DPS软件在中绥12杨光响应曲线拟合中的应用

采用Li-6400光合仪测定了中绥12杨的光响应数据。分别采用直角双曲线模型、非直角双曲线模型、直角双曲线修正模型及指数模型对光响应曲线进行拟合,以探讨几种模型对中绥12杨的适用性并介绍在DPS数据处理系统中的操作步骤。结果表明,直角双曲线、非直角双曲线及指数模型得到的最大净光合速率大于实测值;直角双曲线和非直角双曲线模型得到的光饱和点远小于实测值;应用指数模型时(假设0.9Pn max或0.99Pn max所对应的光强为Isat)则无法求解光饱和点;直角双曲线修正模型拟合得到的各项光合参数均与实际相符,是中绥12杨光合光响应的最适宜模型。     

滇西北4个高原湖泊演变特征及其驱动因子

基于纳帕海、属都湖、碧塔海和剑湖4个高原湖泊矢量数据,利用Arc Map 10、Fragstats 3.3、Excel等软件,系统分析了1955~2012年4个高原湖泊水域面积和形状的时空演变规律及其驱动因子。结果表明,(1)湖泊时空演变规律各异。纳帕海水域面积和形状复杂程度都处于无规律的波动变化过程中。属都湖水域面积和形状复杂程度1955~1994年都处于增加状态,1994~2011年保持不变。碧塔海水域面积和形状复杂程度没有发生变化。剑湖水域面积1957~2012年呈减少趋势,形状复杂程度呈增加趋势。(2)时空变化程度各不相同。4个高原湖泊时空变化程度从大到小的排序为纳帕海﹥剑湖﹥属都湖﹥碧塔海。(3)演变驱动因子不尽一致。纳帕海的驱动因子主要为疏浚落水洞、建闸控水、泥沙淤积、改造河道、气候变化、湖水污染和旅游干扰。剑湖的驱动因子主要为疏浚河道、泥沙淤积、湖滩开发、建闸控水、气候变化和湖水污染。属都湖的驱动因子主要为筑坝蓄水。人为干扰和气候变化没有导致碧塔海湖泊的变化。(4)受人为干扰程度有差异。4个高原湖泊受人为干扰程度由重到轻的排序为纳帕海﹥剑湖﹥属都湖﹥碧塔海。     

应用微机对TM图像进行森林资源分类的研究

遵义地区地形破碎,地貌很复杂,同谱异物、同物异谱现象较严重,地类界限不明显,而且只有一景单时相(质量7分)的TM数据,在微机上以计算机分类对遵义地区进行森林资源遥感图像分类。通过研究波段的选择、样地参数测算、图像的光谱特征、训练样地的选择和分类方法的选择,提出了可实际应用的技术方法,并全部由微机实现,得出了定量结果和分色图件。     

松毛虫早期灾害点遥感监测研究初报

松毛虫早期灾害点遥感监测研究初报武红敢，黄建文，乔彦友（中国林业科学研究院资源信息所北京１００００１）严小君，陈林洪（浙江省江山市森林防疫站江山３２４１００）关键词松毛虫害，ＴＭ数据，影像，监测松毛虫为我国最为严重的森林害虫［１］。几十年来，各级政府...     

VEGETATION植被指数数据预处理方法

VEGETATION数据的植被指数,受云雾影响,含有大量的噪声信号,使用之前必须进行去噪声处理.结合实例数据介绍了半中值滤波法、半均值滤波法、等间隔极大值法用于植被指数的去噪声处理,效果良好;该处理方法同样适用于NOAA和MODIS等数据的植被指数预处理.     

滇中地区森林火险等级预报方法的研究

经引起滇中地区森林火灾的主要细小可燃物的含水率及初始蔓延速度的大量试验数据为基础，结合大量林火历史资料，气象历史资料统计分析，找出规律，在对森林火险等级进行实测的基础上，建立数学模型和预报方法。经验证及对预报效果分析证明，本预报方法符合客观实际，是一种符合科学规律、简易、实用的预报方法，同时对引起滇中地区特大森林火灾的大气环流形势及物理量的变化作了分析，掌握了有关规律，为该地区的特大森林火险预报提     

基于GeoMedia环境下的森林防火系统数据处理

以森林防火智能决策支持系统为例，介绍了GeoMedia的功能，重点讨论如何运用GeoMedia处理林火信息系统所涉及的空间数据、属性数据以及栅格文本数据，具有一定的实际意义。     

电力远程自动集中抄表系统

本文主要论述电力远程自动集中抄表系统的构成、特点以及系统的发展前景。     

Preliminary recovery of coastal sharks in the south‐east United States

Relative abundance of many shark species in the Atlantic is assessed by compiling data from several independently conducted, but somewhat spatially limited surveys. Although these localized surveys annually sample the same populations, resulting trends in yearly indices often conflict with one another, thereby hindering interpretation of abundance patterns at broad spatial scales. We used delta‐lognormal generalized linear models (GLMs) to generate indices of abundance for seven Atlantic coastal shark species from six fishery‐independent surveys along the US east coast and Gulf of Mexico from 1975 to 2014. These indices were further analysed using dynamic factor analysis (DFA) to produce simplified, broad‐scale common trends in relative abundance over the entire sampled distribution. Effects of drivers including the North Atlantic Oscillation index, the Atlantic Multidecadal Oscillation index, annually averaged sea surface temperature and species landings were evaluated within the DFA model. The two decadal oscillations and species landings were shown to affect shark distribution along south‐east US coast. Estimated common trends of relative abundance for all large coastal shark species showed similar decreasing patterns into the early 1990s, periods of sustained low index values thereafter and recent indications of recovery. Small coastal shark species exhibited more regional variability in their estimated common trends, such that two common trends were required to adequately describe patterns in relative abundance throughout the Gulf of Mexico and Atlantic. Overall, all species’ (except the Gulf of Mexico blacknose shark) time series concluded with an increasing trend, suggestive of initial recovery from past exploitation.     

The pelagic habitat analysis module for ecosystem‐based fisheries science and management

We have developed a set of tools that operate within an aquatic geographic information system to improve the accessibility, and usability of remote‐sensed satellite and computer‐modeled oceanographic data for marine science and ecosystem‐based management. The tools form the Pelagic Habitat Analysis Module (PHAM), which can be applied as a modeling platform, an investigative aid in scientific research, or utilized as a decision support system for marine ecological management. Applications include fisheries, marine biology, physical and biological oceanography, and marine spatial management. The GIS provides a home for diverse data types and automated tools for downloading remote sensed and global circulation model data. Within the GIS environment, PHAM provides a framework for seamless interactive four‐dimensional visualization, for matching between disparate data types, for flexible statistic or mechanistic model development, and for dynamic application of user developed models for habitat, density, and probability predictions. Here we describe PHAM in the context of ecosystem‐based fisheries management, and present results from case study projects which guided development. In the first, an analysis of the purse seine fishery for tropical tuna in the eastern Pacific Ocean revealed oceanographic drivers of the catch distribution and the influence of climate‐driven circulation patterns on the location of fishing grounds. To support management of the Common Thresher Shark (Alopias vulpinus) in the California Current Ecosystem, a simple empirical habitat utilization model was developed and used to dynamically predict the seasonal range expansion of common thresher shark based on oceanographic conditions.