Geomorphologic Domination on Urban Sprawl of Southern Riyadh, Saudi Arabia Using Differential Interferometric Synthetic Aperture Radar (DInSAR)

This study tests the southern part of the Riyadh City growth domination by the Early Quaternary-Holocene trans-tensional Central Arabian graben system reactivation and the subsequent dissolution-induced collapses and karstification. This study utilizes Synthetic Aperture Radar (SAR) and Differential Interferometric Synthetic Aperture Radar (DinSAR) to examine the morphology of arid landscape, south of Riyadh. Eight Single Look Complex (SLC) amplitude images are calibrated, filtered, georeferenced and orthorectified at a resolution of 20 meters, and compared with one another by producing 17 diachronic images of the pairs at different intervals (1996, 2003-2005, 2008). The diachronic SAR intensity imageries suggest a downthrown displacement reaching 600 m and eastward tilting at the bottoms of the grabens. Also, the structurally-controlled valleys are developing an eastward-running drainage system towards the oasis of Al-Kharj and capturing an older hydrologic system. Moreover, a 12-year period (1996-2008) of the SAR data was obtained to examine the average annual rate of southern Riyadh’s urban sprawl, which is estimated at approximately 390 metres/year over the 12 years and constrained by geomorphological features towards the deformed area. DInSAR imageries show the primary results obtained from the 26 May 2004 and 31 Jan. 2005 pair of images, merged with 30 m resolution DEM-SRTM data for the arid region south of Riyadh to eliminate the influence of topography. DInSAR is applied in this study for its ability to detect small displacements at the centimetre scale (1/2 wavelength). Although the DInSAR’s coherence and phase imageries suggest a fairly stable region since the last tectonic and subsequent geomorphic events, erosional and artificial changes are observed, bounded within the valleys and depressions, primarily due to aeolian and fluvial processes and agriculture. It is highly recommended to preserve the area for sustainability and economy.

Detecting and Distinguishing Ophiolite Rocks via Multispectral Remote Sensing in Bi’r Umq, Southeast Al Madinah Al Munawarrah, Saudi Arabia

The objective of this work is to show the contribution of multispectral remote sensing data to detect and distinguish the ophiolite rocks of Bi’r Umq and their geologic setting, southeast of Al Madinah Al Munawarrah in Saudi Arabia. This work includes detailed fieldwork lab studies, as well as processing operations, were performed on The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER 2006, 2007) and Satellite Pour L’Observation de la Terre SPOT 5 (2005) images of the study region. Among the processing techniques applied are band ratio, histogram stretching, the combination of spectral bands, image fusion. The techniques used permitted a clearly show that the ultramafic rocks are distinct from other rock units and contain important economic minerals. This research also illustrates the tectonic parameters and that the remains of the oceanic crust are exposed and juxtaposed to the rocks of the continental crust of the Arabian Shield. The results of the spatial data processing revealed a good positive concordance with the results of the field investigations.

Developing a multi-label tinyML machine learning model for an active and optimized greenhouse microclimate control from multivariate sensed data

In the uncertainties within which the worldwide food security lies nowadays,the agricultural industry is raising a crucial need for being equipped with the state-of-the-art technologies for a more efficient,climate-resilient and sustainable production.The traditional production methods have to be revisited,and opportunities should be given for the innovative solutions henceforth brought by big data analytics,cloud computing and internet of things(IoT).In this context,we develop an optimized tinyML-oriented model for an active machine learningbased greenhouse microclimate management to be integrated in an on-field microcontroller.We design an experimental strawberry greenhouse from which we collect multivariate climate data through installed sensors.The obtained values'combinations are labeled according to a five-action multi-label control strategy,then used to prepare a machine learning-ready dataset.The dataset is used to train and five-fold cross-validate 90 Multi-Layer Perceptrons(MLPs)with varied hyperparameters to select the most performant–yet optimized–model instance for the addressed task.Our multi-label control approach enables designing highly scalable models with reduced computational complexity,comprising only n control neurons instead of(1+∑n k=1Cn k)neurons(usually generated from a classic single-label approach from n input variables).Our final selected model incorporates 2 hidden layers with 7 and 8 neurons respectively and 151 parameters;it scored a mean accuracy of 97%during the cross-validation phase,then 96%on our supplementary test set.The model enables an intelligent and autonomous greenhouse management with the less required computations.It can be efficiently deployed in microcontrollers within real world operating conditions.