Integration of Sentinel-1 and Sentinel-2 for temporal identification of aquacultural ponds

Vaishnavi Joshi , Dipanwita Haldar , Subhadip Dey
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Abstract

Accurate classification of ponds under aquaculture practices (AP) and not under aquaculture practices (NAP) is essential for effective monitoring and sustainable management practices. In this study, we have integrated optical and SAR data alongside key spectral indices, such as the Normalized Difference Turbidity Index (NDTI) and the Normalized Difference Chlorophyll Index (NDCI), to distinguish between AP and NAP ponds across different seasons. The analysis reveals distinct spectral and surface characteristics between the two classes for both optical and SAR modalities. Especially for SAR data, NAP ponds exhibit higher anisotropy (A) and lower entropy (H), while AP ponds show lower A and higher H, reflecting complex management practices. Optical indices further highlight differences, with higher water clarity and nutrient enrichment in AP ponds. The Random Forest classifier obtained a maximum overall accuracy of 94 % by combining optical and SAR data, significantly outperforming other classifiers. The advantages of combining optical and SAR data, as supported by the t-SNE plots showing enhanced separability between the AP and NAP ponds. Seasonal variability also plays a critical role, with AP pond areas expanding during the monsoon season and contracting in the summer due to maintenance and evaporation. A notable shift in aquaculture practices was observed, with AP ponds covering 4617.47 ha in January and increasing to 4686.73 ha in September, highlighting the influence of seasonal factors. Spatial analysis revealed a dynamic shift in the usage of ponds, with aquaculture practices peaking during favorable monsoon conditions and declining during summer. The integration of multisource data significantly improves classification accuracy and captures the nuanced variability of aquaculture practices. Although environmental conditions, seasonal changes, and pond management practices influence classification performance, the proposed methodology offers a robust, scalable approach to monitoring aquaculture systems.
基于Sentinel-1和Sentinel-2的水产养殖池塘时间识别
对采用水产养殖做法(AP)和不采用水产养殖做法(NAP)的池塘进行准确分类,对于有效监测和可持续管理做法至关重要。在这项研究中,我们综合了光学和SAR数据以及关键的光谱指数,如归一化浊度指数(NDTI)和归一化叶绿素指数(NDCI),以区分不同季节的AP和NAP池塘。分析揭示了两类光学和SAR模式之间不同的光谱和表面特征。特别是对于SAR数据,NAP池表现出更高的各向异性(A)和更低的熵(H),而AP池表现出更低的A和更高的H,反映了复杂的管理实践。光学指标进一步凸显了差异,AP池的水体清晰度和营养物富集程度更高。随机森林分类器通过结合光学和SAR数据获得了94%的最大总体准确率,显著优于其他分类器。结合光学和SAR数据的优势,如t-SNE图所示,AP和NAP池之间的可分离性增强。季节变化也起着关键作用,AP池面积在季风季节扩大,在夏季由于维护和蒸发而缩小。水产养殖方式变化显著,1月AP池面积为4617.47 ha, 9月增至4686.73 ha,季节性因素影响显著。空间分析揭示了池塘利用的动态变化,水产养殖活动在有利的季风条件下达到高峰,在夏季下降。多源数据的整合显著提高了分类准确性,并捕获了水产养殖实践的细微差异。虽然环境条件、季节变化和池塘管理实践会影响分类效果,但所提出的方法为监测水产养殖系统提供了一种可靠的、可扩展的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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