Relascope sum of Sitka spruce Picea sitchensis in Fusa and Tysnes

<p>Invasive species can be considered a threat to biodiversity, and remote sensing has been proposed as a tool for detection and monitoring of invasive species. In this study, we test the ability to discriminate between two tree species of the same genera, using data from Landsat 8 satellite imagery, aerial images, and airborne laser scanning. Ground observations from forest stands dominated by either Norway spruce (Picea abies) or Sitka spruce (Picea sitchensis) were coupled with variables derived from each of the three sets of remote sensing data. Random forest, support vector machine, and logistic regression classification models were fit to the data, and the classification accuracy tested by performing a cross-validation. Classification accuracies were compared for different combinations of remote sensing data and classification methods. The overall classification accuracy varied from 0.53 to 0.79, with the highest accuracy obtained using logistic regression with a combination of data derived from Landsat imagery and aerial images. The corresponding kappa value was 0.58. The contribution to the classification accuracy from using airborne data in addition to Landsat imagery was not substantial in this study. The classification accuracy varied between models using data from individual Landsat images.</p>
<p>The study area is within the Fusa and Tysnes municipalities on the western coast of Norway (60°2′N, 5°46′E, 0–500 m above sea level, Figure 1). The forest is naturally dominated by Scots pine and deciduous species, mainly birch (Betula pubescens). From the 1940s and throughout the second part of the twentieth century, regeneration using non-native tree species—such as Sitka spruce—was common in this region on the west coast of Norway. Note that Norway spruce is also considered non-native in parts of this region. The productive forest area is about 260 km2, and the species composition is approximately 13% spruce, 66% pine, and 20% deciduous trees. Three sets of field observations were utilized in the present study, with observations from a total of 240 individual locations. All locations were situated in a spruce-dominated forest, and the proportions of Sitka spruce and Norway spruce were recorded for all locations. A total of 113 locations were dominated by Sitka spruce, and 127 were dominated by Norway spruce. Two of the sets were initially collected as a part of the data acquisition in other research and forest inventory projects.</p>
<p>Field measurements with the main purpose of increasing the number of observations from locations dominated by Sitka spruce was carried out during the summer of 2015. From an initial set of all forest stands in the study area dominated by Sitka spruce, 30 stands were subjectively chosen for measurements. With an initial goal of having the observations evenly spread out in the study area, the selection was ultimately guided by accessibility from, e.g., forest roads. The selection of the 30 stands were carried out prior to visiting the stands in the field, with the exception of a few occasions in which a nearby stand was measured instead due to severe storm felling in the originally chosen stand. Within the selected stands, three locations were subjectively chosen, guided by these criteria: the locations are evenly spread out in the stand and are preferably not close to stand borders. At each of the three locations, the proportions of the basal area of Sitka spruce versus other species were recorded using a relascope.</p>

<p>入侵物种被视为对生物多样性的威胁，而遥感技术已被提议作为入侵物种的检测与监测工具。在本研究中，我们利用Landsat 8卫星影像、航空影像和机载激光扫描数据，测试了区分同属两种树木物种的能力。通过将森林群落中主要由挪威云杉（Picea abies）或Sitka云杉（Picea sitchensis）构成的地面观测数据与来自三组遥感数据集的变量相结合，对随机森林、支持向量机和逻辑回归分类模型进行了拟合，并通过交叉验证测试了分类精度。对不同遥感数据组合与分类方法的分类精度进行了比较。总体分类精度介于0.53至0.79之间，使用Landsat影像和航空影像数据组合进行逻辑回归分析时，达到了最高的分类精度，相应的Kappa值为0.58。在本研究中，除Landsat影像外，使用机载数据进行分类对分类精度的贡献并不显著。使用单个Landsat影像数据的模型间的分类精度存在差异。</p><p>研究区域位于挪威西部海岸的Fusa和Tysnes市政区域（北纬60°2′，东经5°46′，海拔0-500米，见图1）。该地区的森林自然以苏格兰松和落叶树种为主，其中以桦树（Betula pubescens）为主。自20世纪40年代以来，直至20世纪后半叶，在该挪威西部海岸地区，使用非本地树种（如Sitka云杉）进行更新的做法十分普遍。需要注意的是，在该区域的部分地区，挪威云杉也被视为非本地物种。生产性森林面积约为260平方公里，物种组成约为13%的云杉、66%的松树和20%的落叶树。本研究利用了三组现场观测数据，共计240个观测点。所有观测点均位于以云杉为主的森林中，并记录了所有地点Sitka云杉和挪威云杉的比例。共计113个地点以Sitka云杉为主，127个地点以挪威云杉为主。其中两组数据最初是作为其他研究项目和森林资源清查的一部分进行收集的。</p><p>2015年夏季进行了野外测量，主要目的是增加Sitka云杉主导的地点的观测数量。从研究区域所有以Sitka云杉为主的森林群落中，主观选择了30个林分进行测量。最初的目的是使观测在研究区域内均匀分布，但最终选择受到从森林道路等地点的可进入性的指导。在实地访问林分之前，就选择了这30个林分，除了在原选定的林分因严重暴风倒木而无法测量的情况下，在附近选择了一些林分进行测量。在选定的林分中，根据以下标准主观选择了三个地点：这些地点在林分中分布均匀，且最好不靠近林分边界。在每个三个地点，使用relascope记录了Sitka云杉与其他物种基面积的比例。</p>