Ze-Meng Fan

A Multi-Grid Method of High Accuracy Surface Modeling and Its Validation

A method of high accuracy surface modeling (HASM) has been constructed to find a solution for error problems that had long troubled surface modeling in geographical information systems (GIS). It is found that when a preconditioned conjugate gradient (PCG) algorithm is used to solve the large sparse linear system, which HASM can be transferred into, HASM performs best in terms of simulation compared with all other algorithms. But its computing speed is not fast enough for all applications. A multi-grid method is introduced into HASM to try to shorten its computing time. Both numerical and real-world tests demonstrate that there is a range of stop error (SE). The multi-grid method of HASM (HASM-MG) greatly increases computing speed when SEs are within this range, compared with the PCG algorithm of HASM (HASM-PCG). HASM-MG is suitable for applications with a need for less accuracy and a shorter computing time. HASM-PCG is appropriate for issues needing higher accuracy. HASM-MG performs better than HASM-PCG in flat areas, while HASM-PCG does better in complex terrainm in terms of accuracy and computing time.

Sensitivity studies of a high accuracy surface modeling method

The sensitivities of the initial value and the sampling information to the accuracy of a high accuracy surface modeling (HASM) are investigated and the implementations of this new modeling method are modified and enhanced. Based on the fundamental theorem of surface theory, HASM is developed to correct the error produced in geographical information system and ecological modeling process. However, the earlier version of HASM is theoretically incomplete and its initial value must be produced by other surface modeling methods, such as spline, which limit its promotion. In other words, we must use other interpolators to drive HASM. According to the fundamental theorem of surface theory, we modify HASM, namely HASM.MOD, by adding another important nonlinear equation to make it independent of other methods and, at the same time, have a complete and solid theory foundation. Two mathematic surfaces and monthly mean temperature of 1951–2010 are used to validate the effectiveness of the new method. Experiments show that the modified version of HASM is insensitive to the selection of initial value which is particular important for HASM. We analyze the sensitivities of sampling error and sampling ratio to the simulation accuracy of HASM.MOD. It is found that sampling information plays an important role in the simulation accuracy of HASM.MOD. Another feature of the modified version of HASM is that it is theoretically perfect as it considers the third equation of the surface theory which reflects the local warping of the surface. The modified HASM may be useful with a wide range of spatial interpolation as it would no longer rely on other interpolation methods.

A robust estimator for the accuracy assessment of remote-sensing-derived DEMs

A robust estimator based on the M-estimation principle (REMP) has been developed for digital elevation model (DEM) accuracy assessment. Adaptive weights were employed to respond to a broad class of DEM error distributions, and an iterative procedure in terms of REMP starting from robust initial estimates with a high breakdown point was introduced. Original DEMs with the resolution of 2 m were obtained by means of light detection and Ranging (LiDAR) from two study sites. DEM errors in each study site were, respectively, calculated based on 100 checkpoints captured by real-time kinematic (RTK) in terms of stratified random sampling strategy. Each group of DEM errors was, respectively, contaminated by five groups of outliers from different distributions. Thus, ten groups of simulated DEM errors were employed to comparatively assess the estimation accuracies of REMP and the classical estimators. The results indicated that under the non-normal distribution of DEM errors, the classical non-robust estimators are seriously influenced by the non-normality. Some robust estimators such as 10%-trimmed or Winsorized mean and normalized median absolute deviation (MADN) are not very robust to resist the influence of outliers. REMP, slightly affected by the non-normal distribution of DEM errors, is more accurate than the classical estimators. The robust methodology can adapt to the DEMs, especially the ones derived from remote sensing such as LiDAR or digital photogrammetry in non-open terrain.

A review of recent developments in HASM

Ground observation is able to obtain highly accurate data with high temporal resolution at observation points, but these observation points are too sparsely to satisfy the application requirements at regional scale. Satellite remote sensing can frequently supply spatially continuous information on earth surface, which is impossible from ground-based investigations, but remote sensing description is not able to directly obtain process parameters. In fact, in terms of fundamental theorem of surfaces, a surface is uniquely defined by the first fundamental coefficients, about the details of the surface observed when we stay on the surface, and the second fundamental coefficients, the change of the surface observed from outside the surface. A method for high accuracy surface modeling (HASM) has been developed initiatively to find solutions for error problem and slow-speed problem of earth surface modeling since 1986. HASM takes global approximate information (e.g., remote sensing images or model simulation results) as its driving field and local accurate information (e.g., ground observation data and/or sampling data) as its optimum control constraints. Its output satisfies the iteration stopping criterion which is determined by application requirement for accuracy. This paper reviews problems to be solved in every development stage and applications of HASM.

Land-cover changes of national nature reserves in China

For preventing ecosystem degradation, protecting natural habitats and conserving biodiversity within the habitats, 2588 nature reserves have been established in China at the end of 2010. The total area is up to 149.44 million ha and covers over 15% of Chinese terrestrial surface. Land-cover change, as the primary driver of biodiversity change, directly impacts ecosystem structures and functions. In this paper, 180 National Nature Reserves (NNRs) are selected and their total area is 44.71 million ha, accounting for 29.9% of all NNRs in China. In terms of the ecosystem characteristics and their major protected object, all selected NNRs are classified into 7 types. A Positive and Negative Change Index of Land-cover (PNCIL) was developed to analyze the land-cover change of each NNRs type from the late 1980s to 2005. The results show that the land-cover of all selected NNRs types have degradated to a certain degree except the forest ecosystem reserves with a decreasing rate, but the rate of degradation alleviated gradually. The mean positive and negative change rates of land-cover in all core zones decreased by 0.69% and 0.16% respectively. The landscape pattern of land-cover in the core zones was more stable than that in the buffer zones and the experimental zones. Furthermore, the ecological diversity and patch connectivity of land-cover in selected NNRs increased generally. In short, the land-cover of 180 selected NNRs in China had a beneficial change trend after NNRs established, especially between 1995 and 2005.

HASM-based Climatic Downscaling Model over China

Compared with statistical downscaling methods and dynamical downscaling methods,HASM-based downscaling methods,which do not need large-scale predictor,can directly create high-resolution climatic surfaces under GCM scenarios.HASM downscaling methods separate future climate elements into climate base value and prospect climatic change value.This method is termed HASM-Constant Coefficient Downscaling Model(HASM-CDM) because climatic base value is fitted by global constant regression model and climatic change value is interpolated by HASM.Although HASM can obtain higher accuracy than other classical methods,precipitation base value fitted by(HASM-CDM) lost spatial non-stationary features of precipitation,which decreases the accuracy of precipitation simulation.The relationship between precipitation and auxiliary variables such as DEM and some topographical factors may change according to geographical location which can not be represented by HASM-CDM.HASM-Spatially Variable Coefficient Downscaling Model(HASM-SVDM) was developed which integrated with spatially variable coefficient regression model and data transformation in this paper.HASM-SVDM uses variable coefficient regression and data transformation to solve accuracy problem of climatic base value.The mean annual temperature(MAT) and mean annual precipitation(MAP) are constructed under different scenarios of HadCM3 A1Fi,A2a and B2a during the periods T1(1961-1990),T2(2010-2039),T3(2040-2069) and T4(2070-2099) by HASM downscaling models.The results show that HASM-constant coefficient downscaling model integrated with global linear model is applicable to the temperature downscaling simulation,while HASM-spatially variable coefficient downscaling model improves spatial non-stationary base value,and is appropriate for the precipitation downscaling modeling at the national level.

Effects of land use planning on aboveground vegetation biomass in China

Dramatic land use change is expected to take place in China in the coming decades, which will exert great impact on the vegetation biomass. The paper assesses how land use change will influence the size and spatial distribution of the vegetation biomass in China. A spatially explicit land use change model, Dyna-CLUE, is employed together with a biomass density approach to account for the effects of forest age on biomass change. Two scenarios have been developed. The first is a trend scenario where land use trends during the period 1985–1995 are extrapolated, representing the situation before implementing the national ecological restoration program. The second is a planning scenario which takes into consideration a range of land use planning measures. Possible temporal and spatial distribution of land use and vegetation biomass across China was simulated. The results indicate that under the planning scenario, vegetation biomass would be 15.47 Gt in 2030, showing an increase by 0.11 Gt compared to that in 2005. Under the trend scenario, however, the biomass would be 14.62 Gt in 2030, 0.02 Gt higher than that in 2005 due to the forest devastation and forest age effect. Seeming to be relatively trivial, the differences between the scenarios are huge in absolute terms, illustrating the impact of land use planning on biomass variation. Compared with the trend scenario, the area of planted forests under the planning scenario will be larger and biomass density lower. Thus under the planning scenario, the vegetation biomass will be more likely to increase, acting as a carbon sink in the future.

Trends of Vegetation Ecosystem Distribution in Jiangxi Province

Climate is one of the most important factors in determining vegetation types and their distribution on Earth, while vegetation is the most direct reflection of Earth’s climate change. In 1807, Humboldt and Bonpland studied the distribution of climate and species in their book “ Essai sur la geographie des plantes” [1]. In 1823, Schouw explored the relationship between plant flora and vegetation type distribution and multiple climatic factors [2]. In 1872 Griesebach formed the first map of the vegetation distribution according to the vegetation appearance and its climatic factors.

An integration framework of ecological models

How to evaluate the accuracy of integration model is becoming more important for studying various complex issues (e.g. ecological issue). The existing integration methods mainly focused on considering the realized technology and integration environment, however, the relations between these sub-models being used in one complex issues and the accuracy of integrated model are not discussed enough. In terms of the idea of Meta-Synthesis and the different relations of each sub-model being used, an integration framework is creatively developed for evaluating the integrated ecological model. The integration framework includes series-wound pattern, shunt-wound pattern, and mix pattern. The evaluation method of each integration patterns among the integration framework is respectively put forward and discussed in this paper.