scholarly journals Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2

2019 ◽  
Vol 11 (10) ◽  
pp. 1257 ◽  
Author(s):  
Ovidiu Csillik ◽  
Mariana Belgiu ◽  
Gregory P. Asner ◽  
Maggi Kelly
Keyword(s):  
Dynamic Time Warping ◽  
Vegetation Indices ◽  
Single Band ◽  
Time Constraints ◽  
Time Warping ◽  
Object Based ◽  
Crop Mapping ◽  
Dynamic Time ◽  
Sentinel 2 ◽  
Multi Band

The increasing volume of remote sensing data with improved spatial and temporal resolutions generates unique opportunities for monitoring and mapping of crops. We compared multiple single-band and multi-band object-based time-constrained Dynamic Time Warping (DTW) classifications for crop mapping based on Sentinel-2 time series of vegetation indices. We tested it on two complex and intensively managed agricultural areas in California and Texas. DTW is a time-flexible method for comparing two temporal patterns by considering their temporal distortions in their alignment. For crop mapping, using time constraints in computing DTW is recommended in order to consider the seasonality of crops. We tested different time constraints in DTW (15, 30, 45, and 60 days) and compared the results with those obtained by using Euclidean distance or a DTW without time constraint. Best classification results were for time delays of both 30 and 45 days in California: 79.5% for single-band DTWs and 85.6% for multi-band DTWs. In Texas, 45 days was best for single-band DTW (89.1%), while 30 days yielded best results for multi-band DTW (87.6%). Using temporal information from five vegetation indices instead of one increased the overall accuracy in California with 6.1%. We discuss the implications of DTW dissimilarity values in understanding the classification errors. Considering the possible sources of errors and their propagation throughout our analysis, we had combined errors of 22.2% and 16.8% for California and 24.6% and 25.4% for Texas study areas. The proposed workflow is the first implementation of DTW in an object-based image analysis (OBIA) environment and represents a promising step towards generating fast, accurate, and ready-to-use agricultural data products.

scholarly journals Mapping Winter Wheat in North China Using Sentinel 2A/B Data: A Method Based on Phenology-Time Weighted Dynamic Time Warping

2020 ◽  
Vol 12 (8) ◽  
pp. 1274 ◽  
Author(s):  
Qi Dong ◽  
Xuehong Chen ◽  
Jin Chen ◽  
Chishan Zhang ◽  
Licong Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
Dynamic Time Warping ◽  
Vegetation Indices ◽  
Policy Formulation ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Time Warping ◽  
Sentinel 2A ◽  
Dynamic Time

Accurate mapping of winter wheat over a large area is of great significance for guiding policy formulation related to food security, farmland management, and the international food trade. Due to the complex phenological features of winter wheat, the cloud contamination in time-series imagery, and the influence of the soil/snow background on vegetation indices, there remains no effective method for mapping winter wheat at a medium spatial resolution (10–30 m). In this study, we proposed a novel method called phenology-time weighted dynamic time warping (PT-DTW) for identifying winter wheat based on Sentinel 2A/B time-series data. The main advantages of PT-DTW include (1) the use of phenological features in two periods, i.e., the greenness increase before winter and greenness decrease after heading, which are common to all winter wheat and are distinct from the features of other land cover types, and (2) the use of the normalized differential phenology index (NDPI) instead of traditional vegetation indices to provide more robust vegetation information and to suppress the adverse impacts of soil and snow cover, especially during the before-winter growth period. The proposed PT-DTW method was employed for winter wheat mapping based on Sentinel 2A/B data on the Huang-Huai Plain, China. Validation with visually interpreted samples showed that the produced winter wheat map achieved an overall classification accuracy of 89.98% and a kappa coefficient of 0.7978, outperforming previous winter wheat classification methods. Moreover, the planting area derived from PT-DTW agreed well with census data at the municipal level, with a coefficient of determination of 0.8638, indicating that the winter wheat map produced at 20 m resolution was reliable overall. Therefore, the PT-DTW method is recommended for winter wheat mapping over large areas.

scholarly journals COHERENCE and BACKSCATTER BASED CROPLAND MAPPING USING MULTI-TEMPORAL SENTINEL-1 with DYNAMIC TIME WARPING

2021 ◽  
Vol XLIII-B5-2021 ◽  
pp. 37-41
Author(s):  
O. G. Narin ◽  
S. Abdikan ◽  
C. Bayik ◽  
A. Sekertekin ◽  
A. Delen ◽  
...  
Keyword(s):  
Time Series ◽  
Dynamic Time Warping ◽  
Weather Conditions ◽  
Data Sets ◽  
Atmospheric Conditions ◽  
Time Warping ◽  
Multi Temporal ◽  
Crop Mapping ◽  
Dynamic Time ◽  
Cropland Mapping

Abstract. Cropland mapping is an important inventory for food security and decision making operated by governments. Crop mapping is used to identify the croplands and their spatial distribution. For a reliable analysis and forecast for projection, multi-temporal data play a key role. Even current open and frequent optical satellite data such as Sentinel-2 and Landsat support monitoring, they are not always operational due to atmospheric conditions (rain, cloud cover, haze, etc.). On the other hand, Synthetic Aperture Radar (SAR) satellites provide alternative data sets compared to optical satellites since they can acquire images under all weather conditions. In this study, an annual cropland monitoring study is conducted using Sentinel-1 SAR. For the investigation, Tokat Province an agricultural region of Turkey, where the main source of income is agriculture, was selected. There are 4 different vegetation species (wheat, sunflower, sugar beet, corn) in the study area. Sentinel-1 data was used to generate time-series of each class and phenological structures of the crops. In this context, backscatter images of both vertical-vertical (VV) and vertical-horizontal (VH) polarized data, and coherence of both VV and VH were produced from Sentinel-1 data. Time-Weighted Dynamic Time-Warping (TWDTW) classification approach was used over cropland. The produced time-series are classified under different scenarios. The results showed that only coherence has provided higher accuracies about 81% compared to using only backscatter images as 49%.

Multichannel sparse spike deconvolution based on dynamic time warping

2021 ◽  
Author(s):  
Xiaowei Zhao ◽  
Shangxu Wang ◽  
Sanyi Yuan ◽  
Liang Cheng ◽  
Youjun Cai
Keyword(s):  
Dynamic Time Warping ◽  
Time Warping ◽  
Dynamic Time
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