Recently, Google Earth Engine (GEE) provides a new way to effectively classify land cover utilizing available in-built classifiers. However, there have a few studies on the applications of the GEE so far. Therefore, the goal of this study is to explore the capacity of the GEE platform in terms of land cover classification in Dien Bien Province of Vietnam. Land cover classification in the year of 2003 and 2010 were performed using multiple-temporal Landsat images. Two algorithms – GMO Max Entropy and Classification and Regression Tree (CART) integrated into the Google Earth Engine (GEE) plat-form – were applied for this classification. The results indicated that the CART algorithm performed better in terms of mapping land use. The overall accuracy of this algorithm in the year of 2003 and 2010 were 80.0% and 81.6%, respective-ly. Significant changes between 2003 and 2010 were found as an increase in barren land and a reduction in forest land. This is likely due to the slash-and-burn agricultural practice of ethnic minorities in the province. Barren land seems to occur more at locations near water sources, reflecting the local people’s unsuitable farming practice. This study may provide use-ful information in land cover change in Dien Bien Province, as well as analysis mechanisms of this change, supporting en-vironmental and natural resource management for the local authorities.
In recent years, the rate of urban growth has increased rapidly especially in Egypt, due to the increase in population growth. The Egyptian government has set up new cities and established large factories, roads and bridges in new places to solve this trouble. This paper investigates the change monitoring of land surface temperature, urban and agricultural area in Egypt especially Kafr EL-Sheikh city as case study using high resolution satellite images. Nowadays, satellite images are playing an important role in detecting the change of urban growth. In this paper, cadastral map for Kafr El-Sheikh city with scale 1:5000, images from Landsat 7 with accuracy 30 meters; images from Google Earth with accuracy 0.5 meter; and images from SAS Planet with accuracy 0.5 m are used where all images are available during the study period (for year’s 2003, 2006, 2009, 2012, 2015 and 2017). The analysis has been performed in a platform of Geographical Information System (GIS) configured with Remote Sensing system using ArcGIS 10.3 and ERDAS Imagine image processing software. From the processing and analysis of the specified images during the studied time period, it is found that the building area was increased by 28.8% from year 2003 up to 2017 from Google Earth images and increased by percentage 34.4% from year 2003 up to year 2017 from supervised Landsat 7 images but for unsupervised Landsat 7 images, the building area was increased by percentage 35.9%. In this study, land surface temperature (LST) was measured also from satellite images for different years through 2003 until 2017. It is deduced that the increase in the building area (urban growth) in the specified city led to increase the land surface temperature (LST) which will affect some agricultural crops. Depending on the results of images analysis, Forecasting models using different algorithms for the urban and agricultural area was built. Finally, it is deduced that integration of spacebased remote sensing technology with GIS tools provide better platform to perform such activities.
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