基于Landsat 8 OLI的的城市绿地信息提取——以兰州市为例

作为城市生态系统的重要组成部分之一,绿地起着尤为重要的作用。然而传统的调查方法不仅费时费力,且动态更新较为困难。本文以兰州市区为例,采用遥感数据Landsat 8 OLI影像结合目视解译、植被指数和混合像元分解方法提取绿地信息,初步比较了三种方法的分析结果。最后据此分析兰州市绿地的空间分布特征,为城市的发展提供有益的建议。     

基于植被指数的藏北牧区土壤湿度反演

土壤湿度的遥感动态监测在农牧业生产中具有重要意义。近年来,多种基于遥感指数的土壤湿度监测方法被提出并得到广泛关注,但当前对不同深度土壤湿度的反演及植被指数反映土壤湿度滞后性的研究较少。该文针对遥感指数反演土壤湿度的精度问题,对MODIS(moderate resolutionimaging spectroradiometer)的2种植被指数产品归一化差异植被指数(normalized difference vegetation index,NDVI)和增强型植被指数(enhanced vegetation index,EVI)与土壤湿度实测值进行相关分析,并利用在其中一个样点得到相关系数最高的回归模型对距离较远的其它点进行土壤湿度值估算,最后用土壤湿度实测值对模型的精度进行验证。结果表明,2种植被指数均与土壤湿度值呈现出较强的相关性,且利用植被指数估算土壤湿度的延迟天数为5～10 d。在相同气候模式、土壤类型和植被类型的条件下,高程为影响回归模型精度的主要因素。该研究可为牧区多层深度土壤湿度反演方法的选择和监测提供参考依据。     

基于MODIS数据的科尔沁区植被覆盖时空变化分析

我国植被覆盖辽阔,自然环境相对优越,但是随着时代发展,植被的生态系统遭到严重破坏,因此对植被空间格局的变化进行及时的研究至关重要。以通辽市科尔沁区为例,利用该区域2004—2013年的MODIS遥感数据产品,结合时间序列、均值法、插值法等理论,分析科尔沁区归一化植被指数(Normalized Difference Vegetation Index,NDVI)的时间和空间变化特征,得出了科尔沁区近10 a的植被覆盖演变情况:近10 a间科尔沁区植被整体上呈增加趋势,增加速率为0.033/a,其中植被覆盖度较好的年份是2005,2010,2011,2012和2013年,对整个科尔沁区而言,西辽河流域植被覆盖度最高;从NDVI值看出,对植被覆盖度贡献最大的为夏季,增加速率为0.002 8/a。研究成果将为科尔沁地区日后地表生态环境的改善和治理提供决策依据和理论基础。     

基于遥感和气象数据的蒙古高原1982~2013年植被动态变化分析

蒙古高原生态系统及其变化对中国北方乃至整个东北亚的生态安全有着重要影响,了解蒙古高原干旱半干旱区植被生长的动态如何在不同时间和空间尺度上响应气候变化十分必要。利用NDVI数据构建长时间序列,分析植被生长动态变化的过程和时空特征,并与气象数据进行相关性分析。主要结论如下:①NDVI的分布具有地带性;②大约39.5%的区域NDVI呈显著的增加(P=0.1),7.3%的区域NDVI显著减少(P=0.1),说明植被条件在蒙古高原有所好转;③蒙古高原NDVI的变异系数均值16.99%,这表明过去32年里植被覆盖变化情况有较强的波动性;④蒙古高原植被的生长状况与降水量有极显著的正相关关系,与气温有极显著的负相关关系。     

黄河三角洲NDVI变化与气候因子的相关分析

利用MOD IS/Terra资料,得到了黄河三角洲地区2000~2004年逐月NDVI统计资料,详细分析了NDVI的时空变化特征,同时研究了NDVI与降水、气温的关系。结果表明:一年中,黄河三角洲NDVI具有明显的周期变化规律,但近年来黄河三角洲的NDVI年际间变化不明显;黄河三角洲地区的气候变化,尤其是气温、降水与NDVI存在着一定的相关性,但相对来说当月NDVI对气温的响应较强,而降雨则对滞后1个月的NDVI的影响较大。     

On the need to observe vegetation canopies in the near-infrared to estimate visible light absorption

This paper examines the rationale for and implications of using a near-infrared band to estimate the absorption of visible light by vegetation canopies. The benefits of using near-infrared observations have already been documented extensively in the literature, notably in the context of applications based on vegetation indices. These include, for instance, a degree of normalization with respect to undesirable perturbing factors. Our intent here is twofold: provide the theoretical basis to justify using measurements outside the main absorption band of vegetation for the purpose of retrieving canopy properties, and uncover the implications of doing so. On the basis of simple radiation transfer considerations, we conclude that near-infrared observations are critical to ensure the accurate retrieval of absorption estimates in the visible domain, and that observations within the absorption band help control the perturbing effect of the soil background.The analytical approach implemented here is conceptually similar to a scale analysis which permits us assessing the most significant contributions to the absorption and scattering processes in the vast majority of geophysical situations. Our final conclusions derived from a series of intermediate steps that need to be performed first. To this end, we illustrate in Section 2 the fact that a suitably-defined one-dimensional radiation transfer model can always be setup to represent accurately the reflected, transmitted and absorbed fraction of vertical fluxes in any vegetation volume at medium spatial resolutions (100 m or lower), and this irrespective of the local variability exhibited by the canopy attributes. This finding is exploited throughout the paper to show that 1) measurements performed in the near-infrared band are needed to ensure a large dynamic range in albedo for dense canopy conditions, by contrast to the visible domain, 2) measurements in the visible domain are effective to remove the contribution due to the background below vegetation for low to intermediate LAI conditions. This is made possible thanks to the soil line concept and the spectral invariance of the interception process, and 3) the estimation of visible light absorption in a canopy on the basis of combinations of spectral bands (as implemented in traditional vegetation indices) hinges on spectral correlations between variables, most notably those controlling the absorbing and scattering properties of the soil and leaves. A series of implications and consequences is drawn from our analysis and, in particular, the suggestion to adopt modern interpretation techniques, superseding the commonly used vegetation index approaches. These advances allow us to improve on current approaches, in particular by lifting some of the hypotheses associated with approaches based on combinations of spectral bands.     

利用ERDAS IMAGINE从遥感影像中提取植被指数

在遥感影像处理中，植被指数提取广泛应用于定性和定量评价植被覆盖及其生长活力。主要介绍了用ERDAS IMAGINE遥感图像处理软件对遥感影像进行植被指数提取。对植被指数提取的关键部分进行分析，并给出植被指数提取的技术关键。     

Use of NOAA-AVHRR NDVI images for the estimation of dynamic fire risk in Mediterranean areas

Wildfires are a major cause of land degradation in the Mediterranean region due to their frequent recurrence in the same areas. The evaluation of fire risk is therefore of high practical importance, particularly during the summer arid season, when fires are most frequent and harmful. Recent studies have demonstrated that the evaluation of dynamic fire risk can be carried out by the use of remotely sensed images, and specifically of NOAA-AVHRR Normalized Difference Vegetation Index (NDVI) data. This use relies on the sensitivity of the index to vegetation dryness, which is a major predisposing factor for fire occurrence. Several problems, however, remain linked to the spatial variability of the risk in environmentally heterogeneous areas, which requires the application of suitable processing techniques to the low-resolution imagery.The current work reports on the development and testing of different methodologies for estimating dynamic fire risk by the use of NOAA-AVHRR data. The investigation was conducted in Tuscany (Central Italy) using a large archive of fires that occurred in the region and NOAA-AVHRR NDVI data of 16 years (1985-2000). Relying on previous methodological achievements of our group and other research groups, several procedures were tested to extract information related to fire risk from the remotely sensed images. These trials led to define an optimum method which is based on the identification of pixels where the accordance between interyear variations in fire probabilities and NDVI values is maximum. The accuracy of the risk estimates from this optimum method was finally evaluated by a leave-one-out cross-validation strategy. In this way, the performance of the methodology was assessed, together with its potential for operational fire risk monitoring and forecasting in Mediterranean areas.     

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.     

Remote Sensing Long-term Monitoring of Cyanobacterial Blooms in Yuqiao Reservoir

Reservoir eutrophication leads serious threat to water supply safety. This paper apples Landsat time series satellite data from 2008 to 2017 to extract the distribution and degree of water bloom in Yuqiao Reservoir based on a threshold method to the correlation analysis results between Normalized Difference Vegetation Index (NDVI) and measured water quality parameters. Through the collaborative analysis of both natural and artificial factors, the water bloom was jointly drive by temperature, precipitation, and human activities. Among them, the ecological restoration project with human intervention could inhibit or slow down the blooms and effectively improve the water quality. Meteorological and spaceborne remote sensing data with higher temporal resolution will be more conducive the analyze the driver force of cyanobacteria blooms on small and medium-sized drinking water surfaces. Meanwhile, remote sensing data based monitoring and early warning technology could be promoted.