Forest Disturbance in China from 1986 to 2020: 30 m disturbance attributes and 1 km annual density products
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2025
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| author | Jia, Xiang Zhang, Xiaoli Du, Jun Huo, Langning Chai, Guoqi Wang, Yueting Wang, Jingxu Tian, Xin Lei, Lingting Chen, Long Li, Caixia Xu, Haifeng Qiu, Shike Yu, Weiwei Yin, Sanjun Wang, Ran Huang, Tiecheng Yao, Zongqi Chen, Mengyu Hou, Pengfei He, Danni Li, Yangguang |
| author_facet | Jia, Xiang Zhang, Xiaoli Du, Jun Huo, Langning Chai, Guoqi Wang, Yueting Wang, Jingxu Tian, Xin Lei, Lingting Chen, Long Li, Caixia Xu, Haifeng Qiu, Shike Yu, Weiwei Yin, Sanjun Wang, Ran Huang, Tiecheng Yao, Zongqi Chen, Mengyu Hou, Pengfei He, Danni Li, Yangguang |
| contents | <p><strong><span lang="EN-US">Abstract:</span></strong><span lang="EN-US"> This repository hosts a comprehensive forest disturbance dataset for China, spanning the period from 1986 to 2020. The dataset was generated using the Continuous Change Detection and Classification (CCDC) algorithm on the Google Earth Engine (GEE) platform, utilizing all available Landsat time-series imagery from 1985 to 2021. This dataset provides a dual-scale perspective on forest dynamics, offering both fine-scale disturbance attributes (30 m resolution) and aggregated disturbance density (1 km resolution) to support diverse ecological and climatic applications.</span></p> <p><span lang="EN-US">The dataset consists of two major components:</span></p> <p><strong><span lang="EN-US">1. 30 m Resolution Forest Disturbance Attributes:</span></strong><span lang="EN-US"> These raster files record the specific characteristics of detected forest disturbance events at a 30 m spatial resolution.</span></p> <p><span lang="EN-US">(1) CFD_year.tif: Records the year of the forest disturbance event. The pixel values are stored as indices relative to the base year 1985. Value Range: 1 represents 1986, 2 represents 1987, ..., and 35 represents 2020. Nodata value: -128 (indicating no disturbance or non-forest areas).</span></p> <p><span lang="EN-US">(2) CFD_mag.tif: This dataset records the maximum disturbance magnitude (abbreviated as "mag"), representing the maximum spectral change detected within a specific time segment and spectral band. It is quantified by the difference between the end-point of the pre-disturbance segment and the start-point of the post-disturbance segment, where a larger value indicates a more severe disturbance. To facilitate interpretation, the raw magnitude values were reclassified into five discrete classes using the Jenks Natural Breaks method. The final pixel values correspond to the following ranges: Class 1 (0 < mag </span><span>≤</span><span> <span lang="EN-US">0.09), Class 2 (0.09 <mag </span></span><span>≤</span><span> <span lang="EN-US">0.19), Class 3 (0.19 < mag </span></span><span>≤</span><span> <span lang="EN-US">0.30), Class 4 (0.30 <mag </span></span><span>≤</span><span> <span lang="EN-US">0.45), and Class 5 (mag > 0.45).</span></span></p> <p><span lang="EN-US">(3) CFD_num.tif: Records the total number of disturbance events detected within the pixel during the study period.</span></p> <p><strong><span lang="EN-US">2. 1 km Resolution Annual Forest Disturbance Density (CFDD):</span></strong><span lang="EN-US"> Derived from the 30 m disturbance year data, this product quantifies the spatiotemporal density of disturbances. CFDD[Year].tif (e.g., CFDD2000.tif): Represents the annual forest disturbance density. Unit: The pixel value indicates the ratio of disturbed forest area within each 1 km × 1 km grid cell (Unit: % or dimensionless fraction).</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17905156 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Forest Disturbance in China from 1986 to 2020: 30 m disturbance attributes and 1 km annual density products Jia, Xiang Zhang, Xiaoli Du, Jun Huo, Langning Chai, Guoqi Wang, Yueting Wang, Jingxu Tian, Xin Lei, Lingting Chen, Long Li, Caixia Xu, Haifeng Qiu, Shike Yu, Weiwei Yin, Sanjun Wang, Ran Huang, Tiecheng Yao, Zongqi Chen, Mengyu Hou, Pengfei He, Danni Li, Yangguang Forest Forest deterioration froest disturbance China <p><strong><span lang="EN-US">Abstract:</span></strong><span lang="EN-US"> This repository hosts a comprehensive forest disturbance dataset for China, spanning the period from 1986 to 2020. The dataset was generated using the Continuous Change Detection and Classification (CCDC) algorithm on the Google Earth Engine (GEE) platform, utilizing all available Landsat time-series imagery from 1985 to 2021. This dataset provides a dual-scale perspective on forest dynamics, offering both fine-scale disturbance attributes (30 m resolution) and aggregated disturbance density (1 km resolution) to support diverse ecological and climatic applications.</span></p> <p><span lang="EN-US">The dataset consists of two major components:</span></p> <p><strong><span lang="EN-US">1. 30 m Resolution Forest Disturbance Attributes:</span></strong><span lang="EN-US"> These raster files record the specific characteristics of detected forest disturbance events at a 30 m spatial resolution.</span></p> <p><span lang="EN-US">(1) CFD_year.tif: Records the year of the forest disturbance event. The pixel values are stored as indices relative to the base year 1985. Value Range: 1 represents 1986, 2 represents 1987, ..., and 35 represents 2020. Nodata value: -128 (indicating no disturbance or non-forest areas).</span></p> <p><span lang="EN-US">(2) CFD_mag.tif: This dataset records the maximum disturbance magnitude (abbreviated as "mag"), representing the maximum spectral change detected within a specific time segment and spectral band. It is quantified by the difference between the end-point of the pre-disturbance segment and the start-point of the post-disturbance segment, where a larger value indicates a more severe disturbance. To facilitate interpretation, the raw magnitude values were reclassified into five discrete classes using the Jenks Natural Breaks method. The final pixel values correspond to the following ranges: Class 1 (0 < mag </span><span>≤</span><span> <span lang="EN-US">0.09), Class 2 (0.09 <mag </span></span><span>≤</span><span> <span lang="EN-US">0.19), Class 3 (0.19 < mag </span></span><span>≤</span><span> <span lang="EN-US">0.30), Class 4 (0.30 <mag </span></span><span>≤</span><span> <span lang="EN-US">0.45), and Class 5 (mag > 0.45).</span></span></p> <p><span lang="EN-US">(3) CFD_num.tif: Records the total number of disturbance events detected within the pixel during the study period.</span></p> <p><strong><span lang="EN-US">2. 1 km Resolution Annual Forest Disturbance Density (CFDD):</span></strong><span lang="EN-US"> Derived from the 30 m disturbance year data, this product quantifies the spatiotemporal density of disturbances. CFDD[Year].tif (e.g., CFDD2000.tif): Represents the annual forest disturbance density. Unit: The pixel value indicates the ratio of disturbed forest area within each 1 km × 1 km grid cell (Unit: % or dimensionless fraction).</span></p> |
| title | Forest Disturbance in China from 1986 to 2020: 30 m disturbance attributes and 1 km annual density products |
| topic | Forest Forest deterioration froest disturbance China |
| url | https://doi.org/10.5281/zenodo.17905156 |