• 한국토양비료학회지
• /
• 제50권5호
• /
• pp.409-421
• /
• 2017

Recently Unmanned Aerial Vehicle (UAV) technology offers new opportunities for assessing crop growth condition using UAV imagery. The objective of this study was to select optimal vegetation indices and regression model for estimating of rice growth using UAV images. This study was conducted using a fixed-wing UAV (Model : Ebee) with Cannon S110 and Cannon IXUS camera during farming season in 2016 on the experiment field of National Institute of Crop Science. Before heading stage of rice, there were strong relationships between rice growth parameters (plant height, dry weight and LAI (Leaf Area Index)) and NDVI (Normalized Difference Vegetation Index) using natural exponential function ($R{\geq}0.97$). After heading stage, there were strong relationships between rice dry weight and NDVI, gNDVI (green NDVI), RVI (Ratio Vegetation Index), CI-G (Chlorophyll Index-Green) using quadratic function ($R{\leq}-0.98$). There were no apparent relationships between rice growth parameters and vegetation indices using only Red-Green-Blue band images.

• 대한원격탐사학회지
• /
• 제40권1호
• /
• pp.9-18
• /
• 2024

Small streams, despite their rich ecosystems, face challenges in vegetation assessment due to the limitations of traditional, time-consuming methods. This study presents a groundbreaking approach, combining unmanned aerial vehicles(UAVs), convolutional neural networks(CNNs), and the vegetation differential vegetation index (VDVI), to revolutionize both assessment and management of stream vegetation. Focusing on Idong Stream in South Korea (2.7 km long, 2.34 km² basin area)with eight diverse revetment methods, we leveraged high-resolution RGB images captured by UAVs across five dates (July-December). These images trained a ResNeXt101 CNN model, achieving an impressive 89% accuracy in classifying vegetation cover(soil,water, and vegetation). This enabled detailed spatial and temporal analysis of vegetation distribution. Further, VDVI calculations on classified vegetation areas allowed assessment of vegetation vitality. Our key findings showcase the power of this approach:(a) TheCNN model generated highly accurate cover maps, facilitating precise monitoring of vegetation changes overtime and space. (b) August displayed the highest average VDVI(0.24), indicating peak vegetation growth crucial for stabilizing streambanks and resisting flow. (c) Different revetment methods impacted vegetation vitality. Fieldstone sections exhibited initial high vitality followed by decline due to leaf browning. Block-type sections and the control group showed a gradual decline after peak growth. Interestingly, the "H environment block" exhibited minimal change, suggesting potential benefits for specific ecological functions.(d) Despite initial differences, all sections converged in vegetation distribution trends after 15 years due to the influence of surrounding vegetation. This study demonstrates the immense potential of UAV-based remote sensing and CNNs for revolutionizing small-stream vegetation assessment and management. By providing high-resolution, temporally detailed data, this approach offers distinct advantages over traditional methods, ultimately benefiting both the environment and surrounding communities through informed decision-making for improved stream health and ecological conservation.

• 한국물환경학회지
• /
• 제28권4호
• /
• pp.582-594
• /
• 2012

A simple ecohydorlogic model that simulates hydrologic components and vegetation dynamics simultaneously based on equations of soil water dynamics and vegetation's growth and mortality is discussed. In order to simulate ungauged watersheds, the proposed model is calibrated with indirected estimated observation data set; 1) empirically estimated annual vaporization, 2) monthly surface runoff estimated by NRCS-CN method, and 3) vegetation fraction estimated by SPOT/VEGETATION NDVI. In order to check whether the model is performed well with indirectly estimated data or not, four upper dam watersheds (Andong, Habcheon, Namgang, Milyang) in Nakdong River watershed are selected, and the model is verified.

• Ecology and Resilient Infrastructure
• /
• 제1권2호
• /
• pp.68-81
• /
• 2014

본 연구에서는 2차원 흐름/유사이동 모형에 식생생장모형을 추가하여 하도의 식생 활착 및 성장에 의한 지형변화 과정과 특성을 수치모의 하였다. 교호사주가 발달하는 조건에서 식생 이입 및 활착은 사주의 이동을 감소시켰다. 식생면적 및 하폭의 변화는 저유량 지속시간보다 상류유량 변화에 더 크게 영향을 받았다. 상류유량이 감소하면 식생면적은 증가하고 하도폭은 감소하였다. 망상하도가 발달하는 조건에서 하도내 식생 이입 및 활착은 지형변화 특성에 크게 영향을 미쳤다. 망도하도에서 식생은 망상의 수를 감소시키고 결국 상류유량이 크게 감소하면 하도지형을 망상하도에서 단일수로로 변화시켰다. 식생면적은 상류유량이 증가함에 따라 감소하였다. 하도폭은 식생 도입 후 급격히 줄어들었고 상류유량 감소와 함께 감소하였다. 수치모의를 이용하여 홍수량 감소가 하도 내의 식생 이입 및 활착을 가속시키고 이로 인해 하도변화 특성에 미치는 영향을 정성적으로 확인할 수 있음을 보였다.

• 한국토양비료학회지
• /
• 제46권4호
• /
• 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.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
• /
• pp.1076-1080
• /
• 2003

$CO_2$ uptake of vegetation is one of the important variables in order to estimate photosynthetic activity, plant growth and carbon budget estimations. The objective of this research was to develop a new estimation method of $CO_2$ uptake of vegetation based on airborne hyperspectral remote sensing measurements in combination with a photosynthetic rate curve model. In this study, a compact airborne spectrographic imager (CASI) was used to obtain image over a field that had been set up to study the $CO_2$ uptake of corn on August 7, 2002. Also, a field survey was conducted concurrently with the CASI overpass. As a field survey, chlorophyll a content, photosynthetic rate curve, Leaf area, dry biomass and light condition were measured. The developed estimation method for $CO_2$ uptake consists of three major parts: a linear mixture model, an enhanced big leaf model and a photosynthetic rate curve model. The Accuracy of this scheme indicates that $CO_2$ uptake of vegetation could be estimated by using airborne hyperspectral remote sensing data in combination with a physiological model.

• 한국환경과학회지
• /
• 제32권3호
• /
• pp.161-171
• /
• 2023

The present study evaluated growth factors affecting oak community plantations through literature review and a field survey. Specifically, 41 related literature sources were analyzed and field surveys were conducted to collect growth data. Previous studies were analyzed to identify variables with high frequency of use. The frequency of use was in the order of tree size > environment > planting density > forest age. Analysis of factors impacting height and diameter growth revealed that the growth rate of species other than Quercus variabilis was negative in the field survey. This may be because of differences between the actual trees planted and specifications in the construction drawings, which may be attributed to the site conditions and decisions made by the project subject during construction. Furthermore, simple linear regression analysis was conducted with time, height at planting, density, and species code as the independent variables and growth rate as the dependent variable. A strong positive linear correlation was noted between height and diameter. This work builds a foundation for developing a forest restoration model and simulation program based on a regression model derived from the four variables tested.

• 한국수자원학회논문집
• /
• 제54권9호
• /
• pp.681-692
• /
• 2021

식생 프로세스는 증발산 제어를 통해 강우 유출 프로세스에 상당한 영향을 미치지만, 개념적인 집중형 수문 모형에서는 거의 고려되지 않는다. 본 연구는 인공위성에서 원격으로 감지된 엽면적지수 자료를 표현하는 생태 모듈을 수문 분할 모듈에 통합하여 합천댐 유역에 대한 모형 성능을 평가하였다. 제안된 생태 수문 모형은 습윤 지역의 생태수문 프로세스를 더 잘 표현하기 위하여 크게 세 가지 주요한 특징을 가진다. 1) 식생의 성장률은 유역의 물 부족 스트레스에 의해 제약을 받는다. 2) 식생의 최대 성장은 유역 기후에 의한 에너지에 의해 제약을 받는다. 3) 식생과 대수층의 상호작용이 반영된다. 제안된 모형은 유역 단위의 수문 성분과 식생 동역학을 동시에 모의한다. SCEM 알고리즘에 의해 추정된 모형 매개변수를 이용한 검증 결과로부터 아래와 같은 발견할 수 있었다. 1) 엽면적지수와 하천유량 자료를 이용하여 생태수문모형의 매개변수를 추정하는 것이 생태 모듈이 없는 수문 모형과 비슷한 정확도 및 견고함으로 하천유량을 예측할 수 있다. 2) 필터링이 안된 원격으로 감지된 엽면적지수를 그대로 입력자료로 이용하는 것은 하천유량 예측에 도움이 안된다. 3) 통합된 생태수문모형은 엽면적지수의 계절적인 변동성에 대한 우수한 추정치를 제공할 수 있다.

• Journal of Biosystems Engineering
• /
• 제34권4호
• /
• pp.269-277
• /
• 2009

In this research, the vegetation growth and rice taste of the liquid fertilizer applied fields (LF) were compared with those of chemical fertilizer applied fields(CF) in order to confirm the possibility of methane fermentation digested sludge as liquid fertilizer using precision agriculture and remote sensing technology. In panicle initiation stage, the vegetation growth at LF was 60%~80% of it at CF and there were significant difference of nitrogen contents between CF and LF. The estimation model of nitrogen contents was established by GNDVI (R=0.607, RMSE=$1.04\;g/m^2$, n=36, p<0.01). In heading stage, vegetation growth at LF went close to it at CF as ratio of 80%~95%. The nitrogen content estimation model was also established (R=0.650, RMSE=$1.73\;g/m^2$, n=35, p<0.01) and there were significant difference of spatial variability between LF and CF. There were not significant difference of rice taste and it's elements, when three samples, which were more than twice of standard deviation, were excepted. The protein contents estimation model using GNDVI of before harvesting (R=0.700, RMSE=0.470%, n=29, p<0.01) were more suitable to predict the protein contents at harvesting comparing with it of heading stage(R=0.610, RMSE=0.521%, n=29, p<0.01).

• KIEAE Journal
• /
• 제13권5호
• /
• pp.89-96
• /
• 2013

Green roofs can reduce surface water runoff, provide a habitat for wildlife moderate the urban heat island effect, improve building insulation and energy efficiency, improve the air quality, create aesthetic and amenity value, and preserve the roof's waterproofing. Green roofs are mainly divided into three types : intensive, simple-intensive, and extensive. Especially, extensive roof environment is a harsh one for plant growth; limited water availability, wide temperature fluctuations, high exposure to wind and solar radiation create highly stressed environment. This study, aimed at extensive green roof, was carried out on the rooftop of the library at Seoul Women's Univ. from October to November, 2012 and from March to August, 2013. To suggest the most effective vegetation model for biodiversity and heat island mitigation, surface temperatures were monitored by each vegetation model. We found that herbaceous plants of Aster sphathulifolius, Aceriphyllum rossii and Belamcanda chinensis, shrub of Syringa patula 'Miss Kim', Thymus quinquecostatus var. japonica, Sedum species can mixing each other. Among them, the vegetation models including Sedum takesimense, Aster sphathulifolius, Thymus quinquecostatus var. japonica was more effective on the surface temperature mitigation, because the species have the tolerance and high ratio of covering, and also in water. Especially, in the treatment of bark mulching, they helped to increase the temperature of vegetation models. In the case of summer, temperature mitigation of vegetation models were no significant difference among vegetation types. Compared to surface temperature of June, July and August were apparent impact of temperature mitigation, it shows that temperature mitigation are strongly influenced by substrate water content.