This paper aimed to assess the status of land degradation in arid and semi-arid areas based on a correlation analysis between spectral and statistical neo-bands. The methodology uses vegetation and soil spectral indices as the second Modified Soil Adjusted Vegetation Index (MSAVI2), Normalized Difference Bare Soil Index (NDBSI), Texture Index (NDTeI), Crust Index (CI), Top Soil Grain Size Index (GSI), Normalized Difference Sand Dune Index (NDSDI) and the first Specific Principal Component of the red, near infrared, shortwave infrared bands stacking (SPC1 R-NIR-SWIR1-SWIR2). The vegetation is considered here as the main object of soil sub-surface. Thus after all the spectral and the statistic neo-bands are performed on Landsat8 OLI sensor image, a linear regression is generated to assess their correlation with MSAVI2. Based on the visual interpretation and the regression curves the results show that the determination coefficient R 2 and the P values all significant as less than 0.0001. Each neo-band is weighted with its R 2 to improve its contribution to the model and the synthesis image obtained enhances the land degradation sensing in six classes; these are respectively named as ''severe'' (3139 km 2), ''high'' (6763 km 2), ''moderate'' (8341 km 2), ''low'' (7454 km 2), ''very low'' (6947 km 2) and ''close to nil'' (5437 km 2). This last image is summed with population layer to produce a decision map helpful for further government decision. At the end the degradation image has given interesting results for the detection of land degradation comparatively to derivation and comparison of individual indices.

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.

Background NASA’s developers recently proposed the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms. This double method uses Landsat 8 satellite images and daily rainfall data for a real-time mapping of this geohazard. This study adapts the processing to face the issues of data quality and unavailability/gaps for the mapping of the recent landslide events in west-Cameroon’s highlands. Methods The SLIP algorithm is adapted, by integrating the inverse Normalized Difference Vegetation Index (NDVI) to assess the soil bareness, the Modified Normalized Multi-Band Drought Index (MNMDI) combined with the hydrothermal index to assess soil moisture, and the slope inclination to map the recent landslide. Further, the DRIP algorithm uses the mean daily rainfall to assess the thresholds corresponding to the recent landslide events. Their probability density function (PDF) curves are superimposed and their intersections are used to propose sets of dichotomous variables before (1948–2018) and after the 28 October 2019 landslide event. In addition, a survival analysis is performed to correlate landslide occurrence to rainfall, with the first known event in Cameroon as starting point, and using the Cox model. Results From the SLIP model, the Landslide Hazard Zonation (LHZ) map gives an overall accuracy of 96%. Further, the DRIP model states that 6/9 ranges of probability are rainfall-triggered landslides at 99.99%, between June and October, while 3/9 ranges show only 4.88% of risk for the same interval. Finally, the survival probability for a known site is up to 0.68 for the best value and between 0.38 and 0.1 for the lowest value through time. Conclusions The proposed approach is an alternative based on data (un)availability, completed by the site’s lifetime analysis for a more flexibility in observation and prediction thresholding.

No abstract

No abstract