Changes in ecosystem service values in Zhoushan Island using remote sensing time series data

2017 ◽  
Author(s):  
Yanpei Qin ◽  
Guangwei Zhen ◽  
Chaokui Li ◽  
Fang Gong ◽  
Xiaoping Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Ecosystem Service ◽  
Time Series Data ◽  
Series Data ◽  
Sensing Time

scholarly journals Determination of Key Phenological Phases of Winter Wheat Based on the Time-Weighted Dynamic Time Warping Algorithm and MODIS Time-Series Data

2021 ◽  
Vol 13 (9) ◽  
pp. 1836
Author(s):  
Fa Zhao ◽  
Guijun Yang ◽  
Xiaodong Yang ◽  
Haiyan Cen ◽  
Yaohui Zhu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Winter Wheat ◽  
Time Series Data ◽  
Series Data ◽  
Time Warping ◽  
Accumulated Temperature ◽  
Dynamic Time ◽  
Sensing Time ◽  
Phenological Phases

Accurate determination of phenological information of crops is essential for field management and decision-making. Remote sensing time-series data are widely used for extracting phenological phases. Existing methods mainly extract phenological phases directly from individual remote sensing time-series, which are easily affected by clouds, noise, and mixed pixels. This paper proposes a novel method of phenological phase extraction based on the time-weighted dynamic time warping (TWDTW) algorithm using MODIS Normalized Difference Vegetation Index (NDVI) 5-day time-series data with a spatial resolution of 500 m. Firstly, based on the phenological differences between winter wheat and other land cover types, winter wheat distribution is extracted using the TWDTW classification method, and the results show that the overall classification accuracy and Kappa coefficient reach 94.74% and 0.90, respectively. Then, we extract the pure winter-wheat pixels using a method based on the coefficient of variation, and use these pixels to generate the average phenological curve. Next, the difference between each winter-wheat phenological curve and the average winter-wheat phenological curve is quantitatively calculated using the TWDTW algorithm. Finally, the key phenological phases of winter wheat in the study area, namely, the green-up date (GUD), heading date (HD), and maturity date (MD), are determined. The results show that the phenological phase extraction using the TWDTW algorithm has high accuracy. By verification using phenological station data from the Meteorological Data Sharing Service System of China, the root mean square errors (RMSEs) of the GUD, HD, and MD are found to be 9.76, 5.72, and 6.98 days, respectively. Additionally, the method proposed in this article is shown to have a better extraction performance compared with several other methods. Furthermore, it is shown that, in Hebei Province, the GUD, HD, and MD are mainly affected by latitude and accumulated temperature. As the latitude increases from south to north, the GUD, HD, and MD are delayed, and for each 1° increment in latitude, the GUD, HD, and MD are delayed by 4.84, 5.79, and 6.61 days, respectively. The higher the accumulated temperature, the earlier the phenological phases occur. However, latitude and accumulated temperature have little effect on the length of the phenological phases. Additionally, the lengths of time between GUD and HD, HD and MD, and GUD and MD are stable at 46, 41, and 87 days, respectively. Overall, the proposed TWDTW method can accurately determine the key phenological phases of winter wheat at a regional scale using remote sensing time-series data.

scholarly journals Identification of dynamically homogeneous areas with time series segmentation of remote sensing data

2018 ◽  
Vol 42 (3) ◽  
pp. 447-456 ◽  
Author(s):  
D. E. Plotnikov ◽  
P. A. Kolbudaev ◽  
S. A. Bartalev
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Time Series Data ◽  
Computation Time ◽  
Remote Sensing Data ◽  
Identification Accuracy ◽  
Series Data ◽  
Homogeneous Surface ◽  
Multi Temporal ◽  
Sensing Time

We propose a method of segmentation of remote sensing time series data, which exploits multi-temporal information to identify objects’ boundaries. Extracting homogeneous objects with similar temporal behavior, the method analyzes large volumes of multi-temporal input data in a piecewise way and produces a consistent output segmentation layer for large territories. Segment building logic is simplified to minimize the computation time, while objects’ boundary identification accuracy remains sufficient for remote monitoring and mapping of vegetation, and specifically, agricultural crops. At the Space Research Institute of the RAS, the proposed method is currently applied for automated on-line satellite imagery analysis for recognition and mapping of (winter and spring) crops on large territories and land-use evaluation. The method successfully deals with gaps in remote sensing time series data and performs well even when input images are contaminated with speckle noise. Due to its  ability to map dynamically homogeneous surface areas with partially missing data, the method provides a potential for their recovery.

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