Assessment of pre-treatment and classification methods for Java paddy field cropping pattern detection on MODIS images

Abstract

Intensive paddy cropping in Java island has an intricate pattern both in spatial and temporal distribution due to landscape and scheduling complexity, in this case the temporal pattern is a key factor in predicting the paddy cropping season. Cropping pattern relevant to the temporal vegetation indices which can be obtained from MODIS temporal imagery. Monitoring of the cropping pattern is a critical factor in preserving the national food security of Indonesia. Remote sensing can be used as an alternative solution to monitor paddy cropping pattern by incorporating statistics analysis and signal processing methods into pre-processing and classification techniques. The main focus of this research is to evaluate the combination of those techniques implemented in MODIS images to classify the annual cropping patterns of paddy crop in Java island. The evaluated techniques involve combinations of four parameters including (1) temporal reconstruction (R0: untreated; R1: linear interpolation), (2) noise reduction (F0: untreated; F1: band-pass FFT; F2: wavelet filter), (3) unsupervised k-means classification using distance parameters (D1: Euclidean; D2: Mahalanobis), and (4) k number of classes (64, 128, 256). The classification validation was evaluated based on the paddy field site visit in several locations across Java island from the years of 2014 until 2017. The best combination (R1-F1-D1-256, the score of 74.5) was determined by the average score calculated from overall classification accuracy, percentage normality of class member, and the average correlation of class member. This combination can describe the highest number of cropping patterns (i.e., 26 patterns) that cover four types of paddy cropping systems (i.e., non-paddy, single paddy cropping, double paddy cropping, and triple paddy cropping). This selected technique can also be used to calculate the annual paddy cropping area.