运用NOAA AVHRR和Landsat TM数据估算多年水稻种植面积

介绍了综合运用NOAA AVHRR和Landsat TM数据进行多年水稻种植面积监测的一种方法,以湖北省为例,首先运用Landsat TM数据计算了该省1992年的水稻种植面积;接着运用1992年和1994年的NOAA AVHRR数据分别计算这两年的水稻像元数,以这两年水稻像元数的变化来反映水稻种植面积的变化;最后运用线性模型,估算1994年的水稻种植面积。所得的1994年水稻种植面积与湖北省农调队资料相比精度为84.5%。运用同样的方法估算1995年该省的水稻种植面积,精度达90%以上。     

Temporal stability of some global NDVI products derived from NOAA/AVHRR GVI

The Normalized DilTerence Vegetation Index (NDVI) derived from NOAA's Advanced Very High Resolution Radiometer (AVHRR) has been widely used in monitoring continental and global vegetation distribution and dynamics, drought severity and location, and environmental deterioration. Since 1982, NOAA has produced the Weekly Global Vegetation Index (GVI) product from AVHRR. The analyses of the GVI product have revealed many problems due to the simplified radiometric correction involved in the processing. Those limitations have inspired several elTorts to reprocess the NOAA GVI data sets to produce an improved representation of global NDVI patterns. In this paper, the quality of three Global NDVI products resulting from very simple to rather sophisticated reprocessing was examined by using a global approach. In general, the quality of data improves with increasing sophistication of radiometric correction. However, this study reveals some significant errors common in all three products assessed. The problems include a systematic annual increase in values computed from a single satellite and jumps between consecutive satellites. These errors are large enough to alTect results of the long term time-series analyses. This pattern suggests an additional radiometric distortion in NOAA/ AVHRR data. It is found that the values computed from data of the first year after satellite launch are roughly the same statistically for NOAA satellites. Thus, the discontinuity ofNDVls between satellites appears to be mainly caused by the systematic drift. Therefore, data collected in the first year of satellite launch might be considered as a baseline for correcting the systematic errors. By comparing NDVI from the first year of satellites in space, it is also found that NDVI increases at higher latitude and decreases or keeps constant at lower latitude. This change of NDVI with time might signal the change of global climate.     

Dryland observation at local and regional scale — Comparison of Landsat TM/ETM+ and NOAA AVHRR time series

During the last few decades, many regions have experienced major land use transformations, often driven by human activities. Assessing and evaluating these changes requires consistent data over time at appropriate scales as provided by remote sensing imagery. Given the availability of small and large-scale observation systems that provide the required long-term records, it is important to understand the specific characteristics associated with both observation scales. The aim of this study was to evaluate the potentials and limits of remote sensing time series for change analysis of drylands. We focussed on the assessment and monitoring of land change processes using two scales of remote sensing data. Special interest was given to the influence of the spatial and temporal resolution of different sensors on the derivation of enhanced vegetation related variables, such as trends in time and the shift of phenological cycles. Time series of Landsat TM/ETM+ and NOAA AVHRR covering the overlapping time period from 1990 to 2000 were compared for a study area in the Mediterranean. The test site is located in Central Macedonia (Greece) and represents a typical heterogeneous Mediterranean landscape. It is undergoing extensification and intensification processes such as long-term, gradual processes driven by changing rangeland management and the extension of irrigated arable land. Time series analysis of NOAA AVHRRR and Landsat TM/ETM+ data showed that both sensors are able to detect this kind of land cover change in complementary ways. Thereby, the high temporal resolution of NOAA AVHRR data can partially compensate for the coarse spatial resolution because it allows enhanced time series methods like frequency analysis that provide complementary information. In contrast, the analysis of Landsat data was able to reveal changes at a fine spatial scale, which are associated with shifts in land management practice.     

基于NOAA/AVHRR数据的地表覆盖变化检测方法与监测

应用图像差分技术、NDVI斜率、生物量斜率计算了过去20年间(1981～2001)我国西部植被的变化状况，总结了计算结果的相关特征。统计表明三种方法所揭示的植被变化信息基本一致，而且前10年(1981～1991)与后10年(1991～2001)相比，植被生长状况呈负相关，并且前10年的植被生长状况要好于后10年。对比主成分分析结果。认为生物量斜率方法比图像差分方法、NDVI斜率方法较为准确，相对于主成分变换计算简明、生物物理意义明确，更适合于植被变化的检测和监测。在讨论卫星更替等外部因素对NDVI影响的基础上，对生物量斜率分割图像的统计揭示了我国西部在1981—2001的20年间的植被演化状况：林地、草地都发生了高比例、大面积的退化。只有很小比例的植被得以改善。表现出局部改善、总体恶化的局面。     

AVHRR multitemporal compositing techniques for burned land mapping

The performance of several criteria to generate multitemporal composites of daily AVHRR images for burned land mapping was tested on some large fires affecting the Iberian Peninsula. The experiment was based on four tests that assessed the discriminability between burned and unburned areas, the presence of artefacts, the verticality of the viewing angle, and the spatial coherency. The maximum temperature was found to be the most appropriate compositing technique for burned land mapping, since it provides the highest performance for the four assessments, with close to maximum discrimination power, no clouds or cloud shadows, high spatial coherency and close to nadir observation angles. Traditional compositing criterion based on maximizing NDVI values provided the lowest ranks in most tests.     

Improving the estimation of noise from NOAA AVHRR NDVI for Africa using geostatistics

The accuracy of NOAA AVHRR NDVI data can be poor because of interference from several sources, including cloud cover. A parameter of the variogram model can be used to estimate the contribution of noise from the total variation in an image. However, remotely sensed information over large areas incorporates non-stationary (regional) trend and directional effects, resulting in violation of the assumptions for noise estimation. These assumptions were investigated at five sites across Africa for a range of ecological environments over several seasons. An unsupervised spectral classification of multi-temporal NDVI data partially resolved the problem of non-stationarity. Quadratic polynomials removed the remaining regional trend and directional effects. Isotropic variograms were used to estimate the noise contributing variation to the image. Standardized estimates of noise ranged from a minimum of 18.5% in west Zambia to 68.2% in northern Congo. Cloud cover and atmospheric particulates (e.g. dust) explained some of the regional and seasonal variations in noise levels. Image artifacts were also thought to contribute noise to image variation. The magnitude of the noise levels and its temporal variation appears to seriously constrain the use of AVHRR NDVI data.     

Early detection system of drought in East Asia using NDVI from NOAA/AVHRR data

Abstract

The influence of surface bidirectional reflectance factors, including shadowing, and of atmospheric aerosol variability are modelled for their effects on the remote sensing of desert targets from space in the 0·?μm region at high spatial resolution. The white sand reflectance data of Salomonson are used as the basis for the simulation. The effects of the surface bi-directional reflectance and atmospheric aerosol on the nadir-normalized reflectance measured at the satellite are discussed individually and jointly. The net influence of these two factors is shown to depend on the magnitude of other parameters, such as the surface reflectance and solar zenith angle.     

Mapping burned areas from Landsat TM/ETM+ data with a two-phase algorithm: Balancing omission and commission errors

Maps of burned area have been obtained from an automatic algorithm applied to a multitemporal series of Landsat TM/ETM+ images in two Mediterranean sites. The proposed algorithm is based on two phases: the first one intends to detect the more severely burned areas and minimize commission errors. The second phase improves burned patches delimitation using a hybrid contextual algorithm based on logistic regression analysis, and tries to minimize omission errors. The algorithm was calibrated using six study sites and it was validated for the whole territory of Portugal (89,000 km²) and for Southern California (70,000 km²). In the validation exercise, 65 TM/ETM+ scenes for Portugal and 35 for California were used, all from the 2003 fire season. A good agreement with the official burned area perimeters was shown, with kappa values close to 0.85 and low omission and commission errors (< 16.5%). The proposed algorithm could be operationally used for historical mapping of burned areas from Landsat images, as well as from future medium resolution sensors, providing they acquire images in two bands of the Short Wave Infrared (1.5-2.2 μm).     

Comparing ten classification methods for burned area mapping in a Mediterranean environment using Landsat TM satellite data

Various methods have been developed during the past three decades to improve the classification accuracy in burned area mapping using satellite data captured by different sensors. In this article, we compare ten such classification approaches using Landsat Thematic Mapper (TM) imagery on three Mediterranean test sites by evaluating the classification accuracy using (i) a traditional pixel-based approach, (ii) the concept of the Pareto boundary of efficient solution and (iii) linear regression analysis. Additionally, we make a discrimination of errors depending on their distribution and causal factor. The classification approaches compared resulted in not statistically significant differences in the accuracy of the burned area maps. Differences between the methods were also observed when considering the accuracy along the edges of the burned patches; however, again these were not statistically significant. The findings of our study in a Mediterranean environment clearly demonstrate that, for the selection of the most suitable classification approach, other factors could be given more weight, such as computational resources, imagery characteristics, availability of ancillary data, available software and the analyst's experience. Maybe the most important finding of our work is that the variance imposed by the methods is less than the variance imposed by factors differentiated locally in the three study sites since the between-group variance of the overall accuracy is higher than that of the within groups.     

NDVI from low altitude aircraft and composited NOAA AVHRR data for scaling Arctic ecosystem fluxes

The relationship between normalized difference vegetation index (NDVI) patterns obtained from high spatial resolution aircraft and low spatial resolution satellite data (Advanced Very High Resolution Radiometer (AVHRR)) was investigated with the intent of using multilevel data to scale carbon flux models in Arctic tundra ecosystems. Despite variable illumination conditions during the aircraft missions and maximum value compositing of the AVHRR data, the difference between 3?km average aircraft and AVHRR NDVI values was generally constant along each flight transect. However, the magnitude of the offset differed between flight dates and small lakes had a greater effect on area averaged aircraft NDVI values than on the satellite values. A cloud index was calculated using incident solar radiation measured by the aircraft and this index was used to identify periods when the aircraft NDVI values may have been biased by cloud cover. Removal of NDVI values based on a cloud index threshold did not appear to be justified given the marginal improvement in the relationship between the two NDVI datasets. If the systematic difference between AVHRR and aircraft NDVI values can be determined, then the scaling of carbon flux models based on the NDVI should be a viable approach in Arctic ecosystems.     

Linking spatial patterns of bird and butterfly species richness with Landsat TM derived NDVI

The ability to predict spatial patterns of species richness using a few easily measured environmental variables would facilitate timely evaluation of potential impacts of anthropogenic and natural disturbances on biodiversity and ecosystem functions. Two common hypotheses maintain that faunal species richness can be explained in part by either local vegetation heterogeneity or primary productivity. Although remote sensing has long been identified as a potentially powerful source of information on the latter, its principal application to biodiversity studies has been to develop classified vegetation maps at relatively coarse resolution, which then have been used to estimate animal diversity. Although classification schemes can be delineated on the basis of species composition of plants, these schemes generally do not provide information on primary productivity. Furthermore, the classification procedure is a time- and labour-intensive process, yielding results with limited accuracy. To meet decision-making needs and to develop land management strategies, more efficient methods of generating information on the spatial distribution of faunal diversity are needed. This article reports on the potential of predicting species richness using single-date Normalized Difference Vegetation Index (NDVI) derived from Landsat Thematic Mapper (TM). We use NDVI as an indicator of vegetation productivity, and examine the relationship of three measures of NDVI—mean, maximum, and standard deviation—with patterns of bird and butterfly species richness at various spatial scales. Results indicate a positive correlation, but with no definitive functional form, between species richness and productivity. The strongest relationships between species richness of birds and NDVI were observed at larger sampling grains and extent, where each of the three NDVI measures explained more than 50% of the variation in species richness. The relationship between species richness of butterflies and NDVI was strongest over smaller grains. Results suggest that measures of NDVI are an alternative approach for explaining the spatial variability of species richness of birds and butterflies.     

A simple cloud masking approach using NOAA AVHRR daytime data for tropical areas

A simple method for cloud detection for AVHRR daytime data is presented and checked for equatorial/tropical areas based on a study area in northeastern Brazil. Five different cloud masking techniques based on visible and infrared spectral information for cloud detection are calibrated. The significant differences between the equatorial threshold obtained in this work and the midlatitude thresholds given by Saunders and Kriebel and by Thiermann and Ruprecht are compared and discussed. Results from the cloud masking algorithm are presented and comments are made about the problems related to the automatic cloud detection algorithm presented in this study.     

Compression of NOAA/AVHRR data with a wavelet transform

An attempt has been made to assess the efficiency of image data compression by wavelet transform encoding using National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) images. Raw and derived images were compressed to various levels and a number of parameters in the decompressed images compared with those obtained using raw data as a yardstick against which to measure the loss of information due to compression. Unsupervised classification, Normalized Difference Vegetation Index (NDVI) values and brightness temperatures appeared to suffer little degradation and only for fractal dimensions was there significant loss of integrity at compression rates of up to a factor of 32. The general conclusion from a visual inspection of the effect of such compressions on artificially generated geometrical imagettes confirms the effectiveness of this method of compression.     

A preliminary assessment of Landsat TM imagery for mapping vegetation and sediment distribution in the Wash estuary

Abstract

Analysis of Landsat Thematic Mapper (TM) image of 14 May 1984 has shown that such data can be used to survey vegetation and sediment distributions in the intertidal zone of the Wash estuary at a spatial detail comparable with current methods practised by the.Nature Conservancy Council. Multispectral classification of this TM image showed good separation of salt-marsh vegetation communities which had recently been surveyed by the Nature Conservancy Council and for which reliable training data could be taken. The sensitivity of classification performance, using both parametric and non-parametric algorithms, to apparently minor differences in phenology at training site locations demonstrates two requirements for improved salt-marsh classification. They are the need for strict definition of training data and that TM wave bands 2, 3, 4 and 5 provide suitable spectral vectors for classifying intertidal environments.     

The role of topographic correction in mapping recently burned Mediterranean forest areas from LANDSAT TM images

Operational use of remote sensing as a tool for post‐fire, Mediterranean forest management has been limited by problems of classification accuracy arising from confusion of burned and non‐burned areas. Frequently, this occurs as a result of slope illumination and shadowing effects caused by the complex topography encountered in many forested areas. Cloud shadows can also be a problem. The aim of this work was to investigate how image classification results could be improved by removing the illumination effects of topography from satellite images. This was achieved by applying supervised classification to both uncorrected and topographically corrected LANDSAT TM data for a site on the Greek island of Thasos. The classification methodology included atmospheric and geometric correction, field‐based training, seperability/contingency analysis and maximum likelihood processing. The classification scheme was determined on the basis of consultation with the Greek Forest Service. Overlay of the resulting class maps enabled comparison of the total burned area and its spatial extent using the two different approaches to processing. The results of each approach were compared with the forest perimeter map generated by the Forest Service using traditional survey methods. Accuracy assessment and error analysis clearly indicated that the removal of the topographic effect from the satellite image before its classification resulted in more accurate mapping of the burned area. It is concluded that operational use of satellite remote sensing for forest fire management depends on accurate, robust, widely available and proven techniques. Topographic correction should now be regarded as an essential element of any classification methodology which will be used for operational, post‐fire management of forests in complex Mediterranean landscapes.     

基于MODIS NDVI的LandsatTM影像地形阴影区光谱信息恢复方法研究

遥感为获取山区生态环境与资源信息提供了重要的观测手段。然而受地形遮蔽影响,山区光学影像大量的地形阴影给山区土地覆被解译以及生态参量的遥感反演带来了巨大困难。针对地形阴影光谱信息的恢复,提出了一种基于MODIS NDVI的Landsat TM影像地形阴影区光谱信息恢复方法。该方法首先利用MODIS上午、下午星(Terra和Aqua)不同时间过境能够对地形阴影区信息实现互补的特点,采用最大值合成法合成MODIS上、下午星16dNDVI产品(MOD13Q1和MYD13Q1),获得低空间分辨率影像上的阴影区光谱信息;在此基础上,考虑MODIS与Landsat的观测角度、光谱差异,设置滑动窗口及筛选规则提取MODIS与TM影像相匹配的同质纯像元;基于中、低空间分辨率影像中均匀同质像元存在一定统计关系的假设,进一步建立同质区域中TM影像光照区域与对应MODIS NDVI的回归树模型,利用该统计关系和阴影区MODIS的NDVI信息推导得出地形阴影区的光谱信息。将阴影光谱信息恢复后的影像与SCS+C校正后的影像进行比较和分析,结果表明该方法恢复得到的地形阴影的光谱信息能够更好地反映阴影区信息,同时光谱保真程度较好。随着越来越多的中高空间分辨率卫星影像的发展,采用多源卫星数据进行山地地形阴影区信息恢复将成为一个新的发展趋势,该方法以期为同类影像处理提供参考。     

Using the mixture-tuned matched filtering method for lithological mapping with Landsat TM5 images

Hyperspectral images are widely employed for geological mapping because of their high spectral resolution. In this article, we develop the mixture-tuned matched filtering (MTMF) method, which can provide highly accurate mapping using the minimum ground-based data. This method is applied in the Malayer region of western Iran, which is composed of various lithological units. MTMF and minimum noise fraction (MNF) methods were applied to a Thematic Mapper 5 (TM5) image, and minimum number of training data based on field observation were used to produce a suitable false-colour image in which locations of lithological units were given. Finally, classification of six desired lithological units was done by the maximum likelihood classification (MLC) method. Results of lithological mapping show that although minimum ground-based data were used, the accuracy of classification is 82.3%. In addition, evaluation of the above-mentioned false-colour image reveals that the algorithm presented enhances the separability of units in the image by 7.3%, which can partially compensate for the low spectral resolution of the image used.     

Landsat TM mapping of evidence for current wind activity in northern Fennoscandia

Hybrid classification of 11 Landsat TM quarter images ( circa 81000km2) was performed in an attempt to locate and quantify areas of sand transported by wind in Lapland. The data were employed in the k-means clustering algorithm after grey-level thresholding, and the resulting normal distribution parameters were used in a maximum likelihood classification. In order to achieve an unbiased classification result, post-classificational manual interpretation was used for selecting the best possible class combination with the aid of ground data and field checks. Wind activity was found on early Holocene anchored inland dunes, glaciofluvial deposits,and in localised areas on fell slopes.Aeolian processes are mainly operative in the mountain birch woodland and treeless tundra north of the Scots Pine forest zone. Sixty-three current localities with a total area of deflated surfaces of circa 10km2 (1000ha) were identified in the study region.     

Comparison of automatic thresholding methods for snow-cover mapping using Landsat TM imagery

Knowledge of snow cover is essential to understanding the global water and energy cycle. Thresholding the normalized difference snow index (NDSI) image is a method frequently used to map snow cover from remotely sensed data. However, the threshold is dependent on the scenario and needs to be determined accordingly. In this study, nine automatic thresholding methods were tested on the NDSI. Comparisons of the automatic thresholding methods, optimal threshold, and support vector machine (SVM) classification show that Otsu's and Nie's methods appear to be the most robust among the nine automatic thresholding methods, achieving comparable accuracies with the latter two approaches. In addition, NDSI from the digital number (DN) can be an efficient substitution for NDSI obtained from atmospherically or topographically corrected data, with similar accuracy.     

Assessment of multitemporal compositing techniques of MODIS and AVHRR images for burned land mapping

The performance of several criteria to generate multitemporal composites of daily Moderate Resolution Imaging Spectrometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) images for burned land mapping was tested using data acquired over the Iberian Peninsula in 2001, 2003 and 2004. The experiment was based on four tests that assessed the discriminability between burned and unburned areas, the presence of artifacts (clouds and clouds shadows), the verticality of the sensor viewing angle, and the spatial coherency of the composite images. Seven different compositing techniques were tested, based on maximizing normalized difference vegetation index (NDVI) and brightness/surface temperature, and minimizing reflectance and sensor zenith angles. The composite criterions that provide the most accurate images for burned land mapping were based on maximizing brightness/surface temperatures, either as the only criterion or in conjunction with minimizing sensor zenith angle or near infrared (NIR) reflectance. These composites present high discrimination capacity between burned and unburned areas, remove most clouds and cloud shadows, offer high spatial coherency and present middle-to-low sensor zenith angles. Traditional compositing criterion based on maximizing NDVI values provided poor results in most tests. Finally, standard NASA MODIS composite provides close to nadir observation angles, and good spatial coherency, but it offered lower discrimination between burned and unburned areas that those composites based on thermal data.