Study On Low-cost And Rapid Estimation Of Leaf Area Index Of Forest Stands

Leaf area index of leaf area index (LAI) is a description of one of the important parameters of the vegetation canopy in forestry and ecology, commonly used in the detection of plant growth status, by pests pest invasion.Because the traditional method of measuring the leaf area index has certain damage to the forest, so the leaf area index is usually measured by plant canopy analyzer (such as LAI-2000) or hemispherical photography (e.g. Hemiview) and has in scientific research is widely recognized and used. However, for large area or continuous ground Lai survey, these instruments in a higher price limits a large number of instruments at the same time, the survey, timeliness is low; in addition the use of these instruments require a higher professional limit the use of large area.And at the grass-roots level in the forest, forest fire prevention patrol, resources survey, investigation of pests forecasting business work, if we can make full use of grass-roots staff, using inexpensive but convenient Lai measurement equipment, a wide range, sampling high frequency of Lai, providing large numbers according to the level of Lai sample data, to quickly grasp the macroeconomic situation of the forest resources and its change.Therefore, it is important to explore the new method of remote sensing observation of the leaf area index, which can be a large area to accurately and rapidly, and it has a great effect on the forest health monitoring.To this end, this study proposes a method for rapid estimation of leaf area index using a mobile phone equipped with a spherical lens(this method is called LAI-Mobile),Then the satellite data are combined to obtain a large area of LAI. In order to verify LAI-Mobile measurements of leaf area index of feasibility study in Beijing Forestry University Campus and Xiangyun County, Dali City, Yunnan Province, day mountain two plots to carry out the investigation and testRespectively using canopy analyzer LAI-2200 and LAI-Mobile hemispherical photography from two kinds of measurement methods on the same single tree and stand was measured, and the measurement of LAI-2200 value to calibrate LAI-Mobile of hemispherical photography measurement data.In order to verify the mobile phone measurement method in the fast, convenient measurement of the accuracy of the same time can reach the measurement requirements.On the campus of the Beijing Forestry University and mountain, the accuracy of Lai measurements of single wood better, R2 respectively 0.843 and 0.812, root mean square error (RMSE is 0.187 and 0.223.Measurement precision in Xiangyun County Tian Fengshan, this study chose 30 plots also use two methods were measured and explore the LAI-Mobile hemispherical photography measurement method in the stand.Results:the results of LAI-2200 measurement and LAI-Mobile measurement results are very close to a linear relationship, R2 is 0.791, RMSE is 0.212.In the observation time,1/4.Further and satellite data are combined to carry out a large range of leaf area index inversion.The study uses the GF-1 data from China to extract NDVI, RVI, SAVI three index, and the method of field measurement of leaf area index to establish the regression statistical model between the two.The results showed that there was a strong correlation between the Yunnan pine forest and the three planting index in the study area.Among them the linear model of NDVI-LAI correlation coefficient is the best to achieve 0.868. use the highestaccuracy model to sample the ground data of the remaining 15 samples found. The average relative error is RMSE=0.238.The research proposed by the use of LAI-Mobile to carry on the hemisphere photography method has the advantage of fast, convenient, simple operation and so on.Although lacking the measurement accuracy but because cell phones are now living necessities, by this method in the forestry work group in a large number of photographs by large data sample size harvest some samples of leaf area index of. Then the inversion of ground data and remote sensing data can be used to quickly get the numerical value of the leaf area index, and has great potential in the large scale measurement of forest indicators.