Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 9-13, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-9-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
28 Apr 2015
Estimation of grass yield in large region on geographically weighted regression model
L. Chengfeng1, Y. Xiujuan2, L. Caijuan3, and D. Yingkun4 1Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing 100830, P.R. China
2National Calibration Center for Electro-optical Distance Meter, Chinese Academy of Surveying and Mapping, Beijing 100830, P.R. China
3Geomatics of Colleg, Shandong University of Science and Technology, Qingdao 266590, P.R. China
4College of Information Science and Engineering, Shandong Agricultural University,Taian 271018, P.R. China
Keywords: Geographically Weighted Regression (GWR) Model;Grass Yield;QingHai Province;Remotely Sensed Data Abstract. The grass yield embodies its productivity,and also is ground for developing animal husbandry production management. Now the remote sensing technology has been becoming an efficient and feasible mean to estimate the grass yield. In the study, the thought about Geographically Weighted Regression (GWR) was involved in estimating the grass yield. The special characteristics of samples measured on field were considered, and then each sample has a local function covering area around. And the parameters for the function are decided by the weighted function which is associated with the spatial distance between the sample and others around. GWR is a good solution to the model without spatial stationarity, as a consequence a significant model-fitting degree comes out. Based on GWR model an ideal production of grassland can be estimated. In this study, Qinghai province, about 0.72 million square kilometres, was taken as an example. The province is an important one on the Qinghai Tibet Plateau. Here the grassland not only closely relates with the local animal husbandry economy, but also directly affects the regional ecosystem security. Landsat TM data in 2013 and samples on field were used to estimate the production. As input parameters, OSAVI and FVC have high correlation coefficient more than 97% with grass yield. There were 201 samples involved in modelling, and the accuracy is 87.27%, above about 47% than that of multiple linear regression model, a widely used traditional statistic model. Another 220 samples were used to verify the results, and here the accuracy can reach 81.3%. Out results indicated that in 2013 the yield of grass in Qinghai province is 1.018*108 ton. The difference between our data and that from professional sector is less than 10%.
Conference paper (PDF, 1150 KB)


Citation: Chengfeng, L., Xiujuan, Y., Caijuan, L., and Yingkun, D.: Estimation of grass yield in large region on geographically weighted regression model, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 9-13, https://doi.org/10.5194/isprsarchives-XL-7-W3-9-2015, 2015.

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