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.

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.

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.

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.