首页> 外文会议>22nd Annual Canadian Remote Sensing Symposium Aug 21-25, 2000, Victoria, British Columbia, Canada >Classification of Soil Moisture on Digitized False Color Aerial Photography for Artificial Scots Pine Regeneration Suitability Assessment
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Classification of Soil Moisture on Digitized False Color Aerial Photography for Artificial Scots Pine Regeneration Suitability Assessment

机译:数字化虚假航空摄影对人工苏格兰松树更新适宜性评估中土壤水分的分类

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Artificial regeneration of Scots pine in Finnish Lapland has been problematic on moist sites previously classified as Norway spruce - downy birch dominated Hyloconium-myrtillus -type. High soil water content may have attributed to the majority of diebacks. High resolution (0.8 m) false color aerial photography were scanned, classified and filtered in an attempt to provide methodology to process this affordable, readily available data source for practical site-specific forestry planning of site suitability for regeneration by Scots pine. Overall highest user's (up to 87%) and producer's (up to 92%) accuracy measures for the suitable and unsuitable Scots pine regeneration areas were obtained using the maximum likelihood classifier. The isodata clustering and k-means classifier provided almost identical results of 77% overall accuracy. Based on the results of this preliminary study, we conclude that image processing of digital false color aerial photography can be a reasonably reliable site-specific method for pine regeneration suitability mapping on site-prepared clear-cuts.
机译:芬兰拉普兰的苏格兰松树的人工更新在以前被归类为挪威云杉(霜降桦树为主的Hyconconium-myrtillus型)的湿地上一直存在问题。高土壤水分含量可能归因于大部分的枯萎病。对高分辨率(0.8 m)假彩色航空摄影进行了扫描,分类和过滤,以期为处理这种可负担得起的,易于获得的数据源提供方法,以进行针对特定地点的实用林业规划,以适应苏格兰松树的再生。使用最大似然分类器,针对合适和不合适的苏格兰松树再生区域,获得了总体最高的使用者(最高87%)和生产者的最高准确率(最高92%)。 isodata聚类和k均值分类器提供了几乎相同的结果,总体准确率为77%。根据此初步研究的结果,我们得出结论,数字假彩色航空摄影的图像处理可以是在现场准备的明确路段上进行松树再生适宜性制图的合理可靠的针对特定地点的方法。

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