Integration of WorldView-2 and airborne LiDAR data for tree species level carbon stock mapping in Kayar Khola watershed, Nepal
| Autor: | Hammad Gilani, Chitra Bahadur Baniya, Faisal Mueen Qamer, Bhaskar Singh Karky, M.C. Bronsveld, Xu Aigong, M. S. R. Murthy, Thakur Bhattarai, Yogendra K. Karna, Yousif A. Hussin |
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| Přispěvatelé: | Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation, UT-I-ITC-FORAGES |
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Canopy
Global and Planetary Change Shorea robusta Tree canopy Watershed biology Forestry Management Monitoring Policy and Law biology.organism_classification n/a OA procedure ITC-HYBRID Tree (data structure) Geography Lidar ITC-ISI-JOURNAL-ARTICLE Mallotus Computers in Earth Sciences Schima wallichii Earth-Surface Processes Remote sensing |
| Zdroj: | International Journal of Applied Earth Observation and Geoinformation (JAG), 38, 280-291. Elsevier |
| ISSN: | 1569-8432 |
| Popis: | Integration of WorldView-2 satellite image with small footprint airborne LiDAR data for estimation of tree carbon at species level has been investigated in tropical forests of Nepal. This research aims to quantify and map carbon stock for dominant tree species in Chitwan district of central Nepal. Object based image analysis and supervised nearest neighbor classification methods were deployed for tree canopy retrieval and species level classification respectively. Initially, six dominant tree species ( Shorea robusta, Schima wallichii, Lagerstroemia parviflora, Terminalia tomentosa, Mallotus philippinensis and Semecarpus anacardium ) were able to be identified and mapped through image classification. The result showed a 76% accuracy of segmentation and 1970.99 as best average separability. Tree canopy height model (CHM) was extracted based on LiDAR’s first and last return from an entire study area. On average, a significant correlation coefficient ( r ) between canopy projection area (CPA) and carbon; height and carbon; and CPA and height were obtained as 0.73, 0.76 and 0.63, respectively for correctly detected trees. Carbon stock model validation results showed regression models being able to explain up to 94%, 78%, 76%, 84% and 78% of variations in carbon estimation for the following tree species: S. robusta, L. parviflora, T. tomentosa, S. wallichii and others (combination of rest tree species). |
| Databáze: | OpenAIRE |
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