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Forest Disturbance in China from 1986 to 2020: 30 m disturbance attributes and 1 km annual density products

Jia, XiangBeijing Key Laboratory of Precision Forestry, College of Forestry, Beijing Forestry UniversityZhang, XiaoliBeijing Key Laboratory of Precision Forestry, College of Forestry, Beijing Forestry UniversityDu, JunInstitute of Geographical Sciences, Henan Academy of SciencesHuo, LangningDepartment of Forest Resource Management, Swedish University of Agricultural SciencesChai, GuoqiInstitute of Forest Resource Information Techniques, Chinese Academy of ForestryWang, YuetingInternational Center of Bamboo and Rattan, National Forestry and Grassland AdministrationWang, JingxuInstitute of Geographical Sciences, Henan Academy of SciencesTian, XinInstitute of Forest Resource Information Techniques, Chinese Academy of ForestryLei, LingtingInstitute of Forest Resource Information Techniques, Chinese Academy of ForestryChen, LongInstitute of Forest Resource Information Techniques, Chinese Academy of ForestryLi, CaixiaLand Consolidation and Rehabilitation Center (Land Science and Technology Innovation Center), Ministry of Natural ResourcesXu, HaifengCollege of Big Data and Intelligent Engineering, Southwest Forestry UniversityQiu, ShikeKey Laboratory of Remote Sensing and Geographic Information Systems in Henan ProvinceYu, WeiweiInstitute of Geographical Sciences, Henan Academy of SciencesYin, SanjunHenan Forestry Resources Monitoring InstituteWang, RanHenan Provincial Forestry Ecological Construction and Development CenterHuang, TiechengCollege of Forestry and Landscape Architecture, Xinjiang Agricultural UniversityYao, ZongqiBeijing Key Laboratory of Precision Forestry, College of Forestry, Beijing Forestry UniversityChen, MengyuBeijing Key Laboratory of Precision Forestry, College of Forestry, Beijing Forestry UniversityHou, PengfeiInstitute of Geographical Sciences, Henan Academy of SciencesHe, DanniInstitute of Geographical Sciences, Henan Academy of SciencesLi, YangguangInstitute of Geographical Sciences, Henan Academy of Sciences

Zenodo (CERN European Organization for Nuclear Research)repository2025en

ABI

Аннотация

Abstract: 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. The dataset consists of two major components: 1. 30 m Resolution Forest Disturbance Attributes: These raster files record the specific characteristics of detected forest disturbance events at a 30 m spatial resolution. (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). (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 ≤ 0.09), Class 2 (0.09 <mag ≤ 0.19), Class 3 (0.19 < mag ≤ 0.30), Class 4 (0.30 <mag ≤ 0.45), and Class 5 (mag > 0.45). (3) CFD_num.tif: Records the total number of disturbance events detected within the pixel during the study period. 2. 1 km Resolution Annual Forest Disturbance Density (CFDD): 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).

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Идентификаторы

DOI: 10.5281/zenodo.17905156

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