基于遥感技术的陕西省渭河流域污染演变研究

水污染是当今社会面临的一个重大问题.论文应用遥感技术,以NDVI为指标,对陕西省渭河流域水质变化趋势进行了分析.结果表明:水质是影响植物生长的重要的因素之一.渭河流域的植被指数极值下降,反映出渭河流域的水质从1998年到2008年整体呈下降趋势,污染日趋严重,且污染程度居高不下.证实了遥感技术进行水质监测的可行性.     

基于植被地下水关系的保水采煤研究

为研究我国西部生态脆弱矿区植被与地下水关系及其对煤层开采的约束,采用路线穿越法剖析了典型区植被随潜水埋深变化的演替规律,利用遥感获取煤层开大规模采前(2000年)植被指数,并与同期地下水位埋深建立了统计关系。结果表明:研究区天然状态下植被随地下水位埋深的增加呈现明显的分带特征,潜水埋深0~4.0 m时植被对地下水依赖性较强;综合考虑水文地质条件和植被与地下水关系,榆神矿区可划分为植被约束区、地下水约束区和无约束区3个区;矿区开采15 a后,2014年矿区地下水位明显下降和植被盖度普遍升高现象并存,这与煤炭资源高强度开采区集中在无约束区有关。生态脆弱矿区井田规划和煤层开采必须重视植被和地下水约束研究,因地制宜地制定保水采煤技术预案。     

近10年来金沙江石鼓上游NDVI时空变化研究

为分析过去10余年长江源区保护治理的效果,采用归一化植被指数（EOS/MODIS的NDVI）,依据地貌、气候、土壤和植被类型等影响植被生长发育的几个主导因素,对金沙江石鼓以上流域近10 a来的归一化植被指数时空分布特征进行了分析。结果表明,2000~2010年NDVI均值为0.51,总体上呈略增大的趋势。NDVI均值增大的趋势主要表现在低植被覆盖地区,高植被覆盖区变化不大;从高程上看,4 000 m以下地区NDVI值较大且历年变化不大,而4 000 m以上地区NDVI值较小且呈略增大的趋势;从气候和植被类型看,森林植被气候区NDVI均值较大但历年变化不大,草原植被气候区NDVI均值较小,但呈增大的趋势;从土壤类型分布看,森林土壤的NDVI均值较大但无增减性变化趋势,草甸、草原土NDVI均值较低但呈增大的趋势。     

基于MODIS与TM遥感影像的植被盖度提取算法比较

利用MODIS NDVI与Landsat TM多光谱遥感影像,提出在缺少地面实测数据的情况下如何快速获得精准的大范围高时效植被覆盖度。利用经验模型与像元二分模型分别计算MODIS影像的植被盖度,然后以TM影像估算出的精度较高的植被盖度为基准,选取检验样本,比较不同模型的结果。分析结果表明：在地表植被分布均匀的研究区内,较大样方建立的非线性经验模型比像元二分模型能更好地提取较大范围内的植被盖度信息,有效地减少MODIS因噪声和几何配准问题使提取结果产生的误差。     

环境减灾卫星中国陆面多时相遥感数据NDVI产品的批处理方法研究

作为大范围、全天候、动态监测灾害时空变化的高技术手段,卫星遥感在灾害监测和环境保护中的应用日益广泛。但在国内,产品化的遥感数据很少。因此,如何快速制作遥感数据产品是国内遥感数据是否能得到广泛应用的关键。本文提出了一种快速自动化多时相遥感数据处理方法,该方法通过用ERDAS的Batch Command对NDVI数据产品进行批处理,大大节省了人力和时间。该方法也可供类似的大数据量遥感影像处理借鉴。     

鄱阳湖区植被覆盖度的遥感估算

植被覆盖度是一个十分重要的生态学参数,对于全球环境变化和监测研究具有重要意义.为了有效地从遥感资料中提取植被覆盖度,在像元二分模型两个重要参数推导的基础上,对已有模型进行了改进,建立了用归一化植被指数(NDVI)估算植被覆盖度的模型.运用该模型对鄱阳湖区2003年的植被覆盖度进行了估算,通过鄱阳湖部分地区的实地考察,对2003年的植被覆盖度估算结果进行了验证.结果表明使用这种改进模型进行植被覆盖度遥感监测是有效可行的.     

时序NDVI数据集重建综合方法研究

时序NDVI数据集已经成功地应用于全球与区域环境变化、植被动态变化、土地覆盖变化和植物生物物理量参数反演等多方面的研究。时序NDVI数据集受到云和气溶胶等大气条件和传感器自身等因素的影响包含很多噪声,影响了其进一步的应用。基于对近几年来普遍使用的5种重建方法的对比分析结果,发展了基于标准差权重和噪声点性质的两种综合方法。以黑河流域2009年16 d最大值合成的MODIS NDVI数据为例,对比了两种综合方法与5种重建方法的效果;并用2009年5月下旬至8月上旬的地面实测NDVI数据验证了两种综合方法的重建效果。结果表明这两种综合方法的效果都优于对比的5种重建方法,它们既保留了原始数据中大部分的点,又最大限度地修正了噪声点,所生产的时序NDVI数据集,可以更好地用来开展全球与区域土地覆盖和植被动态变化监测等研究。     

Extraction of Agricultural Phenological Parameters of Sri Lanka Using MODIS,NDVI Time Series Data

Management of crops is an essential part in the food production procedure. Having a thorough knowledge of growth stages of each crop is of paramount importance in this respect. Phenology (transplanting, panicle formation, flowering etc) is the study of cyclic and seasonal natural phenomena that are controlled by environmental and climatic factors. Monitoring the crop condition manually in the field is difficult and time consuming. Therefore recently, several methods have been introduced by using satellite derived vegetation indices. Extraction of phenological parameters is helpful for the purposes like irrigation management, nutrient management, health management, yield prediction and crop type mapping. Easily extracted parameters will be the important data base for agricultural researchers. This research is an attempt to extract paddy phenological parameters of Sri Lanka by using 16 years’ (2000 to 2015) Time series MODIS Normalised Difference Vegetation Index (NDVI), which is highly sensitive for the green vegetation and the data were analysed using SPIRITS and TIMESAT software's. Periodicity converter in SPIRITS and Savitzky Golay filtering in TIMESAT and SPIRITS are helpful in smoothing the time series which are perturbed by noise due to missing values and Clouds. Phenology is considered as a sensitive climate change indicator but, it is very essential to have a comprehensive familiarity about the method of water supply that the study area is irrigated or rain fed so as to eliminate the wrong interpretation. As results, average of long time series of NDVI profile for a few agro ecological zones of Sri Lanka with extracted seven parameters (Start of the season, End of the season, Length of the season, Booting date, Base value, Maximum NDVI during the Season, Amplitude) and generated phenological parameter maps are presented here. The crop phenology is a very important element of agricultural monitoring, to ensure the security of the food crop production.     

Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks

This paper discusses an assessment of Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data products for detecting forest defoliation from European gypsy moth (Lymantria dispar). This paper describes an effort to aid the United States Department of Agriculture (USDA) Forest Service in developing and assessing MODIS-based gypsy moth defoliation detection products and methods that could be applied in near real time without intensive field survey data collection as a precursor. In our study, MODIS data for 2000-2006 were processed for the mid-Appalachian highland region of the United States. Gypsy moth defoliation maps showing defoliated forests versus non-defoliated areas were produced from temporally filtered and composited MOD02 and MOD13 data using unsupervised classification and image thresholding of maximum value normalized difference vegetation index (NDVI) datasets computed for the defoliation period (June 10-July 27) of 2001 and of the entire time series. These products were validated by comparing stratified random sample locations to relevant Landsat and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) reference data sets. Composites of 250 m daily MOD02 outperformed 16-day MOD13 data in terms of classifying forest defoliation, showing a lower omission error rate (0.09 versus 0.56), a similar Kappa (0.67 versus 0.79), a comparable commission error rate (0.22 versus 0.14), and higher overall classification agreement (88 versus 79%). Results suggest that temporally processed MODIS time-series data can detect with good agreement to available reference data the extent and location of historical regional gypsy moth defoliation patches of 0.25 km² or more for 250-meter products. The temporal processing techniques used in this study enabled effective broad regional, “wall to wall” gypsy moth defoliation detection products for a 6.2 million ha region that were not produced previously with either MODIS or other satellite data. This study provides new, previously unavailable information on the relative agreement of temporally processed, gypsy moth defoliation detection products from MODIS NDVI time series data with respect to higher spatial resolution Landsat and ASTER data. These results also provided needed timely information on the potential of MODIS data for contributing near real time defoliation products to a USDA Forest Service Forest Threat Early Warning System.