Generation of Normalized Difference Vegetation Index Map for Precision Agriculture Using Small-Type Unmanned Aerial Vehicle

2014 ◽  
Author(s):  
Kazuhisa Chiba ◽  
Takuya Kamei ◽  
Tomonori Ito ◽  
Shinya Sugai ◽  
Satoshi Ohta ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Aerial Vehicle

scholarly journals New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle

2020 ◽  
Vol 12 (24) ◽  
pp. 4170
Author(s):  
Pengfei Chen ◽  
Fangyong Wang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Biomass Estimation ◽  
Growth Stages ◽  
Spectral Indices ◽  
Green Band ◽  
Validation Data ◽  
Texture Parameters ◽  
Aerial Vehicle

Although textural information can be used to estimate vegetation biomass, its use for estimating crop biomass is rare, and previous methods lacked a mechanistic explanation for the relationship to biomass. The objective of the present study was to develop mechanistic textural indices for estimating cotton biomass and solving saturation problems at medium and high biomass levels. A nitrogen (N) fertilization experiment was established, and unmanned aerial vehicle optical images and field measured biomass data were obtained during critical cotton growth stages. Based on these data, two textural indices, namely the normalized difference texture index combining contrast and the inverse difference moment of the green band (NBTI (CON, IDM)g) and normalized difference texture index combining entropy and the inverse difference moment of the green band (NBTI (ENT, IDM)g), were proposed by analyzing the mechanism of texture parameters for biomass prediction and the law of texture parameters changing with biomass. These indices were compared with spectral indices commonly used for biomass estimation using independent validation data, such as the normalized difference vegetation index (NDVI). The results showed that the proposed textural indices performed better than the spectral indices with no saturation problems occurring. The combination of spectral and textural indices using a stepwise regression method performed better for biomass estimation than using only spectral or textural indices. This method has considerable potential for improving the accuracy of biomass estimations for the subsequent delineation of precise cotton management zones.

UAV Remote Sensing Assessment of Crop Growth

2021 ◽  
Vol 87 (12) ◽  
pp. 891-899
Author(s):  
Freda Elikem Dorbu ◽  
Leila Hashemi-Beni ◽  
Ali Karimoddini ◽  
Abolghasem Shahbazi
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Crop Growth ◽  
Infrared Sensors ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Growth Indicators

The introduction of unmanned-aerial-vehicle remote sensing for collecting high-spatial- and temporal-resolution imagery to derive crop-growth indicators and analyze and present timely results could potentially improve the management of agricultural businesses and enable farmers to apply appropriate solution, leading to a better food-security framework. This study aimed to analyze crop-growth indicators such as the normalized difference vegetation index (NDVI), crop height, and vegetated surface roughness to determine the growth of corn crops from planting to harvest. Digital elevation models and orthophotos generated from the data captured using multispectral, red/green/blue, and near-infrared sensors mounted on an unmanned aerial vehicle were processed and analyzed to calculate the various crop-growth indicators. The results suggest that remote sensing-based growth indicators can effectively determine crop growth over time, and that there are similarities and correlations between the indicators.

scholarly journals Quantitative Identification of Maize Lodging-Causing Feature Factors Using Unmanned Aerial Vehicle Images and a Nomogram Computation

2018 ◽  
Vol 10 (10) ◽  
pp. 1528 ◽  
Author(s):  
Liang Han ◽  
Guijun Yang ◽  
Haikuan Feng ◽  
Chengquan Zhou ◽  
Hao Yang ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Characteristics ◽  
Canopy Structure ◽  
Canopy Cover ◽  
Structural Features ◽  
Discrimination Ability ◽  
Maize Varieties ◽  
Aerial Vehicle

Maize (zee mays L.) is one of the most important grain crops in China. Lodging is a natural disaster that can cause significant yield losses and threaten food security. Lodging identification and analysis contributes to evaluate disaster losses and cultivates lodging-resistant maize varieties. In this study, we collected visible and multispectral images with an unmanned aerial vehicle (UAV), and introduce a comprehensive methodology and workflow to extract lodging features from UAV imagery. We use statistical methods to screen several potential feature factors (e.g., texture, canopy structure, spectral characteristics, and terrain), and construct two nomograms (i.e., Model-1 and Model-2) with better validation performance based on selected feature factors. Model-2 was superior to Model-1 in term of its discrimination ability, but had an over-fitting phenomenon when the predicted probability of lodging went from 0.2 to 0.4. The results show that the nomogram could not only predict the occurrence probability of lodging, but also explore the underlying association between maize lodging and the selected feature factors. Compared with spectral features, terrain features, texture features, canopy cover, and genetic background, canopy structural features were more conclusive in discriminating whether maize lodging occurs at the plot scale. Using nomogram analysis, we identified protective factors (i.e., normalized difference vegetation index, NDVI and canopy elevation relief ratio, CRR) and risk factors (i.e., Hcv1) related to maize lodging, and also found a problem of terrain spatial variability that is easily overlooked in lodging-resistant breeding trials.

scholarly journals Hybrid Approach of Unmanned Aerial Vehicle and Unmanned Surface Vehicle for Assessment of Chlorophyll-a Imagery Using Spectral Indices in Stream, South Korea

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1930
Author(s):  
Eun-Ju Kim ◽  
Sook-Hyun Nam ◽  
Jae-Wuk Koo ◽  
Tae-Mun Hwang
Keyword(s):  
Chlorophyll A ◽  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Hybrid Approach ◽  
Weather Conditions ◽  
Field Application ◽  
Spectral Indices ◽  
Unmanned Surface Vehicle ◽  
Aerial Vehicle

The purpose of this study is to compare the spectral indices for a two-dimensional river algae map using an unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV) hybrid system. The UAV and USV hybrid systems can overcome the limitation of not being able to effectively compare images of the same region obtained at different times and under different seasonal conditions, when using a method of comparing and analyzing with absolute values in remote sensing. Radiometric correction was performed to minimize the interference that could distort the analysis results of the UAV imagery, and the images were taken under weather conditions that would minimally affect them. Three spectral indices, namely, normalized difference vegetation index (NDVI), normalized green–red difference index (NGRDI), green normalized difference vegetation index (GNDVI), and normalized difference red edge index (NDRE) were compared for the chlorophyll-a images. In field application and correlational analysis, the NDVI was strongly correlated with chlorophyll-a (R2 = 0.88, p < 0.001), and the GNDVI was moderately correlated with chlorophyll-a (R2 = 0.74, p < 0.001). As a result of comparing the chlorophyll-a concentration with the in-situ chlorophyll-a imagery by UAV, we obtained the RMSE of NDVI at 2.25, and the RMSE of GNDVI at 3.41.

scholarly journals UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2530 ◽  
Author(s):  
Vittorio Mazzia ◽  
Lorenzo Comba ◽  
Aleem Khaliq ◽  
Marcello Chiaberge ◽  
Paolo Gay
Keyword(s):  
Satellite Imagery ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Management Practices ◽  
Normalized Difference Vegetation Index ◽  
High Efficiency ◽  
Cost Effective ◽  
Inaccurate Information ◽  
Agricultural Applications ◽  
Aerial Vehicle

Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated description of the local status of crops is required. Remote sensing, and in particular satellite-based imagery, proved to be a valuable tool in crop mapping, monitoring, and diseases assessment. However, freely available satellite imagery with low or moderate resolutions showed some limits in specific agricultural applications, e.g., where crops are grown by rows. Indeed, in this framework, the satellite’s output could be biased by intra-row covering, giving inaccurate information about crop status. This paper presents a novel satellite imagery refinement framework, based on a deep learning technique which exploits information properly derived from high resolution images acquired by unmanned aerial vehicle (UAV) airborne multispectral sensors. To train the convolutional neural network, only a single UAV-driven dataset is required, making the proposed approach simple and cost-effective. A vineyard in Serralunga d’Alba (Northern Italy) was chosen as a case study for validation purposes. Refined satellite-driven normalized difference vegetation index (NDVI) maps, acquired in four different periods during the vine growing season, were shown to better describe crop status with respect to raw datasets by correlation analysis and ANOVA. In addition, using a K-means based classifier, 3-class vineyard vigor maps were profitably derived from the NDVI maps, which are a valuable tool for growers.

scholarly journals Ứng dụng công nghệ UAV (drones) theo dõi và hỗ trợ cảnh báo sớm dịch hại vùng canh tác lúa tỉnh Sóc Trăng

2021 ◽  
Vol 57 (2) ◽  
pp. 28-38
Author(s):  
Võ Quốc Tuấn ◽  
Tấn Lợi Nguyễn ◽  
Thị Dal Quãng ◽  
Trương Chí Quang ◽  
Quốc Việt Phạm
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Red Edge ◽  
Aerial Vehicle

Đồng bằng sông Cửu Long là vùng canh tác lúa trọng điểm của cả nước, tuy nhiên việc thâm canh tăng vụ trong nhiều năm đã làm cho tình hình sâu bệnh diễn biến phức tạp. Nghiên cứu được thực hiện nhằm ứng dụng công nghệ máy bay không người lái (UAV - unmanned aerial vehicle) để theo dõi và cảnh báo sớm dịch hại.  Nghiên cứu phân tích mối quan hệ giữa mức độ nhiễm dịch hại trên lúa dựa trên chỉ số khác biệt thực vật (NDVI - normalized difference vegetation index), chỉ số khác biệt rìa đỏ (NDRE - normalized difference red edge index), và số liệu điều tra thực địa được thu thập tại thời điểm chụp ảnh. Kết quả phân tích đã phân loại được 4 mức độ nhiễm dịch hại trên lúa: nhiễm dịch hại nặng, nhiễm dịch hại trung bình, nhiễm dịch hại nhẹ và không nhiễm dịch hại với tổng diện tích nhiễm là 11,37 ha. Trong đó, nhiễm nặng chiếm 2,1 ha, nhiễm trung bình chiếm 2,76 ha, nhiễm nhẹ chiếm 6,51 ha và không nhiễm là 12,33 ha. Qua đó cho thấy khả năng ứng dụng công nghệ UAV trong theo dõi và hỗ trợ cảnh báo sớm dịch hại trên cây lúa mang lại nhiều hiệu quả, góp phần nâng cao hiệu quả sản xuất lúa tại tỉnh Sóc Trăng nói riêng và vùng Đồng bằng sông Cửu Long nói chung.

scholarly journals Using unmanned aerial vehicle for identifying the vegetative vigor and quantify the area occupied by eucalyptus sprouts after chemical weeding in the state of Bahia, Brazil

2020 ◽  
pp. 165
Author(s):  
Caique Carvalho Medauar ◽  
Samuel de Assis Silva ◽  
Luis Carlos Cirilo Carvalho ◽  
Rafael Augusto Soares Tibúrcio ◽  
Paullo Augusto Silva Medauar
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Supervised Classification ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
New Technologies ◽  
Sampling Methods ◽  
Control Method ◽  
Aerial Images ◽  
Aerial Vehicle

Currently, the efficiency of chemical weeding for controlling eucalyptus sprouts is measured by field sampling, but the inefficiency of the sampling methods has led to the investigation of new technologies, such as using unmanned aerial vehicle (UAV) to help to identify the vegetative vigor of eucalyptus after chemical weeding. This study, therefore, used aerial images obtained by a UAV embedded with a sensor to identify the vegetative vigor and quantify the area occupied by eucalyptus sprouts 90 days after the chemical weeding. The study was conducted in three fields planted with eucalyptus whose sprouts had been previously controlled by the chemical weeding with the Scout® herbicide in November 2016. The vegetative vigor of the eucalyptus sprouts was evaluated from the aerial images obtained by the UAV with embedded sensor, during flights conducted in November 2016 and February 2017, that were used to calculate the normalized difference vegetation index and later, a random sample grid was constructed for each image by supervised classification of the area (m2) to determine the percentage occupied by the sprouts. The used chemical control method neither eradicated the sprouts nor reduced the sprout occupied area. The normalized difference vegetation index and supervised classification tools allowed determining with high precision sprout health status and size, generating interpretable data on the different evaluated fields and periods. The processing of the images obtained by the UAV provided a viable alternative of management to evaluate sprout status in reforestation areas.

scholarly journals Detection of Spatial and Temporal Variability of Wheat Cultivars by High-Resolution Vegetation Indices

Agronomy ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 226 ◽  
Author(s):  
Stefano Marino ◽  
Arturo Alvino
Keyword(s):  
High Resolution ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Growth Stages ◽  
Spatial And Temporal Variability ◽  
Area Index ◽  
Aerial Vehicle ◽  
High Resolution Images

An on-farm research study was carried out on two small-plots cultivated with two cultivars of durum wheat (Odisseo and Ariosto). The paper presents a theoretical approach for investigating frequency vegetation indices (VIs) in different areas of the experimental plot for early detection of agronomic spatial variability. Four flights were carried out with an unmanned aerial vehicle (UAV) to calculate high-resolution normalized difference vegetation index (NDVI) and optimized soil-adjusted vegetation index (OSAVI) images. Ground agronomic data (biomass, leaf area index (LAI), spikes, plant height, and yield) have been linked to the vegetation indices (VIs) at different growth stages. Regression coefficients of all samplings data were highly significant for both the cultivars and VIs at anthesis and tillering stage. At harvest, the whole plot (W) data were analyzed and compared with two sub-areas characterized by high agronomic performance (H) yield 20% higher than the whole plot, and low performances (L), about 20% lower of yield related to the whole plot). The whole plot and two sub-areas were analyzed backward in time comparing the VIs frequency curves. At anthesis, more than 75% of the surface of H sub-areas showed a VIs value higher than the L sub-plot. The differences were evident also at the tillering and seedling stages, when the 75% (third percentile) of VIs H data was over the 50% (second percentile) of the W curve and over the 25% (first percentile) of L sub-plot. The use of high-resolution images for analyzing the frequency value of VIs in different areas can be a useful approach for the detection of agronomic constraints for precision agriculture purposes.

scholarly journals IMAGENS DE VEÍCULO AÉREO NÃO TRIPULADO APLICADAS NA OBTENÇÃO DO ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA

2021 ◽  
Vol 36 (1) ◽  
pp. 111-122
Author(s):  
Felipe de Souza Nogueira Tagliarini ◽  
Mikael Timóteo Rodrigues ◽  
Bruno Timóteo Rodrigues ◽  
Yara Manfrin Garcia ◽  
Sérgio Campos
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
High Spatial Resolution ◽  
Spectral Response ◽  
Infrared Sensors ◽  
Aerial Vehicle ◽  
E Mail

IMAGENS DE VEÍCULO AÉREO NÃO TRIPULADO APLICADAS NA OBTENÇÃO DO ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA   FELIPE DE SOUZA NOGUEIRA TAGLIARINI1, MIKAEL TIMÓTEO RODRIGUES2-3, BRUNO TIMÓTEO RODRIGUES1; YARA MANFRIN GARCIA1 E SÉRGIO CAMPOS1   1 Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas (FCA) - Universidade Estadual Paulista (UNESP), Avenida Universitária, nº 3780, Altos do Paraíso, CEP: 18610-034, Botucatu, São Paulo, Brasil. E-mail: [email protected]; [email protected]; [email protected]; [email protected] 2 Centro Universitário Dinâmica das Cataratas (UDC), Rua Castelo Branco, nº 440, Centro, CEP: 85852-010, Foz do Iguaçu, Paraná, Brasil. E-mail: [email protected] 3 Parque Tecnológico Itaipu (PTI), Avenida Tancredo Neves, nº 6731, Jardim Itaipu, Caixa Postal: 2039, CEP: 85867-900, Foz do Iguaçu, Paraná, Brasil. E-mail: [email protected].   RESUMO: O advento dos Veículos Aéreos Não Tripulados (VANT) como ferramenta no sensoriamento remoto possibilitou uma plataforma atuante em diferentes áreas para o mapeamento com elevada precisão e resolução. O objetivo deste estudo consistiu na análise do Índice de Vegetação por Diferença Normalizada (NDVI) para elaboração de mapa temático por meio de aerofotogrametria e fotointerpretação, com maior detalhamento da vegetação devido à altíssima resolução espacial alcançada com o uso de imagens coletadas por VANT em trecho do rio Lavapés, dentro dos limites da Fazenda Experimental Lageado no município de Botucatu-SP. As imagens foram obtidas por meio dos sensores MAPIR Survey3W RGB e Survey3W NIR/InfraRED, embarcados em VANT multirrotor 3DR SOLO. Para construção dos ortomosaicos RGB e NDVI, as imagens foram processadas no software Pix4Dmapper 3.0. O resultado do NDVI proporcionou transição bem nítidas entre os alvos bióticos (vegetação) e os alvos abióticos (corpo d'água, solo e edificações), e também entre a própria vegetação, possibilitando a distinção da vegetação de porte arbóreo, com maior vigor vegetativo, em relação a vegetação de porte herbáceo. As imagens com elevada resolução espacial coletadas por VANT, demonstraram flexibilidade de utilização, possuindo elevado potencial para o mapeamento de dinâmica da paisagem e a resposta espectral da vegetação.   Palavras-chaves: drone, índice radiométrico, sensoriamento remoto   IMAGES OF UNMANNED AERIAL VEHICLE APPLIED TO OBTAIN THE NORMALIZED DIFFERENCE VEGETATION INDEX   ABSTRACT: The advent of Unmanned Aerial Vehicle (UAV) as a tool in remote sensing has enabled a platform acting in different areas for mapping with high precision and resolution. This study aimed to analyze the Normalized Difference Vegetation Index (NDVI) for the elaboration of thematic map through aerophotogrammetry and photointerpretation, with greater detail of vegetation due to high spatial resolution achieved with the use of images collected by UAV in a stretch of Lavapés river, inside the domains of Lageado Experimental Farm in the municipality of Botucatu-SP. The images were obtained through MAPIR Survey3W RGB and Survey3W NIR/InfraRED sensors, aboard a 3DR SOLO multirotor UAV. For constructing RGB and NDVI orthomosaics, the images were processed using Pix4Dmapper 3.0 software. The NDVI result provided a clear transition among biotic targets (vegetation) and abiotic targets (water, soil and buildings), and among the vegetation itself, with greater vegetative vigor, making possible the distinction of arboreal vegetation, in relation to herbaceous vegetation. The images with high spatial resolution collected by UAV demonstrated the flexibility of use, having high potential to mapping landscape dynamics and the spectral response of vegetation.   Keywords: drone, radiometric index, remote sensing.

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