Spatial change analysis of paddy cropping pattern using MODIS time series imagery in Central Java

3rd International Symposium on LAPAN-IPB Satellite For Food Security and Environmental Monitoring 2016

地球科学;轻工业;生态环境科学

Fatoni, Muhammad Arif^1 ; Nugroho, Kreshna Dwi^1 ; Fatikhunnada, Alvin^1 ; Liyantono^1 ; Setiawan, Yudi^2

Faculty of Agricultural Engineering and Technology, Bogor Agricultural University, Fateta Building, Kampus IPB Darmaga, West Java, Jl. Kamper, Bogor

16680, Indonesia^1

Center for Environmental Research (PPLH), Bogor Agricultural University, PPLH Building, Kampus IPB Darmaga, West Java, 2 nd - 4 th, Jl. Lingkar Akademik, Bogor

16680, Indonesia^2

关键词: Annual time series;    Cropping patterns;    K-Means clustering algorithm;    Long time series;    Modis time series;    Similarity patterns;    Vegetation index;    Water availability;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/54/1/012012/pdf
DOI  :  10.1088/1755-1315/54/1/012012

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Central Java had the diverse paddy field cropping patterns and it was influenced by several factors such as water availability, land condition, paddy fields ownership, and local culture. This research was aimed to analyze dynamic changes of paddy cropping pattern using MODIS imagery (MOD13Q1 16-day composite from 2001 to 2015). This research used k-means clustering algorithm for classified cropping pattern in Central Java based on similarity pattern of annual data from vegetation index. The result of this research classified cropping pattern become a main class and produced 15 maps of distribution cropping patterns (from 2001 to 2015). The result also divided Central Java's paddy fields become 2 section (constant and change) based on cropping pattern that majority was caused by water availability. This research got the better accuracy (77.67%) of cropping pattern than long time series analysis from previous research. Although some classes successfully obtained upon annual time series analysis, MODIS still difficult to detect mixed crop pattern.