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A satellite image time series (SITS) contains a significant amount of temporal information. By analysing this type of data, the pattern of the changes in the object of concern can be explored. The natural change in the Earth’s surface is relatively slow and exhibits a pronounced pattern. Some natural events (for example, fires, floods, plant diseases, and insect pests) and human activities (for example, deforestation and urbanisation) will disturb this pattern and cause a relatively profound change on the Earth’s surface. These events are usually referred to as disturbances. However, disturbances in ecosystems are not easy to detect from SITS data, because SITS contain combined information on disturbances, phenological variations and noise in remote sensing data. In this paper, a novel framework is proposed for online disturbance detection from SITS. The framework is based on long short-term memory (LSTM) networks. First, LSTM networks are trained by historical SITS. The trained LSTM networks are then used to predict new time series data. Last, the predicted data are compared with real data, and the noticeable deviations reveal disturbances. Experimental results using 16-day compositions of the moderate resolution imaging spectroradiometer (MOD13Q1) illustrate the effectiveness and stability of the proposed approach for online disturbance detection.
Yun-Long Kong; Qingqing Huang; Chengyi Wang; Jingbo Chen; Jiansheng Chen; Dongxu He. Long Short-Term Memory Neural Networks for Online Disturbance Detection in Satellite Image Time Series. Remote Sensing 2018, 10, 452 .
AMA StyleYun-Long Kong, Qingqing Huang, Chengyi Wang, Jingbo Chen, Jiansheng Chen, Dongxu He. Long Short-Term Memory Neural Networks for Online Disturbance Detection in Satellite Image Time Series. Remote Sensing. 2018; 10 (3):452.
Chicago/Turabian StyleYun-Long Kong; Qingqing Huang; Chengyi Wang; Jingbo Chen; Jiansheng Chen; Dongxu He. 2018. "Long Short-Term Memory Neural Networks for Online Disturbance Detection in Satellite Image Time Series." Remote Sensing 10, no. 3: 452.
Elucidating the performance of collaborative development within the Beijing Tianjin Hebei (BTH) region and developing an understanding of mechanisms underlying this process are of paramount importance to regional sustainable development as well as for the realization of Chinese national strategy. Thus, utilizing socioeconomic data for 13 districts within the BTH region between 2000 and 2014, this study applies the Gini coefficient alongside the technique for order preference by similarity to an ideal solution (TOPSIS) method supported by the entropy weight model and impulse response functions in order to assess the performance of collaborative development in this region and elucidate underlying mechanisms. The results of this study reveal that collaborative development within the BTH region has tended to slowly increase over time, but with fluctuations. Although some progress has been made in promoting urbanization, constructing traffic networks, protecting the environment, and improving living standards, very significant expansion space nevertheless remains for further improvements. The collaborative development of this region has also been increasingly affected by globalization, with either the equalization of per capita fixed asset investment or fiscal expenditure exerting a definite impact. The results show that although the equalization of per capita fixed asset investment boosts collaborative development at the start of this process, it is likely to impede it over longer time scales, while the equalization of per capita fiscal expenditure will contribute to this process within the BTH region over both the short and long term. A number of policy suggestions are therefore proposed in this paper to promote smooth collaborative development of the BTH region, including optimizing investment structures and establishing an ecological compensation mechanism.
Chuanglin Fang; Kui Luo; Yunlong Kong; Haoxi Lin; Yufei Ren. Evaluating Performance and Elucidating the Mechanisms of Collaborative Development within the Beijing–Tianjin–Hebei Region, China. Sustainability 2018, 10, 471 .
AMA StyleChuanglin Fang, Kui Luo, Yunlong Kong, Haoxi Lin, Yufei Ren. Evaluating Performance and Elucidating the Mechanisms of Collaborative Development within the Beijing–Tianjin–Hebei Region, China. Sustainability. 2018; 10 (2):471.
Chicago/Turabian StyleChuanglin Fang; Kui Luo; Yunlong Kong; Haoxi Lin; Yufei Ren. 2018. "Evaluating Performance and Elucidating the Mechanisms of Collaborative Development within the Beijing–Tianjin–Hebei Region, China." Sustainability 10, no. 2: 471.
Satellite Image Time Series (SITS) have recently been of great interest due to the emerging remote sensing capabilities for Earth observation. Trend and seasonal components are two crucial elements of SITS. In this paper, a novel framework of SITS decomposition based on Ensemble Empirical Mode Decomposition (EEMD) is proposed. EEMD is achieved by sifting an ensemble of adaptive orthogonal components called Intrinsic Mode Functions (IMFs). EEMD is noise-assisted and overcomes the drawback of mode mixing in conventional Empirical Mode Decomposition (EMD). Inspired by these advantages, the aim of this work is to employ EEMD to decompose SITS into IMFs and to choose relevant IMFs for the separation of seasonal and trend components. In a series of simulations, IMFs extracted by EEMD achieved a clear representation with physical meaning. The experimental results of 16-day compositions of Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Index (NDVI), and Global Environment Monitoring Index (GEMI) time series with disturbance illustrated the effectiveness and stability of the proposed approach to monitoring tasks, such as applications for the detection of abrupt changes.
Yun-Long Kong; Yu Meng; Wei Li; An-Zhi Yue; Yuan Yuan. Satellite Image Time Series Decomposition Based on EEMD. Remote Sensing 2015, 7, 15583 -15604.
AMA StyleYun-Long Kong, Yu Meng, Wei Li, An-Zhi Yue, Yuan Yuan. Satellite Image Time Series Decomposition Based on EEMD. Remote Sensing. 2015; 7 (11):15583-15604.
Chicago/Turabian StyleYun-Long Kong; Yu Meng; Wei Li; An-Zhi Yue; Yuan Yuan. 2015. "Satellite Image Time Series Decomposition Based on EEMD." Remote Sensing 7, no. 11: 15583-15604.
In this paper, we propose a novel method to continuously monitor land cover change using satellite image time series, which can extract comprehensive change information including change time, location, and “from-to” information. This method is based on a hidden Markov model (HMM) trained for each land cover class. Assuming a pixel’s initial class has been obtained, likelihoods of the corresponding model are calculated on incoming time series extracted with a temporal sliding window. By observing the likelihood change over the windows, land cover change can be precisely detected from the dramatic drop of likelihood. The established HMMs are then used for identifying the land cover class after the change. As a case study, the proposed method is applied to monitoring urban encroachment onto farmland in Beijing using 10-year MODIS time series from 2001 to 2010. The performance is evaluated on a validation set for different model structures and thresholds. Compared with other change detection methods, the proposed method shows superior change detection accuracy. In addition, it is also more computationally efficient.
Yuan Yuan; Yu Meng; Lei Lin; Hichem Sahli; Anzhi Yue; Jingbo Chen; Zhongming Zhao; Yunlong Kong; Dongxu He. Continuous Change Detection and Classification Using Hidden Markov Model: A Case Study for Monitoring Urban Encroachment onto Farmland in Beijing. Remote Sensing 2015, 7, 15318 -15339.
AMA StyleYuan Yuan, Yu Meng, Lei Lin, Hichem Sahli, Anzhi Yue, Jingbo Chen, Zhongming Zhao, Yunlong Kong, Dongxu He. Continuous Change Detection and Classification Using Hidden Markov Model: A Case Study for Monitoring Urban Encroachment onto Farmland in Beijing. Remote Sensing. 2015; 7 (11):15318-15339.
Chicago/Turabian StyleYuan Yuan; Yu Meng; Lei Lin; Hichem Sahli; Anzhi Yue; Jingbo Chen; Zhongming Zhao; Yunlong Kong; Dongxu He. 2015. "Continuous Change Detection and Classification Using Hidden Markov Model: A Case Study for Monitoring Urban Encroachment onto Farmland in Beijing." Remote Sensing 7, no. 11: 15318-15339.