• Ecology and Resilient Infrastructure
• /
• v.3 no.3
• /
• pp.207-211
• /
• 2016

This study focuses on the categorization of the phenomenon of vegetative recruitment on riparian channels, so called, the phenomenon from "white river" to "green river", and proposes for the corresponding research direction. According to the literature review and research outputs obtained from the authors' previous research performed in Korea within a limited scope, the necessary and sufficient conditions for the recruitment and retrogression of riparian vegetation may be the mechanical disturbance (riverbed tractive stress), soil moisture (groundwater level, topography, composition of riverbed material, precipitation etc.), period of submergence, extreme weather, and nutrient inflow. In this study, two categories, one for the reduction in spring flood due to the change in spring precipitation pattern in unregulated rivers and the other for the increase in nutrient inflow into streams, both of which were partially proved, have been added in the categorization of the vegetative recruitment and retrogression on the riparian channels. In order to scientifically investigate further the phenomenon of the riparian vegetative recruitment and retrogression and develop the working riparian vegetative models, it is necessary to conduct a systematic nationwide survey on the "white to green" rivers, establishment of the categorization of the vegetation recruitment and retrogression based on the proof of those hypotheses and detailed categorization, development of the working mathematical models for the dynamic riparian vegetative recruitment and retrogression, and adaptive management for the river changes.

• Journal of Korean Society of Forest Science
• /
• v.86 no.3
• /
• pp.365-377
• /
• 1997

Much of the Cumberland Plateau of Tennessee, USA is in mixed hardwoods for which there are no applicable growth and yield predictors. Use of site index as a variable in growth and yield prediction models is limited in most stands because their history is not known and many may not be even-aged. Landtypes may offer an alternative to site index for these mixed stands because they were designed to include land of about equal productivity. To determine vegetation by landtype, dependency between landtype and detailed forest type was tested with Chi-square. Differences in productivity among landtypes were tested by employing regression analyses and analysis of variance(ANOVA). Basal area growth was fitted to the nonlinear models developed by Moser and Hall(1969). Basal area growth and volume growth were also predicted as a function of initial total basal area and initial volume with linear regression by landtype and by landtype class. Differences in basal area growth and volume growth by landtype were tested with ANOVA. Dependency between site class and landtype was tested with Chi-square. Vegetation types seem to be related to landtypes in the study area although the validity of the test is questionable because of a high proportion of sparsely occupied cells. No statistically significant differences in productivity among landtypes were found in this study.

• Journal of Korea Water Resources Association
• /
• v.53 no.10
• /
• pp.833-843
• /
• 2020

Ecohydraulics is a newly born discipline in the early 1990s by the interdisciplinary approach combined with aquatic ecology in one discipline and geomorphology, hydrology, and fluid hydrodynamics in another. Major areas of ecohydraulics can be delineated as habitat hydraulics (including environmental flow), vegetation hydraulics, eco-corridor hydraulics, eutrophication hydraulics, and ecological restoration hydraulics. Reviews of relevant international journals and literature reveal that ecohydraulics has remained in the limited areas of fish response, hydraulic modeling, and physical habitat response. It has not reached a truly interdisciplinary stage. Literature reviews in Korea reveal that only 3% of the total number of the papers listed in the Journal of KWRA during the last 24 years is related to ecohydraulics. It is about 20% of the total listed in the Journal of Ecology and Resilient Infrastructure. Most of those related to ecohydraulics in Korea concern vegetation hydraulics, habitat hydraulics, and ecological restoration hydraulics. In contrast, dynamic flow modeling areas, including turbulence, fauna motion simulation, and eutrophication hydraulics, are not found. Areas of further research in ecohydraulics in Korea may be specified as follows: 1) environmental flows adapted to the traits of the rivers in Korea, 2) development of the dynamic floodplain vegetation models (DFVM) to assess the changes from the white river to green river, 3) development of the eutrophication hydraulic model to predict the freshwater algal blooms, and 4) development of the models to evaluate the physical, chemical, and biological impacts of the stream restoration, decommissioning and removal of old weirs or small dams.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1489-1503
• /
• 2022

Drone and sensor technologies are enabling digitalization of agricultural crop's growth information and accelerating the development of the precision agriculture. These technologies could be able to assess damage of crops when natural disaster occurs, and contribute to the scientification of the crop insurance assessment method, which is being conducted through field survey. This study was aimed to calculate lodged damage rate from the vegetation indices extracted by drone based RGB images for soybean. Support Vector Classifier (SVC) models were considered by adding vegetation indices to the Crop Surface Model (CSM) based lodged damage rate. Visible Atmospherically Resistant Index (VARI) and Green Red Vegetation Index (GRVI) based lodged damage rate classification were shown the highest accuracy score as 0.709 and 0.705 each. As a result of this study, it was confirmed that drone based RGB images can be used as a useful tool for estimating the rate of lodged damage. The result acquired from this study can be used to the satellite imagery like Sentinel-2 and RapidEye when the damages from the natural disasters occurred.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_1
• /
• pp.1413-1425
• /
• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Journal of Ecology and Environment
• /
• v.35 no.3
• /
• pp.157-166
• /
• 2012

Many models intending to explain the latitudinal gradient of increasing species diversity from the poles to the equator are presented, which are a formalisation of the species-energy hypothesis. The model predictions are consistent with patterns of increasing species number with increasing mean air or water temperatures for plants and animals. An increase in species richness is also correlated with net primary production or the Normalised Difference Vegetation Index. This implies that increased availability of resources favours increased diversity capacity. The explanatory variables included in the biodiversity prediction models represent measures of water, energy, water-energy, habitat, history/evolution and biological responses. Water variables tend to be the best predictors when the geographic scope of the data is restricted to tropical and subtropical areas, whereas water-energy variables dominate when colder areas are included. In major models, about 20-35% of species in the various global regions (European, Africa, etc.) will disappear from each grid cell by 2050 and >50% could be vulnerable or threatened by 2080. This study provides good explanations for predictive models and future changes in biodiversity depending on various scenarios.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.49 no.5
• /
• pp.394-406
• /
• 2004

A split-plot designed experiment including four rice varieties and 10 nitrogen levels was conducted in 2003 at the Experimental Farm of Seoul National University, Suwon, Korea. Before heading, hyperspectral canopy reflectance (300-1100nm with 1.55nm step) and nine crop variables such as shoot fresh weight (SFW), leaf area index, leaf dry weight, shoot dry weight, leaf N concentration, shoot N concentration, leaf N density, shoot N density and N nutrition index were measured at 54 and 72 days after transplanting. Grain yield, total number of spikelets, number of filled spikelets and 1000-grain weight were measured at harvest. 14,635 narrow-band NDVIs as combinations of reflectances at wavelength ${\lambda}l\;and\;{\lambda}2$ were correlated to the nine crop variables. One NDVI with the highest correlation coefficient with a given crop variable was selected as the NDVI of the best fit for this crop variable. As expected, models to predict crop variables before heading using the NDVI of the best fit had higher $r^2$ (>10\%)$ than those using common broad- band NDVI red or NDVI green. The models with the narrow-band NDVI of the best fit overcame broad- band NDVI saturation at high LAI values as frequently reported. Models using NDVIs of the best fit at booting showed higher predictive capacity for yield and yield component than models using crop variables.

• Korean Journal of Hydrosciences
• /
• v.5
• /
• pp.1-16
• /
• 1994

In this paper distributed models for simulating spatially and temporally varied moving storm in a watershed were developed. The complete simulation in a watershed is achieved through two sequential flow simulations which are overland flow simulation and channel network flow simulation. Two dimensional continuity equation and momentum equation of kinematic approximation were used in the overland flow simulation. On the other hand, in the channel network simulation two types of governing equations which are one dimensional continuity and momentum equations between two adjacent sections in a channel, and continuity and energy equations at a channel junction were applied. The finite difference formulations were used in the channel network model. Macks Creek Experimental Watershed in Idaho, USA was selected as a target watershed and the moving storm on August 23, 1965, which continued from 3:30 P.M. to 5:30 P.M., was utilized. The rainfall intensity fo the moving storm in the watershed was temporally varied and the storm was continuously moved from one place to the other place in a watershed. Furthermore, runoff parameters, which are soil types, vegetation coverages, overland plane slopes, channel bed slopes and so on, are spatially varied. The good agreement between the hydrograph simulated using distributed models and the hydrograph observed by ARS are Shown. Also, the conservations of mass between upstreams and downstreams at channel junctions are well indicated and the wpatial and temporal vaiability in a watershed is well simulated using suggested distributed models.

• Journal of Biosystems Engineering
• /
• v.43 no.2
• /
• pp.148-159
• /
• 2018

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.1174-1176
• /
• 2003

Recently 3D city models are required for many applications such as urban microclimate, transportation navigation, landscape planning and visualization to name a few. The existing 3D city models mostly target on modeling buildings, but vegetation also plays an important role in the urban environment. To represent a more realistic urban environment through the 3D city model, in this research, an investigation is conducted to extract the position of trees from high resolution IKONOS imagery along with Airborne Laser Scanner data. Later, a tree growth model is introduced to simulate the growth of trees in the identified tree-positions.