• 제목/요약/키워드: Optimal vegetation indices

검색결과 13건 처리시간 0.019초

Selection of Optimal Vegetation Indices and Regression Model for Estimation of Rice Growth Using UAV Aerial Images

  • Lee, Kyung-Do;Park, Chan-Won;So, Kyu-Ho;Na, Sang-Il
    • 한국토양비료학회지
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    • 제50권5호
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    • pp.409-421
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    • 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.

무인기를 이용한 이탈리안 라이그라스의 파종계절별 식생지수 비교 (Comparative Analysis of Italian Ryegrass Vegetation Indices across Different Sowing Seasons Using Unmanned Aerial Vehicles)

  • 양승학;정종성;최기춘
    • 한국초지조사료학회지
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    • 제43권2호
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    • pp.103-108
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    • 2023
  • 본 연구는 드론의 초분광장치를 이용하여 이탈리안 라이그라스 생육기간 중의 파종계절에 따른 식생지수 변화 및 생산성을 조사하였다. 수량성을 조사한 결과, 봄파종구의 건물수량이 가을파종구의 약 52%였으며 초장은 유의적으로 차이가 없었다. 식생지수를 산정하여 연속적인 패턴을 분석한 결과, 가을파종구의 대부분 식생지수가 시간이 지날수록 낮아지며, 봄파종구는 높아지는 유형을 보였으나 RGRI는 반대의 유형을 나타냈다. 재배기간에 따른 가을파종구의 건물수량과 RGRI의 상관성이 높았다.

Estimating Optimal-Band of NDVI and GNDVI by Vegetation Reflectance Characteristics of Crops.

  • Shin, Hyoung-Sub;Park, Jong-Hwa;Park, Jin-Ki;Kim, Seong-Joon;Lee, Mi-Seon
    • 대한원격탐사학회:학술대회논문집
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    • 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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    • pp.151-154
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    • 2008
  • Information on the area and spatial distribution of crop fields is needed for biomass production, arrangement of water resources, trace gas emission estimates, and food security. The present study aims to monitor crops status during the growing season by estimating its aboveground biomass and leaf area index (LAI) from field reflectance taken with a hand-held radiometer. Field reflectance values were collected over specific spectral bandwidths using a handheld radiometer(LI-1800). A methodology is described to use spectral reflectance as indicators of the vegetative status in crop cultures. Two vegetation indices were derived from these spectral measurements. In this paper, first we analyze each spectral reflectance characteristics of vegetation in the order of growth stage. Vegetation indices (NDVI, GNDVI) were calculated from crop reflectance. And assess the nature of relationships between LAI and VI, as measured by the in situ NDVI and GNDVI. Among the two VI, NDVI showed predictive ability across a wider range of LAI than did GNDVI. Specific objectives were to determine the relative accuracy of these two vegetation indices for predicting LAI. The results of this study indicated that the NDVI and GNDVI could potentially be applied to monitor crop agriculture on a timely and frequent basis.

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무인기 기반 동계 사료작물의 건물수량 예측을 위한 최적 식생지수 선정 (Selection of Optimal Vegetation Indices for Predicting Winter Crop Dry Matter Based on Unmanned Aerial Vehicle)

  • 신재영;이준민;양승학;임경재;이효진
    • 한국초지조사료학회지
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    • 제40권4호
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    • pp.196-202
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    • 2020
  • 본 연구는 동계사료작물의 무인기기반 생육모니터링을 위하여 호밀, 총체보리, IRG를 대상으로 다중분광영상으로 건물수량을 예측하기 위한 최적식생지수를 테스트하였다. 2019년 2월부터 4월까지 나주의 실경작지에서 무인기 다중분광카메라로 분광영상을 수집하여 4종류의 식생지수(Normalized Difference Vegetation Index; NDVI, Green Normalized Difference Vegetation Index; GNDVI, Normalized Green Red Difference Index; NGRDI and Normalized Difference Red Edge Index; NDREI)를 산출하고 지상에서 건물수량을 조사하여 식생지수와 건물수량의 상관관계를 조사하였다. 호밀, 총체보리, IRG에 대하여 건물수량과 NDVI의 상관관계(R2)는 0.91~0.92, GNDVI는 0.92~0.94, NGRDI는 0.71~0.85, NDREI는 0.84~0.91로 GNDVI가 가장 효과적이었다.

드론원격탐사 기반 SVM 알고리즘을 활용한 하천 피복 분류 모델 개발 (Development of Stream Cover Classification Model Using SVM Algorithm based on Drone Remote Sensing)

  • 정경수;고승환;이경규;박종화
    • 농촌계획
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    • 제30권1호
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    • pp.57-66
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    • 2024
  • This study aimed to develop a precise vegetation cover classification model for small streams using the combination of drone remote sensing and support vector machine (SVM) techniques. The chosen study area was the Idong stream, nestled within Geosan-gun, Chunbuk, South Korea. The initial stage involved image acquisition through a fixed-wing drone named ebee. This drone carried two sensors: the S.O.D.A visible camera for capturing detailed visuals and the Sequoia+ multispectral sensor for gathering rich spectral data. The survey meticulously captured the stream's features on August 18, 2023. Leveraging the multispectral images, a range of vegetation indices were calculated. These included the widely used normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) that factors in soil background, and the normalized difference water index (NDWI) for identifying water bodies. The third stage saw the development of an SVM model based on the calculated vegetation indices. The RBF kernel was chosen as the SVM algorithm, and optimal values for the cost (C) and gamma hyperparameters were determined. The results are as follows: (a) High-Resolution Imaging: The drone-based image acquisition delivered results, providing high-resolution images (1 cm/pixel) of the Idong stream. These detailed visuals effectively captured the stream's morphology, including its width, variations in the streambed, and the intricate vegetation cover patterns adorning the stream banks and bed. (b) Vegetation Insights through Indices: The calculated vegetation indices revealed distinct spatial patterns in vegetation cover and moisture content. NDVI emerged as the strongest indicator of vegetation cover, while SAVI and NDWI provided insights into moisture variations. (c) Accurate Classification with SVM: The SVM model, fueled by the combination of NDVI, SAVI, and NDWI, achieved an outstanding accuracy of 0.903, which was calculated based on the confusion matrix. This performance translated to precise classification of vegetation, soil, and water within the stream area. The study's findings demonstrate the effectiveness of drone remote sensing and SVM techniques in developing accurate vegetation cover classification models for small streams. These models hold immense potential for various applications, including stream monitoring, informed management practices, and effective stream restoration efforts. By incorporating images and additional details about the specific drone and sensors technology, we can gain a deeper understanding of small streams and develop effective strategies for stream protection and management.

원격탐사 기반 맥류 작황 추정을 위한 최적 식생지수 선정 - UAV와 현장 측정자료를 활용하여 - (Selection of Optimal Vegetation Indices for Estimation of Barley & Wheat Growth based on Remote Sensing - An Application of Unmanned Aerial Vehicle and Field Investigation Data -)

  • 나상일;박찬원;정영근;강천식;최인배;이경도
    • 대한원격탐사학회지
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    • 제32권5호
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    • pp.483-497
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    • 2016
  • 무인항공기 영상은 작물의 생육단계에 따라 고해상도로 신속한 수집이 가능하기 때문에 정밀농업 관리를 위한 공간 변이 분석에 활용되고 있다. 본 연구의 목적은 무인항공기를 이용한 최적 식생지수를 선정하여 맥류 작황 추정식을 유도하는 것이다. 무인항공기 영상은 맥류 생육 기간인 2월 하순부터 6월 하순까지 6회에 걸쳐 촬영하였으며, 같은 기간 동안 5개 품종(큰알보리, 흰찰쌀보리, 새찰쌀보리, 금강밀, 조품밀)을 대상으로 휴대용 분광복사계를 이용하여 현장분광반사율을 측정하고 초장, 지상부건물중, 단위면적당 경수 등 생육인자를 조사하였다. 그 결과, 6개의 식생지수 중 RVI, NDVI, NGRDI 및 GLI가 생육인자와 높은 상관관계를 나타내었다. 또한 현장 생육조사 자료를 사용하여 식생지수와 생육인자의 비교를 시도하였다.

Using Chlorophyll Fluorescence and Vegetation Indices to Predict the Timing of Nitrogen Demand in Pentas lanceolata

  • Wu, Chun-Wei;Lin, Kuan-Hung;Lee, Ming-Chih;Peng, Yung-Liang;Chou, Ting-Yi;Chang, Yu-Sen
    • 원예과학기술지
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    • 제33권6호
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    • pp.845-853
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    • 2015
  • The objective of this study was to predict the timing of nitrogen (N) demand through analyzing chlorophyll fluorescence (ChlF), soil-plant analysis development (SPAD), and normalized difference vegetation index (NDVI), which are positively correlated with foliar N concentration in star cluster (Pentas lanceolata). The plants were grown in potting soil under optimal conditions for 30 d, followed by weekly irrigation with five concentrations (0, 4, 8, 16, and 24 mM) of N for an additional 30 d. These five N application levels corresponded to leaf N concentrations of 2.62, 3.48, 4.00, 4.23, and 4.69%, respectively. We measured 13 morphological and physiological parameters, as well as the responses of these parameters to various N-fertilizer treatments. The general increases in Dickson's quality index (DQI), above-ground dry weight (DW), total DW, flowering rate, ${\Delta}F/Fm$', and qP in response to treatment with 0 to 8 mM N were similar to those of SPAD, NDVI, and Fv/Fm. Consistent and strong correlations ($R^2$= 0.60 to 0.85) were observed between leaf N concentration (%) and SPAD, NDVI, ${\Delta}F/Fm$', and above-ground DW. Validation of leaf S PAD, NDVI, and ${\Delta}F/Fm$' revealed that these vegetation indices are accurate predictors of leaf N concentration that can be used for non-destructive estimation of the proper timing for N-solution irrigation of P. lanceolata. Moreover, irrigation with 8 mM N-fertilizer i s recommended w hen leaf N concentration, SPAD, NVDI, and ${\Delta}F/Fm$' ratios are reduced from their saturation values of 4.00, 50.68, 0.64, and 0.137%, respectively.

SPOT/VEGETATION 자료를 이용한 한반도의 광합성유효복사율(FPAR)의 산출 (Retrieval of the Fraction of Photosynthetically Active Radiation (FPAR) using SPOT/VEGETATION over Korea)

  • 피경진;한경수
    • 대한원격탐사학회지
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    • 제26권5호
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    • pp.537-547
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    • 2010
  • FPAR는 다양한 육상 생태계 모텔에서 중요한 입력변수로 사용된다. 이 때문에 다양한 global product의 형태로 제공되고 있다. 하지만 한반도를 영역으로 하는 연구에 이를 바로 적용 시 오차가 발생할 수 있고, 이것은 위성자료를 이용한 지면 정보 산출에 있어서 직접적인 오차요인이 된다. 따라서 본 연구에서는 Terra/MODIS와 SPOT/VEGETATION 그리고 ECOCLIMAP 자료를 이용해 한반도에 최적화된 FPAR를 산출 하였고, 또한 기존에 사용하였던 LAI와의 관계식을 사용하지 않고, SPOT/VGT NDVI 로부터 계산된 FVC (Fraction Vegetation Cover)를 직접 이용하여 FPAR를 산출 하였다. 이를 위해 식생지수의 선형/비선형 관계를 이용하여 구하는 경험적인 방법을 적용하여 회귀분석을 수행한 결과 cropland와 forest에서 각각 결정계수 (Coefficient of Determination, $R^2$)가 0.9039. 0.7901으로 정확도가 높은 관계식을 도출해내었다. 최종적으로 Reference FPAR 자료와의 비교 분석을 통해 본 연구에서 산출된 FPAR가 전반적인 패턴을 잘 표현하면서 불규칙하게 발생하던 노이즈 또한 보정된 것을 확인 할 수 있었다. 이렇게 한반도에 최적화된 입력변수의 사용은 산출물의 정확도뿐만 아니라 연구의 질 향상에도 도움을 줄 것으로 사료된다.

Effects of Environmental Conditions on Vegetation Indices from Multispectral Images: A Review

  • Md Asrakul Haque;Md Nasim Reza;Mohammod Ali;Md Rejaul Karim;Shahriar Ahmed;Kyung-Do Lee;Young Ho Khang;Sun-Ok Chung
    • 대한원격탐사학회지
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    • 제40권4호
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    • pp.319-341
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    • 2024
  • The utilization of multispectral imaging systems (MIS) in remote sensing has become crucial for large-scale agricultural operations, particularly for diagnosing plant health, monitoring crop growth, and estimating plant phenotypic traits through vegetation indices (VIs). However, environmental factors can significantly affect the accuracy of multispectral reflectance data, leading to potential errors in VIs and crop status assessments. This paper reviewed the complex interactions between environmental conditions and multispectral sensors emphasizing the importance of accounting for these factors to enhance the reliability of reflectance data in agricultural applications.An overview of the fundamentals of multispectral sensors and the operational principles behind vegetation index (VI) computation was reviewed. The review highlights the impact of environmental conditions, particularly solar zenith angle (SZA), on reflectance data quality. Higher SZA values increase cloud optical thickness and droplet concentration by 40-70%, affecting reflectance in the red (-0.01 to 0.02) and near-infrared (NIR) bands (-0.03 to 0.06), crucial for VI accuracy. An SZA of 45° is optimal for data collection, while atmospheric conditions, such as water vapor and aerosols, greatly influence reflectance data, affecting forest biomass estimates and agricultural assessments. During the COVID-19 lockdown,reduced atmospheric interference improved the accuracy of satellite image reflectance consistency. The NIR/Red edge ratio and water index emerged as the most stable indices, providing consistent measurements across different lighting conditions. Additionally, a simulated environment demonstrated that MIS surface reflectance can vary 10-20% with changes in aerosol optical thickness, 15-30% with water vapor levels, and up to 25% in NIR reflectance due to high wind speeds. Seasonal factors like temperature and humidity can cause up to a 15% change, highlighting the complexity of environmental impacts on remote sensing data. This review indicated the importance of precisely managing environmental factors to maintain the integrity of VIs calculations. Explaining the relationship between environmental variables and multispectral sensors offers valuable insights for optimizing the accuracy and reliability of remote sensing data in various agricultural applications.

랜�V-5호(號) TM 데이타를 이용(利用)한 구분후(區分后) 비교(比較) 및 영상대차(映像對差)의 습지대(濕地帶) 변화(變化) 탐지(探知) 기법(技法)에 관(關)한 비교연구(比較硏究) (A Comparative Study of Wetland Change Detection Techniques Using Post-Classification Comparison and Image Differencing on Landsat-5 TM Data)

  • 정성학
    • 한국산림과학회지
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    • 제81권4호
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    • pp.346-356
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    • 1992
  • 미서부(美西部)의 광대한 Snake강(江) 범람평원은 홍수로 인하여 수로(水路) 및 식생형(植生型)의 빈번한 변화 및 침해를 받아왔다. 1985년과 1988년 기간 동안의 습지대 식생형의 변화를 탐지하기 위하여, 원격탐사의 변화탐지 기법(技法) 중 구분후(區分后) 비교(比較) 및 영상대차법(映像對差法) 등을 Landsat-5호 TM 디지탈 데이타를 이용하여 비교 고찰 하였다. 대차(對差)된 적외선대(外線帶) 영상들이 가시대(可視帶) 영상을보다 나은 정확도 지표(指標)를 보였으며, 역기법(閾技法)을 적용하여, 영상대차법에 의하여 변형된 영상들로부터 변화(變化)와 무변화(無變化)를 구분하였다. 또한, 여러 정확도 지표들 즉, 카파 일치계수(一致係數), 총정확도, 생산자 정확도, 이용자 정확도 및 평균정확도(생산자 및 이용자 정확도 등에 근거한) 등을 이용하여 최적역영역(最適閾領域)을 결정함에 있어서의 문제점들을 고찰하였다.

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