近10 a阿克苏流域植被覆盖时空演变特征及影响因素

植被覆盖度是指示地表植被分布及生态环境变化的重要指标。为深入了解阿克苏流域植被覆盖度的时空演变特征及驱动机理,基于Landsat TM和高分1号遥感影像,采用NDVI像元二分模型和差值运算方法,对阿克苏流域2005—2015年的植被覆盖动态变化进行了监测分析及评价。结果表明近10 a来阿克苏流域植被覆盖整体呈良性发展趋势,8月份植被覆盖最丰富,植被覆盖度空间分布不均,差异较大;影响阿克苏流域植被覆盖的主要因素有降水、气象灾害、土地利用以及生态工程。阿克苏流域植被覆盖的时空演变研究结果能够为流域未来生态空间重构等提供重要的参考借鉴。     

基于MODIS时间序列森林扰动监测指数比较研究

森林扰动是影响陆地生态系统的重要因素之一,遥感可定期地获得大面积森林覆盖数据,成为定期和连续森林扰动监测的重要手段,基于时间序列数据的森林监测成为主要方式。研究利用2001~2013年MODIS时间序列遥感影像,以福建省为例,利用NDVI、NBRI、NDMI、IFZ和DI 5种森林扰动监测指数,结合植被变化追踪算法提取森林扰动区域,并从光谱响应特征和对不同扰动类型的响应能力等方面,分析了对我国南方森林扰动的监测能力。结果表明:DI对森林砍伐、森林病虫害和植树造林3种扰动类型的响应能力较强,NBR对森林火灾最为敏感,NDVI对4种扰动类型的响应能力相对较弱;5种指数中DI对森林扰动的响应能力较强,森林扰动提取精度最高,IFZ次之,NDMI和NBR监测精度相当,且优于NDVI。     

Airborne high‐resolution images for grape classification: changes in correlation between technological and late maturity in a Sangiovese vineyard in Central Italy

Background and Aims: The goal of this study was to investigate the relationships between NDVI values at different phenological stages and measurements of grape parameters at two different harvest dates. Methods and Results: The research was done on a Sangiovese vineyard in Central Italy. Over four seasons, airborne NDVI measurements acquired between June and August were related to grape parameters (yield per vine, pH, °Brix, anthocyanins and polyphenols) at technological harvest (H1) and two weeks later (H2). Correlations were higher at H1 and decreased at H2 with a different rate depending on the parameter. °Brix and pH correlations showed a moderate rate of variation between H1 and H2; bigger differences and a different inter‐annual dynamic were observed in anthocyanins and polyphenols between H1 and H2. Conclusions: The ability of NDVI to discriminate different grape classes was confirmed, but its efficacy substantially varies depending on the harvest date. These results suggest the existence, within the same vineyard, of different grape populations having specific timing and shape of ripening curve; as a consequence, distinct vigour zones of the vineyard show a different evolution of the content of grape parameters between the two harvests thus influencing the degree of correlation between grape quality and NDVI measurements. Significance of the Study: This is the first study in which harvest date has been considered for its influence on the predictive skill of RS. It therefore highlights not only the importance of spatial variation within the single vineyard, but also the importance of ripening dynamics.     

虎杖黄色素的稳定性及抗氧化作用的研究

对虎杖黄色素的稳定性进行了详细研究 ,并以猪油为底物 ,用碘量法测其抗氧化性。结果表明 :该色素水溶性强 ,耐光、热性强 ,在酸性介质中可稳定存在 ,常用的食品添加剂对其无不良影响。对猪油有明显的抗氧化作用。     

1982～2000年黄淮海地区植被覆盖变化特征分析

利用1982～2000年8km NOAA-AVHRR数据,采用均值法、差值法和一元线性回归模拟法,分析了中国黄淮海地区植被的动态变化及其空间分布特征,模拟了年NDVI均值的变化趋势,并对不同植被类型的NDVI年内和年际变化规律进行了分析。结果表明:黄淮海地区植被覆盖总体上呈增加趋势,20世纪90年代末相对80年代初平均NDVI值增加了近0.03。从空间分布上看,大部分地区NDVI都呈增加的趋势,其中NDVI极显著增加的区域主要分布在山东的西北部和西部、河南的东部、河北的北部及江苏的北部地区,F检验的显著性水平达到了99%。NDVI呈减少趋势的地区很少,主要分布在北京、天津、山西中部。各植被类型的NDVI在年内的变化呈很强的季节性,年际变化规律大致相同,呈波动上升的趋势。     

在GIS支持下利用MODIS数据监测多种作物和果树种植面积

MODIS数据以其时间分辨率高、监测范围大、可免费接收、获取方便及时等优势,已经成为土地利用研究的重要信息源。以河北省38°N带为研究区,选取不同时相的MODIS数据,分别计算出NDVI,根据植被的生长过程中叶面积的变化规律,观察其NDVI的变化,建立分类规则,确定出主要植被的种植区域。在GIS软件的支持下,利用分类精度为91%的TM数据分类结果从提取区域的面积和形状两个方面对MODIS数据的分类结果进行像元尺度上的精度分析。     

仪征地区农田深层土壤湿度遥感反演初探

利用MODIS合成产品数据MOD11A2和MOD13A2获取的陆地表面温度(Ts)和归一化植被指数(NDVI)构建Ts/NDVI特征空间,依据该特征空间计算温度植被干旱指数(TVDI),进而反演了仪征地区不同季节的40 cm土壤相对湿度。使用野外同步实测数据进行验证,结果显示,总体平均相对误差为11.83%,2004年11月误差最小,为4.30%。遥感反演的仪征地区土壤湿度分布图表明该地区存在两个土壤湿度高值区,分别位于仪征南部的长江冲积平原和西北部的谷底平原地带,并且土壤平均相对湿度越大,其高值区与低值区之间的差异越小。     

遥感定量分析水电梯级开发对流域植被环境影响

针对水电梯级开发将改变水资源时空分布并引起流域植被环境的变化,以湖北省清江流域为例,采用基于归一化差异植被指数的像元分解模型和TM/ETM+遥感数据对研究区域水电梯级开发不同时期的植被环境态变化进行了定量估算.结果表明,该法有效可行,且水电梯级开发对清江流域植被境的影响较大,1987～2004年清江中下游的植被环境有较大改善.     

内蒙古植被稳定性及其影响因素分析

植被是生态系统的主要组成部分,在调节全球气候变化和生态平衡方面有重要作用,是气候变化的指示器.因此,研究植被弹性和抗性及影响因素对区域植被稳定性现状和生态可持续发展具有重要意义.基于2000—2019年MOD13A2 NDVI数据、气象数据和植被类型数据,通过自回归模型、趋势分析及地理空间分析方法,对内蒙古植被稳定性及...     

Estimating spatio-temporal patterns of agricultural productivity in fragmented landscapes using AVHRR NDVI time series

The characteristics of Normalized Difference Vegetation Index (NDVI) time series can be disaggregated into a set of quantitative metrics that may be used to derive information about vegetation phenology and land cover. In this paper, we examine the patterns observed in metrics calculated for a time series of 8 years over the southwest of Western Australia—an important crop and animal production area of Australia. Four analytical approaches were used; calculation of temporal mean and standard deviation layers for selected metrics showing significant spatial variability; classification based on temporal and spatial patterns of key NDVI metrics; metrics were analyzed for eight areas typical of climatic and production systems across the agricultural zone; and relationships between total production and productivity measured by dry sheep equivalents were developed with time integrated NDVI (TINDVI). Two metrics showed clear spatial patterns; the season duration based on the smooth curve produced seven zones based on increasing length of growing season; and TINDVI provided a set of classes characterized by differences in overall magnitude of response, and differences in response in particular years. Frequency histograms of TINDVI could be grouped on the basis of a simple shape classification: tall and narrow with high, medium or low mean indicating most land is responsive agricultural cover with uniform seasonal conditions; broad and short indicating that land is of mixed cover type or seasonal conditions are not spatially uniform. TINDVI showed a relationship to agricultural productivity that is dependent on the extent to which crop or total agricultural production was directly reduced by rainfall deficiency. TINDVI proved most sensitive to crop productivity for Statistical Local Areas (SLAs) having rainfall less than 600 mm, and in years when rainfall and crop production were highly correlated. It is concluded that metrics from standardized NDVI time series could be routinely and transparently used for retrospective assessment of seasonal conditions and changes in vegetation responses and cover.