基于尺度转换与重构的FY-3B林火监测影像可视化增强研究

苏维; 尹俊玥; 叶江霞; 周汝良; 温庆忠; 王磊; 李元杰; 赵俊

doi:10.12302/j.issn.1000-2006.202408006

苏维, 尹俊玥, 叶江霞, 周汝良, 温庆忠, 王磊, 李元杰, 赵俊

南京林业大学学报（自然科学版） ›› 2026, Vol. 50 ›› Issue (2) : 37-47.

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南京林业大学学报（自然科学版） ›› 2026, Vol. 50 ›› Issue (2) : 37-47. DOI: 10.12302/j.issn.1000-2006.202408006

专题报道（Ⅰ）：森林资源智能感知与精准监测（执行主编李凤日曹林张怀清）

基于尺度转换与重构的FY-3B林火监测影像可视化增强研究

苏维 ¹ ,
尹俊玥 ² ,
叶江霞 ³^,^* ,
周汝良 ¹ ,
温庆忠 ⁴ ,
王磊 ⁵ ,
李元杰 ⁴ ,
赵俊 ⁵

作者信息 +

Research on visualization enhancement of FY-3B image for forest fire monitoring based on scale conversion and reconstruction

SU Wei ¹ ,
YIN Junyue ² ,
YE Jiangxia ³^,^* ,
ZHOU Ruliang ¹ ,
WEN Qingzhong ⁴ ,
WANG Lei ⁵ ,
LI Yuanjie ⁴ ,
ZHAO Jun ⁵

Author information +

文章历史 +

摘要

【目的】针对千米级卫星林火监测影像模糊及火场微地形信息缺失问题,研究基于地形尺度转换与影像重构的处理方法,增强影像火环境清晰度与可视化效果,为火场信息快速提取及防灭火决策提供支撑。【方法】以2019年“5·17”昆明市晋宁区双河彝族乡干河村森林火灾和“5·18”曲靖市陆良县小百户镇永清村雨台子山火灾为研究对象,基于FY-3B影像与30 m数字高程模型(DEM)地形数据,利用点扩散函数(point spread function, PSF)与亚像元分解方法构建不同尺度的地形单元,结合信息熵理论确定适宜的重构尺度,借助增加权重的HSV(hue, saturation, value)变换实现正立体地形阴影(shaded relief model, SRM)数据与FY-3B影像融合,并比较上推与下推尺度转换方法的重构效果。【结果】①相较常规插值方法,点扩散函数尺度上推效果更优,高程均值差和均方根误差最小,与原始DEM等高线匹配度最高。②分析上推DEM载荷的地形信息熵表明,研究区适宜的DEM重构尺度为≤120 m。③不同尺度重构影像显著增强了原始1.1 km分辨率FY-3B影像的可视化效果,反立体现象得以校正,提升了对火环境地形细节的表征能力。④15 m尺度融合影像在定量指标和火场地形特征增强效果上表现最佳,林火覆盖面积提取最为准确。【结论】研究通过地形尺度转换与图像重构方法,融合多尺度DEM地形数据,显著提升FY-3B林火监测影像的火环境信息表达能力及可视化效果,为提高卫星林火监测成效和支撑防灭火决策提供了重要支持。针对不同尺度、不同面积范围的卫星林火影像解译与火环境研究,综合考虑更多受灾点区域统计特性,以匹配最佳尺度地形单元,是进一步提升林火监测影像可视化效果的研究方向。

Abstract

【Objective】This study developed a processing method based on terrain scale conversion and image reconstruction to address the challenges of blurred kilometer-scale satellite imagery and the absence of micro-topography information in forest fire monitoring. Aiming to enhance the clarity and visualization effect of the fire environments in satellite images, thereby supporting rapid-fire information extraction and informed decision-making for fire prevention and suppression.【Method】The study focused on two forest fire events: the “5·17” fire in Ganhe Village, Shuanghe Yi Township, Jinning District, Kunming City, and the “5·18” fire in Yutaizi Mountain, Yongqing Village, Xiaobaihu Town, Luliang County, Qujing City, both occurring in 2019. Utilizing FY-3B satellite imagery and 30 m resolution Digital Elevation Model (DEM) data, terrain units at varying scales were constructed using the point spread function (PSF) and sub-pixel decomposition method. The optimal reconstruction scale was determined through information entropy theory. A weighted Hue, Saturation, Value (HSV) transformation was employed to fuse shaded relief model (SRM) data with FY-3B images. The reconstruction effects of the scaling up and scaling down methods were systematically compared.【Result】(1) The PSF-based scaling up method demonstrated superior performance to conventional interpolation techniques, exhibiting the minimum mean elevation error and RMSE and the highest alignment with original DEM contour lines. (2) Terrain information entropy analysis revealed that the optimal DEM reconstruction scale for the study area was ≤120 m. (3) Reconstructed images at multiple scales significantly improved the visualization of the original 1.1 km resolution FY-3B imagery, effectively correcting anti-stereo phenomena and enhancing the representation of terrain details in fire environments. (4) The 15 m scale fused image exhibited the best performance in quantitative metrics, including improved terrain feature contrast and the highest accuracy in forest fire area extraction.【Conclusion】Integrating multi-scale DEM data through terrain scale conversion and image reconstruction methods markedly enhanced the fire environment representation and visualization quality of FY-3B forest fire monitoring imagery. These advancements provide critical technical support for improving satellite-based forest fire monitoring efficacy and optimizing fire management strategies. Future research should explore the statistical characteristics of disaster-affected regions across varying scales to further refine the matching of terrain units for enhanced visualization outcomes.

导出引用

苏维, 尹俊玥, 叶江霞, 等. 基于尺度转换与重构的FY-3B林火监测影像可视化增强研究[J]. 南京林业大学学报（自然科学版）. 2026, 50(2): 37-47 https://doi.org/10.12302/j.issn.1000-2006.202408006

SU Wei, YIN Junyue, YE Jiangxia, et al. Research on visualization enhancement of FY-3B image for forest fire monitoring based on scale conversion and reconstruction[J]. Journal of Nanjing Forestry University (Natural Sciences Edition）. 2026, 50(2): 37-47 https://doi.org/10.12302/j.issn.1000-2006.202408006

中图分类号： TP75;P931;S757

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