scholarly journals Using UAV Borne, Multi-Spectral Imaging for the Field Phenotyping of Shoot Biomass, Leaf Area Index and Height of West African Sorghum Varieties under Two Contrasted Water Conditions

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 850
Author(s):  
Boubacar Gano ◽  
Joseph Sékou B. Dembele ◽  
Adama Ndour ◽  
Delphine Luquet ◽  
Gregory Beurier ◽  
...  
Keyword(s):  
Plant Height ◽  
Vegetation Index ◽  
Plant Biomass ◽  
Vegetation Indices ◽  
West African ◽  
Severe Drought ◽  
Destructive Sampling ◽  
Area Index ◽  
Field Phenotyping ◽  
Water Treatments

Meeting food demand for the growing population will require an increase to crop production despite climate changes and, more particularly, severe drought episodes. Sorghum is one of the cereals most adapted to drought that feed millions of people around the world. Valorizing its genetic diversity for crop improvement can benefit from extensive phenotyping. The current methods to evaluate plant biomass, leaves area and plants height involve destructive sampling and are not practical in breeding. Phenotyping relying on drone based imagery is a powerful approach in this context. The objective of this study was to develop and validate a high throughput field phenotyping method of sorghum growth traits under contrasted water conditions relying on drone based imagery. Experiments were conducted in Bambey (Senegal) in 2018 and 2019, to test the ability of multi-spectral sensing technologies on-board a UAV platform to calculate various vegetation indices to estimate plants characteristics. In total, ten (10) contrasted varieties of West African sorghum collection were selected and arranged in a randomized complete block design with three (3) replicates and two (2) water treatments (well-watered and drought stress). This study focused on plant biomass, leaf area index (LAI) and the plant height that were measured weekly from emergence to maturity. Drone flights were performed just before each destructive sampling and images were taken by multi-spectral and visible cameras. UAV-derived vegetation indices exhibited their capacity of estimating LAI and biomass in the 2018 calibration data set, in particular: normalized difference vegetative index (NDVI), corrected transformed vegetation index (CTVI), seconded modified soil-adjusted vegetation index (MSAVI2), green normalize difference vegetation index (GNDVI), and simple ratio (SR) (r2 of 0.8 and 0.6 for LAI and biomass, respectively). Developed models were validated with 2019 data, showing a good performance (r2 of 0.92 and 0.91 for LAI and biomass accordingly). Results were also promising regarding plant height estimation (RMSE = 9.88 cm). Regression plots between the image-based estimation and the measured plant height showed a r2 of 0.83. The validation results were similar between water treatments. This study is the first successful application of drone based imagery for phenotyping sorghum growth and development in a West African context characterized by severe drought occurrence. The developed approach could be used as a decision support tool for breeding programs and as a tool to increase the throughput of sorghum genetic diversity characterization for adaptive traits.

scholarly journals Performances of Vegetation Indices on Paddy Rice at Elevated Air Temperature, Heat Stress, and Herbicide Damage

2020 ◽  
Vol 12 (16) ◽  
pp. 2654
Author(s):  
Jae-Hyun Ryu ◽  
Hoejeong Jeong ◽  
Jaeil Cho
Keyword(s):  
Heat Stress ◽  
Plant Height ◽  
Spectral Reflectance ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Paddy Rice ◽  
Stress Conditions ◽  
Area Index ◽  
Air Temperatures

Spectral reflectance-based vegetation indices have sensitive characteristics to crop growth and health conditions. The performance of each vegetation index to a certain condition is different and needs to be interpreted, correspondingly. This study aimed to assess the most suitable vegetation index to identify the crop response against elevated air temperatures, heat stress, and herbicide damage. The spectral reflectance, yield components, and growth parameters such as plant height, leaf area index (LAI), and above-ground dry matter of paddy rice, which was cultivated in a temperature gradient field chamber to simulate global warming conditions, were observed from 2016 to 2018. The relationships between the vegetation indices and the crop parameters were assessed considering stress conditions. The normalized difference vegetation index (NDVI) represented the changes in plant height (R-square = 0.93) and the LAI (R-square = 0.901) before the heading stage. Furthermore, the NDVI and the cumulative growing degree days had a Sigmoid curve and an R-square value of 0.937 under the normal growth case, but it decreased significantly in the herbicide damage case. This characteristic was useful for detecting the damaged crop growth condition. Additionally, to estimate the grain yield of paddy rice, the medium resolution imaging spectrometer (MERIS) terrestrial chlorophyll index was better: R-square = 0.912; root mean square error = 95.69 g/m2. Photochemical reflectance index was sensitive to physiological stress caused by the heatwave, and it decreased in response to extremely high air temperatures. These results will contribute towards determining vegetation indices under stress conditions and how to effectively utilize them.

scholarly journals Relationships of NDVI, Biomass, and Leaf Area Index (LAI) for six key plant species in Barrow, Alaska

2015 ◽  
Author(s):  
Santonu Goswami ◽  
John Gamon ◽  
Sergio Vargas ◽  
Craig Tweedie
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Species ◽  
Vegetation Index ◽  
Plant Biomass ◽  
Plant Cover ◽  
Strongly Correlated ◽  
Area Index ◽  
Spectral Bands ◽  
Biomass Plant

Here we investigate relationships between NDVI, Biomass, and Leaf Area Index (LAI) for six key plant species near Barrow, Alaska. We explore how key plant species differ in biomass, leaf area index (LAI) and how can vegetation spectral indices be used to estimate biomass and LAI for key plant species. A vegetation index (VI) or a spectral vegetation index (SVI) is a quantitative predictor of plant biomass or vegetative vigor, usually formed from combinations of several spectral bands, whose values are added, divided, or multiplied in order to yield a single value that indicates the amount or vigor of vegetation. For six key plant species, NDVI was strongly correlated with biomass (R2 = 0.83) and LAI (R2 = 0.70) but showed evidence of saturation above a biomass of 100 g/m2 and an LAI of 2 m2/m2. Extrapolation of a biomass-plant cover model to a multi-decadal time series of plant cover observations suggested that Carex aquatilis and Eriophorum angustifolium decreased in biomass while Arctophila fulva and Dupontia fisheri increased 1972-2008.

scholarly journals Estimation of Peanut Leaf Area Index from Unmanned Aerial Vehicle Multispectral Images

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6732
Author(s):  
Haixia Qi ◽  
Bingyu Zhu ◽  
Zeyu Wu ◽  
Yu Liang ◽  
Jianwen Li ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Field Experiments ◽  
Predictive Accuracy ◽  
Vegetation Indices ◽  
Planting Density ◽  
Coefficient Of Determination ◽  
Multispectral Images ◽  
Area Index

Leaf area index (LAI) is used to predict crop yield, and unmanned aerial vehicles (UAVs) provide new ways to monitor LAI. In this study, we used a fixed-wing UAV with multispectral cameras for remote sensing monitoring. We conducted field experiments with two peanut varieties at different planting densities to estimate LAI from multispectral images and establish a high-precision LAI prediction model. We used eight vegetation indices (VIs) and developed simple regression and artificial neural network (BPN) models for LAI and spectral VIs. The empirical model was calibrated to estimate peanut LAI, and the best model was selected from the coefficient of determination and root mean square error. The red (660 nm) and near-infrared (790 nm) bands effectively predicted peanut LAI, and LAI increased with planting density. The predictive accuracy of the multiple regression model was higher than that of the single linear regression models, and the correlations between Modified Red-Edge Simple Ratio Index (MSR), Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), and LAI were higher than the other indices. The combined VI BPN model was more accurate than the single VI BPN model, and the BPN model accuracy was higher. Planting density affects peanut LAI, and reflectance-based vegetation indices can help predict LAI.

scholarly journals Fluxo de CO2 e os Índices de Vegetação do Parque Nacional das Nascentes do Rio Parnaíba, Piauí, Brasil

2020 ◽  
Vol 13 (07) ◽  
pp. 3585
Author(s):  
Luana De Castro Pereira ◽  
Arnon Batista Nunes ◽  
Israel Lobato Rocha ◽  
Janeil Lustosa De Oliveira ◽  
Maria Letícia Stefany Monteiro Brandão ◽  
...  
Keyword(s):  
Carbon Balance ◽  
National Park ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Carbon Flow ◽  
Natural Environments ◽  
Area Index ◽  
Vegetation Indexes ◽  
Co2 Flow ◽  
Good Coverage

As emissões dos gases de efeito estufa na atmosfera trazem consequências para o meio ambiente e saúde pública. Logo, ambientes naturais, como as Florestas Nativas do Cerrado são essenciais no processo de equilíbrio de carbono, pela fixação do mesmo. Com o objetivo estimar o fluxo de CO2 com base em diferentes índices de vegetação do Parque Nacional das Nascentes do Rio Parnaíba (PNNRP), essa pesquisa, utilizou-se dos seguintes índices: Pré Processamento das Imagens (PPI), Índice de Vegetação por Diferença Normalizada – NDVI, Índice de Vegetação Fotossintético – PRI, Índice de Vegetação Ajustado ao Solo – SAVI, Índice de Área Foliar- IAF e CO2FLUX.  Referente ao Índice de Vegetação por Diferença Normalizada (NDVI), verificou-se que a maior parte da área PNNRP se encontra sob a vegetação considerada densa, sendo os  valores de SAVI encontrados próximos aos valores de NDVI, que pode estar relacionado a uma boa cobertura vegetal presente, indicando pouca influência das características do solo sob os índices de vegetação. A partir dos resultados encontrados através do IAF do PNNRP verificou que em áreas que os valores são maiores encontram-se as vegetações com o melhor desenvolvimento. Levando em conta os valores relacionados ao CO2Flux, IAF, NDVI e os demais índices, percebeu-se a capacidade do Parque no aproveitamento da luz solar e a realização da fotossíntese, além de abrigar uma vegetação saudável, podendo assim afirmar o grande potencial do PNNRP em armazenar carbono. Portanto, evidencia-se que o Parque Nacional das Nascentes do Rio Parnaíba possuí uma alto potencial de fluxo de carbono.   CO2 flow and vegetation indices of the Parque Nacional das Nascentes do Rio Parnaíba, Piauí, Brazil A B S T R A C TEmissions of greenhouse gases into the atmosphere have consequences for the environment and public health. Therefore, natural environments, such as the Cerrado's Native Forests are essential in the carbon balance process, due to its fixation. With the objective of estimating the CO2 flow based on different vegetation indexes of the Nascentes do Rio Parnaíba National Park (PNNRP), this research used the following indexes: Pre-Processing of Images (PPI), Vegetation Index by Difference Normalized - NDVI, Photosynthetic Vegetation Index - PRI, Soil Adjusted Vegetation Index - SAVI, Leaf Area Index - IAF and CO2FLUX. Regarding the Index of Vegetation by Normalized Difference (NDVI), it was found that most of the PNNRP area is under dense vegetation, with SAVI values found close to NDVI values, which may be related to good coverage present, indicating little influence of soil characteristics on vegetation indexes. From the results found through the IAF of the PNNRP verified that in areas with higher values are the vegetation with the best development. Taking into account the values related to CO2Flux, IAF, NDVI and other indexes, the Park's capacity to use sunlight and photosynthesis was observed, as well as to house healthy vegetation, thus confirming the great potential of PNNRP in storing carbon. Therefore, it is evident that the Parnaíba River National Park has a high carbon flow potential.Keywords: biomass, cerrado biome, carbon flow

scholarly journals NEW VEGETATION INDICES FOR FULL AND COMPACT POLARIMETRIC SAR DATA: IN PREPARATION FOR THE RADARSAT CONSTELLATION MISSION (RCM)

2020 ◽  
Vol IV-3/W2-2020 ◽  
pp. 41-46
Author(s):  
D. Ratha ◽  
D. Mandal ◽  
S. Dey ◽  
A. Bhattacharya ◽  
A. Frery ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Geodesic Distance ◽  
Vegetation Indices ◽  
Series Data ◽  
Biophysical Parameters ◽  
Area Index ◽  
Plant Area Index ◽  
Sar Data

Abstract. In this paper, we present two radar vegetation indices for full-pol and compact-pol SAR data, respectively. Both are derived using the notion of a geodesic distance between observation and well-known scattering models available in the literature. While the full-pol version depends on a generalized volume scattering model, the compact-pol version uses the ideal depolariser to model the randomness in the vegetation. We have utilized the RADARSAT Constellation Mission (RCM) time-series data from the SAMPVEX16-MB campaign in the Manitoba region of Canada for comparing and assessing the indices in terms of the change in the biophysical parameters as well. The compact-pol data for comparison is simulated from the full-pol RCM time series. Both the indices show better performance at correlating with biophysical parameters such as Plant Area Index (PAI) and Volumetric Water Content (VWC) for wheat and soybean crops, in comparison to the state-of-art Radar Vegetation Index (RVI) of Kim and van Zyl. These indices are timely for the upcoming release of the data from the RCM, which will provide data in both full and compact-pol modes, aimed at better crop monitoring from space.

scholarly journals The Ongoing Greening in Southwest China despite Severe Droughts and Drying Trends

2021 ◽  
Vol 13 (17) ◽  
pp. 3374
Author(s):  
Xin Chen ◽  
Tiexi Chen ◽  
Qingyun Yan ◽  
Jiangtao Cai ◽  
Renjie Guo ◽  
...  
Keyword(s):  
Vegetation Change ◽  
Vegetation Index ◽  
Southwest China ◽  
Vegetation Indices ◽  
Climate Extremes ◽  
Global Scale ◽  
Area Index ◽  
Huge Impact ◽  
Increasing Trends

Vegetation greening, which refers to the interannual increasing trends of vegetation greenness, has been widely found on the regional to global scale. Meanwhile, climate extremes, especially several drought, significantly damage vegetation. The Southwest China (SWC) region experienced massive drought from 2009 to 2012, which severely damaged vegetation and had a huge impact on agricultural systems and life. However, whether these extremes have significantly influenced long-term (multiple decades) vegetation change is unclear. Using the latest remote sensing-based records, including leaf area index (LAI) and gross primary productivity (GPP) for 1982–2016 and enhanced vegetation index (EVI) for 2001–2019, drought events of 2009–2012 only leveled off the greening (increasing in vegetation indices and GPP) temporally and long-term greening was maintained. Meanwhile, drying trends were found to unexpectedly coexist with greening.

scholarly journals Estimativa de emissão de metano na formação do reservatório da hidrelétrica Serra do Facão / Estimate of methane emission from the filling of hydroelectric dam of Serra do Facão in the State of Goiás - Brazil

2017 ◽  
Vol 27 (48) ◽  
pp. 1-26
Author(s):  
Simone Pereira Ferreira ◽  
Rita de Cássia Marques Alves ◽  
Flavio Varone Gonçalves
Keyword(s):  
Vegetation Index ◽  
Hydroelectric Plant ◽  
Vegetation Indices ◽  
Hydroelectric Power Plant ◽  
Methane Emissions ◽  
Landsat Tm ◽  
Hydroelectric Power ◽  
Area Index ◽  
Cerrado Vegetation ◽  
Hydroelectric Dam

hidrelétrica Serra do Facão utilizando imagens Landsat TM. A metodologia desenvolvida neste trabalho compreende as etapas das correções geométricas e radiométricas, recorte e processamento das imagens. Para realizar a estimativa foi necessário calcular índices de vegetação e identificar características como biomassa, índice de área foliar, atividade fotossintética, produtividade. Os resultados obtidos neste trabalho são do metano estimado durante o enchimento do lago da represa da usina hidrelétrica Serra do Facão. Os valores estimados de metano variaram entre 2,38 e 64,08 kg/km2/dia e estão de acordo com os dados publicados no Relatório de Referência do Ministério de Ciência e Tecnologia (MCT) e com outros trabalhos desenvolvidos em reservatórios tropicais. A metodologia aqui descrita pode servir para mitigar os efeitos resultantes do enchimento de grandes reservatórios. Com a técnica pode-se identificar regiões prioritárias para a supressão da vegetação que ficará submersa.Palavras–chave: Sensoriamento remoto, Landsat, Cerrado, Índices de vegetaçãoAbstract The aim of this study is to estimate methane emissions during the filling of the reservoir of the Serra do Facão hydroelectric plant using Landsat TM images. The methodology developed in this work comprises the steps of radiometric and geometric corrections, cropping and image processing. To estimate methane emissions, we had to calculate vegetation indices, identify features like biomass, leaf area index, photosynthetic activity, productivity. The results obtained in this work are the estimated methane during the filling of the Lake of the dam of Serra do Facão hydroelectric power plant. The estimated values of methane varied between 2.38 and 64.08 kg/km2/day and are according to the data published in the report of Ministry of science and technology (MCT) and with other projects developed in tropical reservoirs. The methodology described here can serve to mitigate the effects of filling of large reservoirs. With the technique can identify priority regions for the removal of vegetation that will be submerged. Keywords: Remote sensing, Landsat, Cerrado, Vegetation index. 

scholarly journals The Impact of Non-Photosynthetic Vegetation on LAI Estimation by NDVI in Mixed Grassland

2020 ◽  
Vol 12 (12) ◽  
pp. 1979
Author(s):  
Dandan Xu ◽  
Deshuai An ◽  
Xulin Guo
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Vegetation Indices ◽  
Series Data ◽  
Area Index ◽  
Plant Area Index ◽  
The Impact

Leaf area index (LAI) is widely used for algorithms and modelling in the field of ecology and land surface processes. At a global scale, normalized difference vegetation index (NDVI) products generated by different remote sensing satellites, have provided more than 40 years of time series data for LAI estimation. NDVI saturation issues are reported in agriculture and forest ecosystems at high LAI values, creating a challenge when using NDVI to estimate LAI. However, NDVI saturation is not reported on LAI estimation in grasslands. Previous research implies that non-photosynthetic vegetation (NPV) reduces the accuracy of LAI estimation from NDVI and other vegetation indices. A question arises: is the absence of NDVI saturation in grasslands a result of low LAI value, or is it caused by NPV? This study aims to explore whether there is an NDVI saturation issue in mixed grassland, and how NPV may influence LAI estimation by NDVI. In addition, in-situ measured plant area index (PAI) by sensors that detect light interception through the vegetation canopy (e.g., Li-cor LAI-2000), the most widely used field LAI collection method, might create bias in LAI estimation or validation using NDVI. Thus, this study also aims to quantify the contribution of green vegetation (GV) and NPV on in-situ measured PAI. The results indicate that NDVI saturation (using the portion of NDVI only contributed by GV) exists in grassland at high LAI (LAI threshold is much lower than that reported for other ecosystems in the literature), and that the presence of NPV can override the saturation effects of NDVI used to estimate green LAI. The results also show that GV and NPV in mixed grassland explain, respectively, the 60.33% and 39.67% variation of in-situ measured PAI by LAI-2000.

scholarly journals Spectral variables, growth analysis and yield of sugarcane

2005 ◽  
Vol 62 (3) ◽  
pp. 199-207 ◽  
Author(s):  
Maurício dos Santos Simões ◽  
Jansle Vieira Rocha ◽  
Rubens Augusto Camargo Lamparelli
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Lower Amount ◽  
Total Biomass ◽  
Area Index ◽  
Spectral Vegetation Indices ◽  
Green Vegetation ◽  
Highly Correlated ◽  
Simple Ratio

Spectral information is well related with agronomic variables and can be used in crop monitoring and yield forecasting. This paper describes a multitemporal research with the sugarcane variety SP80-1842, studying its spectral behavior using field spectroscopy and its relationship with agronomic parameters such as leaf area index (LAI), number of stalks per meter (NPM), yield (TSS) and total biomass (BMT). A commercial sugarcane field in Araras/SP/Brazil was monitored for two seasons. Radiometric data and agronomic characterization were gathered in 9 field campaigns. Spectral vegetation indices had similar patterns in both seasons and adjusted to agronomic parameters. Band 4 (B4), Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), and Soil Adjusted Vegetation Index (SAVI) increased their values until the end of the vegetative stage, around 240 days after harvest (DAC). After that stage, B4 reflectance and NDVI values began to stabilize and decrease because the crop reached ripening and senescence stages. Band 3 (B3) and RVI presented decreased values since the beginning of the cycle, followed by a stabilization stage. Later these values had a slight increase caused by the lower amount of green vegetation. Spectral variables B3, RVI, NDVI, and SAVI were highly correlated (above 0.79) with LAI, TSS, and BMT, and about 0.50 with NPM. The best regression models were verified for RVI, LAI, and NPM, which explained 0.97 of TSS variation and 0.99 of BMT variation.

scholarly journals Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring

2020 ◽  
Vol 12 (7) ◽  
pp. 1207 ◽  
Author(s):  
Jian Zhang ◽  
Chufeng Wang ◽  
Chenghai Yang ◽  
Tianjin Xie ◽  
Zhao Jiang ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Growth Monitoring ◽  
Multispectral Images ◽  
Area Index ◽  
Uav Images

The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled spatial resolution in these studies might not be applicable to in situ UAV images. In order to obtain optimal spatial resolution of in situ UAV multispectral images for crop growth monitoring, a RedEdge Micasense 3 camera was installed onto a DJI M600 UAV flying at different heights of 22, 29, 44, 88, and 176m to capture images of seedling rapeseed with ground sampling distances (GSD) of 1.35, 1.69, 2.61, 5.73, and 11.61 cm, respectively. Meanwhile, the normalized difference vegetation index (NDVI) measured by a GreenSeeker (GS-NDVI) and leaf area index (LAI) were collected to evaluate the performance of nine vegetation indices (VIs) and VI*plant height (PH) at different GSDs for rapeseed growth monitoring. The results showed that the normalized difference red edge index (NDRE) had a better performance for estimating GS-NDVI (R2 = 0.812) and LAI (R2 = 0.717), compared with other VIs. Moreover, when GSD was less than 2.61 cm, the NDRE*PH derived from in situ UAV images outperformed the NDRE for LAI estimation (R2 = 0.757). At oversized GSD (≥5.73 cm), imprecise PH information and a large heterogeneity within the pixel (revealed by semi-variogram analysis) resulted in a large random error for LAI estimation by NDRE*PH. Furthermore, the image collection and processing time at 1.35 cm GSD was about three times as long as that at 2.61 cm. The result of this study suggested that NDRE*PH from UAV multispectral images with a spatial resolution around 2.61 cm could be a preferential selection for seedling rapeseed growth monitoring, while NDRE alone might have a better performance for low spatial resolution images.

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