Previous studies monitoring the spatial distribution of rocky desertification have used single indices, a comprehensive index, or image classification. However, these approaches could not distinguish between the degrees of rocky desertification as they did not consider various influencing factors and their interactions. To avoid the above shortcomings, this study used the feature space model and seven typical land surface parameters to establish two categories of rocky desertification model: (1) a point-to-point model; and (2) a point-to-line model. A novel model for the optimal monitoring of rocky desertification was then proposed, which could take the comprehensive impacts of the human-nature system on the process of rocky desertification. The results showed that: (1) the feature space models provided a novel approach to large-scale monitoring of rocky desertification; (2) the point-to-line model incorporating the rock bare index (RBI)-dryness index feature space showed the optimal applicability for monitoring of rocky desertification, with a precision of 93.4%; and (3) the RBI performed the best in indicating the process of rocky desertification, with an average precision of 88.5%. The results of this study can act as a reference within the investigation of the spatiotemporal evolution of rocky desertification for karst mountain areas.

中文翻译：

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基于LANDSAT_8 OLI典型地表参数特征空间模型的石漠化新型最优监测模型

以往监测石漠化空间分布的研究使用单一指数、综合指数或图像分类。然而，这些方法无法区分石漠化程度，因为它们没有考虑各种影响因素及其相互作用。为避免上述不足，本研究利用特征空间模型和7个典型地表参数建立两类石漠化模型：（1）点对点模型；(2) 点对线模型。然后提出了一种新的石漠化优化监测模型，该模型可以综合考虑人类-自然系统对石漠化过程的影响。结果表明：(1) 特征空间模型为大规模石漠化监测提供了一种新方法；(2)包含岩石裸露指数(RBI)-干燥指数特征空间的点线模型对石漠化监测具有最佳适用性，精度为93.4%；(3) RBI 在指示石漠化过程中表现最好，平均精度为 88.5%。本研究结果可为喀斯特山区石漠化时空演化研究提供参考。(3) RBI 在指示石漠化过程中表现最好，平均精度为 88.5%。本研究结果可为喀斯特山区石漠化时空演化研究提供参考。(3) RBI 在指示石漠化过程中表现最好，平均精度为 88.5%。本研究结果可为喀斯特山区石漠化时空演化研究提供参考。