PHENOLOGY-BASED CLASSIFICATION OF MAJOR CROPS AREAS IN CENTRAL LUZON, PHILIPPINES FROM 2001-2013

摘要

Annual crops such as rice, corn and sugarcane are the major source of livelihood for a third of the Philippine population. In view of a changing climate and increasing demand for food, information on the spatial extent and distribution of these crops are important for farmers and policymakers alike. This paper will present a method developed to map crop areas in the Central Luzon Region of the Philippines using time-series Normalized Difference Vegetation Index (NDVI) maps calculated from the MODIS 8-day surface reflectance product in 250-m resolution (MOD09Q1) from 2001-2013. Reference points for classifier training and subsequent accuracy assessment were obtained using a 2003 Land Use System map. Phenology or the seasonally of the vegetation was extracted from the training points. The algorithm applied a filter to smoothen the time-series NDVI and removed spikes and outliers. The processed dataset was then used to extract seasonality parameters including start of season, end of season, peak of season, and length of growing season. A supervised classification scheme using the phenological parameters as inputs was implemented using an artificial neural network trained using resilient backpropagation. Annual maps were produced using the algorithm to reflect the changing crop between years. Accuracy assessment yielded 55.9% and 0.56 overall accuracy and kappa statistic, respectively.