In the field of remote sensing applications, scientists have developed vegetation indices (VI) for qualitatively and quantitatively evaluating vegetative covers using spectral measurements. The spectral response of vegetated areas presents a complex mixture of vegetation, soil brightness, environmental effects, shadow, soil color and moisture. Moreover, the VI is affected by spatial‐temporal variations of the atmosphere. Over forty vegetation indices have been developed during the last two decades in order to enhance vegetation response and minimize the effects of the factors described above. This paper summarizes, refers and discusses most of the vegetation indices found in the literature. It presents different existing classifications of indices and proposes to group them in a new classification.

L'analyse géomorphologique à partir de bandes spectrales débordant le rayonnement visible est encore au stade expérimental. La stratigraphie et la structure interne des dépôts meubles ne peuvent être directement détectées, mais la granulométrie et les formes de surface peuvent l'être. Dans le cas des surfaces dénudées, la granulométrie est reliée à la réflectance dans le visible et le proche infrarouge et à la rugosité détectable par le radar. Dans le cas des surfaces recouvertes de végétation, la granulométrie peut être déduite indirectement des conditions d'humidité des sols, qui commandent l'alimentation en eau des plantes, dans le visible, le proche infrarouge, l'infrarouge thermique, le radar (constante diélectrique) et théoriquement les micro-ondes passives. Sans compter la traditionnelle photointerprétation stéréoscopique, les formes de surface peuvent être reconnues soit de façon automatisée, surtout dans le cas des formes linéaires, lorsque l'on possède des documents numérisés, soit de façon visuelle par rehaussement d'image lors de la prise de vue (radar, thermographies). Les formes de surface peuvent aussi être rehaussées au moment du traitement par incorporation d'un modèle numérique de terrain ou avec des images de composantes spectrales. Des expériences ont été conduites en Estrie afin de distinguer les tills des dépôts fluvioglaciaires ou glacio-lacustres. Il en résulte que l'imagerie la plus efficace dans le cas d'une analyse visuelle est l'infrarouge thermique diurne en automne quand les effets de l'insolation sur la topographie sont maximaux et où ceux des écarts d'albédo sont réduits.

L'expansion spectaculaire des processus d'erosion des sols est, au Maroc, un indicateur d'aspect inquietant relie a la degradation des sols. Pour analyser son etat et en evaluer les risques d'expansion et d'aggravation, la teledetection s'avere un excellent outil. Dans cette etude, on caracterise l'etat de degradation des sols d'un petit bassin versant mediterraneen soumis a une forte activite anthropique en utilisant les donnees du capteur ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) et des donnees spectroradiometriques prises sur le terrain. Les donnees considerees correspondent au bassin experimental de l'oued Saboun, situe dans le Rif occidental du Maroc, et pour lequel on dispose d'une bonne banque de donnees pedologiques, hydrologiques et d'erosion. Pour atteindre les objectifs de l'etude, on a compare les approches des indices spectraux et du SAM (Spectral Angle Mapper) comme methodes de classification. Les resultats obtenus montrent que l'integration des bandes 2, 6 et 8 du capteur ASTER dans le calcul des indices spectraux apporte plus de precision et decrit bien la realite de terrain. Toutefois, la comparaison des resultats obtenus a l'aide des deux approches en terme de performance accorde une meilleure precision a l'approche des indices spectraux avec un coefficient kappa de 0,75 contre 0,61 pour l'approche du SAM.

La cedraie du Moyen Atlas, au Maroc, est caracterisee par l’heterogeneite de ses peuplements ainsi que par la fragmentation de son espace forestier. Ces caracteristiques resultent de l’interaction de divers facteurs anthropiques, pedologiques et climatiques. Ces heterogeneites spatiale et spectrale limitent la fiabilite des methodes conventionnelles de classification de l’imagerie satellitaire. Dans la presente etude, on suggere d’utiliser les methodes basees sur la similarite spectrale pour cartographier les especes forestieres dominantes de l’ecosysteme de la cedraie, soit l’analyse de mixture spectrale lineaire (AMSL) et le Spectral angle mapper (SAM). Les objectifs poursuivis consistent a comparer des procedures d’extraction des signatures spectrales « pures » prototypes, dites endmembers, et les approches de l’AMSL et du SAM en termes de cartographie des especes vegetales dominantes de cette foret. Pour atteindre ces objectifs, on a utilise des images acquises par le capteur ASTER (Advanced spaceborne thermal emission and reflection radiometer). Les resultats obtenus montrent que l’utilisation des methodes de l’AMSL et du SAM a abouti a des resultats similaires en termes de repartition des especes cartographiees, mais avec des differences au plan des superficies occupees par ces especes. La comparaison des resultats obtenus a l’aide de l’AMSL et du SAM avec ceux de la classification par maximum de vraisemblance (notre reference) demontre que l’AMSL a permis de classifier les especes forestieres dominantes avec une meilleure precision que le SAM, ce qui s’exprime par un coefficient Kappa de l’ordre de 0,7 pour la methode de l’AMSL contre 0,66 pour l’approche du SAM.

This study investigates the utility of the land degradation index (LDI) approach to express land degradation in a small Mediterranean watershed with enhanced thematic mapper plus (ETM+) data. The LDI is based on the concept of the soil line, and was elaborated from advanced spaceborne thermal emission and reflection radiometer (ASTER) sensor data and field spectra measurements. The implementation of the LDI index uses all the bands of the spectral domain in the visible, near infrared (NIR), and short wave infrared (SWIR). Field measurements were carried out in the Saboun experimental basin located in a marl soil region of the Moroccan western Rif for which soil, hydrological, and erosion databases were readily available. The present study provided the opportunity for studying and characterizing the state of soil degradation using the LDI. Our results show the interest of the use of the LDI with ASTER data to study or to map land degradation. It provides more accurate results (Kappa = 0,79) when compared with results obtained with ETM+ data (Kappa = 0,57). Globally, the results represent ground reality with sufficient accuracy to help decision makers in their soil conservation planning process.

Shrimp culture is a sector of aquaculture that has a high potential for poverty alleviation and rural development in Vietnam. However, the development of this activity induces changes that potentially have negative impacts on the environment, one of which is wetland deterioration. This paper describes the use of a proposed change detection methodology in the assessment of mangrove forest alterations caused by aquaculture development, as well as the effectiveness of the measures taken to mitigate deforestation in the district of Giao Thuy, Vietnam, between 1986, 1992 and 2001. Geometric and radiometric corrections were applied to Landsat images prior to identifying changes through comparison of unsupervised classifications. Changes were afterwards validated using a thresholding method based on Tasselled Cap feature image differencing and a rule-based feature selection matrix. The matrix is used to identify the feature that is most efficient at detecting the presence of change between given land-cover classes. The proposed approach aims to minimize commission errors in the post-classification change detection process. The results suggest that 63% of mangrove areas apparent in 1986 had been replaced by shrimp ponds in 2001. Between 1986 and 1992, 440 ha of adult mangrove trees had disappeared, whereas the mangrove extent increased by 441 ha between 1992 and 2001. This recovery is attributed to reforestation projects and conservation efforts that promoted natural regeneration.

This study investigates the utility of the land degradation index (LDI) approach to express land degradation in a small Mediterranean watershed with enhanced thematic mapper plus (ETM+) data. The LDI is based on the concept of the soil line, and was elaborated from advanced spaceborne thermal emission and reflection radiometer (ASTER) sensor data and field spectra measurements. The implementation of the LDI index uses all the bands of the spectral domain in the visible, near infrared (NIR), and short wave infrared (SWIR). Field measurements were carried out in the Saboun experimental basin located in a marl soil region of the Moroccan western Rif for which soil, hydrological, and erosion databases were readily available. The present study provided the opportunity for studying and characterizing the state of soil degradation using the LDI. Our results show the interest of the use of the LDI with ASTER data to study or to map land degradation. It provides more accurate results (Kappa = 0,79) when compared with results obtained with ETM+ data (Kappa = 0,57). Globally, the results represent ground reality with sufficient accuracy to help decision makers in their soil conservation planning process.Ce travail est basé sur l'utilisation de l'indice LDI (« land degradation index ») pour caractériser les conditions de surface à partir des données images ASTER (« advanced spaceborne thermal emission and reflection radiometer ») et ETM+ (« enhanced thematic mapper plus »). LDI est un nouvel indice basé sur le concept de la droite des sols. Celui-ci a été mis au point pour la première fois à partir des mesures spectroradiométriques de terrain et de données ASTER. Il se distingue par l'utilisation de l'ensemble des bandes pour un capteur donné. Les données considérées correspondent au bassin expérimental de Saboun, situé dans le Rif occidental du Maroc, et qui dispose d'une base de données pédologiques, hydrologiques et d'érosion. La comparaison des résultats obtenus à partir des données ETM+ et ASTER en terme de performance accorde une meilleure précision à l'utilisation d'ASTER avec un coefficient Kappa de 0,79 contre 0,57 pour ETM+. L'étude fait ressortir l'intérêt du LDI pour la caractérisation de la dégradation des sols, quel que soit le type de capteur.

Flagrant soil erosion in Morocco is an alarming sign of soil degradation. Due to the considerable costs of detailed ground surveys of this phenomenon, remote sensing is an appropriate alternative for analyzing and evaluating the risks of the expansion of soil degradation. In this paper, we characterize the state of land degradation in a small Mediterranean watershed using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and ground-based spectroradiometric measurements. The two visible, the near-infrared and six shortwave infrared bands of the above sensor were calibrated using ground measurements of the spectral reflectance. Field measurements were carried out in the Saboun experimental basin located in the marl soil region of the Moroccan western Rif. The study leads to the development and evaluation of a new spectral approach to express land degradation. This index called Land degradation index (LDI) is based on the concept of the soil line derived from spectroradiometric ground measurements. In this study, we compare LDI and the spectral angle mapping (SAM) approaches to assess and map land degradation. Results show that LDI provides more accurate results for mapping land degradation (Kappa=0.79) when compared to the SAM method (Kappa=0.61). Validation and evaluation of the results are based on the thematic maps derived from the ground data (organic matter, clay, silt and sand) by kriging, DEM, slope gradient and photointerpretation.

Crop residues are efficient in reducing erosion and surface water runoff on agricultural soils. Evaluating the crop residue cover fraction and its spatial distribution is important to scientists involved in the modelling of soil erosion and surface runoff, and also to authorities wishing to assess soil conservation adoption by farmers. This study focuses on the evaluation of four remote sensing techniques to estimate the cover fraction of cereal crop residues (i.e., wheat and corn) from multispectral and hyperspectral measurements. These are the Soil Adjusted Corn Residue Index (SACRI), the Crop Residue Index Multiband (CRIM), the Normalized Difference Index (NDI) and the spectral mixture analysis technique (SMA). Field campaigns that were carried out by the FLOODGEN project in Sainte-Angèle-de-Monnoir, Québec, Canada and in the Pays-de-Caux located in the Normandy region of France, allowed us to gather digital photographs, spectra and other measurements to determine the actual ground cover fraction. A linear regression analysis between results derived from Landsat-5 TM simulated field spectra and the actual ground cover fractions showed best results for the CRIM on the Ste-Angèle-de-Monnoir study site (R2=0.96), and equally good results for the Pays-de-Caux study site (R2=0.94). Results were not as good when SMA was applied to the same Landsat-5 TM simulated field spectra with R2 values of 0.70 and 0.68 for both sites, respectively. However, results improved significantly when SMA was applied to the hyperspectral data in which case the R2 values increased to 0.92 for the Sainte-Angèle-de-Monnoir site and 0.89 for the Pays-de-Caux study site. Results obtained with the NDI and SACRI from both simulated TM and hyperspectral field spectra were not conclusive.

Estimating surface parameters by radar-image inversion requires the use of well-calibrated backscattering models. None of the existing models is capable of correctly simulating scatterometer or satellite radar data. We propose a semi-empirical calibration of the Integral Equation Model (IEM) backscattering model in order to better reproduce the radar backscattering coefficient over bare agricultural soils. As correlation length is not only the least accurate but also the most difficult to measure of the parameters required in the models, we propose that it be replaced by a calibration parameter that would be estimated empirically from experimental databases of radar images and field measurements. This calibration was carried out using a number of radar configurations with different incidence angles, polarization configurations, and radar frequencies. Using several databases, the relationship between the calibration parameter and the surface roughness was determined for each radar configuration. In addition, the effect of the correlation function shape on IEM performance was studied using the three correlation functions (exponential, fractal, and Gaussian). The calibrated version of the IEM was then validated using another independent set of experimental data. The results show good agreement between the backscattering coefficient provided by the radar systems and that simulated by the calibrated version of the IEM. This calibrated version of the IEM can be used in inversion procedures to retrieve surface roughness and/or moisture values from radar images.

This paper presents an application of neural networks to the extraction of bare soil surface parameters such as roughness and soil moisture content using synthetic aperture radar (SAR) satellite data. It uses a fast learning algorithm for training a multilayer feedforward neural network using the Kalman filter technique. Two different databases (theoretical and empirical) were used for the learning stage. Each database was configured as single and multiangular sets of input data (data acquired at two different incidence angles) that are compatible with data from one and two satellite images, respectively. All the configurations are trained and then evaluated using RADARSAT-1 and simulated data. The empirical (measured) database with the multiangular set of input data configuration had the best accuracy with a mean error of 1.54 cm for root mean square (rms) height of the surface roughness and 2.45 for soil dielectric constant in the study area. Based on these results the proposed approach was applied on RADARSAT-1 images from the Chateauguay watershed area (Quebec, Canada) and the final results are presented in the form of roughness and humidity maps.Key words: neural networks, Kalman filter, RADARSAT, SAR, soil roughness, soil moisture.

Earth observation (EO) from space has become one of the most powerful environmental management tools. Today's preoccupations such as the effects of climate change, the prevention of natural hazards and the degradation of land resources impose new challenges to the Earth observation community. Terrestrial ecosystems respond to changes caused by climate or by human action in a progressive manner, but also sometimes by quantum leaps. Therefore, long series of calibrated and validated EO are essential in order to monitor and understand these changes, But the information extracted from EO data is only an indicator which has to be integrated into more complete decision support systems, easily accessible to end users. On the other hand, the number and variety of available EO systems is increasing, with a wide range of spatial and spectral resolutions and more and more temporal coverage and flexibility. Micro-satellites, experimental platforms, commercial systems and free systems coexist in space. Ground based sensor webs, innovative linkages and convergence of technologies are appearing and allow "intelligent" monitoring. But some developing countries, which depend strongly on their natural resources, still lack the basic information on the status of their environment. The technology gap appears to widen between rich and poor regions, and this is also a gap in the accession to knowledge, which in turn is a key to development. Earth observation can be an extraordinary tool for a more sustainable planet, but only if it is used wisely and for the benefit of the majority.

Synthetic Aperture Radar (SAR) provides a remote sensing tool to estimate soil moisture. Mapping surface soil moisture from the grey level of SAR images is a demonstrated procedure, but several factors can interfere with the interpretation and must be taken into account. The most important factors are surface roughness and the radar configuration (frequency, polarization and incidence angle). This Letter evaluates the influence of these variables for estimation of bare soil moisture using RADARSAT-1 SAR data. First, the parameters of two linear backscatter models, the Ji and Champion models (Ji et al. 1995, Champion 1996), were tested and the constants recalculated. rms error based on the backscattering coefficient was reduced from 6.12 and 6.48 dB to 4.28 and 1.68 dB for the Ji and Champion models respectively. Secondly, a new model is proposed which had an rms error of only 1.21 dB. The results showed a marked increase in accuracy compared with the previous models.

This paper presents a simulation comparative study of the effect of salt on the imaginary part (epsilon") of the dielectric constant (e) and therefore on the backscattering coefficient (sigma(0)). Mixing model is adapted to estimate this effect on Soil of Wadi El-Natrun area, west desert of Egypt, as a representative area of salt affected soils in the arid zone. Small perturbation backscattering coefficient model (SPM) is also used to estimate the best mode of RADARSAT-1. S1 and S3 were found to be the best choice to detect such type of soil for this area and similar one.

Le milieu côtier du Viêt-nam occupe un rôle primordial pour l'économie et le développement du pays. Pour faciliter la planification des mesures de conservation et de developpement de ce secteur, il convient de réaliser un suivi continu des changements du trait de côte. Des images HRV de SPOT-1 pour 1986 et de SPOT-2 pour 1991 sont utilisées pour déterminer le bilan accumulation-érosion pour une portion de 14 km de côte. Des photographies aériennes de 1986 et une campagne de terrain (1994) servent de réalité de terrain. Après des corrections géométriques rigoureuses et un exercice d'harmonisation des capteurs visant à stabiliser la dynamique des images HRV de façon à simuler l'enregistrement des deux images par un même capteur, nous sommes en mesure d'effectuer une bonne comparaison des images. L'indice de végétation SAVI (Soil Adjusted Vegetation Index) sert de base pour la réalisation de masque binaire uniquement pour la couverture végétale sur les images de télédétection. La soustraction du masque de 1991 de celui de 1986 fait ressortir la domination de l'érosion dans le secteur d'étude, soit 0.62 km 2 en cinq ans. Cependant, le caractére multi-date des données de télédétection permet de prédire la tendance générale de l'évolution de l'environnement côtier. The coastal environment occupies a prime role in the economy and the development of Vietnam. To facilitate the planning of conservation and development measures in this sector, it is necessary to monitor on a continuous basis the changes occurring in the coastline. HRV SPOT-1 and SPOT-2 images, acquired respectively in 1986 and 1991, are used for determining the accumulation-erosion budget for a 14 km portion of coastline. Both aerial photographs acquired in 1986 and ground campaign results (1994) are used as ground data. After applying rigorous geometric corrections and sensor harmonization for stabilizing the dynamics of the HRV images in order to simulate the acquisition of the two images as if recorded by the same sensor, we are able to make a good image comparison. The SAVI vegetation index (Soil Adjusted Vegetation Index) is used as the basis for developing a binary mask solely for the vegetation cover on the remote sensing images. The removal of the 1991 mask from the 1986 mask enhances the predominance of erosion in the study area, i.e. 0.62 km over five years. However, because of the multi-date character of remote sensing data it is possible to predict the overall trend in the evolution of the coastal environment.

The Guadalentin basin, located in the SE of Spain, has a semiarid climate and presents typical characteristics of Mediterranean landscapes vulnerable to land degradation processes and desertification risks. In such an environment, when the vegetation cover is low, the signal received by satellites is dominated by the spectral properties of soils. Changes in these properties can be interpreted in terms of varying soil surface conditions. These optical changes underline the major modifications affecting soil surface under land degradation processes. The present research uses remote sensing techniques to characterise land degradation based on two approaches: spectral mixture analysis and a set of indices describing the spectrum shape. It also presents an integrated approach for evaluating ecosystem vulnerability to land degradation, through the combined analysis of spectrally-derived land units and geomorphometric units. Specific objectives consist of evaluating the potential of extending the indices describing the spectrum shape to the short-wave infrared region, and of identifying landscape units according to their sensitivity to land degradation. Our results demonstrate that the spatial distribution of regional patterns of land degradation can be reliably mapped by using both indices describing the spectrum shape and spectral unmixing. The latter holds great potential for operational mapping of soil conditions and erosion features from optical images. Moreover, landscape-unit analysis shows that DEM ( Digital Elevation Model) variables combined with spectral information are very useful for land degradation assessment. This approach allowed us to segment the landscape into different units according to their lithology and vegetation characteristics, as well as their susceptibility to water erosion.

Soil surface roughness and moisture content both have a significant effect on microwave backscatter to the satellite. The purpose of this work is to evaluate the optimum sensor configuration for existing radar satellites to quantify soil surface roughness. A simulation study using theoretical and empirical models permits the estimation of the sensitivity of the backscatter coefficient to relative variations in soil parameters in terms of radar characteristics. Two different configurations for estimating surface roughness were tested, multi-polarization (co-polarizations) and multi-angular, and the results of the multi-angular configuration provided the best results. A normalized radar backscatter soil roughness index (NBRI) is presented for estimating soil roughness from a multi-angular approach using sensors such as RADARSAT-1. This index was tested using the geometric optics model (GOM) and RADARSAT data. Coefficients of determination of 99% and 83%, respectively, were obtained for each simulation.

This article presents a method to study and correct radiometric distortions caused by topography in SAR images. The method is easy to implement and requires neither sophisticated software nor code-level programming. It also considers the case of a flat surface having an elevation different from the one for which calibration parameters were derived. An ortho-image of the slant range distance is used with a Digital Elevation Model to generate images of the local incident angle along the range and azimuth directions. The method compensates for variations in the terrain area of each pixel and for the angular dependence of backscatter, allowing the choice of either an empirical or semi-empirical scattering model. The method is applied to high-resolution C-SAR subsets of an agricultural area in the Central Cordillera of Costa Rica. The removal of topographic features appears excellent for local incident angles up to 80°, but small-scale structures have pronounced effects on the radar return for higher local incident angles and are not adequately corrected.