Abstract
Forests function as crucial carbon reservoirs on land, and their carbon sinks can efficiently reduce atmospheric CO2 concentrations and mitigate climate change. Currently, the overall trend for monitoring and assessing forest carbon stocks is to integrate ground monitoring sample data with satellite remote sensing imagery. This style of analysis facilitates large-scale observation. However, these techniques require improvement in accuracy. We used GF-1 WFV and Landsat TM images to analyze Huize County, Qujing City, Yunnan Province in China. Using the style transfer method, we introduced Swin Transformer to extract global features through attention mechanisms, converting the carbon stock estimation into an image translation.
Abstract (translated)
森林作为陆地上的关键碳储存库，其碳汇能够有效减少大气中的二氧化碳浓度并缓解气候变化。目前监测和评估森林碳储量的趋势是将地面监测样本数据与卫星遥感图像相结合。这种分析方法有助于大规模观测，但这些技术在准确性方面仍需改进。 我们使用GF-1 WFV和Landsat TM影像对中国云南省曲靖市会泽县的森林进行了分析。采用风格迁移的方法，我们将Swin Transformer引入其中，利用注意力机制提取全局特征，并将碳储量估算转化为图像翻译问题。
URL
https://arxiv.org/abs/2502.00784
