Abstract
The distribution of C3 and C4 vegetation plays an important role in the global carbon cycle and climate change. Knowledge of the distribution of C3 and C4 vegetation at a high spatial resolution over local or regional scales helps us to understand their ecological functions and climate dependencies. In this study, we classified C3 and C4 vegetation at a high resolution for spatially heterogeneous landscapes. First, we generated a high spatial and temporal land surface reflectance dataset by blending MODIS (Moderate Resolution Imaging Spectroradiometer) and ETM+ (Enhanced Thematic Mapper Plus) data. The blended data exhibited a high correlation (R2 = 0.88) with the satellite derived ETM+ data. The time-series NDVI (Normalized Difference Vegetation Index) data were then generated using the blended high spatio-temporal resolution data to capture the phenological differences between the C3 and C4 vegetation. The time-series NDVI revealed that the C3 vegetation turns green earlier in spring than the C4 vegetation, and senesces later in autumn than the C4 vegetation. C4 vegetation has a higher NDVI value than the C3 vegetation during summer time. Based on the distinguished characteristics, the time-series NDVI was used to extract the C3 and C4 classification features. Five features were selected from the 18 classification features according to the ground investigation data, and subsequently used for the C3 and C4 classification. The overall accuracy of the C3 and C4 vegetation classification was 85.75% with a kappa of 0.725 in our study area.
Highlights
Research on biogeochemical cycling, the global carbon cycle and climate change have shown that the spatial distribution of C3 and C4 vegetation is relevant to atmospheric CO2 and temperature changes [1,2,3,4].C4 plants tend to favor warmer environments, and C3 plants thrive in areas with lower temperatures in the mid-latitudes [3,5]
The predicted Normalized Difference Vegetation Index (NDVI) was calculated using the blended reflectance at red (R) and near infrared (NIR)
We presented a framework for high spatial resolution C3 and C4 vegetation classification in regions with fragmented landscapes
Summary
The global carbon cycle and climate change have shown that the spatial distribution of C3 and C4 vegetation is relevant to atmospheric CO2 and temperature changes [1,2,3,4].C4 plants tend to favor warmer environments (warm season plants), and C3 plants thrive in areas with lower temperatures (cool season plants) in the mid-latitudes [3,5]. The balance between C3 and C4 plants changes with the atmospheric CO2 content variation [6,7]. Mapping of C3 and C4 plants is important for the study of regional climate change and carbon cycle. Previous studies have attempted to map C3 and C4 plants using remote sensing data. Hyperspectral data have proven to be effective in mapping C3 and C4 plants. Irisarri claimed that the hyperspectral data could discriminate C3 and C4 plants inside a laboratory [8]. The chlorophyll fluorescence derived from hyperspectral remote sensing data was used for discriminating C3 and C4 plants [10,11,12]
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