scholarly journals Land Cover Classification of Landsat Data with Phenological Features Extracted from Time Series MODIS NDVI Data

2014 ◽  
Vol 6 (11) ◽  
pp. 11518-11532 ◽  
Author(s):  
Kun Jia ◽  
Shunlin Liang ◽  
Xiangqin Wei ◽  
Yunjun Yao ◽  
Yingru Su ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Land Cover Classification ◽  
Landsat Data ◽  
Modis Ndvi

scholarly journals Time-Series Model-Adjusted Percentile Features: Improved Percentile Features for Land-Cover Classification Based on Landsat Data

2020 ◽  
Vol 12 (18) ◽  
pp. 3091
Author(s):  
Shuai Xie ◽  
Liangyun Liu ◽  
Jiangning Yang
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Land Cover Classification ◽  
Time Series Model ◽  
Training Data ◽  
Series Data ◽  
Landsat Data ◽  
Data Sampling ◽  
Continuous Change

Percentile features derived from Landsat time-series data are widely adopted in land-cover classification. However, the temporal distribution of Landsat valid observations is highly uneven across different pixels due to the gaps resulting from clouds, cloud shadows, snow, and the scan line corrector (SLC)-off problem. In addition, when applying percentile features, land-cover change in time-series data is usually not considered. In this paper, an improved percentile called the time-series model (TSM)-adjusted percentile is proposed for land-cover classification based on Landsat data. The Landsat data were first modeled using three different time-series models, and the land-cover changes were continuously monitored using the continuous change detection (CCD) algorithm. The TSM-adjusted percentiles for stable pixels were then derived from the synthetic time-series data without gaps. Finally, the TSM-adjusted percentiles were used for generating supervised random forest classifications. The proposed methods were implemented on Landsat time-series data of three study areas. The classification results were compared with those obtained using the original percentiles derived from the original time-series data with gaps. The results show that the land-cover classifications obtained using the proposed TSM-adjusted percentiles have significantly higher overall accuracies than those obtained using the original percentiles. The proposed method was more effective for forest types with obvious phenological characteristics and with fewer valid observations. In addition, it was also robust to the training data sampling strategy. Overall, the methods proposed in this work can provide accurate characterization of land cover and improve the overall classification accuracy based on such metrics. The findings are promising for percentile-based land cover classification using Landsat time series data, especially in the areas with frequent cloud coverage.

scholarly journals Land Cover Classification Based on Fused Data from GF-1 and MODIS NDVI Time Series

2016 ◽  
Vol 8 (9) ◽  
pp. 741 ◽  
Author(s):  
Fanjie Kong ◽  
Xiaobing Li ◽  
Hong Wang ◽  
Dengfeng Xie ◽  
Xiang Li ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Land Cover Classification ◽  
Modis Ndvi ◽  
Ndvi Time Series
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