Since the 1990s, forest resource maps and forest variable estimates for small areas have been produced by combining national forest inventory (NFI) field plot data, optical satellite images and numerical map data. A non-parametric |$k$|-NN method has frequently been employed. In Finland, such multi-source NFI (MS-NFI) forest variable estimates for municipalities have been produced eight times. A relatively large variation has been observed between subsequent estimates. In this study, a large-scale evaluation of small-area estimates from an MS-NFI conducted in 2013 was carried out in comparison with pure NFI field data-based estimates and error estimates. The proportion of municipalities with significant differences was larger than expected, e.g. over 10% for the mean volume, which indicates systematic error in the small-area estimates. A multi-temporal data fusion combining MS-NFI estimators from three time points—2011, 2013 and 2015—was tested as a means to improve single time point MS-NFI estimates of the mean volumes of growing stock and of tree species groups. A generalized least squares (GLS) technique and unweighted averaging were tested. The improvement was small but consistent when validated against the NFI field data-based estimates for the municipalities. The unweighted averaging worked nearly as well as a GLS estimator.

中文翻译：

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通过多时相数据融合减少多源森林清单小面积估算中的误差

自1990年代以来，通过结合国家森林清单（NFI）田间地块数据，光学卫星图像和数字地图数据，制作了小区域的森林资源图和森林变量估计值。非参数| $ k $ |-NN方法已被频繁使用。在芬兰，已经为市政当局提供了这种多源NFI（MS-NFI）森林变量估计值。在随后的估计之间观察到相对较大的变化。在这项研究中，与基于纯NFI现场数据的估算和误差估算相比，2013年对MS-NFI进行的小面积估算进行了大规模评估。具有显着差异的市政当局的比例比预期的要大，例如，平均数量超过10％，这表明小区域估算中存在系统误差。结合来自三个时间点（2011年，2013年和2015年）的MS-NFI估计量的多时相数据融合进行了测试，以此作为一种方法来改进MS-NFI对生长种群和树木种类平均数量的单个时间点估计。测试了广义最小二乘（GLS）技术和未加权平均。相对于市政当局基于NFI实地数据的估计值进行验证时，改善幅度很小，但始终如一。未加权平均的效果几乎与GLS估算器一样好。