Data for: How Much More Carbon Can Be Realistically Captured from Grassland Vegetation? Quantitative Assessment Using Focal Analysis on Soil-Topography-Vegetation Unit in the Inner Mongolia

The data provided in this work were processed from five original datasets, including 1) soil map from the Harmonized World Soil Database (ver. 1.2) (http://www.fao.org/soils-portal), 2) topography data (DEM from http://earthexplorer.usgs.gov), 3) Climate dataset (http://cdc.cma.gov.cn), 4) vegetation type map (from http://www.nsii.org.cn/chinavegetaion), and 5) MODIS net primary productivity (NPP) (MODIS 17A3, http://e4ftl01.cr.usgs.gov/MOLT), which were downloaded for the Inner Mongolia Autonomous Region (IMAR) of China. The time window for most of the datasets was during 2000-2014. Topography, after classifying the DEM into three groups (<500m, 500m~1500m, and >1500m), and monthly precipitation and temperature data collected from about 680 weather stations from the climate dataset were applied to make climate grid maps at 1 km×1 km using an ANUSPLIN approach (Price et al., 2000). The climate grid maps were used to model potential NPP (PNPP) using the Miami NPP model (Adams et al., 2004; Gang et al., 2014; Lieth, 1973). Tiles from MODIS 17A3 were mosaicked and taken to represent actual NPP (ANPP) of the grassland vegetation. A differential analysis between PNPP and ANPP at pixel level, was presented to represent a theoretical potential space in carbon capture. The maps of soil (S), topography (T), and vegetation (V) were overlaid to segment the area into spatially homogeneous S-T-V patches. Three types of focal statistics, including mean (Mean), maximum (Max), and 95% percentile threshold (95%PCT), of ANPP for each S-T-V unit were computed as the target level for ANPP. The gap from ANPP to each target level for each S-T-V patch was computed as being the practically realistic potential space. The gaps for the entire IMAR area were aggregated. The temporally averaged maps of the gaps derived from the pixel-based and focal analysis approaches along with ANPP and PNPP were provided. The temporal trajectories of spatially averaged gaps as well as ANPP and PNPP were illustrated.

本研究所用数据均由5套原始数据集加工处理得到，具体包括：1）《世界土壤数据库协调版（版本1.2）（Harmonized World Soil Database, ver.1.2）》配套土壤图，数据来源为http://www.fao.org/soils-portal；2）地形数据（数字高程模型（Digital Elevation Model, DEM），数据来源http://earthexplorer.usgs.gov）；3）气候数据集（数据来源http://cdc.cma.gov.cn）；4）植被类型图（数据来源http://www.nsii.org.cn/chinavegetaion）；5）MODIS净初级生产力（Net Primary Productivity, NPP）数据（MODIS 17A3，数据来源http://e4ftl01.cr.usgs.gov/MOLT），所有数据均下载自中国内蒙古自治区（Inner Mongolia Autonomous Region, IMAR）。多数数据集的时间覆盖范围为2000-2014年。研究人员先将DEM划分为3个高程组（<500m、500m~1500m及>1500m），再结合从气候数据集获取的约680个气象站点的月降水与气温数据，采用ANUSPLIN方法（Price等，2000）生成分辨率为1km×1km的气候格网数据。基于气候格网数据，借助迈阿密NPP模型（Miami NPP model）模拟得到潜在净初级生产力（Potential Net Primary Productivity, PNPP）（Adams等，2004；Gang等，2014；Lieth，1973）。将MODIS 17A3的影像瓦片拼接后，用以表征草原植被的实际净初级生产力（Actual Net Primary Productivity, ANPP）。通过像素级PNPP与ANPP的差值分析，得到可表征理论碳汇潜力的空间分布。将土壤（S）、地形（T）与植被（V）的分布图叠加，将研究区划分为空间均质的S-T-V斑块单元。针对每个S-T-V单元，计算其ANPP的三类焦点统计量：均值（Mean）、最大值（Max）及95%百分位阈值（95%PCT），以此作为该单元的ANPP目标水平。计算每个S-T-V斑块的ANPP与对应目标水平的差值，即实际可实现的碳汇潜力空间。将内蒙古全区的上述差值进行汇总，并提供基于像素级与焦点统计分析得到的差值时间平均格网图，同时附带ANPP与PNPP的相关数据。研究还展示了空间平均差值、ANPP及PNPP的时间变化轨迹。