Estimating above ground biomass for eucalyptus plantation using data from unmanned aerial vehicle imagery

2018 ◽  
Author(s):  
Panu Srestasathiern ◽  
Suramongkon Siripon ◽  
Rattawat Wasuhiranyrith ◽  
Phalakorn Kooha ◽  
Sitthisak Moukomla
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Above Ground Biomass ◽  
Eucalyptus Plantation ◽  
Ground Biomass ◽  
Aerial Vehicle ◽  
Using Data

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 Above Ground Biomass Estimation of Syzygium aromaticum using structure from motion (SfM) derived from Unmanned Aerial Vehicle in Paninggahan Agroforest Area, West Sumatra

2021 ◽  
Vol 9 (1) ◽  
pp. 39
Author(s):  
Try Surya Harapan ◽  
Ahsanul Husna ◽  
Thoriq Alfath Febriamansyah ◽  
Mahdi Mutashim ◽  
Andri Saputra ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Aerial Image ◽  
Biomass Estimation ◽  
Syzygium Aromaticum ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
West Sumatra ◽  
Aerial Vehicle ◽  
The Difference ◽  
The Individual

Above ground biomass (AGB) is all living organic matters above the soil including stem, seed and leaves. This study aimed to estimate the individual clove (Syzygium aromaticum) and it’s above ground biomass using Unmanned Aerial Vehicle in the Agroforestry area in Paninggahan, West Sumatra. This study used a photogrammetry method to calculate trees and estimated the AGB. We detected 257 numbers of trees based on aerial image analysis and observed 270 after we validated on ground check in the field. The result was slightly different between estimated AGB from UAV and observed AGB from our ground validation. The estimated AGB was 5.9 ton/ Ha where the surveyed AGB was 5.6 ton/Ha. The difference between estimated AGB and observed AGB was 0.3 ton/Ha.

scholarly journals Evaluation of the Sensitivity of SMOS L-VOD to Forest Above-Ground Biomass at Global Scale

2020 ◽  
Vol 12 (9) ◽  
pp. 1450
Author(s):  
Arnaud Mialon ◽  
Nemesio J. Rodríguez-Fernández ◽  
Maurizio Santoro ◽  
Sassan Saatchi ◽  
Stéphane Mermoz ◽  
...  
Keyword(s):  
Large Scale ◽  
Correlation Coefficients ◽  
Global Scale ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Northern Latitudes ◽  
Ocean Salinity ◽  
Good Proxy ◽  
Using Data ◽  
The Tropics

The present study evaluates the L band Vegetation Optical Depth (L-VOD) derived from the Soil Moisture and Ocean Salinity (SMOS) satellite to monitor Above Ground Biomass (AGB) at a global scale. Although SMOS L-VOD has been shown to be a good proxy for AGB in Africa and Tropics, little is known about this relationship at large scale. In this study, we further examine this relationship at a global scale using the latest AGB maps from Saatchi et al. and GlobBiomass computed using data acquired during the SMOS period. We show that at a global scale the L-VOD from SMOS is well-correlated with the AGB estimates from Saatchi et al. and GlobBiomass with the Pearson’s correlation coefficients (R) of 0.91 and 0.94 respectively. Although AGB estimates in Africa and the Tropics are well-captured by SMOS L-VOD (R > 0.9), the relationship is less straightforward for the dense forests over the northern latitudes (R = 0.32 and 0.69 with Saatchi et al. and GlobBiomass respectively). This paper gives strong evidence in support of the sensitivity of SMOS L-VOD to AGB estimates at a globale scale, providing an interesting alternative and complement to exisiting sensors for monitoring biomass evolution. These findings can further facilitate research on biomass now that SMOS is providing more than 10 years of data.

scholarly journals Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales

2019 ◽  
Vol 11 (22) ◽  
pp. 2678 ◽  
Author(s):  
Zhu ◽  
Sun ◽  
Peng ◽  
Huang ◽  
Li ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Spatial Scales ◽  
Point Clouds ◽  
Support Vector ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
3D Point Clouds ◽  
Vehicle Data ◽  
Aerial Vehicle ◽  
Multispectral Camera

Crop above-ground biomass (AGB) is a key parameter used for monitoring crop growth and predicting yield in precision agriculture. Estimating the crop AGB at a field scale through the use of unmanned aerial vehicles (UAVs) is promising for agronomic application, but the robustness of the methods used for estimation needs to be balanced with practical application. In this study, three UAV remote sensing flight missions (using a multiSPEC-4C multispectral camera, a Micasense RedEdge-M multispectral camera, and an Alpha Series AL3-32 Light Detection and Ranging (LiDAR) sensor onboard three different UAV platforms) were conducted above three long-term experimental plots with different tillage treatments in 2018. We investigated the performances of the multi-source UAV-based 3D point clouds at multi-spatial scales using the traditional multi-variable linear regression model (OLS), random forest (RF), backpropagation neural network (BP), and support vector machine (SVM) methods for accurate AGB estimation. Results showed that crop height (CH) was a robust proxy for AGB estimation, and that high spatial resolution in CH datasets helps to improve maize AGB estimation. Furthermore, the OLS, RF, BP, and SVM methods all maintained an acceptable accuracy for AGB estimation; however, the SVM and RF methods performed slightly more robustly. This study is expected to optimize UAV systems and algorithms for specific agronomic applications.

scholarly journals Dry Above Ground Biomass for a Soybean Crop Using an Empirical Model in Greece

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 201
Author(s):  
Christos Vamvakoulas ◽  
Stavros Alexandris ◽  
Ioannis Argyrokastritis
Keyword(s):  
Empirical Model ◽  
Above Ground Biomass ◽  
Crop Evapotranspiration ◽  
Area Index ◽  
Soybean Crop ◽  
Ground Biomass ◽  
Cultivation Period ◽  
Proposed Model ◽  
Glycine Max L ◽  
Using Data

A new empirical equation for the estimation of daily dry above ground biomass (D-AGB) for a hybrid of soybean (Glycine max L.) is proposed. This equation requires data for three crop dependent parameters; leaf area index, plant height, and cumulative crop evapotranspiration. Bilinear surface regression analysis was used in order to estimate the factors entering in the empirical model. For the calibration of the proposed model, data yielded from a well-watered soybean crop for the year 2015, in the experimental field (0.1 ha) of the agricultural University of Athens, were used as a reference. Verification of the validity of the model was obtained by using data from a 2014 cultivation period for well-watered soybean cultivation (100% of crop evapotranspiration water treatment), as well as data from three irrigation treatments (75%, 50%, 25% of crop evapotranspiration) for two cultivation periods (2014–2015). The proposed method for the estimation of D-AGB may be proven as a useful tool for estimations without using destructive sampling.

scholarly journals Individual Tree Detection in a Eucalyptus Plantation Using Unmanned Aerial Vehicle (UAV)-LiDAR

2020 ◽  
Vol 12 (5) ◽  
pp. 885 ◽  
Author(s):  
Juan Picos ◽  
Guillermo Bastos ◽  
Daniel Míguez ◽  
Laura Alonso ◽  
Julia Armesto
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Single Point ◽  
Tree Height ◽  
Individual Tree ◽  
Eucalyptus Plantation ◽  
Local Maxima ◽  
Eucalypt Plantations ◽  
Aerial Vehicle ◽  
Precision Forestry

The present study addresses the tree counting of a Eucalyptus plantation, the most widely planted hardwood in the world. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) was used for the estimation of Eucalyptus trees. LiDAR-based estimation of Eucalyptus is a challenge due to the irregular shape and multiple trunks. To overcome this difficulty, the layer of the point cloud containing the stems was automatically classified and extracted according to the height thresholds, and those points were horizontally projected. Two different procedures were applied on these points. One is based on creating a buffer around each single point and combining the overlapping resulting polygons. The other one consists of a two-dimensional raster calculated from a kernel density estimation with an axis-aligned bivariate quartic kernel. Results were assessed against the manual interpretation of the LiDAR point cloud. Both methods yielded a detection rate (DR) of 103.7% and 113.6%, respectively. Results of the application of the local maxima filter to the canopy height model (CHM) intensely depends on the algorithm and the CHM pixel size. Additionally, the height of each tree was calculated from the CHM. Estimates of tree height produced from the CHM was sensitive to spatial resolution. A resolution of 2.0 m produced a R2 and a root mean square error (RMSE) of 0.99 m and 0.34 m, respectively. A finer resolution of 0.5 m produced a more accurate height estimation, with a R2 and a RMSE of 0.99 and 0.44 m, respectively. The quality of the results is a step toward precision forestry in eucalypt plantations.

scholarly journals Development of an Unmanned Aerial Vehicle for the Measurement of Turbulence in the Atmospheric Boundary Layer

Atmosphere ◽  
2017 ◽  
Vol 8 (10) ◽  
pp. 195 ◽  
Author(s):  
Brandon Witte ◽  
Robert Singler ◽  
Sean Bailey
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Atmospheric Boundary Layer ◽  
Unmanned Aerial Vehicle ◽  
Sensor Data ◽  
Velocity Sensor ◽  
Aerial Vehicle ◽  
Probe Velocity ◽  
Convective State ◽  
Using Data

This paper describes the components and usage of an unmanned aerial vehicle developed for measuring turbulence in the atmospheric boundary layer. A method of computing the time-dependent wind speed from a moving velocity sensor data is provided. The physical system built to implement this method using a five-hole probe velocity sensor is described along with the approach used to combine data from the different on-board sensors to allow for extraction of the wind speed as a function of time and position. The approach is demonstrated using data from three flights of two unmanned aerial vehicles (UAVs) measuring the lower atmospheric boundary layer during transition from a stable to convective state. Several quantities are presented and show the potential for extracting a range of atmospheric boundary layer statistics.

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