Abstract: A study was carried out to investigate whether basic soil properties can be predicted by using reflectance spectrometry (RS) in the visible-near infraredshortwave infrared (VIS-NIR-SWIR, 350-2500 nm) region using an artificial neural network (ANN) approach. Over 330 soil samples from three agroforestry areas, representative of the pedo-environmental variability of the Campania region, Southern Italy, were used. The soil properties determined using conventional analyses were sand, silt, clay, organic carbon (OC), and calcium carbonate (CaCO3). Spectral reflectance (SR) measurements on soil samples were carried out under laboratory conditions, using a high resolution ASD FieldSpec spectroradiometer. Relationships between soil properties and soil SR were determined using ANN algorithms. The results obtained showed that clay content and OC can be predicted with high accuracy, while Sand and CaCO3 can be predicted with moderate to relatively high accuracy, respectively, and silt with relatively low accuracy. Improvement in the prediction of soil properties is expected using a larger number of soil samples for the training of the ANN algorithm, in combination with statistically based methods.
Abstract: A field experiment was carried out to assess the effects of irrigation with highly saline (T1 treatment), moderately saline (T0.5) and non saline (T0) water on soil and eggplant (Solanum melongena) properties and spectral response. The investigated soil was a clay-loam Alfic Xerarent, with swelling clay minerals (smectites). After 5 years of irrigation, T1 and T0.5 treatments resulted in soils that were saline-sodic and sodic, respectively. The soil salinity/sodicity status noticeably affected dry biomass, leaf area index, plant water content, leaf water potential and spectral properties of eggplant. Significant relationships were obtained between plant characteristics and normalised difference vegetation index (NDVI) and water index (WI). Soil salinity conditions had significant impacts on NDVI and WI, but not on the red-edge position in the spectral response. Therefore spectral response was considered a useful criterion for discriminating salt-affected soils from non salt-affected soils.
Abstract: The increasing demand for effective forest fire prevention instruments has faced operational and future Earth observation instruments with the challenge of producing updated and reliable maps of vegetation moisture. Various empirical band-ratio indexes have been proposed so far, based on multispectral remote sensing data, that have been found to be related to vegetation moisture expressed in terms of equivalent water thickness (EWT), which is defined as the weight of liquid water per unit leaf area. More sophisticated retrieval methodologies can be adopted when hyperspectral data are available, e.g. based on spectral curve fitting in selected water absorption bands or radiative transfer model inversion, allowing for better estimates of EWT. Problems arise with the evaluation of fuel moisture content (FMC), which is the percentage weight of water per unit of oven-dried leaf weight, due to its weak signal in vegetation spectrum. FMC is essential in fire models, and it is not interchangeable with EWT.
Basing on simulated vegetation spectra, this study aims at demonstrating that hyperspectral images of vegetated areas can be effectively used to evaluate FMC with accuracies not achievable with multispectral data. To this purpose, radiative transfer models PROSPECT and SAILH have been used to simulate canopy reflectance. Vegetation spectra have then been convolved to hyperspectral data basing on the design specifications of a formerly planned ASI-CSA hyperspectral mission (JHM configuration C), similar to those of the forthcoming PRISMA. For comparison against multispectral instruments, measurements from the Operational Land Imager (OLI) have also been simulated. Two retrieval methods have been tested, based on spectral indexes and on partial least squares (PLS) regression. The latter methodology is particularly suited to analyse high-dimensional data.
Results confirm that spectral indexes are good predictors of vegetation moisture expressed as EWT, but their performance in evaluating FMC is poor. By using PLS regression on hyperspectral data, a linear model can be built that accurately predicts FMC. No such result is achievable from OLI simulated data.
Abstract: Forest fires are one of the major environmental hazards in Mediterranean Europe. Biomass burning reduces carbon fixation in terrestrial vegetation, while soil erosion increases in burned areas. For these reasons, more sophisticated prevention tools are needed by local authorities to forecast fire danger, allowing a sound allocation of intervention resources. Various factors contribute to the quantification of fire hazard, and among them vegetation moisture is the one that dictates vegetation susceptibility to fire ignition and propagation. Many authors have demonstrated the role of remote sensing in the assessment of vegetation equivalent water thickness (EWT), which is defined as the weight of liquid water per unit of leaf surface. However, fire models rely on the fuel moisture content (FMC) as a measure of vegetation moisture. FMC is defined as the ratio of the weight of the liquid water in a leaf over the weight of dry matter, and its retrieval from remote sensing measurements might be problematic, since it is calculated from two biophysical properties that independently affect vegetation reflectance spectrum.
The aim of this research is to evaluate the potential of the Moderate Resolution Imaging Spectrometer (MODIS) in retrieving both EWT and FMC from top of the canopy reflectance. The PROSPECT radiative transfer code was used to simulate leaf reflectance and transmittance as a function of leaf properties, and the SAILH model was adopted to simulate the top of the canopy reflectance. A number of moisture spectral indexes have been calculated, based on MODIS bands, and their performance in predicting EWT and FMC has been evaluated. Results showed that traditional moisture spectral indexes can accurately predict EWT but not FMC. However, it has been found that it is possible to take advantage of the multiple MODIS short-wave infrared (SWIR) channels to improve the retrieval accuracy of FMC (r2 = 0.73). The effects of canopy structural properties on MODIS estimates of FMC have been evaluated, and it has been found that the limiting factor is leaf area index (LAI). The best results are recorded for LAI>2 (r2 = 0.83), while acceptable results (r2 = 0.58) can still be achieved for lower vegetation cover density.
Abstract: Vegetation moisture is a key parameter in fire risk modeling. Many authors have demonstrated the role of remote sensing in the assessment of the equivalent water thickness (EWT), which is defined as the weight of liquid water per unit of leaf surface. However, forest fire danger models rely on fuel moisture content (FMC) as a measure of vegetation moisture. FMC is defined as the ratio of the leaf liquid water weight over the leaf dry weight. In a previous research it has been shown the potential of the Moderate Resolution Imaging Spectroradiometer (MODIS) ground reflectance data in retrieving both EWT and FMC at leaf level. Though the atmosphere alters ground signature recorded by the sensor, in this paper it will be shown that a simple index can be designed that allows a fast and accurate estimation of FMC (r2 = 0.83) with MODIS data.
Abstract: Forest fires are one of the major environmental issues in large areas of Southern Italy, and more generally in Mediterranean Europe. Biomass burning reduces carbon fixation in terrestrial vegetation, while risk of soil erosion increases in burned areas. The premier action against fires is prevention, and in this context fire risk mapping is an invaluable tool.
Various factors, either static or dynamic, contribute to the definition of fire risk. Among them, vegetation moisture plays a key role, since forests susceptibility to fire increases with increasing plant water stress and biomass dryness. A tool is needed to allow a timely detection of such forest conditions, and space-borne and airborne remote sensing can be very effective to this end.
Many authors have demonstrated the role of remote sensing in the assessment of vegetation moisture. Various multi-spectral systems have been reported to be useful, such as Landsat TM, SPOT or NOAA AVHRR. We have recently started a research to evaluate fire risk in the rural environment of Southern Italy using the Moderate Resolution Imaging Spectrometer (MODIS), carried on board of EOS Terra and Aqua satellites. The MODIS systems have 20 spectral wavebands covering the visible, the near infrared and the shortwave infrared with a spectral resolution of 10-50 nm.
This paper describes the results of a preliminary experiment to identify the most useful bands or band combinations (spectral indexes) for the detection of biological indicators of plant water stress. PROSPECT radiative transfer code has been adopted to simulate leaf reflectance as a function of leaf properties. Results highlighted the potential of single and combined simulated MODIS bands in the retrieval of vegetation moisture indicators related to fire risk.
Abstract: In the present work we show the potential of multiangular hyperspectral PROBA-CHRIS data to estimate aerosol optical properties over dense dark vegetation. Data acquired over San Rossore test site (Pisa, Italy) have been used together with simultaneous ground measurements. Additionally, spectral measurement over the canopy have been performed to describe the directional behavior of a Pinus pinaster canopy.
Determination of aerosol properties from optical remote sensing images over land is an under-determined problem, and some assumptions have to be made on both the aerosol and the surface being imaged. Radiance measured on multiple directions add extra information that help in reducing retrieval ambiguity. Nevertheless, multiangular observations don’t allow to ignore directional spectral properties of vegetation canopies. Since surface reflectivity is the parameter we wish to determine with remote sensing after atmospheric correction, at least the shape of the bi-directional reflectance factor has to be assumed. We have adopted a Rahman BRF, and have estimated its geometrical parameters from ground spectral measurements. The inversion of measured radiance to obtain aerosol optical properties has been performed, allowing simultaneous retrieval of aerosol model and optical thickness together with the vegetation reflectivity parameter of the Rahman model.
Abstract: Measurements of spectro-directional radiances done with the imaging spectrometer CHRIS on-board the agile platform PROBA are being used to determine key properties of terrestrial vegetation at the appropriate spatial resolution. These data on vegetation properties can then be used to improve the accuracy and the parameterizations of models describing biosphere processes, i.e. photosynthesis and water use by irrigated crops and trees.
The vegetation properties considered are: albedo, Leaf Area Index (LAI), fractional cover, fraction of absorbed photosynthetically active radiation (fAPAR) and canopy chlorophyll content.
The Natural Park of San Rossore (Pisa, Central Italy) is a primary test site for several national and international research projects dealing with forest ecosystem monitoring. In particular, since 1999 measurements of transpiration and ecosystem gas-exchange have been regularly taken in the park pine forest to characterize its main water and carbon fluxes. In the same period, several aerial flights have been carried out with onboard hyper-spectral sensors (MIVIS, VIRS, AISA), while a series of satellite images have been acquired using both conventional (NOAAAVHRR, Landsat-TM/ETM+) and advanced sensors (CHRIS-PROBA).
The final objective of these activities is to calibrate and validate methodologies which integrate remotely sensed and ancillary data for monitoring forest ecosystem. More specifically, a major research effort has been focused on evaluating the additional information content provided by advanced hyper-spectral multi-angular sensors about the main parameters needed for forest characterization (species, LAI, pigment content, etc.). These activities are part of projects which are financed by the Italian and European Space Agencies (ASI and ESA, respectively) within the framework of the CHRIS-PROBA and SPECTRA missions.
During 2002 and 2003 nine complete multi-angular acquisitions were successfully performed over the San Rossore site. This paper summarizes first results of the evaluation of data acquired so far, particularly forward modeling of Top Of Canopy (TOC) reflectances. The models KUUSK, SAIL and GeoSAIL were used to simulate spectro-directional reflectance of different stands in the forest and compared with PROBA – CHRIS and airborne hyperspectral observations. Deviations of simulated from observed reflectances were significant.
