Impatti, adattamento e vulnerabilita Le cause e le responsabilita dei cambiamenti climatici sono state trattate sul numero di ottobre della rivista Cda. Approfondiamo l’argomento presentando il documento: “Cambiamenti climatici 2007: impatti, adattamento e vulnerabilita” votato ad aprile 2007 dal secondo gruppo di lavoro del Comitato Intergovernativo sui Cambiamenti Climatici (Intergovernmental Panel on Climate Change). Si tratta del secondo di tre documenti che compongono il quarto rapporto sui cambiamenti climatici.

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Climate Change 2007: Impacts, Adaptation and Vulnerability.

Framework Convention on Climate Change

A Viking Lander 1 image was modeled as mixtures of reflectance spectra of palagonite dust, gray andesitelike rock, and a coarse rocklike soil. The rocks are covered to varying degrees by dust but otherwise appear unweathered. Rocklike soil occurs as lag deposits in deflation zones around stones and on top of a drift and as a layer in a trench dug by the lander. This soil probably is derived from the rocks by wind abrasion and/or spallation. Dust is the major component of the soil and covers most of the surface. The dust is unrelated spectrally to the rock but is equivalent to the global-scale dust observed telescopically. A new method was developed to model a multispectral image as mixtures of end-member spectra and to compare image spectra directly with laboratory reference spectra. The method for the first time uses shade and secondary illumination effects as spectral end-members; thus the effects of topography and illumination on all scales can be isolated or removed. The image was calibrated absolutely from the laboratory spectra, in close agreement with direct calibrations. The method has broad applications to interpreting multispectral images, including satellite images.

Spectral mixture modeling - A new analysis of rock and soil types at the Viking Lander 1 site. [on Mars]

Land use and land cover change research has been applied to landslides, erosion, land planning and global change. Based on the CA-Markov model, this study predicts the spatial patterns of land use in 2025 and 2036 based on the dynamic changes in land use patterns using remote sensing and geographic information system. CA-Markov integrates the advantages of cellular automata and Markov chain analysis to predict future land use trends based on studies of land use changes in the past. Based on Landsat 5 TM images from 1992 and 2003 and Landsat 8 OLI images from 2014, this study obtained a land use classification map for each year. Then, the genetic transition probability from 1992 to 2003 was obtained by IDRISI software. Based on the CA-Markov model, a predicted land use map for 2014 was obtained, and it was validated by the actual land use results of 2014 with a Kappa index of 0.8128. Finally, the land use patterns of 2025 and 2036 in Jiangle County were determined. This study can provide suggestions and a basis for urban development planning in Jiangle County.

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Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques-A case study of a hilly area, Jiangle, China.

Chapter 1 provides a general introduction to GLIM4 and its features of which we can make particular use, especially the presentation graphics. Chapter 2 gives a detailed discussion of the population modeling process, with a full discussion of frequency, Bayesian, and likelihood inferential approaches to simple models. Chapter 3 discusses the normal models, including regression models and analysis of variance. It also introduces factorial designs and unbalanced cross-classification designs. Chapter 4 discusses the binomial models for the binary response data, along with construction of the contingency tables from binary data. Chapter 5 discusses multinomial and Poisson models as well as their relations and covers model fitting for cross-classified counts and multicategory responses in detail. Chapter 6 discusses survival models, including exponential, gamma, and Weibull distributions for survival data, and introduces Kaplan–Meier estimator and Cox proportional hazard models. Chapter 7 discusses finite mixtures of distributions with maximum likelihood and kernel density estimates. Chapter 8 discusses random-effects models with conjugate random effects, normal random effects, as well as arbitrary random effects. Chapter 9 discusses variance component models with shared random effects arising through variance component or repeated-measures structure. In addition to the theoretical analyses for model fitting and practical examples for illustration of the use of GLIM4, the authors also provide readers with datasets of the examples and GLIM4 programs for their analyses. The programs are written in a very concise form and should be easy to use and understand. The datasets and programs used in this book can be downloaded from the Oxford University Press website. Overall, this book examines the theory of statistical modeling with generalized linear models. It also provides a full description of the use of GLIM4 for model fitting, with detailed discussions of many examples. This book is an ideal textbook for graduate and advanced undergraduate courses in statistics, in conjunction with interactive use of GLIM. It may also be used as a self-teaching manual by researchers and students in applied statistics and other quantitative disciplines, such as biology, medicine, agriculture, industry, and social sciences. Readers may refer to the books by McCullagh and Nelder (1989) for detailed principles of the generalized linear models and Francis, Green, and Payne (1993) for a full GLIM4 manual.

Modeling Longitudinal Data

A multiple linear model was developed for individual tree crown width of Cunninghamia lanceolata (Lamb.) Hook in Fujian province, southeast China. Data were obtained from 55 sample plots of pure China-fir plantation stands. An Ordinary Linear Least Squares (OLS) regression was used to establish the crown width model. To adjust for correlations between observations from the same sample plots, we developed one level linear mixed-effects (LME) models based on the multiple linear model, which take into account the random effects of plots. The best random effects combinations for the LME models were determined by the Akaike’s information criterion, the Bayesian information criterion and the -2logarithm likelihood. Heteroscedasticity was reduced by three residual variance functions: the power function, the exponential function and the constant plus power function. The spatial correlation was modeled by three correlation structures: the first-order autoregressive structure [AR(1)], a combination of first-order autoregressive and moving average structures [ARMA(1,1)], and the compound symmetry structure (CS). Then, the LME model was compared to the multiple linear model using the absolute mean residual (AMR), the root mean square error (RMSE), and the adjusted coefficient of determination (adj-R2). For individual tree crown width models, the one level LME model showed the best performance. An independent dataset was used to test the performance of the models and to demonstrate the advantage of calibrating LME models.

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Linear Mixed-Effects Models to Describe Individual Tree Crown Width for China-Fir in Fujian Province, Southeast China

Monitoring and mapping forest carbon is critical for informing climate change mitigation measures. Evidence indicates that forest edges hold less carbon than the forest interior. In this study, usi...

Forest edge effect on biomass carbon along altitudinal gradients in Chinese Fir (Cunninghamia lanceolata): a study from Southeastern China.

Protected areas serve two objectives, biodiversity conservation and securing of ecosystem services. But the recent expansion of human activities around Cameroon’s Rumpi Hills Forest Reserve in this...

Assessing the drivers of land use change in the Rumpi hills forest protected area, Cameroon

A systematic evaluation of nonlinear mixed-effect taper models for volume prediction was performed. Of 21 taper equations with fewer than 5 parameters each, the best 4-parameter fixed-effect model according to fitting statistics was then modified by comparing its values for the parameters total height (H), diameter at breast height (DBH), and aboveground height (h) to modeling data. Seven alternative prediction strategies were compared using the best new equation in the absence of calibration data, which is often unavailable in forestry practice. The results of this study suggest that because calibration may sometimes be a realistic option, though it is rarely used in practical applications, one of the best strategies for improving the accuracy of volume prediction is the strategy with 7 calculated total heights of 3, 6 and 9 trees in the largest, smallest and medium-size categories, respectively. We cannot use the average trees or dominant trees for calculating the random parameter for further predictions. The method described here will allow the user to make the best choices of taper type and the best random-effect calculated strategy for each practical application and situation at tree level.

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Sun Yujun

Papers

Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques-A case study of a hilly area, Jiangle, China.

Linear Mixed-Effects Models to Describe Individual Tree Crown Width for China-Fir in Fujian Province, Southeast China

Forest edge effect on biomass carbon along altitudinal gradients in Chinese Fir (Cunninghamia lanceolata): a study from Southeastern China.

Assessing the drivers of land use change in the Rumpi hills forest protected area, Cameroon

A mixed-effects model with different strategies for modeling volume in cunninghamia lanceolata plantations