Using Thermal Remote Sensing in Environmental Studies

Thermovision is a relatively new method of remote sensing with applications in areas such as military operations, residential monitoring, technological process control and emergency management. Surprisingly, it has not seen much application in environmental studies. The article presents a method of using thermovision for topoclimatic studies. The method is based on the spatial distribution of land surface temperature (LST). The LST distribution indicates the amount of solar energy reaching the Earth surface and depends primarily on terrain shape and land cover types. By analyzing the LST distribution, one can determine spatial topoclimatic variability. The LST derived topoclimatic classification was compared with the theoretical topoclimatic classification based on heat balance. New classes of topoclimates were created and some of the existing types were diversified into more detailed subtypes. The analysis of selected lowland areas in north‐western Poland revealed that both land cover and terrain shape characteristics had a significant impact on the LST distribution, contrary to the expectation of land cover characteristics being more important than terrain shape. The article demonstrates the possibilities of using thermovision in environmental research and presents a new method of topoclimate delimitation based on thermal remote sensing data and geographical information systems (GIS) techniques comparing. The LST classification method with conventional methods based on DEM and land cover analysis.     

遥感影像时—空融合的“点”—“线”—“面”质量评价

遥感影像时—空融合可集成多源数据高空间分辨率和高时间分辨率互补优势,生成时间连续的高空间分辨率影像,在遥感影像的动态监测与时序分析等方面具有重要应用价值.然而,现有多数研究往往基于单一数据产品对时—空融合算法进行评价,而在实际生产应用中,需要验证算法在多种遥感产品数据的融合表现;此外,目前研究大多基于“单点时刻”进行评...     

利用Landsat TM影像进行地表温度像元分解

提出了一种基于Landsat TM的地表温度二次像元分解方法,将地表温度的空间分辨率从120 m提高到30 m。首先,利用地表类型的线性统计模型（E-DisTrad）获取初次分解子像元的地表温度,计算得到初次分解子像元的辐亮度;然后,利用面向对象的图像分割方法获取二次分解子像元的权重,实现对地表温度的二次分解;最后,采用升尺度再分解的验证方法进行精度分析,并选取了北京市TM影像进行实例分析。实验结果表明,二次像元分解模型不仅能有效地提高地表温度的空间分辨率,反映出不同地表类型地表温度的空间差异性,而且保证了像元分解前后能量值的一致性,非常适合于复杂地表覆盖地区的热红外波段遥感影像数据的降尺度处理。     

地形效应校正的遥感地热异常提取

在山地复杂地形条件下,利用热红外遥感获得的地表温度分布显著受到地形的影响,真实的地热异常信息往往难以识别,热红外遥感应用于山区地热勘探受到极大限制。以广东龙川地热勘查区为研究区,初步探讨了山地环境中如何抑制地形效应,以有效提取地热异常。首先,基于Landsat ETM+遥感数据反演地表温度,分析坡向和坡度两个地形因子与地表温度的关系;然后,在此基础上,将研究区的地表温度按坡向分成3个子区(阳坡、过渡坡和阴坡),根据阳坡地表温度与坡向的线性拟合关系将其校正到水平坡度上;最后,结合地质构造分布和地表覆被情况,在3个子区识别了4处地热异常,并与已知地热点进行比较验证。结果表明:坡向分区和阳坡坡度校正能够有效抑制地形效应,提高遥感地热异常识别精度,为山区地热资源的预测评价提供新思路。     

地表温度日变化模型偏差系数解算的地表温度降尺度

地表温度LST(Land Surface Temperature)是全球气候变化研究的关键参数,遥感是获取全球和区域尺度地表温度的一种切实可行手段,但现有的单一传感器无法提供高时空分辨率的LST数据,限制了遥感地表温度数据的深入广泛应用.现有的降尺度方法难以生成无缝高时空分辨率的地表温度数据,且降尺度效果易受高空间分辨...     

Landsat卫星热红外数据地表温度遥感反演研究进展

作为驱动地表与大气之间能量交换的关键物理量,地表温度在众多领域中都发挥着重要作用,包括气候变化、环境监测、蒸散发估算以及地热异常勘探等.Landsat热红外数据因其时间连续性和高空间分辨率等特点被广泛应用于地表温度反演中.本文详细地介绍了Landsat热红外传感器及其可用的数据与产品的现状,梳理了2001年-2020年...     

Downscaling of Land Surface Temperature Maps in the Texas High Plains with the TsHARP Method

High-resolution evapotranspiration (ET) maps can assist demand-based irrigation management. Development of high-resolution daily ET maps requires high-resolution land surface temperature (LST) images. Earth-observing satellite sensors such as the Landsat 5 Thematic Mapper (TM) and MODerate resolution Imaging Spectroradiometer (MODIS) provide thermal images that are coarser than simultaneously acquired visible and near-infrared images. In this study, we evaluated the TsHARP downscaling technique for its capability to downscale coarser LST images using finer resolution normalized difference vegetation index (NDVI) data. The TsHARP technique was implemented to downscale seven coarser scale (240, 360, 480, 600, 720, 840, and 960 m) synthetic images to a 120 m LST image. The TsHARP was also evaluated for downscaling a coarser 960 m LST image to 240 m to mimic MODIS datasets. Comparison between observed 120 m LST images and 120 m LST images downscaled from coarser 240, 360, 480, 600, 720, 840, and 960 m images yielded root mean square errors of 1.0, 1.3, 1.5, 1.6, 1.7, 1.8, and 1.9°C, respectively. This indicates that the TsHARP method can be used for downscaling coarser (960 m) MODIS-based LST images using finer Landsat (120 m) or MODIS (240 m)-derived NDVI images. However, the TsSHARP method should be evaluated further with real datasets before using it for an operational ET remote sensing program for irrigation scheduling purposes.     

Agriculture pest and disease risk maps considering MSG satellite data and land surface temperature

Pest risk maps for agricultural use are usually constructed from data obtained from in-situ meteorological weather stations, which are relatively sparsely distributed and are often quite expensive to install and difficult to maintain. This leads to the creation of maps with relatively low spatial resolution, which are very much dependent on interpolation methodologies. Considering that agricultural applications typically require a more detailed scale analysis than has traditionally been available, remote sensing technology can offer better monitoring at increasing spatial and temporal resolutions, thereby, improving pest management results and reducing costs. This article uses ground temperature, or land surface temperature (LST), data distributed by EUMETSAT/LSASAF (with a spatial resolution of 3 × 3 km (nadir resolution) and a revisiting time of 15 min) to generate one of the most commonly used parameters in pest modeling and monitoring: “thermal integral over air temperature (accumulated degree-days)”. The results show a clear association between the accumulated LST values over a threshold and the accumulated values computed from meteorological stations over the same threshold (specific to a particular tomato pest). The results are very promising and enable the production of risk maps for agricultural pests with a degree of spatial and temporal detail that is difficult to achieve using in-situ meteorological stations.     

Spatial analysis of high-resolution urban thermal patterns in Vojvodina,Serbia

Main objective of this study was to establish a relationship between land cover and land surface temperature (LST) in urban and rural areas. The research was conducted using Landsat, WorldView-2 (WV-2) and Digital Mapping Camera. Normalised difference vegetation index and normalised difference built-up index were used for establishing the relation between built-up area, vegetation cover and LST for spatial resolution of 30 m. Impervious surface and vegetation area generated from Digital Mapping Camera from Intergraph and WV-2 were used to establish the relation between built-up area, vegetation cover and LST for spatial resolutions of 0.1, 0.5 and 30 m. Linear regression models were used to determine the relationship between LST and indicators. Main contribution of this research is to establish the use of combining remote sensing sensors with different spectral and spatial resolution for two typical settlements in Vojvodina. Correlation coefficients between LST and LST indicators ranged from 0.602 to 0.768.     

基于AutoCAD的遥感影像处理方法及其在电力工程中的应用

卫星遥感和低空遥感等多平台多传感器遥感技术的快速发展,使得多层次遥感影像数据越来越丰富,越来越廉价,使其在电力工程的前期选址选线、可行性研究、施工图等设计过程中的使用越来越广泛。针对大多数电力工程主要采用AutoCAD平台而丢失了遥感影像的地理信息(图像的地理坐标,分辨率等),本文提出了一种新的方法,实现AutoCAD平台对遥感影像带地理参考插入,并以插件的形式实现与系统平台的对接,实现了将遥感影像引入到现有基于AuotoCAD电力工程设计平台之中,简化制图流程,提高设计水平,达到一个平台即能完成所有操作的目的,并通过实际工程应用验证了方法的可靠与高效。     

Downscaling Landsat-8 land surface temperature maps in diverse urban landscapes using multivariate adaptive regression splines and very high resolution auxiliary data

ABSTRACT

We propose a method for spatial downscaling of Landsat 8-derived LST maps from 100(30?m) resolution down to 2–4?m with the use of the Multiple Adaptive Regression Splines (MARS) models coupled with very high resolution auxiliary data derived from hyperspectral aerial imagery and large-scale topographic maps. We applied the method to four Landsat 8 scenes, two collected in summer and two in winter, for three British towns collectively representing a variety of urban form. We used several spectral indices as well as fractional coverage of water and paved surfaces as LST predictors, and applied a novel method for the correction of temporal mismatch between spectral indices derived from aerial and satellite imagery captured at different dates, allowing for the application of the downscaling method for multiple dates without the need for repeating the aerial survey. Our results suggest that the method performed well for the summer dates, achieving RMSE of 1.40–1.83?K prior to and 0.76–1.21?K after correction for residuals. We conclude that the MARS models, by addressing the non-linear relationship of LST at coarse and fine spatial resolutions, can be successfully applied to produce high resolution LST maps suitable for studies of urban thermal environment at local scales.     

我国西部1∶5万地形图空白区地物解译研究——以西藏那曲其香错地区为例

我国西部部分地区1∶5万地形图数据尚未完成,给区域和国家经济发展带来极大不便。空间技术、计算机技术和信息技术的高速发展为遥感技术应用提供了技术支持,高分辨率影像获取能力的提高为大比例尺地物特征的提取提供了数据可能。本文采用SPOT5遥感影像,以1∶5万地形图图式标准为依据,进行青藏高原其香错地区地物判读,结果表明SPOT5能基本满足1∶5万地形图制图要求。为进一步提高判读结果,可采用多时相、多分辨率影像进行1∶5万地形图地物判读。     

Variographic analysis of tropical forest cover from multi-scale remotely sensed imagery

Tropical forest mapping is one of the major environmental concerns at global and regional scales in which remote sensing techniques are firmly involved. This study examines the use of the variogram function to analyse forest cover fragmentation at different image scales. Two main aspects are considered here: (1) analysis of the spatial variability structure of the forest cover observed at three different scales using fine, medium and coarse spatial resolution images; and (2) the study of the relationship between rescaled images from the finest spatial resolution and those of the medium and coarse spatial resolutions. Both aspects are analysed using the variogram function as a basic tool to calculate and interpret the spatial variability of the forest cover. An example is presented for a Brazilian tropical forest zone using satellite images of different spatial resolutions acquired by Landsat TM (30 m), Resurs MSU (160 m) and ERS ATSR (1000 m). The results of this study contribute to establishing a suitable spatial resolution of remotely sensed data for tropical forest cover monitoring.     

Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data

With the availability of high frequent satellite data, crop phenology could be accurately mapped using time-series remote sensing data. Vegetation index time-series data derived from AVHRR, MODIS, and SPOT-VEGETATION images usually have coarse spatial resolution. Mapping crop phenology parameters using higher spatial resolution images (e.g., Landsat TM-like) is unprecedented. Recently launched HJ-1 A/B CCD sensors boarded on China Environment Satellite provided a feasible and ideal data source for the construction of high spatio-temporal resolution vegetation index time-series. This paper presented a comprehensive method to construct NDVI time-series dataset derived from HJ-1 A/B CCD and demonstrated its application in cropland areas. The procedures of time-series data construction included image preprocessing, signal filtering, and interpolation for daily NDVI images then the NDVI time-series could present a smooth and complete phenological cycle. To demonstrate its application, TIMESAT program was employed to extract phenology parameters of crop lands located in Guanzhong Plain, China. The small-scale test showed that the crop season start/end derived from HJ-1 A/B NDVI time-series was comparable with local agro-metrological observation. The methodology for reconstructing time-series remote sensing data had been proved feasible, though forgoing researches will improve this a lot in mapping crop phenology. Last but not least, further studies should be focused on field-data collection, smoothing method and phenology definitions using time-series remote sensing data.     

热红外遥感地表温度与发射率地面验证进展

地表温度与发射率是地表—大气系统长波辐射和潜热通量交换的直接驱动力,是描述区域和全球尺度上地表能量平衡与水平衡的重要参数,其时空变化信息在气象预测、气候变化、水循环、地质勘探、农林监测和城市热环境等诸多领域具有广泛的应用.热红外遥感作为当前获取区域或全球尺度上地表温度和发射率的最有效手段之一,相较于传统的地面点位测量方...     

光谱指数趋势面的城市地表温度降尺度转换

针对城市及周边区域建造区和自然地表交织分布的特点,探讨了利用归一化植被指数(NDVI)和归一化建造指数(NDBI)构造趋势面的地表温度(LST)降尺度方法,以北京市市区及周边较平坦区域为例实现了LST自960 m向120 m的降尺度转换。分析了LST空间分布特征及NDVI、NDBI对地物的指示性特征;以北京市四至六环为界分析NDVI、NDBI趋势面对地表温度的拟合程度及各自的适用区域;在120 m、240 m、480 m和960 m 4个尺度上评价了NDVI、NDBI和NDVI+NDBI趋势面对LST的拟合程度和趋势面转换函数的尺度效应;对NDVI、NDBI和NDVI NDBI等3种方法的降尺度结果分覆盖类型、分区域对比评价。实验结果表明结合两种光谱指数的NDVI NDBI方法降尺度转换精度有所改善,改善程度取决于地表覆盖类型组合。     

基于时刻独立脉冲耦合神经网络的高空间分辨率摇感影像分割

高空间分辨率遥感影像中地物目标内部光谱信息复杂性的增强,使得传统基于光谱特征值的数据处理方法效果不再显著,影像分割为解决这一问题提供了一种思路,成为当前高空间分辨率遥感影像处理的研究焦点.时刻独立脉冲耦合神经网络具有状态相近、空间相邻神经元相互耦合同步脉冲激发和区域之间神经元脉冲激发时刻独立两大特点,已被应用于非遥感影像分割中,并取得较好效果.本文结合高空间分辨率遥感影像特点,通过对网络参数进行实验和分析,提出一个基于时刻独立脉冲耦合神经网络的高空间分辨率遥感影像分割方法,并利用空间分辨率0.3m的航空影像进行了数据试验,将分割结果进行讨论并与现有时刻独立脉冲耦合神经网络方法和ISODATA方法分割结果进行对比分析.结果表明:时刻独立脉冲耦合神经网络在高空间分辨率遥感影像分割处理中具有很好的应用前景.     

利用高分辨率遥感影像提取建筑物方法研究

随着航空航天技术的发展,人们获取的遥感影像数据的空间分辨率不断提高,同时获取难度大幅降低。在遥感数据日益普及的今天,利用影像处理与分析技术对高空间分辨率遥感影像中的感兴趣目标进行自动提取已经成为目前遥感领域研究的热点之一。建筑物作为与人类生活密切相关的主要人工地物,是城市发展的重要标志。目前,通过对高分辨率遥感影像进行建筑物自动提取已经成为建筑物信息获取的重要手段,而建筑物信息可用于进一步研究城市的扩张与发展、城市土地利用现状与变化、城市规划、城市热岛效应、人口估计与预测、灾害监测预警与评估等。     

迁移学习支持下的高分影像山地滑坡灾害解译模型

无人机低空遥感是近年来新兴的一种快速获取灾情信息的手段,如何利用无人机高分影像构建滑坡灾害解译模型是实现快速自动解译滑坡的关键。针对该问题,对比了多种影像特征提取方法,将迁移学习(TL)特征和支持向量机(SVM)引入到构建滑坡灾害自动解译模型中,提出了一种TL支持下的高分影像滑坡灾害解译模型。选取5·12汶川地震及4·20芦山地震系列无人机影像构建了滑坡灾害样本库并进行了实验,TL特征方法整体分类准确度ACC为95%,ROC达到0.98,识别准确率达到97%。结果表明,所提方法可用于高分影像滑坡自动解译,同时可用于大面积高分影像中快速山地滑坡灾害定位及检测。