Simulation of Land-Cover Change in Taipei Metropolitan Area under Climate Change Impact

Huang, Kuo-Ching; Huang, Thomas C C

doi:10.1088/1755-1315/18/1/012106

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…There is a need to focus on climate change at a global level as the impact it will have can be experienced in the global warming we are currently experiencing for instance [28]. It is quite clear that the damage from climate changes may not manifest directly like that of COVID-19, and that calls for an even more mature way of dealing with it as it is slowly eating away the earth cover [29]. COVID-19 has made governments and policymakers alike realize that the impact of a pandemic is far more than medical discoveries can deal with [30].…”

Section: Discussionmentioning

confidence: 99%

Building Policies for Climate Changes: Lessons from COVID-19

Halim¹

2021

J Clin Med Res

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Aim: Global pandemics have been there since 3000 B.C. The response to pandemics has always brought with its serious consequences. In this paper, we aim to draw critical lessons derived from the recent novel COVID-19 pandemic and how they can be implemented to bring change in the global climate scene. Methods: The research was primarily based on the published journals and research papers done in response to the COVID-19 global pandemic and the mitigation measures taken. Following an analytical approach to this issue, there was an in-depth look into the statistics of the global impact of climate change and how COVID-19 has a similarity to it. Results: From the 4 major studies that the paper takes a deeper look into, there is the realization of the need for cohesive management of global pandemics. There is enough evidence from COVID-19 that delay can be costly and that the digitization of technological systems used to combat these global issues is very important. Conclusion: Based on the inferences established from the paper, there is a cohesive need to integrate all facets of national matters regarding pandemics and global issues. It is only through this that national matters can be addressed and bring an impact in the long term. The paper provides the backbone from which further research may be conducted in the mitigation of climate change issues.

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Section: Discussionmentioning

confidence: 99%

Building Policies for Climate Changes: Lessons from COVID-19

Halim¹

2021

J Clin Med Res

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“…A support vector machine (SVM) was adopted as a land cover classifier because it has been evaluated as a high-performance machine-learning algorithm and has been investigated in a number of studies [26,30,42,43]. In this study, the pixel-based classification was applied using six bands of three composite scenes.…”

Section: Land Cover In 2011mentioning

confidence: 99%

Predicting Land Cover Change in the Mamminasata Area, Indonesia, to Evaluate the Spatial Plan

Hakim

Matsuoka

Baja

et al. 2020

IJGI

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The spatial plan program for Makassar City and the surrounding area called Mamminasata (Makassar, Maros, Sungguminasa, and Takalar) was created by the Indonesian Government. The program regulates the proportion of land cover, but predictions about land cover changes were not considered. Therefore, in this study, we predict what the land cover may be in 2031 using the multi-layer perceptron neural network and the Markov chain methods. For this purpose, image composite, support vector machine classifier, and change detection were applied to a time series of satellite data. Visual validation showed the hot-spots of land cover changes related to population density, and statistical validation scored 0.99 and 0.78 in no information kappa and grid-cell level location kappa, respectively. The model was performed to predict land cover in 2031, and the predicted result was then compared with the spatial plan using an overlapping method. The results showed that built-up area, dryland agriculture, and wetland agriculture occupied two, twenty, and eight percent of the protected zone, respectively. Meanwhile, fifteen percent of the development zone was covered by forest, mainly in the eastern part of Mamminasata. The result can be used to help the Government decide future plans for the Mamminasata area.

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“…It is good at fine-tuning a model and can find a good model fit without redundant variables, compared with other traditional regression methods. Stepwise regression analysis method is often used in LUCC research [34,35], so it is chosen here to pick out the variables that have significant impact on each type of land cover. Similar to previous research [36,37] in which the GDP and population-related variables were selected as major representative socioeconomic variables, the following independent variables are taken into account: GDP (X 1 ), GDP of the primary industry (X 2 ), GDP of the secondary industry (X 3 ), GDP of the tertiary industry (X 4 ), population (X 5 ), flow of population (X 6 ), outflow of population (X 7 ), and inflow of population (X 8 ).…”

Section: Impact Analysis Of the Flow And Type Of Population On Luccmentioning

confidence: 99%

Investigating the Impact of Human Activity on Land Use/Cover Change in China’s Lijiang River Basin from the Perspective of Flow and Type of Population

Zhang

Qin

et al. 2017

Sustainability

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Exploring how human activity impacts land use/cover change (LUCC) is a hot research topic in the field of geography and sustainability management. Researchers have primarily used socioeconomic variables to measure human activity. However, the human activity indexes mainly based on socioeconomic variables have a spatial resolution that is coarser than traditional LUCC datasets, which hinders a deep and comprehensive analysis. In view of these problems, we selected China's Lijiang River Basin as our study area and proposed the use of GPS trajectory data for analyzing the impact of human activity on LUCC from two perspectives: (1) Type of population: we used the kernel density estimation method to extract the spatial distribution of activity intensity of local residents and tourists, investigated their correlation with the LUCC result, and found these two populations have different impacts on each land cover; (2) Flow of population: we used the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and a network analysis method to build a flow network of population from raw trajectories, conducted regression analysis with LUCC, and found that the flow of population is an important factor driving LUCC and is sometimes a more important factor than the static distribution of the population. Experimental results validated that the proposed method can be used to uncover the impact mechanism of human activity on LUCC at fine-grained scales and provide more accurate planning and instructions for sustainability management.

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Simulation of Land-Cover Change in Taipei Metropolitan Area under Climate Change Impact

Cited by 3 publications

References 2 publications

Building Policies for Climate Changes: Lessons from COVID-19

Building Policies for Climate Changes: Lessons from COVID-19

Predicting Land Cover Change in the Mamminasata Area, Indonesia, to Evaluate the Spatial Plan

Investigating the Impact of Human Activity on Land Use/Cover Change in China’s Lijiang River Basin from the Perspective of Flow and Type of Population

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