Browse > Article
http://dx.doi.org/10.7848/ksgpc.2016.34.4.431

Estimation of Fractional Vegetation Cover in Sand Dunes Using Multi-spectral Images from Fixed-wing UAV  

Choi, Seok Keun (Dept. of Civil Engineering, Chungbuk National University)
Lee, Soung Ki (Dept. of Civil Engineering, Chungbuk National University)
Jung, Sung Heuk (Dept. of Civil Engineering, Chungbuk National University)
Choi, Jae Wan (Dept. of Civil Engineering, Chungbuk National University)
Choi, Do Yoen (Research institute, Terrapix)
Chun, Sook Jin (Dadohaehaesang Marine National Park Western Office)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.34, no.4, 2016 , pp. 431-441 More about this Journal
Abstract
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. 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. Hence, we propose that the FVC map is generated by using multi-spectral imaging. 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. Then, the result map was reclassified into vegetation and non-vegetation. 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. 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. 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. Our results showed that the GI mode ensures the quality of the FVC if the NDVI maintained at a uniform level. This can be easily achieved by using a UAV, which can provide vegetation data to improve the estimation of FVC.
Keywords
FVC; Fixed-wing UAV; Multi-spectral Image; Sand Dune;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Gessner, U., Klein, D., Conrad, C., Schmidt, M., and Dech, S. (2009), Towards an automated estimation of vegetation cover fractions on multiple scales: Examples of Eastern and Southern Africa, In Proceedings of the 33rd International Symposium on Remote Sensing of Environment, International Center for Remote Sensing of Environment, 4-8 May, Stresa, Italy, pp. 1–4
2 Gitelson, A. A., Kaufman, Y.J., Stark, R., and Rundquist, D. (2002), Novel algorithms for remote estimation of vegetation fraction, Remote sensing of Environment, Vol. 80, No. 1, pp. 76–87.   DOI
3 Guijarro, M., Pajares, G., Riomoros, I., Herrera, P. J., Burgos-Artizzu, X. P., and Ribeiro, A. (2011), Automatic segmentation of relevant textures in agricultural images, Computers and Electronics in Agriculture, Vol. 75, No. 1, pp. 75-83.   DOI
4 Guillen-Climent, M. L., Zarco-Tejada, P. J., Berni, J. A., North, P. R. J., and Villalobos, F. J. (2012), Mapping radiation interception in row-structured orchards using 3D simulation and high-resolution Airborne imagery acquired from a UAV, Precision Agriculture, Vol. 13, No. 4, pp. 473-500.   DOI
5 Gutman, G. and Ignatov, A. (1998), The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models, International Journal of Remote Sensing, Vol. 19, No. 8, pp. 1533-1543.   DOI
6 Herwitz, S. R., Johnson, L. F., Dunagan, S. E., Higgins, R. G., Sullivan, D. V., Zheng, J., and Slye, R. E. (2004), Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support, Computers and Electronics in Agriculture, Vol. 44, No. 1, pp. 49-61.   DOI
7 Hu, Z. Q., He, F. Q., Yin, J. Z., Xia, L. U., Tang, S. L., Wang, L. L., and Li, X. J. (2007), Estimation of fractional vegetation cover based on digital camera survey data and a remote sensing model, Journal of China University of Mining and Technology, Vol. 17, No. 1, pp. 116-120.   DOI
8 Jannoura, R., Brinkmann, K., Uteau, D., Bruns, C., and Joergensen, R. G. (2015), Monitoring of crop biomass using true colour aerial photographs taken from a remote controlled hexacopter, Biosystems Engineering, Vol. 129, pp. 341-351.   DOI
9 Lamonaca, A., Corona, P., and Barbati, A. (2008), Exploring forest structural complexity by multi-scale segmentation of VHR imagery, Remote Sensing of Environment, Vol. 112, No. 6, pp. 2839-2849.   DOI
10 Laliberte, A. S., Rango, A., and Fredrickson, E. L. (2006), Separating green and senescent vegetation in very high resolution photography using an intensity-hue-saturation transformation and object based classification, In Proceedings of the American Society for Photogrammetry and Remote Sensing Annual Conference, ASPRS, 1-5 May, Reno, Nevada, pp. 1-5.
11 Larivière, B. and Van den Poel, D. (2005), Predicting customer retention and profitability by using random forests and regression forests techniques, Expert Systems with Applications, Vo. 29, No. 2, pp. 472-484.   DOI
12 Lunetta, R. S. and Lyon, J. G. (2004), Remote Sensing and GIS Accuracy Assessment, CRC press, Boca Raton, Florida.
13 Meyer, G. E. and Neto, J. C. (2008), Verification of color vegetation indices for automated crop imaging applications, Computers and Electronics in Agriculture, Vo. 63, No. 2, pp. 282-293.   DOI
14 Pal, M. (2005), Random forest classifier for remote sensing classification, International Journal of Remote Sensing, Vol. 26, No. 1, pp. 217-222.   DOI
15 Propastin, P. and Panferov, O. (2013), Retrieval of remotely sensed LAI using Landsat ETM+ data and ground measurements of solar radiation and vegetation structure: Implication of leaf inclination angle, International Journal of Applied Earth Observation and Geoinformation, Vol. 25, pp. 38-46.   DOI
16 Pellikka, P. K., Lötjönen, M., Siljander, M., and Lens, L. (2009), Airborne remote sensing of spatiotemporal change (1955–2004) in indigenous and exotic forest cover in the Taita Hills, Kenya, International Journal of Applied Earth Observation and Geoinformation, Vol. 11, No. 4, pp. 221-232.   DOI
17 Purevdorj, T. S., Tateishi, R., Ishiyama, T., and Honda, Y. (1998), Relationships between percent vegetation cover and vegetation indices, International Journal of Remote Sensing, Vol. 19, No. 18, pp. 3519–3535.   DOI
18 Price, J. C. (1993), Estimating leaf area index from satellite data, IEEE Transactions on Geoscience and Remote Sensing, Vol. 31, No. 3, pp. 727-734.   DOI
19 Romeo, J., Pajares, G., Montalvo, M., Guerrero, J. M., Guijarro, M., and De La Cruz, J. M. (2013), A new expert system for greenness identification in agricultural images, Expert Systems with Applications, Vol. 40, No. 6, pp. 2275-2286.   DOI
20 Rogan, J., Franklin, J., and Roberts, D. A. (2002), A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery, Remote Sensing of Environment, Vol. 80, No. 1, pp. 143-156.   DOI
21 Schwender, H., Zucknick, M., Ickstadt, K., and Bolt, H. M. (2004), A pilot study on the application of statistical classification procedures to molecular epidemiological data, Toxicology Letters, Vol. 151, No. 1, pp. 291-299.   DOI
22 Zeng, X., Dickinson, R. E., Walker, A., Shaikh, M., DeFries, R. S., and Qi, J. (2000), Derivation and evaluation of global 1-km fractional vegetation cover data for land modeling, Journal of Applied Meteorology, Vol. 39, No. 6, pp. 826-839.   DOI
23 Torres-Sánchez, J., López-Granados, F., De Castro, A. I., and Peña-Barragán, J. M. (2013), Configuration and specifications of an unmanned aerial vehicle (UAV) for early site specific weed managemen, PLoS One, Vol. 8, No. 3, e58210.   DOI
24 Xiang, H. and Tian, L. (2011), Method for automatic georeferencing aerial remote sensing (RS) images from an unmanned aerial vehicle (UAV) platform, Biosystems Engineering, Vol. 108, No. 2, pp. 104-113.   DOI
25 Xiao, J. and Moody, A. (2005), A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, Remote Sensing of Environment, Vol. 98, No. 2, pp. 237-250.   DOI
26 Delegido, J., Verrelst, J., Meza, C. M., Rivera, J. P., Alonso, L., and Moreno, J. (2013), A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems, European Journal of Agronomy, Vol. 46, pp. 42-52.   DOI
27 Anderson, K. and Gaston, K. J. (2013), Lightweight unmanned aerial vehicles will revolutionize spatial ecology, Frontiers in Ecology and the Environment, Vol. 11, No. 3, pp. 138-146.   DOI
28 Bendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J., and Bareth, G. (2015), Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley, International Journal of Applied Earth Observation and Geoinformation, Vol. 39, pp. 79-87.   DOI
29 Bryson, M., Reid, A., Ramos, F., and Sukkarieh, S. (2010), Airborne vision-based mapping and classification of large farmland environments, Journal of Field Robotics, Vol. 27, No. 5, pp. 632-655.   DOI
30 Choi, S., Lee, S., and Wang, B. (2014), Analysis of vegetation cover fraction on landsat OLI using NDVI, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 32, No. 1, pp. 9-17. (in Korean with English abstract)   DOI
31 Efron, B. (1983), Estimating the error rate of a prediction rule: improvement on cross-validation, Journal of the American Statistical Association, Vol. 78, No. 382, pp. 316-331.   DOI
32 Feng, Q., Liu, J., and Gong, J. (2015), UAV remote sensing for urban vegetation mapping using random forest and texture analysis, Remote Sensing, Vol. 7, No. 1, pp. 1074-1094.   DOI
33 García-Ruiz, J. M., Nadal-Romero, E., Lana-Renault, N., and Beguería, S. (2013), Erosion in Mediterranean landscapes: changes and future challenges, Geomorphology, Vol. 198, pp. 20-36.   DOI