Data-driven regionalization of forested and non-forested ecosystems in coastal British Columbia with LiDAR and RapidEye imagery
| Autor: | Shanley D. Thompson, Ian Giesbrecht, Sari C. Saunders, Gordon W. Frazer, Trisalyn A. Nelson |
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| Rok vydání: | 2016 |
| Předmět: |
0106 biological sciences
geography.geographical_feature_category Forest inventory 010504 meteorology & atmospheric sciences Geography Planning and Development Forestry Wetland Vegetation 15. Life on land 010603 evolutionary biology 01 natural sciences Geography Ecoregion Thematic map Lidar Tourism Leisure and Hospitality Management Satellite imagery Cartography 0105 earth and related environmental sciences General Environmental Science Riparian zone Remote sensing |
| Zdroj: | Applied Geography. 69:35-50 |
| ISSN: | 0143-6228 |
| DOI: | 10.1016/j.apgeog.2016.02.002 |
| Popis: | Traditionally, forest inventory and ecosystem mapping at local to regional scales rely on manual interpretation of aerial photographs, based on standardized, expert-driven classification schemes. These current approaches provide the information needed for forest ecosystem management but constrain the thematic and spatial resolution of mapping and are infrequently repeated. The goal of this research was to demonstrate the utility of an unsupervised, quantitative technique based on Light Detection And Ranging (LiDAR) data and multi-spectral satellite imagery for mapping local-scale ecosystems over a heterogeneous landscape of forested and non-forested ecosystems. We derived a range of metrics characterizing local terrain and vegetation from LiDAR and RapidEye imagery for Calvert and Hecate Islands, British Columbia. These metrics were used in a cluster analysis to classify and quantitatively characterize ecological units across the island. A total of 18 clusters were derived. The clusters were attributed with quantitative summary statistics from the remotely sensed data inputs and contextualized through comparison to ecological units delineated in a traditional expert-driven mapping method using aerial photographs. The 18 clusters describe ecosystems ranging from open shrublands to dense, productive forest and include a riparian zone and many wetter and wetland ecosystems. The clusters provide detailed, spatially-explicit information for characterizing the landscape as a mosaic of units defined by topography and vegetation structure. This study demonstrates that using various types of remotely sensed data in a quantitative classification can provide scientists and managers with multivariate information unique from that which results from traditional, expert-based ecosystem mapping methods. |
| Databáze: | OpenAIRE |
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