In the present study, the ground-based vicarious calibration based on in-situ spectroradiometric measurements and a semi-empirical line approach (SELA) using pseudo-invariant targets (PITs) has been applied to convert the raw data (DN(λ)) acquired with a UAV-multispectral imaging system (Phantom DJI Pro 4+) to the ground-surface reflectance factors (GSRF(λ)) in the visible, red-edge and NIR wavebands. To achieve so, spectral measurements were acquired using an ASD considering four types of homogeneous bright and dark PITs (i.e. white ceramic tiles, concrete surface, asphalt, and very dense vegetation cover). Statistical regressions were established (p2≤ 0.97) for all considered bands. While the GSRF were retrieved with an RMSE ≤ 5.4% for visible, and around 3% for red-edge and NIR. The magnitudes of these RMSE are in agreement with the expected absolute radiometric calibration accuracies reported by the literature, providing good quality-assurance to retrieve biophysical and/or geophysical parameters comparable in time and consistent with other calibrated datasets. Undoubtedly, the SELA can be used with confidence ensuring acceptable accuracies for radiometric calibration and atmospheric corrections of imagery data acquired by UAV-mounted imaging system.

The present study analyzes for the first time the usefulness of the synergy between UAV-multispectral data and biomass-chlorophyll indices to discriminate and map carob trees dieback and damages. To achieve so, the UAV flight was performed over a carob forest located in a valley of a watershed in the Moroccan Middle-Atlas Mountain. The UAV data were rigorously pre-processed and twelve biomass-chlorophyll indices were implemented, analysed (spectrally and radiometrically), and validated using the ground truth. Then, a histogram thresholding classification was applied to the index offering the best performance. The results obtained pointed out that the TDVI and CIGindices have similar and good dynamic range values, and better performance than the other indices tested. They are well correlated, completely independent of soil background artefacts, and relatively avoid linearity and saturation problems. They showed a curvilinear relationship between their computed values and the considered classes (i.e., bare soil, healthy, dieback, and dead trees). The validation shows that TDVI and CIGare sensitive to the carob spatial variations, thus allowing an excellent land-use separating power, predicting early warning signals of dieback, and providing useful bio-physiological information about carob tree conditions. Furthermore, the results highlighted the radiometric and spectral performance of the DJI Phantom-4 camera for powerful sensitivities in discriminating carob forest classes. This simple and quick method can be a useful tool for decision support for monitoring and protecting carob forests on a large scale to promote sustainable development.

The purposes of this paper is an evaluation of the accuracy of DEMs and associated products derived from UAV-data acquired at two different flight altitudes over a narrow-deep mountainous valley area. The assessment of quality and accuracy were undertaken, respectively, qualitatively by reference to the field observations and quantitatively using DGPS in situ measurements. The assigned time for processing and products derivations steps was also analyzed. The images were acquired using UAV Mavic Pro-2 RGB digital camera at two flight altitudes of 80 and 150 m providing, respectively, 2 dissimilar spatial resolutions, 2.0 and 3.5 cm. The "PIX4D-Mapper" software was used for photogrammetric processing. Uniformly distributed over the study area, 35 GCPs were measured with accurate 500-RTK GPS (σ ≤ ± 1 cm). Among them, 26 points were applied for the calibration procedure and 9 as checkpoints. The results demonstrated that the low altitude flight at 80 m enabled the acquisition of 482 overlapping images that were processed during 7 h 41 min, yielding an RMSE ˂ ± 2.0 on planimetry and ˂ ± 2.5 cm on altimetry of checkpoints. Whereas, the flight at 150 m allowed the recording of 250 overlapping images which were processed during 3 h 22 min, reaching an RMSE ˂ ± 3.0 and ˂ ± 3.7 cm on planimetry and altimetry, respectively. Visual analysis pointed out that regardless the considered altitude, the derived products (DEMs, DSMs and ortho-image) are very similar and faithfully reflect the reality on the ground. Accordingly, a flight at 150 m is able to provide satisfactory accuracies of DEM and auxiliary products generated from UAV Mavic Pro-2 camera data for applications in narrow-deep mountain valleys. However, an experienced operator of drone flight control is required to avoid the risks of accidents when maneuvering between narrow rock walls and to land successfully the drone in a safe place. Moreover, these operations are also conditioned by the reliability of telemetry control and the availability of GPS (or GNSS) signals in this harsh environment.

Research on the impact of climate change on land-use change has focused on satellite imagery and/or aerial photography, particularly for extensive areas. However, although close-range remote sensing methods (terrestrial photos) are of great interest for spatiotemporal analysis of landscape change, they are rarely used. In the present study, historical close-range terrestrial photos taken in 1950 and contemporary digital imagery acquired in 2020 over the same areas are combined, processed and analyzed to quantify the extent of vegetation covers (forest and agriculture) during 70 years in the mountainous regions of the Moroccan High-Atlas. We also analyzed to what extent climatic factors (temperature, rainfall, and snowfall) have contributed to this extent. Results showed that climatic observations over the past 70 years, 1950 to 2020, have gradually decreased year by year for snowfall and rainfall (i.e., less or no snow and about 50% decrease of rainfall) due to global warming. However, forest cover site has increased by 16% during this period, while the bare soil class has decreased by the same percentage. Whereas, agricultural site shows a decrease of bare soil class by 29%, contrariwise, vegetation covers and constructions classes have increased by 27 and 2%, respectively. Nevertheless, although the climatic variables point to global warming, there has been significant growth and development of vegetation covers across the study areas. These results are probably due to the migration and expansion of the vegetation species towards mountainous regions at high altitudes, their adaptation to new climatic conditions, and the absence of snowpack. Likewise, we demonstrated how the combination of close-range remote sensing and climate variables could play a complementary role to other data such as satellite images and/or aerial photos to assess long-term land cover change in mountainous areas on a finer scale.

The sulfates, carbonates, and chlorides based minerals have been remotely detected on the Martian surfaces. These evaporite deposits are not only indicative of environmental conditions of their formation, but can provide clues indicating their interaction with water. Due to narrow absorption features, the hyperspectral remote sensing is a powerful technology to identify and discriminat evaporite minerals. This study intends to detect evaporite minerals including gypsum and salt deposits on the Ritchey crater region using hyperspectral data acquired by CRISM carried onboard MRO platform. To achieve this, the data were pre-processed to correct several radiometric, atmospheric, spectral and geometric anomalies. Afterwards, the most popular indices for gypsum and salts detection in arid terrestrial environments such as the NDGI and SSSI were implemented and maps derived. The results obtained indicate that both the indices have mapped the evaporite minerals patterns almost identical. However, the NDGI further highlights the gypsum content, while SSSI product show greater sensitivity to evaporit minerals (i.e., gypsum, sulfate chloride, halite, etc.). Likewise, for validation and comparison procedure, SSSI derived product revealed high similarity with that of chloride published by CRISM-NASA team. Moreover, the spectral signature extracted from the pixels with high evaporite minerals content exhibits the largest hydration signature around 1.95 μm, which is associated with several features typical of gypsum and halite between 1.0 and 2.5 μm. The position, shape, and structure of the absorption features at 1.45, 1.75 and 1.95 μm, as well the gradual decrease in reflectance at 2.4 μm provide some resemblance with the gypsum compared to less hydrated forms of calcium sulfates. However, these absorption characteristics look like a mixture among gypsum, bassanite, anhydrite, and halite.

Soil salinity is a serious environmental problem, particularly in semi-arid and arid regions. Its accumulation is controlled and aggravated by a combination of several factors such as human activities, climatic conditions, landform variability and water table depths. This research aims to analyze the influence of the topographic attributes variability and the depths of the water table on soil salinity accumulation in arid landscape. Moreover, the maps of soil types (properties) and lithology were also considered to support the findings of this work. Topographic attributes (i.e. elevations, slopes, orientations, flow direction and flow accumulation) were derived from the SRTM-V4.1 DEM with 30 m pixel size. While, the water table map was established applying ordinary Kriging interpolation based on a total of 200 hydraulic head measurement points recorded over the study area represented by centre and east parts of the state of Kuwait. All these data were integrated in GIS environment for spatial analysis. A total of 30 samples were randomly selected and localized using a DGPS to statistically analyse$(p < 0.05)$the relationship among soil salinity, elevations variability, and water table depths. The results demonstrate that these variables associated with high temperature exhibited a significant impact on the spatial distribution and accumulation of soil salinity in arid landscape. Generally, areas at a relatively high altitude$(35 \mathrm{m}\leq)$with hard bedrock and deep water table (12 m BGL$\leq$) are less susceptible to salinity$\left(\leq 10 \text{dS} \cdot \mathrm{m}^{-1}\right)$. While, areas at a low altitudes$(\leq 8.0\mathrm{m}$MSL), feeble slopes ($\leq$5%) and groundwater table near to the soil surface$(\leq 1.00\mathrm{m}$below ground level) exacerbating the salt accumulation in the soil through capillary movement and subsequent evaporation. Moreover, in such low areas, the absence of an adequate drainage network contributes significantly to waterlogging. Consequently, the intrusion and emergence of seawater at the surface, coupled with high temperature and high evaporation rates, contribute extensively to the salt accumulation in the soil. Furthermore, statistical analysis shows a significant inverse correlation$(\mathrm{R}=_{-}0.7)$demonstrating the close relationship between soil salinity accumulations, topographic attributes and water table depths.

In precision agriculture, accurate DEMs are very important for the characterisation of topographic attributes (elevation, slope, orientation, curvature, flow direction and accumulation) to understand the spatial variability at the field or within-field scale for managing several agricultural parameters including irrigation, fertilization, runoff production, etc. Unlike conventional methods, the synergy between UAV and digital photogrammetry is a new geomatics approach to generate DEMs with centimetre pixel size. This paper aims a multi-scale analysis of DEMs and related products (DSMs and orthomosaics) derived from UAV data acquired at three different flight altitudes over agricultural field. The quality and accuracies assessment were undertaken, respectively, qualitatively by reference to the field observations and quantitatively using DGPS in situ measurements. The assigned time for processing and products derivations steps was also analyzed. The images were acquired using UAV-Camera (DJI Phantom 4 Pro+ V2.0) at three different flight altitudes (50, 100 and 150 m), providing three dissimilar spatial resolutions (1.25, 2.75 and 4.15 cm). The “Pix4Dmapper” software was used for photogrammetric processing. Uniformly distributed over the study area, 19 GCPs were measured with accurate DGPS (σ ≤ ± 1 cm), LEICA 500-RTK. Among them, 6 points were used for the calibration procedure and 13 as check-points. The results demonstrated that the low altitude flight at 50 m enabled the acquisition of 736 overlapping images that were processed during 5 h 55 min, yielding an RMSE < ± 2 on planimetry and < ± 3 cm on altimetry of check-points. Similar accuracies were achieved for the flight at 100 m, after processing 231 stereoscopic images during 2 h 26 min. Whereas, the flight at 150 m allowed the recording of 103 overlapping images which were processed during 1 h 46 min, reaching an RMSE of ± 2 and ± 4 cm on planimetry and altimetry, respectively. Visual analysis pointed out that regardless the considered altitude, the derived products (DEMs, DSMs and orthomosaics) are very similar and faithfully reflect the reality on the ground. Accordingly, a flight at 150 m is certainly able to provide satisfactory accuracies of DEM and auxiliary products generated from UAV-Camera (DJI Phantom 4 Pro+) data for applications in the context of precision agriculture.

Assessing the evolution of vegetation cover fragmentation over time is a key-issue for monitoring biodiversity and sustainable development in land ecosystems. In the present study, landscape spatial metrics were investigated to assess changes of multitemporal vegetation cover within the Tlemcen National Park (TNP) in Northwest of Algeria over 31 years. To achieve this, five landscape metrics including number of patches (NP), landscape shape index (LSI), class area (CA), largest patch index (LPI), and percentage of landscape (PLAND) were applied and analysed to assess the fragmentation at class level. While, only NP and LSI were analyzed at the landscape level. Three Landsat images acquired during September 1987, 1999 and 2018 with TM, ETM+ and OLI sensors, respectively, were used. After rigorous preprocessing steps, the images were transformed and threshold into NDVI maps representing the major land use classes in TNP, such as forest, shrub lands, open shrub, scattered vegetation and bare soil. For validation purposes, Kappa coefficient analysis was carried out based on 120 ground control points representing these land use classes. The obtained results revealed that the generated confusion matrix ($\mathrm{p} < 0.05$) yield a Kappa coefficient of 0.77 and a significant concordance coefficient of 0.83 between the observed (ground truth) and predicted classes by NDVI-2018. These results indicate that the thresholding procedure is very appropriate and extendable to the other NDVI maps derived for the years 1987 and 1999. Moreover, at the landscape level, the NP and LSI metrics have increased over time by 67.6% in 1999 and 27.9% in 2018, revealing a gradual and important fragmentation of the landscape at TNP. In addition, this trend is corroborated at the class level by the five considered metrics. Accordingly, the synergy between metric indices and remote sensing data offers great potential for sustainable and efficient management policies to address the issues of spatiotemporal fragmentation and degradation across national parks.

Date palm trees play an important role in Oasis economy, particularly in North-Africa and Middle-East regions. Obviously, information about the palm trees number and health conditions is very fundamental for assessing stress and disease, monitoring stage of growth and development, as well as predicting yield. Deep learning is an innovative method, recently adapted and applied in forest environment to detect and delineate individual tree crowns from remote sensing imagery. This paper aims the experimentation of U-N et deep neural network model to detect individual date palm tree crowns from very high spatial resolution images acquired above “Targa N'Touchka” Oasis plantations in south-west of Morocco. To achieve this goal, a scene of Bing-images has been used. It was sampled in 420 of 128x128 pixels and divided into 70% sub-images to train the data and 30% were used to test and validate the output results. The U-Net model was implemented using Tensor-Flow and Keras. The accuracy of results obtained indicates better classification efficiency (96.94%). Overall, individual crowns of date palms are satisfactory delimited and mapped. However, despite its sophistication and performance, the model requires extensive training information and high investment in calibration mode. As well as, the trees with overlapping crowns are not properly recognized as separate entities. Therefore, further investigations are necessary, particularly over large area with dense plantations.

Soil moisture (SM) is an important physical parameter for several hydrological and agricultural applications, weather and climate predictions, as well as early warning of natural hazards such as flood and drought. It varies significantly in space and time, and it is a challenge to map its variations accurately at the regional and local scales. The aim of the present study is a comparison and validation of four models such as NDWI, SIWSI, TOTRAM and OPTRAM for SM mapping before and after flash-flood storm in arid land. The used methodology exploits two pairs of images acquired with Landsat-OLI/TIRS sensors over the study area before and after flood. The first pair was acquired two weeks before the flash flood, and the second one was collected eight days after the flood. These images were radiometrically and atmospherically corrected, as well topographically rectified using SRTM DEM. For validation purposes, before and after storm, two independent SM products were used. Both, they were derived from data acquired simultaneously with those recorded by Landsat-OLI/TIRS. The first is derived from SMOS data, and the second is compiled from rainfall data (SM-RFE) delivered by NOAA climate prediction center RFE (Rainfall Estimator) for Africa. The results revealed that TOTRAM and NDWI models converge towards the same conclusions describing accurately the drastic change of SM before and after flash-flood highlighting the impact of inundation and the mud accumulation over the study area. Their results validation against those of SMOS and SM-RFE products shows a significant agreement (R2 of 0.95). However, in addition to its simplicity and to its easy implementation, the NDWI show a great potential for SM content estimation independently of LST using only optical data such as Landsat-OLI or Sentinel-MSI.

The new Moroccan remote sensing program is composed from two identical satellites named Mohammed-VI (a and B), which operates in panchromatic, visible and near-infrared wavelengths, and provides a multi-swath ability under different angles for stereo imaging. The present paper assesses the capability of these satellites to create 3D stereo model, to retrieve DEM, and to derivate orthorectified images from multispectral data. To achieve these, along-track pair of stereo-images were preprocessed radiometrically and atmospherically, and processed using the Leica Photogrammetry Suite (LPS) module based on digital photogrammetry principles. A field survey was organized and a total of 21 GCPs were measured using a DGPS for stereo model calibration, as well as for verification and validation purposes (i.e. absolute accuracies evaluation for planimetric and altimetric coordinates). The results obtained shows that the orientation operations were achieved with similar accuracies as DGPS GCPs used for the model calibration. The overall obtained RMSE, using 14 checking points, are ±0.20 m and ±0.61 m for planimetry and altimetry, respectively. However, these accuracies are significantly influenced by topographic variations. Indeed, errors are larger for areas with high altitudes and strong slopes, yielding an RMSE of ±0.80 m and ±1.50 m for planimetry and altimetry, respectively. Whereas, significant accuracies improvement is observed for flat areas in the plain, or low to medium-relief areas with indulgent slopes, achieving an RMSE of ±0.04 m for planimetry and ±0.10 m for altimetry. Accordingly, these new satellites enable definitely several large-scale applications requiring high planimetric and altimetric accuracies, such as urban and topographic mapping, civil engineering planning, city modeling, etc.

Constructed on the Tigris River, Mosul dam is the most important hydraulic structure in Iraq, as well is the most dangerous dam in the world. Its geological foundation is unstable due to alternate and variable soluble strata formed from materials such as gypsum, anhydrite, marl and limestone. Consequently, it could collapse at any time and the lives of at least 1.5 million people could be at risk. The present paper investigate the flash-flood scenario due to a hypothetical disaster failure of Mosul dam using the Hydrologic Engineering Center's River Analysis System (HEC-RAS) in concert with HEC-GeoRAS and ArcGIS. The Landsat-OLI image and SRTM-V4.1 DEM data were exploited to digitize the river line, banks, path, and elevation. The cross sections over the river was created based on HEC-GeoRAS model to simulate the spreading of the dam-break flood after the flood-wave exits the narrow valley towards the city of Mosul and its neighbourhood. The 3D and spatial analysts in ArcGIS were applied to create water surface and the depth raster layer for the flash flood scenario. The results revealed that the flow depths can reach a 110 m by reference to bed of the river (or 32 m compared to the topographic surface), covering the Mosul city and extent situated at 75 km downstream of the Dam and sweeping an area about 30 km wide within few hours. The visual impact of this theoretical flood propagation was represented in 3D for land planning and a strategic plan preparation to protect and reduce the potential impact on human life, infrastructure, and environment in Mosul dam watersheds and adjacent regions.

This paper focuses on the evaluation and comparison of the sensitivity of several chlorophyll indices to bare soils optical property variations. To achieve our goal, field spectroradiometric measurements were used as well as hyperspectral data acquired with the Probe-1 airborne and Hyperion EO-1 satellite sensors. The field-based reflectance measurements were acquired above 90 bare soil plots with various optical properties and selected from different agricultural lands. Probe-1 and Hyperion data were spectrally and radiometrically calibrated as well as atmospherically corrected. After these pre-processing steps, sixty spectra of different bare soils with various optical properties were extracted from each dataset for use in the analysis. The obtained results show an excellent agreement between the accuracies estimated from field, airborne and satellite data. Independently from the data source and from the bare soil background, CARI, MCARI and TCARI indices are basically not sensitive to changes in soil optical properties with an RMSE less than 1% and will permit a better estimation of chlorophyll content in sparse crop cover environment.

The objective of this paper is to investigate the relationship between a wide range of hyperspectral chlorophyll indices and wheat crop chlorophyll content using laboratory measurements. These measurements included the GER-3700 spectroradiometric data, leaf chlorophyll content using the Soil-Plant Analyses Development (SPAD)-502 meter and leaf chlorophyll content estimated from chemical laboratory analysis. The SPAD-502 readings were correlated with leaf chlorophyll content extracted in the laboratory to establish calibration equations for the computation of chlorophyll-ab (Chl-ab) and chlorophyll-a (Chl-a) content. This resulted in a coefficient of determination (R2) of 0.72 for the Chl-ab content and 0.69 for the Chl-a content and a root mean square error (RMSE) of 3.53 and 1.94 mug/cm2, respectively. These estimates were used to establish relationships against hyperspectral chlorophyll indices calculated from the GER-3700 data. From the investigated indices, the NPCI showed the best results with R2 of 0.84 and RMSE of 11.0. The other indices, such as GNDVI, OSAVI, PSSRa, PSNDa, CAI, HNDVI, and MTCI did not perform satisfactorily. The better ones, but still showing a relatively week relationship with leaf chlorophyll content, are the indices NDPI, SIPI, PRI and SRPI with R2's of 0.56, 0.62, 0.54, and 0.57, respectively and RMSEs of 11.06, 10.27, 11.32, and 10.96 mug/cm2, respectively.

The objective of this study is to investigate the relationship between a wide range of spectral chlorophyll indices and chlorophyll content of wheat crop using laboratory measurements. The measurements included the GER-3700 spectroradiometric data, leaf chlorophyll content using the SPAD-502 meter, and leaf chlorophyll content estimated from chemical laboratory analysis. The SPAD-502 readings were correlated with laboratory extracted leaf chlorophyll content to establish calibration equations for the computation of chlorophyll-ab (Chl-ab) and chlorophyll-a (Chl-a) contents. This resulted in coefficients of determination (R2) of 0.72 for the Chl-ab contents and 0.69 for the Chl-a contents and a root mean square error (RMSE) of 3.53 and 1.94 mug /cm2, respectively. These estimates based on these equations were used to establish relationships against spectral chlorophyll indices calculated from the GER-3700 data. The spectral chlorophyll indices, NDPI, SIPI, PRI and SRPI show a relative weak relationship with leaf chlorophyll content with R2s of 0.56, 0.62, 0.54 and 0.57 and RMSEs of 11.06, 10.27, 11.32 and 10.96, respectively. The NPCI shows the best results with R2of 0.84 and RMSE of 11.0.

In this paper, we report on the assessment of the spatial variability of soil moisture using synthetic aperture radar (SAR) data. The imagery was acquired during five different periods over the Roseau River watershed in southern Manitoba, Canada. For validation purposes, ground measurements were carried out at 62 locations simultaneous with the satellite data acquisitions. The first step in this analysis was to assess the performance of the Integral Equation Model (IEM) in simulating backscatter coefficients for selected bare soils. In order to reduce the surface roughness effect on radar backscatter behaviour, the semi-empirical calibration technique proposed in (1) was implemented. This calibrated model was then implemented in a simplex inversion routine in order to estimate and map soil moisture. Derived spatial patterns of near-surface moisture content were then examined using exponential semivariogram analyses for spatial extents ranging from tens of meters to kilometers. Despite a dependence of soil moisture on spatial extent, the semivariogram ranges (around 100 m) were found to be similar to previous studies in the literature. Scale analysis of soil moisture maps shows a log-log linear spatial scale with statistical moments. Concave shape dependency of the corresponding slopes with the moment order was observed during all radar acquisition periods. The latter indicates the presence of multiscale effects.

Differences between the analogous spectral bands on Landsat-class sensors can have significant impacts on vegetation index comparisons. Vegetation indices based on satellite image data are widely used for change monitoring but, when derived from different satellite sensors, they will differ as a function of the uncorrectable differences between the analogous spectral bands used to generate the vegetation indices. This issue becomes important as available satellite sensors in the Landsat class are being considered in order to fill the anticipated gap in continuity of the Landsat TM and ETM+ data record. A follow-on mission is not expected any earlier than 2011. Among the current imagers examined in this study, which encompassed four vegetation targets and eight vegetation indices, either one of the IRS-P6 sensors or the SPOT-5 HRG is preferable to the CBERS- 2 HRCC as a replacement sensor from the standpoint of agreement with Landsat-based vegetation indices. Among the vegetation indices considered, the Global Environmental Monitoring Index (GEMI) proved to be the least sensitive to spectral dissimilarities between sensors and hence GEMI is worth considering for quantitative monitoring of environmental change using images from multiple sensors to fill the Landsat data archive.

The calibration of airborne and satellite remote sensing sensors is a fundamental step for the rigorous validation of products derived from satellite data. Because of the inaccessibility of Earth Observation Satellites on orbit, the direct calibration method based on a test site with ground reference data is often considered necessary. However, the problem of radiometric spatial uniformity and temporal stability of test sites constitutes an important issue in the accuracy achieved in calibration operations and the long-term characterization of satellite sensor radiometry. Generally, the coefficient of variation and semivariograms are the most widely used tools for evaluating the radiometric uniformity and stability of a calibration site. In this study, we analyze for the first time the potential of Getis statistics compared to the coefficient of variation for the study of the radiometric spatial uniformity and temporal stability of the Lunar Lake Playa, Nevada (LLPN) test site. The results obtained show the potential and the importance of the synergy generated by these two methods for analyzing the radiometric temporal stability of the LLPN site. Getis statistics provide an excellent spatial analysis of the site while the coefficient of variation provides complementary information on the temporal evolution of the site.

This paper focuses on a comparative study between a semi empirical model, the Modified Soil Adjusted Crop Residue Index (MSACRI), and artificial neutral networks (ANN) for estimating crop residue cover on agricultural fields using hyperspectral imagery. The results indicate the ANN method is more accurate and more representative of the ground reference information than the MSACRI.

This study focused on the characterization of the state of soil degradation by erosion using the hue and coloration indices and on the integration of the SWIR. The results obtained show that the hue and the coloration indices using ETM5 and ETM7 channels of Landsat-7 allow for a better discrimination of the different states of soil degradation. The integration of these indices with the NDVI and a priori knowledge of the study area within an unsupervised classification allowed an excellent characterization of degraded soils and those subject to degradation.

Spectroradiometric analysis of seven types of forest cover (white spruce, trembling aspen, tamarack, balsam fir, black spruce and herbaceous vegetation) in a hyperspectral use perspective to discriminate between forest species was carried out. The preliminary results analysis revealed that the 700 to 1350 nm region was the most appropriate for discriminating between the species compared in this study.

In this study, we present a new vegetation index, the TDVI: transformed difference vegetation index. This index shows the same sensitivity as the soil adjusted vegetation index (SAVI) to the optical proprieties of bare soil subjacent to the cover. It does not saturate like NDVI and SAVI and it shows an excellent linearity as a function of the rate of vegetation cover.