沿海防护林体系热效应的遥感分析

沿海防护林体系热效应的遥感分析王述礼,金昌杰,关德新（中国科学院沈阳应用生态研究所１１００１５）ＲｅｍｏｔｅＳｅｎｓｉｎｇＡｎａｌｙｓｉｓｏｎｔｈｅＨｅａｔＥｆｆｅｃｔｓｏｆＣｏａｓｔａｌＳｈｅｌｔｅｒｂｅｌｔＳｙｓｔｅｍ．￥ＷａｎｇＳｈｕｌｉ;Ｊｉ...     

区域生态经济型防护林体系建设模式智能决策支持系统的研制开发

根据生态经济型防护林体系及其建设模式的内涵,研制开发出了以专家为核心,包括数据及数据库、图形及图形库、模型及模型库等模块的智能决策支持系统,以便为给定区域提供有关生态经济型防护林体系建设方面的决策服务。     

三北防护林体系建设面临的机遇和挑战

在总结三北防护林工程(三北工程)30年建设成就和经验的基础上,立足国内外相关领域发展趋势,全面分析了三北工程在应对气候变化、增加农民收入、拉动内需等方面显现的机遇,剖析了立地条件、发展动力、投资水平和改革创新等带来的严峻挑战,总结提出了三北工程未来发展应坚持以建设完备的区域性防护林体系为目标,以防沙治沙、保持水土为主要任务,以服务新农村建设、发展特色产业、推进林权改革为重点的发展思路和具体措施.     

试论“三北”生态经济型防护林体系

本文介绍了“三北”防护林体系工程的概要、建设指导思想和技术路线,从理论上较深入地探讨了林业观念更新的意义及其基础。从而,提出“生态经济型防护林体系”的学术概念,以及它同建立区域性人工生态系统的相互关系,并结合“三北”黄土高原昕水河流域生态经济型防护林体系示范区的特点进行分析,探讨丘陵山地条件下,生态经济型防护林体系的技术内涵、组成及其生态经济特点。最后,作者提出了由“三北”防护林工程的实践对我国如何建设好其它防护林工程的几点启示。     

发展林业碳汇推动三北防护林体系建设

三北防护林体系建设作为中国六大林业重点工程之一,30年来取得了巨大成就.但长期以来,工程建设资金不足一直困扰着工程发展.林业碳汇的兴起和快速发展,将对其产生重大影响.本文在对林业碳汇进行分析的基础上,提出了加快发展林业碳汇推动三北防护林体系建设的建议.     

多层次多树种防护林的生态效应

多层次多树种防护林的生态效应彭锦钊（广西大桂山林场,贺县５４２８２４）ＥｃｏｌｏｇｉｃａｌＥｆｆｅｃｔｓｏｆＭｕｌｔｉ－ＬａｙｅｒａｎｄＭｕｌｔｉ－ＳｐｅｃｉｅｓＳｈｅｌｔｅｒｂｅｌｔ．￥ＰｅｎｇＪｉｎｚｈａｏ（ＤａｇｕｉｓｈａｎＦｏｒｅｓｔｆｏｒｅ...     

试论“三北”生态经济型防护林体系

本文介绍了“三北”防护林体系工程的概要、建设指导思想和技术路线,从理论上较深入地探讨了林业观念更新的意义及其基础。从而,提出“生态经济型防护林体系”的学术概念,以及它同建立区域性人工生态系统的相互关系,并结合“三北”黄土高原昕水河流域生态经济型防护林体系示范区的特点进行分析,探讨丘陵山地条件下,生态经济型防护林体系的技术内涵、组成及其生态经济特点。最后,作者提出了由“三北”防护林工程的实践对我国如何建设好其它防护林工程的几点启示。     

基于GIS的浙江省水稻遥感估产最佳时相选择

水稻遥感估产最佳时相选择应包括水稻种植面积估算最佳时相和水稻产量预报最佳时相两部分。在水稻遥感估产最佳时相选择中,由于首次引入GIS技术提取水稻可能种植区域,缩小了研究范围,植被种类也较简单一,因此仅用农作物物候历即可确定水稻种植面积估算最佳时相,而不需要考虑所有的植被类型。利用盆栽试验和小区试验研究水稻产量与不同时期的农学参数、农学参数与植被指数及水稻产量与植被指数的关系,结果表明,水稻产量与农业参数、农学参数与光谱变量的关系均以孕穗以抽穗期最好,水稻产量与光谱变量的关系则从分蘖盛期到抽穗期的极显著。因此,以孕穗期到抽穗期作为建立水稻遥感估产模型的最佳时期。再利用1998年各地的水稻发育期观测资料,确定各区水稻产量遥感最佳时相。     

林带的防护成熟与更新

在详细探讨林带防护成熟概念的基础上,依据林带树木生长规律,建立了林带防护成熟龄与更新龄的确定方法,具体确定了北京杨、小钻杨等５个杨树品种林带的初始防护成熟龄、更新龄和更新期,并对林带不同更新方式进行了效益评价．     

城市生态系统研究中遥感技术的应用

本文提出并论证了城市生态系统研究与遥感的总体方案及主要研究方向和研究方法。可以认为,用遥感技术作为获取数据的手段,用动态大系统理论分析数据,并借助于专家系统和地理信息系统处理和管理数据,可以解决城市生态系统研究中全方位信息的收集和巨大信息量的处理两大难题;通过引入中间变量把非空间直接可测变量表述为空间直接可测量的函数,从而在专家系统和地理信息系统的支持下扩大遥感技术的应用领域。     

Space remote sensing for spatial vegetation characterization

The study area, Madhav National Park (MP) represents northern tropical dry deciduous forest. The national park, due to its unique location (nearest to township), is under tremendous biotic pressure. In order to understand vegetation structure and dynamics, vegetation mapping at community level was considered important. Prolonged leafless period and background reflection due to open canopy poses challenge in interpretation of satellite data. The vegetation of Madhav National Park was mapped using Landsat TM data. The ground data collected from sample points were subjected to TWINSPAN analysis to cluster sample point data into six communities. The vegetation classification obtained by interpretation (visual and digital) of remote sensing data and TWINSPAN were compared to validate the vegetation classification at community level. The phytosociological data collected from sample points were analysed to characterize communities. The results indicate that structural variations in the communities modulate spectral signatures of vegetation and form basis to describe community structure subjectively and at spatial level.     

林带疏透度模型及其应用

在应用数字图象处理法测定东北西部典型杨树林带疏透度的基础上,采用逐步回归分析和推理方法分别构筑了疏透度与林带结构因子相关的主导因子模型和因配置方式和树种而异的机理模型,并阐述了两种模型在林带结构调控中的作用．     

遥感和GIS支持的土地利用动态监测研究--以黄河三角洲垦利县为例

以黄河三角洲垦利县为例 ,对比分析了目视解译、机助分类等遥感土地利用信息提取方法 ,采用 1981、1991和 1995年 3时相土地利用现状信息 ,研究确定了统一的监测分类系统 ,提出了土地利用面积及其空间分布动态监测方法 .在此基础上 ,分析了垦利县地类面积增减变化 ,在GIS支持下分析了 1981～ 1995年土地利用空间动态变化 ,同时进行了实地监测点土地利用变化分析 ,探明了该县土地利用动态变化规律 .     

Satellite-based monitoring of tropical seagrass vegetation: current techniques and future developments

Decline of seagrasses has been documented in many parts of the world. Reduction in water clarity, through increased turbidity and increased nutrient concentrations, is considered to be the primary cause of seagrass loss. Recent studies have indicated the need for new methods that will enable early detection of decline in seagrass extent and productivity, over large areas. In this review of current literature on coastal remote sensing, we examine the ability of remote sensing to serve as an information provider for seagrass monitoring. Remote sensing offers the potential to map the extent of seagrass cover and monitor changes in these with high accuracy for shallow waters. The accuracy of mapping seagrasses in deeper waters is unclear. Recent advances in sensor technology and radiometric transfer modelling have resulted in the ability to map suspended sediment, sea surface temperature and below-surface irradiance. It is therefore potentially possible to monitor the factors that cause the decline in seagrass status. When the latest products in remote sensing are linked to seagrass production models, it may serve as an early-warning system for seagrass decline and ultimately allow a better management of these susceptible ecosystems.     

Inventory of the Maldives’ coral reefs using morphometrics generated from Landsat ETM+ imagery

In this study, we present exact measures of the number, area, and basic morphometric statistics for every single reef of the Maldivian archipelago, as derived from the interpretation of remotely sensed data collected by the Landsat-7 ETM+ earth-observing satellite sensor. We classified and mapped seven morphological attributes of reefs (six marine habitats and reef-top islands) to 30-m depth at 30×30 m spatial resolution (pixel size) for the entire archipelago. The total archipelagic area (all coral reef and lagoon habitats) of the 16 atolls, five oceanic faros, and four oceanic platform reefs which comprise the Maldives is 21,372.72±1,068.64 km² (approx. 20% of the Maldives Territorial Sea). A total of 2,041±10 distinct coral reef structures larger than 0.01 km² occur in the Maldives, covering an area of 4,493.85 km² (including enclosed reef lagoons and islands) to 30-m depth. Smaller areas of coral reef substratum cover another 19.29 km², bringing the total area of Maldivian coral reefs to 4,513.14±225.65 km². Shallow coral platforms thus occupy 21.1% of the total area of the archipelago (0.0052% of the EEZ area of the Maldives). Of these reefs, 538 are rim and oceanic reefs, covering 3,701.93 km² (82.5% of the total reef area), and 1,503 are patch reefs within the atoll lagoons, covering 791.92 km² (17.5% of the total reef area). Islands occupy only 5.1% of the total reef area. Mapping the Maldives coral reefs at high spatial resolution is only possible with remote sensing and spatial analysis technologies. These greatly reduce the large uncertainty around current estimates of reef area. Our accurate measure of total reef area is only 50.6% of the current best estimate, a result having significant implications for predictions of the Maldives reef productivity and response to global climate change. Here we present current best practice and compare the methods and measures with previous approaches.