基于ArcEngine的江宁区国土资源业务办公系统的设计与实现

本文针对国土资源管理的业务需求,设计出结构合理、功能全面的国土资源业务办公系统,并利用ArcEngine和C#加以实现,系统的建成使用极大地提高了国土资源管理工作效率。     

Spatial and temporal scaling of nitrous oxide emissions from the field to the regional scale in Scotland

Nitrous oxide (N₂O) is a powerful greenhouse gas. As the UK government is committed to reducing greenhouse gas emissions, it is important to know not only how much of these gases are released but also where and when. Targeted measurements of emissions in relation to crop growth cycles, soil wetness and fertiliser applications were used to derive annual emission rates for specific combinations of soil type, land management and fertiliser practices. These annual emission rates were then spatially scaled to derive regional figures through the development of a Geographic Information System (GIS) based model framework. Digital soil and land use maps at a scale of 1:25000 for two test areas of approximately 200000 ha each (Lothians and the Ayrshire Basin) were overlain with a climate map within the GIS, deriving unique combinations of soil wetness and land use. The calculated annual emission rates (kg N ha^–1 yr^–1) were then applied to these and multiplied by the total area of each soil/land use type to derive annual emission losses for each area. The annual emission of nitrous oxide from the Lothians was determined as approximately 381000 kg N yr^–1, while the emissions from the Ayrshire Basin were predicted to be 794000 kg N yr^–1. This indicates the increased emissions associated with both the wetter soils of Ayrshire and the greater extent of grazed pasture systems in this area. Due to the detailed scale of the input data, localised areas with large emissions were identified. Abatement strategies would be concentrated on areas of high emissions that include a change to crops with lower emission potential, reducing fertiliser and manure inputs, reducing grazing intensity and improving soil drainage.Supplementary material to this paper is available in electronic form at http://dx.doi.org/10.1023/A:1024422604493     

Dynamics in input and output coefficients for land use studies: a case study for nitrogen in crop rotations

Increasingly, model-based approaches play a role in the design and development of new land use systems. Simulation modeling may play a role in the generation of land use systems for land units, and optimization modeling (e.g. linear programming – LP) may be used in the upscaling to farm and region. In the quantification of new land use systems for land units, often equilibrium conditions with respect to soil resources are assumed, following a so-called target-oriented approach. This facilitates ex ante computation of inputs and emissions of nutrients and allows their use in static optimization models based on LP. The condition of equilibrium in soil resources is often not met, nor is it the ultimate aim. Hence, the dynamics in new systems are insufficiently dealt with. This paper presents an approach for the design of land use systems (crop rotations) and their quantification in terms of input and output coefficients, using particular yields and dynamics in soil resources as targets. Interactions between N input and output of succeeding crops are explicitly taken into account. A simple N-balance model is used describing major processes affecting soil N-dynamics. For the Koutiala region in Mali five crop rotations are evaluated that differ in target crop yield, crop choice, crop residue management and external N source. Modeled crop rotations aiming at high yields, in combination with incorporation of crop residues and legumes, result in depletion of soil N stock. Only in crop rotations aiming at high yields and with incorporation of crop residues combined with a supply of large quantities of animal manure, soil N depletion can be prevented. Four approaches are presented of how to use the dynamic input–output coefficients of these systems in land use studies using LP: (i) use of average coefficients, (ii) use of discounted coefficients, (iii) use of pessimistic estimates of coefficients in an optimization of the land use allocation followed by a recalculation of the objective values for the optimized land use with optimistic coefficients, and (iv) a combined use of systems characteristics, i.e. cumulative N-inputs of land use systems over the time horizon and the magnitude of the soil N pool at the end of the time horizon, which can be used as filters for land use systems. Though none of the approaches completely captures the dynamics in input–output coefficients, they enable a well-founded consideration of the consequences of dynamics in, for instance, soil N stocks in static optimization approaches for farm and regional studies.     

临川区土地生态环境安全评价研究

通过对临川区土地生态环境的研究,采用德尔菲法、等距划分法、加权指数法,确定该区域生态环境评价指标、权重、以及安全等级,得出临川区各乡镇的生态环境安全指数,为临川区土地生态环境安全建设提出建议。     

A detection problem: Sensitivity and uncertainty analysis of a land surface temperature approach to detecting dynamics of water use by groundwater-dependent vegetation

Sustainable management of groundwater-dependent vegetation (GDV) requires the accurate identification of GDVs, characterisation of their water use dynamics and an understanding of associated errors. This paper presents sensitivity and uncertainty analyses of one GDV mapping method which uses temperature differences between time-series of modelled and observed land surface temperature (LST) to detect groundwater use by vegetation in a subtropical woodland. Uncertainty in modelled LST was quantified using the Jacobian method with error variances obtained from literature. Groundwater use was inferred where modelled and observed LST were significantly different using a Student's t-test. Modelled LST was most sensitive to low-range wind speeds (<1.5 m s⁻¹), low-range vegetation height (<=0.5 m), and low-range leaf area index (<=0.5 m² m⁻²), limiting the detectability of groundwater use by vegetation under such conditions. The model-data approach was well-suited to detection of GDV because model-data errors were lowest for climatic conditions conducive to groundwater use.     

Development of a coupled land-atmosphere satellite data assimilation system for improved local atmospheric simulations

This study developed a coupled land-atmosphere satellite data assimilation system as a new physical downscaling approach, by coupling a mesoscale atmospheric model with a land data assimilation system (LDAS). The LDAS consists of a land surface scheme as the model operator, a radiative transfer model as the observation operator, and the simulated annealing method for minimizing the difference between the observed and simulated microwave brightness temperature. The atmospheric model produces forcing data for the LDAS, and the LDAS produces better initial surface conditions for the modelling system. This coupled system can take into account land surface heterogeneities through assimilating satellite data for a better precipitation prediction. To assess the effectiveness of the new system, 3-dimensional numerical experiments were carried out in a mesoscale area of the Tibetan Plateau during the wet monsoon season. The results show significant improvement compared with a no assimilation regional atmospheric model simply nested from the global model. The surface soil moisture content and its distribution from the assimilation system were more consistent to in situ observations. These better surface conditions affect the land-atmosphere interactions through convection systems and lead to better atmospheric predictability as confirmed by satellite-based cloud observations and in situ sounding observations. Through the use of satellite brightness temperature, the developed coupled land-atmosphere assimilation system has shown potential ability to provide better initial surface conditions and its inputs to the atmosphere and to improve physical downscaling through regional models.     

Retrieving soil temperature profile by assimilating MODIS LST products with ensemble Kalman filter

Proper estimation of initial state variables and model parameters are vital importance for determining the accuracy of numerical model prediction. In this work, we develop a one-dimensional land data assimilation scheme based on ensemble Kalman filter and Common Land Model version 3.0 (CoLM). This scheme is used to improve the estimation of soil temperature profile. The leaf area index (LAI) is also updated dynamically by MODIS LAI production and the MODIS land surface temperature (LST) products are assimilated into CoLM. The scheme was tested and validated by observations from four automatic weather stations (BTS, DRS, MGS, and DGS) in Mongolian Reference Site of CEOP during the period of October 1, 2002 to September 30, 2003. Results indicate that data assimilation improves the estimation of soil temperature profile about 1 K. In comparison with simulation, the assimilation results of soil heat fluxes also have much improvement about 13 W m^− 2 at BTS and DGS and 2 W m^− 2 at DRS and MGS, respectively. In addition, assimilation of MODIS land products into land surface model is a practical and effective way to improve the estimation of land surface variables and fluxes.     

基于C5.0决策树算法的西北干旱区土地覆盖分类研究——以甘肃省武威市为例

西北干旱区面积广阔,由于土地利用类型多样,成因复杂,对环境变化敏感、变化过程快、幅度大、景观差异明显等特点,在影像上表现出的“同物异谱”现象明显 |利用常规目视解译、监督非监督分类、人工参与的决策树分类等方法在效率或精度等方面各有其缺陷。采用机器学习C5.0决策树算法,综合利用地物波谱、NDVI、TC、纹理等信息,根据样本数据自动挖掘分类规则并对整个研究区进行地物分类。机器学习的决策树可以挖掘出更多的分类规则,C5.0算法对采样数据的分布没有要求,可以处理离散和连续数据,生成的规则易于理解,分类精度高,可以满足西北干旱区大面积的土地利用/覆被变化制图的需要。     

基于遥感和GIS的西安市土地利用时空变化研究

利用2000和2007年两期TM遥感影像,通过最大似然法、地理信息系统空间分析方法以及数理统计方法,借助Erdas Imagine 9.0、ArcGIS 9.2和Matlab软件平台,计算出西安市土地利用类型的动态转移矩阵;构建了土地利用程度变化指数、土地利用变化类型的多度和重要度指数等区域土地利用变化的指数模型,定量分析西安市土地利用时空变化。揭示了西安市土地利用差异以及土地利用空间变化的主要类型、分布特征和区域方向,阐明了西安市土地利用变化的区域特点,为土地可持续利用提供有效的决策支持。     

利用植被覆盖度-地表温度特征空间提取厦门市土壤湿度信息

土壤湿度是水文学、气象学以及农业科学研究领域的一个重要参数。利用1989年和1996年两个时相Landsat TM遥感卫星影像来获取厦门的植被覆盖度和地表温度,并由此构建了植被覆盖度和地表温度组成的特征空间,最后利用该特征空间反演了土壤湿度,并分析湿度在两个时相中的变化情况。结果显示,在所研究的时间段内,厦门西南老城区的湿度得到增加。但是在东北部城市建成区扩展的地区,土壤湿度则明显降低。