多元林业信息融合的立地知识发现研究
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摘要
立地作为森林生长外部环境的总和,直接影响到森林的生产力大小和健康状况。随着森林资源调查技术和数据获取手段的不断发展,森林资源数据已经具备了多元化、海量化和信息化的特点,更加丰富的森林资源数据中隐含着大量具有实用价值的森林立地信息和知识。但是长期以来,有关立地的大量研究(立地生产力,立地因子)仍然使用单一传统的森林资源小班调查数据,多元化的森林资源数据未得到综合和充分的利用,并且研究手段也较为单一,缺少多种方法的对比研究,使得海量森林资源数据面临着日益显著的“信息爆炸但知识匮乏”问题。为了充分利用多元化海量化的森林资源数据,发现其中隐含的信息知识,为立地研究提供新的方法、理论和参考依据。研究以森林资源小班调查数据、多光谱遥感数据、数字高程模型数据(DEM)为基础,采用空间数据挖掘技术,提取多元化的林业信息,以GIS为平台,实现了多元林业信息的融合和可视化表达；采用空间分析、决策树分类、神经网络预测、空间离散场相关分析等一系列数据挖掘理论和方法,对森林立地的环境因子、生产力知识、因子相关性知识进行提取研究和分析。具体研究内容如下：
(1)立地空间数据挖掘
研究基于内蒙古旺业甸林场的DEM数据和多光谱遥感影像,使用聚类、聚合、信息复合、追踪和窗口、光谱波段组合等空间数据挖掘技术进行森林立地微观地形因子、宏观地形因子以及生物因子进行提取和分析,得到海拔、坡度、坡向、坡度变率、坡向变率、地表粗糙度、地面起伏度、坡形、DVI、RVI、NDVI、TSAVI,12项立地环境因子指标,并实现成果因子统一平台的可视化表达。研究成果丰富了立地环境因子的考量范围,为低成本大面积的森林立地条件研究提供有效的方法途径,有助于更进一步的立地知识数据挖掘研究。
(2)立地宜林性能预测研究
基于上述提取的微观地形因子、宏观地形因子、生物因子结合森林资源小班调查数据构成的多元林业信息,本文使用决策树技术和人工神经网络技术对多项立地因子与立地宜林性能间的关系进行了分析,根据多元林业信息的结构特点,研究采用Boosting、交叉验证、Group symbolics以及局部修剪的机制改进了以往利用C5.0决策树,通过引入敏感度分析的机制并采用共轭梯度法改进了BP神经网络模型,以落叶松为例建立了立地宜林性能预测模型,形成8套不同的方案,对比研究了不同方案的预测结果和模型性能,并对结果进行可视化表达。研究结果表明,本文提出的两种改进算法最为适于立地宜林性能的预测,具有理想的预测性能：研究还发现通过空间数据挖掘技术提取的多项环境因子扩充了立地因子的信息量,提高了立地宜林性能的预测精度,并且具有大范围多时相预测的潜力,可为森林立地生产潜力评价提供有效依据。
(3)立地空间离散场相关知识提取
研究选取森林资源小班调查数据中10项典型的离散数据结合小班空间位置属性构成森林立地空间离散场,采用信息熵的理论方法,通过计算局部空间内离散场的信息量以及局部空间与整体的协调性,定量分析并提取多项离散型因子与立地质量等级间的相关指数。结果表明,在10项因子中不同的立地类型和小班内的优势树种与立地质量相关程度最高,森林起源则与立地质量等级表现出相互独立的关系。研究克服了以往使用统计学原理以及灰色系统理论均无法计算立地空间离散场相关性的缺陷,实现了对立地因子中的离散型属性间关系的定量计算和表达。
As the external environment of forest growth, Site directly affects forest productivity and health status. With the development of forest resources survey and data acquisition technology, forest resource data already has a diversified, sea quantization and information characteristics. More abundant forest resources data implies a large number practical information and knowledge of the forest site. But for a long time, a large number site research (site productivity, site factors) still use traditional forest resources survey data, a wide range of forest resource data have not been integrated and fully utilized, and research tools rather monotonous, lack comparison research of variety of methods. This makes the massive forest resources data facing an increasingly significant problem of information explosion but lack the knowledge. In order to take full use of the diversified sea quantified forest resource data, found implied information and knowledge, to provide new method, theory and reference for site research. Use forest resources survey data, multi-spectral remote sensing data, digital elevation model data (DEM), based spatial data mining technology to extract a wide range of forestry information, use GIS as platform achieved multi-forestry information fusion and visualization. Using spatial analysis, decision tree classification, neural network prediction, space discrete field analysis of a series of data mining theory and methods, extracted and analyzed environmental factors on forest site productivity knowledge, factors related knowledge. The specific contents are as follows:
(1) Site spatial data mining
The study based on DEM data and multi-spectral remote sensing image of the Neimengu Wangyedian forest farm. Using Clustering, aggregation, information composite, tracking and window spectral bands combination of spatial data mining technology. For forest site micro-topographic factors, macro topographic factors and biologicalfactor extracted and analyzed to obtain elevation, slope, aspect, slope variability, aspect variability, surface roughness, surface waviness, sloping, DVI, RVI, NDVI, TSAVI12site environmental factors. The research results enrich the site types of environmental factors, provide effective approaches for low-cost large area of forest site data mining researc.
(2) Site barren performance prediction
Based on the Micro-landform factor, macro-terrain factor and bio-factor extracted in the above part, binding the information of forest resources survey data constitutes multivariate forestry information. This article uses decision tree technology and artificial neural network technology analysis the relationship between the number of site factors and site barren performance. According to the structural characteristics of the multivariateforestry information, research using Boosting, Cross-validation, Group Symbolics, as well as local pruning mechanism improved C5.0decision tree, use the mechanism of the sensitivity analysis improved BP neural network model. Established the site barren performance prediction model, form8different experimental programs. The results show that the proposed two improved algorithms most suitable site barren performance prediction, the prediction performance is ideal. The study also found that number of environmental factors extracted by spatial data mining technology can expand the amount of site factors information, improve the prediction accuracy of the site barren performance, and provide potential for wide range of multi-temporal prediction.
(3) Correlation knowledge extraction of site discrete space field
Select10typical discrete data in forest resources sublot survey data combined with small class spatial location attributes formed forest site space discrete field. Use information entropy theory, by icalculating the local space and local space and the overall coordination of quantitative analysis and extracted a number of discrete factors and site quality rating index. The results showed that different site types and dominant tree species the highest degree of correlation between site quality in10factors, and origin of forest showed independent relationship. Research overcomed inadequate that conventional statistical principles and gray system theory can not calculated site space discrete field, Achieved calculation and expression of site discrete factors.
(1)立地空间数据挖掘
研究基于内蒙古旺业甸林场的DEM数据和多光谱遥感影像,使用聚类、聚合、信息复合、追踪和窗口、光谱波段组合等空间数据挖掘技术进行森林立地微观地形因子、宏观地形因子以及生物因子进行提取和分析,得到海拔、坡度、坡向、坡度变率、坡向变率、地表粗糙度、地面起伏度、坡形、DVI、RVI、NDVI、TSAVI,12项立地环境因子指标,并实现成果因子统一平台的可视化表达。研究成果丰富了立地环境因子的考量范围,为低成本大面积的森林立地条件研究提供有效的方法途径,有助于更进一步的立地知识数据挖掘研究。
(2)立地宜林性能预测研究
基于上述提取的微观地形因子、宏观地形因子、生物因子结合森林资源小班调查数据构成的多元林业信息,本文使用决策树技术和人工神经网络技术对多项立地因子与立地宜林性能间的关系进行了分析,根据多元林业信息的结构特点,研究采用Boosting、交叉验证、Group symbolics以及局部修剪的机制改进了以往利用C5.0决策树,通过引入敏感度分析的机制并采用共轭梯度法改进了BP神经网络模型,以落叶松为例建立了立地宜林性能预测模型,形成8套不同的方案,对比研究了不同方案的预测结果和模型性能,并对结果进行可视化表达。研究结果表明,本文提出的两种改进算法最为适于立地宜林性能的预测,具有理想的预测性能：研究还发现通过空间数据挖掘技术提取的多项环境因子扩充了立地因子的信息量,提高了立地宜林性能的预测精度,并且具有大范围多时相预测的潜力,可为森林立地生产潜力评价提供有效依据。
(3)立地空间离散场相关知识提取
研究选取森林资源小班调查数据中10项典型的离散数据结合小班空间位置属性构成森林立地空间离散场,采用信息熵的理论方法,通过计算局部空间内离散场的信息量以及局部空间与整体的协调性,定量分析并提取多项离散型因子与立地质量等级间的相关指数。结果表明,在10项因子中不同的立地类型和小班内的优势树种与立地质量相关程度最高,森林起源则与立地质量等级表现出相互独立的关系。研究克服了以往使用统计学原理以及灰色系统理论均无法计算立地空间离散场相关性的缺陷,实现了对立地因子中的离散型属性间关系的定量计算和表达。
As the external environment of forest growth, Site directly affects forest productivity and health status. With the development of forest resources survey and data acquisition technology, forest resource data already has a diversified, sea quantization and information characteristics. More abundant forest resources data implies a large number practical information and knowledge of the forest site. But for a long time, a large number site research (site productivity, site factors) still use traditional forest resources survey data, a wide range of forest resource data have not been integrated and fully utilized, and research tools rather monotonous, lack comparison research of variety of methods. This makes the massive forest resources data facing an increasingly significant problem of information explosion but lack the knowledge. In order to take full use of the diversified sea quantified forest resource data, found implied information and knowledge, to provide new method, theory and reference for site research. Use forest resources survey data, multi-spectral remote sensing data, digital elevation model data (DEM), based spatial data mining technology to extract a wide range of forestry information, use GIS as platform achieved multi-forestry information fusion and visualization. Using spatial analysis, decision tree classification, neural network prediction, space discrete field analysis of a series of data mining theory and methods, extracted and analyzed environmental factors on forest site productivity knowledge, factors related knowledge. The specific contents are as follows:
(1) Site spatial data mining
The study based on DEM data and multi-spectral remote sensing image of the Neimengu Wangyedian forest farm. Using Clustering, aggregation, information composite, tracking and window spectral bands combination of spatial data mining technology. For forest site micro-topographic factors, macro topographic factors and biologicalfactor extracted and analyzed to obtain elevation, slope, aspect, slope variability, aspect variability, surface roughness, surface waviness, sloping, DVI, RVI, NDVI, TSAVI12site environmental factors. The research results enrich the site types of environmental factors, provide effective approaches for low-cost large area of forest site data mining researc.
(2) Site barren performance prediction
Based on the Micro-landform factor, macro-terrain factor and bio-factor extracted in the above part, binding the information of forest resources survey data constitutes multivariate forestry information. This article uses decision tree technology and artificial neural network technology analysis the relationship between the number of site factors and site barren performance. According to the structural characteristics of the multivariateforestry information, research using Boosting, Cross-validation, Group Symbolics, as well as local pruning mechanism improved C5.0decision tree, use the mechanism of the sensitivity analysis improved BP neural network model. Established the site barren performance prediction model, form8different experimental programs. The results show that the proposed two improved algorithms most suitable site barren performance prediction, the prediction performance is ideal. The study also found that number of environmental factors extracted by spatial data mining technology can expand the amount of site factors information, improve the prediction accuracy of the site barren performance, and provide potential for wide range of multi-temporal prediction.
(3) Correlation knowledge extraction of site discrete space field
Select10typical discrete data in forest resources sublot survey data combined with small class spatial location attributes formed forest site space discrete field. Use information entropy theory, by icalculating the local space and local space and the overall coordination of quantitative analysis and extracted a number of discrete factors and site quality rating index. The results showed that different site types and dominant tree species the highest degree of correlation between site quality in10factors, and origin of forest showed independent relationship. Research overcomed inadequate that conventional statistical principles and gray system theory can not calculated site space discrete field, Achieved calculation and expression of site discrete factors.
引文
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