An Object- and Topology-Based Analysis (OTBA) Method for Mapping Rice-Crayfish Fields in South China

Haodong Wei,Qiong Hu,Qian Song,Jingya Yang,Zhiwen Cai,Gaofei Yin,Baodong Xu

doi:10.3390/rs13224666

Abstract

The rice-crayfish field (i.e., RCF), a newly emerging rice cultivation pattern, has greatly expanded in China in the last decade due to its significant ecological and economic benefits. The spatial distribution of RCFs is an important dataset for crop planting pattern adjustment, water resource management and yield estimation. Here, an object- and topology-based analysis (OTBA) method, which considers spectral-spatial features and the topological relationship between paddy fields and their enclosed ditches, was proposed to identify RCFs. First, we employed an object-based method to extract crayfish breeding ditches using very high-resolution images. Subsequently, the paddy fields that provide fodder for crayfish were identified according to the topological relationship between the paddy field and circumjacent crayfish ditch. The extracted ditch objects together with those paddy fields were merged to derive the final RCFs. The performance of the OTBA method was carefully evaluated using the RCF and non-RCF samples. Moreover, the effects of different spatial resolutions, spectral bands and temporal information on RCF identification were comprehensively investigated. Our results suggest the OTBA method performed well in extracting RCFs, with an overall accuracy of 91.77%. Although the mapping accuracies decreased as the image spatial resolution decreased, satisfactory RCF mapping results (>80%) can be achieved at spatial resolutions greater than 2 m. Additionally, we demonstrated that the mapping accuracy can be improved by more than 10% when near-infrared (NIR) band information was involved, indicating the necessity of the NIR band when selecting images to derive reliable RCF maps. Furthermore, the images acquired in the rice growth phase are recommended to maximize the differences of spectral characteristics between paddy fields and ditches. These promising findings suggest that the OTBA approach performs well for mapping RCFs in areas with fragmented agricultural landscapes, which provides fundamental information for further agricultural land use and water resources management.

Highlights

The rice-crayfish field (i.e., RCF) is a kind of ecological cropping system that combines rice planting with crayfish aquaculture [1]
The overall classification accuracy of RCFs was 91.77% by using the object- and topology-based analysis (OTBA) method and the 0.5 m SuperView-1 image on 19 August 2018 (Table 2)
We presented an OTBA method to map RCFs, which are an increasingly popular farming pattern used in South China

Summary

Introduction

The rice-crayfish field (i.e., RCF) is a kind of ecological cropping system that combines rice planting with crayfish aquaculture [1]. 2021, 13, 4666 are available in RCFs all year round and are generally harvested twice: between April and June and between August and September [3]. The mutualism between rice and crayfish in an RCF has considerable ecological values, such as increasing the rice yield, improving the soil fertility, and reducing the input of pesticides [4]. Farmers can harvest rice once and crayfish twice within an RCF each year, which significantly improves land use efficiency and their agricultural income [5]. Due to these prominent ecological and economic benefits, RCFs have greatly expanded in China in the last decade [6,7]

Methods

Results

Discussion

Conclusion