Abstract
Accurate regional and global information on land cover and its changes over time is crucial for environmental monitoring, land management, and planning. In this study, we selected Fengning County, in China’s Hebei Province, as a case study area. Using satellite data, we generated fused normalized-difference vegetation index (NDVI) data with high spatial and temporal resolution by utilizing the STARFM algorithm to produce a fused GF-1 and MODIS NDVI dataset. We extracted seven phenological parameters (including the start, end, and length of the growing season, base value, mid-season date, maximum NDVI, seasonal NDVI amplitude) from a fused NDVI time-series after reconstruction using the TIMESAT software. We developed four classification scenarios based on different combinations of GF-1 spectral features, the fused NDVI time-series, and the phenological parameters. We then classified the land cover using a support vector machine and analyzed the classification accuracies. We found that the proposed method achieved satisfactory classification results, and that the combination of the fused NDVI data with the extracted phenological parameters significantly improved classification accuracy. The classification accuracy based on the composited GF-1 multi-spectral bands combined with the phenological parameters was the highest among the four scenarios, with an overall classification accuracy of 88.8% and a Kappa coefficient of 0.8714, which represent increases of 9.3 percentage points and 0.1073, respectively, compared with GF-1 spectral data alone. The producer’s and user’s accuracy for different land cover types improved, with a few exceptions, and cropland and broadleaf forest had the largest increase.
Highlights
Mapping and monitoring of land cover have been widely recognized as an important part of research on global environmental change [1]
To of testthe theGF-1 accuracy of this system, we developed four classification scenarios on after various combinations spectral reflectance data, the fused time-series, and the based on various combinations of the GF-1 spectral reflectance data, the fused time-series, and phenological parameters
To explore the roles of normalized-difference vegetation index (NDVI) data with high spatial and temporal resolution and the derived phenological parameters in improving land cover classification accuracy, we developed four classification scenarios, as shown in Table 3: Scenario 1 used only the GF-1 multi-spectral data, including the image obtained in August 2015 (i.e., 4 multi-spectral bands)
Summary
Mapping and monitoring of land cover have been widely recognized as an important part of research on global environmental change [1]. Land cover types, their distributions, and their dynamics over time are major determinants of terrestrial ecosystem processes, global biogeochemical cycles, climate change, biodiversity, and regional sustainable development [2]. Remote sensing technology provides inexpensive, convenient, and timely observations at regional to global scales, and represents a key means to conduct land cover classification and monitoring. To support these tasks, a range of classification methods have been developed. Multispectral image data from a single date have been commonly used to map land cover, but more recently, efforts have focused on using multi-temporal images or time series for vegetation indices such
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