A two-level nested model for extracting positive and negative terrains combining morphology and visualization indicators

摘要: Abstract Topography affects the resource distribution and limits physical living production conditions in mountainous regions. Depicting specific positive negative terrains karst region would support for understanding ecosystem process land resources managing ecologically fragile areas. However, terrains, topographic types region, have been difficult to extract by automatic machine quantitative method. The novelty of this study is developing a two-level nested (TLN) model quantify features through utilizing visualization indicators, openness sky-view factor (SVF), combing an insight morphological analysis. algorithms two indicators detected surface changes 3D sphere realized self-adaptive complicated changes. There were three main modules included model: optimum radius analysis selected threshold judgement edges postprocessing module. chained designed second module reached potential critical thresholds detection optimizing expression boundaries, which integrated analyzing variation trend difference, SVF value change. Through TLN framework, accuracy stability boundaries extraction further strengthened. was tested seven counties where terrain morphologies quite different. extracted our compared with visual interpretation results, assessment validated that overlapping areas test reference results more than 90% boundaries’ distance deviations less one grid. Positive predominant all proportions varied from 75.33 85.23%. Different percentages formations indicated different phases landform evolution process. largest proportion cultivated appeared 0–150 m buffer ring mutations detected, implied increased ecological risk region. Therefore, it urgent resolve increasingly serious environmental issues while ensure agricultural economic development. patterns could provide theoretical rational use planning future.