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Fayma Mushtaq 《国际地球制图》2017,32(10):1090-1104
The present investigation was conducted to analyse the trends in hydrometeorological data to addresses how these trends impacted on the water extent of Wular Lake, Kashmir. Temporal changes in the lake surface area have been analysed, using the Landsat satellite data. The nonparametric Mann–Kendall and Sen’s methods were used to determine trends in hydrometeorological data. In addition, trends in extreme indices of frost days (FD), summer days, days with rainfall > 10 mm (R10), days with rainfall > 20 mm (R20) and dry spell for temperature and precipitation were also analysed. The results suggest an overall decrease in extreme rainfall events, R10 and R20, and annual precipitation pattern with increase in maximum and minimum annual average temperatures. Furthermore, the analysis of extreme temperature events suggests warming trend with increased number of warm days and decreased number of FD. With regard to water extent of the lake, an intense decreasing trend was observed. 相似文献
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利用ALOS多光谱影像及CV模型对山东沿海三种不同类型海岸、人工海岸、砂质海岸、淤泥质海岸进行海岸线提取,为比较不同特征影像对海岸线提取效果的影响,对同一类型海岸又分为近红外影像、421彩色合成影像、NDWI影像、PCA影像并分别进行海岸线提取,最后利用缓冲区分析进行精度比较。分析得出:对于人工海岸、砂质海岸两种海岸,在一个像元缓冲区内,提取精度基本可以达到80%以上,两个像元内可达到95%以上;对于淤泥质海岸,四种特征影像的提取效果均比较差,只有NDWI影像在两个缓冲区内可达到87%;总体来说, ND-WI影像提取的效果最好。 相似文献
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Vegetation water content is an important indicator of the degree of stress experienced by plants. This paper explored the potential of using the remote sensing data of MERIS (Medium Resolution Imaging Spectrometer) and AATSR (Advanced Along-Track Scanning Radiometer) collected during the Loess Plateau land surface process field Experiment 2005 (LOPEX05) to map and monitor vegetation water content for corn canopies. By comparing with the daily ground observation to validate the satellite reflectance data, we established relationships between the vegetation water content and satellite remotely sensed indices. The two indices studied were the NDVI (Normalized Different Vegetation Index) from MERIS and the NDWI (Normalized Different Water Index) from AATSR. We used the daily ground observation to demonstrate that the NDVI was saturated during the study period while the NDWI continued to reflect the changes in VWC. We found that NDWI, based on near infrared channel (0.855-0.875μm) and short wave infrared wavelength channel (1.58-1.64μm), is suggested to be more suitable and robust approach for retrieval of vegetation water content. The proposed method was validated with experimental field data with biases that are 1.0314 kg/m2 and 0.9413 kg/m2 respectively. Therefore the NDWI was recommended to retrieval the vegetation water content. 相似文献
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应用MODIS卫星数据提取植被-温度-水分综合指数的研究 总被引:17,自引:1,他引:16
中分辨率成像光谱仪(MODIS)覆盖可见光、近红外和热红外的36个波段,其波谱分辨率高、信息量丰富.通过分析MODIS数据的波谱特性以及植被和土壤的反射波谱特征,选用可见光波段(0.66 μm)、近红外波段(0.86 μm、1.24 μm)提取修正的土壤调整植被指数(MSAVI)和归一化植被水分指数(NDWI);用两个热红外波段(8.6 μm、11 μm)反演植被冠层温度,并通过分析三者之间的耦合特征来提取反映植被水分状况的综合指标(VTWSI).用实测的植被水分数据和模拟的叶片等效水分厚度数据验证所提取的VTWSI值,拟合结果表明呈显著正相关,说明所提取的VTWSI可有效反映植被的水分状况.该项研究探讨了一种直接从卫星遥感数据提取植被水分指标的新方法,为研究干旱、半干旱地区的区域缺水提供简便途径. 相似文献
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Evaluation of NDWI and MNDWI for assessment of waterlogging by integrating digital elevation model and groundwater level 总被引:1,自引:0,他引:1
Accurate information on the extent of waterlogging is required for flood prediction, monitoring, relief and preventive measures. The rule-based classification algorithms were used for differentiating waterlogged areas from other ground features using Resourcesat-2 AWiFS satellite imagery (Indian Remote Sensing Satellite with spatial resolution of 56 m). Two spectral indices normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) were used for extracting waterlogged areas in Sri Muktsar Sahib district of Punjab, India. These indices extracted the waterlogged areas (cropped areas inundated with water) but the water features were less enhanced in the NDWI-derived image (when compared with MNDWI-derived image) due to negative values of NDWI and, mixing of water with built up features. The water features were more enhanced with MNDWI and the values of MNDWI were positive for water features mixed with vegetation. The overall accuracy of waterlogged areas extracted from the MNDWI image was 96.9% with the Kappa coefficient of 0.89. The digital elevation model (DEM) was extracted from ASTER-GDEM. The relationships among depth to the water table recorded before the incessant rain in the region, DEM and classified MNDWI images explained the differences in the extent of waterlogging in various directions of the study area. These results suggest that MNDWI can be used to better delineate water features mixed with vegetation compared to NDWI. 相似文献
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Land cover changes associated with urbanisation modify microclimate, leading to urban heat islands, whereby cities are warmer than the surrounding countryside. Understanding the factors causing this phenomenon could help urban areas adapt to climate change and improve living conditions of inhabitants. In this study, land surface temperatures (LST) of Aarhus, a city in the high latitudes, are estimated from the reflectance of a thermal band (TIRS1; Band 10; 10.60–11.19 μm) of Landsat 8 on five dates in the summer months (one in 2015, and four in 2018). Spectral indices, modelled on the normalised difference vegetation index (NDVI), using all combinations of the first seven bands of Landsat 8 are calculated and their relationships with LST, analysed. Land cover characteristics, in terms of the percentages of tree cover, building cover and overall vegetation cover are estimated from airborne LiDAR data, building footprints and 4-band aerial imagery, respectively. The correlations between LST, the spectral indices and land cover are estimated.The difference in mean temperature between the rural and urban parts of Aarhus is up to 3.96 °C, while the difference between the warmer and colder zones (based on the mean and SD of LST) is up to 13.26 °C. The spectral index using the near infrared band (NIR; Band 5; 0.85-0.88 μm) and a short-wave infrared band (SWIR2; Band 7; 2.11–2.29 μm) has the strongest correlations (r: 0.62 to 0.89) with LST for the whole study area. This index is the inverse of normalised burn ratio (NBR), which has been used for mapping burnt areas. Spectral indices using different combinations of the infrared bands have stronger correlations with LST than the more widely used vegetation indices such as NDVI. The percentage of tree cover has a higher negative correlation (Pearson’s r: -0.68 to -0.75) with LST than overall vegetation cover (r: -0.45 to -0.63). Tree cover and building cover (r: 0.53 to 0.71) together explain up to 68 % of the variation in LST. Modification of tree and building cover may therefore have the potential to regulate urban LST. 相似文献
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