scholarly journals Correlating the Plant Height of Wheat with Above-Ground Biomass and Crop Yield Using Drone Imagery and Crop Surface Model, A Case Study from Nepal

Drones ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 28 ◽  
Author(s):  
Uma Shankar Panday ◽  
Nawaraj Shrestha ◽  
Shashish Maharjan ◽  
Arun Kumar Pratihast ◽  
Shahnawaz ◽  
...  
Keyword(s):  
Food Security ◽  
Plant Height ◽  
Crop Yield ◽  
Earth Observation ◽  
Wheat Crop ◽  
Surface Model ◽  
Observation Data ◽  
Land Resources ◽  
Above Ground Biomass ◽  
Ground Biomass

Food security is one of the burning issues in the 21st century, as a tremendous population growth over recent decades has increased demand for food production systems. However, agricultural production is constrained by the limited availability of arable land resources, whereas a significant part of these is already degraded due to overexploitation. In order to get optimum output from the available land resources, it is of prime importance that crops are monitored, analyzed, and mapped at various stages of growth so that the areas having underdeveloped/unhealthy plants can be treated appropriately as and when required. This type of monitoring can be performed using ultra-high-resolution earth observation data like the images captured through unmanned aerial vehicles (UAVs)/drones. The objective of this research is to estimate and analyze the above-ground biomass (AGB) of the wheat crop using a consumer-grade red-green-blue (RGB) camera mounted on a drone. AGB and yield of wheat were estimated from linear regression models involving plant height obtained from crop surface models (CSMs) derived from the images captured by the drone-mounted camera. This study estimated plant height in an integrated setting of UAV-derived images with a Mid-Western Terai topographic setting (67 to 300 m amsl) of Nepal. Plant height estimated from the drone images had an error of 5% to 11.9% with respect to direct field measurement. While R2 of 0.66 was found for AGB, that of 0.73 and 0.70 were found for spike and grain weights respectively. This statistical quality assurance contributes to crop yield estimation, and hence to develop efficient food security strategies using earth observation and geo-information.

scholarly journals UAV-Based LiDAR for High-Throughput Determination of Plant Height and Above-Ground Biomass of the Bioenergy Grass Arundo donax

2020 ◽  
Vol 12 (20) ◽  
pp. 3464
Author(s):  
Mauro Maesano ◽  
Sacha Khoury ◽  
Farid Nakhle ◽  
Andrea Firrincieli ◽  
Alan Gay ◽  
...  
Keyword(s):  
Drought Stress ◽  
Plant Height ◽  
Fossil Fuels ◽  
Arundo Donax ◽  
Bioenergy Crops ◽  
Stepwise Multiple Regression ◽  
Surface Model ◽  
Above Ground Biomass ◽  
Crop Height ◽  
Ground Biomass

Replacing fossil fuels with cellulosic biofuels is a valuable component of reducing the drivers of climate change. This leads to a requirement to develop more productive bioenergy crops, such as Arundo donax with the aim of increasing above-ground biomass (AGB). However, direct measurement of AGB is time consuming, destructive, and labor-intensive. Phenotyping of plant height and biomass production is a bottleneck in genomics- and phenomics-assisted breeding. Here, an unmanned aerial vehicle (UAV) for remote sensing equipped with light detection and ranging (LiDAR) was tested for remote plant height and biomass determination in A. donax. Experiments were conducted on three A. donax ecotypes grown in well-watered and moderate drought stress conditions. A novel UAV-LiDAR data collection and processing workflow produced a dense three-dimensional (3D) point cloud for crop height estimation through a normalized digital surface model (DSM) that acts as a crop height model (CHM). Manual measurements of crop height and biomass were taken in parallel and compared to LiDAR CHM estimates. Stepwise multiple regression was used to estimate biomass. Analysis of variance (ANOVA) tests and pairwise comparisons were used to determine differences between ecotypes and drought stress treatments. We found a significant relationship between the sensor readings and manually measured crop height and biomass, with determination coefficients of 0.73 and 0.71 for height and biomass, respectively. Differences in crop heights were detected more precisely from LiDAR estimates than from manual measurement. Crop biomass differences were also more evident in LiDAR estimates, suggesting differences in ecotypes’ productivity and tolerance to drought. Based on these results, application of the presented UAV-LiDAR workflow will provide new opportunities in assessing bioenergy crop morpho-physiological traits and in delivering improved genotypes for biorefining.

scholarly journals Evaluation of root water uptake in the ISBA-A-gs land surface model using agricultural yield statistics over France

2014 ◽  
Vol 11 (5) ◽  
pp. 5421-5461
Author(s):  
N. Canal ◽  
J.-C. Calvet ◽  
B. Decharme ◽  
D. Carrer ◽  
S. Lafont ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Water Uptake ◽  
Land Surface ◽  
Land Surface Model ◽  
Root Water Uptake ◽  
Surface Model ◽  
Above Ground Biomass ◽  
Soil Layers ◽  
Ground Biomass ◽  
Cereal Grain

Abstract. The interannual variability of cereal grain yield and permanent grassland dry matter yield is simulated over French sites by the Interactions between Soil, Biosphere and Atmosphere, CO2-reactive (ISBA-A-gs) generic Land Surface Model (LSM). The two soil profile schemes available in the model are used to simulate the above-ground biomass (Bag) of cereals and grasslands: a 2-layer force-restore (FR-2L) bulk reservoir model and a multi-layer diffusion (DIF) model. The DIF model is implemented with or without deep soil layers below the root-zone. The evaluation of the various root water uptake models is achieved by using the French agricultural statistics of Agreste over the 1994–2010 period at 45 cropland and 48 grassland sites, for a range of rooting depths. The number of sites where the simulated annual maximum Bag presents a significant correlation with the yield observations is used as a metric to benchmark the root water uptake models. Significant correlations (p value < 0.01) are found for up to 29% of the cereal sites and 77% of the grassland sites. It is found that modelling additional subroot zone base flow soil layers does not improve (and may even degrade) the representation of the interannual variability of the vegetation above-ground biomass. These results are particularly robust for grasslands as calibrated simulations are able to represent the extreme 2003 and 2007 years corresponding to unfavourable and favourable fodder production, respectively.

scholarly journals Mangrove Above-Ground Biomass and Carbon Stock in the Karimunjawa-Kemujan Islands Estimated from Unmanned Aerial Vehicle-Imagery

2022 ◽  
Vol 14 (2) ◽  
pp. 706
Author(s):  
Anindya Wirasatriya ◽  
Rudhi Pribadi ◽  
Sigit Bayhu Iryanthony ◽  
Lilik Maslukah ◽  
Denny Nugroho Sugianto ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Carbon Stock ◽  
Digital Terrain Model ◽  
Satellite System ◽  
Allometric Equation ◽  
Surface Model ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Carbon Biomass ◽  
Aerial Vehicle

Blue carbon ecosystems in the Karimunjawa Islands may play a vital role in absorbing and storing the releasing carbon from the Java Sea. The present study investigated mangrove above-ground biomass (AGB) and carbon stock in the Karimunjawa-Kemujan Islands, the largest mangrove area in the Karimunjawa Islands. Taking the aerial photos from an Unmanned Aerial Vehicle combined with Global Navigation Satellite System (GNSS) measurements, we generated Digital Surface Model (DSM) and Digital Terrain Model (DTM) with high accuracy. We calculated mangrove canopy height by subtracting DSM from DTM and then converted it into Lorey’s height. The highest mangrove canopy is located along the coastline facing the sea, ranging from 8 m to 15 m. Stunted mangroves 1 m to 8 m in height are detected mainly in the inner areas. AGBs were calculated using an allometric equation destined for the Southeast and East Asia region. Above-ground carbon biomass is half of AGB. The AGB and carbon biomass of mangroves in the Karimunjawa-Kemujan Islands range from 8 Mg/ha to 328 Mg/ha, and from 4 MgC/ha to 164 MgC/ha, respectively. With a total area of 238.98 ha, the potential above-ground carbon stored in the study area is estimated as 16,555.46 Mg.

scholarly journals Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery

2019 ◽  
Vol 11 (11) ◽  
pp. 1261 ◽  
Author(s):  
Yaxiao Niu ◽  
Liyuan Zhang ◽  
Huihui Zhang ◽  
Wenting Han ◽  
Xingshuo Peng
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Plant Height ◽  
Linear Regression Model ◽  
Vegetation Indices ◽  
Point Clouds ◽  
Above Ground Biomass ◽  
Sensing System ◽  
Ground Biomass ◽  
Application Potential

The rapid, accurate, and economical estimation of crop above-ground biomass at the farm scale is crucial for precision agricultural management. The unmanned aerial vehicle (UAV) remote-sensing system has a great application potential with the ability to obtain remote-sensing imagery with high temporal-spatial resolution. To verify the application potential of consumer-grade UAV RGB imagery in estimating maize above-ground biomass, vegetation indices and plant height derived from UAV RGB imagery were adopted. To obtain a more accurate observation, plant height was directly derived from UAV RGB point clouds. To search the optimal estimation method, the estimation performances of the models based on vegetation indices alone, based on plant height alone, and based on both vegetation indices and plant height were compared. The results showed that plant height directly derived from UAV RGB point clouds had a high correlation with ground-truth data with an R2 value of 0.90 and an RMSE value of 0.12 m. The above-ground biomass exponential regression models based on plant height alone had higher correlations for both fresh and dry above-ground biomass with R2 values of 0.77 and 0.76, respectively, compared to the linear regression model (both R2 values were 0.59). The vegetation indices derived from UAV RGB imagery had great potential to estimate maize above-ground biomass with R2 values ranging from 0.63 to 0.73. When estimating the above-ground biomass of maize by using multivariable linear regression based on vegetation indices, a higher correlation was obtained with an R2 value of 0.82. There was no significant improvement of the estimation performance when plant height derived from UAV RGB imagery was added into the multivariable linear regression model based on vegetation indices. When estimating crop above-ground biomass based on UAV RGB remote-sensing system alone, looking for optimized vegetation indices and establishing estimation models with high performance based on advanced algorithms (e.g., machine learning technology) may be a better way.

scholarly journals High Throughput Determination of Plant Height, Ground Cover, and Above-Ground Biomass in Wheat with LiDAR

2018 ◽  
Vol 9 ◽  
Author(s):  
Jose A. Jimenez-Berni ◽  
David M. Deery ◽  
Pablo Rozas-Larraondo ◽  
Anthony (Tony) G. Condon ◽  
Greg J. Rebetzke ◽  
...  
Keyword(s):  
Plant Height ◽  
High Throughput ◽  
Ground Cover ◽  
Above Ground Biomass ◽  
Ground Biomass

Imidazolinone-Resistant Sunflower Tolerance to Imazapic

2009 ◽  
Vol 23 (1) ◽  
pp. 188-190
Author(s):  
Eric P. Prostko ◽  
Timothy L. Grey ◽  
Jerry W. Davis
Keyword(s):  
Plant Height ◽  
Production Systems ◽  
Field Trials ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Crop Injury ◽  
Seed Head ◽  
Herbicide Treatment ◽  
Biomass Plant ◽  
Sunflower Production

Field trials were conducted in Georgia in 2007 to 2008 to evaluate the tolerance of three imidazolinone-resistant sunflower cultivars to POST applications of imazapic. There was no interaction between sunflower cultivar and herbicide treatment. When averaged over sunflower cultivars, imazapic, at 70 and 140 g ai/ha and applied at 30 d after planting, had no effect on sunflower above-ground biomass, plant height, seed-heads per meter row, and seed-head weights. Sunflower response to imazapic was similar to that of imazamox. Imazapic could be used in imidazolinone-resistant sunflower production systems without risk of unacceptable crop injury.

scholarly journals The Effect of Droughts on Vegetation Condition in Germany: An Analysis Based on Two Decades of Satellite Earth Observation Time Series and Crop Yield Statistics

2019 ◽  
Vol 11 (15) ◽  
pp. 1783 ◽  
Author(s):  
Sophie Reinermann ◽  
Ursula Gessner ◽  
Sarah Asam ◽  
Claudia Kuenzer ◽  
Stefan Dech
Keyword(s):  
Time Series ◽  
Spatial Pattern ◽  
Crop Yield ◽  
Vegetation Index ◽  
Spatial Scales ◽  
Earth Observation ◽  
Observation Data ◽  
Temporal Development ◽  
Vegetation Condition ◽  
Temporal And Spatial

Central Europe experienced several droughts in the recent past, such as in the year 2018, which was characterized by extremely low rainfall rates and high temperatures, resulting in substantial agricultural yield losses. Time series of satellite earth observation data enable the characterization of past drought events over large temporal and spatial scales. Within this study, Moderate Resolution Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) (MOD13Q1) 250 m time series were investigated for the vegetation periods of 2000 to 2018. The spatial and temporal development of vegetation in 2018 was compared to other dry and hot years in Europe, like the drought year 2003. Temporal and spatial inter- and intra-annual patterns of EVI anomalies were analyzed for all of Germany and for its cropland, forest, and grassland areas individually. While vegetation development in spring 2018 was above average, the summer months of 2018 showed negative anomalies in a similar magnitude as in 2003, which was particularly apparent within grassland and cropland areas in Germany. In contrast, the year 2003 showed negative anomalies during the entire growing season. The spatial pattern of vegetation status in 2018 showed high regional variation, with north-eastern Germany mainly affected in June, north-western parts in July, and western Germany in August. The temporal pattern of satellite-derived EVI deviances within the study period 2000–2018 were in good agreement with crop yield statistics for Germany. The study shows that the EVI deviation of the summer months of 2018 were among the most extreme in the study period compared to other years. The spatial pattern and temporal development of vegetation condition between the drought years differ.

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