La télédétection et les indices de végétation pour la détection de la végétation éparse et moyennement dense cas de l'environnement urbain

The specific objectives of this thesis are twofold. First, our goal is to develop a vegetation index which characterizes sparse and moderately dense vegetation covers, independently from exterior physical disturbances namely: the effect of soil optical properties, i.e. color and brightness, related to the heterogeneity and specificities of this environment, the disturbances introduced by the atmosphere which are variable through time and space and, the effect of spatial and spectral resolutions specific to each sensor. These factors control the interaction processes between the electromagnetic radiation, the atmosphere, the vegetation cover and the underlying soil and, consequently introduce quite severe limitations for the detection of vegetation covers using vegetation indices. Secondly, we evaluate the contribution of the vegetation index to classification precision for thematic mapping applications. For this purpose, we carried out our analyses based on ground-based spectroradiometric data, narrow spatial (7 m) and spectral (30 nm) airborne dam (MEIS-II) and other wide spatial and spectral resolution satellite (TM) data. The study of the sensitivity of vegetation indices to atmospheric disturbances was carried out using the H5S radiative transfer model. As to the analysis of the contribution of the vegetation index to classification precision, we used the maximum likelihood algorithm, and verified the precision by means of the Kappa coefficient. In order to study the spectral properties of bare soils on vegetation covers, we propose a radiative transfer model which permits to decompose the resulting reflectance measured at ground level over a"soil-vegetation cover" mixture into two principal components: the first is intrinsect to the vegetation cover and the second, characteristic of the underlying bare soil, is transmitted through the vegetation cover. The results of the ground simulations for different rates of vegetation cover and different soil colors and brightnesses demonstrate the performance of the proposed model for enhancing the effect of soil optical properties on individual spectral reflectances and consequently, on vegetation indices. The analysis of the results based on the ground measurements, the airborne or satellite data and the simulations of the H5S atmospheric model show that the vegetation indices converge towards the same conclusions and demonstrate that none of the indices remains stable and independent in relation to overall exterior effects. However, the TSAVI and ARVI indices are distinct from the others by their complementary characteristics. Based on the individual performances of these two indices, we propose a new vegetation index: the TSARVI (Transformed Soil Atmospherically Resistant Vegetation Index). This new index has the advantage of adequately describing sparse ar moderately sparse vegetation independently from soil effects, the atmosphere and sensor characteristics."-- Résumé abrégé par UMI

Synergy between SMOS-MIRAS and Landsat-OLI/TIRS Data for Soil Moisture Mapping before, during, and after Flash-Flood Storm in Southwestern Morocco

This chapter deals with soil moisture (SM) characterization over the Guelmim city and its neighborhood in the Southwestern Morocco that has been flooded several times over the past 50 years. To achieve this, space-borne SMOS and Landsat-8 OLI/TIRS data were preprocessed to correct several radiometric anomalies, and they were used. The SMOS brightness temperature data acquired before, during, and after the storm with 1-day temporal resolution and coarse spatial resolution (25 km) were transformed to the SM maps. OLI and TIRS data with moderate spatial and temporal resolutions were converted to Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) to retrieve the Soil Moisture Index (SMI) maps. The results obtained were analyzed, intercompared, and validated against the compiled SM values from rainfall database (SM-RFE) delivered by NOAA climate prediction center Rainfall Estimator (RFE) for Africa. SMOS results show how the spatial variation of SM changes extremely at the regional scale before, during, and after the flash flood day-to-day. The SMI results converge toward the same conclusions showing a drastic SM change before and after flash flood highlighting the impact of inundation and the mud accumulation. By reference to the measured SM-RFE datasets, the validation of the derived SM maps exhibits a significant correlation (R2 ≥ 0.89). Globally, we observe a good complementarity among the considered data sources and processing methods for SM spatial information extraction, and the potential of their integration for the development of a prediction and monitoring model for flash flooding at the regional and local scales

Spatial variability mapping of crop residue using hyperion (EO-1) hyperspectral data

Sherpa Romeo green journal; open accessSoil management practices that maintain crop residue cover and reduce tillage improve soil structure, increase organic matter content in the soil, positively influence water infiltration, evaporation and soil temperature, and play an important role in fixing CO2 in the soil. Consequently, good residue management practices on agricultural land have many positive impacts on soil quality, crop production quality and decrease the rate of soil erosion. Several studies have been undertaken to develop and test methods to derive information on crop residue cover and soil tillage using empirical and semi-empirical methods in combination with remote sensing data. However, these methods are generally not sufficiently rigorous and accurate for characterizing the spatial variability of crop residue cover in agricultural fields. The goal of this research is to investigate the potential of hyperspectral Hyperion (Earth Observing-1, EO-1) data and constrained linear spectral mixture analysis (CLSMA) for percent crop residue cover estimation and mapping. Hyperion data were acquired together with ground-reference measurements for validation purposes at the beginning of the agricultural season (prior to spring crop planting) in Saskatchewan (Canada). At this time, only bare soil and crop residue were present with no crop cover development. In order to extract the crop residue fraction, the images were preprocessed, and then unmixed considering the entire spectral range (427 nm–2355 nm) and the pure spectra (endmember). The results showed that the correlation between ground-reference measurements and extracted fractions from the Hyperion data using CLMSA showed that the model was overall a very good predictor for crop residue percent cover (index of agreement (D) of 0.94, coefficient of determination (R2) of 0.73 and root mean square error (RMSE) of 8.7%) and soil percent cover (D of 0.91, R2 of 0.68 and RMSE of 10.3%). This performance of Hyperion is mainly due to the spectral band characteristics, especially the availability of contiguous narrow bands in the short-wave infrared (SWIR) region, which is sensitive to the residue (lignin and cellulose absorption features).Ye

La télédétection et les indices de végétation pour la détection de la végétation éparse et moyennement dense cas de l'environnement urbain

The specific objectives of this thesis are twofold. First, our goal is to develop a vegetation index which characterizes sparse and moderately dense vegetation covers, independently from exterior physical disturbances namely: the effect of soil optical properties, i.e. color and brightness, related to the heterogeneity and specificities of this environment, the disturbances introduced by the atmosphere which are variable through time and space and, the effect of spatial and spectral resolutions specific to each sensor. These factors control the interaction processes between the electromagnetic radiation, the atmosphere, the vegetation cover and the underlying soil and, consequently introduce quite severe limitations for the detection of vegetation covers using vegetation indices. Secondly, we evaluate the contribution of the vegetation index to classification precision for thematic mapping applications. For this purpose, we carried out our analyses based on ground-based spectroradiometric data, narrow spatial (7 m) and spectral (30 nm) airborne dam (MEIS-II) and other wide spatial and spectral resolution satellite (TM) data. The study of the sensitivity of vegetation indices to atmospheric disturbances was carried out using the H5S radiative transfer model. As to the analysis of the contribution of the vegetation index to classification precision, we used the maximum likelihood algorithm, and verified the precision by means of the Kappa coefficient. In order to study the spectral properties of bare soils on vegetation covers, we propose a radiative transfer model which permits to decompose the resulting reflectance measured at ground level over a"soil-vegetation cover" mixture into two principal components: the first is intrinsect to the vegetation cover and the second, characteristic of the underlying bare soil, is transmitted through the vegetation cover. The results of the ground simulations for different rates of vegetation cover and different soil colors and brightnesses demonstrate the performance of the proposed model for enhancing the effect of soil optical properties on individual spectral reflectances and consequently, on vegetation indices. The analysis of the results based on the ground measurements, the airborne or satellite data and the simulations of the H5S atmospheric model show that the vegetation indices converge towards the same conclusions and demonstrate that none of the indices remains stable and independent in relation to overall exterior effects. However, the TSAVI and ARVI indices are distinct from the others by their complementary characteristics. Based on the individual performances of these two indices, we propose a new vegetation index: the TSARVI (Transformed Soil Atmospherically Resistant Vegetation Index). This new index has the advantage of adequately describing sparse ar moderately sparse vegetation independently from soil effects, the atmosphere and sensor characteristics."-- Résumé abrégé par UMI

Sentinel-MSI VNIR and SWIR Bands Sensitivity Analysis for Soil Salinity Discrimination in an Arid Landscape

Depending on the band position on the electromagnetic spectrum, optical and electronic characteristics, sensors collect the reflected energy by the Earth’s surface and the atmosphere. Currently, the availability of the new generation of medium resolution, such as the Multi-Spectral Instrument (MSI) on board the Sentinel-2 satellite, offers new opportunities for long-term high-temporal frequency for Earth’s surfaces observation and monitoring. This paper focuses on the analysis and the comparison of the visible, the near-infrared (VNIR), and the shortwave infrared (SWIR) spectral bands of the MSI for soil salinity discrimination in an arid landscape. To achieve these, a field campaign was organized, and 160 soil samples were collected with various degrees of soil salinity, including non-saline soil samples. The bidirectional reflectance factor was measured above each soil sample in a goniometric laboratory using an ASD (Analytical Spectral Devices) spectroradiometer. In the laboratory work, pHs, electrical conductivity (EC-Lab), and the major soluble cations (Na+, K+, Ca2++, and Mg2+) and anions (CO32−, HCO3−, Cl−, and SO42−) were measured using extraction from a saturated soil paste, and the sodium adsorption ratio (SAR) was calculated using a standard procedure. These parameters, in addition to the field observations, were used to interpret and investigate the spectroradiometric measurements and their relevant transformations using the continuum removed reflectance spectrum (CRRS) and the first derivative (FD). Moreover, the acquired spectra over all the soil samples were resampled and convolved in the solar-reflective spectral bands using the Canadian Modified Herman transfer radiative code (CAM5S) and the relative spectral response profiles characterizing the Sentinel-MSI band filters. The statistical analyses conducted were based on the second-order polynomial regression (p < 0.05) between the measured EC-Lab and the reflectances in the MSI convolved spectral bands. The results obtained indicate the limitation of VNIR bands and the potential of SWIR domain for soil salinity classes’ discrimination. The CRRS and the FD analyses highlighted a serious spectral-signal confusion between the salt and the soil optical properties (i.e., color and brightness) in the VNIR bands. Likewise, the results stressed the independence of the SWIR domain vis-a-vis these soil artifacts and its capability to differentiate significantly among several soil salinity classes. Moreover, the statistical fit between each MSI individual spectral band and EC-Lab corroborates this trend, which revealed that only the SWIR bands were correlated significantly (R2 of 50% and 64%, for SWIR-1 and SWIR-2, respectively), while the R2 between the VNIR bands and EC-Lab remains less than 9%. According to the convergence of these four independent analysis methods, it is concluded that the Sentinel-MSI SWIR bands are excellent candidates for an integration in soil salinity modeling and monitoring at local, regional, and global scales

: Mapping dominant woody species distribution in the Middle Atlas mountains (Morocco) from ASTER imagery.

International audienceThe cedar forest of the Middle Atlas of Morocco is characterized by the heterogeneity of its stands and its fragmentation caused by the interaction between various factors such as human activities, soil variability and climatic fluctuations. This results in a spatial and spectral heterogeneity that limits the reliability of the conventional methods used for classification of satellite imagery. To address this issue, the present study uses methods based on spectral similarity to map major forest species of the cedar forest of Morocco: Linear spectral mixture analysis (LSMA) andSpectral angle mapper (SAM). The aim of the study was to compare: (i) methods used to extract spectral signatures of pure pixels (endmembers) from the imagery, and (ii) the performances of LSMA and SAM in terms of appropriately mapping major forest species of the Middle Atlas. To achieve these goals, we used ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) images for the forest mapping. The results showed that SMA and SAM have led to similar patterns of the spatial distribution of the studied forest species, but these generated noticeable differences in the areas assigned to each mapped class. The classification results obtained by SMA and SAM werecompared to those generated by the maximum likelihood classification method (our reference). This procedure showed that SMA yielded a better classification of the dominant forest species than SAM; this is illustrated by the value of Kappa Coefficient which was about 0.70 for the SMA method and 0.66 for the SAM approach.La cédraie du Moyen Atlas, au Maroc, est caractérisée par l’hétérogénéité de ses peuplements ainsi que par la fragmentation de son espace forestier. Ces caractéristiques résultent de l’interaction de divers facteurs anthropiques, pédologiques et climatiques. Ces hétérogénéités spatiale et spectrale limitent la fiabilité des méthodes conventionnelles de classification de l’imagerie satellitaire. Dans la présente étude, on suggère d’utiliser les méthodes basées sur la similarité spectrale pour cartographier les espèces forestières dominantes de l’écosystème de la cédraie, soit l’analyse de mixture spectrale linéaire (AMSL) et le Spectral angle mapper (SAM). Les objectifs poursuivis consistent à comparer des procédures d’extraction des signatures spectrales « pures » prototypes, dites endmembers, et les approches de l’AMSL et du SAM en termes de cartographie des espèces végétales dominantes de cette forêt. Pour atteindre ces objectifs, on a utilisé des images acquises par le capteur ASTER (Advanced spaceborne thermal emission and reflection radiometer). Les résultats obtenus montrent que l’utilisation des méthodes de l’AMSL et du SAM a abouti à des résultats similaires en termes de répartition des espèces cartographiées, mais avec des différences au plan des superficies occupées par ces espèces. La comparaison des résultats obtenus à l’aide de l’AMSL et du SAM avec ceux de la classification par maximum de vraisemblance (notre référence) démontre que l’AMSL a permis de classifier les espèces forestières dominantes avec une meilleure précision que le SAM, ce qui s’exprime par un coefficient Kappa de l’ordre de 0,7 pour la méthode de l’AMSL contre 0,66 pour l’approche du SAM

Predicting landslide susceptibility based on decision tree machine learning models under climate and land use changes

Landslides are most catastrophic and frequently occurred across the world. In mountainous areas of the globe, recurrent occurrences of landslide have caused huge amount of economic losses and a large number of casualties. In this research, we attempted to estimate the potential impact of climate and LULC on future landslide susceptibility in of Markazi Province of Iran. We considered the boosted tree (BT), random forest (RF) and extremely randomized tree (ERT) models for landslide susceptibility assessment in Markazi Province. The results of evaluation criteria showed that ERT model is most optimal than other models used in this study with AUC values of 0.99 and 0.93 for the training and validation datasets, respectively. According to the ERT model, the spatial coverage of the very high and high land slide susceptible zones for the current period, 2050s considering RCP 2.6 and 2050s considering RCP 8.5 are 428.5 km2, 439.6 km2 and 465.2 km2, respectively. From this analysis it is clear that the impact of climate and LULC changes on future landslide susceptibility is prominent. The results of the present study help managers to reduce landslide damages, not only for current but also for future conditions, based on climate and LULC changes