《周禮》井田土地算法考——兼論“井田法”“溝洫法”區分之不成立

應釐清田制、民户制度、税制、授田制的區别與聯系,討論“井田”時纔不致於概念混亂。《周禮》中的“井田”是對現實田制的抽象概括,不能等同於田地實況,其目的是要實現人對廣闊土地的不同规劃需求。《周禮》經注對田地的三套不同計算方法,建基於“井田”之上,各自服务於不同目的。自贾公彦以來,相傳認為《周禮》存在“井田法”“溝洫法”兩種不同的田制,後世罕有異義。但它們實是賈疏誤解經注的结果,不能成立;抛開此區分纔能順暢解釋經注。

居住用地到期后土地续期费的计算——以武汉市汉口区为例

依据法律规定和不动产特征,土地使用权有偿续期是必要的。以武汉市汉口区居住用地为例,基于基准地价,以2011年为基期,按土地收益还原倒算法,计算1年、2年、5年、10年、30年、50年、70年的住宅用地使用权续期费,分别计算了汉口区不同基准地价级别对应的续期费以及不同容积率对应的单位土地面积续期费,并进一步以汉口区部分地块为例进行验证。从实际角度考虑,具体的续费金额可根据市民的接受程度再按此方法进行调节。该研究方法为制定房产延长土地使用年期方式、延期后土地续期费用的交付金额及标准等方面的相关规定具有实际操作的经验借鉴。

Application Comparison of Association Rules and C4.5 Rules in Land Evaluation

Association rules and C4.5 rules can overcome the shortage of the traditional land evaluation methods and improve the intelligibility and efficiency of the land evaluation knowledge.In order to compare these two kinds of classification rules in the application,two fuzzy classifiers were established by combining with fuzzy decision algorithm especially based on Second General Soil Survey of Guangdong Province.The results of experiments demonstrated that the fuzzy classifier based on association rules obtain a higher accuracy rate,but with more complex calculation process and more computational overhead;the fuzzy classifier based on C4.5 rules obtain a slightly lower accuracy,but with fast computation and simpler calculation.

基于C4.5算法的土地利用图像识别分类

以地物类型较复杂的福州市的城郊结合区一小块区域的土地利用分类为例,利用C4.5算法从训练样本数据集中发现分类规则,集成遥感影像的光谱特征、纹理特征和地形特征进行分类实验,并与传统的监督分类和逻辑通道分类方法进行比较及分析。结果表明,基于C4.5算法的分类方法的分类精度高于传统的监督分类和逻辑通道分类方法。因而利用C4.5算法构建决策树获取的分类规则是合理的,它可以快速、有效地获取大量分类规则,是促进基于知识的遥感影像分类方法在土地利用分类中广泛应用的一项有效手段。

Land Evaluation Method Based on Decision Tree Produced by C4.5 and Fuzzy Decision

[Objective]The aim was to overcome the shortage of being difficult to build land evaluation model when the impact factors had continuous value in the traditional land evaluation process,as well as to improve the intelligibility of the land evaluation knowledge.[Method] The land evaluation method combining classification rule extracted by C4.5 algorithm with fuzzy decision was proposed in this study.[Result] The result of Second General Soil Survey of Guangdong Province had demonstrated that the method was convenient to extract classification rules,and by using only 100 rules,quantity correct rate 86.67% and area correct rate 84.80% of land evaluation could be obtained.[Conclusions] The use of C4.5 algorithm to obtain the rules,combined with fuzzy decision algorithm to build classifiers had got satisfactory results,which provided a practical algorithm for the land evaluation.

The Application of BP Networks to Land Suitability Evaluation

The back propagation (BP) model of artificial neural networks (ANN) has many good qualities comparing with ordinary methods in land suitability evaluation.Through analyzing ordinary methods’ limitations,some sticking points of BP model used in land evaluation,such as network structure,learning algorithm,etc.,are discussed in detail,The land evaluation of Qionghai city is used as a case study.Fuzzy comprehensive assessment method was also employed in this evaluation for validating and comparing.

A Multi-agent Model to Simulate Regional Land Use Change with an Application to the Poyang Lake Area of China

In many regions both urban expansion and rural development take place simultaneously, and for the purpose of understanding the dynamic process of land use/cover change （LUCC） in such large areas, this study develops a multi-agent based land use model. Taking the Poyang Lake area of China as a typical case, this study applies the mechanism of diffusion-limited aggregation to simulate the behavior of urban agents, while rural land use is illustrated with a bottom-up based model consisting of agent and environment layers. In the agent layer, each household agent makes its own decisions on land use, and at each time interval a government agent takes control of land use by implementing policies. According to incomes and the rate of migrant workers, household agents are divided into six categories, among which different decision rules are followed. For complex LUCC in the Poyang Lake area of China from 1985 to 2005, the artificial society model developed in this study yields results highly consistent with observations. Importantly, it is shown that governmental policies can impose significant effects on the decisions of individual household agents on land use and the multi-agent-based land use model developed in this study provides a robust means for assessing the effectiveness of governmental policies.

A 30 meter land cover mapping of China with an efficient clustering algorithm CBEST

Remote sensing based land cover mapping at large scale is time consuming when using either supervised or unsupervised clas- sification approaches. This article used a fast clustering method---Clustering by Eigen Space Transformation （CBEST） to pro- duce a land cover map for China. Firstly, 508 Landsat TM scenes were collected and processed. Then, TM images were clus- tered by combining CBEST and K-means in each pre-defined ecological zone （50 in total for China）. Finally, the obtained clusters were visually interpreted as land cover types to complete a land cover map. Accuracy evaluation using 2159 test sam- pies indicates an overall accuracy of 71.7% and a Kappa coefficient of 0.64. Comparisons with two global land cover products （i.e., Finer Resolution Observation and Monitoring of Global Land Cover （FROM-GLC） and GlobCover 2009） also indicate that our land cover result using CBEST is superior in both land cover area estimation and visual effect for different land cover types.

Land cover classification of remote sensing imagery based on interval-valued data fuzzy c-means algorithm

There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.

Improvement of snow depth retrieval for FY3B-MWRI in China

The primary objective of this work is to develop an operational snow depth retrieval algorithm for the FengYun3B Microwave Radiation Imager(FY3B-MWRI)in China.Based on 7-year(2002–2009)observations of brightness temperature by the Advanced Microwave Scanning Radiometer-EOS(AMSR-E)and snow depth from Chinese meteorological stations,we develop a semi-empirical snow depth retrieval algorithm.When its land cover fraction is larger than 85%,we regard a pixel as pure at the satellite passive microwave remote-sensing scale.A 1-km resolution land use/land cover(LULC)map from the Data Center for Resources and Environmental Sciences,Chinese Academy of Sciences,is used to determine fractions of four main land cover types(grass,farmland,bare soil,and forest).Land cover sensitivity snow depth retrieval algorithms are initially developed using AMSR-E brightness temperature data.Each grid-cell snow depth was estimated as the sum of snow depths from each land cover algorithm weighted by percentages of land cover types within each grid cell.Through evaluation of this algorithm using station measurements from 2006,the root mean square error(RMSE)of snow depth retrieval is about 5.6 cm.In forest regions,snow depth is underestimated relative to ground observation,because stem volume and canopy closure are ignored in current algorithms.In addition,comparison between snow cover derived from AMSR-E and FY3B-MWRI with Moderate-resolution Imaging Spectroradiometer(MODIS)snow cover products(MYD10C1)in January 2010 showed that algorithm accuracy in snow cover monitoring can reach 84%.Finally,we compared snow water equivalence(SWE)derived using FY3B-MWRI with AMSR-E SWE products in the Northern Hemisphere.The results show that AMSR-E overestimated SWE in China,which agrees with other validations.

An Insight into Machine Learning Algorithms to Map the Occurrence of the Soil Mattic Horizon in the Northeastern Qinghai-Tibetan Plateau

Soil diagnostic horizons, which each have a set of quantified properties, play a key role in soil classification. However, they are difficult to predict, and few attempts have been made to map their spatial occurrence. We evaluated and compared four machine learning algorithms, namely, the classification and regression tree(CART), random forest(RF), boosted regression trees(BRT), and support vector machine(SVM), to map the occurrence of the soil mattic horizon in the northeastern Qinghai-Tibetan Plateau using readily available ancillary data. The mechanisms of resampling and ensemble techniques significantly improved prediction accuracies(measured based on area under the receiver operator characteristic curve score(AUC)) and produced more stable results for the BRT(AUC of 0.921 ± 0.012, mean ± standard deviation) and RF(0.908 ± 0.013) algorithms compared to the CART algorithm(0.784 ± 0.012), which is the most commonly used machine learning method. Although the SVM algorithm yielded a comparable AUC value(0.906 ± 0.006) to the RF and BRT algorithms, it is sensitive to parameter settings, which are extremely time-consuming.Therefore, we consider it inadequate for occurrence-distribution modeling. Considering the obvious advantages of high prediction accuracy, robustness to parameter settings, the ability to estimate uncertainty in prediction, and easy interpretation of predictor variables, BRT seems to be the most desirable method. These results provide an insight into the use of machine learning algorithms to map the mattic horizon and potentially other soil diagnostic horizons.

An improved algorithm to estimate the surface soil heat flux over a heterogeneous surface: A case study in the Heihe River Basin

Surface soil heat flux(G0) is an indispensable component of the surface energy balance and plays an important role in the estimation of surface evapotranspiration(ET). This study calculated G0 in the Heihe River Basin based on the thermal diffusion equation, using the observed soil temperature and moisture profiles, with the aim to analyze the spatial-temporal variations of G0 over the heterogeneous area(with alpine grassland, farmland, and forest). The soil ice content was estimated by the difference in liquid soil water content before and after the melting of the frozen soil and its impact on the calculation of G0 was further analyzed. The results show that:(1) the diurnal variation of G0 is obvious under different underlying surfaces in the Heihe River Basin, and the time when the daily maximum value of G0 occurs is a few minutes to several hours earlier than that of the net radiation flux, which is related to the soil texture, soil moisture, soil thermal properties, and the vegetation coverage;(2) the net radiation flux varies with season and reaches the maximum in summer and the minimum in winter, whereas G0 reaches the maximum in spring rather than in summer, because more vegetation in summer hinders energy transfer into the soil;(3) the proportions of G0 to the net radiation flux are different with seasons and surface types, and the mean values in January are 25.6% at the Arou site, 22.9% at the Yingke site and 4.3% at the Guantan site, whereas the values in July are 2.3%, 1.6% and 0.3%, respectively; and(4) G0 increases when the soil ice content is included in thermal diffusion equation, which improves the surface energy balance closure by 4.3%.

Non-Algorithmically Integrating Land Use Type with Spatial Interpolation of Surface Soil Nutrients in an Urbanizing Watershed

Integrating land use type and other geographic information within spatial interpolation has been proposed as a solution to improve the performance and accuracy of soil nutrient mapping at the regional scale. This study developed a non-algorithm approach, i.e., applying inverse distance weighting （IDW） and ordinary kriging （OK）, to individual land use types rather than to the whole watershed, to determine if this improved the performance in mapping soil total C （TC）, total N （TN）, and total P （TP） in a 200-km2 urbanizing watershed in Southeast China. Four land use types were identified by visual interpretation as forest land, agricultural land, green land, and urban land. One hundred and fifty soil samples （0-10 cm） were taken according to land use type and patch size. Results showed that the non-algorithm approach, interpolation based on individual land use types, substantially improved the performance of IDW and OK for mapping TC, TN, and TP in the watershed. Root mean square errors were reduced by 3.9% for TC, 10.770 for TN, and 25.9% for TP by the application of IDW, while the improvements by OK were slightly lower as 0.9% for TC, 7.7% for TN, and 18.1% for TP. Interpolations based on individual land use types visually improved depiction of spatial patterns for TC, TN, and TP in the watershed relative to interpolations by the whole watershed. Substantial improvements might be expected with denser sampling points. We suggest that this non-algorithm approach might provide an alternative to algorithm-based approaches to depict watershed-scale nutrient patterns.