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AI InsightFinally, the different type of session categorises and determines the NDVI of Nepali sessions and months.
📌 3 Maddede Bu Makale
Abstract
This article presents an extraction of valuable information with the help of trend analysis from a long time series of spatial data demands precision and the enormous amount of scientific computation. The trends themselves are exciting estimators of studying spatial and temporal changes in climate and its effects at global and regional scales. The present study was done to investigate the impact of the modifiable temporal unit problem (MTUP) which arises due to temporal aggregation. In this study, an attempt has been made in analysing vegetation change detection that took place between 2001 and 2016 using Terra MODIS13A3 monthly 1 km resolution time series data on a monthly basis. With the launch of National Aeronautics and Space Administration (NASA) on-board aqua and terra platform, a new generation of satellite sensor data is now available. Normalized Difference Vegetation Index method has been employed for accurate classification of images and has proved to be successful. The results of this research work were the significant trend maps which were helpful in analysing spatial patterns in varying trends access different aggregation level to show the effect of MUTP on NDVI and climate forcing data over Nepal. The analysis showed that the average NDVI was higher during May to October in Nepal and lower during the rest of the months. While analysing the data from 2001 to 2016, the NDVI was least in 2001 and highest in 2015. Finally, the different type of session categorises and determines the NDVI of Nepali sessions and months.