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李晓雅，田昕，段涛，曹晓明，杨凯捷，卢琦，王锋.2023.融合无人机和卫星影像的温带疏林草原木本和草本植物覆盖度遥感估算.遥感学报，27（9）： 2139-2152
Li X Y，Tian X，Duan T，Cao X M，Yang K J，Lu Q and Wang F. 2023. Estimation of fractional woody and herbaceous vegetation cover in Temperate Sparse Forest Grassland using fusion of UAV and Satellite imagery. National Remote Sensing Bulletin， 27（9）：2139-2152
分布于中国半干旱区的温带疏林草原生态系统，是森林到草原之间过渡的一种生态系统类型，是在独特的气候和地形条件下发育于沙地的地带性顶级植被群落。疏林草原具有乔木、灌木和草本植被混合生长的特点、空间异质性高。植被遥感监测难度大，至今仍是全球范围内最不精确的土地覆盖类型。如何兼顾精度和范围，实现温带疏林草原区域尺度不同类型植物生长状态监测是当前干旱区植被遥感的热点和难点。本研究基于机器学习算法，通过近地面无人机遥感观测平台获取地表植被类型信息构建训练数据集，结合高分辨率卫星影像，建立疏林草原木本、草本植物覆盖度估算模型，实现了由无人机到卫星的温带疏林草原木本和草本植物覆盖度的同步估算，并比较了两种高分辨率卫星影像对疏林草原木本和草本植被覆盖度估算的差异。研究结果表明：（1）利用无人机近地遥感影像能够准确分类地表覆盖类型，为区域温带疏林草原木本、草本植物覆盖度估算模型提供大量精确的训练样本数据；（2）基于机器学习算法，利用高分六号（GF-6）和哨兵二号（Sentinel-2）两种高分辨率卫星影像建立的疏林草原覆盖度模型均可较好的实现木本、草本植物覆盖度的估算。其中，基于GF-6的疏林草原木本、草本覆盖度估计结果与无人机观测值的决定系数分别为0.72和0.66，均方根误差分别为6.76%和10.69%，估算精度分别为46.31%和77.88%；基于Sentinel-2的疏林草原木本、草本覆盖度估计结果与无人机观测值的决定系数分别为0.72和0.81，均方根误差分别为6.53%和8.20%，估算精度分别为54.30%和83.17%；（3）基于Sentinel-2卫星影像的疏林草原木本和草本植物覆盖度估算精度稍高于GF-6卫星，基于两个卫星影像的草本植物覆盖度的估算精度都要显著高于木本植物。本研究为实现疏林草原木本植物和草本植物覆盖度由景观尺度扩展到区域尺度的估测提供了新的思路，从无人机到卫星跨尺度协同观测的方法能够为区域温带疏林草原不同生活型植物生长状况监测提供有效的方法支撑，未来基于多时相的高分辨率卫星数据可进一步实现区域尺度温带疏林草原木本植物和草本植物的动态监测。
China’s temperate sparse forest grassland is a transition ecosystem between forest and grassland and is the top-level ecosystem that evolves under the unique climate and topography of northern China. Sparse forest grassland is characterized by mixed woody and herbaceous vegetation
which are difficult to directly distinguish by remote sensing even when using high-spatial-resolution satellite data. Consequently
mapping Fractional Woody and Herbaceous Vegetation Cover (FWHVC) in this ecosystem is challenging. How to precisely monitor the growth status of woody and herbaceous vegetation in sparse forest grassland on a regional scale is a popular and difficult topic in vegetation remote sensing in dryland.
This study proposed a novel method of FWHVC estimation in temperate sparse forest grassland based on Unmanned Aircraft Vehicle (UAV) aerial images and high-spatial-resolution satellite images (GF-6 and Sentinel-2) via a machine learning algorithm. Training datasets of FWHVC were derived from a very-high-resolution aerial image (2.41 cm/pixel). Meanwhile
this study compared the results of FWHVC estimated from GF-6 and Sentinel-2 images.
The results were as follows (1) The UAV platform could precisely capture the land cover type and provide a large number of reliable training datasets of FWHVC. (2) FWHVC could be estimated well on a regional scale based on both GF-6 and Sentinel-2 high-resolution satellite data through the machine learning algorithm. The FWHVCs derived from GF-6 images and UAV aerial images had determination coefficients
R
2
of 0.72 and 0.66
root-mean-square errors (RMSEs) of 6.76% and 10.69%
and estimated accuracies (EAs) of 46.31% and 77.88%
respectively. The FWHVCs derived from Sentinel-2 images and UAV aerial images had determination coefficients
R
2
of 0.72 and 0.81
RMSEs of 6.53% and 8.20%
and EAs of 54.30% and 83.17%
respectively. (3) The EA of the FWHVC estimated from Sentinel-2 was slightly better than that of GF-6. Meanwhile
the EA of fractional herbaceous vegetation cover estimation was higher than that of woody vegetation cover estimation for both satellite images.
This paper provides a new way to estimate FWHVC in temperate sparse forest grassland on a regional scale by using multisource remote sensing data and a machine learning algorithm. The multiscale approach could provide new methodological support to accurately monitor woody and herbaceous vegetation cover in temperate sparse forest grassland. In the future
FWHVC in sparse forest grassland can be monitored dynamically by utilizing long-term and high-spatial-resolution satellite remote sensing data on a regional scale.
遥感榆树疏林无人机高分六号哨兵二号随机森林分类与回归树
remote sensingElm sparse forest grasslandUnmanned Aerial Vehicle (UAV)GF-6Sentinel-2random forestClassification and Regression Tree(CART)
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