Assuntos
Panthera , Tigres , Animais , Panthera/genética , Tigres/genética , Haplótipos , Genoma , Cromossomos/genéticaRESUMO
To investigate the resources of medicinal plant, such as wild Apocynum, supervised classification based on Principal Component Analysis (PCA) and texture feature were used to monitor wild medicinal plants from image captured by ZY-3 and World-view-2 and compare which satellite Image are more appropriate to monitor the wild medicinal plants. The research results shows that: for more complex growth conditions wild medicinal plants Apocynum, high-resolution images Worldview-2 is more suitable for its remote identification, the low-resolution satellite ZY-3 can only recognizes the wild medicinal plants which distributed intensively. If the study target distribution is more intensive and larger scale, and cultivated type medicinal plants, the use of satellite ZY-3 in low resolution remote sensing data to identify the target can be a good choice, it is not necessary to buy high-resolution data, in order to avoid waste of expenditure, for the scattered distribution, the high-resolution satellite imagery data may be indispensable to identify targets.
Assuntos
Apocynum/crescimento & desenvolvimento , Plantas Medicinais/crescimento & desenvolvimento , Tecnologia de Sensoriamento Remoto/métodos , Apocynum/química , China , Conservação dos Recursos Naturais , Sistemas de Informação Geográfica , Dispersão Vegetal , Plantas Medicinais/químicaRESUMO
To improve accuracy of estimation in planted safflower acreage,we selected agricultural area in Yumin County, Xinjiang as the study area. There safflower was concentrated planted. Supervised classification based on Principal Component Analysis (PCA) and texture feature were used to obtain the safflower acreage from image captured by ZY-3. The classification result was compared with only spectral feature and spectral feature with texture feature. The research result shows that this method can effectively solve the problem of low accuracy and fracture classification result in single data source classification. The overall accuracy is 87.519 1%, which increases by 7.117 2% compared with single data source classification. Therefore, the classification method based on PCA and texture features can be adapted to RS image classification and estimate the acreage of safflower. This study provides a feasible solution for estimation of planted safflower acreage by image captured by ZY-3 satellite.
