Abstract 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.

中文翻译：

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基于概率密度函数的 MODIS NDVI 时间序列数据分类与植被生长周期监测

摘要 卫星图像可以在监测时间变化中发挥关键作用的重要区域之一是植被覆盖区域。特别是，人类主导地区自然植被覆盖类型的转换仍在全球范围内发生变化，对环境产生了许多未知的后果。在这些困难的情况下，人们提出了植被生长监测技术来监测农田在不同作物生长过程中主要单峰（即一年或一年的生长信息）和双峰（即两年或半年的生长信息）变化的植被格局。 . 本文考虑的研究区域包含许多农田，至少有两个栽培期可以观察单峰和双峰绿度变化信息。这种类型的信息可以通过正确分析时间序列卫星图像来获得，它提供了良好的时间分辨率卫星图像，可能具有中等空间分辨率和中等分辨率成像光谱仪（MODIS）可能是一个很好的选择。目的。为了分析单峰和双峰绿化，重要的是将农业区分开，然后标记单峰和双峰绿化易发区的区域。因此，在本文中，我们尝试使用调和分析来分析研究区域的一年卫星数据，并提出了一种基于概率密度函数 (PDF) 的用于划分农业区域的分类技术。进一步利用这些隔离区域来获得单峰和双峰绿化倾向区域。