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Land cover changes, especially excessive economic forest plantations, have significantly threatened the ecological security of West Dongting Lake wetland in China. This work aimed to investigate the spatiotemporal dynamics of forests in the West Dongting Lake region from 2000 to 2018 using a reconstructed monthly Landsat NDVI time series. The multi-type forest changes, including conversion from forest to another land cover category, conversion from another land cover category to forest, and conversion from forest to forest (such as flooding and replantation post-deforestation), and land cover categories before and after change were effectively detected by integrating Breaks For Additive Seasonal and Trend (BFAST) and random forest algorithms with the monthly NDVI time series, with an overall accuracy of 87.8%. On the basis of focusing on all the forest regions extracted through creating a forest mask for each image in time series and merging these to produce an ‘anytime’ forest mask, the spatiotemporal dynamics of forest were analyzed on the basis of the acquired information of multi-type forest changes and classification. The forests are principally distributed in the core zone of West Donting Lake surrounding the water body and the southwestern mountains. The forest changes in the core zone and low elevation region are prevalent and frequent. The variation of forest areas in West Dongting Lake experienced three steps: rapid expansion of forest plantation from 2000 to 2005, relatively steady from 2006 to 2011, and continuous decline since 2011, mainly caused by anthropogenic factors, such as government policies and economic profits. This study demonstrated the applicability of the integrated BFAST method to detect multi-type forest changes by using dense Landsat time series in the subtropical wetland ecosystem with low data availability.

The use of remote sensing technology to diagnose heavy metal stress in crops is of great significance for environmental protection and food security. However, in the natural farmland ecosystem, various stressors could have a similar influence on crop growth, therefore making heavy metal stress difficult to identify accurately, so this is still not a well resolved scientific problem and a hot topic in the field of agricultural remote sensing. This study proposes a method that uses Ensemble Empirical Mode Decomposition (EEMD) to obtain the heavy metal stress signal features on a long time scale. The method operates based on the Leaf Area Index (LAI) simulated by the Enhanced World Food Studies (WOFOST) model, assimilated with remotely sensed data. The following results were obtained: (i) the use of EEMD was effective in the extraction of heavy metal stress signals by eliminating the intra-annual and annual components; (ii) LAIdf (The first derivative of the sum of the interannual component and residual) can preferably reflect the stable feature responses to rice heavy metal stress. LAIdf showed stability with an R2 of greater than 0.9 in three growing stages, and the stability is optimal in June. This study combines the spectral characteristics of the stress effect with the time characteristics, and confirms the potential of long-term remotely sensed data for improving the accuracy of crop heavy metal stress identification.

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Monitoring of heavy metal stress in crops is vital for food security and agricultural production management. Traditional remote sensing methods focus on the stress-induced changes to the aerial organs of plants, whereas roots are considered to be more directly and severely stressed. In this study, the dry weight of rice roots (WRT) was used as an indicator for monitoring cadmium (Cd) stress levels in rice tissues. The World Food Study (WOFOST) model is a widely used analysis tool for describing the fundamental processes of crop growth, and has been tested for similar applications. We used this model to incorporate a Cd stress factor (fCd), allowing us to simulate the WRT values more accurately. Then, an optimized method of assimilating remotely sensed leaf area index (LAI) into the modified WOFOST model was used to optimize the simulation process and obtain the optimum value of f Cd. Thus, the dynamic simulation of WRT under Cd stress was adjusted. Based on the WRT values of two sample plots with different soil Cd concentrations, the ratio between them (WRTStress/WRTSafe) was calculated subsequently. The variation in the ratio curve generally reflected the stress mechanism in time scale, indicating that the dynamic simulation of WRT was reliable. This study suggests that the method of assimilating remote sensing data into the crop growth model is applicable for simulating crop growth under Cd stress on spatial-time scale, providing a reference for dynamically monitoring heavy metal contamination in rice tissues.

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