• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1400-1402
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• 2003

The objective of this paper is to determine the suitable spectral bands for monitoring growth status change during a long period. The long-term ground-level reflectance spectra as well as LAI and biomass were obtained in xiaotangshan area, Beijing, 2001. The narrow-band NDVI type spectral indices by all possible two bands were calculated their correlation coefficients R$^2$ with biomass and LAI. The best NDVIs must have higher R$^2$ with both biomass and LAI. The reasonable band centers and band widths were determined by a systematically increasing bandwidth centered over a wavelength. In addition, the first 19 bands of MODIS were simulated and investigated. Each developed spectral indices was then validated by the biomass and LAI time series using the generalized vector angle. It turned out that six new NDVI type indices within 750-1400nm were developed. NDVI(811_10,957_10) and NDVI(962_10,802_10) performed best. No satisfactory conventional NDVI formed by red and NIR bands were found effective. MODIS_NDVI(band19, band17) and MODIS_NDVI(band19, band2) were much better than MODIS_NDVI(band2,band1) for growth monitoring.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.405-407
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• 2003

An attempt has been in this study to delineate the characteristics of spectral signatures of the vegetation in terms of various VIs, particularly made the Normalized Difference Vegetation Index(NDVI), Modified Soil Adjusted Vegetation Index2(MSAVI2) and Enhanced Vegetation Index(EVI). Multitemporal SPOT-4 VEGETATION data from 1998 to 2002 have been used for the analysis. They have been compared with each other for their similarities and differences. The correlations between the vegetation indices observed at various degree of vegetation coverage during their different stages of growth were examined. All of the VIs have shown qualitative relationships to variations in vegetation. Apparently, the NDVI and MSAVI2 are highly correlated for all of the temporal changes, representing the different stages of phenology.

• Korean Journal of Remote Sensing
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• v.22 no.3
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• pp.183-197
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• 2006

Combinations of visible and near-infrared (NIR) bands in an image are widely used for estimating vegetation vigor and productivity. Using this approach to understand within-field grain crop variability could allow pre-harvest estimates of yield, and might enable mapping of yield variations without use of a combine yield monitor. The objective of this study was to estimate within-field variations in crop yield using vegetation indices derived from hyperspectral images. Hyperspectral images were acquired using an aerial sensor on multiple dates during the 2003 and 2004 cropping seasons for corn and soybean fields in central Missouri. Vegetation indices, including intensity normalized red (NR), intensity normalized green (NG), normalized difference vegetation index (NDVI), green NDVI (gNDVI), and soil-adjusted vegetation index (SAVI), were derived from the images using wavelengths from 440 nm to 850 nm, with bands selected using an iterative procedure. Accuracy of yield estimation models based on these vegetation indices was assessed by comparison with combine yield monitor data. In 2003, late-season NG provided the best estimation of both corn $(r^2\;=\;0.632)$ and soybean $(r^2\;=\;0.467)$ yields. Stepwise multiple linear regression using multiple hyperspectral bands was also used to estimate yield, and explained similar amounts of yield variation. Corn yield variability was better modeled than was soybean yield variability. Remote sensing was better able to estimate yields in the 2003 season when crop growth was limited by water availability, especially on drought-prone portions of the fields. In 2004, when timely rains during the growing season provided adequate moisture across entire fields and yield variability was less, remote sensing estimates of yield were much poorer $(r^2<0.3)$.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.256-261
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• 1999

Global vegetation change is one of major global concerns. Remote sensing images provide an efficient and useful data source to estimate global vegetation covers, and a number of methods have been proposed to estimate them. Among them, the NDVI is one of the most popular indices, and it is_easy to calculate with simple image computing. However, this index is very much affected by the radiometric environment of sensing such as atmospheric conditions and the sun illumination angle. Therefore, it is not appropriate to apply the NDVI to investigate seasonal changes. This paper discusses these problems and proposes an alternative index, MODVI(Modified Vegetation Index), that is less affected by radiometric environment changes. An experiment was conducted to compare these two indices using temporal Landsat TM sub-scenes.

• Korean Journal of Remote Sensing
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• v.18 no.5
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• pp.263-271
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• 2002

Recently, spatial information technologies using remotely sensed imagery and functionality of GIS (Geographic Information Systems) have been widely utilized to various types of transportation-related applications. In this study, extraction programs of some practical indices, to be effectively used in transportation reference planning problem, were designed and implemented as prototyped extensions in GIS development environment: traffic flow estimation (TFL/TFB), urban rural index (URI), and accessibility index (AI). In TFL/TFB, user can obtain quantitative results on traffic flow estimation at link/block using high-resolution satellite imagery. Whereas, URI extension provides urban-rural characteristics related to road system, being considered one of important factors in transportation planning. Lastly, AI extension helps to obtain accessibility index between nodes of road segments and surrounding district areas touched or intersected with the road network system, and it also provides useful information for transportation planning problems. This approach is regarded as one of RS-T (Remote Sensing in Transportation), and it is expected to expand as new application of remotely sensed imagery.

• Korean Journal of Remote Sensing
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• v.31 no.1
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• pp.11-20
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• 2015

This study aimed to investigate the canopy growth conditions and the accuracy of phenological stages of paddy rice using ground-based remote sensing data. Plant growth variables including Leaf Area Index (LAI) and canopy reflectance of paddy rice were measured at the experimental fields of Chonnam National University, Gwangju, Republic of Korea during the crop seasons of 2011, 2012, and 2013. LAI values were also determined based on correlations with Vegetation Indices (VIs) obtained from the canopy reflectance. Three phenological stages (tillering, booting, and grain filling) of paddy rice could be identified using VIs and a spatial index (NIR versus red). We found that exponential relationships could be applied between LAI and the VIs of interest. This information, as well as the relationships between LAI and VIs obtained in the present study, could be used to estimate and monitor the relative growth and development of rice canopies during the growing season.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.673-678
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• 2002

A satellite remote sensing is useful for vegetation monitoring. But it has some problem. One of these, it is difficult to find a difference of vegetation surface roughness using satellite remote sensing. Each vegetation type has unique surface roughness, for example needle leaves forest, broad leaves forest and grassland. Difference of vegetation surface roughness can be detected by satellite multiangular observation. In this study, objective is to propose index of vegetation surface roughness using BRF property. General vegetation indices are calculated from nadir data of satellite data. A proposed index is calculated from two different observation zenith angle data. Two different zenith data can provide BRF (Bi-directional Reflectance Factor) property of satellite observation data. A proposed index was able to detect different value on where NDVI shows similar high value areas of rice field and forest. This index is useful for vegetation monitoring.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.144-144
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• 2022

Identifying waterbody from remote sensing images, namely water detection, helps understand continuous redistribution of terrestrial water storage and accompanying hydrological processes. It also allows us to estimate available surface water resources and help effective water management. For this problem, NDWI (Normalized Difference Water Index) and MNDWI (Modified Normalized Difference Water Index) are widely used. Although remote sensing indexes can highlight remote sensing image in the water, the noise and the spatial information of the remote sensing image are difficult to be considered, so the accuracy is difficult to be compared with the visual interpretation (the most accurate method, but it requires a lot of labor, which makes it difficult to apply). In this study, we attempt to improve existing NDWI and MNDWI to better water detection. We establish waterbody database of South Korea first and then used it for assessing waterbody indices.

• Korean Journal of Remote Sensing
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• v.20 no.3
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• pp.175-188
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• 2004

The objectives of this study were to estimate leaf area index (LAI) as a function of image-derived vegetation indices, and to compare measured and estimated LAI to the results of crop model simulation. Soil moisture, crop phenology, and LAI data were obtained several times during the 2001 growing season at monitoring sites established in two central Missouri experimental fields, one planted to com (Zea mays L.) and the other planted to soybean (Glycine max L.). Hyper- and multi-spectral images at varying spatial. and spectral resolutions were acquired from both airborne and satellite platforms, and data were extracted to calculate standard vegetative indices (normalized difference vegetative index, NDVI; ratio vegetative index, RVI; and soil-adjusted vegetative index, SAVI). When comparing these three indices, regressions for measured LAI were of similar quality $(r^2$ =0.59 to 0.61 for com; $r^2$ =0.66 to 0.68 for soybean) in this single-year dataset. CERES(Crop Environment Resource Synthesis)-Maize and CROPGRO-Soybean models were calibrated to measured soil moisture and yield data and used to simulate LAI over the growing season. The CERES-Maize model over-predicted LAI at all corn monitoring sites. Simulated LAI from CROPGRO-Soybean was similar to observed and image-estimated LA! for most soybean monitoring sites. These results suggest crop growth model predictions might be improved by incorporating image-estimated LAI. Greater improvements might be expected with com than with soybean.

• Korean Journal of Soil Science and Fertilizer
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• v.36 no.2
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• pp.92-103
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• 2003

Research and technological advances in the field of remote sensing have greatly enhanced the ability to detect and quantify physical and biological stresses that affect the productivity of agricultural crops. Reflectance in specific visible and near-infrared regions of the electromagnetic spectrum have proved useful in detection of nutrient deficiencies. Especially crop canopy sensors as a ground remote sensing measure the amount of light reflected from nearby surfaces such as leaf tissue or soil and is in contrast to aircraft or satellite platforms that generate photographs or various types of digital images. Multi-spectral vegetation indices derived from crop canopy reflectance in relatively wide wave band can be used to monitor the growth response of plants in relation to environmental factors. The normalized difference vegetation index (NDVI), where NDVI = (NIR-Red)/(NIR+Red), was originally proposed as a means of estimating green biomass. The basis of this relationship is the strong absorption (low reflectance) of red light by chlorophyll and low absorption (high reflectance and transmittance) in the near infrared (NIR) by green leaves. Thereafter many researchers have proposed the other indices for assessing crop vegetation due to confounding soil background effects in the measurement. The green normalized difference vegetation index (GNDVI), where the green band is substituted for the red band in the NDVI equation, was proved to be more useful for assessing canopy variation in green crop biomass related to nitrogen fertility in soils. Consequently ground remote sensing as a non destructive real-time assessment of nitrogen status in plant was thought to be useful tool for site specific crop nitrogen management providing both spatial and temporal information.