Classifying wetland-related land cover types and habitats using fine-scale lidar metrics derived from country-wide Airborne Laser Scanning

编号 030025805

推送时间 20200928

研究领域 森林经理 

年份 2020 

类型 期刊 

语种 英语

标题 Classifying wetland-related land cover types and habitats using fine-scale lidar metrics derived from country-wide Airborne Laser Scanning

来源期刊 REMOTE SENSING IN ECOLOGY AND CONSERVATION

期 第258期

发表时间 20200513

关键词 Habitat classification;  Phragmites australis;  reedbed;  structural heterogeneity;  vegetation complexity;  wetland conservation; 

摘要 Mapping 3D vegetation structure in wetlands is important for conservation and monitoring. Openly accessible country-wide Airborne Laser Scanning (ALS) data-using light detection and ranging (lidar) technology-are increasingly becoming available and allow us to quantify 3D vegetation structures at fine resolution and across broad spatial extents. Here, we develop a new, open-source workflow for classifying wetland-related land cover types and habitats using fine-scale lidar metrics derived from country-wide ALS data. We developed a case study in the Netherlands with a workflow consisting of four routines: (1) pre-processing of ALS data, (2) calculation of lidar metrics (i.e. 31 features representing cover, 3D shape, vertical variability, horizontal variability and height of vegetation as well as microtopography), (3) assessing feature importance of lidar metrics for classifying wetland habitats, and (4) applying a Random Forest algorithm for mapping and prediction. We used an expert-based vegetation map for annotation and generated 100, 500 and 1000 annotation points for each class. Using a three-level hierarchical approach, we differentiated at level 1 planar surfaces (e.g. roads and agricultural fields) from wetland vegetation with 82% mean overall accuracy, using predominantly height and horizontal variability metrics. At level 2, we classified wetland vegetation into four land cover types (forest, grassland, reedbeds, shrubs) with 71% mean overall accuracy, using lidar metrics related to vegetation height and horizontal and vertical variability. At level 3, we differentiated two types of land reed as well as water reed with 78% mean overall accuracy, using predominantly vertical variability metrics. Our results demonstrate that lidar metrics (related to vegetation height, cover, vertical and horizontal variability) derived from country-wide ALS data can differentiate land cover types and habitats within wetlands at high resolution. Given appropriate annotation data, our workflow can be up-scaled to a country-wide extent to allow the comprehensive mapping and monitoring of wetlands at national scales.

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