Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables
Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables
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DOI：
10.1016/s0168-1699(99)00046-0
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发表时间：
1999-12-01
影响因子：
8.3
通讯作者：
Dean, DJ
中科院分区：
文献类型：
--
作者：
Blackard, JA;Dean, DJ
这项研究比较了从地图变量预测森林覆盖类型的两种替代技术。这项研究评估了位于科罗拉多州北部前方山脉的罗斯福国家森林的四个荒野地区。覆盖类型数据来自美国林业局调查信息，而用于预测覆盖类型的地图变量包括高程、坡向和其他信息，这些信息来自地理信息系统(GIS)处理的标准数字空间数据。比较结果表明，前馈人工神经网络模型比基于高斯判别分析的传统统计模型更准确地预测森林覆盖类型。(C)1999 Elsevier Science B.V.保留所有权利。
This study compared two alternative techniques for predicting forest cover types from cartographic variables. The study evaluated four wilderness areas in the Roosevelt National Forest, located in the Front Range of northern Colorado. Cover type data came from US Forest Service inventory information, while the cartographic variables used to predict cover type consisted of elevation, aspect, and other information derived from standard digital spatial data processed in a geographic information system (GIS). The results of the comparison indicated that a feedforward artificial neural network model more accurately predicted forest cover type than did a traditional statistical model based on Gaussian discriminant analysis. (C) 1999 Elsevier Science B.V. All rights reserved.