIS and Vegetation images. The Landsat subareas had different climatic and habitat characteristics, located in the north, center and south of Portugal. Different species richness datasets were used depending on the image spatial resolution: data with metric resolution were used for Landsat, and with 1 km resolution, for MODIS and Vegetation images. The NDVI indexes and all the images were calculated/processed in an open source software (Quantum GIS). Several plug-ins were applied in order to automatize several procedures. We did not find any correlation between the species richness of amphibians and reptiles (not even after separating both groups by species of Atlantic and Mediterranean affinity) and the NDVI calculated with Landsat, MODIS and Vegetation images. Our results may fail to find a relationship because as the species richness is not correlated with only one variable (NDVI), and thus other environmental variables must be considered.

Water temperature influences physical and chemical parameters of rivers and streams and is an important parameter for water quality. It is a crucial factor for the existence and the growth of animal and plant species in the river ecosystem. The aim of the research project ”Remote sensing of water surface temperature” at the Federal Institute of Hydrology (BfG), Germany, is to supplement point measurements of water temperature with remote sensing methodology. The research area investigated here is the Upper and Middle Rhine River, where continuous measurements of water temperature are already available for several water quality monitoring stations. Satellite imagery is used to complement these point measurements and to generate longitudinal temperature profiles for a better systematic understanding of the changes in river temperature along its course. Several products for sea surface temperature derived from radiances in the thermal infrared are available, but for water temperature from rivers less research has been carried out. Problems arise from the characteristics of the river valley and morphology and the proximity to the riverbank. Depending on the river width, a certain spatial resolution of the satellite images is necessary to allow for an accurate identification of the river surface and the calculation of water temperature. The bands from the Landsat ETM+ sensor in the thermal infrared region offer a possibility to extract the river surface temperatures (RST) of a sufficiently wide river such as the Rhine. Additionally, problems such as cloud cover, shadowing effects, georeferencing errors, different emissivity of water and land, scattering of thermal radiation, adjacency and mixed pixel effects had to be accounted for and their effects on the radiance temperatures will be discussed. For this purpose, several temperature data sets derived from radiance and in situ measurements were com- pared. The observed radiance temperatures are strongly influenced by the atmosphere. Without atmospheric correction, the absolute mean difference between RST and in situ measurements was 1.1°C with a standard devi- ation of 1.3°C. Thus, a correction of atmospheric influences on radiances measured at the top of the atmosphere was necessary and two different methods for atmospheric correction (ATCOR2 and the Atmospheric Correction Parameter Calculator) were applied. The correction results showed that for both methods, the correct choice of atmospheric profiles is very important. With the calculator, an absolute mean difference of 0.8 ± 1.0°C and with the selected overall best scenes, an absolute mean difference of 0.5 ± 0.7°C was achieved. The selected corrected RST can be used to interpolate between in situ measurements available only for a limited number of points along the river course and longitudinal example profiles of the surface water temperature in the Upper and Middle Rhine could be calculated for different seasons. On the basis of these profiles, the increasing temperature gradient along the Upper Rhine could be identified and the possibility to detect heat or cooling discharge from tributaries and other sources is evaluated.

In this paper we present hyperspectral and time resolved laser induced fluorescence spectra of oil slicks remotely sensed by means of a new fluorescence LIDAR prototype. The sensor provides both sub-nanometric spectral resolution and sub-nanosecond time resolution. The fluorescence induction is achieved by means of a frequency-tripled pulsed Nd:YAG laser @355 nm. The light is collect by a 250-mm diameter telescope and the fluorescence spectra are measured by a spectrometer and a 512-spectral channels gateable intensified CCD camera. The temporal resolution is achieved by sequentially delaying the gating time to obtain fluorescence data, which are resolved both in the spectral and temporal domains. Measurements were performed in the lab on three artificial 100-μm thick oil films at a distance of 10 m. The oils were a crude oil and two different types of diesel fuel.

Different remote sensing methods for detecting variations in agricultural fields have been studied in last two decades. There are already existing systems for planning and applying e.g. nitrogen fertilizers to the cereal crop fields. However, there are disadvantages such as high costs, adaptability, reliability, resolution aspects and final products dissemination. With an unmanned aerial vehicle (UAV) based airborne methods, data collection can be performed cost-efficiently with desired spatial and temporal resolutions, below clouds and under diverse weather conditions. A new Fabry-Perot interferometer based hyperspectral imaging technology implemented in an UAV has been introduced. In this research, we studied the possibilities of exploiting classified raster maps from hyperspectral data to produce a work task for a precision fertilizer application. The UAV flight campaign was performed in a wheat test field in Finland in the summer of 2012. Based on the campaign, we have classified raster maps estimating the biomass and nitrogen contents at approximately stage 34 in the Zadoks scale. We combined the classified maps with farm history data such as previous yield maps. Then we generalized the combined results and transformed it to a vectorized zonal task map suitable for farm machinery. We present the selected weights for each dataset in the processing chain and the resultant variable rate application (VRA) task. The additional fertilization according to the generated task was shown to be beneficial for the amount of yield. However, our study is indicating that there are still many uncertainties within the process chain.

Alpine grasslands on the Tibetan Plateau (TP) are suffering from pasture degradation induced by over-grazing, climate change and improper livestock management. Meanwhile, the status of pastures is largely unknown especially in poor accessible western parts on the TP. The aim of this case study was to assess the suitability of hyperspectral imaging to predict quality and amount of forage on the western TP. Therefore, 18 ground- based hyperspectral images taken along two transects on a winter pasture were used to estimate leaf chlorophyll content, photosynthetic-active vegetation cover (PV) and proportion of grasses. For calibration and validation purposes, chlorophyll content of 20 grass plants was measured in situ. From the images reference spectra of grass and non-grass species were collected. PV was assessed from similarity of images to mean vegetation spectra using spectral angle mapper and threshold classifications. A set of 48 previously published hyperspectral vegetation indices (VI) was used as predictors to estimate chlorophyll content and to discriminate grass and non-grass pixels. Separation into grass and non-grass species was performed using partial least squares (PLS) discriminant analysis and chlorophyll content was estimated with PLS regression. The accuracy of the models was assessed with leave-one-out cross validation and normalised root mean square errors (nRMSE) for chlorophyll and contingency matrices for grass classification and total PV separation. Highest error rates were observed for discrimination between vegetated and non-vegetated parts (Overall accuracy = 0.85), whilst accuracies of grass and non grass separation (Overall accuracy = 0.98) and chlorophyll estimation were higher (nRMSE = 10.7).

Hyperspectral imaging based precise fertilization is challenge in the northern Europe, because of the cloud conditions. In this paper we will introduce schemes for the biomass and nitrogen content estimations from hyperspectral images. In this research we used the Fabry-Perot interferometer based hypespectral imager that enables hyperspectral imaging from lightweight UAVs. During the summers 2011 and 2012 imaging and flight campaigns were carried out on the Finnish test field. Estimation mehtod uses features from linear and non-linear unmixing and vegetation indices. The results showed that the concept of small hyperspectral imager, UAV and data analysis is ready to operational use.

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.

Mediterranean forests are every year affected by wildfires which have a significant effect on the ecosystem. Mapping burned areas is an important field of application for optical remote sensing techniques and several methodologies have been developed in order to improve mapping accuracy. We developed an automated procedure based on spectral indices and fuzzy theory for mapping burned areas from atmospherically corrected Landsat TM images. The algorithm proved to provide consistent accuracy over Mediterranean areas. We further tested algorithm’s performance to assess the influence of the atmospheric correction on the accuracy of burned areas. In particular, we ran the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) code with different Atmospheric Optical Thickness (AOT) levels and two aerosol models (continental and maritime) on one TM image acquired over Portugal (12/08/2003). Burned area maps derived from atmospherically corrected images and from the non corrected image (Top Of Atmosphere, TOA) have been analyzed. In the output burned areas maps the omission error varies in the range 4.6-6.5% and the commission error fluctuates between 11.9 and 22.2%; the highest omission (commission) errors occur with the continental (maritime) model. The accuracy of burned area maps derived from non corrected image is very low, with omission error greater than 90%. These results show that, although atmospheric correction is needed for the application of the algorithm, the AOT value does not significantly affect the performance.

A method was developed to normalize multitemporal remote images based in vegetative pseudo-invariant features (VPIFs), as following: 1) defining the same parcel for each selected VPIF in each multitemporal image; 2) extracting the VIPF spectral bands data for each image; 3) calculating the correction factor (CF) for each image band to fit it to the same expected values, normally for each band the average of the series; 4) obtaining the normalized images by transforming each original image band through the corresponding CF linear functions. We have validated ARIN using a series of six GeoEye-1 satellite images taken over the same Southern of Spain scene, from early April to October. Citrus orchards (CIT), riparian trees (POP), olive orchards (OLI) and Mediterranean forest trees (MFO) were the VPIFs chosen, among others. The VPIFs spectral band correction factors (CFs) to implement the ARIN linear normalization procedure largely varied among spectral bands for any given image and among images for any given spectral band. For the ARIN normalized images, the range and standard deviation of any spectral bands and vegetation indices values were considerably reduced as compared to the original images, regardless the VPIF or the combination of VPIFs selected for normalization, which proves the method efficacy. Moreover, ARIN method was easier and efficient than the absolute calibration QUAC method, and somehow similarly efficient as the highly tunable FLAASH, in which solar position and weather calibration parameters are required. ARIN® software was developed to automatically achieve the previously described procedure.

The use of information obtained by remote sensing has been suggested to model the behavior of crops through the use of vegetation indices (IV) due to its qualities spatial sampling, temporal and radiometric. In this study, we examined the growth cycle of Sugarcane using a traditional IV and spectral information in the near infrared region (NIR) to check its use as a tool for monitoring the state of development. In the development of work we employed a set of 25 satellite images ETM + sensor, with information in the spectral region of the NIR and Red (R). The imagery covered temporally the end of one season and all of the next season. The results showed a similar behavior between NDVI and reflectance of the NIR by relating to the state of development of the crop in clear sky conditions. Furthermore, with analyzed information derived, it was found that the utility of NDVI decreases when cloudiness problems occur even in small amounts, while the reflectance information from NIR band can be used for purposes of modeling development cultivation under these conditions. We conclude that the NIR information can be used reliably even in cloudy conditions for modeling the state of development of sugar cane cultivation.

The author regards fundamental root functions as underpinning photosynthesis activities by vegetation and as affecting environmental issues, grain production, and desertification. This paper describes the present development of monitoring and near real-time forecasting of environmental projects and crop production by approaching established operational monitoring step-by-step. The author has been developing a thematic monitoring structure (named RSEM system) which stands on satellite-based photosynthesis models over several continents for operational supports in environmental fields mentioned above. Validation methods stand not on FLUXNET but on carbon partitioning validation (CPV). The models demand continuing parameterization. The entire frame system has been built using Reanalysis meteorological data, but model accuracy remains insufficient except for that of paddy rice. The author shall accomplish the system that incorporates global environmental forces. Regarding crop production applications, industrialization in developing countries achieved through direct investment by economically developed nations raises their income, resulting in increased food demand. Last year, China began to import rice as it had in the past with grains of maize, wheat, and soybeans. Important agro-potential countries make efforts to cultivate new crop lands in South America, Africa, and Eastern Europe. Trends toward less food sustainability and stability are continuing, with exacerbation by rapid social and climate changes. Operational monitoring of carbon sequestration by herbaceous and bore plants converges with efforts at bio-energy, crop production monitoring, and socio-environmental projects such as CDM A/R, combating desertification, and bio-diversity.

Forest biomass estimation is essential for greenhouse gas inventories, terrestrial carbon accounting and climate change modeling studies. Although a lot of efforts have been made in estimating biomass using both field-based and remote sensing techniques, no universal and transferable technique has been developed so far to quantify biomass carbon sources and sinks due to the complexity of the environmental, topographic and biophysical characteristics of forest ecosystems. This study investigated the potential of SAR (RADARSAT-2 dual polarizations) and optical (AVNIR-2) image fusion for biomass estimation using wavelet transform. Six different types of wavelets (haar, daubechies, symlet, coiflet, biorthogonal and discrete meyer) were tested with different rules and three decomposition levels for four different image combinations of SAR and optical data. The highest accuracy (r) of 0.84 was obtained from the fusion of NIR and HV polarization data, compared to 0.70 (r) from the NIR band alone. The results indicated a substantial improvement of biomass estimation from the fused images, and this accuracy is very promising, especially when using only one fused image in the high biomass situation of the study area, and gives a clear message to the research community that biomass estimation can be improved using the fusion of SAR and optical data due to their complementary information. Furthermore this fusion process can significantly reduce the saturation problem of optical and SAR data for biomass estimation.

Flooding is one of the most common disasters worldwide. This paper strives to highlight the hydrological effects of multi-temporal land use changes in flood hazard. The study area is Potamia basin established in the broader Yialias catchment basin located in Nicosia –Cyprus. The certain area has suffered from several flood disasters in the recent past. Initially, the land cover regime of the study area (last 20 years) was thoroughly studied using multi-temporal satellite (ASTER) and implementing g sophisticated classification methodologies such as Object oriented analysis. Land use/ Land cover (LULC) maps for the periods of 1990, 2000 and 2010 were developed. All these maps were incorporated in the CA-Markov chain analysis model and the LULC map of 2020 was constructed. The Markov chain analysis describes the probability of land cover change from one period to another by developing a transition probability matrix between two different time periods. Following, the hydrological analysis was performed using the Hydrological Modeling System (HEC-HMS). HEC-HMS is designed to simulate the precipitation-runoff processes of dendritic watershed systems. The HEC-HMS model was set up in distributed mode, enabling the utilization of the spatial information on the land use via the Curve number coefficient. Thus, the 2020LULC map was incorporated in the hydrological model in order to predict the hydrological behavior of the catchment area for the next decade. The results were compared with the present hydrological regime and denoted the future increase of runoff due o the predicted extensive urban sprawl phenomenon.

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.

Radar-based satellite altimetry is a well recognized measuring technique with good precision for oceanographic applications. For continental hydrology, its use is complicated by a number of factors such as river width, satellite crossing angle and noise from the river banks or islands. These factors make precision vary significantly. The satellite crossing points can be made into virtual gauging stations that can complement the existing network of in situ stations. This article describes a series of spatially explicit processing to correct or exclude altimetry measurements not related to the water level. While some processing take advantage of a priori information such as the centerline of the river, other processing are based on pattern recognition to characterize the shape described by the sequence of points. These problems are dealt with by fitting a second degree polynomial curve to the sequence of points and characterizing its shape. The correction is applied by determining a weight for each point in the crossing sequence of measurements. These processing approaches have been combined into a single tool called VHSTOOL. The method is tested on a 1000 km stretch of the S˜ao Francisco River in Brazil. Data from Envisat cover the 2003-2010 period while the recently launched Altika sensor provided data for a few months in 2013. Results show that the average accuracy of 60 cm obtained (45 cm by removing outliers) is comparable to that of completely manual methods. Altika measurements could not be validated since no recent in situ data was available but initial evaluation suggests increased details should bring some improvements over Envisat data.

Surface soil moisture content is one of the key variables used for many applications especially in hydrology, meteorology and agriculture. Hyperspectral remote sensing provides effective methodologies for mapping soil moisture content over a broad area by different indices such as NSMI [1,2] and SMGM [3]. Both indices can achieve a high accuracy for non-vegetation influenced soil samples, but their accuracy is limited in case of the presence of vegetation. Since, the increase of the vegetation cover leads to non-linear variations of the indices. In this study a new methodology for moisture indices correcting the influence of vegetation is presented consisting of several processing steps. First, hyperspectral reflectance data are classified in terms of crop type and growth stage. Second, based on these parameters 3D plant models from a database used to simulate typical canopy reflectance considering variations in the canopy structure (e.g. plant density and distribution) and the soil moisture content for actual solar illumination and sensor viewing angles. Third, a vegetation correction function is developed, based on the calculated soil moisture indices and vegetation indices of the simulated canopy reflectance data. Finally this function is applied on hyperspectral image data. The method is tested on two hyperspectral image data sets of the AISA DUAL at the test site Fichtwald in Germany. The results show a significant improvements compared to solely use of NSMI index. Up to a vegetation cover of 75 % the correction function minimise the influences of vegetation cover significantly. If the vegetation is denser the method leads to inadequate quality to predict the soil moisture content. In summary it can be said that applying the method on weakly to moderately overgrown with vegetation locations enables a significant improvement in the quantification of soil moisture and thus greatly expands the scope of NSMI.

This study presents a strategy to improve the evapotranspiration estimates in semi arid areas using data assimilation in a SVAT (Soil Vegetation Atmosphere Transfer) modeling, the ISBA scheme (Interaction Soil Biosphere Atmosphere). In the perspective to use remote sensing products, the overall objective of this work is to identify the best combination of data (surface soil moisture / surface temperature / evapotranspiration), the temporal repetitiveness of acquisition (daily / tri-daily / weekly / bi-monthly / monthly) and the kind of data assimilation technique (two dimensional variational method / Extended Kalman filter) to constraint evapotranspiration predictions. Within this preliminary study, synthetic data referring to a wheat crops experimental site located in the Haouz Plain, part of the Tensift basin near Marrakesh in Morocco have been used (from January to May 2003). The results show that in order to improve the evapotranspiration through the analysis of the root zone soil moisture, the surface soil moisture is the most informative observation to use in the assimilation process (roughly 40% improvement in evapotranspiration RMSE). Combinations of observations improve the results but not significantly (few % improvement in evapotranspiration RMSE). Assimilation is very efficient for short assimilation windows. It is also shown that the propagation of the background error matrix done through the Extended Kalman filter doesn’t represent a significant added value with regards to the constant matrix used with two dimensional variational method.

Accurate estimation of physical quantities depends on the availability of High Resolution (HR) observations of the Earth surface. However, due to the unavoidable tradeoff between spatial and time resolution, the acquisition instants of HR data hardly coincides with those required by the estimation algorithms. A possible solution consists in constructing a synthetic HR observation at a given time k by exploiting Low Resolution (LR) and HR data acquired at different instants. In this work we recast this issue as a smoothing problem, thus focusing on cases in which observations acquired both before and after time k are available. The proposed approach is validated on a region of interest for the IRRISAT irrigation management project in which the surface thermal inertia estimation, requiring multiple HR images at specific instants, constitute a key step.

Soil water content is directly connected with soil evaporation and plant transpiration processes; in particular, soil water content within the root zone, is readily available to evapotranspiration. Thus, in agricultural sciences, the assessment of the spatial distribution of soil water content could be of utmost importance in evaluating crop water requirement. In spite of limitations to applicability due to contingent cloud cover, water content of the upper part of the soil can be determined by applying the thermal inertia approach by coupling optical and thermal infrared images. The thermal inertia formulation, rigorously retrieved on bare soil, has been also verified on soils partially covered by vegetation. In each case, one of the crucial steps is the assessment of the phase difference between surface temperature and solar irradiation. Different approaches allow determining this latter parameter. To this aim, three formulations to retrieve the phase difference were tested: i) the first, assuming a spatially constant value based on the knowledge of the time when maximum surface temperature occurs; ii) other two methods, allowing determining its spatial distribution through three or four thermographies. In this framework, this research is focused to establish the simplest operational approach providing reliable results over time using low-resolution MODIS images collected over an agricultural area of South Italy (Campania). Temporal evolution of the remote sensing estimates have been compared to data collected by the micro-meteorological station installed in a vineyard within the area.

A desert plain in Egypt is being investigated to develop new techniques of multisensor data integration for groundwater exploration. A combination of multispectral, thermal and microwave data (ASTER and PALSAR) and supported by ground measurements (GPR, field spectroradiometry and magnetometry) were used to investigate surface sediment characteristics of the El-Gallaba Plain area, NW of Aswan. This desert plain once hosted an ancestral river system long before the Nile even existed. Nowadays the fluvial deposits are largely covered by Aeolian and gravelly sands and thus only detectible with radar and thermal images. Results show two broad strips of thermal cooling anomalies arranged in a linear fashion and diagonally crossing the alluvial basin. Spectral signatures collected along the linear land surface temperature (LST) anomalies show generally higher reflectance values (higher albedo) than the surrounding sediments. Both, the cooler LST and higher albedo, suggest that the surface sediments within the anomaly strips have lower emissivity values (low heat storage capacity). Furthermore, the homogeneity of these sediments was measured with a ground penetrating radar (GPR) using 250 and 100 MHz shielded antenna. The 12 GPR profiles across the LST anomalies confirmed that the near-surface sediments (up to 10 m depth) consist of thin horizontal layers of sandstone with very low gravel content. They show very different textural and compositional characteristics with respect to the surrounding areas suggesting a different depositional environment. Thus 12 magnetic profiles with 1.5 km average length were acquired across the LST anomalies to investigate deep seated structures. The results confirmed the existence of graben-like structures with a maximum depth to the basement of 150 m and shallower depths toward the edges of the LST anomalies. Consequently, these structurally controlled basins could be promising areas for ground water accumulation and exploration in the El-Gallaba Plain of the Western Desert in Egypt.

The Nilo Coelho irrigation scheme, located in the semi-arid region of Brazil, is highlighted as an important agricultural irrigated perimeter. Considering the scenario of this fast land use change, the development and application of suitable tools to quantify the trends of the water productivity parameters on a large scale is important. To analyse the effects of land use change within this perimeter, the large-scale values of biomass production (BIO) and actual evapotranspiration (ET) were quantified from 1992 to 2011, under the naturally driest conditions along the year. Monteith´s radiation model was applied for estimating the absorbed photosynthetically active radiation (APAR), while the SAFER (Simple Algorithm For Evapotranspiration Retrieving) algorithm was used to retrieve ET. The highest incremental BIO values happened during the years of 1999 and 2005, as a result of the increased agricultural area under production inside the perimeter, when the average differences between irrigated crops and natural vegetation were more than 70 kg ha-1 d^-1. Comparing the average ET rates of 1992 (1.6 mm d^-1) with those for 2011 (3.1 mm d^-1), it was verified that the extra water consumption doubled because of the increments of irrigated areas along the years. More uniformity along the years on both water productivity parameters occurred for natural vegetation, evidenced by the lower values of standard deviation when comparing to irrigated crops. The heterogeneity of ET values under irrigation conditions are due to the different species, crop stages, cultural and water managements.

The use and timing of many agronomical practices such as the scheduling of irrigation and harvesting are dependent on accurate vineyard sampling of qualitative and productive parameters. Crop forecasting also depends on the representativeness of vineyard samples during the whole phenological period. This manuscript summarizes the last two years of precision viticulture in Sicily (Italy); agronomic campaigns were carried out in 2012 and 2013 within the “Tenute Rapitalà” and “Donnafugata” farms. Normalized Difference Vegetation Index derived from satellite images (RapidEye) acquired at berry set, pre-veraison and ripening phenological stages (occurred at June, July and August respectively) have been related to production parameters (sugar and anthocyanins contents) at harvesting of a selected red autochthonous cultivar (Nero D’Avola). The research aims to assess how robust are prediction models based on simple linear regression analysis, in particular: 1) whether there is a suitable period for acquiring the remote sensing image to evaluate these parameters at harvesting, when their knowledge is required; 2) if these relationships are consistent between years or need to be re-calibrated; 3) the models transferability to other vineyard of the same cultivar.

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.

An operational use of the actual evapotranspiration estimates requires the integration from instantaneous to daily values. This can commonly be achieved under the hypothesis of daytime self-preservation of the evaporative fraction. In this study, it has been evaluated the effect of this assumption on the assessment of daily evapotranspiration from proximity sensing images acquired at hourly intervals over a homogeneous olive groove. Results have been validated by comparison with observations made by a micrometeorological (EC-flux tower) and an eco-physiological (sap flux) sensor. SEBAL model has been applied to thermal and multispectral images acquired during a clear day on August 2009 trough a FLIR A320G thermal camera and a Tetracam MCA II multispectral camera, installed on a tethered helium balloon. Thermal and multispectral images were characterized by very high spatial resolution. This experiment aims to analyze two effects: 1) the consistency of the self-preservation hypothesis for daily estimates of the actual evapotranspiration from hourly assessments at different times of the day; 2) the effects of the spatial resolution on the performances of the energy balance model. To evaluate the effects of the spatial resolution, semi-hourly observations made by a flux tower and sap-flow measures were compared to the evapotranspiration estimates performed using downscaled images at resolutions close to canopy sizes (2, 5 and 10 m). Results show that the best estimates are obtained with a spatial resolution comparable to the average size of the canopy with images taken approximately at 10 UTC.

In the semi-arid areas of Petrolina municipality, Northeast Brazil, irrigated agriculture has replaced the natural vegetation, being important the quantification of the energy exchanges between the plants and the low atmosphere. MODIS satellite images and agro-meteorological data for the years of 2010 and 2011 were used together, for modelling the energy balance components under these conditions. Surface albedo (α₀), NDVI and surface temperature (T₀) were the remote sensing parameters necessary to calculate the latent heat flux (λE) and the surface resistance to evapotranspiration (r_s) on a large scale. The daily net radiation (R_n) was retrieved from α0, air temperature (T_a) and transmissivity (τ_sw), allowing the quantification of the sensible heat flux (H) by residual in the energy balance. With threshold values for r_s, it was possible to do a simplified vegetation classification. The incident solar radiation (RS↓) partitioned as Rn ranged from 0.40 to 0.51, corresponding respectively to periods after the rainy season and the driest conditions of the year, with the differences between irrigated crops and natural ecosystem not significant. Considering all periods along the year the averaged fractions of Rn partitioned as H, were 31 and 78%, for irrigated crops and natural vegetation, respectively, while as λE the corresponding ratios were 69 and 22%. It was observed heat advection from the dry areas to irrigated plots, with λE exceeding R_n by 9% during the coldest periods. The models tested here can be used for monitoring the energy exchanges in agro-ecosystems under conditions of land use and climate changes.

This paper is concerned with vegetation wetland mapping using multi-temporal SAR imagery. Whilst wetlands play a key role in controlling flooding and nonpoint source pollution, sequestering carbon and providing an abundance of ecological services, knowledge of the flora and fauna of these environments is patchy, and understanding of their ecological functioning is still insufficient for a reliable functional assessment on areas larger than a few ha. The aim of this paper is to evaluate multitemporal TerraSAR-X imagery to map precisely the distribution of vegetation formations within wetlands, in determining seasonally flooded areas of wetlands. A series of six dual-polarization TerraSAR-X images (HH/VV) were acquired in 2012 during dry and wet seasons. Polarimetric and intensity parameters, which present a temporal variation that depends on wetland flooding status and vegetation roughness, were firstly extracted. The parameters were then classified based on Support Vector Machines (SVM) techniques using a specific kernel adapted to the comparison of time-series data. The results show that the Shannon entropy parameter allows discriminating vegetation formations within wetland with more accuracy than intensity parameters.

Sustainability of modern agro-hydrology requires the knowledge of spatial and temporal variability of vegetation biomass to optimize management of land and water resources. Diversely from optical imaging, temporal resolution of active sensors, such as SAR, is not limited by sky cloudiness; thus, they may be combined with optical imageries to provide a more continuous monitoring of land surfaces. Several new SAR missions (e.g., ALOS-PALSAR, COSMO-SkyMed 1 and 2, TerraSAR-X, TerraSAR-X2, Sentinel 1) acquiring at X-, C- and L-bands and dual polarization capability, are characterized by a short revisit time (from 12 h to ~10 days) and high spatial resolution (<20 m). These satellites could provide suitable data for operative crop monitoring. The new COSMO-SkyMed (owned by Italian Space Agency-ASI) represents a valuable source of SAR data in X-band, opening new opportunities to develop agro-hydrological applications. Although, X-band backscattering is not the most suitable to model agricultural and hydrological processes; it could help assessing the vegetation development if combined with optical vegetation indices (VIs). Recently, two models to infer a VI from σ° have been setup. One of these achieved accurate results in VI retrieval at a certain time using σ°(VISAR), once known the NDVI derived from optical images (VIopt) at a reference time. This paper aims to further validate this model through two independent datasets. The model was firstly implemented on COSMO-SkyMed and optical DEIMOS-1 data acquired over the Sele plain (Campania, Italy). It has been further validated using different optical images. To this aim, a dataset of 2 COSMOSkyMed images and 2 Landsat 7 SLC-off images were acquired in the southwestern part of Sicily (Italy) between 8 and 25 August 2011. Determination coefficients of the validation set were similar to those of the calibration set. Results confirm that VISAR obtained using the combined model is a suitable surrogate of VIopt if estimated at parcel scale.

The paper debates the impact of sensor configuration diversity on the crop classification performance. More specifically, the analysis accounts for multi-temporal and polarimetric C-Band SAR information used individually and in synergy with Multispectral imagery. The dataset used for the investigation comprises several multi-angle Radarsat-2 (RS2) fullpol acquisitions and RapidEye (RE) images both at fine resolution collected over the Indian Head (Canada) agricultural site area and spanning the summer crop growth cycle from May to September. A quasi-Maximum Likelihood (ML) classification approach applied at per-field level has been adopted to integrate the different data sources. The analysis provided evidence on the overall accuracy enhancement with respect to the individual sensor performances, with 4%-8% increase over a single RE image, a 40%-10% increase over a single 1-pol/full-pol image and 15%-0% increase over multitemporal 1-pol/full-pol RS2 series respectively. A more detailed crop analysis revealed that in particular canola and the cereals benefit from the integration, whereas lentil and flax can experience similar or worse performance when compared to the RE-based classification. Comments and suggestions for further development are presented.

Remote sensing has long been used as a means of detecting and classifying changes on the land. Analysis of multi-year time series of land surface attributes and their seasonal change indicates a complexity of land use land cover change (LULCC). In semi-arid region, where fragile ecosystems are dominant, the land cover change often reflects the most significant impact on the environment due to excessive human activities. This study focused on the monitoring and mapping of land use land cover change in the Gash Agricultural Scheme (Eastern Sudan), Sudan through monitoring the changes that occurred in land use pattern due to drought, climate change and mismanagement. The study attempted to analyze variation of land use land cover changes and it is impacts particularly the changes in the agricultural territories in Gash river scheme, Eastern Sudan. Multi-temporal remotely sensed data (Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper plus (ETM+) between 1972 and 2010 (MSS 1972, TM 1987, ETM+1999 and ASTER 2010) were used together with Geographical Information System (GIS).The study reveals that there was rapid increase of invasion of mesquite tree as well as increase in sand land during the period of study and clear decrease in cultivated and grass land.

Coastal islands are located in a transitional environment where land and ocean interact. During the past 20 years, large areas of the natural landscape of Zhoushan islands have been replaced by human features. There is limited research detailing the amount of impervious surface growth in the Zhoushan islands and trends in the land environment using remotely sensed technologies. The purpose of this paper is to assess the influences of anthropogenic activities on land ecological environment in Zhoushan islands based on remotely sensed impervious surfaces. First the impervious surfaces information of 1986, 1995 and 2006 were estimated using Landsat Thematic Mapper (TM) images, and then an integrated state indicator was built. The results reveal that imperious surfaces areas (ISA) of Zhoushan islands remarkably increased from 19.2 km² (2.73%) in 1986 to 29.5 km² (4.20%) in 1995, and to 58.2 km² (8.27%) in 2006. The average state value for the total area was 0.70, 0.65, and 0.55 respectively for 1986, 1995, and 2006. Of the surrounding islands, the Lujiazhi, Xiaogan, Panzhi, and Cezi suffered the most land disturbance intensity from human activities, followed by the Jintang, Xiushan, and Damao, and the Changbai and Changzhi had the lowest disturbance values. It indicates that the land ecological environment of Zhoushan islands was variously disturbed by growing human activities over time. Further, we found that the topography, island size, spatial location from Zhoushan Island and economic policy had an influence on change of impervious surfaces for each island.

Monte Carlo Ray Tracing (MCRT) method is a versatile application for simulating radiative transfer regime of the Solar - Atmosphere - Landscape system. Moreover, it can be used to compute the radiation distribution over a complex landscape configuration, as an example like a forest area. Due to its robustness to the complexity of the 3-D scene altering, MCRT method is also employed for simulating canopy radiative transfer regime as the validation source of other radiative transfer models. In MCRT modeling within vegetation, one basic step is the canopy scene set up. 3-D scanning application was used for representing canopy structure as accurately as possible, but it is time consuming. Botanical growth function can be used to model the single tree growth, but cannot be used to express the impaction among trees. L-System is also a functional controlled tree growth simulation model, but it costs large computing memory. Additionally, it only models the current tree patterns rather than tree growth during we simulate the radiative transfer regime. Therefore, it is much more constructive to use regular solid pattern like ellipsoidal, cone, cylinder etc. to indicate single canopy. Considering the allelopathy phenomenon in some open forest optical images, each tree in its own ‘domain’ repels other trees. According to this assumption a stochastic circle packing algorithm is developed to generate the 3-D canopy scene in this study. The canopy coverage (%) and the tree amount (N) of the 3-D scene are declared at first, similar to the random open forest image. Accordingly, we randomly generate each canopy radius (r_c). Then we set the circle central coordinate on XY-plane as well as to keep circles separate from each other by the circle packing algorithm. To model the individual tree, we employ the Ishikawa’s tree growth regressive model to set the tree parameters including DBH (dt), tree height (H). However, the relationship between canopy height (Hc) and trunk height (H_t) is unclear to us. We assume the proportion between Hc and Ht as a random number in the interval from 2.0 to 3.0. De Wit’s sphere leaf angle distribution function was used within the canopy for acceleration. Finally, we simulate the open forest albedo using MCRT method. The MCRT algorithm of this study is summarized as follows (1) Initialize the photon with a position (r₀), source direction (Ω0) and intensity (I₀), respectively. (2) Simulate the free path (s) of a photon under the condition of (r', Ω, I’) in the canopy. (3) Calculate the new position of the photon r=r +sΩ’. (4) Determine the new scattering direction (Ω)after collision at, r and then calculate the new intensity I = Υ_L(Ω_L,Ω'→Ω)I'.(5) Accumulate the intensity I of a photon escaping from the top boundary of the 3-D Scene, otherwise redo from step (2), until I is smaller than a threshold. (6) Repeat from step (1), for each photon. We testify the model on four different simulated open forests and the effectiveness of the model is demonstrated in details.

The study contains the results of forest monitoring at three levels: the forests condition assessment at the time of recording or mapping for this indicator, the seasonal changes assessment in the forests condition, mainly during the vegetation period and the evaluation of long-term changes in the values of the studied parameters on the example of the forests recovery after a fire. The use of two indices - NDVI and Image Difference in the boreal forests monitoring is treated. NDVI assesses the state of plant biomass and its productivity. The rate of Image Difference characterizes the optical density and allows estimate the density of the forest stand. In addition, by identifying Image Difference on summer and autumn pictures it can makes a distinction of different wood species, to divide forest areas, which consist of deciduous and coniferous species and larch which shedded needles at the end of the vegetation period. Therefore, it is possible to differentiate the pine, cedar, spruce forests on the one side and birch, larch, alder on the other side. The optical density of the forest decreases after the needles- and the leaf sheddings. Using the index Image Difference in estimates of long-term changes of the forest stand shows the trend of changes of the forest density and the tree species composition. The results of the analysis of the recovery process of the forest after a fire in the period from 1995 to 2009 showed how shoots of birch, larch and pine recover wastelands.

Snow is a component of the cryosphere which has played an important role in Earth energy balance. Northern hemisphere snow cover extent (SCE) has steadily decreased since 1980 and in recently the trend of SCE is sharply decreased. Because Himalaya region's shows most significant changes except for the Arctic, we analyzed this region for SCE. We used Moderate Resolution Imaging Spectroradiometer (MODIS) snow product from 2001 to 2011 in august. Analysis was made by considering some conditions (region, elevation, longitude and climate) which can affect the changes in SCE. The entire SCE in Himalaya for 11 years has steadily increased(+55,098 km²). Trends for SCE in western region has increased(+77,781km²), But trend for central and eastern have decreased -3,453 km², -19,230km², respectively. According to elevation increases, the ratio of snow in each study area is increased. In 30°N~35°N SCE shows increased trend, 27°N~28°N shows decreased trend. In tundra climate, trends for SCE are similar to regional analysis. whereas the result in tropical climate's trend was increased. these performed result shows different side for change of SCE depending on each condition. The result of this study were similar to the rapid decline of the northern hemisphere SCE area in recent. The result of this study can be used to help management to water budget in Central-Asia country located to Himalayas.

Satellite data can be used as a valuable tool for the detection of water pipeline leakages in semiarid areas. However the use of multi-temporal satellite images for this purpose can be problematic since reflectance values may change due to phenological changes of plants, radiometric errors during the pre-processing of satellite data, etc. It is therefore important to establish a spectral signature library with “ground truth data” for different scenarios of water leakages in a control site minimizing other potential errors. For this purpose, the GER 1500 spectroradiometer was used for measuring the reflectance values of three different targets: soil, vegetation and asphalt. The targets were saturated with a specific amount of water and then several spectroradiometric measurements were taken. The narrowband reflectance values were then re-scaled to spectral bands of Landsat 5 TM and spectral signature variations were highlighted for all targets before and after moisture level were increased. Using these data, threshold values were defined in order to be used for multispectral satellite data analysis. Specifically, this data was used for detection of water leakages in pipelines in Cyprus using Landsat 5 TM images,.

The assessment of the spatial distribution of soil water content could improve the effectiveness of agro-hydrological models. Although it is possible to retrieve the spatial distribution of the soil water content using thermal inertia, the main limit is its applicability to bare soils only. Recently, a variation of the thermal inertia approach has been setup also on vegetated soils characterized by low fractional cover. In particular, the methodology proposes to attenuate the solar radiation at the top of the canopy to the one reaching the soil trough an extinction factor. In situ data were acquired in June 2011 and July 2012 over two fields of maize and sunflowers; both were at their early growing stages. An airborneplatform provided images in the visible/near infrared and thermal infrared, both in day and night time. Results of the 2011 experiment demonstrated that the vegetation cover correction is required even with low fractional cover; indeed, not applying this correction would results in strong overestimation. The 2012 experiment (REFLEX) further validates the model on an independent dataset, thus, confirming the reliability of the methodology. Furthermore, a spatial resolution analysis highlighted that retrievals at low spatial resolution best compares with in situsoil water content than those obtained at high-resolution. Finally, the availability of a thermal image acquired after irrigating demonstrated the unreliability of the method when soil water content significantly changes between the two thermal acquisitions.

Hyperspectral camera was applied to establish the models of catechin concentration for green tea. The possibility of improvement for the models was checked by the multi-year models and the mutual prediction. ECg, EGCg and the ester catechin (ECg and EGCg) decreased with the growth but EC, EGC and the free catechin (EC and EGC) were changed by the covering. In partial least square regression (PLSR) models for each catechin, R² (Relative Error for validation) was more than 0.785 (13.4%) for a single year data, 0.723 (13.3%) for two years data, and 0.756 (13.6%) for three years data except several catechins. It was possible to improve the precision and accuracy of models using the combination of catechin (free and ester type) or the combination of multi-year data. When each and each type of catechin model was predicted by the other year data, the accuracy of two years model improved comparing with it of a single year data. It means that the multi-year models might be more accurate than a single year models to predict the unknown data.

Many vegetation biophysical parameters, e.g. Leaf Area Index, have nonlinear relationships with spectral reflectance, while Support Vector Regression (SVR) has fantastic nonlinear fitting ability owing to kernel inner product and the sparseness of Support Vectors (SVs). Currently, most of the LAI inversion methods over Huanjing optical satellites (HJ) are based on empirical relationship between LAI and spectral index, which have some limitations. In this paper, we developed an algorithm combining SVR and physical model-PROSAIL to retrieve LAI over HJ-CCD image. The model adopted a new HJ vegetation index (HJVI), which can lessen saturation on high LAI domain. Experiments over simulations generated by PROSAIL model proved the new algorithm’s good performance on noising resisting and effectiveness of HJVI. Finally, we applied the algorithm on a HJ1B CCD2 image and validated it with the field measured data in Xinxiang, Henan Province, China. The RMSE of 0.5230 indicated the applicability of the SVR-based physical method over HJ data.

Agricultural monitoring is an important and continuously spreading activity in remote sensing and applied Earth observations. It supplies valuable information on crop condition and growth processes. Much research has been carried out on vegetation phenology issues. In agriculture, the timing of seasonal cycles of crop activity is important for species classification and evaluation of crop development, growing conditions and potential yield. The correct interpretation of remotely sensed data, however, and the increasing demand for data reliability require ground-truth knowledge of the seasonal spectral behaviuor of different species and their relation to crop vigour. For this reason, we performed groundbased study of the seasonal response of winter wheat reflectance patterns to crop growth patterns. The goal was to quantify crop seasonality by establishing empirical relationships between plant biophysical and spectral properties in main ontogenetic periods. Phenology and agr-specific relationships allow to assess crop condition during different portions of the growth cycle and thus effectively track plant development and make yield predictions. The applicability of different vegetation indices for monitoring crop seasonal dynamics, health condition, and yield potential was examined.

Characterization of landscapes is crucial in modelling potential soil erosion to ascertain environmental services that are provided by the main land use in the ecosystem. Remote sensing techniques have proved successful in characterization of landscapes. In this study area of a rain-fed Kibos-Miwani sugar zone of Kenya, we used Normalized difference vegetation index (NDVI) data extracted from satellite imagery to characterize the spatial and temporal heterogeneity of the vegetation conditions, and to model potential soil erosion. Data used included Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m NDVI acquired in the period 2000 to 2013; 30 m Landsat5 time series images acquired between November 2010 and June 2011; a 30 m digital elevation model (DEM); and ground observations (land cover and soil characteristics). Temporal NDVI was extracted directly from MODIS 250 m images to study the changes in seasonal vegetation conditions with time, and spatial NDVI was extracted by analysing Landsat5 images at the field scale. NDVI extracted from Landsat images for a specific date, represented vegetation conditions for that simulation period. To compute potential soil erosion, we used Landsat 5 NDVI, the slope, aspect, curvature and soil physical properties as input data sets in the spatially explicit Fuzzy-based dynamic soil erosion model (FuDSEM). Land cover data collected revealed that sugarcane was the main land use, occupying 76% of the land cover. Results were consistent with crop management practices, illustrating a spatially heterogeneous land scape with varied vegetation conditions throughout the year. Out of simulations, we noted a homogeneous low erosion risk in areas with natural land cover with a global mean of 0.42. Medium to intense erosion risk in cropped areas was evident, with erosion risk varying from one pixel to the other. Simulation results suggest that crop management practices (planting and harvesting processes) are the drivers of erosion in sugar cane cultivated areas.

Air temperature (Ta) plays important role for the circulation of energy and water between the surface and atmosphere. Ta was accurately measured from ground observation stations. However, the number of ground observation stations is limited, and Ta is influenced from temporal and spatial change. In this study, Ta was estimated using satellite data from April 2011 to March 2012 in the Northeast Asia where consist of the various ecosystem. States of surface and atmosphere were considered through Normalized Difference Water Index (NDWI) and the differences of brightness temperature values of 11μm (TBB1) and 12μm (TBB2). Dataset was divided into nine cases that had seasonal characteristics according surface states (NDWI) and atmosphere states (TBB1-TBB2). Ta was acquired from 174 ground observation stations, and multiple regression equation of each case was consisted of LST, NDVI, TBB1-TBB2. The weighting region was set to be within 8.33% of total density from boundary area of cases in order to reduce the errors that can occur due to the small value. The weighting was applied as distance from the nearest four points. The spatial representativeness of estimated Ta was determined as 9 by 9 window size. R-squared of estimated Ta from satellite was 0.94, RMSE was 2.98 K, Bias was 0.56 K.

Terminology is a key issue for a better understanding among people using various languages. Terminology accuracy is essential during all phases of international cooperation. It is crucial to keep up with the latest quantitative and qualitative developments and novelties of the terminology in advanced technology fields such as aerospace science and industry. This is especially true in remote sensing and geoinformatics which develop rapidly and have wide and ever extending applications in various domains of human activity. The importance of the correct use of remote sensing terms refers not only to people working in this field but also to experts in many disciplines who handle remote sensing data and information products. The paper is devoted to terminology issues that refer to all aspects of remote sensing research and application areas. The attention is drawn on the recent needs and peculiarities of compiling specialized dictionaries in the subject area of remote sensing. Details are presented about the work in progress on the preparation of an English-Bulgarian dictionary of remote sensing terms focusing on Earth observations and geoinformation science. Our belief is that the elaboration of bilingual and multilingual dictionaries and glossaries in this spreading, most technically advanced and promising field of human expertise is of great practical importance. Any interest in cooperation and initiating of suchlike collaborative multilingual projects is welcome and highly appreciated.

Urban vegetation land cover change is a direct measure of quantitative increase or decrease in sources of urban pollution and the dimension of extreme climate events and changes that determine environment quality. Spatio- temporal monitoring of urban vegetation land cover changes is a very important task for establishing the links between policy decisions, regulatory actions and subsequent land use activities. Former studies incorporating two-date change detection using Landsat TM/ETM data had limited performance for urban biophysically complex systems applications. In this paper, we describe recent results using data from NASA’s Moderate Resolution Imaging Spectroradiometer and NOAA/AVHRR satellite to study urban vegetation land cover dynamics. This study explored the use of time-series MODIS Terra/Aqua Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI), data to provide change detection information for metropolitan area of Bucharest in Romania. Training and validation are based on a reference dataset collected from IKONOS high resolution remote sensing data. The mean detection accuracy for period 2002- 2012 was assessed to be of 89%, with a reasonable balance between change commission errors (21.7%), change omission errors (28.5%), and Kappa coefficient of 0.69. Annual change detection rates across the urban/periurban areas over the study period (2002–2012) were estimated at 0.78% per annum in the range of 0.45% (2002) to 0.75% (2012).Vegetation dynamics in urban areas at seasonal and longer timescales reflect large-scale interactions between the terrestrial biosphere and the climate system.

In frame of global warming, the field of urbanization and urban thermal environment are important issues among scientists all over the world. This paper investigated the influences of urbanization on urban thermal environment as well as the relationships of thermal characteristics to other biophysical variables in Bucharest metropolitan area of Romania based on satellite remote sensing imagery Landsat TM/ETM+, time series MODIS Terra/Aqua data and IKONOS acquired during 1990 - 2012 period. Vegetation abundances and percent impervious surfaces were derived by means of linear spectral mixture model, and a method for effectively enhancing impervious surface has been developed to accurately examine the urban growth. The land surface temperature (Ts), a key parameter for urban thermal characteristics analysis, was also retrieved from thermal infrared band of Landsat TM/ETM+, from MODIS Terra/Aqua datasets. Based on these parameters, the urban growth, urban heat island effect (UHI) and the relationships of Ts to other biophysical parameters have been analyzed. Results indicated that the metropolitan area ratio of impervious surface in Bucharest increased significantly during two decades investigated period, the intensity of urban heat island and heat wave events being most significant. The correlation analyses revealed that, at the pixel-scale, Ts possessed a strong positive correlation with percent impervious surfaces and negative correlation with vegetation abundances at the regional scale, respectively. This analysis provided an integrated research scheme and the findings can be very useful for urban ecosystem modeling.

Erosion is a natural, gradual and continuous process of earth surface displacement caused by various agents of denudation. It is also caused by some anthropogenic activities. Erosion rate of an area at any point in time is dependent mainly on climate and geological factors. Physical aspects of the erosive force experienced in gullies are mainly dependent on the local prevailing climate condition. In this study, remotely sensed data was used in the analysis of gully erosion progression at Nyaba River in Enugu Urban, aimed at mapping and monitoring gully erosion at the study site. Methodologies employed include; data acquisition from field observation and satellite images; data processing and analyses using ilwis 3.7 and Arc GIS 9.3 software. The result showed that gully progressed from 578,713,735 square meters in 1986 to 1, 002,819,723 in 2011. Prediction showed that the magnitude of the gully area is expected to increase as the years go by if measures are not taken to control the expansion rate. The forecast put the expected coverage of gully erosion at Nyaba River to be 45,210,440 square meters by the year 2040. Consequently, recommendations made include: constant monitoring to detect early stages of gully formation; regulation of grazing of pasture in the area; restriction of sand mining from the river bank and construction of water ways to stabilize river flow. In conclusion, monitoring clearly showed that there was a geometric progression in gully formation at Nyaba over years; the expansion was aided more by anthropogenic activities than natural factors.