• Journal of Korean Society of Forest Science
• /
• v.101 no.3
• /
• pp.356-364
• /
• 2012

This study was to estimate the biomass of Mt. Changbai mountain area using the IKONOS imagery and field survey data. Then, we prepared the regression function using the vegetation index derived from the IKONOS and biomass estimated from field measured data of previous studies, respectively. The five vegetation index which used in the regression model was SAVI, NDVI, SR, ARVI, and EVI. As a result, the rank of the R-square from coefficient of correlation was as follow, SAVI(0.84), NDVI(0.73), SR(0.59), ARVI(0.0036), EVI(0.0026). Finally, we estimated the biomass of non-measured area using the Soil Adjusted Vegetation Index (SAVI). This study can be used as reference methodology for the estimation of carbon sinks of primary forest.

• Proceedings of the KSRS Conference
• /
• 2007.10a
• /
• pp.449-451
• /
• 2007

In the commercially managed timber lands, the information such as height, age, stand density, canopy closure and leaf area index need to be collected periodically. Stand volume is the most fundamental information in the valuation of timber, however, stand age information is the primary element of forest inventory and these two are highly correlated. Conventional method of collecting stand age information by field surveys such as ring count method is accurate; however, it is expensive, labor-intensive and time consuming. In the present study it was aimed to collect stand age information using modem techniques in a commercially managed timberland situated in Tennessee, USA. The Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+) of three different periods, Shuttle Radar Topography Mission (SRTM), National elevation dataset (NED) and field inventory data were used. Normalized difference vegetation index (NDVI) and Tasselled Cap (TC) transformation techniques were applied on the TM and ETM+ data. The regression analysis was carried out to identify the correlation between stand age and NDVI, TC. In the present study about 2,469 datasets were analyzed. The $R^{2}$ value for stand age estimation was 0.713. The NDVI, TC2 and TC3 were found to produce accurate timber age information.

• Journal of Korean Society of Forest Science
• /
• v.101 no.1
• /
• pp.163-174
• /
• 2012

CBI(Composite Burn Index) developed by USDA Forest Service is a index to measure burn severity based on remote sensing. In Korea, the CBI has been used to investigate the burn severity of fire sites for the last few years. However, it has been an argument on that CBI is not adequate to capture unique characteristics of Korean forests, and there has been a demand to develop KCBI(Korean Composite Burn Index). In this regard, this study aimed to develop KCBI by adjusting the CBI and to validate its applicability by using remote sensing technique. Uljin and Youngduk, two large fire sites burned in 2011, were selected as study areas, and forty-four sampling plots were assigned in each study area for field survey. Burn severity(BS) of the study areas were estimated by analyzing NDVI from SPOT images taken one month later of the fires. Applicability of KCBI was validated with correlation analysis between KCBI index values and NDVI values and their confusion matrix. The result showed that KCBI index values and NDVI values were closely correlated in both Uljin (r = -0.54 and p<0.01) and Youngduk (r = -0.61 and p<0.01). Thus this result supported that proposed KCBI is adequate index to measure burn severity of fire sites in Korea. There was a number of limitations, such as the low correlation coefficients between BS and KCBI and skewed distribution of KCBI sampling plots toward High and Extreme classes. Despite of these limitations, the proposed KCBI showed high potentials for estimating burn severity of fire sites in Korea, and could be improved by considering the limitations in further studies.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.46 no.3
• /
• pp.27-35
• /
• 2018

In the mandatory application of the CPTED, only negative parts of urban parks and vegetation were reflected. Therefore, this study tries to present the positive effects of urban parks and vegetation. The purpose of this study is to demonstrate the effects of urban parks and vegetation on crime occurrence and to suggest the planning implications of this to CPTED based on theory related to crime, environmental psychology, and crime occurrence analysis. This study used the crime occurrence data of Seoul, NDVI, population, distance from urban park, floating population, and the like. This study collected data from the Statistics Korea, the local government, and Landsat 8 satellite images provided by the USGS and created data of environmental variables and social variables by district using ArcGIS and statistical program. Literature analysis, correlation analysis, regression analysis, and geographically weighted regression were used to determine the relationship between crime occurrence and environmental variables, and to discuss its implication. It was found that crime occurrence has a relationship with the total population (${\beta}=.663$), the number of amusement facilities (${\beta}=.447$) and the area of a police station jurisdiction (${\beta}=.395$). This confirms that a crime rate is low when the floating population is large (${\beta}=-.241$) and vegetation vitality is high (NDVI, ${\beta}=-.281$). Vegetation vitality (NDVI) is effective in lowering violence through psychological stabilization, strengthening territoriality and improving regional image. The implications for the allocation of urban park and vegetation, program and management plan of urban park and vegetation to reduce crime occurrence have therefore been presented.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.965-967
• /
• 2003

Information on forest volume, forest coverage and biomass are important for developing global perspectives about CO$_{2}$ concentration changes. Forest biomass cannot be directly measured from space yet, but remotely sensed greenness can be used to estimate biomass on decadal and longer time scales in regions of distinct seasonality, as in the north. Hence, in this research, numerical methods were used to estimate forest biomass in higher northern regions. A regression model linking Normalized Difference Vegetation Index(NDVI), to forest biomass extracted from SPOT/4 VEGETATION data and PAL 8km data in regional and continental area (N40-N70) respectively. Statistical tests indicated that the regression model can be used to represent the changes of forest biomass carbon pools and sinks at high latitude regions over years 1982-2000. This study suggests that the implementation of estimation of biomass based on 8-km resolution NOAA/AVHRR PAL and SPOT-4/VEGETATION data could be detected over a range of land cover change processes of interest for global biomass change studies.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.16 no.2
• /
• pp.114-124
• /
• 2014

For classifying fire damaged areas and analyzing burn severity of two large-fire areas damaged over 100 ha in 2011, three methods were employed utilized supervised classification, unsupervised classification and Normalized Difference Vegetation Index (NDVI). In this paper, the post-fire imageries of SPOT were used to compute the Maximum Likelihood (MLC), Minimum Distance (MIN), ISODATA, K-means, NDVI and to evaluate large-scale patterns of burn severity from 1 m to 5 m spatial resolutions. The result of the accuracy verification on burn severity from satellite images showed that average overall accuracy was 88.38 % and the Kappa coefficient was 0.8147. To compare the accuracy between burn severity and field survey at Uljin and Youngduk, two large fire sites were selected as study areas, and forty-four sampling plots were assigned in each study area for field survey. The burn severities of the study areas were estimated by analyzing burn severity (BS) classes from SPOT images taken one month after the occurrence of the fire. The applicability of composite burn index (CBI) was validated with a correlation analysis between field survey data and burn severity classified by SPOT5, and by their confusion matrix. The result showed that correlation between field survey data and BS by SPOT5 were closely correlated in both Uljin (r = -0.544 and p<0.01) and Youngduk (r = -0.616 and p<0.01). Thus, this result supported that the proposed burn severity analysis is an adequate method to measure burn severity of large fire areas in Korea.

• Korean Journal of Environment and Ecology
• /
• v.18 no.1
• /
• pp.53-60
• /
• 2004

This study was carried out to investigate the effect of green area in apartment complexes to variation of temperature. The inside temperature of each site was estimated by analyzing Landsat ETM＋ image data. The factors on variation of temperature were landcover type, building density, and Normalised Difference Vegetation Index(NDVI). The results of correlation between inside temperature of apartment complex and land cover type showed that the green area ratio had negative(－) correlation and impermeable pavement ratio had positive(＋) correlation. Building-to-land ratio was not significant with inside temperature. A coefficient of correlation between the temperature value and the value of permeable pavement ratio added up green area ratio was higher than a coefficient of correlation between the temperature value and the value of permeable pavement ratio added up impermeable pavement ratio. Thus we may define that permeable pavement area decrease urban temperature with green area in apartment complex. Floor area ratio had no significant correlation with inside temperature. Inside temperature was decreased as the NDVI was increased. To establish the temperature distribution model in a development apartment complex, As the result of regression analysis between inside temperature as dependent variable and permeable pave ratio+green area ratio, green area ratio, building-to-land ratio and NDIT as independent variables, only permeable pavement ratio added up green area ratio of the independent variables was accepted fur regression equation in both two seasons and adjusted coefficient of determination was 41.4 on September, 2000 and 40.4 on June,2001.

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

• Journal of Biosystems Engineering
• /
• v.41 no.3
• /
• pp.273-280
• /
• 2016

Purpose: This experiment was conducted to detect water stress in terms of the moisture content of cucumber seedlings under water stress condition using a hyperspectral image acquisition system, linear regression analysis, and partial least square regression (PLSR) to achieve a non-destructive measurement procedure. Methods: Changes in the reflectance spectrum of cucumber seedlings under water stress were measured using hyperspectral imaging techniques. A model for estimating moisture content of cucumber seedlings was constructed through a linear regression analysis that used the moisture content of cucumber seedlings and a normalized difference vegetation index (NDVI). A model using PLSR that used the moisture content of cucumber seedlings and reflectance spectrum was also created. Results: In the early stages of water stress, cucumber seedlings recovered completely when sub-irrigation was applied. However, the seedlings suffering from initial wilting did not recover when more than 42 h passed without irrigation. The reflectance spectrum of seedlings under water stress decreased gradually, but increased when irrigation was provided, except for the seedlings that had permanently wilted. From the results of the linear regression analysis using the NDVI, the model excluding wilted seedlings with less than 20% (n=97) moisture content showed a precision ($R^2$ and $R^2_{\alpha}$) of 0.573 and 0.568, respectively, and accuracy (RE) of 4.138% and 4.138%, which was higher than that for models including all seedlings (n=100). For PLS regression analysis using the reflectance spectrum, both models were found to have strong precision ($R^2$) with a rating of 0.822, but accuracy (RMSE and RE) was higher in the model excluding wilted seedlings as 5.544% and 13.65% respectively. Conclusions: The estimation model of the moisture content of cucumber seedlings showed better results in the PLSR analysis using reflectance spectrum than the linear regression analysis using NDVI.

• Korean Journal of Remote Sensing
• /
• v.24 no.6
• /
• pp.551-558
• /
• 2008

It is well known that the protein content of rice grain is an indicator of taste of cooked rice in the countries where people as the staple food. Ground-based optical sensing over the crop canopy would provide information not only on the mass of plant body which reflects the light, but also on the crop nitrogen content which is closely related to the greenness of plant leaves. The vegetation index has been related to crop variables such as biomass, leaf nitrogen, plant cover, and chlorophyll in cereals. The objective of this study was to investigate the correlation between GNDVI and NDVI values, and grain protein content at different dates and to estimate the grain protein content using G(NDVI) values. We measured Green normalized difference vegetation index [$GNDVI=({\rho}0.80{\mu}m-{\rho}0.55{\mu}m)/({\rho}0.80{\mu}m+{\rho}0.55{\mu}m)$] and [$GNDVI=({\rho}0.80{\mu}m-{\rho}0.68{\mu}m)/({\rho}0.80{\mu}m+{\rho}0.68{\mu}m)$] by using two different active sensors. The study was conducted during the rice growing season for three years from 2005 through 2007 at the experimental plots of National Institute of Agricultural Science and Technology. The experiments were carried out by randomized complete block design with the application of four levels of nitrogen fertilizers(0, 70, 100, 130kg N/ha) and the same amount of phosphorous and potassium content of the fertilizers. After heading stage, relationships between GNDVI of rice canopy and grain protein content showed the highly positive correlation at different dates for three years. GNDVI values showed higher correlation coefficients than that of NDVI during growing season in 2005-07. The correlation between GNDVI values at different dates and grain protein contents was highly correlated at early July. We attempted to estimate the grain protein content at harvesting stage using GNDVI values from early July for three years. The determination coefficients of the linear model by GNDVI values were 0.9l and the measured and estimated grain protein content at harvesting stage using GNDVI values highly correlated($R^2=0.96^{***}$). Results from this study show that GNDVI appeared very effective to estimate leaf nitrogen and grain protein content of rice canopy.