Application of Surface Modeling for Large Regions: A Case Study for Forest Carbon Stocks in China

2016 ◽  
pp. 539-562
Keyword(s):  
Surface Modeling ◽  
Carbon Stocks ◽  
Forest Carbon

The imprint of logging on tropical forest carbon stocks: A Bornean case-study

2018 ◽  
Vol 417 ◽  
pp. 154-166 ◽  
Author(s):  
Andes Hamuraby Rozak ◽  
Ervan Rutishauser ◽  
Karsten Raulund-Rasmussen ◽  
Plinio Sist
Keyword(s):  
Tropical Forest ◽  
Carbon Stocks ◽  
Forest Carbon

scholarly journals A 100-year conservation experiment: Impacts on forest carbon stocks and fluxes

2013 ◽  
Vol 310 ◽  
pp. 242-255 ◽  
Author(s):  
Tara Sharma ◽  
Werner A. Kurz ◽  
Graham Stinson ◽  
Marlow G. Pellatt ◽  
Qinglin Li
Keyword(s):  
Carbon Stocks ◽  
Forest Carbon

The effect of cassava-based bioethanol production on above-ground carbon stocks: A case study from Southern Mali

Energy Policy ◽  
2012 ◽  
Vol 41 ◽  
pp. 575-583 ◽  
Author(s):  
Laura Vang Rasmussen ◽  
Kjeld Rasmussen ◽  
Torben Birch-Thomsen ◽  
Søren B.P. Kristensen ◽  
Oumar Traoré
Keyword(s):  
Carbon Stocks ◽  
Bioethanol Production

An adapted method to assess soil organic carbon stocks in a high mountain region: A LDN case study from Kirgistan

2021 ◽  
Author(s):  
Margarete Korintenberg ◽  
Judith Walter ◽  
Katja Märten ◽  
Jutta Zeitz
Keyword(s):  
Bulk Density ◽  
Land Degradation ◽  
Carbon Stocks ◽  
Mountain Region ◽  
High Mountain ◽  
Fine Soil ◽  
Soil Organic Carbon Stocks ◽  
Global Data ◽  
Soc Stocks

<p>The Sustainable Development Goals (SDGs) adopetd by the United Nations in 2016 include the SDG 15.3 „Land Degradation Neutrality (LDN)“, which aims to reduce land degradation by national efforts of the member states. Three indicators for land degradation were gloablly identified: landcover, land productivity and soil organic carbon stocks (SOC). In particular, the assessment of SOC is challenging in countries where (a) spatial digital data is largely missing and (b) SOC mapping is difficult due to remotness typical for high mountain regions . Global data provided by the Secretariat of the United Nations Convention to Combat Desertification (UNCCD) may be used for reporting, but experience from various countries indicates inaccuracies due to generalisation. This is especially the case for SOC. Moreover, to report on changes in SOC stocks, a comprehensive baseline is mandatory. In order to approach these challenges, Kirgistan, which has signed the SDG’s but still lacks a baseline for SOC, has been chosen for a case study.</p><p>In a multinational project we developed a scientifically based method to map and assess SOC stocks enabling a nationwide upscaling of SOC data (baseline). Using globally available data on landcover, elevation, climate and national soil data, „representative SOC units“ were identified prior to sampling. We assume that mainly these factors determine the spatial variability of SOC and that similar SOC stocks can be expected at comparable site conditions. More than 90% of the surface area, that potentially store SOC, is coverd by only 20 representative units, which were sampled 3-fold in the field. Sampling location within a single unit was determined using a drone to identify a representative location. Using the drone was especially helpful as sampling sites in a high mountain region were often extremely remote. During sampling small-scale variability of SOC was considered in the field. To determine SOC stocks, bulk density of the fine soil, coarse fragments and amount of roots were measured in the laboratory. Furthermore, pH, clay, silt and sand content were analysed to identify further drivers for SOC distribution.</p><p>Results show that spatial distribution of SOC in such a high mountain region is mainly controlled by landcover (cropland, grassland, forest), elevation, bulk density and clay content. Within single landcover classes topographic indices, such as aspect, further determine SOC distribution. This is especially the case for grassland, which is the dominant landcover in Kirgistan (53%). For the assessment of SOC stocks different approaches were compared. For instance, precise assessment of stocks using the bulk density of the fine soil corrected for coarse fragments leads to significantly lower SOC stocks when compared to the global data provided by the UNCCD.</p>

scholarly journals Closing a gap in tropical forest biomass estimation: taking crown mass variation into account in pantropical allometries

2016 ◽  
Vol 13 (5) ◽  
pp. 1571-1585 ◽  
Author(s):  
Pierre Ploton ◽  
Nicolas Barbier ◽  
Stéphane Takoudjou Momo ◽  
Maxime Réjou-Méchain ◽  
Faustin Boyemba Bosela ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Reference Model ◽  
Original Data ◽  
Carbon Stocks ◽  
Forest Carbon ◽  
Biomass Estimation ◽  
Systematic Bias ◽  
Data Set ◽  
Allometric Models ◽  
Large Trees

Abstract. Accurately monitoring tropical forest carbon stocks is a challenge that remains outstanding. Allometric models that consider tree diameter, height and wood density as predictors are currently used in most tropical forest carbon studies. In particular, a pantropical biomass model has been widely used for approximately a decade, and its most recent version will certainly constitute a reference model in the coming years. However, this reference model shows a systematic bias towards the largest trees. Because large trees are key drivers of forest carbon stocks and dynamics, understanding the origin and the consequences of this bias is of utmost concern. In this study, we compiled a unique tree mass data set of 673 trees destructively sampled in five tropical countries (101 trees > 100 cm in diameter) and an original data set of 130 forest plots (1 ha) from central Africa to quantify the prediction error of biomass allometric models at the individual and plot levels when explicitly taking crown mass variations into account or not doing so. We first showed that the proportion of crown to total tree aboveground biomass is highly variable among trees, ranging from 3 to 88 %. This proportion was constant on average for trees < 10 Mg (mean of 34 %) but, above this threshold, increased sharply with tree mass and exceeded 50 % on average for trees  ≥  45 Mg. This increase coincided with a progressive deviation between the pantropical biomass model estimations and actual tree mass. Taking a crown mass proxy into account in a newly developed model consistently removed the bias observed for large trees (> 1 Mg) and reduced the range of plot-level error (in %) from [−23; 16] to [0; 10]. The disproportionally higher allocation of large trees to crown mass may thus explain the bias observed recently in the reference pantropical model. This bias leads to far-from-negligible, but often overlooked, systematic errors at the plot level and may be easily corrected by taking a crown mass proxy for the largest trees in a stand into account, thus suggesting that the accuracy of forest carbon estimates can be significantly improved at a minimal cost.

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