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首页> 外文期刊>GIScience & remote sensing >Estimating fractional green vegetation cover of Mongolian grasslands using digital camera images and MODIS satellite vegetation indices
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Estimating fractional green vegetation cover of Mongolian grasslands using digital camera images and MODIS satellite vegetation indices

机译：利用数码相机图像和MODIS卫星植被指数估算蒙古草地的绿色植被覆盖率

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摘要

Fractional green vegetation cover (FVC) is a useful indicator for monitoring grassland status. Satellite imagery with coarse spatial but high temporal resolutions has been preferred to monitor seasonal and inter-annual FVC dynamics in wide geographic area such as Mongolian steppe. However, the coarse spatial resolution can cause a certain uncertainty in the satellite-based FVC estimation, which calls attention to develop a robust statistical test for the relationship between field FVC and satellite-derived vegetation indices. In the arid and semi-arid Mongolian steppe, nadir pointing digital camera images (DCI) were collected and used to produce a FVC dataset to support the evaluation of satellite-based FVC retrievals. An optimal DCI processing method was determined with respect to three color spaces (RGB, HIS, L*a*b*) and six green pixel classification algorithms, from which a country-wide dataset of DCI-FVC was produced and used for evaluating the accuracy of satellite-based FVC estimates from MODIS vegetation indices. We applied three empirical and three semi-empirical MODIS-FVC retrieval models. DCI data were collected from 96 sites across the Mongolian steppe from 2012 to 2014. The histogram algorithm using the hue (H) value of the HIS color space was the optimal DCI method (r(2) = 0.94, percent root-mean-square-error (RMSE) = 7.1%). For MODIS-FVC retrievals, semi-empirical Baret model was the best-performing model with the highest r(2) (0.69) and the lowest RMSE (49.7%), while the lowest MB (+1.1%) was found for the regression model with normalized difference vegetation index (NDVI). The high RMSE (>50% or so) is an issue requiring further enhancement of satellite-based FVC retrievals accounting for key plant and soil parameters relevant to the Mongolian steppe and for scale mismatch between sampling and MODIS data.
机译：部分绿色植被覆盖度（FVC）是监测草地状况的有用指标。具有较宽的空间分辨率和较高的时间分辨率的卫星图像已被广泛用于监测蒙古草原等广阔地理区域的季节性和年际FVC动态。但是，粗略的空间分辨率可能会在基于卫星的FVC估算中引起一定的不确定性，这引起了人们的注意，需要针对田间FVC与卫星衍生的植被指数之间的关系进行可靠的统计检验。在干旱和半干旱的蒙古草原，收集了最低点数码相机图像（DCI），并将其用于生成FVC数据集，以支持对基于卫星的FVC检索进行评估。针对三种色彩空间（RGB，HIS，L * a * b *）和六种绿色像素分类算法确定了一种最佳的DCI处理方法，由此产生了全国范围的DCI-FVC数据集并用于评估基于MODIS植被指数的卫星FVC估算的准确性。我们应用了三个经验和三个半经验的MODIS-FVC检索模型。从2012年至2014年从蒙古草原的96个地点收集DCI数据。使用HIS颜色空间的色相（H）值的直方图算法是最佳DCI方法（r（2）= 0.94，均方根百分比） -错误（RMSE）= 7.1％）。对于MODIS-FVC检索，半经验的Baret模型是表现最佳的模型，具有最高的r（2）（0.69）和最低的RMSE（49.7％），而发现最低的MB（+ 1.1％）用于回归具有标准化差异植被指数（NDVI）的模型。 RMSE较高（> 50％左右）是一个问题，需要进一步增强基于卫星的FVC检索，这要考虑与蒙古草原有关的关键植物和土壤参数，以及采样和MODIS数据之间的比例失配。

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