## 1 Introduction  
Land use involves modifying human beings' natural or wild environment to an urban environment, e.g., converting forests to cultivated land or pastures and settlements or other management practices [1]. According to FAO [2], land where crops have been planted and irrigated has increased due to the great demand for food, while the land allocated to permanent headows and pastures has declined significantly. The rapid growth of urban areas has negatively changed all kinds of agricultural land uses, which has led to changes in current ecosystems and the water cycle/micro-climate [3; 4; 5; 6]. This is why climate change (CC), land use, and land change (LULC) are among the most current research topics that international environment, food and agriculture program organizations are working on [7; 8]. Several studies have been carried out in the framework of remote sensing for land cover and vegetation assessment at scale worldwide [9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. In Central Puget Sound, [PERSON] et al. [19] used the normalized difference vegetation index (NDVI) to assess vegetative land cover change. The results indicate that the green-up from logging linked to human use and development and seasonal weather patterns were the main reason for an increase in NDVI values. Using LANDSAT8 time series land cover for classification based on NDVI, [PERSON] et al. (2018) observed that there is not much or any significant change in dense vegetation at Tirupati region (India) since most of it is covered with settlements and built-up area. Using hyper-temporal MODIS NDVI databases, [PERSON] et al. (2018) analyzed land-use land-cover (LULC) classification at the Giba catchment (northern Ethiopia). The analysis indicates that that the MODIS NDVI image is applicable for mapping large areas with an overall accuracy of 87.7%. In the northwest Hexi region of China, [PERSON] et al. (2018) characterized land cover and elevation using trends analysis of NDVI variation for the 1982-2015 period. The results revealed an increase in the vegetable class, especially on grass land and forest areas due to their adaptation to climate change. Moreover, the NDVI values were sensitive to rainfall trend variability, where the crop was mostly influenced by human activities. In the Rio de la Plata Grasslands, [PERSON] and Paruelo (2018) analyzed LULC change during the 2000-2014 period using MODIS NDVI Time Series. The results indicate an increase of 23% in the agricultural area, which added over 50,000 km\({}^{2}\) of new crops that replaced grassland concentrated on both sides of the western portion of Inland Pampa and the Uruguay river. In many regions of Saudi Arabia, numerous studies and researches have elaborated, [PERSON] et al. (2018) used remote sensing and GIS for the assessment and mapping of land use/land cover in Al-Qassim region, central Saudi Arabia. The analysis revealed that the plant lands are surrounded by sand dunes, which will lead to the loss of some producing lands. In the desert cities of Makkah and Al-Taif (Saudi Arabian), land use and land cover change were analyzed using Landsat satellite data (Saudi Arabia, 2018). The results show that built areas have augmented over the period from 1986 to 2013 by approximately 174% and 113% in Makkah and Al-Taif, respectively. In addition, an increasing vegetation cover has been observed due to rising aforestation in the urban areas. At the scale of Al-Khobar region in Saudi Arabia, [PERSON] (2018) used multi-temporal remote sensing data (Landsat TM, ETM+, and OLI data) for change detection of land use/land cover and urban sprawl. The results indicate that built-up areas augmented by 117% and 43.51% between 1990 and 2001 and from 2001 to 2013, respectively. Where the vegetation increased by 110% and by 52% for the same periods, respectively. [PERSON] et al.