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.

本研究中提供的数据经过五个原始数据集的处理而成，包括：1）来源于协调世界土壤数据库（版本1.2）的土壤图（http://www.fao.org/soils-portal），2）地形数据（来自 http://earthexplorer.usgs.gov 的数字高程模型DEM），3）气候数据集（http://cdc.cma.gov.cn），4）植被类型图（来自 http://www.nsii.org.cn/chinavegetaion），以及5）MODIS净初级生产力（NPP）数据（MODIS 17A3，http://e4ftl01.cr.usgs.gov/MOLT），这些数据均下载自中国内蒙古自治区（IMAR）。大多数数据集的时间范围为2000-2014年。将数字高程模型DEM分类为三个等级（<500m、500m~1500m和>1500m）后，并结合来自约680个气象站的月降水量和温度数据，采用ANUSPLIN方法（Price et al., 2000）制作了1 km×1 km的气候网格图。这些气候网格图被用于基于迈阿密NPP模型（Adams et al., 2004；Gang et al., 2014；Lieth, 1973）模拟潜在NPP（PNPP）。MODIS 17A3的图块经过镶嵌，用以代表草地植被的实际NPP（ANPP）。通过像素级别的PNPP与ANPP的差异分析，展示了碳捕获的理论潜在空间。将土壤（S）、地形（T）和植被（V）的地图叠加，将区域划分为空间同质的S-T-V斑块。计算了每个S-T-V单元的ANPP的均值（Mean）、最大值（Max）和95%分位数阈值（95%PCT）的三种焦点统计量，作为ANPP的目标层级。每个S-T-V斑块从ANPP到每个目标层级的差距被计算为实际可实现的潜在空间。整个IMAR区域的差距被汇总。提供了基于像素和焦点分析方法的差距时间平均图，以及ANPP和PNPP。同时，绘制了空间平均差距以及ANPP和PNPP的时间轨迹。