This study aims to present a technique that combines multi-sensor spatial data to monitor wetland areas after a flash-flood event in a Saharan arid region. To extract the most efficient information, seven satellite images (radar and optical) taken before and after the event were used. To achieve the objectives, this study used Sentinel-1 data to discriminate water body and soil roughness, and optical data to monitor the soil moisture after the event. The proposed method combines two approaches: one based on spectral processing, and the other based on categorical processing. The first step was to extract four spectral indices and utilize change vector analysis on multispectral diachronic images from three MSI Sentinel-2 images and two Landsat-8 OLI images acquired before and after the event. The second step was performed using pattern classification techniques, namely, linear classifiers based on support vector machines (SVM) with Gaussian kernels. The results of these two approaches were fused to generate a collaborative wetland change map. The application of co-registration and supervised classification based on textural and intensity information from Radar Sentinel-1 images taken before and after the event completes this work. The results obtained demonstrate the importance of the complementarity of multi-sensor images and a multi-approach methodology to better monitor changes to a wetland area after a flash-flood disaster.

Imlili Sebkha is a stable and flat depression in southern Morocco that is more than 10 km long and almost 3 km wide. This region is mainly sandy, but its northern part holds permanent water pockets that contain fauna and flora despite their hypersaline water. Google Earth Engine (GEE) has revolutionized land monitoring analysis by allowing the use of satellite imagery and other datasets via cloud computing technology and server-side JavaScript programming. This work highlights the potential application of GEE in processing large amounts of satellite Earth Observation (EO) Big Data for the free, long-term, and wide spatio-temporal wet/dry permanent salt water cavities and moisture monitoring of Imlili Sebkha. Optical and radar images were used to understand the functions of Imlili Sebkha in discovering underground hydrological networks. The main objective of this work was to investigate and evaluate the complementarity of optical Landsat, Sentinel-2 data, and Sentinel-1 radar data in such a desert environment. Results show that radar images are not only well suited in studying desertic areas but also in mapping the water cavities in desert wetland zones. The sensitivity of these images to the variations in the slope of the topographic surface facilitated the geological and geomorphological analyses of desert zones and helped reveal the hydrological functions of Imlili Sebkha in discovering buried underground networks.

This paper explores a spectral vector-based methodology on Landsat 8 bands of the visible wavelengths, that is deep-blue (1) to shortwave infrared (7), to improve the urban land features classification. Using two different ratio models, based on two and three bands’ combinations in the cloud environment of Google Earth Engine, the Uncertainty reducing Spectral Vector (USV_r), the Onward Continuous Spectral Vector (OSV_c) and the Onward Discontinuous Spectral Vector (OSV_d) are proposed as new entries for the land use land cover (LULC) classification. Two different sizes of arrays are built, i.e. 42 vectors and 15 vectors corresponding to the same number of derivative bands and new pixels′ values. A decision tree is built in J.48 and applied to select the most suitable derivative bands for the analysis. Hereafter, the selected ones are stacked and submitted to five machine learning classifiers using a supervised process, namely, Classification and Regression Trees (CART), Random Forest (RF) Gradient Boosting (GBR), Support Vector Machine (SVM) and Minimum Distance (MD). This method was tested in the two cities of Bamenda and Foumban in west-Cameroon highlands, due to their good representativeness of tropical hilly urban areas’ spatial heterogeneity. The results are satisfying for 4/5 classifiers, up to 87% Overall Accuracy, OA, for 0.82 kappa coefficient, KC, in Bamenda, while combining SVM/OSV_d. Whereas, in Foumban, the classifiers perform up to 85%OA and 0.78 KC for the combination SVM/USV_r. Only the MD classifier has always performed below 80%OA. The process has been found better than performing classifiers directly on the multispectral (MS) image, by providing more possibilities of hidden spectral indices not yet explored, as far as we know, to detect and discriminate between LULC features, plus an accurate extraction of human settlements.

Drought affects all human activities and ecosystems. Nearly 40 percent of the world’s population inhabit Drylands, and they depend on agriculture for their food, security and livelihoods. Among the remote sensing indices developed, the Land Surface General Drought Index (LSGDI) was recently proposed. This paper proposes an improved model of LSGDI to face the issue of drought in semi-arid and arid regions. The experiment was conducted for the Maga’s floodplain, in North-Cameroon. The method uses satellite images of Landsat in 1987, 2003 and 2018, for January and March or April, corresponding to the middle and the end of the dry season. A Vegetation Moisture Index (VMI) and a Normalized Difference Soil Drought Index (NDSoDI) are both developed. On an orthogonal plan, their projections give a drought line that expresses the improved LSGDI (LSGDI2) as the root sum square of the NDSoDI and the VMI. The LSGDI2 results are ranged in [0.09 – 0.14] interval, which is used to define the threshold and ease the qualifiers for drought classes. The visual patterns easily match the sandy areas of the original Landsat images with the highest values, while the vegetation and water areas match the lowest values. Compared with the LSGDI and Second Modified Perpendicular drought Index (MPDI1), the new index reflectance values are higher. Finally, although LSGDI2 curve’s evolution follows the NDSoDI one at 94%, the new spectral index values depends on the both components, helping to map highest values of drought and moisture in Maga’s floodplain, for a sustainable rice culture expansion.