基于地表定量参数的沙漠化遥感监测方法

利用野外沙漠化调查的定位数据和ETM+ 遥感数据，在实验分析的基础上，探讨了沙漠化程度与地表参数之间的定量关系，即沙漠化与植被指数（NDVI）、地表辐射温度（LST）之间的关系，提出了综合反映沙漠化土地生物物理特征的遥感监测指数——沙漠化遥感监测差值指数（DDI），为沙漠化遥感监测提供有效的定量化方法。     

非均匀陆面条件下区域蒸散量计算的遥感模型

非均匀陆面条件下的区域蒸散计算是一个复杂的问题。文中首先在利用遥感资料求取地表特征参数 (如植被覆盖度、地表反照率等 )的基础上 ,建立了裸露地表条件下的裸土蒸发和全植被覆盖条件下植被蒸腾计算模型 ,然后结合植被覆盖度 (植被的垂直投影面积与单位面积之比 )给出非均匀陆面条件下的区域蒸散计算方法。实测资料验算表明该模型具有较高的计算精度。文章最后利用该模型对中国北方地区的蒸散量进行了计算 ,并对该研究区蒸散的特点进行了分析     

Impact of Southern Oscillation on the Frequency of Monsoon Depressions in the Bay of Bengal

The impact of Southern Oscillation on thecyclogenesis over the Bay of Bengal duringthe summer monsoon has been investigated.The analysis of correlation coefficients(CCs) between the frequency of monsoondepressions and the Southern OscillationIndex (SOI) reveals that more depressionsform during July and August of El Niñoyears. Due to this, the seasonal frequencyof monsoon depressions remains little higherduring El Niño epochs even though thecorrelations for June and September are notsignificant. The CCs for July and August aresignificant at the 99% level.The El Niño-Southern Oscillation (ENSO)is known to affect Indian MonsoonRainfall (IMR) adversely. The enhancedcyclogenesis over the Bay of Bengal duringJuly and August is an impact of ENSO whichneeds to be examined closely. Increasedcyclogenesis over the Bay of Bengal may bereducing the deficiency in IMR duringEl Niño years by producing more rainfallover the eastern parts of India duringJuly and August. Thus there is a considerablespatial variation in the impact of ENSOon the monsoon rainfall over India and El Niñoneed not necessarily imply a monsoonfailure everywhere in India.The area of formation of monsoon depressionsshifts eastward during El Niño years.Warmer sea surface temperature (SST) anomaliesprevail over northwest and adjoiningwestcentral Bay of Bengal during premonsoon andmonsoon seasons of El Niño years.May minus March SOI can provide useful predictionsof monsoon depression frequencyduring July and August.     

生态干扰度:一种评价植被天然性程度的方法

地球上现存的植物或森林群落都是植被与其环境长期适应及人类长期干扰影响的结果 ,因此 ,现实的植被反映着历史上人类对它们不同程度的影响。‘生态干扰’的概念常常被用来描述历史上人类对植被或森林影响的大小 ,即‘生态干扰度’;或用来表示现实植被离开它的‘天然植被’的距离 ,即现实植被的‘天然性程度’。如此的‘天然植被’可以是 :( 1 )历史上从未受过人类任何干扰的‘原始植被’;( 2 )基于当前立地的‘潜在的自然植被’。然而 ,成百上千年以来人类对自然的利用干扰 ,致使真正的‘原始植被’现在几乎无处可寻。因此 ,‘潜在的自然植被’被用来作为生态干扰度估计或植被天然性程度评价的参照系。作为一种很实用的生态分析手段 ,生态干扰度的方法已被广泛地应用在农、林、水、景观、城市及自然保护等诸多领域的生态评价上。为此 ,本文论述了生态干扰度的概念、原理、方法和应用实例。     

塔里木河中游天然植被的数量分类与排序研究

通过塔里木河中下游天然植被带的调查研究，应用数量分类(TWINSPAN)和排序(CCA)方法，对塔里木河中下游地区天然植被类型进行了划分，并探讨了决定该地区天然植物群落类型的主要环境因子。该地区天然植被可分为4个植被型组，4个植被型，6种植被亚型，9个群系，15个群丛。通过对8个环境因子的CCA排序分析，结果表明制约塔里木河中下游天然植被组成和结构的主导环境因子为地下水位、地下水矿化度、地下水酸碱度。通过CCA二维排序图将16种植物对干旱、盐碱的适应性划分5类型。21个样地在CCA二维排序图上可聚集成9个植物群落类群，即胡杨(Populus euphratica)群落、铃铛刺(Halimodendron halodendron)群落、库尔勒沙拐枣(Calligonum Kuerlese)群落、多枝柽柳(Tamarix ramosissima)群落、黑果枸杞(Lycium ruthenicum)群落、盐穗木(Halostashys caspica)群落、花花柴(Karelinia caspica)群落、疏叶骆驼剂(Alhagi sparsifolia)群落、罗布麻(Apocynum venetum)群落，与TWINSPAN结果中的群系分类单位一致。CCA连续排序与TWINSPAN分类结果吻合较好。9种植物群落类型中，能耐受最大盐胁迫的为盐穗木群落，能耐受最大干旱胁迫的为铃铛刺群落，能耐受最大地下水碱胁迫的为黑果枸杞群落。     

叶面积指数(LAI)的遥感定量方法综述

总结了当前遥感定量研究叶面积指数(LAI)的两种主要方法:统计模型法和光学模型法,阐述了各自的机理和研究进展,在此基础上,讨论了两种方法的优缺点及未来的发展趋势。     

地区发展水平衡量指标体系设计及实例分析

选定地区发展水平衡量指标，测度区域社会，经济，文化发展水平或发育程度是一项有重要的工作，本文试图为量度我国西部地区发展水平提供一套昼完整的指标体系，并对一个具体实例进行衡量和计算，由此说明地区的区内发展程度的差异，划分区域发展的不同类型。     

宝鸡某场地污染土的分析评价

宝鸡某场地由于长期堆放食盐，造成地基上污染、通过勘察，查明了污染土层，进行了腐蚀性测试和评价，对场地污染区与非污染区土的物理力学性质指标进行了对比，并分析了土的污染机理，提出了防护措施。     

Vegetation change,erosion risk and land management on the Nullarbor Plain,Australia

 Arid karst landscapes that have been degraded by human activities provide a challenge for rehabilitation and an opportunity to test ideas about the stability and resilience of limestone ecosystems. The Nullarbor Plain is the largest arid karst area in Australia (220 000 km²) and is divided into extensive closed karstic depressions separated by low rocky ridges, while the dominant vegetation is chenopod shrubland. Since European settlement there has been considerable change in the vegetation, with significant reduction in shrub and grass cover over large areas of the plain. These changes are related to a state and transition model of vegetation dynamics which incorporates climatic variability, fire history and grazing pressure from sheep, kangaroos and rabbits. A partial sediment budget using ¹³⁷Cs inventories reveals local and regional patterns of soil redistribution within this arid karst landscape. Rehabilitation of eroded soil in pastoral lands has been accomplished at several sites but is labour intensive and vulnerable to climatic fluctuations. Given the low stock numbers, limited number of people involved, and poor economic returns, it would be sensible to make pastoral activities on the Nullarbor secondary to conservation priorities. This would necessitate a change in land ethic to stewardship, with emphasis on rehabilitation and control of feral animals. Management of increased numbers of visitors to the caves and karst also requires that resource inventories and management plans for each area be drawn up and used. Received: 1 June 1995 · Accepted: 4 December 1995     

Leaf area index retrieval based on canopy reflectance and vegetation index in eastern China

The aim of this paper is to investigate the feasibility of using Landsat TM data to retrieve leaf area index (LAI). To get a LAI retrieval model based ground reflectance and vegetation index, detailed field data were collected in the study area of eastern China, dominated by bamboo, tea plant and greengage. Plant canopy reflectance of Landsat TM wavelength bands has been inversed using software of 6S. LAI is an important ecological parameter. In this paper, atmospheric corrected Landsat TM imagery was utilized to calculate different vegetation indices (VI), such as simple ratio vegetation index (SR), shortwave infrared modified simple ratio (MSR), and normalized difference vegetation index (NDVI). Data of 53 samples of LAI were measured by LAI-2000 (LI-COR) in the study area. LAI was modeled based on different reflectances of bands and different vegetation indices from Landsat TM and LAI samples data. There are certainly correlations between LAI and the reflectance of Tm3, TM4, TM5 and TM7. The best model through analyzing the results is LAI = 1.2097*MSR + 0.4741 using the method of regression analysis. The result shows that the correlation coefficient R2 is 0.5157, and average accuracy is 85.75%. However, whether the model of this paper is suitable for application in subtropics needs to be verified in the future.