Progress in the remote sensing of groundwater-dependent ecosystems in semi-arid environments

Rampheri, Mangana B.; Dube, Timothy; Dondofema, Farai; Dalu, Tatenda

doi:10.1016/j.pce.2023.103359

Cited by 7 publications

(2 citation statements)

References 71 publications

(203 reference statements)

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“…Table advances the article and space utilization of machine learning in the intelligent urban communities, where rampheri [15] presented the advancement of remote sensing technology has allowed for a new perspective on GDEs in ecosystems that are changing due to the combined effects of climate change and other types of human activities. Chithaluru [16] In light of the inherent challenges associated with IoT resource management, they developed the Dynamic Clustering Routing (DCR) protocol to address these issues.…”

Section: ) Support Vector Machinementioning

confidence: 99%

Smart Cities: An In-Depth Study of AI Algorithms and Advanced Connectivity

Yaswanth,

Babu,

Vineetha

et al. 2023

IJRITCC

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The goal of smart city development is to improve the quality of life by incorporating technology into daily activities. Artificial intelligence (AI) is critical to the ongoing development of future smart cities. The Internet of Things (IoT) idea connects every internet-enabled device for improved access and control. AI in various domains has changed ordinary towns into highly equipped smart cities. Machine learning and deep learning algorithms have proven indispensable in a variety of industries, and they are now being implemented into smart city concepts to automate and improve urban activities and operations on a large scale. IoT and machine learning technology are frequently used in smart cities to collect data from various sources. This article delves deeply into the significance, scope, and developments of AI-based smart cities. It also addresses some of the difficulties and restrictions associated with smart cities powered by AI. The goal of the study is to inspire and encourage academics to create original smart city solutions based on AI technologies.

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Section: ) Support Vector Machinementioning

confidence: 99%

Smart Cities: An In-Depth Study of AI Algorithms and Advanced Connectivity

Yaswanth,

Babu,

Vineetha

et al. 2023

IJRITCC

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“…The case studies include landscape mosaics [48], monitoring sedimentation processes [49], dynamics of sandy dunes in the Sahara [50], geomorphological modelling [51] and monitoring coastal regions [52,53]. The generic approach of RS and advantages of the use of spaceborne data have been noted earlier [54]. These include the high sensitivity of satellite images, which enables the detection of small-size patches of landscapes, regularity of survey by various space sensors and flexibility of data coverage.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Monitoring Seasonal Fluctuations in Saline Lakes of Tunisia Using Earth Observation Data Processed by GRASS GIS

Lemenkova

2023

Land

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This study documents the changes in the Land Use/Land Cover (LULC) in the region of saline lakes in north Tunisia, Sahara Desert. Remote sensing data are a valuable data source in monitoring LULC in lacustrine landscapes, because variations in the extent of lakes are visible from space and can be detected on the images. In this study, changes in LULC of the salt pans of Tunisia were evaluated using a series of 12 Landsat 8-9 Operational Land Imager (OLI) and Thermal Infrared (TIRS) images. The images were processed with the Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS) software. The study area included four salt lakes of north Tunisia in the two regions of the Gulf of Hammamet and Gulf of Gabès: (1) Sebkhet de Sidi el Hani (Sousse Governorate), (2) Sebkha de Moknine (Mahdia Governorate), (3) Sebkhet El Rharra and (4) Sebkhet en Noual (Sfax). A quantitative estimate of the areal extent analysed in this study is 182 km × 185 km for each Landsat scene in two study areas: Gulf of Hammamet and Gulf of Gabès. The images were analysed for the period 2017–2023 on months February, April and July for each year. Spatio-temporal changes in LULC and their climate–environmental driving forces were analysed. The results were interpreted and the highest changes were detected by accuracy assessment, computing the class separability matrices, evaluating the means and standard deviation for each band and plotting the reject probability maps. Multi-temporal changes in LULC classes are reported for each image. The results demonstrated that changes in salt lakes were determined for winter/spring/summer months as detected changes in water/land/salt/sand/vegetation areas. The accuracy of the classified images was evaluated using pixel rejection probability values, which were filtered out using the ‘r.mapcalc’ module of GRASS GIS. The confidence levels were computed and visualised with a series of maps along with the error matrix and measured convergence level of classified pixels. This paper contributes to the environmental monitoring of Tunisian landscapes and analysis of climate effects on LULC in landscapes of north Africa.

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