Plant Adaptation to Global Climate Change

Plant Adaptation to Global Climate Change discusses the issues of the impact of climate change factors (abiotic and biotic) on vegetation. This book also deals with simulation modeling approaches to understanding the long-term effects of different environmental factors on vegetation. This book is a valuable resource for the environmental science research community, including those interested in assessing climate change impacts on vegetation and researchers working on simulation modeling

Agro-ecological evaluation of sustainable area for citrus crop production in Ramsar District, Iran

Citrus growing is regarded as an important cash crop in Ramsar, Iran. Ramsar District has a temperate climate zone, while citrus is a sub-tropical fruit. Few studies on citrus crop in terms of negative environmental factors have been carried out by researchers around the world. This study aims to integrate Geographical Information System (GIS) and Analytical Network Process (ANP) model for determination of citrus suitability zones. This study evaluates the agro-ecological suitability, determine potentials and constraints of the region based on effective criteria using ANP model. ANP model was used to determine suitable, moderate and unsuitable areas based on (i) socio-economic, morphometry and hydro-climate factors using 15 layers based on experts’ opinion; (ii) Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite image of the year 2003 with 98.45% overall accuracy, and (iii) developed Multiple Linear Regression (MLR) model for citrus prediction. Thereby, weighted overlay of 15 factors was obtained using GIS. In this study, the citrus orchards map of 2003 and the new map of the citrus areas of 2014 namely Citrus State Development Program (CSDP) of the study area were compared. The results of this study demonstrated: (i) suitable areas (free risk areas) based on negative environmental factors and areas which are susceptible to citrus plantation; (ii) high-risk areas which are unsuitable for citrus plantation, and (iii) the high weights derived by ANP model were assigned to altitude, frost and minimum temperature. The MLR model was successfully developed to predict citrus yield in Ramsar District by 10% error. The MLR model would propose optimum citrus crop production areas. As conclusion, the main outcome of this study could help growers and decision makers to enhance the current citrus management activities for current and future citrus planning

Mapping Torreya grandis Spatial Distribution Using High Spatial Resolution Satellite Imagery with the Expert Rules-Based Approach

Rapid expansion of Torreya forests in the mountainous region in Zhejiang Province in the past three decades has produced many environmental problems such as soil erosion and poor water quality, requiring an update of its spatial distribution in a timely way. However, to date there are no suitable approaches available for mapping Torreya forest distribution, especially the new Torreya plantations, due to the complex landscapes. This research used high spatial resolution Chinese Gaofen (GF-1) and Ziyuan (ZY-3) satellite images and digital elevation model (DEM) data to extract old Torreya forests and new Torreya plantations using a newly proposed expert rules-based approach. Different variables such as spectral bands, vegetation indices, textural images, and DEM-derived variables were examined, and separability analyses of different land covers were explored. An expert rules-based approach was developed for the extraction of old Torreya forests and new Torreya plantations. The accuracy assessment using field survey data and Google Earth images indicates that this newly-proposed approach can effectively distinguish both old Torreya forests and new Torreya plantations from other land covers with producer’s accuracies of 84% and 92%, and user’s accuracies of 77% and 85%, respectively, much better classification accuracies than the maximum likelihood classifier. This new approach may be used for other study area for extracting Torreya forest distribution. This research provides valuable data sources for better managing existing Torreya forests and planning potential Torreya expansions in this region in the near future