Dynamic response of the vegetation carbon storage in the sanjiang plain to changes in land use/cover and climate

Abstract

Large-scale human activities especially the destruction of forest land, grassland, and unused land result in a large amount of carbon release into the atmosphere and cause drastic changes in land use/cover in the Sanjiang Plain. As a climate change-sensitive and ecologically vulnerable area, the Sanjiang Plain ecosystem’s carbon cycle is affected by significant climate change. Therefore, it is important that studying the impact of the changes in land use/cover and climate on vegetation carbon storage in the Sanjiang Plain. Remote sensing, temperature, and precipitation data in four periods from 2001 to 2015 are used as bases in conducting an analysis of land use/cove types and spatio-temporal variation of vegetation carbon density and carbon storage in growing season using model and related analysis methods. Moreover, the impact of land use/cover change and climate change on vegetation carbon density and carbon storage is discussed. The findings are as follows. (1) Cultivated land in the Sanjiang Plain increased, while forest land, grassland and unused land generally decreased. (2) Vegetation carbon density increased, in which the average carbon density of cultivated land, grassland, and unused land varied insignificantly, while that of forest land increased continuously from 4.18 kg C/m2 in 2001 to 7.65 kg C/m2 in 2015. Vegetation carbon storage increased from 159.18 Tg C in 2001 to 256.83 Tg C in 2015, of which vegetation carbon storage of forest land contributed 94% and 97%, respectively. (3) Conversion of land use/cover types resulted in a 22.76-TgC loss of vegetation carbon storage. Although the forest land area decreased by 3389.5 km2, vegetation carbon storage in the research area increased by 97.65 Tg C owing to the increase of forest carbon density. (4) Pixel-by-pixel analysis showed that vegetation carbon storage in the majority of the areas of the Sanjiang Plain are negatively correlated with temperature and positively correlated with precipitation. The results showed that changes of land use/cover types and vegetation carbon density directly lead to a change in vegetation carbon storage, with the change of forest vegetation carbon density being the main driver affecting vegetation carbon storage variation. The increase of temperature mainly suppresses the vegetation carbon density, and the increase of precipitation mainly promotes it.

Spatiotemporal Variation in Aboveground Biomass and Its Response to Climate Change in the Marsh of Sanjiang Plain

“…The marsh on the Sanjiang Plain plays a crucial role in regional biodiversity conservation and the carbon cycle (Sui et al, 2019 ). Some studies have analyzed the variations in marsh AGB and the effects of climatic change on marsh AGB in the Sanjiang Plain (Shi et al, 2015 ; Li et al, 2021 ). For example, Ni et al ( 1996 ) studied the influences of hydrothermal conditions and the AGB of Deyeuxia angustifolia in the Sanjiang Plain marshes.…”

“…For example, Ni et al ( 1996 ) studied the influences of hydrothermal conditions and the AGB of Deyeuxia angustifolia in the Sanjiang Plain marshes. Shi et al ( 2015 ) analyzed the effects of different water levels on the AGB of vegetation in the freshwater marshes of the Sanjiang Plain and found that increasing water levels promoted the biomass of D. angustifoli a. Li et al ( 2021 ) analyzed the influences of temperature and precipitation on AGB and found that the increase of precipitation can increase the AGB in the Sanjiang Plain. However, these studies focused on analyzing changes in marsh AGB and their responses to climatic change at a species scale, and no research has been conducted over the entire Sanjiang Plain due to the limitations of field investigations.…”

An improved Carnegie-Ames-Stanford Approach model for estimating ecological carbon sequestration in mountain vegetation

“…Mountainous vegetation is rich and diverse, and improving the estimation accuracy of the ecological carbon sequestration in mountainous areas can provide a clearer understanding of the ecological carbon sequestration capacity of mountain vegetation (Xu et al, 2020;Jiang et al, 2021;Li H. et al, 2021;Zhang et al, 2021). Wang et al (2020) showed that from 2010 to 2016 in mainland China, the carbon sequestration of terrestrial ecosystems was 45% of the anthropogenic carbon emissions during the same period, approximately 1.11 billion tons, and these carbon sequestration contributions are mainly due to the strong ecological carbon sequestration capacity in the southwestern mountainous areas of China.…”

“…To some extent, Geng et al revised the CASA model's parameters based on the Digital Elevation Model (DEM) data to increase the estimation accuracy of the CASA (Wang H. et al, 2019;Wang Y. et al, 2019), but this method's correcting effect on terrain falling shadow is subpar. Early studies have shown (Sun et al, 2016;Li H. et al, 2021) that changing model parameters is challenging when assessing vegetation carbon sequestration at a wide range of regional scales. They only established the value range of ε max based on experimental data but did not establish a functional relation between terrain parameters and the ε max (Han, 2020;Peng et al, 2021).…”

Space Technologies for Sustainable Heritage: 10th anniversary of HIST

“…More recently, natural heritage sites and biosphere reserves are recognized as sensitive areas for climate change and ecological degradation. In Li et al [ 9 ], the dynamic response of the vegetation carbon storage to the land use/cover was assessed by synergistically using remote sensing images and temperature and precipitation data in the Sanjiang Plain in China. Apart from the visible environmental changes, the imperceptible deformations for the decline of architectural heritages were monitored in Xu et al using multi-temporal synthetic aperture interferometry (MTInSAR) approaches taking advantage of the capability in estimating spatiotemporal deformations with accuracy up to millimeters, as verified by the comparative case study of the Shanhaiguan section of the Great Wall (China) [ 10 ].…”