Forest Vulnerability to Climate Change: A Review for Future Research Framework
Abstract
:1. Introduction
2. Methodology
2.1. Synthesis of Metadata
2.2. Literature Survey
3. Results
3.1. Metadata Analysis
3.1.1. Publication Trend Analysis
3.1.2. Geographical Distribution of the Studies
3.1.3. Keyword Analysis
3.2. Mechanism Analysis
4. Discussion
4.1. Climate Change Impact and Forest Vulnerability
4.2. The Adaptive Capacity and Strategies of the Forest and Dependent Communities
4.3. Forest Management and Its Scope
4.4. Framework for Future Research
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Year of Publication | Author | Methods, Models and Tools |
---|---|---|
2002 | M.J. Lexer | Bayesian probability theory |
2006 | Wilfried Thuiller | BIOMOD framework, Simpson’s diversity index |
2009 | M.J. Lexer | PROMETHEE outranking method |
2010 | Yuko Ogawa-Onishi | Multiple linear regressions |
2010 | Patrick Gonzalez | Correlation coefficient |
2011 | Paul H. Evangelista | Jackknife testing |
2011 | Marcus Klaus | Logistic regression analysis, Wald-test |
2011 | Wei Ren | leaf area index (LAI) |
2011 | N. H. Ravindranath | PCA, agricultural vulnerability index (AVI), water vulnerability index (WVI), forest vulnerability index (FVI) |
2012 | Doo-Ahn Kwak | Inverse distance squared weighting (IDW), Non-linear regression analysis |
2012 | Rupert Seidl | ANOVA, significance of the squared Mahalanobis |
2014 | Mingshi Li | NDVI, normalized burn ratio (NBR) |
2014 | Jun Du | Kriging method |
2014 | Pavel Tucek | Multivariate statistical techniques, PCA |
2014 | Nicholas A. Fisichelli | Pearson’s correlation coefficient |
2014 | Adrianon Mazziotta | Habitat suitability index (HSI), generalized estimating equations (GEEs) Methods |
2014 | V.S. Chitale | Geographic weighted regression (GWR) |
2015 | Sujata Upgupta | Forest vulnerability index (FVI) |
2016 | Adam Bauer | Regression analysis |
2016 | Alan J. Tepley | Non-parametric multiplicative regression |
2016 | Rauls Anchez-Salguero | COFECHA program, ARSTAN program |
2016 | Mark J. Lara | Regression analysis, maximum likelihood algorithm, error matrix and Kappacoefficient |
2017 | Ji-Zhong Wan | MaxEnt software |
2017 | Jan C. Thiele | Site index, leaf area index (LAI), Kolmogorov–Smirnoff test |
2017 | Craig R. Nitschke | Linear regression, Program Dendroclim, Kalman filtering, multiple regression, Kappa coefficient |
2017 | Rhys Manners | fuzzy cognitive mapping |
2017 | Alexandra Paige Fischer | Z-Scores, PCA |
2017 | Duncan Ray | Pearson correlation method |
2017 | I. Aubin | Climate moisture index (CMI), drought sensitivity index |
2017 | Camille S. Stevens-Rumann | Z-score, Chi-square test |
2018 | Alexandra Paige Fischer | NVivo data analysis software |
2018 | Qinli Yang | CCDC algorithm, F-mask algorithm, ANUSPLIN software, simple linear trend (SLT) method, Mann–Kendall, correlation analysis |
2018 | Rita Sousa-Silva | Kruskal–Wallis test, Spearman’s correlation analysis |
2018 | Malay Pramanik | Pearson correlation coefficient, PCA |
2018 | Simone Matias Reis | Fragstats software, Mann–Kendall, Kruskal–Wallis test |
2018 | Chidiebere Ofoegbu | SPSS, Chi-square, Bonferroni tests, logistic regression model |
2018 | Xiongwen Chen | Simpson’s index, t-test |
2018 | Nguyen Thi Lan Huong | Livelihood vulnerability index (LVI) and livelihood effect index (LEI), t-tests |
2018 | Getnet Feyissa | Diversity index |
2018 | M. Peraza-Castro | Differential split-sampling test (DSST), linear regressions |
2018 | Manoj Kumar | Climate data operator (CDO) software, correlation coefficient |
2019 | Cristian Gheorghe Sidor | Basal area increment (BAI), CooRecorder software, COFECHA software, XLSTAT software, QGIS software |
2019 | Brandi A. Gaertner | Asymmetric Gaussian function, Penman–Monteith equation, Mann–Kendall, PCA |
2019 | Tamir Klein | Global forest watch (GFW) analysis tool, standardized precipitation evapotranspiration index (SPEI), ANOVA |
2019 | Adriana Almeida de Lima | Maximum entropy algorithm |
2019 | Mathieu Bouchard | Poisson distribution |
2019 | Sara Alibakhshi | Leaf area index (LAI) |
2019 | Muhammad Naeem | MaxEnt software, Pearson’s correlation coefficient, Markov chain analysis |
2019 | A.J. Hester | t-tests, Bray–Curtis distance matrix, ANOVA |
2019 | Francis K. Dwomoh | Relative delta normalized burn ratio (RdNBR) |
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Share and Cite
Roshani; Sajjad, H.; Kumar, P.; Masroor, M.; Rahaman, M.H.; Rehman, S.; Ahmed, R.; Sahana, M. Forest Vulnerability to Climate Change: A Review for Future Research Framework. Forests 2022, 13, 917. https://doi.org/10.3390/f13060917
Roshani, Sajjad H, Kumar P, Masroor M, Rahaman MH, Rehman S, Ahmed R, Sahana M. Forest Vulnerability to Climate Change: A Review for Future Research Framework. Forests. 2022; 13(6):917. https://doi.org/10.3390/f13060917
Chicago/Turabian StyleRoshani, Haroon Sajjad, Pankaj Kumar, Md Masroor, Md Hibjur Rahaman, Sufia Rehman, Raihan Ahmed, and Mehebub Sahana. 2022. "Forest Vulnerability to Climate Change: A Review for Future Research Framework" Forests 13, no. 6: 917. https://doi.org/10.3390/f13060917