Dynamic Mapping of Inland Freshwater Aquaculture Areas in Jianghan Plain, China

Abstract

Freshwater aquaculture in Jianghan Plain plays a key role in the whole industry of China. It was expanding rapidly to meet the fast-growing demand of consumption in these decades. The spatial distribution change of aquaculture in Jianghan Plain has attracted many researchers in recent years and it is worth further investigating. However, the accuracy and the quality of inland aquaculture classification and mapping still have space to be improved. Our study attempts to use Sentinel-1 and Sentinel-2 data to classify aquaculture areas, non-aquaculture water, and non-water areas. We applied image segmentation and pixel-based feature computation to obtain candidate datasets. Recursive feature elimination (RFE) was used to find the optimal datasets which were then imported into a modified UNet for classification. This workflow was then applied to the annual datasets for better observation of the spatial change of aquaculture areas. In total 12 indices and features were found most influential and preserved after RFE. The overall classification accuracy reached 96.83%, while the precision and recall of aquaculture areas reached 84.47% and 90.48%, respectively. The combination of object-based and pixel-based image analysis showed its advantage in inland aquaculture classification compared to other studies. Integrated rice-crawfish farming was found a key factor motivating the rapid development of aquaculture. Policies about the fishing ban and ecosystem restoration were also found deeply affecting the spatial change of aquaculture in Jianghan Plain.

Freshwater Aquaculture Mapping in “Home of Chinese Crawfish” by Using a Hierarchical Classification Framework and Sentinel-1/2 Data

“…The fine classification of inland aquaculture areas has significant and far-reaching implications for understanding the spatial distribution and production allocation of different types of inland aquaculture, as well as for achieving efficient management and sustainable development. Previous studies have primarily focused on satellite image extraction for individual aquaculture types, leaving a gap in research that simultaneously extracts multiple types of inland aquaculture areas [4][5][6]8,19,53]. For instance, Cai et al [8] proposed the RAUNet deep learning method and utilized high-quality GF-2 images covering the entire Qianjiang City for rice-crawfish field extraction.…”

“…However, rice-crawfish fields undergo dynamic changes throughout the year, and stitching together only instantaneous images may overlook crucial seasonal or cyclical variations in the extraction process. Han et al [53] employed an improved UNet model to extract aquaculture areas from Sentinel images near December 1st each year, ultimately obtaining aquaculture area maps for the Jianghan Plain from 2016 to 2021. However, a considerable portion of rice-crawfish fields may not exhibit distinct inundation signals around December 1st each year.…”

Monitoring Monthly Net-Pen Aquaculture Dynamics of Shallow Lakes Using Sentinel-1 Data: Case Study of Shallow Lakes in Jiangsu Province, China

“…Liu et al (2022) [13] developed a multi-feature-based, object-oriented method to extract aquaculture ponds data using fully polarimetric GF-3 SAR data. Han et al (2023) [14] introduced a novel method that utilizes Sentinel-1 and Sentinel-2 data to classify aquaculture, non-aquaculture water, and non-water areas. They also analyzed the spatial distribution and change in the aquaculture industry in Jianghan Plain, China.…”