A geospatial study of the drought impact on surface water reservoirs: study cases from Texas,USA

Satellite images have been used historically to measure and monitor fluctuations in the surface water reservoirs. This study integrates remote sensing and Geographic Information System (GIS) technologies to investigate the impact of drought on 10 selected surface water reservoirs in San Angelo and Dallas, Texas. Oscillations in summer and winter months throughout the 2005–2016 period were assessed using multispectral images from Landsat-5, ?7, and ?8, and changes in the reservoirs were characterized and correlated against local climate data of each reservoir. For quantitative comparisons of the time-series measurements, a robust density slicing approach was employed to classify the range of values of the raster cells in the near-infrared band of Landsat images for each lake into three desired classes (deep water, shallow water, and dry area) based on the natural breaks inherent in the dataset. Statistical analysis shows that the overall accuracy of the classification is about 94%, which demonstrates the efficiency of the density slicer to accurately estimate surface water area changes from an individual Landsat band. Shrinkage in the surface water area over the study period reveals the concrete impact that the drought along with other factors have on the 10 selected lakes. The San Angelo lakes located in west central Texas experienced a nearly consistent pattern of change during most of the study period; whereas the Dallas lakes in northeast Texas followed the oscillating pattern of drought and correlated closely to the local conditions. Shockingly, the extreme drought caused complete vanishing of several lakes, and consequently Texas had to remove them from its recreational plans. Our new findings can certainly help with the water resource management in Texas and our study approach can be adapted for monitoring lake oscillations in other areas across the world. This geospatial study demonstrates the societal benefits from incorporating remote sensing and GIS in investigating geo-environmental problems associated with severe climate changes.     

从Landsat8影像反演地表温度的劈窗算法研究

以厦门市为研究区域,以2013年的Landsat8为研究数据,研究亮度温度反演以及地表温度反演的劈窗算法,推导出该算法的演算过程,根据Landsat8的特性,确定该算法的参数取值。通过ENVI/IDL软件编程实现该算法,采用厦门市遥感数据反演地表温度,并且利用厦门市气象局实测温度验证地表温度反演结果的有效性。     

基于Landsat卫星数据的山东临沂市热岛效应研究

利用Landsat卫星数据分别反演了2005年和2014年临沂市的地表温度和不透水层指数,分析了城市化进程对临沂市热岛效应的影响。结果表明,2005年临沂市表现为中等强度的热岛效应,2014年表现为强热岛效应。利用地面站点资料统计分析来看,2005～2014年,临沂市热岛强度总体呈波动增加的趋势,冬季最强,春秋季次之,夏季较弱。分析城市化因子发现,城市经济、人口、用电消耗、城市房屋面积增量等多个因素对城市热岛强度变化的影响,其相关系数分别为0.86、0.82、0.67、0.81,其中房屋面积增量与热岛强度增强密切相关。从不透水层指数分布图的动态变化来看,也说明了城市化进程中城镇建筑和硬化的路面的增多导致了热岛强度的增强。     

基于Landsat/TM资料研究广州城市热岛现象

利用Landsat/TM卫星资料及气象观测资料,基于覃志豪单窗算法对地表温度进行反演,根据热岛强度定义进行热岛强度、热岛范围的定量计算。研究结果表明,在考虑初始地温、总辐射量、植被状况等因素的影响之后,2000年至2005年间,广州市热岛现象呈现明显增强趋势,2000年11月1日、2002年11月7日、2005年11月23日热岛强度指数分别为1.15、1.20、2.89,热岛面积分别为235.44 km~2、261.09 km~2、381.42 km~2。人口增长刺激城市化加剧、温室效应增强使得地表蓄积热量增加是导致广州城市热岛效应增强的主要原因。     

归一化水汽指数与地表温度的关系

城市热岛的遥感研究,传统上主要利用归一化植被指数作为指标来评估地表温度-植被之间的关系。本研究探讨了以归一化水汽指数作为评估其与地表温度关系的适应性,比较了归一化水汽指数和归一化植被指数在表达城市热岛效应方面的效力。利用"单窗算法"和3个不同时段的TM/ETM+数据获取了珠江三角洲核心区域的地表温度分布图,分析了两种指数与地表温度之间的关系。对局部区域逐像元和对区域总体分析的结果表明:不同时段的地表温度与归一化水汽指数之间均存在较强的线性关系,而地表温度与归一化植被指数之间的线性关系相对较弱。说明在城市化的环境下,与传统上常用归一化植被指数作为指标来定量分析地表温度相比,归一化水汽指数提供了一种互补的、甚至更加有效的衡量指标。     

Analysis of Spatio-temporal Land Surface Temperature and Normalized Difference Vegetation Index Changes in the Andassa Watershed,Blue Nile Basin,Ethiopia

Analysis of the nexus between vegetation dynamics and climatic parameters like surface temperature is essential in environmental and ecological studies and for monitoring of the natural resources. This study explored the spatio-temporal distribution of land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) and the relationship between them in the Andassa watershed from 1986 to 2016 periods using Landsat data. Monthly average air temperature data of three meteorological sites were used for validating the results. The findings of the study showed that the LST of the Andassa watershed has increased during the study periods. Overall, average LST has been rising with an increasing rate of 0.081°C per year. Other results of this study also showed that there has been a dynamic change in vegetation cover of the watershed in all seasons. There was also a negative correlation between LST and NDVI in all the studied years. From this study we can understand that there has been degradation of vegetation and intensification of LST from 1986 to 2016.     

呼图壁至乌苏一带新构造变形特征及油气勘探方向预测

以覆盖北天山山前呼图壁至乌苏一带的四景 L andsat TM卫星遥感图像的地质解译为基础 ,结合野外实地考察所获取的地质资料以及烃源岩的生烃和排烃的盆地模拟分析结果 ,对呼图壁—乌苏地区第二、三排构造带上新世以来的新构造运动变形特征、背斜和断裂构造的形成时代以及油气勘探方向进行分析和预测。该区第二、三排背斜构造的变形非常强烈 ,它们的构造变形始于上新世末期 ,早更新世末期是新构造变形最强烈的时期 ,这些背斜构造带在中更新世早期已基本形成。第二排背斜构造带的构造变形强于第三排构造带。有利烃源岩安集海河组的排烃高峰期在距今 0 .3Ma左右 ,晚于背斜构造的形成时期 ,其生成和排出的油气很有可能聚集于这些背斜构造中。发育于背斜构造核部或北翼一侧的逆冲断裂构造为油气的垂向运移提供了良好通道 ,但也可能导致油气的逸散和破坏。综合分析表明第三排背斜构造带中的西湖背斜、独南背斜、安集海背斜和呼图壁背斜具有良好的油气勘探前景     

基于ETM影像数据融合的土地覆盖分类研究

在ERDAS软件支持下，对ETM遥感影像数据的TM1-TM5，TM7与其全色波段TM8进行融合，采用主成分、乘积法、Brovey转换三种融合方法，重采样方法分别为邻域法、立方卷积法及双线性内插法。采用相同的训练样本区及最大似然法分类方法，对融合产生的9幅影像及未融合影像进行土地覆盖分类，通过对分类影像的Producers Accuracy，Users Accuracy，Kappa三者的精度数据和地物波谱信息的对比分析，在总体上，上述的影像融合方法对提高土地覆盖分类的精度不明显，但就某些地物类型来说，还是值得采用的；三种融合方法和三种重采样方式它们之问相比较而言，乘积法融合法和立方卷积重采样法相对较为可取。     

基于Landsat 8数据的城市不透水面及其与热环境的关系研究

利用Landsat8数据,以成都市为研究对象,采用光谱混合分解模型研究估算成都市的不透水面分布信息。通过MNF变换和PPI指数运算,确定高、低反照率,植被及土壤四类光谱端元,并利用Landsat8新增的卷云波段去除云、土壤、沙地等噪声和MNDWI指数去除水体。修正后的高、低反照率组分含量之和即为不透水面含量的估计值。通过将不透水面信息与利用TIRS11波段反演的地表温度进行回归分析,发现两者呈正相关。     

华南地区典型种植园地遥感分类研究

华南地区种植园地广泛分布,类型混杂多样,导致园地分布信息难以正确获取,为农业管理造成了较大困难。本研究基于Landsat8 OLI数据,通过数据融合、特征优化,应用随机森林算法构建面向对象的种植园地分类规则集,对华南地区典型经济作物香蕉、柑橘、葡萄、蒲葵、海枣、番木瓜和火龙果等进行类别识别,同时对比贝叶斯分类法、K最邻近分类法、支持向量机法、决策树分类法的分类效果。结果表明：数据融合会在一定程度上影响分类结果精度;植株形态、光谱特征接近,种植期交错是影响华南地区典型园地分类精度的重要原因;以中分辨率影像为数据源,面向对象的随机森林算法应用于种植园地分类研究总体精度可达88.05%,Kappa系数0.87,可以有效区分华南地区典型种植园地类别;相比于其他算法,随机森林算法在分类精度、可靠性和稳定性上具有一定优势,可为园地作物生长监测和种植管理提供科学依据。