Biochemical components of vegetation canopies, such as chlorophyll and nitrogen, are among the parameters controlling physiological processes and therefore essential for the characterization of these processes and their integration in hydrological or vegetation modeling. AVIS (Airborne Visible/near Infrared imaging Spectrometer), built at the department for environmental sciences of the Ludwig-Maximilians-University Munich, is a cost-effective tool for environmental monitoring. Its spectral range lies between 550 and 1000nm and its multitemporal application enables observation of the development of chlorophyll and nitrogen content of plants throughout a vegetation period. Twelve and nine airborne data sets were gathered between April and September 1999 and 2000 respectively from three maize fields in a test site south-west of Munich in the Bavarian Alpine foothills, Germany (48° 6’, 11° 17’ E). Weekly ground-based measurements of plant parameters (plant height, phenology, biomass, nitrogen content, chlorophyll content) during the vegetation periods provided data validation. The chlorophyll and nitrogen content of the maize canopies were derived using the Chlorophyll Absorption Integral (CAI), which exhibited a high correlation with the chlorophyll content per area and the nitrogen content, both per area (g/m²) and in percentage of dry matter (nitrogen=%DM; chlorophyll=mg/g), during vegetative growth before emergence of the ear. The chlorophyll content per mass cannot be derived with the CAI, due to distinct variations of the chlorophyll per mass during plant growth caused by the low chilling tolerance of maize. The mean field values and the spatial distribution of parameter values within one of the fields will be presented, demonstrating the capabilities of AVIS.

AVIRIS data from Blythe were acquired in June 1997 to study the agricultural spectra from different crops and for identification of crops in other areas with similar environmental factors and similar spectral properties. In this respect; the main objectives of this study are: 1) to compare the spectral and radiometric characteristics of AVIRIS data from agriculture crops with the spectra measured by FieldSpec ASD radiometer; 2) to explore the use of AVIRIS images in identifying agricultural crops; 3) to study the impact of environmental factors on selected crops and; 4) to build a spectral library for the crops that were studied. A long-term goal is to extend the spectral library for different vegetation or crops in different stages of growth. In order to support our study, on July 18-19, 2000 we collected spectra using the FieldSpec spectrometer from selected fields planted with different crops at Blythe area, California (at the Longitude 114° 33.28 W and Latitude 33° 25.42 N to Longitude of 114° 44.53 W and 33° 39.77 N Latitude. The teff grass spectra were studied. Teff grass fields were treated with different types of irrigation (i.e. wet to dry conditions). Additional parameters were studied such as the soil water content (WC), pH, organic matter (OM) and nitrogen (N%). The results of this study showed that there is a significant correlation between the data that were collected by AVIRIS image scene in 1997 and spectral data collected by the FieldSpec spectrometer. This correlation allowed us to build a spectral library to be used in ENVI-IDL software. This leads to identification of different crops and in particular the visible part of the spectra. AVIRIS data are in agreement with FieldSpec data. Using IDL algorithms of Spectral Angle Mapper classification (SAM), Spectral Feature Fitting (SFF) and Spectral Binary Encoding (SPE) showed that there is an excellent agreement between the predicted and the actual crop type (i.e. the correlation is between 85 - 90% match). Further use of the AVIRIS images can be of a value to crops identification or crops yield for commercial use. The yield data of Teff grass were correlated significantly with the spectral data from the AVIRIS and from the hand-held radiometer and it showed the impact of irrigation on the yield of the crop.

Remote sensing technology provides useful information on landuse/landcover of a geographical area because of its capability of synoptic viewing multispectral data and repetitive coverage.Its spatial distribution are essential prerequisite for proper planing .In the present study,an attempt has been made to identify and assessing different landuse/landcover classes on the scale of 1:100,000 for centeral region of Iran.Different seasonal satellite data (i.e.landsatTM 1998 19993-4 and 1998 IRS1C LISSIII data) were analyzed to generate complete landuse/landcover information of the aera.The total images was 4 quadrants in both digital and visual interpretation techniques were adopted for landuse/landcover assessing of level III landuse/cover classification, the information available in both seasonal data were further aggregated to generate a single output depicting the information available in both the images. More then 21 landuse/landcover categories were identified and mapped in the area using both the interpretation techniques. The different steps involved in this study area could be explained as below. 1- Defining a comprehensive legend according to the scale of TM,IRS images. 2- Field checking and necessary modification of the primary maps applying the auxiliary data and extant maps to promote the formation depicted on the land use /cover maps. 3- Measurement of land unit surface by applying geographical information system (GIS) facilities to accomplish the work .the total of 4 TM images have been interpreted is 11 sheet with the size of 50 by 70 cm and geographic coordinate of 30 by 30 minutes were produced.

The aim of this study is the discrimination of the main cereals cultivated in Greece (namely soft and durum wheat, barley and oat), using spectral data. In a field experiment, the spectral response in the visible and the near infrared was measured over the above crops, during a 17-week period from early growth to harvesting, for five days a week. An 8-channel ground-based hand-held radiometer, with spectral wavelength range 500-850 nm, was used for the measurements of the spectral reflectance. The ratio vegetation index (RVI) and the normalised difference vegetation index (NDVI) values were also calculated, using all combinations between the near infrared and the visible bands. The results show that all reflectance values are developed in similar way, as canopy geometry was almost similar for all four crops. However, oat presents remarkable differences with regards to the three other crops, in bands 7 and 8. NDVI and RVI values for oat also present the same differences with respect to the rest crops and these differences are better distinguished in some of the studied weeks. A statistical analysis based on the above observations confirmed the significance of the differences of the spectral values between crop pairs, for only the first eight weeks of the studied period. Moreover, differences between the three other crops (soft and durum wheat and barley) are very small. For discrimination applications, the more bands available, the better the chances for detection and identification.

Landsat TM thermal bands have generally not been used for land-use classification because of their inferior spatial resolution. But thermal band data is potentially useful, highlighting reductions in temperature associated with recent irrigation, and between the different stages of growth of the crops. This paper presents the results of a remote sensing study for land use classification, based upon Landsat 7 ETM+ data, aimed at estimating irrigation water demand on the basis of the areas cultivated with different types of crops, and local irrigation practices. A time series of images has been acquired for an area along the Syr Darya River (Kazakhstan), one of the two major rivers feeding the Aral Sea. Once the fourth largest inland sea in surface area in the world, the Aral Sea has been reduced to less than 20% of its original volume as a result of large-scale irrigation, causing extensive environmental damage. A rational method of managing irrigation is urgently required if the sea is to return to its former condition. This paper explores the use of the Landsat ETM+ thermal bands alongside those more commonly used for agricultural land classification. Strategies for determining irrigation water demand are discussed, and observations are compared with ground truth.

The Japanese forestry industry has been significantly affected by the increase in low-price timber imports and a diminishing work force. This has had a direct impact on the level of maintenance of the coniferous forests. In this paper, we examine the development of local domestic energy supply schemes in conjunction with effective forest maintenance plans based on data from Landsat/TM imagery. We examined regions in which coniferous forests experienced various levels of blight and to investigated the introduction of energy supply systems using such forests. Both sound and blighted coniferous forests were examined and the characteristics of each area quantified. The supply of energy to a small town using a local blighted coniferous forest was examined. It is estimated that the use of existing wood burning technologies and an effective forest maintenance plan would result in the supply of power to 30% of the households in the town.

The important role of nitrogen (N) in limiting or enhancing vegetation productivity is relatively well understood, although the interaction of N with other environmental variables in natural and agricultural ecosystems needs more study. In 2001, a suite of optical, fluorescence, and biophysical measurements were collected on leaves of corn (Zea Mays L.) from field plots provided four N fertilizer application rates: 20%, 50%, 100% and 150% of optimal N levels. Two complementary sets of high-resolution (< 2 nm) optical spectra were acquired for both adaxial and abaxial leaf surfaces. The first was comprised of leaf optical properties (350-2500 nm) for reflectance, transmittance, and absorptance. The second was comprised of reflectance spectra (500-1000 nm) acquired with and without a long pass 665 nm filter to determine the fluorescence contribution to "apparent reflectance" in the 670-750 nm spectrum that includes the 685 and 740 nm chlorophyll fluorescence (ChlF) peaks. Two types of fluorescence measurements were also made on adaxial and abaxial surfaces: 1) fluorescence images in four 10 nm bands (blue, green, red, far-red) resulting from broadband irradiance excitation; and 2) emission spectra at 5 nm resolution produced by three excitation wavelengths (280, 380, and 532 nm). The strongest relationships between optical properties and foliar chemistry were obtained for a "red-edge" optical parameter versus C/N and chlorophyll b. Select optical indices and ChlF parameters were correlated. A significant contribution of steady-state ChlF to apparent reflectance was observed, averaging 10-25% at 685 nm and 2-6% at 740 nm over the range of N treatments. From all measurements assessing fluorescence, higher ChlF was measured from the abaxial leaf surfaces.

A microwave based sensor concept for measuring the ground profile is presented. It offers the advantage of detecting even hidden obstacles as radar signals penetrate a covering layer. Advanced algorithms such as reconstruction by range stacking are investigated for accurate ground profile determination.

The Upper Midwest Aerospace Consortium has developed a crop and range alert system to provide farmers, ranchers, land managers from the Native American Community, government agencies and non-governmental organizations with frequent and near real time remote sensing data to enable decisions that both maximize the producer's income and protect the environment. The project, started in 1999, includes the establishment of a learning community network of end users, fast delivery of data to remote locations, applications development and training. More than a hundred and fifty end users and research scientists participated in this learning group in which information is shared in all directions. Over fifty end users were connected via high-bandwidth satellite link to a central distribution system at the University of North Dakota. They received and shared products derived from AVHRR, MODIS, Landsat, IKONOS and aerial platforms. A number of practical applications were developed for precision farming, such as zone-based nitrogen management, stress detection, spray drift detection, and for rangeland management, such as weed detection, livestock carrying capacity, and livestock field rotations. Several instances of cost savings and higher earnings occurred. More importantly, the imagery use resulted in lesser use of chemicals in farming and ranching, leading to environmental benefits.

Relative time NDVI mosaics are proposed as a tool for crop growth monitoring and yield forecasting. The mosaics are constructed for the region of interest for a given phenological phase of crop development (for example, flowering). Mosaics for different years, created for the same characteristic time of crop development, are used for crop growth monitoring and yield forecasting. The approach is illustrated through two case studies: - forecasting of wheat yield in the countries of Northern Africa (relative time NDVI mosaics are constructed for the flowering stage of crop development); - assessing winter crop status in southern areas of Russia after the winter season (mosaics are constructed two dekads before the establishment of snow cover and two dekads after its disappearance). NDVI values, calculated from SPOT4-Vegetation data, were used in both cases. Dates of crop phenological phases were determined applying the WOFOST crop growth model and ECMWF-derived meteorological grid data. Results demonstrate the validity of the approach and the improvements obtained as compared with traditional methods.

NASA/NOAA Pathfinder AVHRR Land (PAL) 10 day composited NDVI data with a spatial resolution of 8 km was used to estimate carbon-fixation and biomass over Indian territory. The study area was classified into agricultural and non-agricultural based upon the NDVI-climatological modeling technique. The Production Efficiency Model (PEM), which decomposits productivity into independent parameters, was used to evaluate the Net Primary Productivity (NPP). The NDVI data for the three years was used to estimate fraction of PAR absorbed (fAPAR) based upon the relationship provided by SAIL model. Incident PAR (IPAR) data set for India was extracted from the monthly global IPAR data set already generated using UV reflectivity data. The IPAR data when combined with the fAPAR data provided absorbed PAR (APAR). APAR was converted to NPP using the mean PAR conversion efficiency values calculated based upon literature survey. The NPP was finally converted to biomass and carbon-fixation. It was observed that about 50 per cent of the carbon-fixation and consequently biomass production over India is through agricultural crops. This appears to be quite substantial compared to the global scenario. Annual variations in carbon-fixation have been explained by changing cropping seasons whereas the inter-annual variations by the anomalies in the monsoon in the region.

Temporal data acquired during January to March 1997 pertaining to two study areas dominated by rubber, teak and mixed forest was processed. This particular period of study included wintering and post-wintering times during which defoliation and refoliation take place in rubber. An increase in the reflectance in green band and near infrared band over time from January to March 97 while a decrease was seen for red band in rubber plantations. Highest temporal variation was seen in rubber. In teak a decrease in the reflectance for green band and near infrared band from January to March 97 and an increase in red band were seen, opposite to that of rubber vegetation. In mixed forest there was a decrease in the reflectance in band 2 and band 4 from January to March 97 followed by an increase in band 3, similar to that found in teak plantation. Though teak and mixed forests also shed leaves, the pattern and timing are different such that rubber vegetation could easily be isolated from the other two types of vegetation. This study provided essential information that helps in mapping and monitoring rubber plantations in India or elsewhere.

Classification of land cover and land use is one of ROCSAT-2 primary applications. Spectral reflectance experiments were conducted for classification of the main land use and land cover in Taiwan. The four Level I categories of USGS classification system were picked out for classification of the major territory in Taiwan. The fourteen subcategories with fourteen different crops were further classified for the agricultural land use. The barren land is classified into five subcategories. The spectral reflectance of each category or subcategory was recorded with a spectro-radiometer, GER3700, with 702 channels. Since the spectral range of GER3700 covers the four color bands, blue, green, red and infrared of the ROCSAT-2 Remote Sensing Imager, the recorded data were derived into simulated reflectance in the color bands of the ROCSAT-2 Remote Sensing Imager. To use a multiple end-member spectral mixed model, the correlation among the category and/or subcategory was calculated. Yam, dry sand and water were selected as the local three end-members for their low correlation of spectral reflectance. The spectral reflectance of other categories is coded as the components of the local end-members.

Knowledge of the characteristics of the vegetation cover is of great interest due to its role in the mass and energy exchanges at the surface/atmosphere interface (e.g. water and carbon cycles). This study is part of DARFEM experiments, EU-funded HySens project (DLR), designed to provide a better understanding of the capability of airborne hyperspectral and directional observations to retrieve biophysical vegetation parameters. Different airborne hyperspectral data were acquired in late June 2001 on the experimental site, a poplar plantation belonging to CARBOEUROFLUX network, located in Northern Italy. An intensive field campaign was accomplished during the aerial survey to collect vegetation parameters and radiometric measurements. Leaf area index (LAI) and vegetation fractional cover (Fc), were retrieved from remote sensing data by statistical relationships with ground measurements. A radiative transfer model was used in direct mode to simulate and analyse the canopy spectral signature changes for varying overstory LAI and different understory conditions. In order to minimize the influence of the extensive understory vegetation on the relationship between spectral Vegetation Index (VI) and LAI, an optical index exploiting short wave infrared (SWIR) was evaluated. A comparison of different VIs performance is presented and relative advantages and drawbacks of SWIR exploitation are discussed.

Airborne hyperspectral images collected over San Rossore Natural Park (Pisa, Italy) by the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) on June 21st, 2000 were analyzed in order to assess the best indices for forest LAI estimation. Hemispherical photography was used for ground truth measurements, simultaneously with the overflights, in hardwood and conifer stands characterized by a LAI ranging between 1.2 and 4.5. All band combinations expressed as simple ratios and normalized indices (a total of 89 single bands, and 7832 and 3916 indices, respectively) were linearly correlated to LAI in order to detect the best correlations. Determination coefficients were analyzed by means of a graphical matrix to highlight relevant spectral regions. Normalized indices composed by the red chlorophyll absorption wavelength (680 nm) and the wavelengths after the green reflectance peak (580-640 nm) in the orange region were strongly correlated to LAI. Best results were obtained with the newly proposed Orange Slope Vegetation Index [OSVI=(ρ₆₂₀-ρ₆₈₀)/(ρ₆₂₀+ρ₆₈₀), R²=0.88, RMSE=0.5). The index performed better than the normalized difference vegetation index (NDVI=(ρ₇₈₀-ρ₆₈₀)/(ρ₇₈₀+ρ₆₈₀), R²=0.47) Using SAIL radiative transfer model, canopy reflectance at different viewing angles and a wide range of LAI was simulated in order to verify the sensitivity of OSVI and NDVI. For LAI between 0.25 and 8 both indices resulted stable for viewing zenith angles between -60° and +60°. OSVI, being saturated with values greater than 4, could be used to estimate a wider range of LAI than NDVI. Application of GeoSail model resulted in a good agreement between simulated and measured OSVI.

Forest fires affect significant areas of the Portuguese forest annually, depending upon seasonal moisture and temperature conditions. Although a large percentage of those fires are not originated by natural causes, there is still a need to develop an effective and timely warning system for fire-prevention. The combination of Earth observation (EO) information with ancillary data of natural parameters for daily monitoring of fire risk, which is allowed by Geographic Information Systems (GIS), offers an appropriate response to that need. The PREMFIRE project was funded by the European Spatial Agency (ESA) for the selection and implementation of the most adequate method for production of fire risk maps for Portugal. The project aims at building a wireless system, enabling its use by fire prevention services in the field with real-time or near real-time exchange of data. We present a methodology to produce fire risk maps using satellite imagery and ancillary data. The approach combines a detailed land cover map and other spatial data sets with vegetation greenness maps and meteorological information to produce a fire potential index map. The vegetation greenness is characterized using NDVI 10-day composites derived from NOOA AVHRR imagery. This methodology is being tested in central Portugal yielding encouraging results.

The Advanced Spaceborne Thermal Emission Reflectance Radiometer (ASTER) has acquired more than a dozen clear sky scenes over the Jornada Experimental Range in New Mexico since the launch of NASA's Terra satellite in December, 1999. To support the ASTER overpasses there were simultaneous field campaigns for the 5/09/00, 5/12/01, 9/17/01 and 5/15/02 scenes. Also, data from an airborne simulator, MASTER, were obtained for the 5/12/01 and 5/15/02 scenes to provide high resolution (3 m) data roughly coincident with ASTER. The Jornada Experimental Range is a long term ecological reserve (LTER) site located at the northern end of the Chihuahuan desert. The site is typical of a desert grassland where the main vegetation components are grass and shrubs. The White Sands National Monument is also within several of the scenes. ASTER has 5 channels in the 8 to 12 micrometer wave band with 90 meter resolution and thus is able to provide information on both the surface temperature and emissivity. The Temperature Emissivity Separation (TES) algorithm was used to extract emissivity values from the ASTER data for 5 sites on the Jornada and for the gypsum sand at White Sands. The results are in good agreement with values calculated from the lab spectra for gypsum and with each other. The results for sites in the Jornada show reasonable agreement with the lab results when the mixed pixel problem is taken into account. These results indicate ASTER and TES are working very well. The surface brightness temperatures from ASTER were in reasonable agreement with measurements made on the ground during the field campaigns.

We compare methods used to measure the water content near the soil surface. The primary objective of this project is to link remotely sensed surface water contents to the soil water regime, in particular to the regime of structured soils. We attempt to use the dynamics of spatially averaged surface water contents measured with microwave radiometry to predict preferential infiltration and drainage. Under field conditions the so-called macropore flow plays an important role in the infiltration and drainage behavior of a soil, as well as in the mass transfer of all kinds of solutes to larger soil depths. These rapid processes are only detectable during the first few hours after a rainfall event, when most of the larger pores are still water filled. The main focus of our project lies in an areal integration of such processes on a field scale. For this reason, we depend on areal data with a high temporal resolution that allow to characterize the soil water dynamics. In this study we report on a field experiment with two different ground-based radiometers (1.4 GHz and 11.4 GHz, respectively) centered at a 5 m × 10 m bare soil plot. The brightness temperature measured with passive microwave sensors contains information on surface water content that is already spatially averaged. Furthermore the water content was measured in-situ with time domain reflectometry probes (TDR) assembled at five depths. In the same depths we measured matric potential (pressure head of soil water) and soil temperature. These data were recorded every 30 min from May to July 2002. In addition, we determined the moisture profile over the top 15 cm using neutron radiography. Transmission radiographs of soil slabs vertically taken from the surface horizon allow for a high spatial resolution of the water distribution. In order to characterize the surface roughness of the soil on a mm-scale we used optical measurement techniques. We illustrate the implications of the results from this field campaign on the dynamics of surface water content.

Surface soil temperature is an important input parameter to a variety of environment models, such as global circulation models, radiative transfer, and land surface process models. Soil temperature is especially important for normalizing microwave radiobrightness temperatures in inverse radiative transfer modelling for soil moisture and vegetation optical depth retrieval. To ensure maximum accuracy of soil moisture retrieval models on a regional or global scale, spatially averaged temperature data are necessary. Since the variability of surface temperature in time and space is extremely high due to incoming solar radiation, air temperature, vegetation, soil physical properties, and topography, an aggregation of a few point measurements rarely provides a good spatial average. Remote sensing methods typically provide spatially averaged values needed. Thermal infrared sensors (TIR) measure the skin temperature, but usually require some atmospheric correction, and during periods of cloud cover they become unusable. Microwave sensors also have the potential for providing reliable estimates of spatially averaged soil temperature. Microwave instruments are also much less affected by atmospheric conditions and thus require little or no correction. A technique to estimate the effective temperature with vertical polarized high-frequency microwave brightness temperatures is presented. Calibration procedures with field observations are discussed, and a technique to estimate the soil temperature at the soil moisture sampling depth for 6.6 GHz is shown.

Accurate, spatially distributed surface temperatures are required for modeling evapotranspiration (ET) over agricultural fields under wide ranging conditions, including stressed and unstressed vegetation. Modeling approaches that use surface temperature observations, however, have the burden of estimating surface emissivities. Emissivity estimation, the subject of much recent research, is facilitated by observations in multiple thermal infrared bands. But it is nevertheless a difficult task. Using observations from multiband thermal sensors, ASTER and MASTER, estimated surface emissivities and temperatures are retrieved in two different ways: the temperature emissivity separation approach (TES), and the normalized emissivity approach (NEM). Both rely upon empirical relationships, but the assumed relationships are different. TES relies upon a relationship between the minimum spectral emissivity and the range of observed emissivities. NEM relies upon an assumption that at least one thermal band has a predetermined emissivity (close to 1.0). Experiments comparing TES and NEM were performed using simulated observations from spectral library data, and with actual data from two different landscapes-- one in central Oklahoma, USA, and another in southern New Mexico, USA. The simulation results suggest that TES's empirical relationship is more realistic than NEM's assumed maximum emissivity, and therefore TES temperature estimates are more accurate than NEM estimates. But when using remote sensing data, TES estimates of maximum emissivities are lower than expected, thus causing overestimated temperatures. Work in progress will determine the significance of this overestimation by comparing ground level measurements against the remote sensing observations.

A series of validation studies for a recently developed soil moisture retrieval algorithm is presented. The approach is largely theoretical, and uses a non-linear iterative optimisation procedure to solve for soil moisture and vegetation optical depth with a radiative transfer model from satellite microwave observations. The new theoretical approach is not dependent on field observations of soil moisture or canopy biophysical measurements and can be used at any wavelength in the microwave region. Details of the model and its development are discussed. Satellite retrievals were derived from 6.6 GHz Nimbus/SMMR brightness temperatures, and were validated with soil moisture data sets from the U.S., Mongolia, and Turkmenistan. Time series of the satellite-derived surface moisture compared well with the available ground observations and precipitation data. The vegetation optical depth showed similar seasonal patterns as the NDVI.

Soil hydraulic properties are needed in many applications. One of the most difficult quantities to assess is the hydraulic conductivity function. One reason for this is the influence of soil structure on the infiltration capacity. In this paper we present an approach to estimate the hydraulic properties based on time-series of water contents measured in the topsoil of an experimental field plot. Based on the van Genuchten-Mualem model for the soil hydraulic functions we investigate how these properties affect the dynamics of the topsoil water content. We postulate that the water retention curve can be estimated from the range of the top soil water contents observable in the field. The experimental evidence obtained in the plot experiment supports this theoretical conjection. With regard to the hydraulic conductivity function simulations of the drying process demonstrate that there is no straightforward, linear effect of the saturated conductivity K_s on the drying rate. Depending on the initial conditions and the water retention curve drying may be faster or slower with increasing values of K_s. Despite this non-linear behavior the simulation results indicate that for certain soils the influence of soil structure on the conductivity function may be observed by monitoring diurnal cycles of water content. The lack of these cycles in the measured data points to a small K_s-value for the soil matrix of the experimental plot. This is in agreement with the infiltration patterns observed on that plot. A further way to detect bimodal pore-size distributions consists in measuring during a number of drying periods that differ substantially in the initial water distribution in the profile. Simulations show larger effects on the drying rate caused by larger K_s-values.

Atmospheric eddies cause transient spatial and temporal variations of surface temperature and can limit the precision of satellite surface temperature retrievals. If a thermal IR sensor has sufficiently high spatial resolution, the effects of these transient changes of temperature will be seen as variations of the thermal spatial pattern. Nine thermal IR images of a uniform emissivity area on Mauna Loa caldera are carefully compared to document spatial differences between them. These images were obtained from the Dept. of Energy Multispectral Thermal Imager satellite at about 20m GSD. Spatial patterns with a 1C - 6C magnitude are present but not repeated in any of the images. In order to better understand the characteristics and impact of turbulence induced temperature fluctuations for quantitative remote thermal IR sensing, an effort to model the spatial variation of surface temperature as driven by turbulent energy fluxes has been initiated. Stochastic models initially examined showed a close coupling between surface temperature and turbulent fluxes but were not successful. Traditional energy balance models used in this type of simulation are insufficient to model skin temperature because of the importance of the skin layer and its small depth compared to soil depths used in the models. A new treatment based on surface renewal theory is introduced.

The paper analyses seasonal effects on L-band backscatter in boreal forests and the implications for stem volume retrieval (JERS-1 mission). As test sites, the estate of Kattbole, Sweden, and two compartments in Bolshe-Murtinsky, Siberia, were considered. The in-situ measured stem volumes ranged from 5 to 350 m³/ha in Kattbole and to 400 m³/ha in Bolshe-Murtinsky, at stand level. For each site nine SAR images were available. Forest backscatter strongly depended on seasonal conditions. With respect to other seasons, in frozen conditions the dynamic range was smaller and the forest backscatter at least 3 dB lower. When precipitation occurred, the backscatter showed saturation. In Kattbole, no saturation was found in images acquired at dry/unfrozen conditions. By means of a semi-empirical model, a regression between stem volume and backscatter was performed. Stem volume was then retrieved for an independent set of backscatter measurements. Images acquired at dry/unfrozen conditions showed a relative RMS error of around 30 % for the images acquired over Kattbole. At both sites the retrieval error was higher for other weather conditions, around 50%. When dry/unfrozen conditions occurred, multi-temporal combination of stem volume estimates showed the smallest error (22%). Hence, for boreal forest monitoring L-band images acquired at dry/unfrozen conditions should be used.

In this paper we consider the problem of estimating chlorophyll content in vegetation using an experimental optical method from noisy spectral data. It is shown that the quantitative analysis of the spectral curves for the reflection of plant leaves may be the basis for development of new methods for interpretation of the data obtained by the remote measurement of plants. A mathematical model of vegetation reflectance is proposed to estimate the chlorophyll concentration from spectral data. Estimates are defined as minimizers of penalized cost functionals with sequential quadratic programming (SQP) methods. An estimation tool is related to the local scoring procedure for an generalized additive model. Experimental and simulation results are shown for different agricultural plants using a functional parametric fitting of spectral curves.

Radar images are presently being used in association with optical remote sensing data to characterize the different processes of land use in Brazilian Amazon region. Considering the current development in remote sensing techniques for estimating forest biomass, where L, X and C band images have their limitations, it was recently accomplished a scientific airborne mission with image polarimetric P-band imagery acquisition at lower Rio Tapajós region, Brazil. This study analyses the biomass variation of the primary forest and secondary succession and it's influence on the response of backscatter values in the P-band polarimetric images. The start of this study was the understanding of the behavior of the structural variables of the vegetation cover (measured during the field survey) and its'correlation with the backscatter data obtained from P_HH -, P_HV - and P_VV - band data. A statistical regression model was used to verify the relationship between biomass (estimate by different allometric equations) and P-band polarimetric data. Based on the regression equation that best fits the data sets, a biomass map was elaborated. This was done through the segmentation of the backscatter image, using Caesar 3.0 rwseg algorithm (based on the successive edge detecting and region growing procedures), with the σ° of each resulting segment was converted into biomass values by the best fit function. The final goal of this P-band experiment is to improve the regional inventory and monitoring biomass dynamics, as well as landscape changes, due to human action in Amazon.

Hyperspectal remote sensing is one of the main trends in the domain of remote sensing technology. Hyperspectral data contain plenty of information about space, radiation and spectrum, which makes plant classification more precise. In the west of China, plant distribution is heavily dispersed because the loess terrain is liable to erosion by wind or rain. This makes it very difficult to survey plant distribution using normal multispectral remote sensing methods. The paper introduces the methods of plant classification using imaging spectral data obtained by OMIS I in detail, including traditional methods after the best features selecting from hyperspectral data, and ones based on spectrum matching technique uniquely applied in hyperspectral remote sensing, such as spectral angel mapping, derivate spectrum shape matching etc. The classification result verifies the effectiveness of hyperspectral remote sensing in plant classification.

A hyperspectral imaging system demonstrated potential to detect surface fecal and ingesta contaminants on poultry carcasses. Hyperspectral data were analyzed with four pre-processing methods considering two parameters: calibration and 20-nm spectral smoothing. A band-ratio image-processing algorithm, using band equation including 2-wavelengths (565 nm / 517 nm) and 3-wavelengths (576 nm - 616 nm)/(529 nm - 616 nm) equations, was then applied to each pre-processed method that included applying a background mask to the ratio of images, and finally applying a fecal threshold. Based on a high accuracy of 96.2% for predicting surface contaminants and significantly less false positives on the 64 birds measured, the calibrated smooth method was considered the best pre-processing method for contaminant detection. In conjunction with an appropriate image-processing algorithm, the hyperspectral imaging system is an effective technique for the identification of fecal and ingesta contaminants on poultry carcasses. Specifically, band ratio with 2-wavelength equation (565/517) performed very well with 96.4% accuracy and 147 false positives for detecting both feces (duodenum, ceca, colon) and ingesta contaminants.

The global land-surface temperature (LST) and normalized difference vegetation index (NDVI) products retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) data in 2001 were used in this study. The yearly peak values of NDVI data at 5km grids were used to define six NDVI peak zones from -0.2 to 1 in steps of 0.2, and the monthly NDVI values at each grid were sorted in decreasing order, resulting in 12 layers of NDVI images for each of the NDVI peak zones. The mean and standard deviation of daytime LSTs and day-night LST differences at the grids corresponding to the first layer of NDVI images characterize the thermal status of terrestrial ecosystems in the NDVI peak zones. For the ecosystems in the 0.8-1 NDVI peak zone, daytime LSTs distribute from 0-35 °C and day-night LST differences distribute from -2 to 22 °C. The daytime LSTs and day-night LST differences corresponding to the remaining layers of NDVI images show that the growth of vegetation is limited at low and high LSTs. LSTs and NDVI may be used to monitor photosynthetic activity and drought, as shown in their applications to a flood-irrigated grassland in California and an unirrigated grassland in Nevada.

Previous forest research using time-of-flight lidar suggests that there exists some quantile of the distribution of laser canopy heights that could provide an estimate of various forest biophysical properties. The results presented here not only support this theory, but also extend it by suggesting that a quantile of the distribution of all laser heights could provide estimates of aboveground biomass for forests with similar stand structure. Tolerant northern hardwood forests, composed predominantly of mature sugar maple (Acer saccharum Marsh.) and yellow birch (Betula alleghaniensis Britton), were surveyed using an ALTM 1225 (Optech Inc.) in August 2000. Field data for 49 circular plots, each 400 m² in area, were collected in July 2000. Using site-specific allometric equations, total aboveground biomass and biomass components (i.e., stem wood, stem bark, live branches, and foliage) were derived for each plot. Three laser height metrics were derived from the lidar data: (i) maximum laser height; (ii) mean laser height; and (iii) mean laser height calculated from lidar returns filtered based on a threshold applied to the intensity return data Lh_IR). Lh_IR was identified as the best predictor of total aboveground biomass (R² = 0.85) and biomass components (R² between 0.84 to 0.85) when all plot types were considered.

Drought years are a very frequent phenomenon in Israel. Since 1995 Israel had suffered from three years of drought. Yatir, as a pine forest, is located on the border of the desert has suffered like other plants from the water shortage. The aim of this research was to detect and monitor the changes within the forest, during the growing season, and between the years. The use of the Normalized Vegetation Index NDVI to detect the state of a forest under stress has been applied in many studies. In the current study seven Landsat TM and ETM+ images along the past eight years were radiometricly, atmospherically and geometrically corrected. NDVI images were produced. In order to detect the changes along the years, eight change detection NDVI image differencing techniques were applied. The results indicated correlation between the photosynthetic activity along the growing cycle of the trees in one year (1994-1995) and NDVI dynamics. Also it was found that NDVI decreases between the year 1995 and 2000 due to drought events accruing along those years. The correlation between rainfall events and NDVI is a lag of one to two months. We concluded this study by stating that the Change Detection NDVI Image Differencing technique can be used not only to compare changes in cover types, but also to detect changes in the state of the same plant species.

The objective of this study is to show the operational capacity of a "linear spectral mixture model" using TM/Landsat data for the characterization/monitoring of the annual deforestation and the timber logging exploitation process in the Amazon. In the methodological procedure, the original TM bands were initially converted to "vegetation", "shade" and "soil" fraction images, derived from the linear spectral mixture model. After the selection of fraction images, the scene segmentation was made using a region growing algorithm, and then an unsupervised classifier (per region) as applied. Afterwards, the thematic polygons were manually edited to generate the final maps. An analysis was made on the proportion of "vegetation", "shade" and "soil" components, for primary forest, selective logging, regrowth, and deforestation areas, for the timeframe 1997-2001. This analysis demonstrates, through the ternary diagram, that the variations in the spatial attributes of these component fractions were caused by a land cover/land use change process. A set of images and maps, showing the temporal identification of deforested and timber logging exploitation areas is shown, as a result of the operational use of this technique. The spatial distribution of these landscape changes provides subsidies to environmental agencies for the control and enforcement of specific conservation policies referring to the Amazon forest resources.

A multidisciplinary investigation of the Tunguska site (Central Siberia) devastated in 1908 by the explosion of a cosmic body has been carried out in July 14-29, 1999 by the Tunguska99 expedition (see http://www-th.bo.infn.it/tunguska/). In this framework, the remote sensing of a 300-km² territory has been performed in collaboration with the Russian “State Research Institute of Aviation Systems”(GosNIIAS). An aerophotosurvey and a line scanner survey in 6 spectral bands, from optical to thermal infrared, have been made simultaneously. The 1999 surveys are used to re-examine the 1938 aerophotographic material in order to check details of the 1908 explosion and to verify some recent hypothesis on the event. The 1938 photographic material has been analyzed with the help of the “Tomsk Creative Collective” to obtain new information on the fallen tree distribution. The comparison between the two aerophotosurveys will make it possible to map more accurately the areas with trees surviving the 1908 catastrophe and those with flora variation due to the impact. From the comparison we shall obtain new data on the effects of a cosmic body impact on the forestland coverage, on the spectra reflected from the flora cover, on the Leaf Area Index and other vegetation indices.

We investigated the relations between leaf-air temperature and photosynthetic ratio of cherry trees in order to obtain the fundamental data for applying the biological information to the remote sensing system. Some branches of Prunus jamasakura were cut and put into the water pot prompt once per month from May to October 2001. We measured the surface temperature of ten leaves and photosynthetic ratio every five second for ten minutes every measurement air temperature condition at 20, 25, 30 and 35°C with 1000 PAR light intensity. Result as, there was recognized the small significantly relation between leaf temperature and photosynthetic ratio because leaf temperature is usually changed with air temperature. Although, there was recognized large significantly correlations between the difference of the leaf temperature and air temperature and photosynthetic ratio. It is thought that transpiring in healthy plants are active for absorption of water and it cause to drop the leaf temperature. This research showed that the health of cherry trees could be diagnosed for measurement of the difference of leaf and air temperature.

In intensive agricultural regions, monitoring land use and cover change represents an important stake. Some land cover changes in agro-systems cause modifications in the management of land use that contribute to increase environmental problems, including an important degradation of water quality. In this context, the identification of land-cover dynamics at high spatial scales constitutes a prior approach for the restoration of water resources. The modeling approach used to study land use and cover changes at a field-scale is adapted from a vector change analysis method generally applied to assess land cover changes from regional to global scales. The main objective of this study is to identify vegetation changes at the field scale during winter, in relation with crop successions. Magnitude and direction of the vector of changes with remote sensing data and GIS, calculated on a small watershed located in Western France for a six-year period (1996-2001) indicate both intensity and nature of observed changes in this area. The results allow to qualify accurately (i.e. at the scale of the field) the type of changes, to quantify them and weigh up their intensity. Then, all the results are integrated in a probabilistic model to build-up a short time land use prediction.

An approach to classification of satellite images aimed at vegetation mapping in a wetland ecosystem has been presented. The wetlands of the Biebrza Valley located in the NE part of Poland has been chosen as a site of interest. The difficulty of using satellite images for the classification of a wetland land cover lies in the strong variability of the hydration state of such ecosystem in time. Satellite images acquired by optical or microwave sensors depend heavily on the current water level which often masks the most interesting long-time scale features of vegetation. Therefore the images have to be interpreted in the context of various ancillary data related to the investigated site. In the case of Biebrza Valley the most useful information was obtained from the soil and hydration maps as well as from the old vegetation maps. The object oriented classification approach applied in eCognition software enabled simultaneous use of satellite images together with the additional thematic data. Some supplementary knowledge concerning possible plant cover changes was also introduced into the process of classification. The accuracy of the classification was assessed versus ground-truth data and results of visual interpretation of aerial photos. The achieved accuracy depends on the type of vegetation community in question and is better for forest or shrubs than for meadows.

A method to calibrate a pushbroom hyperspectral imaging system for "near-field" applications in agricultural and food safety has been demonstrated. The method consists of a modified geometric control point correction applied to a focal plane array to remove smile and keystone distortion from the system. Once a FPA correction was applied, single wavelength and distance calibrations were used to describe all points on the FPA. Finally, a percent reflectance calibration, applied on a pixel-by-pixel basis, was used for accurate measurements for the hyperspectral imaging system. The method was demonstrated with a stationary prism-grating-prism, pushbroom hyperspectral imaging system. For the system described, wavelength and distance calibrations were used to reduce the wavelength errors to <0.5 nm and distance errors to <0.01mm (across the entrance slit width). The pixel-by-pixel percent reflectance calibration, which was performed at all wavelengths with dark current and 99% reflectance calibration-panel measurements, was verified with measurements on a certified gradient Spectralon panel with values ranging from about 14% reflectance to 99% reflectance with errors generally less than 5% at the mid-wavelength measurements. Results from the calibration method, indicate the hyperspectral imaging system has a usable range between 420 nm and 840 nm. Outside this range, errors increase significantly.

Alentejo (South of Portugal) water reservoirs have, due to many factors, more probability of suffering eutrophication problems, which makes essential continuous monitoring. These water reservoirs are also one of the main water sources supply for population. A preliminary study concerning the project "LANDSAT / IKONOS Applied to Water Quality Monitoring (LIWQ)" is presented. It aims to develop a water quality monitoring system based on advanced remote sensing techniques for inland water reservoirs, using LANDSAT and IKONOS imagery. In order to determine the trophic state for Alentejo and Algarve most important drinking water reservoirs, we used the empirical approach developed by Palmeirim and Mariano1 and estimated Carlson Trophic State Index2 from chlorophyll and transparency values. This was related with field surveys data. This model was applied to Roxo, Monte Novo, Caia, Vigia, Alvito, Divor and Santa Clara water reservoirs using Landsat 5 TM (year 1997. IKONOS data have four multispectral bands similar to Landsat TM bands 1-4 and high spatial resolution (4 m multispectral and 1 m panchromatic), which is potentially well suited for assessment of small lakes, such as Alentejo water reservoirs.

This paper reports on research to estimate leaf area index (LAI) in Swedish forests with satellite sensor data. The study is part of a research programme that aims at generating input data for process-oriented forest carbon models. Field-work was carried out in two areas in Sweden about 530 km apart, in the nemoral and boreo-nemoral forest regions. Various ways of estimating LAI in the field were tested, including litter-traps, allometric equations, and light transmission measurements. The capability of Landsat TM and ETM+ for LAI-mapping was investigated with the Nilson and Kuusk forest reflectance model. Results point to channel 3 and the mid-IR channels as particularly important for LAI estimation in coniferous stands, however, modelled reflectances were strongly influenced by background reflectances (particularly at low densities) and leaf optical properties. Top-of-canopy reflectances were derived from Landsat TM and ETM+, and their relationships with field-estimated LAI analysed. Among several vegetation indices tested, the Moisture Stress Index (TM5 / TM4) was one of the best indices for LAI in coniferous stands. In deciduous stands relationships based on the Simple Ratio were superior, however, the explanatory power in deciduous stands was lower than in coniferous stands.

We detected a change in the area of rice-planted fields using multitemporal satellite images, and investigated the cause from a geographical point of view. Rice is the staple food of Asian people. Rice is cultivated in warm places. In marginal zones, external causes, such as socioeconomic and natural causes strongly affect the decision of a farmer to cultivate rice or not. We selected the northern part of Japan as a test site. The air temperature of this site in a rice-growing season is at the lower limit for rice cropping in a normal year. We first classified the Landsat TM data acquired on 22 May 1999 and estimated the area of rice-planted fields. Then, we determined rice-planted fields using the RADARSAT SAR data acquired on 22 May and 11 June 2001, and detected a change in the area of rice-planted fields between 1999 and 2001. The result shows that the decrease in the area of rice-planted fields in the coastal zone is greater than that in the inland zone. According to climatic data, the air temperature in the coastal zone is lower than that in the inland zone. The lower temperature in the coastal zone is disadvantageous to rice cropping.

Dryland wheat of semi-arid areas is significantly affected by water and nitrogen availability since deficiency in these resources creates a stress status over the crop, reduce the chlorophyll content in the leaves, and damage the yield production. The objective of the current research was to characterize wheat stresses caused by the lack of nitrogen fertilization on one hand, and water on the other hand. This objective was implemented by measuring the spectral reflectance of Wheat plants in different growth conditions, in the leaf level. The reflectance was measured in the spectral range of 400-1100 nm by a Licor LI-1800 high spectral resolution spectroradiometer, equipped with an integrated sphere. We aimed to create spectral vegetation indices that would be sensitive to changes in chlorophyll, nitrogen and water contents in the leaves and hence serve as indicators to the wheat stress. The following indices were applied to the spectral data: NDVI, Green-NDVI, NDGI, and the ratios R695/R420, R695/R760, and R970/R900 (where R is the reflectance of the marked wavelength). The sensitivity of these indices was estimated by correlating the spectral data with the bio-physiological variables that were taken in parallel. We found that the green range of the electromagnetic spectrum (around 550 nm) is the most sensitive for the nitrogen wheat stress while the NIR range of the spectrum is sensitive for both nitrogen and water stresses. We improved the sensitivity to water status by using a water absorption wavelength in the ratio R970/R900, instead of the entire NIR region. Therefore, using the green range and water absorption wavelengths in different indices, enables the user to distinguish between these two types of stress.

The inventory and monitoring of aquaculture areas are essential tools for decision-making at a governmental level in developing countries. With the use of satellite imagery, these tasks can be performed in an accurate, rapid and objective way. This approach is also economically viable, as the worth of aquaculture far outweighs its cost. This paper describes a methodology for identifying and monitoring shrimp farms by means of multi-temporal satellite SAR data. SAR offer all-weather capabilities, an important characteristic since shrimp farms exist in tropical and sub-tropical areas. Moreover, the backscatter effect created by the dykes surrounding the ponds produces a typical pattern which allows the interpreter to distinguish them from other types of water-covered surfaces. However, the presence of speckle noise limits the interpretability of SAR imagery. To increase it, a multi-temporal set of four scenes covering the study area was processed by using a method that enhances time-invariant spatial features and reduces speckle without compromising the geometrical resolution of the images. The enhanced SAR imagery has proved to be valuable in identifying shrimp farm patterns with a field-tested accuracy of more than 90 percent. The methodology reported in this study has been tested with the ground truth obtained under operative conditions in Sri Lanka, thanks to the support of the FAO TCP/SRL/6712 project.

The Normalized Difference Vegetation Index (NDVI), provide synoptic information on environmental conditions in agricultural regions such as in Ukraine and Russia. However, past studies have raised questions whether the Maximum Value Compositing (MVC) method, used to produce time-series of NDVI, is affected by Bidirectional Reflectance Distributions (BRD), as a result of view geometry, possibly leading to erroneous NDVI values. This study uses Angular Dependence Models (ADM), derived the NOAA/NASA Pathfinder AVHRR Land (PAL) data set, to correct PAL Channels 1 & 2 reflectance for BR. MVC is used to produce ten day NDVI composites, from both the pre-BRD correct channels and the BRD corrected channels. Comparisons of the two NDVI time-series show a slight increase in NDVI for the

Results of this investigation confirm previous results by several other authors that correspondence between the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Scanning Sensor Microwave Instrument (SSMI) - derived snow maps improves as the winter progresses. Early in the season, the SSMI snow mapping algorithms are unable to identify shallow and wet snow as snow cover, while the MODIS snow maps perform well under those circumstances, but cannot map snow through clouds and cannot provide estimates of SWE. By mid winter when the snowpack is deeper, temperatures are colder, and liquid water in the snowpack is minimal, the agreement between MODIS- and SSMI-derived snow maps improves. For North America, the difference between MODIS and SSMI was approximately 35% for early December but averaged about 5% for the maps examined in February of 2002, and for Eurasia (eastern Asia), the difference between the MODIS and SSMI maps was less than about 10% in early January of 2001.

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) launched on NASA's Terra satellite in December 1999 provides anew tool for Earth observations. ASTER provides high-resolution, 15m(VNIR), 30m (SWIR) and 90m (TIR) coverage for limited areas with unique multispectal SWIR and TIR coverage and 15 m stereo coverage for DEM generation. These data have been used extensively for volcano and glacier monitoring. ASTER observations of over 1000 volcanoes around the world represent a significant increase in our ability to monitor volcanic activity and to map the products of eruptions. The SWIR channels have been used for mapping hot areas with temperatures up to 350 C and the multispectral TIR data have been used to map ash and SO₂ plumes. ASTER data are being used in the Global Land Ice Measurements from Space (GLIMS) project to map and catalog the approximately 80,000 glaciers. The objective is to acquire multiple observations to detect changes in ice margins and surface feature velocities. ASTER data acquired over the Jornada Experimental range in New Mexico have been used to extract spectral emissivities in the 8 to 12 micrometer range. These TIR data were also used in models to estimate the surface energy fluxes. Similar analysis of data acquired over the El Reno Oklahoma test site has shown that our satellite estimates of the surface fluxes agree reasonably well with ground measurements.