Analysis of the Driving Force of Spatial and Temporal Differentiation of Carbon Storage in Taihang Mountains Based on InVEST Model

Abstract

The Taihang Mountains are an important ecological barrier in China, and their ecosystems have good carbon sink capacity. Studying the spatial-temporal variation characteristics and driving factors of carbon storage in the Taihang Mountains ecosystem provides decision-making for the construction of “dual carbon” projects and the improvement of ecological environment quality in this region. This paper takes the area in the Taihang Mountains as the research area, based on the land use and carbon density data of 2005, 2010, 2015, and 2019 of the Taihang Mountains, calculates the carbon storage in the region with the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, explores the main factors affecting the spatial differentiation of carbon storage in this region, and analyzes their driving mechanisms by Geodetector. The results show that: (1) From 2005 to 2019, the land use of the Taihang Mountains changed somewhat. The area of forest and construction land increased slightly, while the area of farmland and grassland decreased. (2) The current carbon storage in the Taihang Mountains ranges from 1472.91 × 106 t to 1478.17 × 106 t (t is the abbreviation of ton), and shows a decreasing trend, which is due to the decrease in forest and the increase in construction land. (3) Slope and Normalized Difference Vegetation Index (NDVI) are the main driving factors affecting the spatial variation of carbon storage in the Taihang Mountains ecosystem. Temperature, precipitation, and population density are the secondary factors affecting the spatial variation of carbon storage. (4) The synergy between the driving factors is more potent than the individual factor, which is the most evident between NDVI and slope. This means some areas may have more abundant carbon storage under the combined effect of slope and NDVI.

Blackland Conservation and Utilization, Carbon Storage and Ecological Risk in Green Space: A Case Study from Heilongjiang Province in China

“…The modeling and evaluation of regional carbon storage data rely heavily on this model [ 41 ]. The formula for calculating carbon storage is as follows [ 42 ]: where is the land use type, is the total carbon density of land use type (t/ha), is the carbon density of above-ground part of the vegetation of land use type (t/ha), is the carbon density of below-ground part of the vegetation of land use type (t/ha), is the soil organic carbon density of land use type (t/ha), is the carbon density of dead organic matter of land use type (t/ha), C_total is the total carbon stock, and is the area of land use type . Carbon density data were obtained based on previous research results [ 43 , 44 , 45 , 46 , 47 ], and field measurements of HLJP were preferred; if data comprehensiveness was insufficient, measured or data compiled from the literature near HLJP and in the same climate zone were used ( Table 1 ).…”

Spatial-temporal evolution and prediction of carbon storage in Mohe city by linking the logistic-CA-Markov and InVEST models

“…However, its reliance on subjective criteria for determining the division of drivers results in inadequate categorization and subjectivity (Song et al, 2020). The OPGD model explores the optimal combination of different spatial data discretization methods and spatial layers, and the parameter optimization process enables further extraction of information contained in geographic features and spatial explanatory variables in the GeoDetector (Wang et al, 2023). Consequently, this research chose the OPGD to quantitatively analyze the effects of the driving factors of carbon storage in Mohe City and the interactions among the factors on the spatial differentiation of carbon storage.…”

Dynamic response of carbon storage to ecological restoration measures in Liupanshui City, Mountain, Water, Forest, Field, Lake and Grassland

“…The traditional eld survey method mainly uses regional carbon density pro les of different vegetation and soil types to estimate carbon stocks, which is relatively simple and accurate, but the applicable area is small and the assessment results cannot re ect the dynamic changes of carbon stocks [13]. The use of models can simulate, predict and assess carbon stocks at different scales, such as global, national and regional, among which the InVEST model is the most widely used, and previous studies have shown that the InVEST model can estimate carbon stocks in a simple and reliable way [14]. storage [14], and the model is based on land use type maps and carbon density, and estimates carbon stocks using four carbon pools: aboveground biomass, belowground biomass, soil carbon density, and dead organic carbon, and has been widely used by many scholars to assess regional carbon stocks and their spatial and temporal distribution patterns [15].…”