Spectral mixture analysis for subpixel vegetation fractions in the urban environment: How to incorporate endmember variability?

In the urban environment both quality of life and surface biophysical processes are closely related to the presence of vegetation. Spectral mixture analysis (SMA) has been frequently used to derive subpixel vegetation information from remotely sensed imagery in urban areas, where the underlying landscapes are assumed to be composed of a few fundamental components, called endmembers. A critical step in SMA is to identify the endmembers and their corresponding spectral signatures. A common practice in SMA assumes a constant spectral signature for each endmember. In fact, the spectral signatures of endmembers may vary from pixel to pixel due to changes in biophysical (e.g. leaves, stems and bark) and biochemical (e.g. chlorophyll content) composition. This study developed a Bayesian Spectral Mixture Analysis (BSMA) model to understand the impact of endmember variability on the derivation of subpixel vegetation fractions in an urban environment. BSMA incorporates endmember spectral variability in the unmixing process based on Bayes Theorem. In traditional SMA, each endmember is represented by a constant signature, while BSMA uses the endmember signature probability distribution in the analysis. BSMA has the advantage of maximally capturing the spectral variability of an image with the least number of endmembers. In this study, the BSMA model is first applied to simulated images, and then to Ikonos and Landsat ETM+ images. BSMA leads to an improved estimate of subpixel vegetation fractions, and provides uncertainty information for the estimates. The study also found that the traditional SMA using the statistical means of the signature distributions as endmember signatures produces subpixel endmember fractions with almost the same and sometimes even better accuracy than those from BSMA except without uncertainty information for the estimates. However, using the modes of signature distributions as endmembers may result in serious bias in subpixel endmember fractions derived from traditional SMA.     

Comparison of the influence factors on NDVI for CCD camera and WFI imager on CBERS-02

China Brazil Earth Resources Satellites (CBERS) have many payloads, among them there are a high resolution Charge Coupled Device (CCD) Camera and the Wide Field Image (WFI). CCD’s spatial resolution in nadir is 19.5 meters and its swath width is 113 kilometers. It has 4 wave bands and a panchromatic wave band in visible and near infra- red spectral band. Side looking is one of the main functions of CCD and the side looking range is ±32°. WFI has one visible band and one near inf…     

塔里木河生态输水的累积生态响应

以塔里木河第12～18次生态输水为例,采用经验统计模型和克里金插值法分析了输水前后塔里木河下游地下水埋深和夏季归一化差异植被指数(NDVI)的时空变化,剖析了地下水和植被对生态输水的累积生态响应规律。结果表明:累积生态输水量与最小地下水埋深呈对数曲线正相关关系,其累积效应的增长性由强到弱;地下水埋深对生态输水的累积时间响应具有滞后性,输水80 d后累积效应最显著;沿河道方向,库尔干监测断面累积空间效应最显著;垂直河道方向,地下水对累积输水的空间响应随河道距离增加而减弱;NDVI与近3 a累积输水量呈显著正相关关系,大西海子水库、台特玛湖及河道附近NDVI增长显著。推荐近期输水量为2.5亿～5.0亿m~3/次,合理输水时间为4—6月和9—11月,适宜输水间隔5～12个月,预期连续年份5次输水后累积效应可达最小地下水埋深3.5 m、平均地下水埋深4.2 m。     

汉江上游植被指数变化及其归因分析

植被对调节流域水文过程、维持生态平衡具有重要作用,分析汉江上游植被变化和驱动因子具有重要价值。采用MODIS/Terra的NDVI数据和Land Cover数据分析了汉江上游不同土地利用类型的NDVI的变化规律,结合气象数据和DMSP灯光数据分析了气候变化和人类活动对植被NDVI的影响,分析了植被指数变化的主导性驱动因子。结果表明,2001-2017年汉江上游NDVI整体上呈增加趋势,年均NDVI介于0.5～0.62之间;同种土地利用类型的NDVI年际变化不大,不同土地利用类型的NDVI变化趋势基本一致,NDVI从高到低依次为落叶阔叶林、混交林、草地、农用地、灌丛、常绿针叶林。月平均最高气温是影响月NDVI变化的主导因子,夏季NDVI变化与日照时数和降水量关系较大,冬季NDVI变化与气温相关性较高。气温和降水与NDVI年变化的相关系数高于平均灯光强度变化,即年尺度上气象因素对植被变化的影响高于人类活动。     

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。     

Water leakage detection using remote sensing,field spectroscopy and GIS in semiarid areas of Cyprus

Water pipelines need to be systematically monitored in order to minimize losses from possible leakages. In this paper, remote sensing techniques have been exploited in semiarid areas of Cyprus. In addition, ground spectroradiometer has been used to define the leakage's threshold values. The data were analysed in a GIS environment. Two known leakage problems have been examined. In the first case study, a high resolution QuickBird image was used for the detection of the exact point of leakage. In the second case study a multi-temporal analysis was performed using SPOT images. This methodology was able to record 10 possible leakage points along the pipeline. Throughout the 25 km length of the pipeline, the in-situ observations were minimized to only 0.4%. In both studies the Normalised Difference Vegetation Index (NDVI) was applied. The final outcomes highlight the contribution of remote sensing to the early detection of leakages especially in difficult and near inaccessible areas.     

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。     

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.     

AVHRR derived phenological change in the Sahel and Soudan, Africa, 1982-2005

The Sahel region of Africa has experienced a decrease in rainfall from the early 1960s to mid 1990s. Recent studies have detected an increased in NDVI amplitude and growing season integrated NDVI for the region since 1982. However, these studies have not examined how plant phenology has changed. Phenology examines life cycle events such as bud burst and leaf senescence. Using the software TIMESAT to estimate phenological parameters from the GIMMS AVHRR NDVI dataset, we have found significant positive trends for the length of the growing and end of the growing season for the Soudan and Guinean regions, but significant trends in the Sahel could not be detected. The geographical extent of these trends contrasts with the more northern extent of positive trends of NDVI amplitude and growing season integrated NDVI. Results suggest two types of “greening” trends associated with rainfall change since the drought in the early 1980s.     

Estimation of diurnal air temperature using MSG SEVIRI data in West Africa

Spatially distributed air temperature data with high temporal resolution are desired for several modeling applications. By exploiting the thermal split window channels in combination with the red and near infrared channels of the geostationary MSG SEVIRI sensor, multiple daily air temperature estimates can be achieved using the contextual temperature-vegetation index method. Air temperature was estimated for 436 image acquisitions during the 2005 rainy season over West Africa and evaluated against in situ data from a field test site in Dahra, Northern Senegal. The methodology was adjusted using data from the test site resulting in RMSE = 2.55 K, MBE = − 0.30 K and R² = 0.63 for the estimated versus observed air temperatures. A spatial validation of the method using 12 synoptic weather stations from Senegal and Mali within the Senegal River basin resulted in overall values of RMSE = 2.96 K, MBE = − 1.11 K and R² = 0.68. The daytime temperature curve is interpolated using a sine function based on the multiple daily air temperature estimates from the SEVIRI data. These estimates (covering the 8:00-20:00 UCT time window) were in good agreement with observed values with RMSE = 2.99 K, MBE = − 0.70 K and R² = 0.64. The temperature-vegetation index method was applied as a moving window technique to produce distributed maps of air temperature with 15 min intervals and 3 km spatial resolution for application in a distributed hydrological model.