scholarly journals Above-ground biomass estimation for Quercus rotundifolia using vegetation indices derived from high spatial resolution satellite images

2018 ◽  
Vol 51 (1) ◽  
pp. 932-944 ◽  
Author(s):  
Fabrício L. Macedo ◽  
Adélia M. O. Sousa ◽  
Ana Cristina Gonçalves ◽  
José R. Marques da Silva ◽  
Paulo A. Mesquita ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Satellite Images ◽  
High Spatial Resolution ◽  
Vegetation Indices ◽  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Quercus Rotundifolia

scholarly journals Above‐Ground Biomass Estimation with High Spatial Resolution Satellite Images

10.5772/65665 ◽  
2017 ◽  
Author(s):  
Adlia M.O. Sousa ◽  
Ana Cristina Gonçalves ◽  
José R. Marques da Silva
Keyword(s):  
Spatial Resolution ◽  
Satellite Images ◽  
High Spatial Resolution ◽  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Ground Biomass

Modeling Above-Ground Biomass in Tallgrass Prairie Using Ultra-High Spatial Resolution sUAS Imagery

2014 ◽  
Vol 80 (12) ◽  
pp. 1151-1159 ◽  
Author(s):  
Huan Wang ◽  
Chuyuan Wang ◽  
Kevin P. Price ◽  
Deon van der Merwe ◽  
Nan An
Keyword(s):  
Spatial Resolution ◽  
Tallgrass Prairie ◽  
High Spatial Resolution ◽  
Above Ground Biomass ◽  
Ground Biomass

scholarly journals Evaluation of Above Ground Biomass Estimation Accuracy for Alpine Meadow Based on MODIS Vegetation Indices

2017 ◽  
Vol 12 ◽  
pp. 02003 ◽  
Author(s):  
Bao-Ping Meng ◽  
Tian-Gang Liang ◽  
Jing Ge ◽  
Jin-Long Gao ◽  
Jian-Peng Yin
Keyword(s):  
Alpine Meadow ◽  
Vegetation Indices ◽  
Estimation Accuracy ◽  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Ground Biomass

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 Mapping Above-Ground Biomass of Winter Oilseed Rape Using High Spatial Resolution Satellite Data at Parcel Scale under Waterlogging Conditions

2017 ◽  
Vol 9 (3) ◽  
pp. 238 ◽  
Author(s):  
Jiahui Han ◽  
Chuanwen Wei ◽  
Yaoliang Chen ◽  
Weiwei Liu ◽  
Peilin Song ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Oilseed Rape ◽  
Satellite Data ◽  
High Spatial Resolution ◽  
Above Ground Biomass ◽  
Winter Oilseed Rape ◽  
Ground Biomass

Above-ground biomass estimation of Indian tropical forests using X band Pol-InSAR and Random Forest

2021 ◽  
Vol 21 ◽  
pp. 100462
Author(s):  
Sadhana Yadav ◽  
Hitendra Padalia ◽  
Sanjiv K. Sinha ◽  
Ritika Srinet ◽  
Prakash Chauhan
Keyword(s):  
Random Forest ◽  
Tropical Forests ◽  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
X Band

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 Exploring a uniform procedure to map Eucalyptus plantations based on fused medium–high spatial resolution satellite images

2021 ◽  
Vol 103 ◽  
pp. 102462
Author(s):  
Yaoliang Chen ◽  
Zhongwei Peng ◽  
Yongpeng Ye ◽  
Xiandie Jiang ◽  
Dengsheng Lu ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Satellite Images ◽  
High Spatial Resolution ◽  
Eucalyptus Plantations

scholarly journals Biomass estimation with high resolution satellite images: A case study of Quercus rotundifolia

2015 ◽  
Vol 101 ◽  
pp. 69-79 ◽  
Author(s):  
Adélia M.O. Sousa ◽  
Ana Cristina Gonçalves ◽  
Paulo Mesquita ◽  
José R. Marques da Silva
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Biomass Estimation ◽  
High Resolution Satellite Images ◽  
Quercus Rotundifolia
Sign in / Sign up

Export Citation Format

Share Document