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 Vegetation Structure Index (VSI): Retrieving Vegetation Structural Information from Multi-angular Satellite Remote Sensing

2021 ◽  
Author(s):  
Ram C. Sharma
Keyword(s):  
Remote Sensing ◽  
Vegetation Structure ◽  
Structural Information ◽  
Vegetation Indices ◽  
Model Parameters ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Back Scattering ◽  
Structure Index ◽  
The Right

Utilization of Bidirectional Reflectance Distribution Function (BRDF) model parameters obtained from the multi-angular remote sensing is one of the approaches for the retrieval of vegetation structural information. In this research, the potential of multi-angular vegetation indices, formulated by the combination of multi-spectral reflectance from different view angles, for the retrieval of forest above ground biomass was assessed. This research was implemented in the New England region with the availability of a high quality forest inventory database. The multi-angular vegetation indices were generated by the simulation of the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo Model Parameters Product (MCD43A1 Version 6) based BRDF parameters. The effects of seasonal (spring, summer, autumn, and winter) composites of the multi-angular vegetation indices on above ground biomass, angular relationship of the spectral reflectance with above ground biomass, and the interrelationships between the multi-angular vegetation indices were analyzed. Among the existing multi-angular vegetation indices, only the Nadir BRDF-adjusted NDVI ( and Hot-spot incorporated NDVI ( showed significant relationship (more than 50%) with the above ground biomass. This research proposed two more sensitive vegetation structural indices, Fore-scattering Back-scattering NDVI and Vegetation Structure Index (VSI). The Fore-scattering Back-scattering NDVI showed higher sensitivity (R2 = 0.62, RMSE = 52.46) towards the above ground biomass than existing multi-angular vegetation indices. Furthermore, the VSI performed in the most efficient way explaining 64% variation of the above ground biomass, suggesting that the right choice of the spectral channel and observation geometry should be considered for improving the estimates of the above ground biomass. In addition, the right choice of seasonal data (summer) was found to be important for estimating the forest biomass while other seasonal data were either insensitive or pointless. The promising results shown by the VSI suggest that it could be an appropriate candidate for monitoring vegetation structure from the multi-angular satellite remote sensing.

scholarly journals Above-Ground Biomass Estimation in Oats Using UAV Remote Sensing and Machine Learning

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 601
Author(s):  
Prakriti Sharma ◽  
Larry Leigh ◽  
Jiyul Chang ◽  
Maitiniyazi Maimaitijiang ◽  
Melanie Caffé
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Vegetation Indices ◽  
Support Vector ◽  
Biomass Estimation ◽  
Surface Model ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
South Shore ◽  
Oat Biomass

Current strategies for phenotyping above-ground biomass in field breeding nurseries demand significant investment in both time and labor. Unmanned aerial vehicles (UAV) can be used to derive vegetation indices (VIs) with high throughput and could provide an efficient way to predict forage yield with high accuracy. The main objective of the study is to investigate the potential of UAV-based multispectral data and machine learning approaches in the estimation of oat biomass. UAV equipped with a multispectral sensor was flown over three experimental oat fields in Volga, South Shore, and Beresford, South Dakota, USA, throughout the pre- and post-heading growth phases of oats in 2019. A variety of vegetation indices (VIs) derived from UAV-based multispectral imagery were employed to build oat biomass estimation models using four machine-learning algorithms: partial least squares (PLS), support vector machine (SVM), Artificial neural network (ANN), and random forest (RF). The results showed that several VIs derived from the UAV collected images were significantly positively correlated with dry biomass for Volga and Beresford (r = 0.2–0.65), however, in South Shore, VIs were either not significantly or weakly correlated with biomass. For Beresford, approximately 70% of the variance was explained by PLS, RF, and SVM validation models using data collected during the post-heading phase. Likewise for Volga, validation models had lower coefficient of determination (R2 = 0.20–0.25) and higher error (RMSE = 700–800 kg/ha) than training models (R2 = 0.50–0.60; RMSE = 500–690 kg/ha). In South Shore, validation models were only able to explain approx. 15–20% of the variation in biomass, which is possibly due to the insignificant correlation values between VIs and biomass. Overall, this study indicates that airborne remote sensing with machine learning has potential for above-ground biomass estimation in oat breeding nurseries. The main limitation was inconsistent accuracy in model prediction across locations. Multiple-year spectral data, along with the inclusion of textural features like crop surface model (CSM) derived height and volumetric indicators, should be considered in future studies while estimating biophysical parameters like biomass.

scholarly journals Vegetation Structure Index (VSI): Retrieving Vegetation Structural Information from Multi-Angular Satellite Remote Sensing

2021 ◽  
Vol 7 (5) ◽  
pp. 84
Author(s):  
Ram C. Sharma
Keyword(s):  
Remote Sensing ◽  
Vegetation Structure ◽  
Satellite Remote Sensing ◽  
Structural Information ◽  
Vegetation Indices ◽  
Model Parameters ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Structure Index ◽  
The Right

Utilization of the Bidirectional Reflectance Distribution Function (BRDF) model parameters obtained from the multi-angular remote sensing is one of the approaches for the retrieval of vegetation structural information. In this research, the potential of multi-angular vegetation indices, formulated by the combination of multi-spectral reflectance from different view angles, for the retrieval of forest above-ground biomass was assessed in the New England region. The multi-angular vegetation indices were generated by the simulation of the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo Model Parameters Product (MCD43A1 Version 6)-based BRDF parameters. The effects of the seasonal (spring, summer, autumn, and winter) composites of the multi-angular vegetation indices on the above-ground biomass, the angular relationship of the spectral reflectance with above-ground biomass, and the interrelationships between the multi-angular vegetation indices were analyzed. Among the existing multi-angular vegetation indices, only the Nadir BRDF-adjusted NDVI and Hot-spot incorporated NDVI showed significant relationship (more than 50%) with the above-ground biomass. The Vegetation Structure Index (VSI), newly proposed in the research, performed in the most efficient way and explained 64% variation of the above-ground biomass, suggesting that the right choice of the spectral channel and observation geometry should be considered for improving the estimates of the above-ground biomass. In addition, the right choice of seasonal data (summer) was found to be important for estimating the forest biomass, while other seasonal data were either insensitive or pointless. The promising results shown by the VSI suggest that it could be an appropriate candidate for monitoring vegetation structure from the multi-angular satellite remote sensing.

scholarly journals Comparison of Capability of SAR and Optical Data in Mapping Forest above Ground Biomass Based on Machine Learning

2020 ◽  
Vol 5 (1) ◽  
pp. 13
Author(s):  
Negar Tavasoli ◽  
Hossein Arefi
Keyword(s):  
Machine Learning ◽  
Carbon Stock ◽  
Vegetation Indices ◽  
Forest Biomass ◽  
Principal Component ◽  
Optical Data ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Texture Characteristics ◽  
Sentinel 2

Assessment of forest above ground biomass (AGB) is critical for managing forest and understanding the role of forest as source of carbon fluxes. Recently, satellite remote sensing products offer the chance to map forest biomass and carbon stock. The present study focuses on comparing the potential use of combination of ALOSPALSAR and Sentinel-1 SAR data, with Sentinel-2 optical data to estimate above ground biomass and carbon stock using Genetic-Random forest machine learning (GA-RF) algorithm. Polarimetric decompositions, texture characteristics and backscatter coefficients of ALOSPALSAR and Sentinel-1, and vegetation indices, tasseled cap, texture parameters and principal component analysis (PCA) of Sentinel-2 based on measured AGB samples were used to estimate biomass. The overall coefficient (R2) of AGB modelling using combination of ALOSPALSAR and Sentinel-1 data, and Sentinel-2 data were respectively 0.70 and 0.62. The result showed that Combining ALOSPALSAR and Sentinel-1 data to predict AGB by using GA-RF model performed better than Sentinel-2 data.

scholarly journals Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1221
Author(s):  
Yuki Hamada ◽  
Colleen R. Zumpf ◽  
Jules F. Cacho ◽  
DoKyoung Lee ◽  
Cheng-Hsien Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Environmental Sustainability ◽  
Vegetation Index ◽  
Mean Squared Error ◽  
Spectral Band ◽  
Great Promise ◽  
Optical Remote Sensing

A sustainable bioeconomy would require growing high-yielding bioenergy crops on marginal agricultural areas with minimal inputs. To determine the cost competitiveness and environmental sustainability of such production systems, reliably estimating biomass yield is critical. However, because marginal areas are often small and spread across the landscape, yield estimation using traditional approaches is costly and time-consuming. This paper demonstrates the (1) initial investigation of optical remote sensing for predicting perennial bioenergy grass yields at harvest using a linear regression model with the green normalized difference vegetation index (GNDVI) derived from Sentinel-2 imagery and (2) evaluation of the model’s performance using data from five U.S. Midwest field sites. The linear regression model using midsummer GNDVI predicted yields at harvest with R2 as high as 0.879 and a mean absolute error and root mean squared error as low as 0.539 Mg/ha and 0.616 Mg/ha, respectively, except for the establishment year. Perennial bioenergy grass yields may be predicted 152 days before the harvest date on average, except for the establishment year. The green spectral band showed a greater contribution for predicting yields than the red band, which is indicative of increased chlorophyll content during the early growing season. Although additional testing is warranted, this study showed a great promise for a remote sensing approach for forecasting perennial bioenergy grass yields to support critical economic and logistical decisions of bioeconomy stakeholders.

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.

Remote sensing based shrub above-ground biomass and carbon storage mapping in Mu Us desert, China

2010 ◽  
Vol 53 (S1) ◽  
pp. 176-183 ◽  
Author(s):  
Min Xu ◽  
ChunXiang Cao ◽  
QingXi Tong ◽  
ZengYuan Li ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Carbon Storage ◽  
Above Ground Biomass ◽  
Mu Us Desert ◽  
Ground Biomass

scholarly journals Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape

2020 ◽  
Vol 12 (19) ◽  
pp. 3121
Author(s):  
Roya Mourad ◽  
Hadi Jaafar ◽  
Martha Anderson ◽  
Feng Gao
Keyword(s):  
Linear Regression ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Above Ground Biomass ◽  
Area Index ◽  
Ground Biomass ◽  
Semi Arid ◽  
Sentinel 2

Leaf area index (LAI) is an essential indicator of crop development and growth. For many agricultural applications, satellite-based LAI estimates at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions, Sentinel 2 (ESA) and Landsat 8 (NASA), provides this opportunity. In this study, we evaluated the leaf area index generated from three methods, namely, existing vegetation index (VI) relationships applied to Harmonized Landsat-8 and Sentinel-2 (HLS) surface reflectance produced by NASA, the SNAP biophysical model, and the THEIA L2A surface reflectance products from Sentinel-2. The intercomparison was conducted over the agricultural scheme in Bekaa (Lebanon) using a large set of in-field LAIs and other biophysical measurements collected in a wide variety of canopy structures during the 2018 and 2019 growing seasons. The major studied crops include herbs (e.g., cannabis: Cannabis sativa, mint: Mentha, and others), potato (Solanum tuberosum), and vegetables (e.g., bean: Phaseolus vulgaris, cabbage: Brassica oleracea, carrot: Daucus carota subsp. sativus, and others). Additionally, crop-specific height and above-ground biomass relationships with LAIs were investigated. Results show that of the empirical VI relationships tested, the EVI2-based HLS models statistically performed the best, specifically, the LAI models originally developed for wheat (RMSE:1.27), maize (RMSE:1.34), and row crops (RMSE:1.38). LAI derived through European Space Agency’s (ESA) Sentinel Application Platform (SNAP) biophysical processor underestimated LAI and provided less accurate estimates (RMSE of 1.72). Additionally, the S2 SeLI LAI algorithm (from SNAP biophysical processor) produced an acceptable accuracy level compared to HLS-EVI2 models (RMSE of 1.38) but with significant underestimation at high LAI values. Our findings show that the LAI-VI relationship, in general, is crop-specific with both linear and non-linear regression forms. Among the examined indices, EVI2 outperformed other vegetation indices when all crops were combined, and therefore it can be identified as an index that is best suited for a unified algorithm for crops in semi-arid irrigated regions with heterogeneous landscapes. Furthermore, our analysis shows that the observed height-LAI relationship is crop-specific and essentially linear with an R2 value of 0.82 for potato, 0.79 for wheat, and 0.50 for both cannabis and tobacco. The ability of the linear regression to estimate the fresh and dry above-ground biomass of potato from both observed height and LAI was reasonable, yielding R2: ~0.60.

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