ABSTRACT
Regional land use change is the main cause of carbon storage changes in ecosystems. Predicting the impact of future land use changes on carbon storage is of great significance for the sustainable development of carbon storage functions. In recent years, under the combined action of natural and human factors, the land use in the source region of the Yellow River has changed significantly, and its carbon storage function has also changed accordingly. This study combined InVEST and GeoSoS-FLUS models to evaluate land use change and its impact on carbon storage in the source region of the Yellow River from 2000 to 2020 and from 2020 to 2040 under different scenarios. The results showed that:â from 2000 to 2020, the carbon storage in the source region of the Yellow River showed an overall upward trend, with a total increase of 11.59×106 t. â¡ Over the past 20 years, the land use changes in the source region of the Yellow River included mainly the increase in the area of low-coverage grassland, construction land, and wetland and the decrease in the area of high-coverage grassland, medium-coverage grassland, and unused land, as well as the large-scale reduction of unused land and the reduction of grassland. The increase in the area of wetlands was the main reason for the increase in carbon storage. ⢠Under the natural change scenario in 2040, the ecosystem carbon storage in the source region of the Yellow River was 871.34×106 t, an increase of 3.92×106 t compared with that in 2020. Under the ecological protection scenario, carbon storage increased significantly, with an increase of 13.53×106 t compared with that in 2020. The results of this study can provide a scientific reference for the decision-making of land use management and the sustainable development of carbon storage function in the source region of the Yellow River.
Subject(s)
Ecosystem , Rivers , Humans , Carbon , WetlandsABSTRACT
With the flux data of ChinaFLUX and the concurrent satellite remote sensing data in Changbai Mountain, we recombined parameters of four models, i.e., vegetation photosynthesis model (VPM), eddy covariance-light utility efficiency model (EC-LUE), terrestrial ecosystem model (TEM) and Carnegie-Ames-Stanford approach model (CASA) within 3PG model. The most suitable parameters of 3PG model were determined by comparing the root mean square error, coefficient of determination and average error between measured and observed flux values. To improve its accuracy in estimating gross primary productivity (GPP) of broadleaved Korean pine forest in Changbai Mountain, the fitness of the optimal model was validated using the observed flux data. The results showed that when temperature, enhanced vegetation index, and surface water index were used to characterize the temperature limiting factor, photosynthetic active radiation absorption ratio and water limiting factor in the original model to estimate GPP of broadleaved Korean pine forest, the simulation results were the best. The precision of the optimized model (R2=0.948, RMSE=0.035 mol·m-2·month-1) was better than that of the original model (R2=0.854, RMSE=0.177 mol·m-2·month-1), which could effectively improve the phenomenon of obvious overestimation of the original model in the growing season. Results from the parameter sensitivity analysis showed that the uncertainty of GPP estimation was dominated by temperature, followed by enhanced vegetation index, photosynthetically active radiation and land surface water index, as well as their interactions.
Subject(s)
Carbon/analysis , Ecosystem , Environmental Monitoring , Models, Statistical , Pinus , China , Forests , PhotosynthesisABSTRACT
Forest leaf area index (LAI) is an important indicator to describe the forest canopy structure and growth status of trees. In this paper, the Yigen area of Inner Mongolia was selected as the study area. Taken full account of the differences among different echo types, the LiDAR point cloud data were split into different single lasers. Then, intensity normalization was implemented for LiDAR point cloud data with the range between sensor and target. Based on the normalized intensity data, a new laser penetration index, called single laser beam penetration index (LPIs), was calculated along with the calculation of traditional LPI. These two laser penetration indexes were used to estimate the forest LAI based on the theoretical model and empirical model on four different sampling scales (5, 10, 15, and 20 m), respectively, which aimed to improve the retrieval accuracy of forest LAI through laser beam splitting. The results showed that the forest LAI estimated from mean LPIs (LPImean) was obviously better than that from traditional LPI. In addition, both of the empirical [R2=0.80, mean absolute deviation (MAD)=0.11] and theoretical models (R2=0.77, MAD=0.16) achieved the best performances with sampling scale of 15 m. The mapping of birch forest LAI for the study area was derived by integrating both the advantages of best empirical and theoretical models.
