■草地群落的物种组成和变化是推断数量的重要指标,草原的质量和群落演替,准确的监测是评估的基础,保护,利用草地资源。遥感技术为区域地形信息的测量提供了可靠而有力的手段,遥感识别草地物种将提高草地监测的质量和效果。
■使用Soc710VP成像光谱仪获得了不同季节的绢云母-蒿荒漠草原的地面高光谱图像。采用一阶微分处理计算特征参数。方差分析用于提取主要物种,即,TransilienseSeripium(Poljak),红心耳,Petrosimoniasibirica(Pall),不同季节的裸露土地及光谱特征参数和植被指数。在此基础上,使用Fisher判别分析将样品以7:3的比率分成训练集和测试集。光谱特征参数和植被指数用于识别三种主要植物和裸露土地。
■选择识别对象之间具有显着差异(P<0.05)的参数有效区分了不同的土地特征,并且由于生长期和物种的差异,识别参数也有所不同。植被指数建立的识别模型的总体精度按以下顺序下降:6月(98.87%)>9月(91.53%)>4月(90.37%)。通过特征参数建立的识别模型的总体精度按以下顺序下降:9月(89.77%)>6月(88.48%)>4月(85.98%)。
■基于不同月份植被指数的识别模型优于基于特征参数的识别模型,总体准确度从1.76%到9.40%不等。基于高光谱图像数据,利用植被指数作为识别参数,可以识别绢云母-蒿荒漠草原的主要植物,为进一步对群落图像中的物种进行定量分类提供依据。
UNASSIGNED: The species composition of and changes in grassland communities are important indices for inferring the number, quality and community succession of grasslands, and accurate monitoring is the foundation for evaluating, protecting, and utilizing grassland resources. Remote sensing technology provides a reliable and powerful approach for measuring regional terrain information, and the identification of grassland species by remote sensing will improve the quality and effectiveness of grassland monitoring.
UNASSIGNED: Ground hyperspectral images of a sericite-Artemisia desert grassland in different seasons were obtained with a Soc710 VP imaging spectrometer. First-order differential processing was used to calculate the characteristic parameters. Analysis of variance was used to extract the main species, namely, Seriphidium transiliense (Poljak), Ceratocarpus arenarius L., Petrosimonia sibirica (Pall), bare land and the spectral characteristic parameters and vegetation indices in different seasons. On this basis, Fisher discriminant analysis was used to divide the samples into a training set and a test set at a ratio of 7:3. The spectral characteristic parameters and vegetation indices were used to identify the three main plants and bare land.
UNASSIGNED: The selection of parameters with significant differences (P < 0.05) between the recognition objects effectively distinguished different land features, and the identification parameters also differed due to differences in growth period and species. The overall accuracy of the recognition model established by the vegetation index decreased in the following order: June (98.87%) > September (91.53%) > April (90.37%). The overall accuracy of the recognition model established by the feature parameters decreased in the following order: September (89.77%) > June (88.48%) > April (85.98%).
UNASSIGNED: The recognition models based on vegetation indices in different months are superior to those based on feature parameters, with overall accuracies ranging from 1.76% to 9.40% higher. Based on hyperspectral image data, the use of vegetation indices as identification parameters can enable the identification of the main plants in sericite-Artemisia desert grassland, providing a basis for further quantitative classification of the species in community images.