A new approach to vegetation sample selection, classification and mapping is described that accounts for rare and restricted vegetation communities. The new method (data-informed sampling and mapping: D-iSM) builds on traditional preferential sampling and was developed to guide conservation and land-use planning. It combines saturation coverage of vegetation point data with a preferential sampling design to produce locally accurate vegetation classifications and maps. Many existing techniques rely entirely or in part on random sampling, modelling against environmental variables, or on assumptions that photo-patterns detected through aerial photographic interpretation or physical landscape features can be attributed to a specific vegetation type. D-iSM uses ground data to inform both classification and mapping phases of a project. The approach is particularly suited to local- and regional-scale situations where disputes between conservation and development often lead to poor planning decisions, as well as in circumstances where highly restricted vegetation types occur within a wider mosaic of more common communities. Benefits of the D-iSM approach include more efficient and more representative floristic sampling, more realistic and repeatable classifications, increased user accuracy in vegetation mapping and increased ability to detect and map rare vegetation communities. Case studies are presented to illustrate the method in real-world classification and mapping projects.

中文翻译：

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以数据为依据的采样和制图：一种确保基于地块的分类对稀有和受限植被类型进行定位、分类和制图的方法

描述了一种新的植被样本选择、分类和制图方法，该方法解释了稀有和受限制的植被群落。新方法（基于数据的抽样和制图：D-iSM）建立在传统的优先抽样基础上，旨在指导保护和土地利用规划。它将植被点数据的饱和覆盖率与优先采样设计相结合，以生成局部准确的植被分类和地图。许多现有技术完全或部分依赖于随机采样、针对环境变量的建模，或基于通过航空摄影解释或物理景观特征检测到的照片模式可归因于特定植被类型的假设。D-iSM 使用地面数据为项目的分类和绘图阶段提供信息。这种方法特别适用于地方和区域尺度的情况，在这种情况下，保护和开发之间的争议往往导致规划决策不当，以及在更广泛的更常见社区镶嵌中出现高度受限的植被类型的情况。D-iSM 方法的好处包括更有效和更具代表性的植物区系采样、更现实和可重复的分类、增加植被绘图中的用户准确性以及增加检测和绘制稀有植被群落的能力。案例研究用于说明实际分类和映射项目中的方法。以及在更广泛的更常见社区中出现高度受限的植被类型的情况下。D-iSM 方法的好处包括更有效和更具代表性的植物区系采样、更现实和可重复的分类、增加植被绘图中的用户准确性以及增加检测和绘制稀有植被群落的能力。案例研究用于说明实际分类和映射项目中的方法。以及在更广泛的更常见社区中出现高度受限的植被类型的情况下。D-iSM 方法的好处包括更有效和更具代表性的植物区系采样、更现实和可重复的分类、增加植被绘图中的用户准确性以及增加检测和绘制稀有植被群落的能力。案例研究用于说明实际分类和映射项目中的方法。