偏最小二乘(PLS)回归方法在中国东部植被变化归因研究中的应用

植被变化往往受到不同气候变量的综合作用,人类活动影响又使得植被对气候响应变得更为复杂,如何准确判别各种影响因素的相对重要性是植被变化归因研究中的一个关键点.研究基于偏最小二乘(PLS)回归方法,使用1982-2006 年的归一化植被指数(NDVI)数据,分析了降水、气温、日照、相对湿度和风等气候变量对中国东部植被变化的相对影响,并选取了NDVI变化较为典型的区域,量化了农业活动对该地区植被变化的相对贡献.PLS回归方法兼具了主成分分析和多元回归的优点,克服了众多自变量之间存在强烈交互相关导致的多元共线性问题.研究结果表明:① 1982-2006 年间,中国东部逐月NDVI的年际变化呈现出明显的南北差异.在12、1-5 月,NDVI以显著上升为主,上升区域主要位于淮河以北.在6-10月,NDVI以显著下降为主,下降区域主要为淮河以南的部分区域,特别是6 月江苏一带NDVI的大范围下降尤为明显.不过与NDVI发生显著变化的区域相比,更多区域的NDVI在各月并没有出现显著变化.② 在NDVI显著上升的站点,对NDVI变化最具解释意义的气候变量为气温,特别是冬末春初(2-3 月)的升温对黄淮海区域NDVI的显著上升具有主导控制作用.而对于NDVI 显著下降的站点,多数都不能从气候角度解释这些区域的NDVI 变化.③ 江苏省NDVI在6 月出现的大范围显著下降,与农业种植结构的调整,主要是棉花种植面积的减少以及油菜面积的增加具有显著关系.     

哈尼梯田地区农户粮食作物种植结构及驱动力分析

哈尼稻作梯田系统作为全球重要农业文化遗产（GIAHS）,具有极高的生态、经济、文化价值。近年来,以粮食产量增长为导向的农耕技术和作物品种单一化趋势,给哈尼梯田地区带来了严重的生态和食品安全问题。论文以农户生产行为作为切入点,从主要粮食作物的经济效益、耕地资源特征、村落发展类型、农户的家庭特征与资源禀赋进行实证研究,分析了哈尼梯田地区农户粮食作物种植结构现状及驱动因素。结果表明：1）调查涉及的41.23 hm²有效耕地中,按种植总面积排序,杂交稻、玉米、水果类作物位居前三。2）本地传统粮食作物--梯田红米,种植总面积和户均种植面积远小于经济效益较高的杂交稻和兼有饲料用途的玉米。同时,农户倾向于将其种植在质量较差、海拔较高的耕地上。3）作物的经济效益和耕地海拔及质量对替代性作物（如杂交稻和红米）的种植选择影响较大;个体农户层面上,农户特征与资源禀赋在不同程度上对不同作物的种植选择产生影响。     

The Spanish building crisis and its effect in the gypsum quarry production (1998–2012)

Gypsum is one of the most important mined mineral commodities of the world. As an industrial rock, it is primarily used by the construction industry. Therefore, the economics of the gypsum market can be correlated with parameters that have controlled the origin and development of the Spanish building bubble between years 1998 and 2006. The burst of this building bubble has been interpreted as the main triggering factor for Spain's current crisis.     

回归分析方法在环境领域中的应用评述

回归分析是一种处理变量间相关关系的有效数理统计方法,回归分析模型目前已应用于环境领域的多个方面,并在实际应用中证实了其准确性和可行性。基本回归分析方法有一元线性回归、多元线性回归和逐步回归等,通过概述了这几种基本回归分析方法的原理及其在环境领域中的应用现状,评述了其应用效果,并预测了回归分析技术在环境领域中应用的发展方向;对在众多环境科学与工程研究领域中,更好地发挥回归分析的作用具有很好的参考价值。     

基于SVR的船舶溢油事故预测

船舶溢油事故的预测,是确定所需应急资源多少的重要依据;但船舶溢油事故具有小样本性,传统预测方法不再适用。针对船舶溢油事故的小样本性,该文选择SVR模型对其进行预测;SVR算法的关键是核函数的选择及自由参数的确定,针对上述问题该文分别采用交叉验证法、遗传算法;并在matlab软件上建立SVR模型,最后将该模型应用于上海港水域船舶溢油事故的预测。     

A practical real option methodology for the evaluation of farm-in/out joint venture agreements in mineral exploration

Although the “real option valuation” (ROV) methodology offers extremely valuable insights in optimising investment decisions in the face of uncertainty, its practical acceptance in the mining industry has until recently been slow because of its perceived computational complexity. Recent conceptual advances in the use of binomial lattices and software developments in the areas of decision trees and dynamic programming, have significantly simplified ROV analysis and made it of practical application in day-to-day financial evaluations and decisions involving uncertainty. This paper provides general background on the ROV methodology and an example of how a typical farm-in/out agreement, as a preliminary to the establishment of a joint venture (JV), can be valued as a series of sequential and compound real options. For illustrative and quality assurance purposes, a simple farm-in/out agreement is valued from the point of view of the party acquiring equity in the project (the farm-inee) using two distinct methods, i.e. (a) a binomial lattice and (b) a decision tree in combination with binomial stochastic processes, in both cases neutralising risk using the user-friendly “risk-neutral probability”. The fact that exactly the same ROV is obtained by both methods provides confidence in the modified decision tree approach, which opens up the capacity to value the more complex sequential/compound real options inherent in real-life farm-in/out agreements. The model is then modified to incorporate a number of realistic contractual conditions often encountered in typical exploration and mining farm-in/out deals. The paper demonstrates how the increased complexity of the model can be relatively easily addressed using a decision tree with dynamic programming capability.     

Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition

An important consideration in conservation and biodiversity planning is an appreciation of the condition or integrity of ecosystems. In this study, we have applied various machine learning methods to the problem of predicting the condition or quality of the remnant indigenous vegetation across an extensive area of south-eastern Australia—the state of Victoria. The field data were obtained using the ‘habitat hectares’ approach. This rapid assessment technique produces multiple scores that describe the condition of various attributes of the vegetation at a given site. Multiple sites were assessed and subsequently circumscribed with GIS and remote-sensed data.     

Modelling the abundance of three key plant species in New Zealand hill-pasture using a decision tree approach

Decision tree models were developed to investigate and predict the relative abundance of three key pasture plants [ryegrass (Lolium perenne), browntop (Agrostis capillaris), and white clover (Trifolium repens)] with integration of a geographical information system (GIS) in a naturalised hill-pasture in the North Island, New Zealand, and were compared with regression models with respect to model fit and predictive accuracy. The results indicated that the decision tree models had a better model fit in terms of average squared error (ASE) and a higher percentage of adequately predicted cases in model validation than the corresponding regression models. These decision tree models clearly revealed the relative importance of environmental and management variables in influencing the abundance of these three species. Hill slope was the most significant environmental factor influencing the abundance of ryegrass while soil Olsen P and annual P fertilizer input were the most significant factors influencing the abundance of browntop, and white clover, respectively. Soil Olsen P of approximately 10 μg/g, or a slope of about 10.5° was critical points where the competition between ryegrass and browntop tended to come to an equilibrium. Integrating the decision tree models with a GIS in this study not only facilitated the model development and analyses, but also provided a useful decision support tool in pasture management such as in assisting precision fertilizer placement. The insights obtained from the decision tree models also have important implications for pasture management, for example, it is important to maintain a soil Olsen P higher than 10 μg/g in order to keep the dominance of ryegrass in the hill-pasture.     

The Future of Scattered Trees in Agricultural Landscapes

Abstract: Mature trees scattered throughout agricultural landscapes are critical habitat for some biota and provide a range of ecosystem services. These trees are declining in intensively managed agricultural landscapes globally. We developed a simulation model to predict the rates at which these trees are declining, identified the key variables that can be manipulated to mitigate this decline, and compared alternative management proposals. We used the initial numbers of trees in the stand, the predicted ages of these trees, their rate of growth, the number of recruits established, the frequency of recruitment, and the rate of tree mortality to simulate the dynamics of scattered trees in agricultural landscapes. We applied this simulation model to case studies from Spain, United States, Australia, and Costa Rica. We predicted that mature trees would be lost from these landscapes in 90–180 years under current management. Existing management recommendations for these landscapes—which focus on increasing recruitment—would not reverse this trend. The loss of scattered mature trees was most sensitive to tree mortality, stand age, number of recruits, and frequency of recruitment. We predicted that perpetuating mature trees in agricultural landscapes at or above existing densities requires a strategy that keeps mortality among established trees below around 0.5% per year, recruits new trees at a rate that is higher than the number of existing trees, and recruits new trees at a frequency in years equivalent to around 15% of the maximum life expectancy of trees. Numbers of mature trees in landscapes represented by the case studies will decline before they increase, even if strategies of this type are implemented immediately. This decline will be greater if a management response is delayed.     

华北山区温度推算模式和应用

利用华北山区气象站观测的温度与其所在纬度、经度和高度，分山区建立宏观地面气温场方程。采用１：２０万地形图，以１０＇经、纬度为步长，判读其高度值，代入已建立好的方程中，经地形项订正，推算华北山区年、月平均温度、年较差和ΣＴ＇≥１０℃，其精度达极显著水平。计算结果使我们对华北山区热量资源有了新的认识，最后求算华北山区的主要水果最优品质种植上限高度。