基于ISODATA法的冬小麦产量分级监测预报

以江苏省姜堰市为例,进行了基于TM卫星遥感技术和小麦估产模型的冬小麦产量监测研究。在利用GPS实地采样调查和建立解译标志的基础上,通过影像校正、采用优化的ISODATA分类方法,结合人机交互式判读解译等操作,将样点的作物信息数据贯穿到整个校验分类过程中,信息解译精度在90%以上。利用分类提取的冬小麦数据,反演叶面积指数、生物量信息等,结合冬小麦估产模型,计算单点产量信息,经过线性转换,对整个区域的冬小麦产量进行监测预报,并制作了冬小麦产量分级专题图。     

Assessing the potential of VEGETATION sensor data for mapping snow and ice cover: A Normalized Difference Snow and Ice Index

The VEGETATION (VGT) sensor in SPOT 4 has four spectral bands that are equivalent to Landsat Thematic Mapper (TM) bands (blue, red, near-infrared and mid-infrared spectral bands) and provides daily images of the global land surface at a 1-km spatial resolution. We propose a new index for identifying and mapping of snow/ice cover, namely the Normalized Difference Snow/Ice Index (NDSII), which uses reflectance values of red and mid-infrared spectral bands of Landsat TM and VGT. For Landsat TM data, NDSII is calculated as NDSII_TM=(TM3-TM5)/(TM3+TM5); for VGT data, NDSII is calculated as NDSII_VGT=(B2-MIR)/(B2+MIR). As a case study we used a Landsat TM image that covers the eastern part of the Qilian mountain range in the Qinghai-Xizang (Tibetan) plateau of China. NDSIITM gave similar estimates of the area and spatial distribution of snow/ice cover to the Normalized Difference Snow Index (NDSI=(TM2-TM5)/(TM2+TM5)) which has been proposed by Hall et al. The results indicated that the VGT sensor might have the potential for operational monitoring and mapping of snow/ice cover from regional to global scales, when using NDSII_VGT.     

基于MODIS遥感数据的宏观土地覆盖特征分类方法与精度分析研究

针对宏观土地覆盖遥感分类的现状,充分利用MODIS相对于AVHRR数据具有的多光谱和分辨率优势,提出了利用MODIS数据进行分类特征选择与提取并结合多时相特征进行宏观土地覆盖分类的分类方法,并在中国山东省进行了分类试验,得出以下结论:①不同比例下的训练样本与验证样本影响着总体分类精度;②从MODIS数据中得到的植被指数EVI、白天地表温度Tday、水体指数NDWI、纹理特征局部平稳Homogeneity等可以作为分类特征配合参与到多波段地表反射率Ref1-7遥感影像中,能明显提高分类精度,而土壤亮度指数NDSI则没有贡献;③提取的分类特征对总体分类精度贡献大小为:EVI贡献最大,提高近6个百分点,其次是Homogeneity、NDWI,均提高近4个百分点,而最少的Tday也贡献了近3个百分点;④各分类特征对不同地物类别具有不同的分离度,在提高某些类别的分离性时,有可能降低了其它类别的分离性。试验结果表明:在没有其它非遥感信息的前提下,仅利用MODIS遥感自身信息对宏观土地覆盖分类就可达到较高精度。     

基于MODIS遥感数据的宏观土地覆盖特征分类方法与精度分析研究

针对宏观土地覆盖遥感分类的现状,充分利用MODIS相对于AVHRR数据具有的多光谱和分辨率优势,提出了利用MODIS数据进行分类特征选择与提取并结合多时相特征进行宏观土地覆盖分类的分类方法,并在中国山东省进行了分类试验,得出以下结论:①不同比例下的训练样本与验证样本影响着总体分类精度;②从MODIS数据中得到的植被指数EVI、白天地表温度Tday、水体指数NDWI、纹理特征局部平稳Homogeneity等可以作为分类特征配合参与到多波段地表反射率Ref1-7遥感影像中,能明显提高分类精度,而土壤亮度指数NDSI则没有贡献;③提取的分类特征对总体分类精度贡献大小为:EVI贡献最大,提高近6个百分点,其次是Homogeneity、NDWI,均提高近4个百分点,而最少的Tday也贡献了近3个百分点;④各分类特征对不同地物类别具有不同的分离度,在提高某些类别的分离性时,有可能降低了其它类别的分离性。试验结果表明:在没有其它非遥感信息的前提下,仅利用MODIS遥感自身信息对宏观土地覆盖分类就可达到较高精度。     

A water index for rapid mapping of shoreline changes of five East African Rift Valley lakes: an empirical analysis using Landsat TM and ETM+ data

Because of the complicated shorelines, inaccessibility and vast spread of some lakes, information on changing shorelines is difficult to acquire. A new water index (WI) has been applied to quantify changes in five saline and non‐saline Rift Valley lakes in Kenya using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) data. The WI is based on a logical combination of the Tasseled Cap Wetness (TCW) index and the Normalized Difference Water Index (NDWI). Using regression analysis with estimated shoreline coordinates, the WI detected the shorelines with an accuracy of 98.4%, which was 22.3% higher than the TCW, and 43.2% more accurate than the NDWI. Change detection was derived using image differencing followed by density slicing and unsupervised classification. The saline lakes (Bogoria, Nakuru and Elementaita) changed more with respect to the ratio of the change in the original surface areas than the non‐saline lakes (Baringo and Naivasha).     

The use of SPOT VEGETATION data in a classification tree approach for burnt area mapping in Australian savanna

An algorithm to map burnt areas has been developed for SPOT VEGETATION (VGT) data in Australian woodland savannas. A time series of daily VGT images (15 May to 15 July 1999) was composited into 10-day periods by applying a minimum value criterion to the near-infrared band (0.78-0.89 @m). The algorithm was developed using a classification tree methodology that was confirmed as a powerful means of image classification. This methodology allowed the identification of three classes of burnt surfaces that appear to be differentiated by the proportion of the pixel that is burnt, the intensity of the fire and the density of the tree layer. The performance of the algorithm was assessed by classification of one VGT composite image (31 May-9 June) using, as representative of the ground truth, burnt areas extracted from two Landsat TM scenes (9 June). We randomly extracted 30 windows (each of ～14 km by 14 km) for which we compared the percentage of area burnt as derived from TM and VGT. The estimated mean absolute deviation in the percentage of the area burnt in each window is - 6.3%. In the area common to the two datasets a total amount of 6473 km 2 was estimated to be burnt in the VGT classification against 7536 km 2 that was burnt according to TM images. The accuracy of the classification was found to vary with the vegetation type being the most accurate estimate in low woodland with an underestimation error of 8.6%. These results show that VGT could be a very useful sensor for burnt area mapping over large woodland areas, although the low spatial resolution and the lack of a thermal band can be a limitation in certain conditions (e.g. understorey burns). The same methodology will be applied to map burnt areas for the entire Australian continent.     

Comparison of methods of snow cover mapping by analysing the solar spectrum of satellite remote sensing data in China

Three methods, supervised classification (SC), digital number (DN) statistics and Normalized Difference Snow Index (NDSI), are used to map snow cover and then calculate snow cover area. Data sets from Landsat TM, Moderate Resolution Imaging Spectroradiometer (MODIS) and NOAA/AVHRR are selected because these sensors of different spatial resolution provide the most up to date remote sensing data for China. The results show that the best method for obtaining the snow index is different for each of these sensor products because of their different spatial and temporal resolutions and objectives of application. Reflectivity threshold statistics (RTs) should be used if the data series is incomplete; whereas SC needs a relatively accurate signature file for classification. A valid and rational method has been certified which selects NDSI for extracting snow pixels. Meanwhile, we introduce the brightness compensation method for decreasing the impact of topographic shading on distinguishing of snow pixels.     

Agreement assessment of NOAA/AVHRR NDVI with Landsat TM NDVI for mapping burned forested areas

Meteorological satellites are appropriate for operational applications related to early warning, monitoring and damage assessment of forest fires. Environmental or resources satellites, with better spatial resolution than meteorological satellites, enable the delineation of the affected areas with a higher degree of accuracy. In this study, the agreement of two datasets, coming from National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Landsat TM, for the assessment of the burned area, was investigated. The study area comprises a forested area, burned during the forest fire of 21-24 July 1995 in Penteli, Attiki, Greece. Based on a colour composite image of Landsat TM a reference map of the burned area was produced. The scatterplot of the multitemporal Normalized Difference Vegetation Index (NDVI) images, from both Landsat TM and NOAA/AVHRR sensors, was used to detect the spectral changes due to the removal of vegetation. The extracted burned area was compared to the digitized reference map. The synthesis of the maps was carried out using overlay techniques in a Geographic Information System (GIS). It is illustrated that the NOAA/AVHRR NDVI accuracy is comparable to that from Landsat TM data. As a result NOAA/AVHRR data can, operationally, be used for mapping the extent of the burned areas.     

Water body delineation using index composition and HIS transformation

The use of satellite data for mapping water bodies is important for environmental management. Previous approaches exhibit limited applicability in southeastern China, given its complex and heterogeneous landscapes as well as the difficulty to obtain cloud-free images. To overcome these problems, we proposed an approach using index composition and HIS (hue, intensity and saturation) transformation. First, a colour image was generated using three indices: the Normalized Difference Built-up Index (NDBI), the Normalized Difference Vegetation Index (NDVI) and the Modified Normalized Difference Water Index (MNDWI). Then, HIS transformation was employed to extract water bodies and remove hill shadows. Another colour image, composed by Landsat TM (Thematic Mapper) 4, NDVI, TM1 and HIS transformation, was further applied to separate water bodies from residual shadows. This approach was tested and verified with Landsat TM images in the Tiaoxi watershed, southeastern China. The results indicated the high accuracy and promising applicability of this new approach for fragmented landscapes, given its insensitivity to seasonal and subjective factors.     

The IGBP-DIS global 1km land cover data set,DISCover: First results

The International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) is co-ordinating the development of global land data sets from Advanced Very High Resolution Radiometer (AVHRR) data. The first is a 1 km spatial resolution land cover product 'DISCover', based on monthly Normalized Difference Vegetation Index composites from 1992 and 1993. DISCover is a 17 class land cover dataset based on the science requirements of IGBP elements. Mapping uses unsupervised classification with post-classification refinement using ancillary data. Draft Africa, North America and South America products are now available for peer review.     

Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data

A unique physical feature of paddy rice fields is that rice is grown on flooded soil. During the period of flooding and rice transplanting, there is a large proportion of surface water in a land surface consisting of water, vegetation and soils. The VEGETATION (VGT) sensor has four spectral bands that are equivalent to spectral bands of Landsat TM, and its mid-infrared spectral band is very sensitive to soil moisture and plant canopy water content. In this study we evaluated a VGT-derived normalized difference water index (NDWI VGT =(B3-MIR)/ (B3+MIR)) for describing temporal and spatial dynamics of surface moisture. Twenty-seven 10-day composites (VGT- S10) from 1 March to 30 November 1999 were acquired and analysed for a study area (175 km by 165 km) in eastern Jiangsu Province, China, where a winter wheat and paddy rice double cropping system dominates the landscape. We compared the temporal dynamics and spatial patterns of normalized difference vegetation index (NDVI VGT ) and NDWI VGT . The NDWI VGT temporal dynamics were sensitive enough to capture the substantial increases of surface water due to flooding and rice transplanting at paddy rice fields. A land use thematic map for the timing and location of flooding and rice transplanting was generated for the study area. Our results indicate that NDWI and NDVI temporal anomalies may provide a simple and effective tool for detection of flooding and rice transplanting across the landscape.     

基于决策树分层分类法的粉煤灰堆场信息提取研究

粉煤灰污染环境,危害人类健康。应用遥感方法快速、实时、准确地识别粉煤灰堆场信息,对保护环境和人类健康具有重要意义。通过分析包头市辖区内典型地物的光谱信息,基于Landsat 5 TM影像数据,采用决策树分层分类法对研究区内的粉煤灰堆场进行提取实验。首先,分析研究区内典型地物的光谱特征,对不同地物之间的关系进行比较。其次,建立决策树,利用土壤调节植被指数(SAVI)、改进归一化差异水体指数(MNDWI)、归一化建筑指数(NDBI)以及光谱阈值法对图像进行了分类。最后利用形状特征和空间位置特征等对分类图像进行后处理,分类精度达到70.7%。实验结果表明:该方法适合粉煤灰堆场信息的自动提取,结合目视解译能够达到较高的识别精度。     

Monitoring snow-cover dynamics in Northern Fennoscandia with SPOT VEGETATION images

In order to monitor snow-cover dynamics in the Tana River Basin in Northern Fennoscandia, SPOT VEGETATION (VGT) images of the snowmelt seasons of 1998 and 1999 were used to identify snow-covered areas, employing an algorithm that was originally developed for data from the Moderate Resolution Imaging Spectroradiometer (MODIS). This algorithm is based on the Normalized Difference Snow Index (NDSI), which usually is calculated from the green and mid-infrared bands. In the absence of a green band, the applicability of this algorithm to VGT data from the red and mid-infrared bands was tested by comparing NDSI values with a corresponding Landsat Thematic Mapper (TM) image. The best agreement was found with slightly lower threshold values for the NDSI. Comparison of the snow-cover estimates also allowed testing of the performance of the NDSI-based algorithm in partially snow-free conditions. By applying the algorithm to ten-day syntheses of VGT images, the moment of snow disappearance could be registered for each 1×1?km pixel in the study area. The results were largely consistent with observations at meteorological stations in the area, confirming the effectiveness of VGT images and the algorithm employed in monitoring snow-cover depletion patterns.     

NOAA-AVHRR data processing for the mapping of vegetation cover

The NOAA-AVHRR images have been widely used for global studies due to their low cost, suitable wavebands and high temporal resolution. Data from the AVHRR sensor (Bands 1 and 2) transformed to the Normalized Difference Vegetation Index (NDVI) are the most common product used in global land cover studies. The purpose of this Letter is to present the vegetation, soil, and shade fraction images derived from AVHRR, in addition to NDVI, to monitor land cover. Six AVHRR images from the period of 21 to 26 June 1993 were composed and used to obtain the above mentioned products over Sa o Paulo State, in the south-east of Brazil. Vegetation fraction component values were strongly correlated with NDVI values ( r 0.95; n 60). Also, the fraction image presented a good agreement with the available global vegetation map of Sao Paulo State derived from Landsat TM images.     

Classification of seasonal images for monitoring irrigated crops in a salinity-affected area of Australia

An attempt was made to observe the impact of salinity management on agricultural crops in a highly salinity-affected area of Australia. A classification strategy based on crop phenology and irrigation practices, was developed to identify and monitor the three major irrigated crops. For this, Normalized Difference Vegetation Indices (NDVIs) were derived from Landsat Thematic Mapper (TM) data for two consecutive seasons (summer and autumn) each of 1989-1990 and 1995-1996. Using the 1995-1996 field survey the results of parametric and nonparametric classifiers were compared. Higher accuracies were achieved with the nonparametric classifier. Substantial changes were found in pasture culture from 1989-1990 to 1995-1996.     

Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans

Information about vegetation water content (VWC) has widespread utility in agriculture, forestry, and hydrology. It is also useful in retrieving soil moisture from microwave remote sensing observations. Providing a VWC estimate allows us to control a degree of freedom in the soil moisture retrieval process. However, these must be available in a timely fashion in order to be of value to routine applications, especially soil moisture retrieval. As part of the Soil Moisture Experiments 2002 (SMEX02), the potential of using satellite spectral reflectance measurements to map and monitor VWC for corn and soybean canopies was evaluated. Landsat Thematic Mapper and Enhanced Thematic Mapper Plus data and ground-based VWC measurements were used to establish relationships based on remotely sensed indices. The two indices studied were the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI). The NDVI saturated during the study period while the NDWI continued to reflect changes in VWC. NDWI was found to be superior based upon a quantitative analysis of bias and standard error. The method developed was used to map daily VWC for the watershed over the 1-month experiment period. It was also extended to a larger regional domain. In order to develop more robust and operational methods, we need to look at how we can utilize the MODIS instruments on the Terra and Aqua platforms, which can provide daily temporal coverage.     

A multiple dataset approach for 30-m resolution land cover mapping: a case study of continental Africa

Recent developments in global land-cover mapping have focused on spatial resolution improvement with more heterogeneous features to integrate spatial, spectral and temporal information. In this study, hundreds of features derived from four seasonal Landsat 8 OLI (Operational Land Imager) spectral bands, Moderate Resolution Imaging Spectroradiometer (MODIS) time series vegetation index (VI) data, night-time light (NTL), digital elevation models (DEM) and climatic variables were used for land cover mapping with a target 30-m resolution for the whole African continent. In total, 49,007 training samples (from 11,231 locations) and 23,803 validation samples (from 5,414 locations) interpreted from seasonal Landsat, MODIS Normalized Difference Vegetation Index (NDVI) time series and high-resolution images in Google Earth were used for classifier training (Random Forest) and map validation. Overall accuracy was 76% at 30-m spatial resolution, which is better than previous land cover mapping for the African continent. Besides, accuracies for cropland were improved dramatically by more than 10%. Our method also addressed many remaining issues for 30-m mapping (e.g. boundary effects and declines in resolution). This framework is promising for automatic and efficient global land cover mapping resulting in better visual effects and classification accuracy.     

Use of Normalized Difference Water Index for monitoring live fuel moisture

Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were compared for monitoring live fuel moisture in a shrubland ecosystem. Both indices were calculated from 500?m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data covering a 33‐month period from 2000 to 2002. Both NDVI and NDWI were positively correlated with live fuel moisture measured by the Los Angeles County Fire Department (LACFD). NDVI had R ² values ranging between 0.25 to 0.60, while NDWI had significantly higher R ² values, varying between 0.39 and 0.80. Water absorption measures, such as NDWI, may prove more appropriate for monitoring live fuel moisture than measures of chlorophyll absorption such as NDVI.     

Monitoring Sahelian floodplains using Fourier analysis of MODIS time‐series data and artificial neural networks

Fourier analysis of Moderate Resolution Image Spectrometer (MODIS) time‐series data was applied to monitor the flooding extent of the Waza‐Logone floodplain, located in the north of Cameroon. Fourier transform (FT) enabled quantification of the temporal distribution of the MIR band and three different indices: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Enhanced Vegetation Index (EVI). The resulting amplitude, phase, and amplitude variance images for harmonics 0 to 3 were used as inputs for an artificial neural network (ANN) to differentiate between the different land cover/land use classes: flooded land, dry land, and irrigated rice cultivation. Different combinations of input variables were evaluated by calculating the Kappa Index of Agreement (KIA) of the resulting classification maps. The combinations MIR/NDVI and MIR/EVI resulted in the highest KIA values. When the ANN was trained on pixels from different years, a more robust classifier was obtained, which could consistently separate flooded land from dry land for each year.     

Land cover characterization of Temperate East Asia using multi-temporal VEGETATION sensor data

Temperate East Asia (TEA) is characterized by diverse land cover types, including forest and agricultural lands, one of the world's largest temperate grasslands, and extensive desert and barren landscapes. In this paper, we explored the potential of SPOT-4 VEGETATION (VGT) data for the classification of land cover types in TEA. An unsupervised classification was performed using multi-temporal (March–November 2000) VGT-derived spectral indices (Land Surface Water Index [LSWI] and Enhanced Vegetation Index [EVI]) to generate a land cover map of TEA (called VGT-TEA). Land cover classes from VGT-TEA were aggregated to broad, general class types, and then compared and validated with classifications derived from fine-resolution (Landsat) data. VGT-TEA produced reasonable results when compared to the Landsat products. Analysis of the seasonal dynamics of LSWI and EVI allows for the identification of distinct growth patterns between different vegetation types. We suggest that LSWI seasonal curves can be used to define the growing season for temperate deciduous vegetation, including grassland types. Seasonal curves of EVI tend to have a slightly greater dynamic range than LSWI during the peak growing season and can be useful in discriminating between vegetation types. By using these two complementary spectral indices, VGT data can be used to produce timely and detailed land cover and phenology maps with limited ancillary data needed.