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Object-based image analysis for scaling properties of rangeland ecosystems: Linking field and image data for management decision making.

机译：基于对象的图像分析，用于牧场生态系统的缩放特性：链接田野和图像数据以进行管理决策。

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Management of semi-arid shrub-steppe ecosystems (i.e., rangelands) requires accurate information over large landscapes, and remote sensing is an attractive option for collecting such data. To successfully use remotely-sensed data in landscape-level rangeland management, questions as to the relevance of image data to landscape patterns and optimal scales of analysis must be addressed. Object-based image analysis (OBIA), which segments image pixels into homogeneous regions, or objects, has been suggested as a way to increase accuracy of remotely-sensed products, but little research has gone into how to determine sizes of image objects with regard to scaling of ecosystem properties. The purpose of my dissertation was to determine if OBIA could be used to generate observational scales to match ecological scales in rangelands and to explore the potential for OBIA to generate accurate and repeatable remote-sensing products for managers. The work presented here was conducted in southern Idaho's Snake River Plain region. By comparing OBIA segmentation of satellite imagery into successively coarser objects to pixel-based aggregation methods, I found that canonical correlations between field-collected and image data were similar at the finest scales, but higher for image segmentation as scale increased. I also detected scaling thresholds with image segmentation that were confirmed via semi-variograms of field data. This approach proved useful for evaluating the overall utility of an image to address an objective, and identifying scaling limits for analysis. I next used observations of percent bare-ground cover from 346 field sites to consider how hierarchies of image objects created through OBIA could be used to discover appropriate scales for analysis given a specific objective. Using a regression-based approach, I found that segmentation levels whose predictions of bare-ground cover had spatial dependence that most closely matched the spatial dependence of the field samples had the highest predicted-to-observed correlations. When combined with geostatistical predictors, these changes in spatial variance with scale led to robust predictions across a range of scales. Third, I demonstrated an application of OBIA with the technique of regression kriging (RK), a geostatistical interpolator, to make spatial predictions for three aspects of rangeland condition (percent cover of shrubs, bare ground, and cheatgrass [Bromus tectorum L.]). Comparing spatial predictions from generalized least-squares (GLS) regression to RK, I found that RK implemented with OBIA produced more accurate results than GLS regression alone for all three variables measured by cross-validated root mean-squared error. Finally, I considered why techniques like OBIA, and remote sensing in general, are not more widely used in routine rangeland management. Bolstering decision-making through (1) better information tools and data to support management and (2) adaptive management has been proffered as a means for making sound management decisions, but two recent lawsuits in southern Idaho suggest that neither of these solutions is likely to be effective at managing rangelands at scales commensurate with their threats unless there are changes to the underlying management paradigm governing how the public participates in the management process.
机译：对半干旱灌木草原生态系统（即牧场）的管理需要大面积景观的准确信息，而遥感是收集此类数据的一种有吸引力的选择。为了在景观级牧场管理中成功使用遥感数据，必须解决有关图像数据与景观格局的相关性和最佳分析尺度的问题。基于对象的图像分析（OBIA）将图像像素划分为均匀的区域或对象，已被建议作为提高遥感产品精度的一种方法，但是有关如何确定图像对象的尺寸方面的研究很少扩展生态系统特性。本文的目的是确定OBIA是否可用于生成观测尺度以匹配牧场的生态尺度，并探索OBIA为管理人员生成准确且可重复的遥感产品的潜力。这里介绍的工作是在爱达荷州南部的蛇河平原地区进行的。通过将卫星图像的OBIA分割成依次更粗糙的对象与基于像素的聚合方法进行比较，我发现野外采集的图像数据与图像数据之间的规范相关性在最大尺度上相似，但随着尺度的增加，图像分割的正相关性更高。我还检测了通过图像分割的缩放阈值，这些阈值已通过现场数据的半变异函数确认。事实证明，这种方法对于评估图像的总体实用性以解决某个目标以及确定缩放比例限制以进行分析很有用。接下来，我使用对346个现场站点的裸地覆盖率的观察结果，来考虑如何将通过OBIA创建的图像对象的层次结构用于发现给定特定目标进行分析的合适比例。使用基于回归的方法，我发现，其对裸露覆盖的预测具有与实地样本的空间相关性最接近的空间依赖性的分割级别具有最高的预测到观测的相关性。当与地统计学预测器结合使用时，这些空间变化随尺度的变化会导致在一系列尺度上进行可靠的预测。第三，我展示了OBIA与地统计插值器回归克里金（RK）技术的应用，可以对牧场状况的三个方面（灌木，裸露地面和白茅草的覆盖率[Bromus tectorum L.]）进行空间预测。 。将广义最小二乘（GLS）回归与RK的空间预测进行比较，我发现，使用OBIA实施的RK比通过交叉验证的均方根误差测量的所有三个变量产生的结果均比单独使用GLS回归产生的结果更准确。最后，我考虑了为什么像OBIA这样的技术以及一般的遥感技术在常规牧场管理中没有得到更广泛的应用。通过提供（1）更好的信息工具和数据来支持管理以及（2）自适应管理来增强决策能力，已成为制定合理的管理决策的一种手段，但是爱达荷州南部最近发生的两起诉讼表明，这些解决方案都不可能除非在管理公众如何参与管理过程的基本管理范式发生变化的情况下，否则才能有效地以与其威胁相称的规模来管理牧场。

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