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

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.

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

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.

An Allometric Model Based Approach For Estimating Biomass In Seven Indian Bamboo Species In Western Himalayan Foothills, India

The rapid growth rate, high biomass production, and annual harvesting, makes bamboo as suitable species for commercial production. Allometric equations for many broadleaf and conifer tree species are available. However, knowledge on biomass production and allometric equations of bamboos are limited. This study aims at developing species specific allometric models for predicting biomass and synthetic height values as a proxy variable for seven bamboo species in Himalayan foothills. Two power form based allometric models were used to predict above ground and culm biomass using Diameter at breast height (D) alone and D in combination with culm height (H) as independent variable. This study also extended to establishing H-D allometric model that can be used to generate synthetic H values as proxy to missing H. In the seven bamboo species studied, among three major biomass component (culm, branch and foliage), culm is the most important component with highest share (69.56 to 78.71%).Distribution of percentage (%) share of culm, branch and foliage to above ground fresh weight varies significantly between different bamboo species. D. hamiltonii has highest productivity for above ground biomass components. Ratio of dry to fresh weight of seven bamboo species was estimated for culm, branch, foliage and above ground biomass to convert fresh weight to dry weight.

Partitioning above and below ground interactions and their effects on juvenile Dacrycarpus dacrydioides (kahikatea) and Podocarpus totara (totara)

<p>Competitive and facilitative interactions play an important role in determining plant community structure and development. Historically, competitive interactions have been considered to be more prevalent in nature. However, in the past few decades strong facilitative interactions have been identified as being more important than competition in certain environments. Recent evidence has also suggested that interactions occurring in the above and below ground environments may be unevenly contributing to the net interaction effects between a target plant and nurses species. This study partitions the above and below ground interactions and determines their strength and directions in order to help better understand their relative importance to plant community dynamics. In Chapter 2 I develop species specific allometric models which aim to accurately estimate the total above- and below- ground biomass of individual D. dacrydioides and P. totara juveniles using measurements which are easily and non-destructively obtained in the field. The best model for each species is then used to construct total above and below ground biomass estimates for use in Chapter 3. Eight models using stem height, diameter, and volume either alone or in combination are examined for their predictive power and tested for their goodness of fit. Models using diameter alone are found to be less powerful in predicting total tree biomass, while models containing height either alone or in combination with diameter are more powerful. The absolute best model for predicting D. dacrydioides total biomass was BTOTAL = 0.0099(Height²)⁰˙⁸⁷⁴⁹, whereas the absolute best model for P. totara was BTOTAL = 0.2635((Height*Diameter)²)⁰˙⁵⁶⁹⁵. In Chapter 3 I use the Relative Interaction Index (RII) to determine the strength and direction of the net interactions affecting D. dacrydioides and P. totara juveniles. To partition the above ground interactions, I examined the effects of a conspecific or interspecific neighbour. I found that my two study species D. dacrydioides and P. totara showed different responses to the treatments that they received. D. dacrydioides showed net facilitation and gained biomass when it had access to the mycorrhizal network and a neighbour. Whereas, P. totara showed net neutral interactions and did not gain biomass. P. totara also showed net competition when it did not have access to the mycorrhizal network and was grown next to neighbours. The role of above ground interactions was found to be less important than below ground interactions, overall. In general, these results mean that D. dacrydioides juveniles should be expected to have higher growth, reproductive, and survival rates when grown next to nurse species in comparison to P. totara. Chapter 4 details the significance of this study for the restoration of Wairio wetland, and wetlands in general. Given the result in chapter 3 and the current restoration method at Wairio wetland, this study suggests that it may be worth exploring the benefit of planting new P. totara juveniles farther away from older woody species in order to avoid root competition.</p>

Partitioning above and below ground interactions and their effects on juvenile Dacrycarpus dacrydioides (kahikatea) and Podocarpus totara (totara)

<p>Competitive and facilitative interactions play an important role in determining plant community structure and development. Historically, competitive interactions have been considered to be more prevalent in nature. However, in the past few decades strong facilitative interactions have been identified as being more important than competition in certain environments. Recent evidence has also suggested that interactions occurring in the above and below ground environments may be unevenly contributing to the net interaction effects between a target plant and nurses species. This study partitions the above and below ground interactions and determines their strength and directions in order to help better understand their relative importance to plant community dynamics. In Chapter 2 I develop species specific allometric models which aim to accurately estimate the total above- and below- ground biomass of individual D. dacrydioides and P. totara juveniles using measurements which are easily and non-destructively obtained in the field. The best model for each species is then used to construct total above and below ground biomass estimates for use in Chapter 3. Eight models using stem height, diameter, and volume either alone or in combination are examined for their predictive power and tested for their goodness of fit. Models using diameter alone are found to be less powerful in predicting total tree biomass, while models containing height either alone or in combination with diameter are more powerful. The absolute best model for predicting D. dacrydioides total biomass was BTOTAL = 0.0099(Height²)⁰˙⁸⁷⁴⁹, whereas the absolute best model for P. totara was BTOTAL = 0.2635((Height*Diameter)²)⁰˙⁵⁶⁹⁵. In Chapter 3 I use the Relative Interaction Index (RII) to determine the strength and direction of the net interactions affecting D. dacrydioides and P. totara juveniles. To partition the above ground interactions, I examined the effects of a conspecific or interspecific neighbour. I found that my two study species D. dacrydioides and P. totara showed different responses to the treatments that they received. D. dacrydioides showed net facilitation and gained biomass when it had access to the mycorrhizal network and a neighbour. Whereas, P. totara showed net neutral interactions and did not gain biomass. P. totara also showed net competition when it did not have access to the mycorrhizal network and was grown next to neighbours. The role of above ground interactions was found to be less important than below ground interactions, overall. In general, these results mean that D. dacrydioides juveniles should be expected to have higher growth, reproductive, and survival rates when grown next to nurse species in comparison to P. totara. Chapter 4 details the significance of this study for the restoration of Wairio wetland, and wetlands in general. Given the result in chapter 3 and the current restoration method at Wairio wetland, this study suggests that it may be worth exploring the benefit of planting new P. totara juveniles farther away from older woody species in order to avoid root competition.</p>

Below-Ground Growth of Alpine Plants, Not Above-Ground Growth, Is Linked to the Extent of Its Carbon Storage

Understanding carbon allocation in plants is essential for explaining their growth strategies during environmental adaptation. However, the role of mobile carbon in plant growth and its response to habitat conditions is still disputed. In degraded meadow (alpine sandy grassland) and non-degraded meadow (typical alpine meadow and swamp meadow) on the Qinghai–Tibetan Plateau, we measured the monthly averages of above-ground biomass (AGB) and below-ground biomass (BGB) of the investigated species in each meadow and the average concentration of non-structural carbohydrates (NSCs), an indicator of carbon storage. Below-ground organs had higher concentrations and showed more seasonal variation in NSCs than above-ground organs. BGB had a positive correlation with below-ground NSCs levels. However, AGB had no clear relationship with above-ground NSCs levels. Plants in sandy grasslands had higher total NSC, soluble sugars, fructose, and sucrose concentrations and lower starch concentrations in below-ground organs than plants in alpine or swamp meadows. Overall, NSCs storage, particularly soluble sugars, is a major process underlying the pattern of below-ground growth, but not above-ground growth, in the meadow ecosystem of the Qinghai–Tibetan Plateau, and degraded meadow strengthens this process. These results suggest that the extent of carbon storage in non-photosynthetic organs of alpine herbs impacts their growth and habitat adaptation.

Carbon stock estimation and mapping of mangrove forest using ALOS-2 PALSAR-2 in Benoa Bay Bali, Indonesia

Mangrove forests in tropics coastlines area play an essential role in carbon fixation and carbon storage. Mangrove forests in coastal areas are very effective and efficient in reducing the concentration of carbon dioxide (CO2) in the atmosphere because mangroves can absorb CO2 through photosynthesis by diffusion through stomata and then store carbon in the form of biomass. With the lack of efforts to manage mangrove forests, it needs to be developed so that forest functions can be utilized sustainably. This paper describes examining the use of remote sensing data, particularly dual-polarization ALOS-2 PALSAR-2 data, with the primary objective to estimate the carbon stock of mangrove forests in Benoa Bay, Bali. The carbon stock was estimated by analyzing HV Polarization, Above Ground Biomass (AGB), and ground biomass (BGB). The total carbon stock was obtained by multiplying the total biomass with the organic carbon value of 0.47. The potential carbon stock in the mangrove Benoa Bay area is 209,027.28 ton C to absorb carbon dioxide (CO2) of 767,130.11 ton CO2 Sequestration same with 3.87 X 1011 bottles in 2015 and 204.422,59 ton C to absorb carbon dioxide (CO2) of 750.230,93 ton CO2 Sequestration same with 3.79 x 1011 bottles in 2020.

Estimation of carbon stock in seagrass communities in Central Tapanuli

This study aims to determine the carbon stock in seagrass communities in Central Tapanuli, North Sumatera, Indonesia. The research was conducted from July to August 2020 in the coastal areas of Hajoran and Jago Jago. The parameters measured in this study were density, coverage, biomass, carbon content, and carbon stock in seagrass. Biomass analysis and carbon measurement are divided into the top (above-ground biomass) and the bottom substrate (below-ground biomass). Carbon measurements are conducted using the loss on ignition (LOI) approach. The results showed that the seagrass ecosystem on the coast of Central Tapanuli Regency, which was covered by monospecies Enhalus acoroides, was in a less healthy condition with a cover percentage of 30.3-33.3% and a density of 59-67 shoots/m2. Above-ground and below-ground seagrass biomass reached 140.19-188.72 g/m2 and 368.13-423.69 g/m2 respectively, while carbon stock reached 70.57-94.86 g Corg/m2 and 18731-19603 g Corg/m2 and total standing stock range 257.87-290.90 g Corg/m2. The data obtained from this research can be used as a database to see the potential of seagrass beds as storage of CO2 and as an effort to mitigate and adapt to climate change.