A decision support system for Taiwan’s forest resource management using Remote Sensing Big Data

Wang, Ruei-Yuan; Lin, Pao-An; Chu, Jui-Yuan; Tao, Yi-Huang; Ling, Hsiao-Chi

doi:10.1080/17517575.2021.1883123

Cited by 11 publications

(14 citation statements)

References 56 publications

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“…Natural language processing (NLP) is a technology and application that processes languages and words by using computers [13]. In recent years, with the arrival of the big data era, the rapid improvement of machine computing 2…”

Section: Methodsmentioning

confidence: 99%

Census and Inventory Method of Pollution Sources Based on Big Data Technology under Machine Learning

Liu

Sun

Jing

et al. 2022

Wireless Communications and Mobile Computing

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In order to strengthen the supervision and management of environmental pollution and timely understand and record the basic information of potential environmental pollution of enterprises and institutions, this paper proposes a general survey and inventory method of pollution sources based on big data technology under machine learning. Firstly, this paper evaluates and screens the data provided by government departments, constructs a machine learning classification model, and uses a variety of classification algorithms to compare and analyze according to the basic idea of machine learning to deal with practical problems. Then, the calibration data set constructed is used as the training set to predict and classify the national industrial and commercial data, provincial industrial and commercial data, and municipal industrial and commercial data provided by government departments. The experimental results show that the naive Bayesian classification algorithm is the best algorithm, and the F1 values of each data set are increased by 32.92%, 21.42%, and 14.91%, respectively. The classified prediction of the screened Internet data shows that the accuracy of the final Internet supplementary data is 17.26%, which is similar to the industrial and commercial data of the city. The availability of the machine learning model established in this paper is proven.

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Section: Methodsmentioning

confidence: 99%

Census and Inventory Method of Pollution Sources Based on Big Data Technology under Machine Learning

Liu

Sun

Jing

et al. 2022

Wireless Communications and Mobile Computing

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“…, 2015). The monitoring and evaluation of a forest system and its attributes provides various advantages and is particularly valuable in obtaining useful information for guiding decision-making and attempting greater efforts in forest management (Wang et al. , 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

“…, 2009). Combined with decision support tools, remote sensing data with imagery can help with inventory, monitoring, and evaluation in forest management (Wang et al. , 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

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The efficiency of LiDAR HMLS scanning in monitoring forest structure parameters: implications for sustainable forest management

Sofia

Maetzke

Crescimanno

et al. 2022

EMJB

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PurposeThis article aims to compare the LiDAR handheld mobile laser scanner (HMLS) scans with traditional survey methods, as the tree gauge and the hypsometer, to study the efficiency of the new technology in relation to the accuracy of structural forest attributes estimation useful to support a sustainable forest management.Design/methodology/approachA case study was carried out in a high forest located in Tuscany (Italy), by considering 5 forest types, in 20 different survey plots. A comparative analysis between two survey methods will be shown in order to verify the potential limits and the viability of the LiDAR HMLS in the forest field.FindingsThis research demonstrates that LiDAR HMLS technology allows to obtain a large amount of valuable data on forest structural parameters in a short span of time with a high level of accuracy and with obvious impact in terms of organisational efficiency.Practical implicationsFindings could be useful for forest owners highlighting the importance of investing in science and technology to improve the overall efficiency of forest resources management.Originality/valueThis article adds to the current knowledge on the precision forestry topic by providing insight on the feasibility and effectiveness of using precision technologies for monitoring forest ecosystems and dynamics. In particular, this study fills the gap in the literature linked to the need to have practical examples of the use of innovative technologies in forestry.

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“…Ten social, economic and legal insights have also been summarized in climate science research since mid-2019, highlighting the need for advancing knowledge about the interactions between the human world and planetary systems (Pihl et al, 2021). New methods and data are needed to support monitoring and decision-making systems, such as using remote sensing big data for more precise and efficient forest resource management and policy-making (Wang et al, 2021). Thus, to achieve carbon neutrality, social science knowledge must be developed to understand the multiple aspects of preparing human societies for a sustainable transition for the mitigation of climate change.…”

Section: Introductionmentioning

confidence: 99%

Carbon Neutrality Policies and Technologies: A Scientometric Analysis of Social Science Disciplines

Zhang

Pan

Liao

2021

Front. Environ. Sci.

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Carbon neutrality, or “net zero”, has become the impact assessment project of human impact on Earth, increasingly structured to examine the implications, for the environment and people, of proposed actions and consequences of inaction. International and local collaboration efforts have been made with the aim of achieving carbon neutrality or “net-zero” emissions; thus, policies and technological innovations have been developed. Such impact-oriented risk assessment and control efforts amount to carbon-neutral pathways. Although such pathways may diverge in terms of energy, resources, and cost, it is critical to summarize essential and promising preparatory work on related policies and technologies to inform both policy-makers and social scientists to take actions. Through a scientometric analysis and systematic review of the latest social science literature, the study identified the size, scope and exemplar work for each social science discipline on carbon neutrality, based on 907 articles collected in early 2021 from the Web of Science database. This study reveals a set of disciplines focusing on certain common and distinct aspects of carbon neutrality. By outlining the possibilities and application areas for future research and policy development for socio-technical transition towards a net-zero or post-carbon future, this study has contributed to the understanding of the global efforts to achieve a clearer and viable carbon-neutral pathway. In conclusion, as many aspects of the planet and humans have become datafied, digitized, and networked, carbon neutrality, as the ecological indicator that guide human production and consumption patterns, must take on a central role in guiding our conscious green digital transformation of many political, economic, social and psychological aspects of our societies according to the existing and emerging social science knowledge.

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A decision support system for Taiwan’s forest resource management using Remote Sensing Big Data

Cited by 11 publications

References 56 publications

Census and Inventory Method of Pollution Sources Based on Big Data Technology under Machine Learning

Census and Inventory Method of Pollution Sources Based on Big Data Technology under Machine Learning

The efficiency of LiDAR HMLS scanning in monitoring forest structure parameters: implications for sustainable forest management

Carbon Neutrality Policies and Technologies: A Scientometric Analysis of Social Science Disciplines

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