基于ArcSDE的空间数据组织和管理

重点讨论了ArcsDE的数据组织模型,以及如何组织和管理空间数据;利用AreSDE结合Oraclegi技术,实现了中国--东盟自由贸易区电子政务系统中数据库的建立.     

The delineation of agricultural management zones with high resolution remotely sensed data

Remote sensing (RS) techniques have been widely considered to be a promising source of information for land management decisions. The objective of this study was to develop and compare different methods of delineating management zones (MZs) in a field of winter wheat. Soil and yield samples were collected, and five main crop nutrients were analyzed: total nitrogen (TN), nitrate nitrogen (NN), available phosphorus (AP), extractable potassium (EP) and organic matter (OM). At the wheat heading stage, a scene of Quickbird imagery was acquired and processed, and the optimized soil-adjusted vegetation index (OSAVI) was determined. A fuzzy k-means clustering algorithm was used to define MZs, along with fuzzy performance index (FPI), and modified partition entropy (MPE) for determining the optimal number of clusters. The results showed that the optimal number of MZs for the present study area was three. The MZs were delineated in three ways; based on soil and yield data, crop RS information and the combination of soil, yield and RS information. The evaluation of each set of MZs showed that the three methods of delineating zones can all decrease the variance of the crop nutrients, wheat spectral parameters and yield within the different zones. Considering the consistent relationship between the crop nutrients, wheat yield and the wheat spectral parameters, satellite remote sensing shows promise as a tool for assessing the variation in soil properties and yield in arable fields. The results of this study suggest that management zone delineation using RS data was reliable and feasible.     

Spatio-temporal analysis of yield and weather data for defining site-specific crop management zones

Precision Agriculture - Understanding yield potential and yield-limiting factors is essential for improving profitability and grain yields while avoiding adverse environmental effects. In the USA,...     

Data fusion techniques for delineation of site-specific management zones in a field in UK

Fusion of different data layers, such as data from soil analysis and proximal soil sensing, is essential to improve assessment of spatial variation in soil and yield. On-line visible and near infrared (Vis–NIR) spectroscopy have been proved to provide high resolution information about spatial variability of key soil properties. Multivariate geostatistics tools were successfully implemented for the delineation of management zones (MZs) for precision application of crop inputs. This research was conducted in a 18 ha field to delineate MZs, using a multi-source data set, which consisted of eight laboratory measured soil variables (pH, available phosphorus (P), cation exchange capacity, total nitrogen (TN), total carbon (TC), exchangeable potassium (K), sand, silt) and four on-line collected Vis–NIR spectra-based predicted soil variables (pH, P, K and moisture content). The latter set of data was predicted using the partial least squares regression (PLSR) technique. The quality of the calibration models was evaluated by cross-validation. Multi-collocated cokriging was applied to the soil and spectral data set to produce thematic spatial maps, whereas multi-collocated factor cokriging was applied to delineate MZ. The Vis–NIR predicted K was chosen as the exhaustive variable, because it was the most correlated with the soil variables. A yield map of barley was interpolated by means of the inverse distance weighting method and was then classified into 3 iso-frequency classes (low, medium and high). To assess the productivity potential of the different zones of the field, spatial association between MZs and yield classes was calculated. Results showed that the prediction performance of PLSR calibration models for pH, P, MC and K were of excellent to moderate quality. The geostatistical model revealed good performance. The estimates of the first regionalised factor produced three MZs of equal size in the studied field. The loading coefficients for TC, pH and TN of the first factor were highest and positive. This means that the first factor can be assumed as a synthetic indicator of soil fertility. The overall spatial association between the yield classes and MZs was about 40 %, which reveals that more than 50 % of the yield variation can be attributed to more dynamic factors than soil parameters, such as agro-meteorological conditions, plant diseases and nutrition stresses. Nevertheless, multivariate geostatistics proved to be an effective approach for site-specific management of agricultural fields.     

Delineation of management zones and optimization of irrigation scheduling to improve irrigation water productivity and revenue in a farmland of Northwest China

Precision Agriculture - Precision agriculture has been increasingly practised in recent years. Under precision management, farmland is divided into several management zones to implement different...     

A comparison between spatial clustering models for determining N-fertilization management zones in orchards

Precision Agriculture - Site-specific agricultural management (SSM) relies on identifying within-field spatial variability and is used for variable rate input of resources. Precision agricultural...     

Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data

In light of the increasing demand for food production, climate change challenges for agriculture, and economic pressure, precision farming is an ever-growing market. The development and distribution of remote sensing applications is also growing. The availability of extensive spatial and temporal data—enhanced by satellite remote sensing and open-source policies—provides an attractive opportunity to collect, analyze and use agricultural data at the farm scale and beyond. The division of individual fields into zones of differing yield potential (management zones (MZ)) is the basis of most offline and map-overlay precision farming applications. In the process of delineation, manual labor is often required for the acquisition of suitable images and additional information on crop type. The authors therefore developed an automatic segmentation algorithm using multi-spectral satellite data, which is able to map stable crop growing patterns, reflecting areas of relative yield expectations within a field. The algorithm, using RapidEye data, is a quick and probably low-cost opportunity to divide agricultural fields into MZ, especially when yield data is insufficient or non-existent. With the increasing availability of satellite images, this method can address numerous users in agriculture and lower the threshold of implementing precision farming practices by providing a preliminary spatial field assessment.     

某型飞机航空军械仿真系统中数据融合及算法

介绍了某型飞机航空军械训练仿真系统的多传感器数据融合的特点和一种数据融合模式,结合实例说明了多传感器对众多数据信息进行融合处理,以使系统准确快速反应。     

基于多特征融合和深度信念网络的植物叶片识别

基于叶片数字图像的植物识别是自动植物分类研究的热点。但是随着植物种类的增加,传统的分类方法由于提取的特征比较单一或者分类器结构过于简单,导致叶片识别率较低。为此,本文提出使用纹理特征结合形状特征进行识别,并且使用深度信念网络构架作为分类器。纹理特征通过局部二值模式、Gabor滤波和灰度共生矩阵方法得到。而形状特征向量由Hu氏不变量和傅里叶描述子组成。为了避免过拟合现象,使用“dropout”方法训练深度信念网络。这种基于多特征融合的深度信念网络的植物识别方法,在Flavia数据库中,对32种叶片的识别率为99.37%;在ICL数据库中,对220种叶片的识别率为93.939%。这表明相比一般的叶片识别方法,此方法鲁棒性更强,并且识别率更高。      

Model-based optimal delineation of drip irrigation management zones

Precision Agriculture - Delineation of management zones for applying Variable Rate Irrigation (VRI) in drip irrigation presents unique challenges due to the directionality of the drip lines and the...     

农业空间数据挖掘及其系统开发研究

从海量农业空间数据中提取有价值的信息,对于农业空间信息管理水平的提升非常重要.本文在空间数据挖掘理论和应用研究的基础上,首先介绍了农业空间数据挖掘(Agricultural Spatial Data Mining,简称ASDM)的基础理论、方法和一般处理过程,给出了ASDM系统开发的框架结构,并对基于GIS的ASDM系统开发进行了实例阐述,以期为农业空间数据挖掘的发展提供理论指导.     

Site-specific management zones based on the Rasch model and geostatistical techniques

Delineation of management zones (MZ), i.e. areas within the field which represent subfield regions of similar production potential, is the first stage to implement site-specific management. During the last years different algorithms have been proposed to define MZ, with different results. In this work, the use of an objective method, the formulation of the Rasch model, which synthesizes data with different units into a uniform analytical framework, is considered to get representative measures of soil fertility potential which could be used to delimit MZ.To illustrate the method, a case study was conducted in a experimental field using five soil properties: clay, sand and silt content, and deep (ECd) and shallow (ECs) soil apparent electrical conductivity (approximately 0-90 and 0-30 cm depths, respectively). Two main results were obtained after applying this method: (1) a classification of all locations according to the soil fertility potential, which was the value of the Rasch measure and (2) the influence on the soil fertility of each individual soil property, being ECs the most influential and silt content the less influential property.Later, from the measures of soil fertility potential at sampled points, estimates were carried out using the ordinary kriging technique. Consequently, kriged estimates were utilized to map soil fertility potential and MZ were delimited using an equal-size classification method, which practically coincided with the MZ determined by a unsupervised classification.It is also shown the possibility of using probability maps to delimit MZ or provide information for hazard assessment of soil fertility in a field.     

Automatic harmonization of heterogeneous agronomic and environmental spatial data

Precision Agriculture - The analysis and mapping of agronomic and environmental spatial data require observations to be comparable. Heterogeneous spatial datasets are those for which the...     

基于MapX的空间数据不一致性问题处理

数据预处理是数据挖掘过程中很重要的环节。由于复杂的空间数据更易造成数据的不一致，加上不同应用空间数据的特殊性，对空间数据的预处理往往需要采用特殊的方法。针对林业小班空间数据在数字化过程中产生的空间对象位置的不一致性问题，给出了一种解决的方法：以基准面为基础，通过最外2个交点，在所有的交点上进行修正。实际空间数据对比实验结果显示该方法比节点抓取具有更好的处理效果。图7参10     

Methodology for selective coffee harvesting in management zones of yield and maturation

Precision Agriculture - The non-uniformity of coffee fruit maturation is an intrinsic plant condition that is not favorable for the beverage quality. This could be overcome by using the precision...     

Maximizing the relationship of yield to site-specific management zones with object-oriented segmentation of hyperspectral images

Quick and low cost delineation of site-specific management zones (SSMZ) would improve applications of precision agriculture. In this study, a new method for delineating SSMZ using object-oriented segmentation of airborne imagery was demonstrated. Three remote sensing domains—spectral, spatial, and temporal- are exploited to improve the SSMZ relationship to yield. Common vegetation indices (VI), and first and second derivatives (\(\rho^{\prime}\), \(\rho^{\prime\prime}\)) from twelve airborne hyperspectral images of a cotton field for one season \(\rho^{\prime}\) were used as input layers for object-oriented segmentation. The optimal combination of VI, SSMZ size and crop phenological stage were used as input variables for SSMZ delineation, determined by maximizing the correlation to segmented yield monitor maps. Combining narrow band vegetation indices and object-oriented segmentation provided higher correlation between VI and yield at SSMZ scale than that at pixel scale by reducing multi-resource data noise. VI performance varied during the cotton growing season, providing better SSMZ delineation at the beginning and middle of the season (days after planting (DAP) 66–143).The optimal scale determined for SSMZ delineation was approximately 240 polygons for the study field, but the method also provided flexibility enabling the setting of practical scales for a given field. For a defined scale, the optimal single phenological stage for the study field was near July 11 (DAP 87) early in the growing season. SSMZs determined from multispectral VIs at a single stage were also satisfactory; compared to hyperspectral indices, temporal resolution of multi-spectral data seems more important for SSMZ delineation.     

The challenge of reproducing remote sensing data from satellites and unmanned aerial vehicles (UAVs) in the context of management zones and precision agriculture

Precision Agriculture - Mapping the within-field variability of crop status is of great importance in precision agriculture, which seeks to balance agronomic inputs with spatial crop demands....     

江苏滩涂围垦区土壤养分空间变异研究

采用地理信息系统(GIS)和地统计方法,研究20世纪80年代初围垦王竹垦区的土壤养分空间变异特征.结果表明:有机质、碱解氮、全磷、速效磷块金基台比介于7.9%～19%之间,表现出强烈的空间相关性;土壤pH值、全氮、全钾、速效钾的块金基台比值介于29.8%～49.9%之间,表现出中等的空间相关性.土壤pH值、全氮、全磷、全钾的空间分布呈带状,以较大区域变异为主;碱解氮、速效钾的空间渐变性稍差,以中等大小区域变异为主;有机质、速效磷的空间分布呈团状,以较小区域变异为主.土壤各养分的空间分布图可直观表示出各养分的空间变异特征和丰歉程度,样区有机质含量处于中低水平,碱解氮含量极低,速效钾、速效磷含量较高.建议多施氮肥和有机肥,少施或不施钾肥和磷肥.     

A comparison of different algorithms for the delineation of management zones

One approach to the application of site-specific techniques and technologies in precision agriculture is to subdivide a field into a few contiguous homogenous zones, often referred to as management zones (MZs). Delineating MZs can be based on some sort of clustering, however there is no widely accepted method. The application of fuzzy set theory to clustering has enabled researchers to account better for the continuous variation in natural phenomena. Moreover, the methods based on non-parametric density estimation can detect clusters of unequal size and dispersion. The objectives of this paper were to: (1) compare different procedures for creating management zones and (2) determine the relation of the MZs delineated with potential yield. One hundred georeferenced point measurements of soil and crop properties were obtained from a 12 ha field cropped with durum wheat for two seasons. The trial was carried out at the experimental farm of CRA-CER in Foggia (Italy). All variables were interpolated on a 1 × 1 m grid using the geostatistical techniques of kriging and cokriging. The techniques compared to identify MZs were: (1) the ISODATA method, (2) the fuzzy c-means algorithm and (3) a non-parametric density algorithm. The ISODATA method, which was the simplest, subdivided the field into three distinct classes of suitable size for uniform management, whereas the other two methods created two classes. The non-parametric density algorithm characterized the edge properties between adjacent clusters more efficiently than the fuzzy method. The clusters from the non-parametric density algorithm and yield maps for three seasons (2005–2006, 2006–2007 and 2007–2008) were compared and agreement measures were computed. The kappa coefficients for the three seasons were negative or small positive values which indicate only slight agreement. These results illustrate the importance of temporal variation in spatial variation of yield in rainfed conditions, which limits the use of the MZ approach.