Assessment of climatic variability in the catchments of Himalayan Lake,Jammu & Kashmir

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.     

基于指数图像的城镇建设用地信息识别与提取——以岳阳市为例

以岳阳市Landsat ETM+影像为信息源,分析了典型地物的光谱特征及可分性,将地物种类归并为建设用地、植被、水体三大类,分别选用归一化裸露指数( NDBI)、重归一植被指数(RDVI)和改进的归一化水体指数(修改后的NDWI)作为三种地类的指示因子,通过阈值分割、掩膜处理去除了非建设用地区域的噪音信息,得到了比较准...     

环境一号卫星高光谱数据水体信息提取方法

环境一号卫星A星上的超光谱成像光谱仪(HSI)是中国第一个高光谱成像光谱仪.为充分利用HSI数据的高光谱特性,本文以2009年10月5日的影像为研究区,得到HSI数据影像反射率,分析水体等地类光谱特征差异及选择各地类敏感波段;利用传统指数NDVI和NDWI,构建新的基于指数的水体指数IWI,试验得出,IWI指数增加了各...     

基于 ALOS 影像与 CV 模型的瞬时海岸线提取

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。     

Vegetation water content retrieval using MERIS and AATSR data over the Loess Plateau

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.     

黄河口湿地典型地物类型高光谱分类方法

黄河口湿地地物类型具有复杂多样的特点,本文将线性光谱混合分析模型与归一化植被指数(NDVI)和归一化水体指数(NDWI)相结合,建立了一种新的滨海湿地遥感影像分类方法;开展了基于CHRIS高光谱影像的黄河口湿地芦苇、柽柳、碱蓬、大米草、潮滩和水体6种典型地物分类实验,整体分类精度为77.33%,Kappa 系数为 0.71,与经典的最大似然分类(MLC)方法相比较,整体分类精度提高1.6%,Kappa 系数提高0.02,尤其是芦苇、碱蓬、大米草和潮滩的分类精度明显提高。     

应用MODIS卫星数据提取植被-温度-水分综合指数的研究

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.     

Evaluation of NDWI and MNDWI for assessment of waterlogging by integrating digital elevation model and groundwater level

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.     

Normalised difference spectral indices and urban land cover as indicators of land surface temperature (LST)

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.     

基于决策树的蓬莱市土地覆盖信息提取

以蓬莱市为研究区,对TM影像经过预处理后,进行典型地物的光谱分析。在此基础上,运用决策树分类法,选取影像的光谱特征值、NDWI值、NDVI值、K—T变换信息和DEM值等数据作为测试变量,选择适当阈值设定判别规则,建立决策树模型进行土地利用覆盖信息提取并做出精度评价。将其提取精度与监督分类结果精度进行比较,结果表明其分类精度有很大提高,尤其在研究区是丘陵地形的情况下,DEM数据的使用使林地、果园的可分性大大加强。