基于GIS的崇明土壤养分空间变异及肥力综合评价研究
|
摘要
随着人类社会的进步和经济的发展,生态环境及其安全问题愈来愈受到重视,生态安全的预测研究以及在预测研究基础上建立生态安全预警决策成为了人们关注的焦点。位于西太平洋沿岸中国海岸线中部、地处中国最大河流长江入海口的崇明岛,被称为“上海最后一块真正的生态净土”。依据最新规划和崇明的发展定位,崇明岛将“留足自然生态涵养空间”和“留足未来国际级项目的规划选址空间”,真正成为“21世纪生态岛”。因此,与生态安全问题紧密相关的土壤环境污染监测、安全监控以及肥力综合评价就成为了崇明生态安全预测预警研究的重要组成部分。
土壤是一形态和演化过程都十分复杂的自然综合体,开展土壤养分空间变异性研究对于科学合理地制定农田施肥方案,提高养分资源利用率,减少由于过量施肥造成环境污染,实现精确施肥具有重要意义。
本文根据崇明岛的土壤发育状况、土地利用特征,借助地统计空间变异分析和GIS技术在进行详细的土壤养分变异特征分析的基础上,探讨分析了土壤污染监测的采样点优化布局布设方案;并依据研究区成土过程的化学分类区域和近年的土地利用情况,采用隶属度函数和内梅罗指数结合的方法对崇明土壤进行了综合肥力评价,期望在分析了解崇明土壤养分空间分布情况和土壤肥力总体水平的基础上,为科学、合理施肥,保护环境及崇明生态建设提供依据。
研究采用GIS与地统计学相结合的方法,首先,分析崇明土壤重金属的空间变异特征、结合反距离加权插值和克里金插值,对崇明土壤进行空间变异分析,分别获得其预插值污染范围和估计的重金属分布特征。通过研究发现,Zn、Hg无变异角度,其变程分别为2000m、2500m;As、Cu、Pb变异角度在50度左右,变程分别是55000m、35000m、5000m;而Cr、Cd、Ni变异角度在300度左右,变程是7500m、40000m、16000m。变程和变异角度将作为最基本的参数,在满足精度的条件下对崇明土壤做地形分析并针对不同区域进行采样布局设计,在初步采样的基础上,获得土壤二次采样的最优化布局,为进一步更准确地获取研究区土壤养分的空间变异特征奠定基础。
其次,本文研究了上海崇明岛表层土壤有效铜、有效硼、有效铁、有效钼、有效锰、有效锌等微量元素的空间变异特征及分布规律。针对每个指标分别进行详细的试验分析,取得空间相关性最强的步长和分析尺度,并由此选择不同的拟合模型对各因子进行泛克里格插值.从而保证了进行筛选试验时理论插值模型的稳定性及其精度的可比性,同时获得相关微量元素的空间变异分布图。结果表明:6个指标均表现出强变异性。
再次,本文基于对崇明土壤18项化学指标的精确的空间变异特征的研究,借助遥感图像处理软件ERDAS中对2002年的崇明ETM图像进行监督分类;利用主成分分析和因子转轴选择出6个主成分因子,在ERDAS中将6个主成分因子的栅格图进行波段融合获得组合后的三波段图,在ArcGIS中对融合后的图像进行重分类获得:钙质滨海盐渍土带,围垦冲击土带,高铁有机人为土带,半水成半人为土带,有机新成土带,有机人为土带,淤积新成土带等七个化学分类土带。导入选择成土过程中土壤发育的限制因子,以限制因子为评价对象,结合隶属度函数和内梅罗指数法对崇明土壤进行综合肥力评价,将土壤肥力分为五个等级,每个区都分别进行评价。
根据以上研究,本文得到以下结论:
(一)土壤具有高度的空间变异性,不同尺度下土壤养分及重金属均适宜采用地统计学模型进行空间分析与估测。结合GIS的地统计学技术作为研究土壤空间变异的重要工具,不同变量在不同取样尺度下插值分析空间估测结果是不一致的。本文基于原有S型采样分析数据,详细研究分析了土壤的空间变异特性,特别是土壤不同元素的变异角度和变程,并根据地形及土地利用情况进行了相应的坡度分析,在此基础上分析设计了二次采样方案。研究表明,依据二次采样分析数据的土壤空间变异特征更能反映研究区的土壤空间变异状况。因此,基于初步采样数据进行相应的空间分析再进行二次采样方案的设计,为最终选取适合的尺度和插值方法进行相应的区域土壤空间变异的分析是必要的,也是可行的。(二)土壤养分空间变异研究中,最核心的问题是尺度和插值模型及其参数的选择。在ArcGIS中自动计算选取的插值模型和参数不一定能最好地反应土壤数据的空间变异分布特征。本文通过不同的步长尝试,依据块金效应的原则来选择合适的模型参数。在模型的选择上考虑了多尺度的模型,引入双模型插值。结果表明,双尺度的模型提高了插值的精度,能更加准确的反应出土壤养分的空间变异特征。
(三)崇明岛是一个冲击而成的岛,其发育过程相当复杂,每年都会发生不同程度的变化。依据成土过程进行分区,并结合土地利用类型进行肥力分区综合评价是一种新的思路,对了解崇明土壤肥力状况有十分重要的意义。通过研究每一类土壤发育限制因子,及其对崇明18项土壤化学指标的分析进行分类和分区,结合各评价区域的土地利用类型,进行不同区域的土壤肥力综合评价,更能客观地反映研究区土壤的肥力状况。与传统的肥力评价方法相比,本方法需要更多的数据及相应的处理技术支撑,但在现有的条件下,对本研究区域来说,这种方法是可行的且效果良好。
With the development of economic, ecological environment and security issues are paid more attentions, as well as the prediction of ecological security and the construction of ecological security-warning on the base of predict research. Located in the west-pacific coastline and the estuary of China’s largest river, Chongming Island considered as the last ecological land in Shanghai, and it plans to become a real ecological island in 21 century for leaving space to the ecological protection and sampling sites selection to the world level project, according to the latest planning and development orientation. So soil pollution monitor, surveillance and comprehensive evaluation of fertility which are closely related to the ecological security are become one of the most important part in ecological security-warning program. Soil is a nature complex that has complex morphology and evolution, carried out spatial variability research of soil nutrition has great significance to the farmland fertilization, nutrition efficiency and reduction of environmental pollution which induced by over fertilization.
Soil was a natural synthesis with complicated configuration and evolvement process. The study on soil spatial variability of soil nutrients is very important to promote the development of soil science. It is not improving the resource utilization ratio of soil nutrients, but also reducing the environmental pollution.
Based on the soil land use and development conditions,on the one hand ,the study discussed how to optimize the distribution of sampling sites by using geostatistics and GIS technology. On the other hand, according to the process of soil formation and land use, the research also analysis fertility evaluation with method of combining Nemero index. On the basis of spatial distribution of soil nutrient, we hope that we can provide evidence for rational fertilization, environmental protection and ecological construction in Chongming.
In the research, first of all, by analyzing spatial variability of soil heavy metals, spatial interpolations were made by universal kriging and inverse distance weighted methods. The results are as following:
The range of Zn is 2000m which has no variation of the angle; the anisotropy of Hg is not significant, its range is 2500m; the anisotropy of As, Cu and Pb are the same 50°, their range are 55000m, 35000m and 5000m; the Cr, Cd, Ni variation of angle of 300 degrees, variable range is 7500m, 40000m, 16000m. As variable-range and anisotropy are the most important parameters, it is necessary to develop the second sample plan in order to obtain a more accurate spatial variability of soil nutrients. Secondly, this paper studied the spatial variability and distribution of available Cu, Mn, Zn, B, Mo, Fe in the soil surface of Chongming island. According to the analysis of soil trace elements, the best lag size and scale were chosen. The results showed that: six indicators all shown strong variability. Thirdly, based on the Spatial Variability research on the 18 chemical indexes, this paper performed Supervised Classification to the ETM image produced in 2002 by ERDAS. 6 Principal component factors were selected by using PCA and factor axis; 3-band graphic was obtained by composing bands; and 7 classes were eventually reclassified via ArcGIS. After introducing the pedogenic development factors which were selected as evaluated objects to conduct comprehensive evaluation of the soil by combining Membership function and Nemero Index, five levels of soil maturity were discerned by the evaluation in each zone separately.
Based on the above, this paper obtained the following conclusions:
1. The feature of soil behaves as an obvious spatial heterogeneity and different observational scales of soil nutrient and heavy metal elements’distributions are suitable to be analyzed and predicted by geostatistical models. As a magnificent method in analyzing the spatial variation of soil, difference among the interpolations of different elements that being estimated at different sampling scales come up under the help of geostatistics with GIS. In this article, according to the sampling data of traditional“S”-sampling method, detailed analysis on the spatial distribution was performed, especially on the anisotropy and range of each element, with the gradient of terrain and land use factors, to present a reformative schema of sampling. The result shows a discernable advantage of the second sampling schema that the variations are more precise to reflect the status of interesting area. Therefore, the method that to draw up a schema of second sampling referring to the results observed from the first sampling sites is feasible and indispensable.
2. In the research on soil nutrients, the crucial issue is to choose an appropriate model, scale and relevant parameters for interpolation. The model and parameter suggested automatically by ArcGIS software itself could hardly provide a satisfactory portray of the features of soil data. By iterate the lag sizes of different scales, the effective parameters and model could be found out with the minimum value of nugget effect. Introduced multi-scaled models to interpolation in this research, And these models are proved consequently to improve not only the accuracy of interpolation but also the similarity of the variation features of soil nutrients.
3. Chongming island is formed by rush, and its development process is quite complicated. The changes at different degrees take place every year. It is important to understand Chongming’s soil fertility. With the investigation to the limiting factors of soil development, classification and division to 18 chemical index of soil, as well as did comprehensive evaluation to the soil fertility; soil fertility of research area is objectively reflected. This approach needs more data and related technology by comparing with traditional method, but under the existing condition, this approach is feasible and has good results to this research area.
土壤是一形态和演化过程都十分复杂的自然综合体,开展土壤养分空间变异性研究对于科学合理地制定农田施肥方案,提高养分资源利用率,减少由于过量施肥造成环境污染,实现精确施肥具有重要意义。
本文根据崇明岛的土壤发育状况、土地利用特征,借助地统计空间变异分析和GIS技术在进行详细的土壤养分变异特征分析的基础上,探讨分析了土壤污染监测的采样点优化布局布设方案;并依据研究区成土过程的化学分类区域和近年的土地利用情况,采用隶属度函数和内梅罗指数结合的方法对崇明土壤进行了综合肥力评价,期望在分析了解崇明土壤养分空间分布情况和土壤肥力总体水平的基础上,为科学、合理施肥,保护环境及崇明生态建设提供依据。
研究采用GIS与地统计学相结合的方法,首先,分析崇明土壤重金属的空间变异特征、结合反距离加权插值和克里金插值,对崇明土壤进行空间变异分析,分别获得其预插值污染范围和估计的重金属分布特征。通过研究发现,Zn、Hg无变异角度,其变程分别为2000m、2500m;As、Cu、Pb变异角度在50度左右,变程分别是55000m、35000m、5000m;而Cr、Cd、Ni变异角度在300度左右,变程是7500m、40000m、16000m。变程和变异角度将作为最基本的参数,在满足精度的条件下对崇明土壤做地形分析并针对不同区域进行采样布局设计,在初步采样的基础上,获得土壤二次采样的最优化布局,为进一步更准确地获取研究区土壤养分的空间变异特征奠定基础。
其次,本文研究了上海崇明岛表层土壤有效铜、有效硼、有效铁、有效钼、有效锰、有效锌等微量元素的空间变异特征及分布规律。针对每个指标分别进行详细的试验分析,取得空间相关性最强的步长和分析尺度,并由此选择不同的拟合模型对各因子进行泛克里格插值.从而保证了进行筛选试验时理论插值模型的稳定性及其精度的可比性,同时获得相关微量元素的空间变异分布图。结果表明:6个指标均表现出强变异性。
再次,本文基于对崇明土壤18项化学指标的精确的空间变异特征的研究,借助遥感图像处理软件ERDAS中对2002年的崇明ETM图像进行监督分类;利用主成分分析和因子转轴选择出6个主成分因子,在ERDAS中将6个主成分因子的栅格图进行波段融合获得组合后的三波段图,在ArcGIS中对融合后的图像进行重分类获得:钙质滨海盐渍土带,围垦冲击土带,高铁有机人为土带,半水成半人为土带,有机新成土带,有机人为土带,淤积新成土带等七个化学分类土带。导入选择成土过程中土壤发育的限制因子,以限制因子为评价对象,结合隶属度函数和内梅罗指数法对崇明土壤进行综合肥力评价,将土壤肥力分为五个等级,每个区都分别进行评价。
根据以上研究,本文得到以下结论:
(一)土壤具有高度的空间变异性,不同尺度下土壤养分及重金属均适宜采用地统计学模型进行空间分析与估测。结合GIS的地统计学技术作为研究土壤空间变异的重要工具,不同变量在不同取样尺度下插值分析空间估测结果是不一致的。本文基于原有S型采样分析数据,详细研究分析了土壤的空间变异特性,特别是土壤不同元素的变异角度和变程,并根据地形及土地利用情况进行了相应的坡度分析,在此基础上分析设计了二次采样方案。研究表明,依据二次采样分析数据的土壤空间变异特征更能反映研究区的土壤空间变异状况。因此,基于初步采样数据进行相应的空间分析再进行二次采样方案的设计,为最终选取适合的尺度和插值方法进行相应的区域土壤空间变异的分析是必要的,也是可行的。(二)土壤养分空间变异研究中,最核心的问题是尺度和插值模型及其参数的选择。在ArcGIS中自动计算选取的插值模型和参数不一定能最好地反应土壤数据的空间变异分布特征。本文通过不同的步长尝试,依据块金效应的原则来选择合适的模型参数。在模型的选择上考虑了多尺度的模型,引入双模型插值。结果表明,双尺度的模型提高了插值的精度,能更加准确的反应出土壤养分的空间变异特征。
(三)崇明岛是一个冲击而成的岛,其发育过程相当复杂,每年都会发生不同程度的变化。依据成土过程进行分区,并结合土地利用类型进行肥力分区综合评价是一种新的思路,对了解崇明土壤肥力状况有十分重要的意义。通过研究每一类土壤发育限制因子,及其对崇明18项土壤化学指标的分析进行分类和分区,结合各评价区域的土地利用类型,进行不同区域的土壤肥力综合评价,更能客观地反映研究区土壤的肥力状况。与传统的肥力评价方法相比,本方法需要更多的数据及相应的处理技术支撑,但在现有的条件下,对本研究区域来说,这种方法是可行的且效果良好。
With the development of economic, ecological environment and security issues are paid more attentions, as well as the prediction of ecological security and the construction of ecological security-warning on the base of predict research. Located in the west-pacific coastline and the estuary of China’s largest river, Chongming Island considered as the last ecological land in Shanghai, and it plans to become a real ecological island in 21 century for leaving space to the ecological protection and sampling sites selection to the world level project, according to the latest planning and development orientation. So soil pollution monitor, surveillance and comprehensive evaluation of fertility which are closely related to the ecological security are become one of the most important part in ecological security-warning program. Soil is a nature complex that has complex morphology and evolution, carried out spatial variability research of soil nutrition has great significance to the farmland fertilization, nutrition efficiency and reduction of environmental pollution which induced by over fertilization.
Soil was a natural synthesis with complicated configuration and evolvement process. The study on soil spatial variability of soil nutrients is very important to promote the development of soil science. It is not improving the resource utilization ratio of soil nutrients, but also reducing the environmental pollution.
Based on the soil land use and development conditions,on the one hand ,the study discussed how to optimize the distribution of sampling sites by using geostatistics and GIS technology. On the other hand, according to the process of soil formation and land use, the research also analysis fertility evaluation with method of combining Nemero index. On the basis of spatial distribution of soil nutrient, we hope that we can provide evidence for rational fertilization, environmental protection and ecological construction in Chongming.
In the research, first of all, by analyzing spatial variability of soil heavy metals, spatial interpolations were made by universal kriging and inverse distance weighted methods. The results are as following:
The range of Zn is 2000m which has no variation of the angle; the anisotropy of Hg is not significant, its range is 2500m; the anisotropy of As, Cu and Pb are the same 50°, their range are 55000m, 35000m and 5000m; the Cr, Cd, Ni variation of angle of 300 degrees, variable range is 7500m, 40000m, 16000m. As variable-range and anisotropy are the most important parameters, it is necessary to develop the second sample plan in order to obtain a more accurate spatial variability of soil nutrients. Secondly, this paper studied the spatial variability and distribution of available Cu, Mn, Zn, B, Mo, Fe in the soil surface of Chongming island. According to the analysis of soil trace elements, the best lag size and scale were chosen. The results showed that: six indicators all shown strong variability. Thirdly, based on the Spatial Variability research on the 18 chemical indexes, this paper performed Supervised Classification to the ETM image produced in 2002 by ERDAS. 6 Principal component factors were selected by using PCA and factor axis; 3-band graphic was obtained by composing bands; and 7 classes were eventually reclassified via ArcGIS. After introducing the pedogenic development factors which were selected as evaluated objects to conduct comprehensive evaluation of the soil by combining Membership function and Nemero Index, five levels of soil maturity were discerned by the evaluation in each zone separately.
Based on the above, this paper obtained the following conclusions:
1. The feature of soil behaves as an obvious spatial heterogeneity and different observational scales of soil nutrient and heavy metal elements’distributions are suitable to be analyzed and predicted by geostatistical models. As a magnificent method in analyzing the spatial variation of soil, difference among the interpolations of different elements that being estimated at different sampling scales come up under the help of geostatistics with GIS. In this article, according to the sampling data of traditional“S”-sampling method, detailed analysis on the spatial distribution was performed, especially on the anisotropy and range of each element, with the gradient of terrain and land use factors, to present a reformative schema of sampling. The result shows a discernable advantage of the second sampling schema that the variations are more precise to reflect the status of interesting area. Therefore, the method that to draw up a schema of second sampling referring to the results observed from the first sampling sites is feasible and indispensable.
2. In the research on soil nutrients, the crucial issue is to choose an appropriate model, scale and relevant parameters for interpolation. The model and parameter suggested automatically by ArcGIS software itself could hardly provide a satisfactory portray of the features of soil data. By iterate the lag sizes of different scales, the effective parameters and model could be found out with the minimum value of nugget effect. Introduced multi-scaled models to interpolation in this research, And these models are proved consequently to improve not only the accuracy of interpolation but also the similarity of the variation features of soil nutrients.
3. Chongming island is formed by rush, and its development process is quite complicated. The changes at different degrees take place every year. It is important to understand Chongming’s soil fertility. With the investigation to the limiting factors of soil development, classification and division to 18 chemical index of soil, as well as did comprehensive evaluation to the soil fertility; soil fertility of research area is objectively reflected. This approach needs more data and related technology by comparing with traditional method, but under the existing condition, this approach is feasible and has good results to this research area.
引文
[1]武志杰,张海军, 21世纪的肥料科学.科学中国人, 2005. ,(12). 60-62.
[2]毛文永,文剑平,全球环境问题与对策. 1993,中国科学技术出版社.
[3]刘爱民,徐丽明,现代精准农业及我国精准农业的发展方向.中国农业大学学报, 2000. 5(2). 20-25.
[4] Burgess T.M., Webster R., McBRATNEY A.B., Optimal interpolation and isarithmic mapping of soil properties. European Journal of Soil Science, 1980. 31(2). 315-331.
[5] Burrough P.A., Multiscale sources of spatial variability in soil variation. Journal of soil science, 1983. 34. 577-597.
[6]石元春,土壤学的数字化和信息化革命.土壤学报, 2000. 37(3). 289-295.
[7]白颖,于艳萍,吕春晖,精准农业变量施肥技术初探.中国科技财富, 2009. 4.
[8] Huggett R.J., Soil chronosequences, soil development, and soil evolution: a critical review. Catena, 1998. 32(3-4). 155-172.
[9] Fisher E., Thornton B., Hudson G., et al., The variability in total and extractable soil phosphorus under a grazed pasture. Plant and Soil, 1998. 203(2). 249-255.
[10] Wollenhaupt N.C., Wolkowski R.P., Clayton M.K., Mapping soil test phosphorus and potassium for variable-rate fertilizer application. Journal of production agriculture, 1994. 7(4). 441-448.
[11] Cameron D.R., Nyborg M., Toogood J.A. et al., Accuracy of field sampling for soil tests. Can. J. Soil Sci, 1971. 51. 165-175.
[12]张淑娟,方慧,何勇,精细农业田间信息采样策略.农业机械学报, 2004. 35(4). 88-92.
[13]孙波,张桃林,我国东南丘陵山区土壤肥力的综合评价.土壤学报, 1995. 32(4). 362-369.
[14]杨俐苹,张峰民,棉田土壤养分精准管理初探.中国农业科学, 2000. 33(6). 67-72.
[15]杨诗秀,雷志栋,田间土壤含水率的空间结构及取样数目确定.地理学报, 1993. 48(5). 447-456.
[16]张有山,林启美,大比例尺区域土壤养分空间变异定量分析.华北农学报, 1998. 13(1). 122-128.
[17]高义民,同延安,胡正义等.,黄土区村级农田土壤养分空间变异特征研究.土壤通报, 2006. 37(1). 1-6.
[18]盛建东,肖华,武红旗等.,不同取样尺度农田土壤速效养分空间变异特征初步研究.干旱地区农业研究, 2005. 23(2). 63-67.
[19] Sims P.C., Waever R.E., Redfearn H.M., Assessing coastline change: A GIS model for Dawich, Devon, UK. Proceedings of Coast GIS 1995.
[20] White J.G., Welch R.M., Norvell W.A., Soil Zine Map of the USA using Geostatisties and Geographic Information and Systems. Soil Seience Soeiety of Alneriea Journal, 1997. 61. 185-194.
[21]郭旭东,杨福林,基于GIS和地统计学的土壤养分空间变异特征研究:以河北省遵….应用生态学报, 2000. 11(4). 557-563.
[22]侯光炯,农业土壤学-侯光炯在宜宾应用研究17年论文集. 2000,乌鲁木齐:新疆科技卫生出版社.
[23]张敏,陆宏,赵先军等.,慈溪市旱地土壤肥力变化的比较研究.土壤通报, 2004. 35(1). 91-93.
[24]邹加明,单沛祥,李文璧,大理烟区土壤肥力现状与演变趋势.中国烟草科学, 2002. 4. 35-39.
[25]朱鹤健,土壤学与地理学交叉研究. 2006,北京:科学出版社.
[26]周勇,李倩,张海涛,基于RS和GIS的农用土地物元综合评价.水土保持学报, 1999. 5(2). 75-80.
[27]薛红霞,何江华,广东省耕地分等中的土壤肥力评价指标体系.生态环境, 2004. 13(3). 461-462.
[28]高玉蓉,许红卫,周斌,稻田土壤养分的空间变异性研究.土壤通报, 2005. 36(6). 822-825.
[29]石常蕴,周慧珍, GIS技术在土地质量评价中的应用;以苏州市水田为例.土壤学报, 2001. 38(3). 248-255.
[30]周丕生,唐亮,上海野生动物园土壤性状及其综合评价.上海农学院学报, 1996. 14(3). 153-158.
[31]王光复,李亦兵,长春耕地质量评估.农业区划, 1991. 64(2). 13-15.
[32]杨金,马振江,复混肥对秋白菜产量及土壤肥力的影响.土壤通报, 1996. 27(5). 236-238.
[33]杨文元,张先婉,稻田多熟制下小麦连作对土壤肥力的影响.土壤肥力研究进展, 1991. 14-18.
[34]黎孟波,张先婉,土壤肥力概念与模式:土壤肥力研究之一.土壤肥力研究进展, 1991. 208-213.
[35]王克孟,马玉军,淮阴市高产土壤肥力指标的研究.土壤, 1992. 24(5). 239-243.
[36]余存祖,刘耀宗,黄土区土壤肥力形成过程与肥务指标分析.土壤通报, 1990. 21(5). 197-201.
[37]蔡崇法,丁树文,史志华等., GIS支持下乡镇域土壤肥力评价与分析.土壤与环境, 2000. 9(2). 99-102.
[38]张大克,王玉杰,叶海江,水稻土肥力水平分类中主要土壤肥力因素指标的筛选模型.农业工程学报, 1997. 13(2). 91-95.
[39]沈宏,徐志红,曹志洪,用土壤生物和养分指标表征土壤肥力的可持续性.土壤与环境, 1999. 8(1). 31-35.
[40]阚文杰,吴启堂,一个定量综合评价土壤肥力的方法初探.土壤通报, 1994. 25(6). 245-247.
[41]莫治雄,对应分析在土壤肥力研究中的应用.土壤肥料, 1993(4). 4-7.
[42]曹承绵,严长生,张志明等.,关于土壤肥力数值化综合评价的探讨.土壤通报, 1983. 4. 13-15.
[43]吕巧灵,付巧玲,吴克宁等.,郑州市郊区土壤综合肥力评价及空间分布研究.中国农学通报, 2006. 22(1). 166-168.
[44]李丹丹,毕庆文,许自成等.,湖北宣恩烟区土壤养分状况综合评价.郑州轻工业学院学报:自然科学版, 2007. 22(5). 33-36.
[45]黄健,李会民,张惠琳等.,基于GIS的吉林省县级耕地地力评价与评价指标体系的研究——以九台市为例.土壤通报, 2007. 38(3). 422-426.
[46]严昶升,土壤肥力研究方法. 1988,农业出版社.
[47] Burrough P.A., Fuzzy mathematical methods for soil survey and land evaluation. European Journal of Soil Science, 1989. 40(3). 477-492.
[48] Schmidt M.G., Schreier H.E., Shah P.B., A GIS evaluation of land use dynamics and forest soil fertility in a watershed in Nepal. International Journal of Geographical Information Science, 1995. 9(3). 317-327.
[49]周勇,李学垣, ARC/INFO信息系统在农地分等定级中的应用.土壤学报, 1998. 35(4). 450-460.
[50]郑小佳,基于人工神经网络的耕作土壤肥力质量评价. 2006,四川农业大学.
[51]王洋,齐晓宁,德惠市农田黑土肥力评价及施肥措施研究.中国生态农业学报, 2007. 15(2). 26-28.
[52]段项锁,薛正平,杨星卫等.,精准农业土壤养分与水稻产量关系模型研究. Chinese Journal of Eco—Agriculture, 2003. 11(1).
[53]朱青,陈正刚,陈欣等.,数理统计分析在土壤养分评价中的应用.西南农业学报, 2006. 19(5). 847-852.
[54]赵其国, 21世纪土壤科学展望.地球科學進展, 2001. 16(5). 704-709.
[55]童庆禧,张兵,郑兰芬,高光谱遥感的多学科应用. 2006,电子工业出版社.
[56]杜森,高祥照,信息技术在农田施肥管理中的应用.土壤与环境, 2002. 11(2). 189-193.
[57]邹尚辉,遥感图像专题制图. 1990,武汉:华中师范大学出版社. 167-219.
[58]徐冠华,徐吉炎,再生资源遥感研究. 1988,北京:科学出版社.
[59]中国农业工程学会农业遥感专业委员会编,农业遥感论文集. 1991,测绘出版社. 138-144.
[60]傅伯杰,土地潜力评价的类型与方法.国土与自然资源研究, 1990(2). 29-32.
[61]黄杏元,陈丙咸,地理信息系统发展趋势.地理学报, 1989. 44(2). 230-236.
[62] Dumanski T., Objectives and Structure of the Cansde Soil Information System. Canada Journal of Soil Science, 1975. 17(2). 205-213.
[63] Tomlinson R.F., The Development of Geographic InformatinoSystems. Canada- China Bilateral Symposium on Territorial Developmemt and Mnagement,Beijing, 1986.
[64] Tomlinson R.F., Presidential address: Geographic information systems and geographers in the 1990s. Canadian Geographer, 1989. 33. 290-298.
[65]雷能忠,费大鹏,基于GIS的农业面源污染风险评估.中国农学通报, 2007. 23(12). 381-385.
[66]蔡德利,辛刚,张之一等.,区域土壤信息系统数据建模.黑龙江八一农垦大学学报, 2005. 17(5). 10-13.
[67]边馥苓,朱国宾,地理信息系统原理和方法. 1996,北京:测绘出版社.
[68]李德仁,龚健雅,边馥苓,地理信息系统导论. 1993,北京:测绘出敝社.
[69] Goodchild M.F., Intergrating gis and enviromental modeling at globle scales. GIS/LIS'91 Proceedings: 28 October-1 November, The Inforum, Atlanta, Georgia, 1991. 117.
[70]陈育峰,环境地学分析方法及应用. 1994,北京:中国环境科学出版社.
[71]张仁铎,空间变异理论及应用. 2005,科学出版社.
[72]朱长青,史文中,空间分析建模与原理. 2006,北京:科学出版社.
[73]许红卫,王珂,田间土壤采样数据的统计特征与空间变异性研究.浙江大学学报:农业与生命科学版, 2000. 26(6). 665-669.
[74]任振辉,吴宝忠,精细农业中最佳土壤采样间距确定方法的研究.农机化研究, 2006(6). 82-85.
[75]史舟,李艳,地统计学在土壤学中的应用. 2006,北京:中国农业出版社.
[76]钱振华,申广荣,徐敬敬等.,基于GIS的上海崇明耕地土壤主要养分的空间变异研究.上海交通大学学报:农业科学版, 2009. 27(001). 7-12.
[77] DB31/T252-2000上海市地方标准,安全卫生优质农产品(或原料)产地环境标准.
[78] Franzen D.W., Hofman V.L., Halvorson A.D., et al., Sampling for site-specific farming: Topography and nutrient considerations. Better crop, 1996. 80(3). 14-18.
[79]路鹏,彭佩钦,宋变兰等.,洞庭湖平原区土壤全磷含量地统计学和GIS分析.中国农业科学, 2005. 38(6). 1204-1212.
[80] Li X.Y., Zhang S.W., Wang Z.M., Spatial variability and pattern analysis of soil properties in Dehui city of Jilin Province. Journal of Geographical Sciences, 2004. 14(4). 503-511.
[81]刘湘南, GIS空间分析原理与方法. 2005,科学出版社.
[82]郭旭东,傅伯杰,河北省遵化平原土壤养分的时空变异特征.地理学报, 2000. 55(5). 555-566.
[83]刘铮,中国土壤微量元素.地球科学进展, 1998. 13(6).
[84]董国政,刘德辉,姜月华等.,湖州市土壤微量元素含量与有效性评价.土壤通报, 2004. 35(004). 474-478.
[85]南京土壤研究所.中国土壤数据库.http://www.soil.csdb.cn.
[86]李红,卢振兰,李德志等.,上海崇明县植被覆盖度动态变化遥感监测研究.城市环境与城市生态, 2009. 2. 8-13.
[87]崇明年鉴, 2002.
[88]钱振华,基于地统计分析的崇明土壤肥力评价.园艺学进展(第八辑), 2008.
[89]王学军,陈静生,长江三角洲(上海地区)土壤微量元素的时空变化及成因分析.中国环境科学, 1992. 12(6). 450-454.
[90]倪绍祥,陈传康,我国土地评价研究的进展.地理学报, 1983. 48(1). 75-81.
[91]倪绍祥,土地评价类别意见.地利域研究与开发, 1992. 11(3). 10-12.
[2]毛文永,文剑平,全球环境问题与对策. 1993,中国科学技术出版社.
[3]刘爱民,徐丽明,现代精准农业及我国精准农业的发展方向.中国农业大学学报, 2000. 5(2). 20-25.
[4] Burgess T.M., Webster R., McBRATNEY A.B., Optimal interpolation and isarithmic mapping of soil properties. European Journal of Soil Science, 1980. 31(2). 315-331.
[5] Burrough P.A., Multiscale sources of spatial variability in soil variation. Journal of soil science, 1983. 34. 577-597.
[6]石元春,土壤学的数字化和信息化革命.土壤学报, 2000. 37(3). 289-295.
[7]白颖,于艳萍,吕春晖,精准农业变量施肥技术初探.中国科技财富, 2009. 4.
[8] Huggett R.J., Soil chronosequences, soil development, and soil evolution: a critical review. Catena, 1998. 32(3-4). 155-172.
[9] Fisher E., Thornton B., Hudson G., et al., The variability in total and extractable soil phosphorus under a grazed pasture. Plant and Soil, 1998. 203(2). 249-255.
[10] Wollenhaupt N.C., Wolkowski R.P., Clayton M.K., Mapping soil test phosphorus and potassium for variable-rate fertilizer application. Journal of production agriculture, 1994. 7(4). 441-448.
[11] Cameron D.R., Nyborg M., Toogood J.A. et al., Accuracy of field sampling for soil tests. Can. J. Soil Sci, 1971. 51. 165-175.
[12]张淑娟,方慧,何勇,精细农业田间信息采样策略.农业机械学报, 2004. 35(4). 88-92.
[13]孙波,张桃林,我国东南丘陵山区土壤肥力的综合评价.土壤学报, 1995. 32(4). 362-369.
[14]杨俐苹,张峰民,棉田土壤养分精准管理初探.中国农业科学, 2000. 33(6). 67-72.
[15]杨诗秀,雷志栋,田间土壤含水率的空间结构及取样数目确定.地理学报, 1993. 48(5). 447-456.
[16]张有山,林启美,大比例尺区域土壤养分空间变异定量分析.华北农学报, 1998. 13(1). 122-128.
[17]高义民,同延安,胡正义等.,黄土区村级农田土壤养分空间变异特征研究.土壤通报, 2006. 37(1). 1-6.
[18]盛建东,肖华,武红旗等.,不同取样尺度农田土壤速效养分空间变异特征初步研究.干旱地区农业研究, 2005. 23(2). 63-67.
[19] Sims P.C., Waever R.E., Redfearn H.M., Assessing coastline change: A GIS model for Dawich, Devon, UK. Proceedings of Coast GIS 1995.
[20] White J.G., Welch R.M., Norvell W.A., Soil Zine Map of the USA using Geostatisties and Geographic Information and Systems. Soil Seience Soeiety of Alneriea Journal, 1997. 61. 185-194.
[21]郭旭东,杨福林,基于GIS和地统计学的土壤养分空间变异特征研究:以河北省遵….应用生态学报, 2000. 11(4). 557-563.
[22]侯光炯,农业土壤学-侯光炯在宜宾应用研究17年论文集. 2000,乌鲁木齐:新疆科技卫生出版社.
[23]张敏,陆宏,赵先军等.,慈溪市旱地土壤肥力变化的比较研究.土壤通报, 2004. 35(1). 91-93.
[24]邹加明,单沛祥,李文璧,大理烟区土壤肥力现状与演变趋势.中国烟草科学, 2002. 4. 35-39.
[25]朱鹤健,土壤学与地理学交叉研究. 2006,北京:科学出版社.
[26]周勇,李倩,张海涛,基于RS和GIS的农用土地物元综合评价.水土保持学报, 1999. 5(2). 75-80.
[27]薛红霞,何江华,广东省耕地分等中的土壤肥力评价指标体系.生态环境, 2004. 13(3). 461-462.
[28]高玉蓉,许红卫,周斌,稻田土壤养分的空间变异性研究.土壤通报, 2005. 36(6). 822-825.
[29]石常蕴,周慧珍, GIS技术在土地质量评价中的应用;以苏州市水田为例.土壤学报, 2001. 38(3). 248-255.
[30]周丕生,唐亮,上海野生动物园土壤性状及其综合评价.上海农学院学报, 1996. 14(3). 153-158.
[31]王光复,李亦兵,长春耕地质量评估.农业区划, 1991. 64(2). 13-15.
[32]杨金,马振江,复混肥对秋白菜产量及土壤肥力的影响.土壤通报, 1996. 27(5). 236-238.
[33]杨文元,张先婉,稻田多熟制下小麦连作对土壤肥力的影响.土壤肥力研究进展, 1991. 14-18.
[34]黎孟波,张先婉,土壤肥力概念与模式:土壤肥力研究之一.土壤肥力研究进展, 1991. 208-213.
[35]王克孟,马玉军,淮阴市高产土壤肥力指标的研究.土壤, 1992. 24(5). 239-243.
[36]余存祖,刘耀宗,黄土区土壤肥力形成过程与肥务指标分析.土壤通报, 1990. 21(5). 197-201.
[37]蔡崇法,丁树文,史志华等., GIS支持下乡镇域土壤肥力评价与分析.土壤与环境, 2000. 9(2). 99-102.
[38]张大克,王玉杰,叶海江,水稻土肥力水平分类中主要土壤肥力因素指标的筛选模型.农业工程学报, 1997. 13(2). 91-95.
[39]沈宏,徐志红,曹志洪,用土壤生物和养分指标表征土壤肥力的可持续性.土壤与环境, 1999. 8(1). 31-35.
[40]阚文杰,吴启堂,一个定量综合评价土壤肥力的方法初探.土壤通报, 1994. 25(6). 245-247.
[41]莫治雄,对应分析在土壤肥力研究中的应用.土壤肥料, 1993(4). 4-7.
[42]曹承绵,严长生,张志明等.,关于土壤肥力数值化综合评价的探讨.土壤通报, 1983. 4. 13-15.
[43]吕巧灵,付巧玲,吴克宁等.,郑州市郊区土壤综合肥力评价及空间分布研究.中国农学通报, 2006. 22(1). 166-168.
[44]李丹丹,毕庆文,许自成等.,湖北宣恩烟区土壤养分状况综合评价.郑州轻工业学院学报:自然科学版, 2007. 22(5). 33-36.
[45]黄健,李会民,张惠琳等.,基于GIS的吉林省县级耕地地力评价与评价指标体系的研究——以九台市为例.土壤通报, 2007. 38(3). 422-426.
[46]严昶升,土壤肥力研究方法. 1988,农业出版社.
[47] Burrough P.A., Fuzzy mathematical methods for soil survey and land evaluation. European Journal of Soil Science, 1989. 40(3). 477-492.
[48] Schmidt M.G., Schreier H.E., Shah P.B., A GIS evaluation of land use dynamics and forest soil fertility in a watershed in Nepal. International Journal of Geographical Information Science, 1995. 9(3). 317-327.
[49]周勇,李学垣, ARC/INFO信息系统在农地分等定级中的应用.土壤学报, 1998. 35(4). 450-460.
[50]郑小佳,基于人工神经网络的耕作土壤肥力质量评价. 2006,四川农业大学.
[51]王洋,齐晓宁,德惠市农田黑土肥力评价及施肥措施研究.中国生态农业学报, 2007. 15(2). 26-28.
[52]段项锁,薛正平,杨星卫等.,精准农业土壤养分与水稻产量关系模型研究. Chinese Journal of Eco—Agriculture, 2003. 11(1).
[53]朱青,陈正刚,陈欣等.,数理统计分析在土壤养分评价中的应用.西南农业学报, 2006. 19(5). 847-852.
[54]赵其国, 21世纪土壤科学展望.地球科學進展, 2001. 16(5). 704-709.
[55]童庆禧,张兵,郑兰芬,高光谱遥感的多学科应用. 2006,电子工业出版社.
[56]杜森,高祥照,信息技术在农田施肥管理中的应用.土壤与环境, 2002. 11(2). 189-193.
[57]邹尚辉,遥感图像专题制图. 1990,武汉:华中师范大学出版社. 167-219.
[58]徐冠华,徐吉炎,再生资源遥感研究. 1988,北京:科学出版社.
[59]中国农业工程学会农业遥感专业委员会编,农业遥感论文集. 1991,测绘出版社. 138-144.
[60]傅伯杰,土地潜力评价的类型与方法.国土与自然资源研究, 1990(2). 29-32.
[61]黄杏元,陈丙咸,地理信息系统发展趋势.地理学报, 1989. 44(2). 230-236.
[62] Dumanski T., Objectives and Structure of the Cansde Soil Information System. Canada Journal of Soil Science, 1975. 17(2). 205-213.
[63] Tomlinson R.F., The Development of Geographic InformatinoSystems. Canada- China Bilateral Symposium on Territorial Developmemt and Mnagement,Beijing, 1986.
[64] Tomlinson R.F., Presidential address: Geographic information systems and geographers in the 1990s. Canadian Geographer, 1989. 33. 290-298.
[65]雷能忠,费大鹏,基于GIS的农业面源污染风险评估.中国农学通报, 2007. 23(12). 381-385.
[66]蔡德利,辛刚,张之一等.,区域土壤信息系统数据建模.黑龙江八一农垦大学学报, 2005. 17(5). 10-13.
[67]边馥苓,朱国宾,地理信息系统原理和方法. 1996,北京:测绘出版社.
[68]李德仁,龚健雅,边馥苓,地理信息系统导论. 1993,北京:测绘出敝社.
[69] Goodchild M.F., Intergrating gis and enviromental modeling at globle scales. GIS/LIS'91 Proceedings: 28 October-1 November, The Inforum, Atlanta, Georgia, 1991. 117.
[70]陈育峰,环境地学分析方法及应用. 1994,北京:中国环境科学出版社.
[71]张仁铎,空间变异理论及应用. 2005,科学出版社.
[72]朱长青,史文中,空间分析建模与原理. 2006,北京:科学出版社.
[73]许红卫,王珂,田间土壤采样数据的统计特征与空间变异性研究.浙江大学学报:农业与生命科学版, 2000. 26(6). 665-669.
[74]任振辉,吴宝忠,精细农业中最佳土壤采样间距确定方法的研究.农机化研究, 2006(6). 82-85.
[75]史舟,李艳,地统计学在土壤学中的应用. 2006,北京:中国农业出版社.
[76]钱振华,申广荣,徐敬敬等.,基于GIS的上海崇明耕地土壤主要养分的空间变异研究.上海交通大学学报:农业科学版, 2009. 27(001). 7-12.
[77] DB31/T252-2000上海市地方标准,安全卫生优质农产品(或原料)产地环境标准.
[78] Franzen D.W., Hofman V.L., Halvorson A.D., et al., Sampling for site-specific farming: Topography and nutrient considerations. Better crop, 1996. 80(3). 14-18.
[79]路鹏,彭佩钦,宋变兰等.,洞庭湖平原区土壤全磷含量地统计学和GIS分析.中国农业科学, 2005. 38(6). 1204-1212.
[80] Li X.Y., Zhang S.W., Wang Z.M., Spatial variability and pattern analysis of soil properties in Dehui city of Jilin Province. Journal of Geographical Sciences, 2004. 14(4). 503-511.
[81]刘湘南, GIS空间分析原理与方法. 2005,科学出版社.
[82]郭旭东,傅伯杰,河北省遵化平原土壤养分的时空变异特征.地理学报, 2000. 55(5). 555-566.
[83]刘铮,中国土壤微量元素.地球科学进展, 1998. 13(6).
[84]董国政,刘德辉,姜月华等.,湖州市土壤微量元素含量与有效性评价.土壤通报, 2004. 35(004). 474-478.
[85]南京土壤研究所.中国土壤数据库.http://www.soil.csdb.cn.
[86]李红,卢振兰,李德志等.,上海崇明县植被覆盖度动态变化遥感监测研究.城市环境与城市生态, 2009. 2. 8-13.
[87]崇明年鉴, 2002.
[88]钱振华,基于地统计分析的崇明土壤肥力评价.园艺学进展(第八辑), 2008.
[89]王学军,陈静生,长江三角洲(上海地区)土壤微量元素的时空变化及成因分析.中国环境科学, 1992. 12(6). 450-454.
[90]倪绍祥,陈传康,我国土地评价研究的进展.地理学报, 1983. 48(1). 75-81.
[91]倪绍祥,土地评价类别意见.地利域研究与开发, 1992. 11(3). 10-12.