Analysis of urban built-up areas and surface urban heat island using downscaled MODIS derived land surface temperature data

Regional scale urban built-up areas and surface urban heat islands (SUHI) are important for urban planning and policy formation. Owing to coarse spatial resolution (1000 m), it is difficult to use Moderate Resolution Imaging Spectroradiometer (MODIS) Land surface temperature (LST) products for mapping urban areas and visualization, and SUHI-related studies. To overcome this problem, the present study downscaled MODIS (1000 m resolution)-derived LST to 250 m resolution to map and visualize the urban areas and identify the basic components of SUHI over 12 districts of Punjab, India. The results are compared through visual interpretation and statistical procedure based on similarity analysis. The increased entropy value in the downscaled LST signifies higher information content. The temperature variation within the built-up and its environs is due to difference in land use and is depicted better in the downscaled LST. The SUHI intensity analysis of four cities (Ludhiana, Patiala, Moga and Vatinda) indicates that mean temperature in urban built-up core is higher (38.87 °C) as compared to suburban (35.85 °C) and rural (32.41 °C) areas. The downscaling techniques demonstrated in this paper enhance the usage of open-source wide swath MODIS LST for continuous monitoring of SUHI and urban area mapping, visualisation and analysis at regional scale. Such initiatives are useful for the scientific community and the decision-makers.     

Monitoring canopy growth and grain yield of paddy rice in South Korea by using the GRAMI model and high spatial resolution imagery

Monitoring crop conditions and forecasting crop yields are both important for assessing crop production and for determining appropriate agricultural management practices; however, remote sensing is limited by the resolution, timing, and coverage of satellite images, and crop modeling is limited in its application at regional scales. To resolve these issues, the Gramineae (GRAMI)-rice model, which utilizes remote sensing data, was used in an effort to combine the complementary techniques of remote sensing and crop modeling. The model was then investigated for its capability to monitor canopy growth and estimate the grain yield of rice (Oryza sativa), at both the field and the regional scales, by using remote sensing images with high spatial resolution. The field scale investigation was performed using unmanned aerial vehicle (UAV) images, and the regional-scale investigation was performed using RapidEye satellite images. Simulated grain yields at the field scale were not significantly different (p = 0.45, p = 0.27, and p = 0.52) from the corresponding measured grain yields according to paired t-tests (α = 0.05). The model’s projections of grain yield at the regional scale represented the spatial grain yield variation of the corresponding field conditions to within ±1 standard deviation. Therefore, based on mapping the growth and grain yield of rice at both field and regional scales of interest within coverages of a UAV or the RapidEye satellite, our results demonstrate the applicability of the GRAMI-rice model to the monitoring and prediction of rice growth and grain yield at different spatial scales. In addition, the GRAMI-rice model is capable of reproducing seasonal variations in rice growth and grain yield at different spatial scales.     

高灵敏度机载L波段微波辐射计探测海表盐度

海水盐度是研究大洋环流和全球气候变化的重要参数,L波段(1400-1427MHz)微波辐射计是进行海水盐度遥感的最有效工具.在该频段内,微波辐射亮度温度对海水盐度变化的灵敏度为0.5K/psu,而用于海洋研究的盐度分辨率为0.1-0.2psu,要求微波辐射计的灵敏度优于0.1K.高灵敏度机载L波段微波辐射计,应用数字增益波动自动补偿和温度补偿技术,保证了系统的稳定性,并采用高效的滤波技术有效地抑制了全球定位信号(GPS)对接收机的干扰,灵敏度达到0.08K.进行了多次航空和海上对比遥感试验,获得了分辨率优于0.2psu、误差精度0.42psu的测量结果.     

一种基于小波域的分形遥感影像压缩方法

解决大数据量遥感影像传输和存储问题的方案之一,是对遥感影像进行高保真快速压缩。通过对遥感影像进行小波变换产生子带频域之间的相似性,再利用分形方法对具有相似性的相邻高频子带进行编码,由高一级的子影像构造低一级的子影像,然后采用分形迭代解码和小坡变换的逆变换重建影像。实验结果表明,该方法在一定程度上能够提高压缩比,在压缩时间上比传统的分形方法有明显的缩短,并且压缩后影像信息的损失量较少,影像恢复的质量较好。     

一种全自动提取居民地的方法

对遥感影像中居民地提取的各种方法作了一般性的阐述,并在吸收这些算法优势的同时,提出了一种全自动实现遥感影像中居民地目标的提取方法：先求取非最大值抑制后的梯度图,而后进行高斯模糊处理,以提高影像上居民地区域纹理特征一致性;通过增强大津法和形态学的方法全方位腐蚀与膨胀,完成面状地物居民地的提取;最后进行图象矢量化处理。论述方法的同时,展示和分析了实验结果。     

《遥感图像处理》课程教学在地理学专业中的探索与实践

针对地理专业的自身特点和当前遥感图像处理的教学现状，提出了遥感图像处理课程在地理专业中的主要教学内容框架、教学方法以及教学改革方向，并对该课程的实践教学部分做了重点介绍，最后论述了该课程在地理学专业实践教学中的取得一些教改成果。     

基于高分辨率遥感数据的城市居住单元交通环境质量评价——以厦门市为例

居住单元是与人们生活密切相关的社区范围,其交通环境的优劣是衡量居住单元环境好坏的重要标准之一。高分辨遥感影像 的高空间分辨率特点为我们提供了研究居住单元交通环境的可能性。本文从区域交通环境、路网可达性出发,对城市居住单元交通 环境质量的遥感评价方法进行了研究,根据高分辨率遥感影像的特点构建了居住单元交通环境的多级指标评价体系,并对厦门市的 50个居住单元进行了评价分析。实验表明,利用高分辨率影像进行城市交通环境质量评价是一种成本较低,简便可行的方法。     

利用多分类器集成进行遥感影像分类

基于信息相关理论,根据相关度值动态调整分类器的组合和权重,建立了新型的多分类器集成规则,并应用于决策树分类器、BP人工神经网络分类器和SVM分类器的集成。通过对长沙城区TM影像的分类实验发现:①三种分类器的分类结果存在较明显的差异,水体区的差异像元最少,占水体总像元的15.12%,建设用地区的差异像元最多,占建设用地区像元的54.93%;②三种分类器均具有较高的分类精度,总体精度均超过了74%,而且分类器各有优势,决策树分类器能够较好地分出水体和建设用地,BP分类器能够较好地分出水体和林地,SVM分类器对水体、林地和建设用地均有较高的分类精度;③基于全信息相关度的多分类器集成分类法明显地提升了分类结果的精度,分类精度达到了85.71%,Kappa系数达到了80.56%。     

视觉皮层振荡神经网络在遥感影像分割中的应用

对于具有复杂场景的遥感影像,提出了基于各向异性扩散特征保持平滑的简化LEGION算法,弥补传统LEGION图像分割算法对噪声敏感的不足。对于多(高)光谱影像,则采用马氏距离计算光谱向量间的连接权得到扩展LEGION算法进行分割。实验结果表明,LEGION算法较传统的图像分割方法对单波段灰度遥感影像目标及其重要细节部分的分割效果更显著;扩展LEGION算法适用于多光谱遥感影像分割。     

一种利用K均值和SOM进行遥感图像分类的方法

探讨了一种将K均值算法和SOM神经网络算法相结合的方法,并将其应用于多光谱遥感图像分类,通过与K均值算法、ISODATA算法和SOM算法的对比实验,验证了该方法的有效性.