![]() ![]() In particular, the sand dunes in Korea are too small to apply conventional methods. Notwithstanding, spatial and temporal resolutions of satellite-based data improvements, high costs per scene, and unprofitable revisit times remain significant obstacles for many remote sensing applications. In particular, several studies indicate that space-borne sensors can be used to obtain spatially extensive information from landscapes on a global scale (Hu et al., 2007 Lamonaca et al., 2008 Pellikka et al., 2009 Propastin and Panferov, 2013). Remote sensing information offers a unique way to obtain large-scale mapping of FVC. In addition, these methods are unsuitable for real-time monitoring (Anderson and Gaston, 2013). Conventional methods (ground-based methods) are usually time-consuming and impractical for large areas. In the past, FVC was estimated through ground-based methods. Representatively, the FVC was applied in soil erosion models (RUSLE (Revised Universal Soil Loss Equation), SEMMA (Soil Erosion Model for Mountain Areas), and GeoWEPP (Geo-spatial interface for WEPP)) and atmospheric models (NOAH Land-Surface Model and NAM (North American Mesoscale) Eta model) (Choi et al., 2014 Gutman and Ignatov, 1998). Moreover, FVC is extensively applied in fields such as agriculture, forestry, resource and environmental management, disaster risk monitoring, and drought monitoring (Gitelson et al., 2002 Purevdorj et al., 1998)Īccurate estimation of the FVC is required for research on land-surface processes, climate change, and numerical weather prediction (Zeng et al., 2000). Existing vegetation indices, such as NDVI (Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index) and AFRI (Aerosol Free vegetation Index), are useful to indicate the activity of the condition of vegetation, but they do not directly show the vegetation cover ratio of a specific area.įVC is an important parameter to measure the size of the vegetated portion of the land surface additionally, it is an important index for researching the aerosphere, hydrosphere, and biosphere. This can be easily achieved by using a UAV, which can provide vegetation data to improve the estimation of FVC.įVC (Fractional Vegetation Cover) is generally defined as the ratio of the vertical projection area of vegetation to target area. Our results showed that the GI mode ensures the quality of the FVC if the NDVI maintained at a uniform level. In this regard, the availability of the GI model which uses only the values of NDVI is higher than that of RF whose accuracy varies according to the results of classification. ![]() The method of adding NDVI shows a relatively higher accuracy compared to that of adding only RGB, and in particular, the GI model shows a lower RMSE (Root Mean Square Error) with 0.182 than RF. Finally, an FVC map-based RF were generated by using pixel calculation and FVC map-based GI (Gutman and Ignatov) model were indirectly made by fixed parameters. Then, the result map was reclassified into vegetation and non-vegetation. ![]() First, two types of result classifications were obtained based on RF (Random Forest) using RGB images and NDVI (Normalized Difference Vegetation Index) with RGB images. Hence, we propose that the FVC map is generated by using multi-spectral imaging. In this study, the process of estimating FVC (Fractional Vegetation Cover), based on multi-spectral UAV, to overcome the limitations of conventional methods is suggested. Since the use of UAV (Unmanned Aerial Vehicle) is convenient for the acquisition of data on broad or inaccessible regions, it is nowadays used to establish spatial information for various fields, such as the environment, ecosystem, forest, or for military purposes.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |