scholarly journals Machine learning for carbon stock prediction in a tropical forest in Southeastern Brazil

2021 ◽  
Vol 42 (1) ◽  
pp. 131-140
Author(s):  
Daniel Dantas ◽  
Marcela de Castro Nunes Santos Terra ◽  
Luis Paulo Baldissera Schorr ◽  
Natalino Calegario
Keyword(s):  
Machine Learning ◽  
Tropical Forest ◽  
Carbon Stock ◽  
Southeastern Brazil ◽  
Stock Prediction

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.

Comparing the potential reproductive phenology between restored areas and native tropical forest fragments in Southeastern Brazil

2021 ◽  
Author(s):  
Débora Cristina Rother ◽  
Igor Lopes Ferreira Sousa ◽  
Eliana Gressler ◽  
Ana Paula Liboni ◽  
Vinícius Castro Souza ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Southeastern Brazil ◽  
Reproductive Phenology ◽  
Forest Fragments

scholarly journals DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Suppawong Tuarob ◽  
Poom Wettayakorn ◽  
Ponpat Phetchai ◽  
Siripong Traivijitkhun ◽  
Sunghoon Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Collection ◽  
Stock Market ◽  
Real Time ◽  
Stock Prices ◽  
Contextual Information ◽  
Online News ◽  
Stock Prediction ◽  
Stock Market Prediction

AbstractThe explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

scholarly journals Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning

2021 ◽  
Vol 172 ◽  
pp. 79-94
Author(s):  
Maryam Pourshamsi ◽  
Junshi Xia ◽  
Naoto Yokoya ◽  
Mariano Garcia ◽  
Marco Lavalle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tropical Forest ◽  
Forest Canopy ◽  
Canopy Height ◽  
Polarimetric Sar ◽  
Height Estimation ◽  
Tropical Forest Canopy

scholarly journals Outlook for carbon stock of tropical forest in the context of climate change

2019 ◽  
Vol 299 ◽  
pp. 012049
Author(s):  
Dasrizal ◽  
Rahmi ◽  
A Rezki ◽  
Farida ◽  
A Z P Ulni ◽  
...  
Keyword(s):  
Climate Change ◽  
Tropical Forest ◽  
Carbon Stock

scholarly journals Carbon Stock and Sequestration Potential of an Agroforestry System in Sabah, Malaysia

Forests ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 210 ◽  
Author(s):  
Normah Awang Besar ◽  
Herawandi Suardi ◽  
Mui-How Phua ◽  
Daniel James ◽  
Mazlin Bin Mokhtar ◽  
...  
Keyword(s):  
Global Warming ◽  
Tropical Forest ◽  
Tropical Forests ◽  
Oil Palm ◽  
Carbon Stock ◽  
Agroforestry System ◽  
Agroforestry Systems ◽  
Sequestration Potential ◽  
Significant Difference ◽  
The Impact

Total aboveground carbon (TAC) and total soil carbon stock in the agroforestry system at the Balung River Plantation, Sabah, Malaysia were investigated to scientifically support the sustaining of natural forest for mitigating global warming via reducing carbon in the atmosphere. Agroforestry, monoculture, and natural tropical forests were investigated to calculate the carbon stock and sequestration based on three different combinations of oil palm and agarwood in agroforestry systems from 2014 to 2018. These combinations were oil palm (27 years) and agarwood (seven years), oil palm (20 years) and agarwood (seven years), and oil palm (17 years) and agarwood (five years). Monoculture oil palm (16 years), oil palm (six years), and natural tropical forest were set as the control. Three randomly selected plots for agroforestry and monoculture plantation were 0.25 ha (50 × 50 m), respectively, whereas for the natural tropical forest it was 0.09 ha (30 × 30 m). A nondestructive sampling method followed by the allometric equation determined the standing biomass. Organic and shrub layers collected in a square frame (1 × 1 m) were analyzed using the CHN628 series (LECO Corp., MI, USA) for carbon content. Soil bulk density of randomly selected points within the three different layers, that is, 0 to 5, 5 to 10, and 10 to 30 cm were used to determine the total ecosystem carbon (TEC) stock in each agroforestry system which was 79.13, 85.40, and 78.28 Mg C ha−1, respectively. The TEC in the monoculture oil palm was 76.44 and 60.30 Mg C ha−1, whereas natural tropical forest had the highest TEC of 287.29 Mg C ha−1. The forest stand had the highest TEC capacity as compared with the agroforestry and monoculture systems. The impact of planting systems on the TEC showed a statistically significant difference at a 95% confidence interval for the various carbon pools among the agroforestry, monoculture, and natural tropical forests. Therefore, the forest must be sustained because of its higher capacity to store carbon in mitigating global warming.

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