Abstract
Time series vegetable indexes (Vis) have been evidenced a useful data to extract vegetable phenology and identify crop types. This paper conducted such a research in Qinghai Province by using Landsat TM images, via four steps, i) sampling single-crop plots and extracting crop spectrums based on pure signle-crop pixels; ii) building time-series vegetable indexes by using Landsat 8 TM images (2013-2014); iii) extracting seasonal parameters according to algorithms defined in TIMESAT program; vi) generating a decision tree for identifying crop types and validate classification accuracy via ground investigation. The results indicate that crops planted in a larger continuous range, such as spring wheat, potato and rapeseed, achieved an acceptable accuracy of above 70%, while crops planted too dispersedly (like broad bean, which is often inter-planted with other crops) or with a too smaller planting range (like barley), remained a poor recognition rates (below 50%). The value of this work lies in it displayed not only the classification accuracy of crop types in this region by using such methodology, but also the feasibility of integrating VIs calculation, seasonal parameter extracting and decision tree generation into one computer program, which is highly desired in this region.
Highlights
Wide-ranging, long-term, and spatially accurate cropland classification data is a valuable source of information for government agencies, private sector organizations, scientists, educators, and others who use land-cover information
Couples of VIs have been evidenced suitable for usage of crop classification, including Enhanced Vegetation Index (EVI), Green Vegetation Index (GVI), Leaf Area Index (LAI), Normalized Difference Vegetation Index (NDVI), Red Green Ratio Index (RGRI), Soil Adjusted Vegetation Index (SAVI) and WorldView Improved Vegetation Index (WVVI) etc
Namely NDVI, EVI and SAVI (Bannari, Morin, Bonn, & Huete,1995; Yang, Di, Yu, & Chen, 2011), were selected to build time-series VIs based on accounts of: i) they are mostly used VIs in crop related research, ii) they have better discriminating ability on land cover phases of crop lands such as dense green crops, sparse vegetation and bare soil, iii) they are derivable from bands calculation of Landsat 8 TM images
Summary
Wide-ranging, long-term, and spatially accurate cropland classification data is a valuable source of information for government agencies, private sector organizations, scientists, educators, and others who use land-cover information. Phenology is the nature’s calendar on periodic biological events and their controlling factors of environment, many interactions in nature depend on timing. Phenology affects most aspects of the environment, including the abundance, distribution, and diversity of organisms, ecosystem services, food webs, and the global cycles of water and carbon. Phenological event like onset of green, mature and senescence often occurs at a relatively stable time node over a long time period, phenological characteristic is frequently used as an auxiliary information or even priority metric to identify plant type, such as distinguish evergreen forest from seasonal forest and trees from shrubs etc
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