Data and code for: Identifying fine-scale habitat preferences of threatened butterflies using airborne laser scanning

Aim: Light Detection And Ranging (LiDAR) is a promising remote sensing technique for ecological applications because it can quantify vegetation structure at high resolution over broad spatial extents. Using country-wide airborne laser scanning (ALS) data, we test to what extent fine-scale LiDAR metrics capturing low vegetation, medium-to-high vegetation and landscape-scale habitat structures can explain the habitat preferences of threatened butterflies at a national extent. Location: The Netherlands. Methods: We applied a machine learning (random forest) algorithm to build species distribution models (SDMs) for grassland and woodland butterflies in wet and dry habitats using various LiDAR metrics and butterfly presence-absence data collected by a national butterfly monitoring scheme. The LiDAR metrics captured vertical vegetation complexity (e.g. height and vegetation density of different strata) and horizontal heterogeneity (e.g. vegetation roughness, microtopography, vegetation ...研究目标：激光雷达（LiDAR）是一种极具应用前景的生态遥感技术，可在大空间范围内以高分辨率量化植被结构。本研究利用全国范围机载激光扫描（ALS）数据，旨在探究表征低矮植被、中高层植被以及景观尺度生境结构的精细尺度激光雷达指标，在国家尺度下能够在多大程度上解释濒危蝴蝶的生境偏好。 研究区域：荷兰 研究方法：本研究采用机器学习（随机森林）算法，结合多种激光雷达指标与全国蝴蝶监测计划采集的蝴蝶存在-缺失数据，构建针对干湿生境中草原与林栖蝴蝶的物种分布模型（SDMs）。激光雷达指标涵盖植被垂直复杂度（如不同植被层的高度与植被密度）与水平异质性（如植被粗糙度、微地形、植被……）