Abstract
Abstract. Updating of seasonal agricultural crop map is limited by the local knowledge of the mapper. Mapping of previously unaccounted agricultural plots involve massive field works aided by very high-resolution images. The phenological cycle of seasonal crops like sugarcane, with a range of ten (10) to twelve (12) months from planting to harvesting, exhibit a unique characteristic in terms of radar backscatter and time. In this paper, a pattern matching algorithm was tested to detect sugarcane plantations. Dynamic Time Warping (DTW), which was originally used for voice recognition, was used to detect sugarcane plantations from multitemporal Sentinel-1A images. Using known sugarcane plots, temporal signatures were gathered and used to detect other plantations in the area. The result helped the Sugar Regulatory Administration (SRA) in updating the inventory of sugarcane plantations faster with detection accuracy of more than 92 percent.
Highlights
Nationwide crop maps are integral in planning and decision making undertaken by mandated government agencies
One effort to do an updated agricultural map in a national scale was done by the Nationwide Detailed Resources Assessment Using LiDAR (Phil-LiDAR 2 Program) between 2014 and 2017 but the detection focused on areas with LiDAR data (Blanco et al, 2016) which is around 42.43% (Gatdula et al, 2017) of the land area of the country
The objective of this study is to develop a sugarcane mapping algorithm from multitemporal radar images
Summary
Nationwide crop maps are integral in planning and decision making undertaken by mandated government agencies. These types of maps are usually inaccessible or outdated in the Philippines. Digitizing from Very High Resolution (VHR) image produces accurate plots, single scene image can only capture a snapshot of what is on the ground when the image was taken which poses a problem for areas with different planting or harvesting time. Some farmers practice multi-cropping adding complexity to the detection. This is why delineating seasonal crops need to be conducted in a multitemporal manner to properly determine what crops were grown in an area which may not be captured by a single image. Frequent cloud cover adds to the problem which introduces gaps even in temporal datasets making temporal signature detection difficult
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