Remote sensing of seasonal variability of fractional vegetation cover and its object-based spatial pattern analysis over mountain areas
Remote sensing of seasonal variability of fractional vegetation cover and its object-based spatial pattern analysis over mountain areas
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DOI：
10.1016/j.isprsjprs.2012.11.008
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发表时间：
2013-03
影响因子：
12.7
通讯作者：
Guijun Yang;R. Pu;Jixian Zhang;Chunjiang Zhao;Haikuan Feng;Jihua Wang
中科院分区：
文献类型：
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作者：
Guijun Yang;R. Pu;Jixian Zhang;Chunjiang Zhao;Haikuan Feng;Jihua Wang
植被覆盖度是反映山地生态系统状况的重要指标。研究森林植被覆盖率的季节变化，有助于区域生态环境安全，有助于评价山区生态系统的恢复状况，为北京等特大城市的山地森林规划和景观重建提供科学依据。遥感已被证明是调查山区植被的最有力和最可行的工具之一。然而，地形和大气的影响可能会产生巨大的误差，定量反演的FVC数据从卫星图像的山区。此外，用于评估FVC季节性波动的最常用的分析方法是基于每像素分析，而不管空间背景如何，这导致基于像素的FVC值对于景观和生态系统应用是可行的。为了解决这些问题，我们提出了一种新的方法，采用了一个修订的物理为基础的（RPB）模型来纠正大气和地形引起的照明效应对陆地卫星图像，一个改进的植被指数（VI）为基础的技术估计的FVC，和一个自适应均值漂移方法为基础的对象的FVC分割。生成了一系列用于分段FVC分析的指标，包括各种面积指标、斑块指标、形状指标和多样性指标。基于不同时期多幅影像的FVC值和景观尺度，对北京山区的FVC季节变化进行了遥感研究。实验结果表明：（a）RPB-NDVI的四季平均值比大气校正值增加了约10%;（B）地面观测的FVC值与遥感估算的FVC值具有较强的一致性（3）研究区景观特征存在季节变化，景观多样性在5、6月份达到最大值。
Fractional vegetation cover (FVC) is an important indicator of mountain ecosystem status. A study on the seasonal changes of FVC can be beneficial for regional eco-environmental security, which contributes to the assessment of mountain ecosystem recovery and supports mountain forest planning and landscape reconstruction around megacities, for example, Beijing, China. Remote sensing has been demonstrated to be one of the most powerful and feasible tools for the investigation of mountain vegetation. However, topographic and atmospheric effects can produce enormous errors in the quantitative retrieval of FVC data from satellite images of mountainous areas. Moreover, the most commonly used analysis approach for assessing FVC seasonal fluctuations is based on per-pixel analysis regardless of the spatial context, which results in pixel-based FVC values that are feasible for landscape and ecosystem applications. To solve these problems, we proposed a new method that incorporates the use of a revised physically based (RPB) model to correct both atmospheric and terrain-caused illumination effects on Landsat images, an improved vegetation index (VI)-based technique for estimating the FVC, and an adaptive mean shift approach for object-based FVC segmentation. An array of metrics for segmented FVC analyses, including a variety of area metrics, patch metrics, shape metrics and diversity metrics, was generated. On the basis of the individual segmented FVC values and landscape metrics from multiple images of different dates, remote sensing of the seasonal variability of FVC was conducted over the mountainous area of Beijing, China. The experimental results indicate that (a) the mean value of the RPB–NDVI in all seasons was increased by approximately 10% compared with that of the atmospheric correction-NDVI; (b) a strong consistency was demonstrated between ground-based FVC observations and FVC estimated through remote sensing technology (R2=0.8527, RMSE=0.0851); and (c) seasonal changes in the landscape characteristics existed, and the landscape diversity reached its maximum in May and June in the study area.