Complex vegetation cover classification study of the Yellow river basin based on NDVI data

Abstract

In order to better reflect the response of natural geographical environment factors, such as hydrology, climate and soil, to the changes of land use/land cover, land cover classification using multi-temporal and multi-spectral remote sensing observation data has been an effective technique. This paper adopts a multi-dimension classification method to classify the vegetation cover of the Yellow River Basin (YRB) in China by using NOAA/AVHRR remote sensing image as data source. Better classification resolution, which Mill provide scientific basis to study on increasingly prominent ecological-environmental problems, hydrology and water resources of the YRB, is acquired by this complex classification method. With the support of geography information system (GIS) and remote sensing (RS), this paper makes use of principal component transformation (KL transformation) to classify the vegetation cover using the Normalized Difference Vegetation Index (NDVI) data of NOAA/AVHRR remote sensing image as data source with spatial and temporal resolution at 1 km times 1 km and a vegetation growth cycle. Principal component transformation is first carried out on 3 normalized images of bio-temperature (BT), potential evapotranspiration rate (PER) and annual precipitation (P) which are the controlling factors on vegetation distribution pattern and the first principal component is chosen as the first classification vector of the digital image of the vegetation cover classification of the YRB. Because the basin topography is complex and underlaying surface condition has great impact on vegetation growth, Digital Elevation Model (DEM) image of the YRB became the second classification vector. Then, the first 3 principal components from the time series of the 12 monthly NDVI images are combined with the first and the second classification vectors to consist of the integrated image with 5 bands. After integrating the multidimension information, the vegetation cover of the YRB is finally classified into two grads with iterative self-organizing data analysis technique A (ISODATA) and unsupervised classification method, including 8 vegetation types and 25 vegetation subtypes