A Spatio-Temporal Data Fusion Model for Generating NDVI Time Series in Heterogeneous Regions

Spatio-temporal data fusion refers to the technique of combining high temporal resolution from coarse satellite images and high spatial resolution from fine satellite images. However, data availability remains a major limitation in algorithm development. Existing spatio-temporal data fusion algorithms require at least one known image pair between the fine and coarse resolution image. However, data which come from two different satellite platforms do not necessarily have an overlap in their overpass times, hence restricting the application of spatio-temporal data fusion. In this paper, a new algorithm named Hopfield Neural Network SPatio-tempOral daTa fusion model (HNN-SPOT) is developed by utilizing the optimization concept in the Hopfield neural network (HNN) for spatio-temporal image fusion. The algorithm derives a synthesized fine resolution image from a coarse spatial resolution satellite image (similar to downscaling), with the use of one fine resolution image taken on an arbitrary date and one coarse image taken on a predicted date. The HNN-SPOT particularly addresses the problem when the fine resolution and coarse resolution images are acquired from different satellite overpass times over the same geographic extent. Both simulated datasets and real datasets over Hong Kong and Australia have been used in the evaluation of HNN-SPOT. Results showed that HNN-SPOT was comparable with an existing fusion algorithm, the spatial and temporal adaptive reflectance fusion model (STARFM). HNN-SPOT assumes consistent spatial structure for the target area between the date of data acquisition and the prediction date. Therefore, it is more applicable to geographical areas with little or no land cover change. It is shown that HNN-SPOT can produce accurate fusion results with >90% of correlation coefficient over consistent land covers. For areas that have undergone land cover changes, HNN-SPOT can still produce a prediction about the outlines and the tone of the features, if they are large enough to be recorded in the coarse resolution image at the prediction date. HNN-SPOT provides a relatively new approach in spatio-temporal data fusion, and further improvements can be made by modifying or adding new goals and constraints in its HNN architecture. Owing to its lower demand for data prerequisites, HNN-SPOT is expected to increase the applicability of fine-scale applications in remote sensing, such as environmental modeling and monitoring.

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Monitoring cotton root rot by synthetic Sentinel-2 NDVI time series using improved spatial and temporal data fusion

Scientific Reports ◽

10.1038/s41598-018-20156-z ◽

2018 ◽

Vol 8 (1) ◽

Cited By ~ 12

Author(s):

Mingquan Wu ◽

Chenghai Yang ◽

Xiaoyu Song ◽

Wesley Clint Hoffmann ◽

Wenjiang Huang ◽

...

Keyword(s):

Time Series ◽

Data Fusion ◽

Root Rot ◽

Temporal Data ◽

Cotton Root ◽

Ndvi Time Series ◽

Sentinel 2

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Coastal Change Patterns from Time Series Clustering of Permanent Laser Scan Data

10.5194/esurf-2020-34 ◽

2020 ◽

Author(s):

Mieke Kuschnerus ◽

Roderik Lindenbergh ◽

Sander Vos

Keyword(s):

Time Series ◽

Laser Scanning ◽

Clustering Algorithm ◽

Coastal Areas ◽

Small Scale ◽

Temporal Data ◽

Agglomerative Clustering ◽

Data Set ◽

Deformation Processes ◽

Spatio Temporal

Abstract. Sandy coasts are constantly changing environments governed by complex interacting processes. Permanent laser scanning is a promising technique to monitor such coastal areas and support analysis of geomorphological deformation processes. This novel technique delivers 3D representations of a part of the coast at hourly temporal and centimetre spatial resolution and allows to observe small scale changes in elevation over extended periods of time. These observations have the potential to improve understanding and modelling of coastal deformation processes. However, to be of use to coastal researchers and coastal management, an efficient way to find and extract deformation processes from the large spatio-temporal data set is needed. In order to allow data mining in an automated way, we extract time series in elevation or range and use unsupervised learning algorithms to derive a partitioning of the observed area according to change patterns. We compare three well known clustering algorithms, k-means, agglomerative clustering and DBSCAN, and identify areas that undergo similar evolution during one month. We test if they fulfil our criteria for a suitable clustering algorithm on our exemplary data set. The three clustering methods are applied to time series of 30 epochs (during one month) extracted from a data set of daily scans covering a part of the coast at Kijkduin, the Netherlands. A small section of the beach, where a pile of sand was accumulated by a bulldozer is used to evaluate the performance of the algorithms against a ground truth. The k-means algorithm and agglomerative clustering deliver similar clusters, and both allow to identify a fixed number of dominant deformation processes in sandy coastal areas, such as sand accumulation by a bulldozer or erosion in the intertidal area. The DBSCAN algorithm finds clusters for only about 44 % of the area and turns out to be more suitable for the detection of outliers, caused for example by temporary objects on the beach. Our study provides a methodology to efficiently mine a spatio-temporal data set for predominant deformation patterns with the associated regions, where they occur.

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Research on a Prediction Method for Carbon Dioxide Concentration Based on an Optimized LSTM Network of Spatio-Temporal Data Fusion

IEICE Transactions on Information and Systems ◽

10.1587/transinf.2021edl8020 ◽

2021 ◽

Vol E104.D (10) ◽

pp. 1753-1757

Author(s):

Jun MENG ◽

Gangyi DING ◽

Laiyang LIU

Keyword(s):

Carbon Dioxide ◽

Data Fusion ◽

Carbon Dioxide Concentration ◽

Prediction Method ◽

Temporal Data ◽

Spatio Temporal ◽

Lstm Network

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A Simple Spatio–Temporal Data Fusion Method Based on Linear Regression Coefficient Compensation

Remote Sensing ◽

10.3390/rs12233900 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3900

Author(s):

Bingxin Bai ◽

Yumin Tan ◽

Gennadii Donchyts ◽

Arjen Haag ◽

Albrecht Weerts

Keyword(s):

Linear Regression ◽

Data Fusion ◽

Spatial Resolution ◽

Temporal Resolution ◽

Fusion Method ◽

Temporal Data ◽

Resolution Image ◽

Coarse Resolution ◽

Fine Spatial Resolution ◽

Spatio Temporal

High spatio–temporal resolution remote sensing images are of great significance in the dynamic monitoring of the Earth’s surface. However, due to cloud contamination and the hardware limitations of sensors, it is difficult to obtain image sequences with both high spatial and temporal resolution. Combining coarse resolution images, such as the moderate resolution imaging spectroradiometer (MODIS), with fine spatial resolution images, such as Landsat or Sentinel-2, has become a popular means to solve this problem. In this paper, we propose a simple and efficient enhanced linear regression spatio–temporal fusion method (ELRFM), which uses fine spatial resolution images acquired at two reference dates to establish a linear regression model for each pixel and each band between the image reflectance and the acquisition date. The obtained regression coefficients are used to help allocate the residual error between the real coarse resolution image and the simulated coarse resolution image upscaled by the high spatial resolution result of the linear prediction. The developed method consists of four steps: (1) linear regression (LR), (2) residual calculation, (3) distribution of the residual and (4) singular value correction. The proposed method was tested in different areas and using different sensors. The results show that, compared to the spatial and temporal adaptive reflectance fusion model (STARFM) and the flexible spatio–temporal data fusion (FSDAF) method, the ELRFM performs better in capturing small feature changes at the fine image scale and has high prediction accuracy. For example, in the red band, the proposed method has the lowest root mean square error (RMSE) (ELRFM: 0.0123 vs. STARFM: 0.0217 vs. FSDAF: 0.0224 vs. LR: 0.0221). Furthermore, the lightweight algorithm design and calculations based on the Google Earth Engine make the proposed method computationally less expensive than the STARFM and FSDAF.

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Data Fusion of Multiple Spatio-Temporal Data Sources for Improved Localisation in Cellular Network

2018 IEEE International Conference on Big Knowledge (ICBK) ◽

10.1109/icbk.2018.00067 ◽

2018 ◽

Author(s):

Shixin Luo ◽

Ying Li

Keyword(s):

Data Fusion ◽

Cellular Network ◽

Data Sources ◽

Temporal Data ◽

Spatio Temporal

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Freeway Travel Time Prediction Based on Seamless Spatio-temporal Data Fusion: Case Study of the Freeway in Taiwan

Transportation Research Procedia ◽

10.1016/j.trpro.2016.11.087 ◽

2016 ◽

Vol 17 ◽

pp. 452-459 ◽

Cited By ~ 1

Author(s):

Tang-Hsien Chang ◽

Albert Y. Chen ◽

Yu-Ting Hsu ◽

Chien-Li Yang

Keyword(s):

Data Fusion ◽

Travel Time ◽

Temporal Data ◽

Travel Time Prediction ◽

Time Prediction ◽

Spatio Temporal

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Monitoring of Sugarcane Growth Based on the Fused Remote Sensing NDVI Series and Ground Seeding Survey

Transactions of the ASABE ◽

10.13031/trans.13810 ◽

2020 ◽

Vol 63 (6) ◽

pp. 1795-1804

Author(s):

Yanli Chen ◽

Weihua Mo ◽

Jianfei Mo ◽

Meihua Ding

Keyword(s):

Remote Sensing ◽

Time Series ◽

Southern China ◽

Growth Monitoring ◽

List Type ◽

Fusion Model ◽

Growth Status ◽

Modis Data ◽

Timing Data ◽

Ndvi Time Series

HighlightsThe spatial and temporal fusion model ESTARFM was used to obtain NDVI timing data with high fusion accuracy and high spatial and temporal resolution.High-quality NDVI timing data could be obtained by using ESTARFM to fuse HJ-1 CCD and MODIS data.Fused NDVI data coupled with ground seeding survey data could effectively monitor sugarcane growth status.Abstract. This study addressed the instability of clear-sky remote sensing data with high spatial resolution in sugarcane growing areas in southern China and the current inconsistency between traditional survey results and remote sensing results for seedling growth. Moderate-resolution imaging spectroradiometer (MODIS) data and China land resources satellite (HJ-1 CCD) data were used to build high-resolution normalized difference vegetation index (NDVI) time series using the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM). Agronomic indicators of sugarcane were obtained by field sampling and were used for determining the remote sensing monitoring index (NDVI) of sugarcane growth. The method provided satisfactory results for evaluating sugarcane growth, with accuracy exceeding 90%. Moreover, sugarcane growth monitoring in a wider area was highly correlated with yield per unit area. Keywords: Growth status, HJ-1 CCD, MODIS, NDVI time series, Spatial and temporal fusion, Sugarcane.

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