Biomedical subjects

Renchang Bu

Publications and source records attributed to Renchang Bu.

4 recordsLinked to original sources

PubMed2005

[Landscape change in Kangbao County of Hebei {rovince].

Based on RS image TM5 of 1999 and SPOT5 of 2003, this paper studied the landscape change from 1999 to 2003 of Kangbao County, Hebei Province. Logistic regression was adopted to analyze the driving factors, and Kappa index was used to evaluate the accuracy of the landscape classification result, which was 86.72% for 1999, and 89.76% for 2003. The area of cropland in Kangbao County reduced largely, while that of vegetable field, forestland and artificial grassland increased sharply, among which, vegetable field and artificial grassland had the greatest increasing rate. The landscape fragmentation aggravated in the region. The landscape change was mainly caused by the policy of reducing cultivated land, with the main driving factor slope, and the direct driving factor water-heat condition and soil fertility determined by slope aspect.

China↗

PubMed2004

[Spatially explicit landscape model-LANDIS I. Mechanism].

Spatially explicit landscape models are the models that spatially explicitly simulate the ecological processes at landscape scale on heterogeneous landscape. LANDIS is a spatially explicit landscape model of forest landscape disturbance, succession and management. By recording the absence/presence of species in terms of 10-yr age cohorts at site level, the semi-quantitative description of fire and windthrow disturbances, and the representation of age-cohorts by bit-wise array, it is possible for LANDIS to simulate the ecological processes at species, site and landscape level. We addressed in detail the approaches used in simulating seed dispersal, disturbances of fire and windthrow, and harvesting. The deficiencies of the model were also discussed to provide feedback for model development.

Biodiversity↗

PubMed2004

[Sensitivity analysis in ecological modeling].

Sensitivity analysis is used to qualitatively or quantitatively apportion the variation of model output to different source of variation. It is a very useful tool in model parameterization and calibration, and has important ecological significance by identifying the governing factors for a certain ecological process simulated. There are two schools of sensitivity analysis, local sensitivity analysis and global sensitivity analysis. The former examines the local response of the output(s) by varying input parameters one at a time, holding other parameters to a central value; and the latter examines the global response (averaged over the variation of all the parameters) of model output(s) by exploring a finite (or even an infinite) region. Since it is very easy to conduct local sensitivity analysis, it is very popular in ecological models. However, local sensitivity analysis is not computationally effective, because it can only get the sensitivity of a single parameter at a time. It can not take into consideration the effect of interaction of different parameters. Additionally, the value of other parameters will affect the sensitivity of the parameter specified. In view of this, global sensitivity analysis is increasingly preferred to local sensitivity in recent years. However, for most of the ecological modeling study published in Chinese, only local sensitivity analysis is conducted. To provide a toolbox of alternative sensitivity analysis algorithms for ecological model development in future study, we reviewed the main methods of both local sensitivity analysis and global sensitivity analysis, including one at a time method, multivariate regression, Morris' method, Sobol's method, Fourier Amplitude Sensitivity Analysis, and Extended Fourier Amplitude Sensitivity Analysis. Based on the state-of-the-art research on sensitivity analysis, the sensitivity of the interaction between two or more than two model parameters, the sensitivity of common model parameters in a set of models, and the sensitivity analysis in spatially explicit landscape model simulation were identified as the key areas and difficulties of future study on sensitivity analysis in ecological modeling.

Ecology↗

PubMed2004

[Application of spatially explicit landscape model in soil loss study in Huzhong area].

Universal Soil Loss Equation (USLE) has been widely used to estimate the average annual soil loss. In most of the previous work on soil loss evaluation on forestland, cover management factor was calculated from the static forest landscape. The advent of spatially explicit forest landscape model in the last decade, which explicitly simulates the forest succession dynamics under natural and anthropogenic disturbances (fire, wind, harvest and so on) on heterogeneous landscape, makes it possible to take into consideration the change of forest cover, and to dynamically simulate the soil loss in different year (e.g. 10 years and 20 years after current year). In this study, we linked a spatially explicit landscape model (LANDIS) with USLE to simulate the soil loss dynamics under two scenarios: fire and no harvest, fire and harvest. We also simulated the soil loss with no fire and no harvest as a control. The results showed that soil loss varied periodically with simulation year, and the amplitude of change was the lowest under the control scenario and the highest under the fire and no harvest scenario. The effect of harvest on soil loss could not be easily identified on the map; however, the cumulative effect of harvest on soil loss was larger than that of fire. Decreasing the harvest area and the percent of bare soil increased by harvest could significantly reduce soil loss, but had no significant effects on the dynamic of soil loss. Although harvest increased the annual soil loss, it tended to decrease the variability of soil loss between different simulation years.

China↗