Estimating the insurance rates for loss of annual production of grass herbage associated with natural disasters in China
Xing-peng Liu A D , Ji-quan Zhang A , Wei-ying Cai B and Yu-long Bao CA School of Environment, Natural Disaster Research Institute, Northeast Normal University, Changchun, Jilin, 130117, P. R. China.
B College of Tourism, Changchun University, Changchun, Jilin 130607, P. R. China.
C College of Geographical Science, Inner Mongolia Normal University, Hohhot, Inner Mongolia, 010020, P. R. China.
D Corresponding author. Email: liuxp912@nenu.edu.cn
The Rangeland Journal 37(2) 139-146 https://doi.org/10.1071/RJ14040
Submitted: 14 February 2014 Accepted: 11 February 2015 Published: 13 March 2015
Abstract
Grasslands in many parts of China are vulnerable to natural disasters which can bring large economic losses to pastoralists. As an effective method to manage the risk, insurance has gradually become an important means used in the management of grassland disasters. Because of insufficient statistical data on annual production of grass herbage, insurance-rate-making has become the core challenge in grassland insurance programs in China. Taking Xilingol League in Inner Mongolia as the study area, by analysing the correlations of different vegetation indices with annual production of grass herbage, a spatial Normalised Difference Vegetation Index (NDVI) based insurance rate was estimated for the loss of annual production of grass herbage in three steps: (i) the annual NDVIs in mid-to-late August were obtained using Moderate Resolution Imaging Spectroradiometer (MODIS) products and, applying a relationship developed between remote-sensing data and measured grass herbage mass, annual production of grass herbage was estimated, (ii) the Relative Fluctuation Production (RFP) was estimated from trend and fluctuating data on the annual production of grass herbage, and (iii) applying kernel density estimation, the insurance rate of loss of annual production of grass herbage was calculated based on the RFP in each cell of the study area. This approach to estimating the insurance rate for loss of annual production of grass herbage associated with natural disasters can improve the ability of pastoralists to manage their grasslands more effectively.
Additional keywords: insurance rate, kernel density estimation, natural disasters, NDVI, pastoralists.
References
Brown, M. E., Osgood, D. E., and Carriquiry, M. A. (2011). Science-based insurance. Nature Geoscience 4, 213–214.| Science-based insurance.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXjvFKlsbY%3D&md5=994596a111a73d9dd4f962a31a2b2e36CAS |
Govaerts, Y. M., Verstraete, M. M., Pinty, B., and Gobron, N. (1999). Designing optimal spectral indices: a feasibility and proof of concept study. International Journal of Remote Sensing 20, 1853–1873.
| Designing optimal spectral indices: a feasibility and proof of concept study.Crossref | GoogleScholarGoogle Scholar |
Hazell, P. R., and Hess, U. (2010). Drought insurance for agricultural development and food security in dry land areas. Food Security 2, 395–405.
| Drought insurance for agricultural development and food security in dry land areas.Crossref | GoogleScholarGoogle Scholar |
Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25, 295–309.
| A soil-adjusted vegetation index (SAVI).Crossref | GoogleScholarGoogle Scholar |
Huete, A. R., Justice, C., and van Leeuwen, W. (1996). ‘MODIS vegetation index (mod13).’ Algorithm theoretical basis document. Version 2. (NASA Goddard Space Flight Center: Greenbelt, MD, USA.)
Huete, A. R., Liu, H. Q., Batchily, K., and van Leeuwen, W. (1997). A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment 25, 295–309.
| A comparison of vegetation indices over a global set of TM images for EOS-MODIS.Crossref | GoogleScholarGoogle Scholar |
Jeon, Y., and Kim, J. H. T. (2013). A gamma kernel density estimation for insurance loss data. Insurance, Mathematics & Economics 53, 569–579.
| A gamma kernel density estimation for insurance loss data.Crossref | GoogleScholarGoogle Scholar |
Jin, Y. X., Xu, B., and Yang, X. C. (2011). Remote-sensing dynamic estimation of grass production in Xilinguole, Inner Mongolia. Scientia Sinica Vitae 41, 1185–1195.
Karl, W. J., Duniway, C. M., Nusser, M. S., Opsomer, D. J., and Unnasch, R. S. (2012). Using very-large-scale aerial imagery for rangeland monitoring and assessment: some statistical considerations. Rangeland Ecology and Management 65, 330–339.
| Using very-large-scale aerial imagery for rangeland monitoring and assessment: some statistical considerations.Crossref | GoogleScholarGoogle Scholar |
Kawamura, K., Akiyama, T., Yokota, H., Tsutsumi, M., Yasuda, T., Watanabe, O., and Wang, S. (2005). Comparing MODIS vegetation indices with AVHRR NDVI for monitoring the forage quantity and quality in Inner Mongolia grassland, China. Grassland Science 51, 33–40.
| Comparing MODIS vegetation indices with AVHRR NDVI for monitoring the forage quantity and quality in Inner Mongolia grassland, China.Crossref | GoogleScholarGoogle Scholar |
Lee, H., Kawamura, K., Watanabe, N., Sakanoue, S., Sakuno, Y., Itano, S., and Nakagosh, N. (2011). Estimating the spatial distribution of green herbage biomass and quality by geo-statistical analysis with field hyperspectral measurements. Japanese Society of Grassland Science 57, 142–149.
| Estimating the spatial distribution of green herbage biomass and quality by geo-statistical analysis with field hyperspectral measurements.Crossref | GoogleScholarGoogle Scholar |
Leiva-Murillo, M. J., and Artés-Rodríguez, A. (2012). Algorithms for maximum-likelihood bandwidth selection in kernel density estimators. Pattern Recognition Letters 33, 1717–1724.
| Algorithms for maximum-likelihood bandwidth selection in kernel density estimators.Crossref | GoogleScholarGoogle Scholar |
Li, F., Jiang, L., Wang, X., Zhang, X., Zheng, J., and Zhao, Q. (2013). Estimating grassland above-ground biomass using multi-temporal MODIS data in the West Songnen Plain, China. Journal of Applied Remote Sensing 7, 073546.
| Estimating grassland above-ground biomass using multi-temporal MODIS data in the West Songnen Plain, China.Crossref | GoogleScholarGoogle Scholar |
Liu, X. P., Zhang, J. Q., Cai, W. Y., and Tong, Z. J. (2010). Information diffusion-based spatio-temporal risk analysis of grassland fire disaster in northern China. Knowledge-Based Systems 23, 53–60.
| Information diffusion-based spatio-temporal risk analysis of grassland fire disaster in northern China.Crossref | GoogleScholarGoogle Scholar |
Lockwood, J. A., and Latchininsky, A. V. (2000). The risks of grasshoppers and pest management to grassland agro-ecosystems: an international perspective on human well-being and environmental health. In: ‘Grasshoppers and Grassland Health’. NATO Science Series, Vol. 73. (Ed. J. Lockwood.) pp. 193–215. (Springer: Berlin, Germany)
Moreira, C., and Van Keilegom, I. (2013). Bandwidth selection for kernel density estimation with doubly truncated data. Computational Statistics & Data Analysis 61, 107–123.
| Bandwidth selection for kernel density estimation with doubly truncated data.Crossref | GoogleScholarGoogle Scholar |
Moulin, S., and Guerif, M. (1999). Impacts of model parameter uncertainties on crop reflectance estimates: a regional case study on wheat. International Journal of Remote Sensing 20, 213–218.
| Impacts of model parameter uncertainties on crop reflectance estimates: a regional case study on wheat.Crossref | GoogleScholarGoogle Scholar |
Myneni, R. B., Hall, F. G., Sellers, P. J., and Marshak, A. L. (1995). The interpretation of spectral vegetation indexes. IEEE Transactions on Geoscience and Remote Sensing 33, 481–486.
| The interpretation of spectral vegetation indexes.Crossref | GoogleScholarGoogle Scholar |
Parzen, E. (1962). On estimation of a probability density function and mode. The Annals of Mathematical Statistics 33, 1065–1076.
| On estimation of a probability density function and mode.Crossref | GoogleScholarGoogle Scholar |
Rosenblatt, M. (1956). Remarks on some non-parametric estimates of a density function. Annals of Mathematical Statistics 27, 832–837.
| Remarks on some non-parametric estimates of a density function.Crossref | GoogleScholarGoogle Scholar |
Rouse, J. W., Haas, R. H., Schell, J. A., and Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. In: ‘Third ERTS-1 Symposium’. pp. 309–317. (NASA: Washington, DC, USA.)
Sheather, S. J., and Jones, M. C. (1991). A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society. Series B. Methodological 53, 683–690.
Trnka, M., Schaumberger, A., Herbert, F., Eitzinger, J., Hlavinka, P., Semeradova, D., Dubrovsky, M., Mozny, M., Thaler, S., and Zalud, Z. (2011). Evaluating drought risk for permanent grasslands under present and future climate conditions. Procedia Environmental Sciences 3, 50–57.
| Evaluating drought risk for permanent grasslands under present and future climate conditions.Crossref | GoogleScholarGoogle Scholar |
Vrieling, A., Meroni, M., Shee, A., Mude, A. G., Woodard, J., de Bie, C. A. J. M., and Rembold, F. (2014). Historical extension of operational NDVI products for livestock insurance in Kenya. International Journal of Applied Earth Observation and Geoinformation 28, 238–251.
| Historical extension of operational NDVI products for livestock insurance in Kenya.Crossref | GoogleScholarGoogle Scholar |
Xie, Y., Sha, Z., Yu, M., Bai, Y., and Zhang, L. (2009). A comparison of two models with Landsat data for estimating above-ground grassland biomass in Inner Mongolia, China. Ecological Modelling 220, 1810–1818.
| A comparison of two models with Landsat data for estimating above-ground grassland biomass in Inner Mongolia, China.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXntVyqsr8%3D&md5=a8e3e358d4b726bbe88d6a32aa565252CAS |
