• Proceedings of the Korean Society of Agricultural Engineers Conference
• /
• 2002.10a
• /
• pp.21-24
• /
• 2002

Many methods are available to measure leaf area index(LAI) directly and are variations of either Leaf area index(LAI) and leaf angle distribution are widely used indices of canopy structure that are difficult to measure directly. Direct measurements of canopy structure are tedious and labor intensive in small canopies and nearly impossible in large forest canopies. This study introduced fundamental theories in LAI measurements and applied that for several crops.

• Proceedings of the KSRS Conference
• /
• 2004.10a
• /
• pp.166-169
• /
• 2004

NDVI has been used to estimate several ecological variables including leaf area index (LAI). Global MODIS LAI data are partially produced by empirical model that is based on the assumption of high correlation between NDVI and LAI. This study attempts to evaluate the MODIS empirical model by comparing with the result obtained from field LAI measurement and Landsat ETM+ reflectance. MODIS LAI product and ancillary data were analyzed over a small forest watershed near the Seoul metropolitan area. The relationship between NDVI of ETM+ and field measured LAI did not correspond to MODIS LAI estimation. Since the study area is mostly covered by very dense and fully closed forest, the correlation between NDVI and LAI might not be high. Although MODIS LAI product has great potential for global environment studies, it needs to be cautious to use them in regional and local area in particular for the forest of dense canopy situation.

• Korean Journal of Remote Sensing
• /
• v.19 no.5
• /
• pp.371-380
• /
• 2003

The primary objective of this study is to develop a suitable methodology to generate forest leaf area index (LAI) map at regional and local scales. To build empirical models, we collected the LAI values at 30 sample plots over the forest within the kyongan watershed area by the field measurements using an optical instrument. Landsat-7 ETM＋ multispectral data obtained at the same growing season with the field LAI measurement were used. Three datasets of remote sensing signal were prepared for analyzing the relationship with the field measured LAI value and they include raw DN, atmospherically corrected reflectance, and topographically corrected reflectance. From the correlation analysis and regression model development, we found that the radiometric correction of topographic effects was very critical step to increase the sensitivity of the multispectral reflectance to LAI. In addition, the empirical model to generate forest LAI map should be separately developed for each of coniferous and deciduous forest.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2008.05a
• /
• pp.805-809
• /
• 2008

Measurement of leaf area index (LAI) is useful for understanding rice growth, water use, and canopy light interception. The top nitrogen content(TNC) per unit area is an important quantitative index of the condition of nitrogen nutrition in rice production. The rapid and simple method of estimation of TNC, with the use of the existing nondestructive analyzing instruments chlorophyll meter SPAD-502 and plant canopy analyzer (PCA) LAI-2000, was scrutinized. Destructive measurement is time consuming and labor intensive. Our objective was to evaluate sampling procedures using the Li-Cor LI-1800, LAI 2000 plant canopy analyzer (PCA) for nondestructive estimation of rice LAI, and SPAD-502 on the Northern Plains of Cheongju. The LAI estimated by PCA tended to underestimate the LAI determined by actual measurement by about 20%. The estimation of LAI by PCA was judged to have a sufficient accuracy as a practical technique. A high positive correlation was obtained between the values of the SPAD reading and LAI. NDVI and LAI also showed a very high correlation. The values of the SPAD reading and LAI, and NDVI gave a high positive correlation. These results indicated that the method described in this study was effective as a simple and rapid method for the estimation of rice growth.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.21 no.4
• /
• pp.158-174
• /
• 2018

The leaf area index (LAI) is a major factor explaining the photosynthesis of vegetation, evapotranspiration, and energy exchange between the earth surface and atmosphere, and there have been studies on accurate and applicable LAI estimation methods. This study aimed to investigate the relationship between the actual LAI data, UAV image-based vegetation index, canopy height and satellite image (Sentinel-2) LAI and to present an effective LAI estimation method using UAV. As a result, among the six vegetation indices in this study, NDRE ($R^2=0.496$) and CIRE ($R^2=0.443$), which contained red-edge band, showed a high correlation. The application of the canopy height model data to the vegetation index improved the explanatory power of the LAI. In addition, in the case of NDVI, the saturation problem caused by the linear relationship with LAI was addressed. In this study, it was possible to estimate high resolution LAI using UAV images. It is expected that the applicability of such data will be improved if calibration and correction steps are carried out for various vegetation and seasonal images.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.42 no.2
• /
• pp.173-181
• /
• 1997

To estimate the leaf area index(LAI) of rice plant by non-destructive method, spectral reflectance from rice plant canopy was measured by using the spectroradiometer (LI-1800, LICOR Inc.) with one week interval during the rice growing season at Suwon paddy field in 1993. LAI of two medium late maturing varieties, Daechungbyeo and Ilpumbyeo, and one early maturing variety, Jinbubyeo, were observed and compared with those estimated by vegetation index. The reflectance(R) of visible wavelength remained less than 0.1 over entire growing season, but that of near infrared wavelength remained from 0.1 to 0.5 with the significant positive correlation with LAI. Vegetation index determined by the reflectance of visible against near infrared wavelength showed high correlation with LAI of rice canopy. Vegetation index derived from wide band ratio, NIR(720~1, 100nm) /Blue(400~500nm), showed the highest correlation coefficient with LAI. Vegetation index derived from narrow band(10nm interval) ratio, R910/R460, from transplanting to heading stage corresponded well to measured values (Y=0.16799X-0.79776 ; $R^2$=0.94). But another vegetation index, NIR(720~1, 100nm) /Red (600~700nm), showed higher correlation with LAI than NIR /Blue did from heading stage to maturity.

• Korean Journal of Soil Science and Fertilizer
• /
• v.46 no.4
• /
• pp.245-252
• /
• 2013

Leaf area index (LAI) is important in explaining the ability of crops to intercept solar energy for biomass production, amount of plant transpiration, and in understanding the impact of crop management practices on crop growth. This paper describes a procedure for estimating LAI as a function of image-derived vegetation indices from temporal series of RapidEye imagery obtained from 2010 to 2012 using empirical models in a rice plain in Seosan, Chungcheongnam-do. Rice plants were sampled every two weeks to investigate LAI, fresh and dry biomass from late May to early October. RapidEye images were taken from June to September every year and corrected geometrically and atmospherically to calculate normalized difference vegetation index (NDVI). Linear, exponential, and expolinear models were developed to relate temporal satellite NDVIs to measured LAI. The expolinear model provided more accurate results to predict LAI than linear or exponential models based on root mean square error. The LAI distribution was in strong agreement with the field measurements in terms of geographical variation and relative numerical values when RapidEye imagery was applied to expolinear model. The spatial trend of LAI corresponded with the variation in the vegetation growth condition.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.11 no.2
• /
• pp.61-72
• /
• 2008

Leaf area index (LAI) is a key biophysical variable influencing land surface processes such as photosynthesis, transpiration and energy balance, and is a required input to estimate evapotranspiration in various ecological and hydrological models. The development of more correct and useful LAIs estimation techniques is required by these importance, but LAIs had been assumed in most LAI research through simple relations with the normalized difference vegetation index (NDVI) because the field measurement is difficult on wide area. This paper is to evaluate the MODIS LAI Product's practical use by comparing with LAIs that is derived from NOAA AVHRR NDVIs and the 2 years (2003-2004) measured LAIs of Korea Forest Research Institute in Gyeongancheon watershed (561.12 $Km^2$). As a result, the MODIS LAIs of deciduous forests showed higher values about 14 % and 15~30 % than the measured LAIs and NOAA LAIs. In the year of 2003, the MODIS LAIs in coniferous forests were 5 % higher than the measured LAIs, and showed about 7 % differences comparing with the NOAA LAIs except April. These differences come from the insufficient field data measured in partial points of the target area, and the extracted reference data from MODIS LAIs include the limits of spatial resolution and the error of incorrect land cover classification. Thus, using the MODIS data by the proper correction with the measured data can be useful as an input data for ecological and hydrological models which offers the vegetation information and simulates the water balance of a given watershed.

• Korean Journal of Ecology and Environment
• /
• v.46 no.1
• /
• pp.53-59
• /
• 2013

The purpose of this study was to analyze the topographic effect of the LAI (Leaf Area Index), which has been widely used as an index that quantifies the structure of forest vegetation in Deogyusan National Park. With this aim, the study was conducted through a regression analysis which took as explanation the following variables: the elevation, slope, aspect, and soil moisture conditions. The LAI was taken as the response variable. Overall, the correlation between the Field-LAI and topographic factors was less than 0.5, which was relatively low. Except for topographic altitude, there was no statistical significance regarding the correlation with other factors. Meanwhile, regarding the orientation of the correlation, the higher the attitude, the steeper slope, the lower the soil moist, the lower the LAI value. The topographic altitude was found as a statistically significant explanation variable. The TWI (Topographic Wetness Index), which was used in this study to explain the soil moisture conditions, was not significantly related to the LAI distribution. The results of this study are expected to be utilized as basic data in more accurate forecasting the LAI distribution using remote sensing data.

• Korean Journal of Remote Sensing
• /
• v.28 no.6
• /
• pp.597-609
• /
• 2012

Leaf area index (LAI) is important in explaining the ability of the crop to intercept solar energy for biomass production and in understanding the impact of crop management practices. This paper describes a procedure for estimating LAI as a function of image-derived vegetation indices from temporal series of IKONOS, Landsat TM, and MODIS satellite images using empirical models and demonstrates its use with data collected at Missouri field sites. LAI data were obtained several times during the 2002 growing season at monitoring sites established in two central Missouri experimental fields, one planted to soybean (Glycine max L.) and the other planted to corn (Zea mays L.). Satellite images at varying spatial and spectral resolutions were acquired and the data were extracted to calculate normalized difference vegetation index (NDVI) after geometric and atmospheric correction. Linear, exponential, and expolinear models were developed to relate temporal NDVI to measured LAI data. Models using IKONOS NDVI estimated LAI of both soybean and corn better than those using Landsat TM or MODIS NDVI. Expolinear models provided more accurate results than linear or exponential models.