Use of vegetation change tracker, spatial analysis, and random forest regression to assess the evolution of plantation stand age in Southeast China

Diao, Jiaojiao; Feng, Tuo; Li, Mingshi; Zhu, Zhiliang; Liu, Jinxun; Biging, Gregory S.; Zheng, Guang; Shen, Wenjuan; Wang, Heng; Wang, Jian; Ji, Biyong

doi:10.1007/s13595-020-0924-x

Cited by 23 publications

(15 citation statements)

References 66 publications

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“…Based on the above information, we need to rely on remote sensing to obtain more detailed spatial and temporal changes. Vegetation loss and restoration in mining areas often lasts for decades; thus, Landsat has been widely applied in forest change detection due to its ability to cover large scales and its high acquisition time frequency [11,12,54]. However, the processing efficiency and precision assurance of remote sensing images have become some of the challenges of vegetation For the relationships between restored landscape structure and ecological function, we quantitatively determined the increased AGB caused by each restoration process category.…”

Section: Value and Suitability Of Integrating Corona And Landsat Datamentioning

confidence: 99%

“…Zhao et al [58] used VCT and support vector machine to identify forest disturbance types in the Greater Yellowstone Ecosystem, including wildfire, harvest and other disturbance types. Diao et al [54] integrated the VCT algorithm and spatial analysis to characterize the disturbed forests in southeastern China from 1987 to 2017. Zhang et al [41] combined the VCT and the urban impervious surface index to describe the forest loss due to urban expansion in Nanjing.…”

Section: Value and Suitability Of Integrating Corona And Landsat Datamentioning

confidence: 99%

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Integrating Landsat Time Series Observations and Corona Images to Characterize Forest Change Patterns in a Mining Region of Nanjing, Eastern China from 1967 to 2019

Zhang

Shen

et al. 2020

Remote Sensing

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Long-term surface mining and subsequent vegetation recovery greatly alter land cover types, reshape landscape patterns and impose several impacts on local ecosystem services. However, studies on the history of forest changes in mining areas from the 1960s to the present have not been reported. This study developed a new idea to investigate the spatial and temporal dynamics of forest cover in a mining area of Mufu Mountain (Mt. Mufu) from 1967 to 2019 by integrating Landsat and Corona data, and to explore the relationships among the forest changes, landscape structures and ecosystem functions. Firstly, we applied the vegetation change tracker (VCT) algorithm and visual interpretation to create annual forest change datasets. Subsequently, the forest loss process was divided into subdivision, shrinkage, perforation and attrition components. An improved forest restoration model in this study extended the recovery process to bridge, branch, infilling and increment components. Finally, remote sensing variables and crown density were coupled to assess the forest aboveground biomass (AGB) to reflect the ecosystem function in the restoration area. Results showed that the combined use of Corona and the dense time series of Landsat can provide more detailed information on forest changes. Forest cover sharply decreased from 343.89 in 1967 to 298.44 ha in 1990, and after 2003, the forest area substantially increased and finally reached a maximum of 434.16 ha in 2019. Subdivision and bridge not only occupied the larger areas in the process of forest loss and restoration, but also they had strong correlations with forest changes and the Pearson correlation coefficients (r) were respectively 0.96 and 0.91. These all revealed that forest changes mainly affected landscape structure connectivity. The total forest AGB of Mt. Mufu increased from 20,173.35 in 2006 to 31,035.77 t in 2017, but the increases in AGB were only 30-40 t/ha in most recovery areas with high structure connectivity (bridge regions), indicating there is room for improving restoration projects in the future. The obtained findings can provide mining site restoration managers with clear, long-term forest change information and mine restoration assessment methods.

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Section: Value and Suitability Of Integrating Corona And Landsat Datamentioning

confidence: 99%

Section: Value and Suitability Of Integrating Corona And Landsat Datamentioning

confidence: 99%

Integrating Landsat Time Series Observations and Corona Images to Characterize Forest Change Patterns in a Mining Region of Nanjing, Eastern China from 1967 to 2019

Zhang

Shen

et al. 2020

Remote Sensing

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“…Remote sensing images represent a systematic tool for estimating large-scale biophysical variables owing to their wide spatial coverage and frequent data updates (Diao et al, 2020). Generally, the basic physical mechanism for estimating forest age using remote sensing images is that forests of different ages exhibit different physical characteristics, such as spectral reflectance, tree crown texture, light transmittance and 65 biomass (Champion et al, 2014;Kuusinen et al, 2014;Thom and Keeton, 2019).…”

Section: Introductionmentioning

confidence: 99%

30 m Map of Young Forest Age in China

Xiao

Wang

Tong

et al. 2023

Preprint

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Abstract. Young forest age mapping at a fine spatial resolution is important for increasing the accuracy of estimating land-atmosphere carbon fluxes and guiding forest management practices. In recent decades, China has actively conducted afforestation and forest protection projects, thereby, laying the foundation for the realization of carbon neutrality. However, very few studies have been conducted which map the ages of young forests for the whole of China at a fine spatial resolution. In this research, a continuous change detection and classification (CCDC)-based method suitable for large-scale forest age mapping is proposed, and used to estimate young forest ages across China in 2020 at a spatial resolution of 30 m. First, a 10 m spatial resolution land cover dataset (WorldCover2020) from the European Space Agency (ESA) was used to determine the forest cover areas in 2020. Then, the CCDC algorithm was used to identify stand-replacing disturbances to determine the stand age based on 436,967 Landsat tiles across China from 1990 to 2020. A validation sample set composed of multiple land use/land cover (LULC) products was used to calculate the overall accuracy (OA) of the 2020 young forest age (1–31 years) map of China, and the OA was 90.28 %. The reliability and applicability of the proposed CCDC-based forest age mapping method was validated by comparing the forest age map with Hansen’s forest change dataset, Max Planck Institute for Biogeochemistry (MPI-BGC) 1 km global forest age datasets and field measurements. The CCDC-based method has strong application potential in real-time mapping of the age of young forests at the global scale. The produced forest age map provides a basic dataset for research on the forest carbon cycle and forest ecosystem services, and important guidance for government departments, such as the National Forestry and Grassland Administration and National Development and Reform Commission in China.

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“…At present, there are only a few studies in China that have assessed the spatial and temporal patterns of forest loss and gain on a small scale, such as mountains, counties, and multi-counties [17,[39][40][41][42]. Most of these studies directly adopted the forest change detection algorithms in China and the parameters and methods were not localized and improved.…”

Section: Introductionmentioning

confidence: 99%

Contrasting Forest Loss and Gain Patterns in Subtropical China Detected Using an Integrated LandTrendr and Machine-Learning Method

et al. 2022

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China has implemented a series of forestry law, policies, regulations, and afforestation projects since the 1970s. However, their impacts on the spatial and temporal patterns of forests have not been fully assessed yet. The lack of an accurate, high-resolution, and long-term forest disturbance and recovery dataset has impeded this assessment. Here we improved the forest loss and gain detections by integrating the LandTrendr change detection algorithm with the Random Forest (RF) machine-learning method and applied it to assess forest loss and gain patterns in the Zhejiang, Jiangxi, and Guangxi Provinces of the subtropical vegetation in China. The accuracy evaluation indicated that our approach can adequately detect the spatial and temporal distribution patterns in forest gain and loss, with an overall accuracy of 93% and the Kappa coefficient of 0.89. The forest loss area was 8.30 × 104 km2 in the Zhejiang, Jiangxi, and Guangxi Provinces during 1986–2019, accounting for 43.52% of total forest area in 1986, while the forest gain area was 20.25 × 104 km2, accounting for 106.19% of total forest area in 1986. Although the interannual variation patterns were similar among three provinces, the forest loss and gain area and the magnitude of change trends were significantly different. Guangxi has the largest forest loss and gain area and increasing trends, followed by Jiangxi, and the least in Zhejiang. The variations in annual forest loss and gain area can be mostly explained by the timelines of major forestry policies and regulations. Our study would provide an applicable method and data for assessing the impacts of forest disturbance events and forestry policies and regulations on the spatial and temporal patterns of forest loss and gain in China, and further contributing to regional and national forest carbon and greenhouse gases budget estimations.

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Use of vegetation change tracker, spatial analysis, and random forest regression to assess the evolution of plantation stand age in Southeast China

Cited by 23 publications

References 66 publications

Integrating Landsat Time Series Observations and Corona Images to Characterize Forest Change Patterns in a Mining Region of Nanjing, Eastern China from 1967 to 2019

Integrating Landsat Time Series Observations and Corona Images to Characterize Forest Change Patterns in a Mining Region of Nanjing, Eastern China from 1967 to 2019

30 m Map of Young Forest Age in China

Contrasting Forest Loss and Gain Patterns in Subtropical China Detected Using an Integrated LandTrendr and Machine-Learning Method

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