Evaluation of the saturation property of vegetation indices derived from sentinel-2 in mixed crop-forest ecosystem

Aklilu Tesfaye Andualem,Gessesse Awoke Berhan 대한공간정보학회 2021 Spatial Information Research Vol.29 No.1

The saturation property of vegetation indices posed a known limitation and this study was motivated to understand the saturation property of three widely used vegetation indices in mixed crop-forest ecosystem where limited knowledge existed. Normalized Difference Vegetation Index (NDVI), Simple Ratio Index (SRI) and Transformed Vegetation Index (TVI) were computed from sentinel-2 bands and; variations among bands and among vegetation indices were evaluated. The study employed green Leaf Area Index (gLAI) Version 1 product, derived from PROBA-V daily data for discriminating the saturation property of the indices. Although the study applied various methods of image preprocessing and processing, best curve fitting and correlation analysis were the key ones. The three vegetation indices: NDVI, SRI, and TVI computed from sentinel-2 bands: four (red) and five (red edge) coupled with bands 8 and 8a showed some levels of saturation. Nonetheless, TVI computed from bands 8a and 4 is the best outperforming combination, i.e., the least saturated one and it is an interesting output in a sense that a single index with significantly lower values of noise equivalent green Leaf Area Index as well as having strong association with gLAI is obtained that could be very useful for quantification of gLAI in similar ecosystems. For the rest of the bands and vegetation indices combination of the indices via setting thresholds could be one possible solution.

VHI의 모니터링 성능 개선 및 기상학적 요인 변화에 따른 식생 반응의 정량적 평가

정하은(Haeun Jung),최시중(Sijung Choi),김상단(Sangdan Kim) 한국습지학회 2024 한국습지학회지 Vol.26 No.4

To assess vegetation drought, it is important to understand the relationship between meteorological factors and vegetation, and to accurately measure vegetation response to meteorological changes. This study introduced VHIs(Vegetation Health Indices) calculated by applying various weights to assess the relative contribution of VCI(Vegetation Condition Index(i.e. water stress of vegetation) and TCI(Temperature Condition Index)(i.e. temperature stress of vegetation) based on correlation analysis. Through this, the VHIopt(optimal VHI) that best explains the effects of meteorological drought on vegetation activity in the Nakdong River basin has been presented. The EDCI(Ecological Drought Condition Index)-VHIopt was proposed, which uses VHIopt to monitor the effects of meteorological drought on vegetation. EDCI-VHIopt is calculated based on a copula-based joint probability model between SPI(Standardized Precipitation Index) and VHIopt, which quantitatively assesses vegetation response to meteorological drought. As a result, the relative contribution of VCI and TCI in the Nakdong River basin varied from month to month and pixel to pixel. The proposed EDCI-VHIopt in this study showed that it canprovide useful information on vegetation response to spatiotemporally varying meteorological drought. This study can be used as a tool to understand vegetation responses to meteorological drought and provide a basis for addressing the deterioration of vegetation health due to meteorological drought. 식생 가뭄을 평가하기 위해서는 기상학적 요인과 식생 사이의 관계를 이해하고, 기상 변화에 따른 식생 반응을 정확히 측정하는 것이 중요하다. 본 연구에서는 상관성 분석을 기반으로 식생상태지수(VCI, Vegetation Condition Index)(즉, 식생의 수분 스트레스)와 열상태지수(TCI, Temperature Condition Index)(즉, 식생의 온도 스트레스)의 상대적 기여도를 평가하기 위해 다양한 가중치를 적용하여 계산된 식생건강지수들(VHIs, Vegetation Health Indices)을 도입하였다. 이를 통해 낙동강 유역 식생의 활동에 대한 기상학적 가뭄의 영향을 가장 잘 설명하는 최적 VHI(VHIopt, optimal VHI)가 제시되었다. VHIopt를 이용하여 기상학적 가뭄이 식생에 미치는 영향을 모니터링하는 생태가뭄상태지수(EDCI, Ecological Drought Condition Index)-VHIopt가 제안되었다. EDCI-VHIopt는 표준강수지수(SPI, Standardized Precipitation Index)와 VHIopt 사이의 copula 기반 결합 확률 모델을 기반으로 계산되었으며, 이는 기상학적 가뭄에 따른 식생 반응을 정량적으로 평가한다. 그 결과, 낙동강 유역에서의 VCI와 TCI의 상대적 기여도는 월별 및 픽셀별로 다르게 나타났다. 본 연구에서 제안한 EDCI-VHIopt는 시공간적으로 다르게 나타나는 기상학적 가뭄에 따른 식생 반응에 대해 유용한 정보를 제공할 수 있음을 보여주었다. 본 연구는 기상학적 가뭄에 따른 식생 반응에 대해 이해하고, 기상학적 가뭄으로 인한 식생 건강성 악화에 대처할 수 있는 근거를 제공하는 도구로 활용될 수 있을 것이다.

식생 활력도를 고려한 드론 기반의 식생지수 분석

조상호 ( Sang-ho Cho ),이근상 ( Geun-sang Lee ),황지욱 ( Jee-wook Hwang ) 한국지리정보학회 2020 한국지리정보학회지 Vol.23 No.2

Vegetation information is a very important factor used in various fields such as urban planning, landscaping, water resources, and the environment. Vegetation varies according to canopy density or chlorophyll content, but vegetation vitality is not considered when classifying vegetation areas in previous studies. In this study, in order to satisfy various applied studies, a study was conducted to set a threshold value of vegetation index considering vegetation vitality. First, an eBee fixed-wing drone was equipped with a multi-spectral camera to construct optical and near-infrared orthomosaic images. Then, GIS calculation was performed for each orthomosaic image to calculate the NDVI, GNDVI, SAVI, and MSAVI vegetation index. In addition, the vegetation position of the target site was investigated through VRS survey, and the accuracy of each vegetation index was evaluated using vegetation vitality. As a result, the scenario in which the vegetation vitality point was selected as the vegetation area was higher in the classification accuracy of the vegetation index than the scenario in which the vegetation vitality point was slightly insufficient. In addition, the Kappa coefficient for each vegetation index calculated by overlapping with each site survey point was used to select the best threshold value of vegetation index for classifying vegetation by scenario. Therefore, the evaluation of vegetation index accuracy considering the vegetation vitality suggested in this study is expected to provide useful information for decision-making support in various business fields such as city planning in the future.

Development of a Fusion Vegetation Index Using Full-PolSAR and Multispectral Data

김용현,오재홍,김용일 한국측량학회 2015 한국측량학회지 Vol.33 No.6

The vegetation index is a crucial parameter in many biophysical studies of vegetation, and is also a valuable content in ecological processes researching. The OVIs (Optical Vegetation Index) that of using multispectral and hyperspectral data have been widely investigated in the literature, while the RVI (Radar Vegetation Index) that of considering volume scattering measurement has been paid relatively little attention. Also, there was only some efforts have been put to fuse the OVI with the RVI as an integrated vegetation index. To address this issue, this paper presents a novel FVI (Fusion Vegetation Index) that uses multispectral and full-PolSAR (Polarimetric Synthetic Aperture Radar) data. By fusing a NDVI (Normalized Difference Vegetation Index) of RapidEye and an RVI of C-band Radarsat-2, we demonstrated that the proposed FVI has higher separability in different vegetation types than only with OVI and RVI. Also, the experimental results show that the proposed index not only has information on the vegetation greenness of the NDVI, but also has information on the canopy structure of the RVI. Based on this preliminary result, since the vegetation monitoring is more detailed, it could be possible in various application fields; this synergistic FVI will be further developed in the future.

드론원격탐사 기반 SVM 알고리즘을 활용한 하천 피복 분류 모델 개발

정경수,박종화,고승환,이경규 한국농촌계획학회 2024 농촌계획 Vol.30 No.1

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.

무인기를 이용한 이탈리안 라이그라스의 파종계절별 식생지수 비교

양승학,정종성,최기춘 한국초지조사료학회 2023 한국초지조사료학회지 Vol.43 No.2

Due to the recent impact of global warming, heavy rainfall and droughts have been occurring regardless of the season, affecting the growth of Italian ryegrass (IRG), a winter forage crop. Particularly, delayed sowing due to frequent heavy rainfall or autumn droughts leads to poor growth and reduced winter survival rates. Therefore, techniques to improve yield through additional sowing in spring have been implemented. In this study, the growth of IRG sown in Spring and Autumn was compared and analyzed using vegetation indices during the months of April and May. Spectral data was collected using an Unmanned Aerial Vehicle (UAV) equipped with a hyperspectral sensor, and the following vegetation indices were utilized: Normalized Difference Vegetation Index; NDVI, Normalized Difference Red Edge Index; NDRE (I), Chlorophyll Index, Red Green Ratio Index; RGRI, Enhanced Vegetation Index; EVI and Carotenoid Reflectance Index 1; CRI1. Indices related to chlorophyll concentration exhibited similar trends. RGRI of IRG sown in autumn increased during the experimental period, while IRG sown in spring showed a decreasing trend. The results of RGRI in IRG indicated differences in optical characteristics by sowing seasons compared to the other vegetation indices. Our findings showed that the timing of sowing influences the optical growth characteristics of crops by the results of various vegetation indices presented in this study. Further research, including the development of optimal vegetation indices related to IRG growth, is necessary in the future.

SPOT/VEGETATION NDVI 자료를 이용한 북한지역 식생 변화 탐지

염종민 ( Jong Min Yeom ),한경수 ( Kyung Soo Han ),이창석 ( Chang Suk Lee ),박윤영 ( Youn Young Park ),김영섭 ( Young Seup Kim ) 한국지리정보학회 2008 한국지리정보학회지 Vol.11 No.2

In this study, we perform land surface monitoring of NDVI (Normalized Difference Vegetation Index) variation by using remote sensing data during 1999-2005 over North Korea, which can`t easily access to measure directly land surface characteristics due to one of the world`s most closed societies. North Korea forest region has most abundant forest vegetation-so called Lungs of Korea in the Korea peninsula. NDVI represents vegetation activity used in many similar studies. In this study, we detect vegetation variation and analysis factors of the change over North Korea. By using variation of NDVI, we can infer that effect of drought over North Korea, and reduced vegetation indices by typhoon in North Korea. Land surface type except barren ground with decreased NDVI value is considered as when North Korea region was suffering from drought and typhoon effects, which show lower than mean of 7-year NDVI value. Especially, in recently, the food production of North Korea with political and economical issues can be inferred indirectly these trends by using estimated output data from this study.

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

Kyung-Do Lee,Chan-Won Park,Kyu-Ho So,Sang-Il Na 한국토양비료학회 2017 한국토양비료학회지 Vol.50 No.5

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 fixedwing 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≥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≤-0.98). There were no apparent relationships between rice growth parameters and vegetation indices using only Red-Green-Blue band images.

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 한국토양비료학회 2017 한국토양비료학회지 Vol.50 No.5

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.

Enhancing leaf area index estimation in tropical vegetation: a comparative study of multivariate linear regression and Sentinel Application Platform-derived leaf area index

Ali Yasin Ahmed,Abebe Mohammed Ali,Nurhussen Ahmed,Birhane Gebrehiwot The Ecological Society of Korea 2024 Journal of Ecology and Environment Vol.48 No.1

Background: The leaf area index (LAI) quantifies the total one-sided green leaf area per unit of soil area, making it a crucial parameter in models that simulate carbon, nutrient, water, and energy fluxes within forest ecosystems. This study enhances LAI estimation techniques by employing a multivariate linear regression (MVLR) approach specifically tailored to tropical vegetation. We integrated field-collected LAI data with spectral indices and multispectral bands to develop a robust predictive empirical model. The LAI estimates derived from the MVLR approach are rigorously compared with those obtained from the Sentinel Application Platform (SNAP), a widely utilized tool for remote sensing analysis. Results: In developing the MVLR model, nine multispectral bands, seven vegetation indices (VIs), and two biophysical variables derived from Sentinel-2 multispectral image were tested to identify efficient predictors for LAI estimation. To determine significant multispectral bands and VIs (ensuring no multicollinearity, high coefficient of determination (R²), low root mean square error (RMSE), and a p-value < 0.05) for the best representative model, stepwise multiple linear regression (SMLR) was employed. Multispectral bands 7 and 8, along with the VIs soil adjusted vegetation index and normalized difference vegetation index, and the fraction of vegetation cover biophysical variable, produced superior outcomes and serve as strong predictor variables for LAI. The accuracy of the MVLR model was validated using 17 directly measured LAI sample plots with the leave-one-out cross-validation method. The estimated LAI using the MVLR model achieved higher accuracy, with an R² of 0.94, compared to the SNAP toolbox (R² = 0.71). The RMSE and bias of the MVLR model were 0.18 and 0.006, respectively, while for SNAP-derived LAI, the RMSE and bias were 0.53 and 0.31, respectively. Conclusions: The improved accuracy and reduced error of the MVLR model are attributed to its adjustment for tropical vegetation types. Future research should focus on comparing the MVLR model with other global LAI products to further validate and enhance its applicability.