This study showed that GREENLAB model has the ability to capture plant plasticity induced by PPD. The relatively stable parameter values strengthened the hypothesis that one set of equations can govern dynamic organ growth. With further validation, this model can be used for agronomic applications such as yield optimization.
Land use and land cover (LULC) datasets for Beijing in 1978Beijing in , 1987Beijing in , 1992Beijing in , 2000 and 2010 were developed from Landsat images using the object-oriented classification approach. The relationships between social-economic, demographic and political factors and time-series LULC data were examined for the periods between 1978 and 2010. The results showed the effectiveness of using the object-oriented decision tree classification method for LULC classification with time series of Landsat images. Combined with anthropogenic driving forces, our research can effectively explain the detailed LULC change trajectories corresponding to different stages and give new insights for Beijing LULC change patterns. The results show a significant increase in forest and built-up areas, but a decrease in arable lands, due to urbanization and reforestation. Large ecological projects result in an increase of forest areas and population, and economic conditions result in urban expansion. The 10594 anthropogenic driving forces analysis results further prove that both population increase and economic development played important roles in the expansion of built-up areas. Both the qualitative and quantitative anthropogenic driving forces analysis methods were helpful for better understanding the mechanisms of LULC change.
The patterns and drivers of soil microbial communities in forest plantations remain inadequate although they have been extensively studied in natural forest and grassland ecosystems. In this study, using data from 12 subtropical plantation sites, we found that the overstory tree biomass and tree cover increased with increasing plantation age. However, there was a decline in the aboveground biomass and species richness of the understory herbs as plantation age increased. Biomass of all microbial community groups (i.e. fungi, bacteria, arbuscular mycorrhizal fungi, and actinomycete) decreased with increasing plantation age; however, the biomass ratio of fungi to bacteria did not change with increasing plantation age. Variation in most microbial community groups was mainly explained by the understory herb (i.e. herb biomass and herb species richness) and overstory trees (i.e. tree biomass and tree cover), while soils (i.e. soil moisture, soil organic carbon, and soil pH) explained a relative low percentage of the variation. Our results demonstrate that the understory herb layer exerts strong controls on soil microbial community in subtropical plantations. These findings suggest that maintenance of plantation health may need to consider the management of understory herb in order to increase the potential of plantation ecosystems as fast-response carbon sinks.
Land surface temperature (LST) is a critical parameter of surface energy fluxes and has become the focus of numerous studies. LST downscaling is an effective technique for supplementing the limitations of the coarse-resolution LST data. However, the relationship between LST and other land surface parameters tends to be nonlinear and spatially nonstationary, due to spatial heterogeneity. Nonlinearity and spatial nonstationarity have not been considered simultaneously in previous studies. To address this issue, we propose a multi-factor geographically weighted machine learning (MFGWML) algorithm. MFGWML utilizes three excellent machine learning (ML) algorithms, namely extreme gradient boosting (XGBoost), multivariate adaptive regression splines (MARS), and Bayesian ridge regression (BRR), as base learners to capture the nonlinear relationships. MFGWML uses geographically weighted regression (GWR), which allows for spatial nonstationarity, to fuse the three base learners’ predictions. This paper downscales the 30 m LST data retrieved from Landsat 8 images to 10 m LST data mainly based on Sentinel-2A images. The results show that MFGWML outperforms two classic algorithms, namely thermal image sharpening (TsHARP) and the high-resolution urban thermal sharpener (HUTS). We conclude that MFGWML combines the advantages of multiple regression, ML, and GWR, to capture the local heterogeneity and obtain reliable and robust downscaled LST data.
Simplification of field measurement to reduce the time-consuming data collection for calibration is important to facilitate the application of the GREENLAB model. The effect of such simplifications on the accuracy of parameter values should be quantified in order to define to what extent simplifications are valid. This study introduced a new method for model parameter optimization with sparse data of maize using a multi-fitting technique, evaluated the effect of such simplifications on the parameter values, and validated the calibrated model with four independent field data sets. The results showed that coefficients of variance (CV) among different simplifications were below 15% for most parameter values. The parameter values of the beta function varied more compared with those of relative sink strength for different simplifications. Organ biomass under four different climate regimes was simulated based on parameter values optimized with a sparse dataset. Significant (P<0.05) deviations of simulation vs. observation correlations from the 1:1 relationship were only observed for internodes of second experiment in 2003. Thus, multi-fitting with sparse data can provide reasonable accuracy of parameter values.
With the acceleration of urbanization in China, most rural areas formed a widespread phenomenon, i.e., destitute village, labor population loss, land abandonment and rural hollowing. And it formed a unique hollow village problem in China finally. The governance of hollow village was the objective need of the development of economic and social development in rural area for Chinese government, and the research on the evaluation method of rural hollowing was the premise and basis of the hollow village governance. In this paper, several evaluation methods were used to evaluate the rural hollowing based on the survey data, land use data, social and economic development data. And these evaluation indexes were the transition of homesteads, the development intensity of rural residential areas, the per capita housing construction area, the residential population proportion in rural area, and the average annual electricity consumption, which can reflect the rural hollowing degree from the land, population, and economy point of view, respectively. After that, spatial analysis method of GIS was used to analyze the evaluation result for each index. Based on spatial raster data generated by Kriging interpolation, we carried out re-classification of all the results. Using the fuzzy clustering method, the rural hollowing degree in Ningxia area was reclassified based on the two spatial scales of county and village. The results showed that the rural hollowing pattern in the Ningxia Hui Autonomous Region had a spatial distribution characteristics that the rural hollowing degree was obvious high in the middle of the study area but was low around the study area. On a county scale, the specific performances of the serious rural hollowing were the higher degree of extensive land use, and the lower level of rural economic development and population transfer concentration. On a village scale, the main performances of the rural hollowing were the rural population loss and idle land. The evaluation method of rural hollowing constructed in this paper can effectively carry out a comprehensive degree zoning of rural hollowing, which can make orderly decision support plans of hollow village governance for the government.
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