Ahmed, T; Singh, D (2020). Probability density functions based classification of MODIS NDVI time series data and monitoring of vegetation growth cycle. ADVANCES IN SPACE RESEARCH, 66(4), 873-886.

One of the important areas where satellite images can play a key role in the monitoring of the temporal changes is the vegetated cover regions. In particular, the conversion of natural vegetation cover types in human dominated areas is still changing on a global scale with a number of unknown consequences for the environment. In these difficult circumstances, vegetation growth monitoring technique has been proposed to monitor the agriculture fields on vegetation patterns of major unimodal (i.e. annual or yearly growth information) and bimodal (i.e. biannual or half yearly growth information) changes during the growth of the different crops. The study region which is considered in this paper contains many agricultural fields and at least two cultivation period are there to observe the unimodal and bimodal greenness change information. This type of information can be achieved by properly analysing the time series satellite images, which provides a good temporal resolution satellite images which might be having a moderate spatial resolution and Moderate-resolution Imaging Spectroradiometer (MODIS) may be one of the good option for this purpose. To analyse the unimodal and bimodal greenness, it is important to segregate the agriculture areas and then mark the area of unimodal and bimodal greenness prone zone. Therefore, in this paper, we have attempted to analyse one year satellite data of study region using harmonic analysis and proposed a probability density function (PDF) based classification technique for segregating the agriculture areas. These segregated areas are further utilized to obtain the unimodal and bimodal greenness prone zones. (C) 2020 COSPAR. Published by Elsevier Ltd. All rights reserved.