Estimation of the Conifer-Broadleaf Ratio in Mixed Forests Based on Time-Series Data
Estimation of the Conifer-Broadleaf Ratio in Mixed Forests Based on Time-Series Data
复制标题
基于时间序列数据的混交林针阔叶比估计
DOI：
10.3390/rs13214426
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发表时间：
2021-11
期刊：
影响因子：
5
通讯作者：
Yanjun Yang
中科院分区：
文献类型：
--
作者：
Ranran Yang;Lei Wang;Qingjiu Tian;Nianxu Xu;Yanjun Yang
天然林多为针阔混交林，针阔混交林实质上是遥感图像上的一个异质混合像元。卫星任务依赖于建模来获取区域或全球植被参数产品。然而，这些检索模型往往假设在像素级的均匀条件下，导致反演精度下降，这是一个问题，异质森林。因此，混交林冠层组成的信息是利用遥感准确反演植被参数的基础。中、高空间分辨率多光谱时间序列数据频率高、覆盖面广，是估算冠层针阔比的重要数据源。本文介绍了一种在树种多样、林冠结构复杂的混交林中估算针阔比的成功方法。在南京紫金山进行试验，收集了叶面积指数（LAI）时间序列和森林样地调查数据。基于反演森林反射率模型（INFORM），模拟了不同针阔比的归一化植被指数（NDVI）时间序列。时间序列相似性分析，以确定典型的可分离的针叶阔叶树的比例。获得了2015年的15张高分一号（GF-1）卫星图像。基于GF-1 NDVI时间序列和半监督k-means聚类方法估算针阔比，总体精度为83.75%。本研究论证了利用野外实测数据和GF-1时间序列准确估算针阔混交林可分离针阔比的可行性。
Most natural forests are mixed forests, a mixed broadleaf-conifer forest is essentially a heterogeneously mixed pixel in remote sensing images. Satellite missions rely on modeling to acquire regional or global vegetation parameter products. However, these retrieval models often assume homogeneous conditions at the pixel level, resulting in a decrease in the inversion accuracy, which is an issue for heterogeneous forests. Therefore, information on the canopy composition of a mixed forest is the basis for accurately retrieving vegetation parameters using remote sensing. Medium and high spatial resolution multispectral time-series data are important sources for canopy conifer-broadleaf ratio estimation because these data have a high frequency and wide coverage. This paper highlights a successful method for estimating the conifer-broadleaf ratio in a mixed forest with diverse tree species and complex canopy structures. Experiments were conducted in the Purple Mountain, Nanjing, Jiangsu Province of China, where we collected leaf area index (LAI) time-series and forest sample plot inventory data. Based on the Invertible Forest Reflectance Model (INFORM), we simulated the normalized difference vegetation index (NDVI) time-series of different conifer-broadleaf ratios. A time-series similarity analysis was performed to determine the typical separable conifer-broadleaf ratios. Fifteen Gaofen-1 (GF-1) satellite images of 2015 were acquired. The conifer-broadleaf ratio estimation was based on the GF-1 NDVI time-series and semi-supervised k-means cluster method, which yielded a high overall accuracy of 83.75%. This study demonstrates the feasibility of accurately estimating separable conifer-broadleaf ratios using field measurement data and GF-1 time series in mixed broadleaf-conifer forests.
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DOI：
10.3390/rs12182943
发表时间：
2020-09
期刊：
Remote. Sens.
影响因子：
--
作者：
Jingxian Yu;Yalan Liu;Yuhuan Ren;Haojie Ma;Dacheng Wang;Yafei Jing;Linjun Yu
通讯作者：
Jingxian Yu;Yalan Liu;Yuhuan Ren;Haojie Ma;Dacheng Wang;Yafei Jing;Linjun Yu
DOI：
10.1016/0034-4257(85)90072-0
发表时间：
1984-02
期刊：
--
影响因子：
--
作者：
W. Verhoef
通讯作者：
W. Verhoef
DOI：
--
发表时间：
2002-07
期刊：
--
影响因子：
--
作者：
Sugato Basu;A. Banerjee;R. Mooney
通讯作者：
Sugato Basu;A. Banerjee;R. Mooney
DOI：
10.3390/rs6010087
发表时间：
2013-12
期刊：
Remote. Sens.
影响因子：
--
作者：
S. Deng;M. Katoh;Qingwei Guan;Na Yin;Mingyang Li
通讯作者：
S. Deng;M. Katoh;Qingwei Guan;Na Yin;Mingyang Li
影响因子：
2.1
作者：
WELLES, JM;NORMAN, JM
通讯作者：
NORMAN, JM