Year 2017, Volume 2, Issue 2
| Year : | 2017 |
| Volume : | 2 |
| Issue : | 2 |
| Authors : | Ricardo MARTÍNEZ, César VICENTE, Nuria SÁNCHEZ-LÓPEZ, Javier MONTALVO |
| Title : | AUTOMATED MAPPING WITH LiDAR AND SPECTRAL CHARACTERIZATION IN MEDITERRANEAN FOREST AGROECOSYSTEMS |
| Abstract : | Mapping with LiDAR data is not a standardized practice, though LiDAR databases are increasing in all countries in Europe. We develop and test a simple method for automated land-cover mapping. The study area was a farm located at a natural park of southern Spain. It comprises 502 ha covered by Mediterranean forest agroecosystems, like dehesa (a very open woodland of scattered evergreen trees used by grazing animals), woodland and scrubland, and transitions among them, composing a heterogeneous landscape. This heterogeneity is caused by variations in holm and cork oak tree density and a sclerophyllous shrub cover, i.e., 3D structure of woody vegetation. Using aerial photographs digitization, Landsat image classification, and image segmentation of tree crowns, land-cover maps were generated. Besides, other maps were produced from LiDAR-derived canopy cover and height of tree vegetation and shrub stratum. These 3D variables allowed to a wall-to-wall characterization of woody vegetation land-cover classes in the study area, that was completed with a NDVI assessment. The results show that automated mapping with LiDAR is reliable and accurate enough in comparison with other mapping techniques. It outperforms them because its higher spatial resolution, and can be combined with other remote sensing methods to provides an improved understanding of the forest landscape. |
| For citation : | Martínez, R., Vicente, C., Sánchez-López, N., Montalvo, J. (2017). Automated mapping with LiDAR and spectral characterization in Mediterranean forest agroecosystems. AGROFOR International Journal, Volume 2. Issue No. 2. pp. 162-170. DOI: 10.7251/AGRENG1702162M |
| Keywords : | canopy, Dehesa, forest structure, GIS, LiDAR, vegetation structure |
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ISSN 2490-3434 (Printed)
ISSN 2490-3442 (Online)
ISSN 2490-3442 (Online)
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