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The preparation of land cover maps provides the possibility of studying the impact of land surface changes on sustainable development and is significant for a wide range of important issues at the global level. The current research aims to facilitate the preparation of land cover maps using the classification of Normalized Difference Vegetation Index (NDVI) values ​​and prepare land cover maps from it. For this purpose, first, two complete consecutive Landsat-8 scenes of parts of Iran and Turkmenistan were selected for August 30, 2021. Then the images were classified using supervised classification algorithms including Neural Network Classification (NNC), maximum Likelihood Classification (MLC), Support Vector Machine (SVM), Minimum Distance (MinD) and Mahalanobis Distance (MahD). In the next step, to perform an evaluation, by using a thousand ROI for a test, the overall accuracy, kappa coefficient, user accuracy and producer accuracy of the map produced by each of the algorithms were calculated. Then, using the most optimal algorithm, the threshold of NDVI image values ​​was extracted in order to classify it and the obtained map was re-evaluated for accuracy. Among the evaluated algorithms, the MLC algorithm had the most optimal performance with a kappa coefficient of 0.75 and overall accuracy of 80.86%. The results of evaluating the accuracy of the NDVI Based land cover Classification (NBC) index also indicated that this map has extracted the land cover map with an overall accuracy of 83% and a Kappa coefficient of 0.77. This index showed good performance in the classification of Bare Land Class (BLC), Water Area Class (WAC) and Salt Marsh Class (SMC) with user accuracy and producer accuracy above 94%. This is while the Agricultural Land Class (ALC) and Vegetation Class (VC) were classified by this index with producer accuracy of above 73% and user accuracy of 69% and 97%, respectively. The results of this research indicate the acceptable accuracy of NDVI index values ​​for the production of natural land cover maps and can be used in order to prepare these maps for geographic monitoring and achieving sustainable environmental development.

Climate change is one of the most pressing problems among scientists worldwide, with experts warning about it and even referring to it as unfathomable human agony. In this study, we reviewed previous studies and examined two gaps in the existing approach to climate change studies. First, look at the "side effects" of global warming that have been overlooked in the process and then look at the leading "cause" of global warming, namely "humans" and not its "effects". The findings revealed that a 1.4 °C temperature increase (as predicted by United National (UN) projections) would not only raise this amount but also trigger further global warming. As a result, the premise that global warming produces additional global warming was proven. In the Water Area (WA) class, radiant energy increased by 1194.8%, compared to 1205.8%, 1154.9%, 1115.6% and 1229% in the Vegetation Area Class (VAC), Agricultural Area Class (AAC), Bare Area Class (BAC) and Salt Lake Class (SLC), respectively. Although the Land Surface Temperature (LST) of all classes has only increased by about 0.4 °C, these changes in radiant energy are much more pronounced. The current study also revealed that most legitimate research on this subject has focused on the effects of global warming on environmental variations. These studies, which see these changes as "results" of climate change and global warming, have overlooked the primary cause, "human demands", which has prompted humans to alter or exploit their surroundings actively. This study found that concentrating on humans and encouraging them to focus on happiness rather than pleasure is more helpful in addressing global warming issues than focusing on its impacts, such as rising sea level, storms, drought, etc. The results of this study are helpful for a deeper understanding of global warming and a careful study of the cause and dimensions of this phenomenon.