Yearly CO2 emissions from anthropogenic land use change by main driver (2014-2023)

Background Human-induced land use change (LUC), driven by activities such as forestry, logging, and the production of agricultural commodities (e.g. fruits, nuts, and meat) significantly impacts the Global Commons, encompassing the climate system, ice sheets, land biosphere, oceans, and the ozone layer. The convertion of natural forests into areas dedicated to these activities lead to disrupted ecosystems (Foley et al. 2005), severely degraded biodiversity (Newbold et al. 2015), and the release of substantial amounts of greenhouse gases (GHGs) into the atmosphere (Hong et al. 2021), further exacerbating climate change and ocean acidification (Doney et al. 2009). The expansion of the agricultural frontier is identified as the predominant direct cause of deforestation globally, with other industries like timber and mining also playing significant roles (Curtis et al. 2018). To achieve global climate targets, forestry, and other land use GHG emissions must decrease along a nonlinear trajectory and reach carbon neutrality by 2050 (Rockström et al. 2017). However, to successfully address this road map, improving our understanding of deforestation drivers is urgently needed. Summary This dataset is the result of data processing performed to estimate the extent to which commodities and other agricultural products have replaced forests, while mapping the CO2 emission impact making use of the best available spatially explicit data. Results are reported globally for 52 products at national level, as well as agroecological and thermal zones (FAO & IIASA) and a 50km cell vector grid. In order to detect spatially-explicit deforestation drivers, the current extent of commodities and agricultural products was overlapped with global annual tree cover loss in the 10-year period from 2014 to 2023. Carbon stocks in the deforested areas were then assumed to have been emmited into the atmosphere. Recent, detailed crop and pasture maps for relevant commodities were used whenever available, and coarser resolution datasets were used as supplements when needed. Operations were performed in Google Earth Engine. Datasets used Forest and biomass carbon distribution The Global Forest Change dataset (Hansen et al., 2013) is used to estimate deforestation between 2014 and 2023. This tree cover loss dataset measures the first instance of complete removal of tree cover canopy at a 30-meter resolution for all woody vegetation over 5 meters in height. The WCMC Above and Below Ground Biomass Carbon Density (Soto-Navarro et al., 2020), for reference year 2010 at 300m pixel, is overlapped with resulting deforested areas pixels to dermine the biomass carbon present in the areas before deforestation. Generalized deforestation drivers Tree cover loss by dominant driver (Curtis et al., 2022) in 2023 is used to determine wide categories of deforestation drivers (commodities, shifting agriculture, forestry, wildfire and urbanization). Pixels indicating deforestation in the Global Forest Change dataset (Hansen et al., 2013) that overlap the commodities and shifting agriculture pixels from this dataset (Curtis et al., 2022) have their drivers further detailed with the data sources listed in the below. EarthStat pasture areas layer (Ramankutty et al., 2008) is used to identify areas for which specific livestock categories are to be defined. The project provides pasture areas for reference year 2000 at ~10km resolution. Detailed deforestation drivers The Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) commodity distibution layer (Becker-Reshef et al., 2023) is used to identify specific commodities (winter wheat, spring wheat, maize, rice and soybean) to deforestation pixels pertaining to the "commodities" class. The ressource provides commodity distribution mapping at 5km pixel resolution. Values are provided as percentage of pixel area occupied by given crop. The Spatial Production Allocation Model (SPAM) physical area layer (You et al., 2014) for reference year 2020 is used to detail drivers pertaining to the "shifting agriculture" class. The dataset covers 46 crops and crop groups at ~9km pixel resolution. Values are provided as percentage of pixel area occupied by given crop or crop group. The Gridded Livestock of the World (GLW3) (Gilbert et al., 2022) is used to determine which species (cattle, goat, sheep or horse) of livestock is raised in areas identified as pasture in the EarthStat layer and pertaining to the "commodities" class. The project provides livestock distribution for reference year 2015 at ~9km resolution. Values are provided as number of individuals located within the pixel. Values were converted into percentage of pixel area covered by grazing field for given species based on species density thresholds. Data processing Most of data processing takes place in Google Earth Engine, with scripts redacted in javascript. In summary, two strategies were implemented: Proportional driver distribution strategy: When deforestation pixels (Hansen et al., 2013) overlapped with pixels from at least one of the detailed deforestation drivers data sources, the driver describe in the latter were associated with that deforested area. Whenever more than one of these data sources had non-null pixels overlapping the area, a proportional distribution was assumed (i.e. if SPAM indicated 100% of the area to be covered by cowpea crops, GEOGLAM 100% by maize, and GLW3 100% by cattle grazing fields, the pixel is assumed to have 33.3% of its deforested area associated with each of these drivers). Main driver strategy: When deforestation pixels did not overlap with any non-null pixels from any of the detailed drivers sources, the pixel is assumed to have the entirety of its deforested area associated with one single main driver resulting from a crop-livestock mosaic. The mosaic is created by taking the highest value from each of the crop or livestock distribution rasters, and then assigning the raster category to be the new pixel value, ultimately creating a category raster layer containing the main crop, crop group or livestock species occupying that pixel area. Null or zero values in this mosaic are filled-in by nearest neighbour analysis, to a limit of 20 pixels expansion. This was enough to ensure that all deforestation pixels had at least one detailed driver with which it could be associated. The logic behind this operation resides in the fact that the deforestation layer (Hansen et al., 2013) has a larger temporal coverage (with the more recent data point being the reference year 2023), while the detailed driver layers can be as old as reference year 2015. This means we're assuming the main deforestation drivers continued to expand their limits to neighbouring areas during the years for which no data is available. Resulting rasters from both strategies are put together and a zonal statistics operation is performed in order to populate the vector grid cells. Files This repository contains the following files: deforested_area_by_LUC_driver_2014_2023.CSV contains the deforested area (hectares) and the corresponding driver in each grid cell (idenfied by the id field) in each year, in CSV text format. carbon_emissions_by_LUC_driver_2014_2023.CSV contains the carbon emitted (Mg CO2 eq.) and the corresponding driver in each grid cell (idenfied by the id field) in each year, in CSV text format. spatial_grid.gpkg contains the raw 50km cell grid, with identification of country (iso3 and name fields), region, and FAO agroecological zone (zone field) and thermal zone (thermal field), in Geopackage format. In order to visualize the data in a map, the user will need to join one of the csv files to this geopackage file by basing the join on the 'id' field. summary_showcase.png is an image showcasing maps created using the database, as well as a diagram showing the datasets used to create the final dataset. How to cite Iablonovski, G.; Berthet, E. C.; Roberts, S. (2024). Yearly CO2 emissions from anthropogenic land use change by main driver (2014-2023) [Data set]. Zenodo. https://zenodo.org/doi/10.5281/zenodo.13308514 Authors and contact Authors: Guilherme Iablonovski*, Etienne Charles Berthet, Sophie Roberts *Corresponding author: Guilherme Iablonovski ([e-mail])研究背景 由林业、伐木以及农产品（如水果、坚果、肉类）生产等活动驱动的人为土地利用变化（Land Use Change, LUC），对全球公域（Global Commons）造成了显著影响，后者涵盖气候系统、冰盖、陆地生物圈、海洋与臭氧层。天然林被转换为上述活动用地后，会导致生态系统紊乱（Foley等，2005）、生物多样性严重退化（Newbold等，2015），并向大气中释放大量温室气体（Greenhouse Gas, GHG）（Hong等，2021），进一步加剧气候变化与海洋酸化（Doney等，2009）。农业扩张被认为是全球范围内森林砍伐的主要直接诱因，木材、采矿等其他行业也发挥了重要作用（Curtis等，2018）。为实现全球气候目标，林业及其他土地利用活动的温室气体排放必须沿非线性轨迹下降，并于2050年实现碳中和（Rockström等，2017）。然而，要顺利推进这一路线图，亟需加深对森林砍伐驱动因素的认知。 数据集概述 本数据集通过数据处理生成，旨在估算大宗商品与其他农产品替代森林的规模，并结合现有最优空间显性数据绘制二氧化碳排放影响图谱。研究结果以全球尺度呈现，涵盖52种产品的国家级数据，以及农业生态区、热量带（联合国粮农组织（Food and Agriculture Organization, FAO）与国际应用系统分析研究所（International Institute for Applied Systems Analysis, IIASA））和50km分辨率矢量格网数据。 为识别空间显性的森林砍伐驱动因素，本研究将2014—2023年十年间的全球年度林木覆盖损失数据，与当前大宗商品与农产品分布范围进行叠加。随后假设被砍伐森林区域的碳储量全部释放至大气中。研究优先采用已公开的详细作物与牧场分布图集，必要时辅以低分辨率数据集。所有处理均在谷歌地球引擎（Google Earth Engine, GEE）中完成。 所用数据集 森林与生物质碳分布数据集 本研究采用全球森林变化数据集（Global Forest Change, GFC；Hansen等，2013）估算2014—2023年的森林砍伐情况。该林木覆盖损失数据集以30m分辨率，识别所有高度超过5m的木本植被冠层首次完全移除的事件。 将世界保护监测中心（World Conservation Monitoring Centre, WCMC）的地上与地下生物质碳密度数据集（Soto-Navarro等，2020，基准年为2010年，分辨率300m）与上述森林砍伐区域像素叠加，以确定砍伐前该区域的生物质碳储量。 广义森林砍伐驱动因素数据集 采用2023年按主导驱动因素划分的林木覆盖损失数据集（Curtis等，2022），以确定森林砍伐驱动因素的宽泛类别：大宗商品种植、轮垦农业、林业、野火与城市化。将全球森林变化数据集（Hansen等，2013）中与大宗商品种植、轮垦农业像素重叠的森林砍伐像素，结合下文列出的数据源进一步细化驱动因素。 采用地球统计牧场面积图层（EarthStat pasture areas layer；Ramankutty等，2008）识别需定义具体牲畜类别的区域。该数据集以2000年为基准年，提供约10km分辨率的牧场面积数据。 详细森林砍伐驱动因素数据集 采用地球观测组织全球农业监测计划（Group on Earth Observations Global Agricultural Monitoring, GEOGLAM）的大宗商品分布图层（Becker-Reshef等，2023），将“大宗商品种植”类别的森林砍伐像素对应至具体大宗商品（冬小麦、春小麦、玉米、水稻与大豆）。该资源以5km分辨率提供大宗商品分布图谱，数据值为给定作物占像素面积的百分比。 采用空间生产分配模型（Spatial Production Allocation Model, SPAM；You等，2014，基准年为2020年）物理面积图层细化“轮垦农业”类别的驱动因素。该数据集覆盖46种作物与作物群，分辨率约9km，数据值为给定作物或作物群占像素面积的百分比。 采用世界栅格化牲畜数据集第三版（Gridded Livestock of the World 3, GLW3；Gilbert等，2022），确定地球统计图层中被归类为“大宗商品种植”类别的牧场区域内饲养的牲畜种类（牛、山羊、绵羊或马）。该数据集以2015年为基准年，提供约9km分辨率的牲畜分布数据，数据值为像素内的个体数量。研究基于物种密度阈值，将其转换为给定物种放牧场占像素面积的百分比。 数据处理流程 绝大多数数据处理工作在谷歌地球引擎中完成，脚本采用JavaScript编写。总体而言，本研究采用两种策略： 1. 比例驱动因素分配策略：当森林砍伐像素（Hansen等，2013）与至少一个详细驱动因素数据集的像素重叠时，将后者对应的驱动因素关联至该森林砍伐区域。若多个数据集的非空像素均与该区域重叠，则采用比例分配方式（例如，若空间生产分配模型显示100%区域为豇豆种植区、地球观测组织全球农业监测计划显示100%区域为玉米种植区、世界栅格化牲畜数据集第三版显示100%为牛放牧区，则该像素的砍伐面积分别按33.3%关联至三类驱动因素）。 2. 主导驱动因素策略：当森林砍伐像素未与任何详细驱动因素数据集的非空像素重叠时，假设该像素的全部砍伐面积均与单一主导驱动因素相关，该驱动因素由作物-牲畜镶嵌图确定。镶嵌图通过提取每个作物或牲畜分布栅格的最大值，将对应栅格类别赋值为像素新值，最终生成包含该像素区域主要作物、作物群或牲畜种类的类别栅格图层。该镶嵌图中的空值或零值通过最近邻分析填充，扩展范围限定为20个像素。这一操作的逻辑在于：全球森林变化数据集（Hansen等，2013）的时间覆盖范围更广（最新数据点为2023年基准年），而详细驱动因素数据集的基准年最早可至2015年。因此本研究假设，在无数据的年份里，主要森林砍伐驱动因素的扩张范围延伸至了邻近区域。 将两种策略生成的栅格合并后，执行区域统计操作，以填充矢量格网单元。 数据集文件 本仓库包含以下文件： 1. `deforested_area_by_LUC_driver_2014_2023.CSV`：以CSV文本格式存储的数据，包含每个格网单元（由id字段标识）每年的森林砍伐面积（单位：公顷）及其对应驱动因素。 2. `carbon_emissions_by_LUC_driver_2014_2023.CSV`：以CSV文本格式存储的数据，包含每个格网单元（由id字段标识）每年的碳排放总量（单位：Mg CO₂当量）及其对应驱动因素。 3. `spatial_grid.gpkg`：以Geopackage格式存储的原始50km格网数据，包含国家（iso3与name字段）、区域、联合国粮农组织农业生态区（zone字段）与热量带（thermal field）信息。若需在地图中可视化数据，用户需通过'id'字段将CSV文件与该Geopackage文件关联。 4. `summary_showcase.png`：展示使用本数据库生成的地图及数据集构成示意图的图像文件。 引用方式 Iablonovski, G.; Berthet, E. C.; Roberts, S. (2024). 基于主要驱动因素的人为土地利用变化年度二氧化碳排放量（2014—2023）[数据集]. Zenodo. https://zenodo.org/doi/10.5281/zenodo.13308514 作者与联系方式 作者：Guilherme Iablonovski*, Etienne Charles Berthet, Sophie Roberts *通讯作者：Guilherme Iablonovski（邮箱：[e-mail]）