利用不同地表比辐射率对ETM＋数据地表温度反演对比研究

地表温度作为研究环境生态影响的一个重要指标之一,以Landsat ETM+的热红外波段作为数据源,辅助大气透过率、大气平均气温等参数,利用单窗算法基于混合像元、NDVI、植被和裸露地三种不同地表比辐射率反演泉州市地表温度,对比分析结果表明采用这三种地表比辐射率反演地表温度的方法均是可行的,对于具体采用哪一种地表比辐射率应需要结合实际的研究区域地表地物的实际监测地表温度进行对比后,方可选择合适的地表比辐射率进行地表温度反演,会更具实际意义。反演结果可为城市热岛效应等研究提供支持数据源。     

基于决策树分类的大屯矿区地物信息提取及矿区污染分析

本文在对国内外遥感图像分类方法充分研究分析的基础上,选择决策树分类法对大屯矿区的Landsat 8遥感图像进行分类研究。选取样本提取并分析研究区典型地类光谱特征曲线,依据光谱曲线特征和归一化植被指数建立了土地利用分类决策树模型,通过反复试验和修正,筛选出适宜大屯矿区地物分类的决策树最优阈值,对研究区进行分类和精度评价,最后通过分类结果对研究区的水体污染状况进行简要分析。     

遥感技术在植物物候研究中的应用综述

通过遥感技术研究植物物候现象的机理分析,认为植被指数可反映植被各物候期的特征。国内外在探测植物生长季始末日期、花期变化、净第一性生产力变化、全球碳收支等方面的研究促进了植物物候的发展;同样物候研究也可提高遥感影像植物分类和作物估产的精度,同时可促进高光谱遥感的发展。通过我国物候研究从传统的农林业应用转向注重遥感探测、生态学应用的现状分析,展望了我国物候发展方向：关注植物生长季始末时间的时空分布规律;遥感监测植物季相变化;遥感监测植物花期;注重探讨植物生理和生态特征;植物高光谱遥感物候研究;重视物候科普普及工作。     

中国西部地区基于经验模型的土壤水分反演

地表土壤水分含量的时空分布信息是十分重要的,常常作为水文模型、气候模型、生态模型的输入参数,同时,也是干旱预报、农作物估产等工作的重要指标。被动微波遥感是监测土壤含水量最有效的手段之一。相比红外与可见光,它具有波长长,穿透能力强的优势。相比主动微波雷达,被动微波辐射计具有监测面积大、周期短,受粗糙度影响小,对土壤水分更为敏感,算法更为成熟的优势。目前,已研究出许多反演土壤水分的方法.本课题的主要内容是借助AMSR-E土壤水分影像数据、MODIS归一化植被指数(NDVI)影像数据和MODIS分类影像数据,利用ENVI软件进行遥感图像数据处理,运用统计分析方法建立NDVI与土壤水分的经验模型,研究中国西部地区稀疏植被覆盖区土壤水分的反演。     

Spatial associations between NDVI and environmental factors in the Heihe River Basin

Journal of Geographical Sciences - The Heihe River Basin is located in the arid and semi-arid regions of Northwest China. Here, the terrestrial ecosystem is vulnerable, making it necessary to...     

地下水对银川平原植被生长的影响

植被是反映区域性生态环境状况的重要指标之一,而地下水对植被的生长有着重要的影响。结合MODIS-NDVI遥感数据与地下水位观测数据,从大尺度上研究了银川平原地下水与植被的关系,并结合实测的数据,利用最小二乘法拟合出地下水位埋深与NDVI变化率的关系曲线,定量分析了地下水位埋深对植被生长的影响,并对适宜植被生长的地下水位埋深进行了探讨。研究结果表明：地下水位埋深在3 m时,植被长势最好;而适宜植被生长的地下水位埋深范围是1~5 m;当地下水位埋深超过5 m时,地下水位埋深对植被的影响逐渐减弱;当水位埋深超过8 m时,地下水位埋深对植被的生长没有影响。     

2001-2020年新疆伊犁河谷植被主要生长季NDVI时空变化与水热因子关系研究

植被是陆地生态系统的重要组成部分,在气候和生物化学循环中对水和能量的交换起到至关重要的作用。利用MODIS-NDVI数据,采用Theil-Sen中值趋势分析、Mann-Kendall检验及变异系数的方法,研究了2001-2020年伊犁河谷植被主要生长季（5-9月）归一化植被指数（NDVI）的时空变化特征,结合气温降水数据采用相关性分析的方法,探究了植被主要生长季的影响因素。结果表明：（1）2001-2020年伊犁河谷植被主要生长季NDVI在空间上存在明显的差异性,NDVI多年均值较高。（2）区域尺度上,NDVI年际变化趋势较为平缓;像元尺度上,微量减少和微量增加的面积占比较大,显著增加与显著减少的区域较集中,占比较小。（3）伊犁河谷生长季植被的稳定性较好,CV≤0.10基本分布在整个区域,0.3NDVI年均值与年降水相关性较大（r=0.7644）,与气温无显著相关;NDVI月均值与月气温均值强相关,与月降水量弱相关。     

Can a 25-year trend in Soudano-Sahelian vegetation dynamics be interpreted in terms of land use change? A remote sensing approach

This study is based on the premise that, in the Sahel/Sudanian belt of Africa, the main determinants of interannual variation in vegetation dynamics are rainfall and land cover type. We analyzed the spatio-temporal sensitivity of the NOAA-AVHRR 8 km-resolution vegetation index (NDVI) to (i) annual rainfall variability (0.5° × 0.5° resolution) acquired over a 25-year period (1982-2006); and (ii) land use changes in the different eco-climatic regions of the Bani catchment in Mali (130 000 km²). During the period 1982-2006, there was no clear trend in rainfall over the catchment, whereas there was a strong positive trend in the NDVI, both when the NDVI values were corrected using annual rainfall variability and when they were not. We divided the catchment into three eco-climatic regions based on the relationship between the annual NDVI and rainfall. In each region, we analyzed the observed greening in relation to changes in land use after correcting for the effect of annual rainfall on the NDVI. Results show that there is a mixed level of agreement between the land cover changes at the grid cell scale and the spatial pattern of the NDVI trend. Increased cropping does not explain the increase in the annual NDVI, except in the Sahelian part of the catchment. We hypothesize that the natural vegetation dynamics related to the non-linear rainfall patterns during the 25-year study period were responsible for these results.     

Hyperspectral remote sensing analysis of short rotation woody crops grown with controlled nutrient and irrigation treatments

Hyperspectral remote sensing research was conducted to document the biophysical and biochemical characteristics of controlled forest plots subjected to various nutrient and irrigation treatments. The experimental plots were located on the Savannah River Site near Aiken, SC. AISA hyperspectral imagery were analysed using three approaches, including: (1) normalized difference vegetation index based simple linear regression (NSLR), (2) partial least squares regression (PLSR) and (3) machine-learning regression trees (MLRT) to predict the biophysical and biochemical characteristics of the crops (leaf area index, stem biomass and five leaf nutrients concentrations). The calibration and cross-validation results were compared between the three techniques. The PLSR approach generally resulted in good predictive performance. The MLRT approach appeared to be a useful method to predict characteristics in a complex environment (i.e. many tree species and numerous fertilization and/or irrigation treatments) due to its powerful adaptability.     

基于NDVI纹理的山东丘陵地区SPOT-5影像果园信息识别研究

胶东半岛是我国重要的水果生产基地,及时有效地获取果园信息,对指导当地水果生产具有重要意义。在遥感信息提取中,纹理特征是一种有效的辅助信息,但在原始影像中地形阴影对纹理特征有较大影响。本文提出了一种利用NDVI影像的纹理特征和光谱特征从SPOT-5影像自动识别果园信息的方法,消除了地形阴影的影响,取得了86.40%的识别精度。