School of Forestry, Northeast Forestry University, Harbin 150040, China.
Saihanba Mechanized Forest Farm of Hebei, Chengde 067000, Hebei, China.
Ying Yong Sheng Tai Xue Bao. 2023 Mar;34(3):605-613. doi: 10.13287/j.1001-9332.202303.025.
Accurately clarifying the applicable spatial scale of 4-Scale model is conducive to improving the accuracy of its application in canopy reflectance simulation of different vegetation types, and to further improving the inversion accuracy of leaf area index, canopy density, and other parameters. Two forest plots (one for broad-leaved forest and one for mixed forest) with each area of 100 m×100 m in Maoershan Experimental Forest Farm, Shangzhi, Heilongjiang, were divided into the spatial scales of 10, 20, 30, 40 and 50 m, respectively. The 4-Scale model was used to simulate forest canopy reflectance. Local mean method, the nearest neighbor method, bilinear interpolation method, and cubic convolution method were used to convert Sentinel-2 images with spatial resolution of 10 m to other scales, with the results being evaluated. The simulated canopy reflectance and remote sensing pixel reflectance were compared and analyzed. The spatial scale of mixed forest and broad-leaved forest suitable for high-precision inversion parameters of 4-Scale model was determined. The results showed that the 4-Scale model underestimated the pixel forest canopy reflectance as a whole. The canopy reflectance of mixed forest and broad-leaved forest had the worst simulation effect at the 20 m scale. Both the root mean square error (RMSE) and the mean absolute error from (MAE) of red and near-infrared band were large. When the scale was >20 m, the simulation effect became better. The applicability of the model was the best when the mixed forest was 40 m and the broad-leaved forest was 30 m. The mean and standard deviation of the reflectance difference between the simulated value and the remote sensing pixel were the minimum in the red and near near-infrared bands, with the minimum RMSE and MAE. The simulation results of mixed forest and broad-leaved forest at 10 m scale were not stable, the rule of mean and standard deviation was inconsistent, and the difference between RMSE and MAE was large under the same band.
准确厘定 4-Scale 模型的适用空间尺度，有利于提高其在不同植被类型冠层反射率模拟中的应用精度，进一步提高叶面积指数、冠层密度等参数的反演精度。在黑龙江省尚志市帽儿山实验林场，选取面积各为 100 m×100 m 的两片森林样地（一片为阔叶林，一片为混交林），分别划分为 10 m、20 m、30 m、40 m 和 50 m 等空间尺度，采用 4-Scale 模型模拟森林冠层反射率，利用局部均值法、最近邻法、双线性内插法和三次卷积法将空间分辨率为 10 m 的 Sentinel-2 影像转换为其他尺度，并进行评价，对比分析模拟冠层反射率与遥感像元反射率。确定适用于 4-Scale 模型高精度反演参数的森林样地空间尺度。结果表明：4-Scale 模型整体上低估了像元森林冠层反射率，混交林和阔叶林的冠层反射率在 20 m 尺度下模拟效果最差，红、近红外波段的均方根误差（RMSE）和平均绝对误差（MAE）均较大，当尺度＞20 m 时，模拟效果逐渐变好，其中混交林的模型适用性最好的尺度为 40 m，阔叶林的模型适用性最好的尺度为 30 m。在红、近红外波段，模拟值与遥感像元反射率差值的均值和标准差最小，RMSE 和 MAE 最小，模型的适用性最好。10 m 尺度下混交林和阔叶林的模拟结果不稳定，均值和标准差规律不统一，相同波段下 RMSE 和 MAE 的差值较大。
