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首页> 外文期刊>ISPRS International Journal of Geo-Information >Mapping Forest Characteristics at Fine Resolution across Large Landscapes of the Southeastern United States Using NAIP Imagery and FIA Field Plot Data
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Mapping Forest Characteristics at Fine Resolution across Large Landscapes of the Southeastern United States Using NAIP Imagery and FIA Field Plot Data

机译：使用NAIP影像和FIA田地图数据在美国东南部大景观上高分辨率绘制森林特征

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摘要

Accurate information is important for effective management of natural resources. In the field of forestry, field measurements of forest characteristics such as species composition, basal area, and stand density are used to inform and evaluate management activities. Quantifying these metrics accurately across large landscapes in a meaningful way is extremely important to facilitate informed decision-making. In this study, we present a remote sensing based methodology to estimate species composition, basal area and stand tree density for pine and hardwood tree species at the spatial resolution of a Forest Inventory Analysis (FIA) program plot (78 m by 70 m). Our methodology uses textural metrics derived at this spatial scale to relate plot summaries of forest characteristics to remotely sensed National Agricultural Imagery Program (NAIP) aerial imagery across broad extents. Our findings quantify strong relationships between NAIP imagery and FIA field data. On average, models of basal area and trees per acre accounted for 43% of the variation in the FIA data, while models identifying species composition had less than 15.2% error in predicted class probabilities. Moreover, these relationships can be used to spatially characterize the condition of forests at fine spatial resolutions across broad extents.
机译：准确的信息对于有效管理自然资源很重要。在林业领域，对森林特征（例如物种组成，基础面积和林分密度）的野外测量用于指导和评估管理活动。以有意义的方式在大范围内准确量化这些指标对于促进明智的决策至关重要。在这项研究中，我们提出了一种基于遥感的方法，可以在森林清单分析（FIA）程序图（78 m x 70 m）的空间分辨率下估算松树和硬木树种的物种组成，基础面积和林分密度。我们的方法使用在此空间尺度上得出的纹理度量，将森林特征的绘图摘要与广泛范围内的遥感国家农业影像计划（NAIP）航空影像相关联。我们的发现量化了NAIP图像和FIA现场数据之间的紧密关系。平均而言，每英亩的基础面积和树木模型占FIA数据变化的43％，而用于识别物种组成的模型的预测类别概率误差小于15.2％。而且，这些关系可以用来在大范围内以精细的空间分辨率对森林状况进行空间表征。

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