Disturbance of terrestrial ecosystems can significantly affect and alter ecosystem function and structure and thus have a major impact on the spatial and temporal patterns of the terrestrial carbon cycle (Lin et al., 2022). As a major part of terrestrial ecosystems, forest ecosystems play an important role in the global carbon cycle, energy balance, and material exchange (Paul et al., 2002; Bonan, 2008; Pan et al., 2011). The main factors causing forest disturbance are climate change, natural disasters, and human activities, of which human activities are the most significant (Foley et al., 2005; Song et al., 2018; Trisasongko and Paull, 2020).

China is a country with large forest resources and a large population, and the number of forest resources owned per capita is at a low level in the world. During vigorous industrial development, the destruction of forests due to the long-term over-harvesting of natural forests has led to increasing ecological degradation (Zhang et al., 2000; Deng et al., 2014). Deforestation for timber, farming or urban expansion has exacerbated environmental problems such as land desertification, soil erosion, and a sharp decline in biodiversity (Foley et al., 2005). In addition, forest resources, especially those available for logging, were in severe shortage. To restore the ecological environment, China has been developing ecological projects since the 1970s, conserving and restoring natural forests and expanding forest plantations. Plantations are a completely different forest type from natural forests. Plantations are formed entirely by artificial planting, while natural forests grow naturally or regenerate through human promotion. Plantations are characterized by short rotation periods, good materials, and high survival rates. Guangxi Zhuang Autonomous Region of China, a province with rich forest resources, has undergone a large amount of deforestation and land reclamation for economic development, resulting in a sharp decline in forest resources. With the launch of the “Coastal Shelterbelt” Program in 1991 and the “Grain-for-Green” program in 1999 (Deng et al., 2014), Guangxi began planting large areas of forest plantations and has created a large amount of forestry output through rotational logging and replanting.

Plantations are currently the main raw material for timber supply in China, and the repetitive deforestation and afforestation resulting from their rotation is an important influence on changes in regional carbon sinks. Although the rapid expansion of plantation forestry can alleviate the demand for timber for social development, reduce the increasing level of carbon dioxide in the atmosphere (Bonan, 2008), and mitigate climate change to a certain extent, the rapid development in a crude manner has also brought about a series of ecological and environmental problems, which are disruptive to the balance of the original ecosystem. Furthermore, compared to the natural forests, the non-native and exotic species in plantation forestry projects can change the physical, chemical, and biological properties of the soil, which may also reduce soil quality and forest productivity. Thus, the problems caused by massive plantations should not be ignored (Jackson et al., 2005; Brockerhoff et al., 2008). Likewise, timely and accurate information on the distribution of plantation resources and continuous monitoring of the dynamic changes in plantation forests are of great significance to forestry management, economic estimation of plantation forests, ecological environmental protection, and carbon cycle research.

With the launch of satellites carrying various sensors and the rapid development of remote sensing technology, remote sensing has become an important means of monitoring plantation forests. Plantation forests and natural forests both belong to forest land cover type, so they are highly similar in many characteristics, as a result, how to distinguish between them becomes a problem worthy of research. In terms of distinguishing natural forests from plantation forests, most studies are based on the classification of land use and land cover (LULC). As for the classifier selection, the traditional classifiers of remote sensing are involved in many researches, such as Decision Tree (Han et al., 2018), Support Vector Machine (Razak et al., 2018). Recent studies have widely used random forest as the classifier (Senf et al., 2013; Wu et al., 2022), because random forest classifier can achieve higher accuracy, handle high-dimensional features and avoid overfitting. The difference mainly lies in the fact that researchers chose different classification features. Since the spectral features of plantation forests and natural forests are highly similar, it is more difficult to distinguish between them using only spectral features. Some studies have used phenological features (Senf et al., 2013). The advantages of using such features are that some plantation species, such as rubber trees, have a deciduous period, which has obvious phenological features that can be used to separate them from natural forests (Senf et al., 2013). However, methods using phenological features have limitations, because evergreen plantations, for example eucalyptus, that do not have a deciduous period may not be easily separated from natural forests using the phenological characteristics (Wu et al., 2022). Other studies use multiple features (Torbick et al., 2016; Xu et al., 2017; Fagan et al., 2018; Sun et al., 2022; Wu et al., 2022), including spectral, textural, topographic, and ancillary data. The regular planting of plantations results in different texture characteristics than natural forests, and some plantations will be planted in areas with relatively gentle slopes. These combined features can separate plantations from natural forests through multi-feature methods (Torbick et al., 2016). However, when multiple features are used, parameter selection is required to choose the optimal parameters (Wu et al., 2022).

In terms of plantation change detection and spatiotemporal distribution information extraction, there are two main methods. The first is to directly compare the differences after LULC classification, and the second is based on long time series change detection. For the first method, forest change is monitored based on LULC classification by comparing the classification results of images in different time periods, usually through the land conversion matrix (Mahmoud et al., 2011; Liu et al., 2016; Twisa and Buchroithner, 2019; Chen, H et al., 2021; Sun et al., 2021; Wang et al., 2021; Chen et al., 2023). The advantage of this method is that it can reduce the effects of light radiation differences, sensor differences, and seasonal phenology differences, and can clearly represent the land cover type conversion (Twisa and Buchroithner, 2019). The disadvantage is that there are different degrees of error in the classification results for different time phases, and in some cases the error can be even greater than the degree of change (Azizan et al., 2021). Moreover, if only optical satellites are used, it also leads to the problem of susceptibility to cloud influence and difficulty in obtaining high-quality coverage of the complete study area in certain cloudy and rainy areas, especially in large regions (Azizan et al., 2021). In contrast, methods based on long time series change detection can directly detect vegetation changes, and the problem of images being obscured by clouds can be solved by processing multi-year time series data. Breakpoint detection algorithms such as Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) and Continuous Change Detection and Classification (CCDC) can significantly reduce the effects of clouds, cloud shadows, and snow, realizing to monitor the dynamics of different land cover types. Grogan et al. (2015) used the LandTrendr algorithm to study forest disturbance caused by rubber forest expansion. The LandTrendr algorithm, proposed by Kennedy, was designed specifically for Landsat time series imagery to detect trends in forest change (Kennedy et al., 2012). Specifically, LandTrendr uses characteristic points in a time series consisting of annual synthetic values to segment long-term trends in vegetation into segments and simulate the process of vegetation change based on a segmented linear model. In this method, several complex control parameters are necessary and need to be constantly changed depending on both signal-to-noise ratios and spectral characteristics of the different sensors (Kennedy et al., 2012). In addition, the CCDC algorithm, based on all available Landsat images, first initializes the model according to 15 cloud-free observations in each pixel’s time series and then detects changes by comparing the discrepancies between the model predictions and observations (Zhu and Woodcock, 2014). The algorithm can detect a wide range of land cover changes, both gradual (e.g., changes due to vegetation growth and succession, pests, abnormal weather, etc.) and abrupt changes, which also can be applied to improve land cover classification accuracy. Besides, as the CCDC algorithm makes use of all available Landsat images, its change detection results are more comprehensive than that of using only annual composite images and are particularly effective in gradual change detection (Vogelmann et al., 2016). The cost of data storage has fallen dramatically in recent years and we have witnessed a rapid increase in computing power, which has laid the foundation for time series analysis using Landsat data (Zhu, 2017), especially with the advent of the Google Earth Engine (GEE) cloud platform, which provides an efficient solution for the spatial and temporal dynamics analysis of plantations using medium resolution long time series Landsat imagery in large scale.

The objectives of our study are to: (1) explore the possibility of using the CCDC algorithm to discriminate plantations from natural forests through plantations’ temporal features of rapid growth and harvest rotation in a large area of cloudy and rainy conditions, (2) describe the spatiotemporal dynamic changes of plantations from different perspectives, including area changes, expansion years, and expansion times. Based on the above background, we adopted the CCDC algorithm and used all available Landsat images from 2000–2020 to map annual plantation forest distribution in Guangxi Zhuang Autonomous Region and analyze their spatial and temporal dynamic changes. The values and contributions of this study are: (1) using the fitting parameters of CCDC as classification features to extract information on the rapid growth of plantation forests and distinguish them from natural forests, which provides a fast and effective method for long time and large area plantation forest identification and spatio-temporal distribution information extraction and (2) revealing the spatio-temporal distribution information and change characteristics of plantation forests in Guangxi within 20 years, which can provide data support and decision reference for plantation forest resource investigation, management and rotation time adjustment.

In this paper, based on the CCDC algorithm, we used all available Landsat data from 2000 to 2020 to distinguish between plantations and natural forests in Guangxi and describe the expansion years and replanting times of plantations. Applying the CCDC algorithm to plantation forest extraction has the following advantages: (1) The characteristics to discriminate plantation forests from natural forests are derived from the parameters of the CCDC fitting curve, which are different from the spectral, textural or phenological features used in most previous studies. Plantations and natural forests both belong to the forest land cover type, therefore, little difference in spectral or texture features can be detected when using medium-resolution optical satellite data, resulting in hard work to difference the two types. Hence, in previous studies of small areas, high spatial resolution images or optical images fused with LiDAR were frequently used (Fagan et al., 2018; Wu et al., 2022). In large-area studies using only medium-resolution satellites, phenological features are commonly used to extract plantation forests with significant deciduous periods such as rubber forests (Li et al., 2015; Xiao et al., 2019; Yang et al., 2021; Xiong et al., 2022). In contrast, the plantation forests in Guangxi are mainly evergreen forests such as fir, horsetail pine, and eucalyptus, thus they do not have obvious phenological characteristics enough to distinguish them from natural forests. Fortunately, plantation forests are characterized by rapid inter-annual variation in growth due to artificial planting, which is very different from the more slow-growing natural forests. The CCDC algorithm can precisely describe the change trend of pixels (Zhu and Woodcock, 2014), which can be used to more accurately identify plantations, (2) The CCDC algorithm uses all available images with less cloud at the pixel scale, which can greatly improve the utilization of partially cloudy images and reduce the influence of clouds and rain on the classification, thus resulting in more conducive recognition of plantation forests in large areas. Cases are often mentioned in previous studies sorely using optical satellites that cloud and rain contaminated pixels lead to less available data and limitations in classification accuracy and study regions (Wu et al., 2022), and (3) The entire experiment ran on the GEE platform, including preprocessing, segmentation, classification and spatiotemporal distribution description. GEE platform is the world’s most advanced cloud computing platform dedicated to processing geospatial observations such as satellite imagery. The GEE cloud database integrates nearly 40 years of historical archived data from the Landsat series of satellites, providing individual users with strong computing power and cloud storage, as well as a fast and easy JavaScript language API interface for data processing, algorithm implementation, and result analysis (Dong et al., 2016; Gorelick et al., 2017). The area of Guangxi is 2.38 × 10⁵ km², and a total of 15,522 Landsat images were used. In this study, GEE platform’s massive cloud storage and fast calculation speed provided much support to process such a large volume of data.

To further improve the classification accuracy and provide a finer description of the dynamic change of plantations, improvements can be made in the following directions: (1) In this study, plantation and natural forest samples from 2016 were used for classification training and accuracy validation of plantation extraction, while the study period in this paper is up to 20 years, and using samples from one single year may affect the classification accuracy due to inadequate samples. Therefore, samples of plantation forests and natural forests in different years should be added to improve the completeness of the samples in future research work and (2) We discussed the inter-annual variation of plantation forests in this paper. Actually, the fitted model of CCDC algorithm can describe the intra-annual variation, and thus the time of plantation expansion can be further accurate to months. Therefore, the changes of plantation forests on a regular basis of every 6 months or every month can be explored.

In this study, the CCDC algorithm was used to extract plantations in Guangxi and explore their spatial and temporal dynamic changes from 2000 to 2020. The following conclusions are obtained:

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

QZ: methodology, data curation, software, formal analysis, visualization, writing – original draft, and writing – review and editing. LW: methodology, writing – review and editing, funding acquisition, and project administration. FT: visualization and writing – review and editing. SZ: validation and writing – review and editing. NH and KZ: writing – review and editing. All authors contributed to the article and approved the submitted version.

This work was supported by the National Key Research and Development Program of China (grant no. 2021YFE0117900) and the National Natural Science Foundation of China (grant no. U2244230).

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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