Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: A Bayesian ensemble algorithm

Zhao, Kaiguang; Wulder, Michael A.; Hu, Tongxi; Bright, Ryan M.; Wu, Qiusheng; Qin, Haiming; Yang, Li; Toman, Elizabeth Myers; Mallick, Bani K.; Zhang, Xuesong; Brown, M. E.

doi:10.1016/j.rse.2019.04.034

Cited by 262 publications

(172 citation statements)

References 88 publications

(138 reference statements)

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“…Figure 7 shows the significant presence of a yearly cycle (seasonality) in inflow temperature, together with relatively high autocorrelations in other process variables. This periodicity is a typical pattern observed in environmental datasets [43], [44].…”

Section: A Wwtp Data Analysissupporting

confidence: 63%

Forecasting of Wastewater Treatment Plant Key Features Using Deep Learning-Based Models: A Case Study

et al. 2020

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The accurate forecast of wastewater treatment plant (WWTP) key features can comprehend and predict the plant behavior to support process design and controls, improve system reliability, reduce operational costs, and endorse optimization of overall performances. Deep learning technologies as proven data-driven soft-sensors should be developed for WWTP applications to tackle the process of non-linearity and the dynamic nature of environmental data. This study adopts deep learning-based models as softsensors to forecast WWTP key features, such as influent flow, influent temperature, influent biochemical oxygen demand (BOD), effluent chloride, effluent BOD, and power consumption. We constructed six deep learning models derived from long short-term memory (LSTM) and gated recurrent unit (GRU), namely traditional LSTM and GRU, the exponentially smoothed LSTM, and the adaptive version of LSTM and smoothed LSTM. The employment of a smoothed LSTM technique is expected to reduce the outlier effect and to improve forecasting accuracy. Meanwhile, the usage of adaptive deep models will enhance the capabilities of the LSTM to quickly and accurately follow the trend of future data. We compared the performance of these models with Bi-directional LSTM (BiLSTM) and the seasonal decomposition using local regression. The historical records from a coastal municipal WWTP in Saudi Arabia are used to verify the investigated models' effectiveness. The proposed models provide promising forecasting results but require no assumptions on the data distributions. In terms of efficiency, GRU based models converge faster than LSTM based models. In terms of accuracy, the LSTM soft-sensor shows overall the optimal result for all key features followed by the exponentially-smoothed GRU and LSTM. By contrast, the adaptive models achieved the lowest forecasting performance compared to the other models. These findings will benefit practitioners to achieve data-driven WWTP management.

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Section: A Wwtp Data Analysissupporting

confidence: 63%

Forecasting of Wastewater Treatment Plant Key Features Using Deep Learning-Based Models: A Case Study

et al. 2020

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“…However, it is difficult to identify whether the errors are originated from the classification during PALSAR period or the change detection in the gap period. Further improvement could be the use of algorithms which combines the different models (i.e., BEAST) rather than the single-best model (Zhao et al, 2019a). More importantly, oil palm will be cut down and replanted after 20 to 25 years for the next rotation in order to make the maximum profits.…”

Section: Uncertainty Of Aopdmentioning

confidence: 99%

Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016

Xu¹,

Yu²,

Li³

et al. 2019

Preprint

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Increasing global demand of vegetable oils and biofuels results in significant oil palm expansion in Southeast Asia, predominately in Malaysia and Indonesia. The land conversion to oil palm plantations leads to deforestation, loss of biodiversity, and greenhouse gas emission over the past decades. Quantifying the consequences of oil palm expansion requires fine scale and frequently updated datasets of land cover dynamics. Previous studies focused on total changes for a multi-year interval without identifying the exact time of conversion, causing uncertainty in the timing of carbon emission estimates from 15 land cover change. Using Advanced Land Observing Satellite (ALOS) Phased Array Type L-band Synthetic Aperture Radar (PALSAR), ALOS-2 PALSAR-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) datasets, we produced an Annual Oil Palm Area Dataset (AOPD) at 100-meter resolution in Malaysia and Indonesia from 2001 to 2016. We first mapped the oil palm extent using PALSAR/PALSAR-2 data for 2007-2010 and 2015-2016 and then applied a disturbance and recovery algorithm (BFAST) to detect land cover change time-points using MODIS data during the years without PALSAR data (2011-20 2014 and 2001-2006). The new oil palm land cover maps are assessed to have an accuracy of 86.61% in the mapping step (2007-2010 and 2015-2016). During the intervening years when MODIS data are used, 75.74% of the change detected time matched the timing of actual conversion using Google Earth and Landsat images. The AOPD dataset revealed spatiotemporal oil palm dynamics every year and shows that plantations expanded from 2.59 to 6.39 M ha and from 3.00 to 12.66 M ha in Malaysia and Indonesia, respectively (i.e., a net increase of 146.60% and 322.46%) between 2001 and 2016. The increasing 25 trends from our dataset are consistent with those from the national inventories, but slightly greater because of inclusion of smallholder oil palm plantations in our dataset. We highlight the capability of combining multiple resolution radar and optical satellite datasets in annual plantation mapping at large extent using image classification and statistical boundary-based change detection to achieve long time-series. The consistent characterization of oil palm dynamics can be further used in downstream applications. The annual oil palm plantation maps from 2001 to 2016 at 100 m resolution is published in the Tagged Image 30File Format with georeferencing information (GeoTIFF) at https://doi.org/10.5281/zenodo.3467071 .

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“…Dynamic global vegetation models use gross land-use change and thus require high-resolution grid-cell-based annual oil palm conversion maps rather than country-level inventories and bi-decadal land cover maps (Yue et al, 2018a, b). A lack of continuous change information may cause a wrong interpretation of land cover change time and significant bias in global carbon dynamic studies (Zhao and Liu, 2014;Zhao et al, 2009). As a result, oil palm plantation maps at high temporal and spatial resolutions in Malaysia and Indonesia are urgently needed.…”

Section: Introductionmentioning

confidence: 99%

Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016

et al. 2020

Earth Syst. Sci. Data

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Abstract. Increasing global demand of vegetable oils and biofuels results in significant oil palm expansion in southeastern Asia, predominately in Malaysia and Indonesia. The land conversion to oil palm plantations has posed risks to deforestation (50 % of the oil palm was taken from forest during 1990–2005; Koh and Wilcove, 2008), loss of biodiversity and greenhouse gas emission over the past decades. Quantifying the consequences of oil palm expansion requires fine-scale and frequently updated datasets of land cover dynamics. Previous studies focused on total changes for a multi-year interval without identifying the exact time of conversion, causing uncertainty in the timing of carbon emission estimates from land cover change. Using Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), ALOS-2 PALSAR-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) datasets, we produced an annual oil palm area dataset (AOPD) at 100 m resolution in Malaysia and Indonesia from 2001 to 2016. We first mapped the oil palm extent using PALSAR and PALSAR-2 data for 2007–2010 and 2015–2016 and then applied a disturbance and recovery algorithm (Breaks For Additive Season and Trend – BFAST) to detect land cover change time points using MODIS data during the years without PALSAR data (2011–2014 and 2001–2006). The new oil palm land cover maps are assessed to have an accuracy of 86.61 % in the mapping step (2007–2010 and 2015–2016). During the intervening years when MODIS data are used, 75.74 % of the detected change time matched the timing of actual conversion using Google Earth and Landsat images. The AOPD revealed spatiotemporal oil palm dynamics every year and shows that plantations expanded from 2.59 to 6.39×106 ha and from 3.00 to 12.66×106 ha in Malaysia and Indonesia, respectively (i.e. a net increase of 146.60 % and 322.46 %) between 2001 and 2016. The higher trends from our dataset are consistent with those from the national inventories, with limited annual average difference in Malaysia (0.2×106 ha) and Indonesia (−0.17×106 ha). We highlight the capability of combining multiple-resolution radar and optical satellite datasets in annual plantation mapping to a large extent by using image classification and statistical boundary-based change detection to achieve long time series. The consistent characterization of oil palm dynamics can be further used in downstream applications. The annual oil palm plantation maps from 2001 to 2016 at 100 m resolution are published in the Tagged Image File Format with georeferencing information (GeoTIFF) at https://doi.org/10.5281/zenodo.3467071 (Xu et al., 2019).

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Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: A Bayesian ensemble algorithm

Cited by 262 publications

References 88 publications

Forecasting of Wastewater Treatment Plant Key Features Using Deep Learning-Based Models: A Case Study

Forecasting of Wastewater Treatment Plant Key Features Using Deep Learning-Based Models: A Case Study

Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016

Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016

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