Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Changsha 410004, China.
Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China.
Sensors (Basel). 2020 Jul 16;20(14):3957. doi: 10.3390/s20143957.
Increasing the area of planted forests is rather important for compensation the loss of natural forests and slowing down the global warming. Forest growing stem volume (GSV) is a key indicator for monitoring and evaluating the quality of planted forest. To improve the accuracy of planted forest GSV located in south China, four L-band ALOS PALSAR-2 quad-polarimetric synthetic aperture radar (SAR) images were acquired from June to September with short intervals. Polarimetric characteristics (un-fused and fused) derived by the Yamaguchi decomposition from time series SAR images with different intervals were considered as independent variables for the GSV estimation. Then, the general linear model (GLM) obeyed the exponential distribution were proposed to retrieve the stand-level GSV in plantation. The results show that the un-fused power of double bounce scatters and four fused variables derived from single SAR image is highly sensitive to the GSV, and these polarimeric characteristics derived from the time series images more significantly contribute to improved estimation of GSV. Moreover, compared with the estimated GSV using the semi-exponential model, the employed GLM model with less limitations and simple algorithm has a higher saturation level (nearly to 300 m/ha) and higher sensitivity to high forest GSV values than the semi-exponential model. Furthermore, by reducing the external disturbance with the help of time average, the accuracy of estimated GSV is improved using fused polarimeric characteristics, and the estimation accuracy of forest GSV was improved as the images increase. Using the fused polarimetric characteristics (Dbl×Vol/Odd) and the GLM, the minimum RRMSE was reduced from 33.87% from single SAR image to 24.42% from the time series SAR images. It is implied that the GLM is more suitable for polarimetric characteristics derived from the time series SAR images and has more potential to improve the planted forest GSV.
增加人工林面积对于补偿天然林损失和减缓全球变暖至关重要。森林生长量（GSV）是监测和评估人工林质量的关键指标。为了提高中国南方人工林 GSV 的精度，在 6 月至 9 月间，使用短时间间隔获取了四幅 L 波段 ALOS PALSAR-2 四极化合成孔径雷达（SAR）图像。将不同时间间隔的时间序列 SAR 图像的 Yamaguchi 分解得到的极化特征（未融合和融合）作为 GSV 估计的独立变量。然后，提出了服从指数分布的广义线性模型（GLM），用于估算林分水平的 GSV。结果表明，双反照率散射的未融合功率和单幅 SAR 图像衍生的四个融合变量对 GSV 高度敏感，这些源自时间序列图像的极化特征对 GSV 的估计有显著的改进作用。此外，与使用半指数模型估计的 GSV 相比，具有更少限制和简单算法的所采用 GLM 模型的饱和度水平更高（接近 300 m/ha），对高森林 GSV 值的敏感性更高。此外，通过时间平均减少外部干扰，融合极化特征可提高估计的 GSV 的精度，并且随着图像数量的增加，森林 GSV 的估计精度得到提高。使用融合的极化特征（Dbl×Vol/Odd）和 GLM，从单幅 SAR 图像的最小 RRMSE 从 33.87%降低到时间序列 SAR 图像的 24.42%。这表明 GLM 更适合于源自时间序列 SAR 图像的极化特征，并且具有提高人工林 GSV 的更大潜力。
