Identifying Dike-Pond System Using an Improved Cascade R-CNN Model and High-Resolution Satellite Images

Yintao Ma,Xiaoxiong She,Zheng Zhou,Tao Ren,Shishi Liu,Jianwei Lu,Longyu Zhou

doi:10.3390/rs14030717

Abstract

The dike-pond system (DPS) is the integration of a natural or man-made pond and crop cultivation on dikes, widely distributed in the Pearl River Delta and Jianghan plain in China. It plays a key role in preserving biodiversity, enhancing the nutrient cycle, and increasing crop production. However, DPS is rarely mapped at a large scale with satellite data, due to the limitations in the training dataset and traditional classification methods. This study improved the deep learning algorithm Cascade Region Convolutional Neural Network (Cascade R-CNN) algorithm to detect the DPS in Qianjiang City using high-resolution satellite data. In the proposed mCascade R-CNN, the regular convolution layer in the backbone was modified into the deformable convolutional layer, which was more suitable for learning the features of DPS with variable shapes and orientations. The mCascade R-CNN yielded the most accurate detection of DPS, with an average precision (AP) value that was 2.71% higher than Cascade R-CNN and 11.84% higher than You Look Only Once-v4 (YOLOv4). The area of oilseed rape growing on the dikes accounted for 3.42% of the total oilseed rape planting area. This study demonstrates the potential of the deep leaning methods combined with high-resolution satellite images in detecting integrated agriculture systems.

Highlights

The dike-pond system (DPS) is the integration of agriculture and aquaculture
When zooming in on the bounding boxes, we found that the bounding box of mCascade R-Convolutional neural networks (CNNs) had less overlap and fit to the DPS more than the Cascade region-based convolutional neural network (R-CNN)
There is a lack of studies devoted to mapping DPS at a large scale using satellite data due to the complex combination of ponds and crops growing on DPS is a typical eco-agricultural landscape that is distributed in plains or deltas covered by dense waterways

Summary

Introduction

The dike-pond system (DPS) is the integration of agriculture and aquaculture. It is characterized by a natural or man-made pond and dikes on which crop, vegetables, or fruit trees are cultivated [1]. In China, DPSs are concentrated in the Pearl River. The Huzhou Mulberry-dike and Fish-pond system in China was designated a globally important agriculture systems project (GIAHS) by the Food and Agriculture Organization of the United Nations (FAO). The DPS plays a key role in preserving biodiversity [5], enhancing the nutrient cycle [6], and increasing crop production [2]. Identifying the DPS and mapping their spatial distributions are significant for understanding the environmental impacts of the integrated agricultural systems

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Discussion

Conclusion