PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Calculation of Forest Carbon Storage and Carbon Density Based on Forest Resource Inventory and Remote Sensing Data: A Case Study of Tianzhu Tibetan Autonomous County
Version 1
: Received: 10 June 2024 / Approved: 10 June 2024 / Online: 11 June 2024 (11:41:04 CEST)
How to cite:
Yang, X.; Ma, J.; Li, X.; Lang, W.; Liu, X. Calculation of Forest Carbon Storage and Carbon Density Based on Forest Resource Inventory and Remote Sensing Data: A Case Study of Tianzhu Tibetan Autonomous County. Preprints2024, 2024060691. https://doi.org/10.20944/preprints202406.0691.v1
Yang, X.; Ma, J.; Li, X.; Lang, W.; Liu, X. Calculation of Forest Carbon Storage and Carbon Density Based on Forest Resource Inventory and Remote Sensing Data: A Case Study of Tianzhu Tibetan Autonomous County. Preprints 2024, 2024060691. https://doi.org/10.20944/preprints202406.0691.v1
Yang, X.; Ma, J.; Li, X.; Lang, W.; Liu, X. Calculation of Forest Carbon Storage and Carbon Density Based on Forest Resource Inventory and Remote Sensing Data: A Case Study of Tianzhu Tibetan Autonomous County. Preprints2024, 2024060691. https://doi.org/10.20944/preprints202406.0691.v1
APA Style
Yang, X., Ma, J., Li, X., Lang, W., & Liu, X. (2024). Calculation of Forest Carbon Storage and Carbon Density Based on Forest Resource Inventory and Remote Sensing Data: A Case Study of Tianzhu Tibetan Autonomous County. Preprints. https://doi.org/10.20944/preprints202406.0691.v1
Chicago/Turabian Style
Yang, X., Wenzhe Lang and Xuelu Liu. 2024 "Calculation of Forest Carbon Storage and Carbon Density Based on Forest Resource Inventory and Remote Sensing Data: A Case Study of Tianzhu Tibetan Autonomous County" Preprints. https://doi.org/10.20944/preprints202406.0691.v1
Abstract
As the largest carbon pool on Earth, forest ecosystems are essential to maintaining carbon sink balance. However, accurately estimating the carbon sequestration and carbon storage of forest resources remains an urgent issue to address. Plot investigation, model estimation, and machine learning are examples of current approaches. Nevertheless, because of variables such as regional characteristics and plot size, large-scale and extensive region estimates are comparatively scarce. This study, which focuses on Tianzhu County, aims to comprehensively analyze and estimate the Net Primary Productivity (NPP), carbon storage, and carbon density of the forest ecosystem in this area by integrating data from forest resource inventory and the CASA model. The main conclusions are as follows: (1) The main vegetation types in Tianzhu County include coniferous forests, broad-leaved forests, mixed forests, and shrub forests; (2) Accordin g to the CASA model calculations, the annual cumulative NPP in Tianzhu County in 2020 ranges from a maximum of 249.63 gC/m² to a minimum of 3.40 gC/m², with the forest NPP amounting to 134,674.65 tons of carbon. Based on MODIS observation data, the forest’s NPP is 171,728.32 tons of carbon, with a maximum of 325.36 gC/m² and a minimum of 0.02 gC/m²; (3) At the pixel scale, the carbon storage of forests in Tianzhu County is 361,700 tons. Specifically, 46,500 tons of carbon are stored in coniferous forests, 1,100 tons in broad-leaved forests, 500 tons in mixed forests, and 313,600 tons in shrub forests.
Keywords
Tianzhu County; CASA model; NPP; forest carbon storage; carbon density.
Subject
Environmental and Earth Sciences, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
