Browsing by Author "Heine, PR"

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    Estimates and determinants of stocks of deep soil carbon in Gabon, Central Africa
    (Geoderma, 2019-05-01) Wade, AM; Richter, DD; Medjibe, VP; Bacon, AR; Heine, PR; White, LJT; Poulsen, JR
    © 2019 Despite the importance of tropical forest carbon to the global carbon cycle, research on carbon stocks is incomplete in major areas of the tropical world. Nowhere in the tropics is this more the case than in Africa, and especially Central Africa, where carbon stocks are known to be high but a scarcity of data limits understanding of carbon stocks and drivers. In this study, we present the first nation-wide measurements and determinants of soil carbon in Gabon, a nation in Central Africa. We estimated soil carbon to a 2-m depth using a systematic, random design of 59 plots located across Gabon. Soil carbon to a 2-m depth averaged 163 Mg ha −1 with a CV of 61%. These soil carbon stocks accounted for approximately half of the total carbon accumulated in aboveground biomass and soil pools. Nearly a third of soil carbon was stored in the second meter of soil, averaging 58 Mg ha −1 with a CV of 94%. Lithology, soil type, and terrain attributes were found to be significant predictors of cumulative SOC stocks to a 2-m depth. Current protocols of the IPCC are to sample soil carbon from the surface 30 cm, which in this study would underestimate soil carbon by 60% and underestimate ecosystem carbon by 30%. A nonlinear model using a power function predicted cumulative soil carbon stocks in the second meter with an average error of prediction of 3.2 Mg ha −1 (CV = 915%) of measured values. The magnitude and turnover of deep soil carbon in tropical forests needs to be estimated as more countries prioritize carbon accounting and monitoring in response to accelerating land-use change.
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    Limited carbon contents of centuries old soils forming in legacy sediment
    (Geomorphology, 2020-04) Wade, AM; Richter, DD; Cherkinsky, A; Craft, CB; Heine, PR
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    Micro-topographic roughness analysis (MTRA) highlights minimally eroded terrain in a landscape severely impacted by historic agriculture
    (Remote Sensing of Environment, 2019-03-01) Brecheisen, ZS; Cook, CW; Heine, PR; Richter, DDB
    © 2018 Elsevier Inc. The 190 km2 Calhoun Critical Zone Observatory in the Piedmont region of South Carolina, USA lies in an ancient, highly weathered landscape transformed by historic agricultural erosion. Following the conversion of largely hardwood forests to cultivated fields and pastures for ~200 years, excess runoff from fields led to extreme sheet, rill, and gully erosion across the landscape. Roads, terraces, and a variety of other human disturbances have increased the landscape's surface roughness. By the 1950s, cultivation-based agriculture was largely abandoned across most of the Southern Piedmont due to soil erosion, declining agricultural productivity, and shifting agricultural markets. Secondary forests, dominated by loblolly and shortleaf pines, have since regrown on much of the landscape, including the 1500 km2 Sumter National Forest, which was purchased from farmers and private land owners in the 1930s. Although this landscape was intensively farmed for approximately 150 years, there are a few hardwood forest stands and even entire small watersheds that have never been plowed and degraded by farming. Such relatively old hardwood stands and watersheds comprise relic landforms whose soils, regoliths, and vegetation are of interest to hydrologists, environmental historians, biogeochemists, geomorphologists, geologists, pedologists, and others interested in understanding the legacy of land-use history in this severely altered environment. In this work we champion the need for high-resolution terrain mapping and demonstrate how Light Detection And Ranging (LiDAR) digital elevation model (DEM) data and microtopographic terrain roughness analyses (MTRA) can be used to infer land use history and management. This is accomplished by analyzing fine scale variation in terrain slope across the 1190 km2 CCZO using data derived from three independent and overlapping LiDAR datasets at varying spatial resolutions. Terrain slope variability MTRA is further compared to three other methods of capturing and quantifying fine-scale surface roughness. We lastly demonstrate how these analyses can be employed in concert with historic aerial photography from the 1930's, contemporary Landsat remote sensing data, and ecological field data to identify reference relic landforms: hardwood stands, hillslopes, and small watersheds that have experienced minimal anthropogenic erosion for study and conservation.