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Data · dataset · 2019

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

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Description

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) (fao.org/soils-portal), 2) topography data (DEM from earthexplorer.usgs.gov), 3) Climate dataset (cdc.cma.gov.cn), 4) vegetation type map (from nsii.org.cn/chinavegetaion), and 5) MODIS net primary productivity (NPP) (MODIS 17A3, 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).

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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.

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Teesside University Research Data Repositoryoai:data.mendeley.com/x8pdwgrykj.115 h agoJSON v1
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concepts[field].anzsrc:field:401304mapping · researchdata tees ac ukvocabulary-mapper@1.0.0keywords['Remote Sensing']
concepts[field].local:field:earth-environmentalmapping · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0
concepts[modality].local:modality:remote-sensingmapping · researchdata tees ac ukvocabulary-mapper@1.0.0keywords['Remote Sensing']
concepts[subject].local:topic:climatemapping · researchdata tees ac ukvocabulary-mapper@1.0.0keywords['Climate Change']
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