Carbon Dynamics in the Northeastern Qinghai–Tibetan Plateau from 1990 to 2030 Using Landsat Land Use/Cover Change Data

Abstract

Land use/cover change (LUCC) has an important impact on the terrestrial carbon cycle. The spatial distribution of regional carbon reserves can provide the scientific basis for the management of ecosystem carbon storage and the formulation of ecological and environmental policies. This paper proposes a method combining the CA-based FLUS model and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to assess the temporal and spatial changes in ecosystem carbon storage due to land-use changes over 1990–2015 in the Qinghai Lake Basin (QLB). Furthermore, future ecosystem carbon storage is simulated and evaluated over 2020–2030 under three scenarios of natural growth (NG), cropland protection (CP), and ecological protection (EP). The long-term spatial variations in carbon storage in the QLB are discussed. The results show that: (1) Carbon storage in the QLB decreased at first (1990–2000) and increased later (2000–2010), with total carbon storage increasing by 1.60 Tg C (Teragram: a unit of mass equal to 1012 g). From 2010 to 2015, carbon storage displayed a downward trend, with a sharp decrease in wetlands and croplands as the main cause; (2) Under the NG scenario, carbon reserves decrease by 0.69 Tg C over 2020–2030. These reserves increase significantly by 6.77 Tg C and 7.54 Tg C under the CP and EP scenarios, respectively, thus promoting the benign development of the regional ecological environment. This study improves our understanding on the impact of land-use change on carbon storage for the QLB in the northeastern Qinghai–Tibetan Plateau (QTP).

Simulation of Land Use Change and Ecosystem Service Value Dynamics under Ecological Constraints in Anhui Province, China

“…The direction of land use change in the future is uncertain [ 55 ]. Many studies on the simulation of land use change under different scenarios exist, including natural growth scenario, urban expansion scenario, and ecological protection scenario [ 22 ]. There are no restrictions on the conversion of land use types in the natural growth scenario, and this scenario is in full accordance with the natural law of land use type evolution.…”

“…With the development and application of computer science and geographic information system (GIS) technology, research on land use spatial layout simulation models has rapidly increased [ 15 , 16 , 17 , 18 ]. Researchers have used a variety of simulation models to simulate future land use in terms of the quantitative characteristics and spatial layout, including a multi-agent system (MAS) model [ 19 ], cellular automata (CA) model [ 20 ], the conversion of land use and its effects at a small regional extent (CLUE-S) model [ 21 ], and a future land use simulation (FLUS) model [ 22 ], etc. The FLUS model, first proposed by Liu et al [ 14 ], can effectively deal with the complexity and uncertainty of the conversion of various land use types under the mutual influence of the human-land relationship.…”

“…The FLUS model, first proposed by Liu et al [ 14 ], can effectively deal with the complexity and uncertainty of the conversion of various land use types under the mutual influence of the human-land relationship. The model is mainly used in land use simulation, urban expansion simulation, and the delineation of urban growth boundaries [ 13 , 22 , 23 , 24 , 25 ]. The model adds an adaptive inertial competition mechanism based on the traditional CA model.…”

Multi-Scenario Analysis of Habitat Quality in the Yellow River Delta by Coupling FLUS with InVEST Model

“…Artificial neural network (ANN) is an effective tool to iterate, adjust and fit the relationship between the input data and the training target through learning-recall. ANN was proven to be an easier way to deal with the complex and nonlinear relationship between land use and multiple driving factors [ 31 , 34 , 35 ]. …”

Perspectives for multifunctional landscapes in the Amazon : Analyzing farmers’ strategies, perceptions, and scenarios in an agricultural frontier

“…We used the Carbon Storage and Sequestration InVEST 3.7.0 model (Sharp, 2020) to assess carbon storage at the landscape scale in Paragominas. This spatially explicit model has been widely used for the quantification of carbon storage at a regional scale (Azevedo et al, 2017;Zhang et al, 2017;Pavani et al, 2018;Pechanec et al, 2018;Sun et al, 2018;Xiang, 2018;Zarandian et al, 2018;Li et al, 2020). The model aggregates the amount of carbon stored in four carbon pools (aboveground living biomass, belowground living biomass, soil organic matter, and litter) based on LULC raster maps.…”