M.A. Gilabert

A GENERALIZED SOIL-ADJUSTED VEGETATION INDEX

Operational monitoring of vegetative cover by remote sensing currently involves the utilisation of vegetation indices (VIs), most of them being functions of the reflectance in red (R) and near-infrared (NIR) spectral bands. A generalized soil-adjusted vegetation index (GESAVI), theoretically based on a simple vegetation canopy model, is introduced. It is defined in terms of the soil line parameters (A and B) as: GESAVI=(NIRBRA)/(R+Z), where Z is related to the red reflectance at the cross point between the soil line and vegetation isolines. As Z is a soil adjustment coefficient, this new index can be considered as belonging to the SAVI family. In order to analyze the GESAVI sensitivity to soil brightness and soil color, both high resolution reflectance data from two laboratory experiments and data obtained by applying a radiosity model to simulate heterogeneous vegetation canopy scenes were used. VIs (including GESAVI, NDVI, PVI and SAVI family indices) were computed and their correlation with LAI for the different soil backgrounds was analyzed. Results confirmed the lower sensitivity of GESAVI to soil background in most of the cases, thus becoming a very efficient index. This good index performance results from the fact that the isolines in the NIR-R plane are neither parallel to the soil line (as required by the PVI) nor convergent at the origin (as required by the NDVI) but they converge somewhere between the origin and infinity in the region of negative values of both NIR and R. This convergence point is not necessarily situated on the bisectrix, as required by other SAVI family indices. D 2002 Published by Elsevier Science Inc.

Monitoring fire-affected areas using Thematic Mapper data

In this paper three methods for updating inventories of burned areas have been presented and examined. They include Multitemporal Principal Component Analysis (MPCA), Change Vector Analysis (CVA) and Multitemporal NDVI Classification (MNC). First, 11 Landsat-5 Thematic Mapper (TM) images of a forest area were radiometrically corrected to derive a multitemporal series of intercomparable images for each spring from 1984 to 1994. Then, in order to check the feasibility of the three approaches, they were used for mapping fire burns that occurred during 1992. The various procedures yielded different maps of burned areas; the MNC method seemed to be more reliable than the others, because it merges spectral data corresponding not only to 1992 (pre-fire) and 1993 (post-fire) but also to 1994 (the second year after the fires), which is key in the vegetation regeneration. Finally, this methodology was automated to yield an inventory of burned areas for each year during the period of study.

A Mixture Modeling Approach to Estimate Vegetation Parameters for Heterogeneous Canopies in Remote Sensing

In this article, we describe a reflectance model which parametrizes the reflectance of vegetation canopies from optical properties of leaves and soil, and dominant canopy structural parameters. The model assumes certain principles of geometric models, for example, that sensor integrates the radiance reflected from three components, plant, shaded soil, and illuminated soil. Its inversion provides compositional information of the ground surface that is linked with the interpretation of the linear spectral mixture modeling (LSMM). This model also offers the potential for retrieving other meaningful biophysical properties such as LAI. The model has been tested on simulated spectra of spectral mixtures in presence of significant multiple scattering. Results indicate that this modeling approach is a suitable remote sensing tool for retrieving the vegetation abundance in heterogeneous canopies, which is interpreted as the fractional cover of plants with well-defined structural parameters.

Vegetation cover seasonal changes assessment from TM imagery in a semi-arid landscape

This work evaluates the suitability of spectral mixture analysis (SMA) methods to assess vegetation cover seasonal changes in a desertification context. Our main interest is to produce remotely sensed derived maps, sensitive to vegetation activity and quite independent of the soil background. A further aim is to analyse the inter-annual variations of this magnitude for different natural vegetation species, in response to seasonal and climatic changes. Fractional vegetation cover (FVC) was obtained using a Variable Endmember Spectral Mixture Analysis (VESMA) technique. The aim is to identify the main vegetation cover and lithological units and decompose them in separate stages. The use of specific spectral signatures for each pixel allows for a better adaptation of the endmembers to local conditions, which is an important prerequisite to ensure the accuracy of fractions. The method has been tested on a well documented area, the Guadalentin river basin, located in south-eastern Spain. Unlike pine forest and stipa classes, rosmarinus, sparse shrubs and seasonal grasses classes displayed larger inter-annual variability, showing higher stress in response to water availability. A comparative analysis between FVC and the Normalized Difference Vegetation Index (NDVI) was also conducted. Average values were used as indicators of the dynamics of the vegetation cover, with the variance of each vegetation class giving similar results. The correlation between both magnitudes varied from 55% for the class with least coverage to 90% for the densest vegetation class. Regarding seasonal evolution, the average values and standard deviations of the changes in each vegetation class in specific periods were related to seasonal changes and the effects of the rainfall pattern. Significant differences were found between the two methods, with FVC showing a higher coherence.

Extraction of endmembers from spectral mixtures

Abstract Linear spectral mixture modeling (LSMM) divides each ground resolution element into its constituent materials using endmembers which represent the spectral characteristics of the cover types. However, it is difficult to identify and estimate the spectral signature of pure components or endmembers which form the scene, since they vary with the scale and purpose of the study. We propose three different methods to estimate the spectra of pure components from a set of unknown mixture spectra. Two of the methods consist in different optimization procedures based on objective functions defined from the coordinate axes of the dominant factors. The third one consists in the design of a neural network whose architecture implements the LSMM principles. The different procedures have been tested for the case of three endmembers. First, were used simulated and real data corresponding to mixtures of vegetation and soil. Factors that limit the accuracy of the results, such as the number of channels and the level of data noise have been analyzed. Results have indicated that the three methods provide accurate estimations of the spectral endmembers, especially the third one. Moreover, the second method, that is based on the exploration of the mixture positions in the factor space, has demonstrated to be the most appropriate when the dimensionality of the data is reduced. Finally, this procedure was applied on a Landsat-5 TM scene.

Intercomparison and quality assessment of MERIS, MODIS and SEVIRI FAPAR products over the Iberian Peninsula

Abstract The fraction of absorbed photosynthetically active radiation (FAPAR) is a key variable in productivity and carbon cycle models. The variety of available FAPAR satellite products from different space agencies leads to the necessity of assessing the existing differences between them before using into models. Discrepancies of four FAPAR products derived from MODIS, SEVIRI and MERIS (TOAVEG and MGVI algorithms), covering the Iberian Peninsula from July 2006 to June 2007 are here analyzed. The assessment is based on an intercomparison involving the spatial and temporal consistency between products and a statistical analysis across land cover types. In general, significant differences are found over the Iberian Peninsula concentrated on the temporal variation and absolute values. The MODIS and MERIS/MGVI FAPAR products clearly show the highest and lowest absolute values, respectively, along with the lowest intra-annual variation. When considering individual land cover types, the largest FAPAR disagreements among the analyzed products were found between MODIS-MERI/MGVI and MERIS/TOAVEG-MERIS/MGVI over broadleaf and needleaf forests, with discrepancies quantified by RMSE higher than 0.30 and absolute bias higher than 0.25. These discrepancies can lead to relative gross primary production differences up to 65%.

Solar angle and sky light effects on ground reflectance measurements in a citrus canopy

Abstract Ground radiometry was used to gather spectral data from different targets of a citrus canopy, in order to analyze the effect of solar zenith angle and proportion of diffuse radiation on spectral reflectance. Results have shown that the variation in solar angle causes significant changes in nadirsensed reflectance from vegetation, which exhibits a marked diurnal pattern with a minimum slightly shifted from the solar noon. This fact is more noticeable in the near-infrared and middle-infrared regions of the spectrum. Furthermore, the visible part of the spectrum has resulted in being highly influenced by the diffuse radiation incident on the canopy, which has been quantified by two different physical parameters: the proportion of diffuse irradiance kd and the sky clearness ϵ. It has been shown that the reflectance factor increases linearly with increasing diffuse radiation, but only below a threshold value, above which the reflectance remains constant. On the other hand, the reflectance dependence on the parameter ϵ has allowed us to identify three well-defined zones of sky light conditions, in which reflectance presents a different behavior.

Monitoring water stress in Mediterranean semi-natural vegetation with satellite and meteorological data

Abstract In arid and semi-arid environments, the characterization of the inter-annual variations of the light use efficiency ɛ due to water stress still relies mostly on meteorological data. Thus the GPP estimation based on procedures exclusively driven by remote sensing data has not found yet a widespread use. In this work, the potential to characterize the water stress in semi-natural vegetation of three spectral indices (NDWI, SIWSI and NDI7) – from MODIS broad spectral bands – has been analyzed in comparison to a meteorological factor (Cws). The study comprises 70 sites (belonging to 7 different ecosystems) uniformly distributed over Tuscany, and three eddy covariance tower sites. An operational methodology, which combines meteorological and MODIS data, to characterize the inter-annual variations of ɛ due to summer water stress is proposed. Its main advantage is that it relies on existing series of meteorological data characterizing each site and allows calculating a typical Cws profile that can be “updated” ( C w s * ) for the actual conditions using MODIS spectral indices. The results confirm that the modified C w s * can be used as a proxy of water stress that does not require concurrent information on meteorological data.

Usefulness of the temporal analysis and the normalized difference in the study of rice by means of Landsat-5 TM images: identification and inventory of rice fields.

Abstract The multispectral and multitemporal analysis of the spectral response of rice has made it possible to determine at which point in the vegetative cycle of rice it is best to make an inventory, together with the usefulness of the normalized‐difference vegetation index for such an inventory. The results could be usefully included in any classification procedure of the TM image in order to make the inventory in a systematic way. In this case a supervised classification of the image has been made which assumes a Gaussian behaviour for each spectral class. The results obtained are, for the most part, consistent with those obtained by using traditional methods.

Usefulness of the temporal analysis and the normalized difference in the study of rice by means of landsat‐5 TM images: Establishment of relationships for yield prediction purpose

Abstract In this paper, a study has been made of the relationship between rice productivity and rice reflectance with a view to examining the possibility of predicting grain production. To this end a regression analysis between final grain yield values of a group of ricefields and the values which correspond to different vegetation indices constructed from Thematic Mapper bands has been made. It has been found that the indices constructed from TM4 and TM3 bands of an image belonging to the ripenning phase of rice are, fundamentally, those which show a great correlation coefficient with the final grain production.