Daytime fire detection using airborne hyperspectral data

The shortwave infrared region of the electromagnetic spectrum, covering wavelengths from 1400 to 2500 nm, can include significant emitted radiance from fire. There have been relatively few evaluations of the utility of shortwave infrared remote sensing data, and in particular hyperspectral remote sensing data, for fire detection. We used an Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) scene acquired over the 2003 Simi Fire to identify the hyperspectral index that was able to most accurately detect pixels containing fire. All AVIRIS band combinations were used to calculate normalized difference indices, and kappa was used to compare classification ability of these indices for three different fire temperature ranges. The most accurate index was named the Hyperspectral Fire Detection Index (HFDI). The HFDI uses shortwave infrared bands centered at 2061 and 2429 nm. These bands are sensitive to atmospheric attenuation, so the impacts of variable elevation, solar zenith angle, and atmospheric water vapor concentration on HFDI were assessed using radiative transfer modeling. While varying these conditions did affect HFDI values, relative differences between background HFDI and HFDI for 1% fire pixel coverage were maintained. HFDI is most appropriate for detection of flaming combustion, and may miss lower temperature smoldering combustion at low percent pixel coverage due to low emitted radiance in the shortwave infrared. HFDI, two previously proposed hyperspectral fire detection indices, and a broadband shortwave infrared-based fire detection index were applied to AVIRIS scenes acquired over the 2007 Zaca Fire and 2008 Indians Fire. A qualitative comparison of the indices demonstrated that HFDI provides improved detection of fire with less variability in background index values.     

基于静止气象卫星数据的地表温度遥感估算

基于分裂窗算法和地表温度日周期变化模型,探讨了利用多时相热红外遥感数据反演地表温度的方法。首先,利用分裂窗算法及地表温度日周期变化形式,推导了多时相遥感数据反演地表温度的方法。其次,利用辐射传输模型(MODTRAN),以2006年夏季在禹城观测的3 d地表温度、气温及大气水汽数据做为输入参数、变化观测角及比辐射率,模拟了一日多个时刻与风云二号(F-2D)波谱响应函数一致的亮温数据,基于此,模拟数据库对所提算法进行了检验。最后,利用2010年9月30日FY-2D多时相热红外数据对新疆区域地表温度进行了反演,并与相应时刻的MODIS地表温度产品进行了比较。结果表明：利用模拟遥感数据反演地表温度,模拟值与估算值的相关系数达0.9,均方根误差在1.5 K以内;利用在轨FY-2D热红外数据反演得到的地表温度与MODIS温度产品趋势基本一致,两者的相关性达到了0.5,均方根误差为4.4 K。需要说明的是,此方法仅满足于晴朗无云的条件。     

MODIS数据地表温度反演劈窗算法比较

针对MODIS数据,分析比较了QIN和Wan-Dozier两种劈窗算法地表温度(LST)反演精度和误差分布。首先利用辐射传输模型MODTRAN4.0,结合TIGR大气廓线数据,评价两种算法绝对精度,然后基于误差传递理论分析评价二者的总精度,最后对两种算法的LST反演结果进行比较。研究表明针对所有廓线数据,两种算法绝对精度相差不大,但Wan-Dozier算法绝对精度受地表温度和水汽含量变化的影响程度要大于QIN算法;两种算法总精度相差不大,且主要误差源均为算法绝对精度和地表比辐射率精度,QIN算法反演结果对地表比辐射率的敏感性要略高于Wan-Dozier算法;两种算法得到研究区LST分布情况基本一致,均可表现空间LST分布差异,其中水体和裸土的LST反演结果差异较大,城镇和植被平均温度差异在0.5 K以内。     

大气、地表条件对目标-背景对比度的影响分析

本文利用辐射传输软件包MODTRAN．详细分析了大气模式、地表状况以及气溶胶模式等诸多参数对0．43～0．75μm波段范围内的目标背景对比度的影响，主要结果为；①季节变化给目标识别带来一定程度的影响，在同一种大气模式下，在冬季比在夏季更容易识别目标，但是季节变化仅改变C的大小，且变化幅度较小；②不同的大气模式下的C值随波长变化的分布形式发生变化，纬度较高的大气模式下C值较大，或者变化趋势较为明显；③地面反照率对C值的影响较小，同一波长对应的对比度的相对变化较小；④气溶胶模式的变化不仅影响C值的大小，而且改变了C值随波长的分布情况，但是这种影响与其他影响因素相比是很小的．     

探测条件对红外热成像系统MRTD影响的研究

在考虑目标的尺寸、探测等级、探测概率及大气环境等因素情况下,研究了热成像系统的最小可分辨温差（MRTD）,建立了一个改进的MRTD模型。由于大气透过率与波长和距离相关,利用光谱等分法计算大气透过率,将探测波长范围等分为一个个窄波段,利用MODTRAN软件计算在一定大气条件和距离下每个窄波段的大气透过率。通过调用对应波长的透过率,计算了探测波长范围内热成像系统的MRTD,提高了MRTD的准确性和适用性。     

Validation of clear-sky radiances over oceans simulated with MODTRAN4.2 and global NCEP GDAS fields against nighttime NOAA15-18 and MetOp-A AVHRR data

An accurate and globally representative forward radiative transfer model (RTM) is needed to explore improvements in sea surface temperature (SST) retrievals from spaceborne infrared observations. This study evaluates the biases in top-of-atmosphere (TOA) brightness temperatures (BT) modeled with the moderate resolution transmission (MODTRAN4.2) band RTM, bounded by a Fresnel's reflective flat sea surface. This model is used to simulate global clear-sky Advanced Very High Resolution Radiometer (AVHRR) nighttime BTs from NOAA-15 through 18 and MetOp-A platforms for one full day of 18 February 2007. Inputs to RTM (SST fields and vertical profiles of atmospheric relative humidity, temperature, pressure, and geopotential height) are specified from the National Centers for Environmental Prediction's (NCEP) Global Data Assimilation System (GDAS) data. Model BTs in AVHRR channels 3B (3.7 μm), 4 (11 μm), and 5 (12 μm) are then compared with their respective measured counterparts, available in the NESDIS operational SST files. Ideally, the RTM should match the observations, but in fact, the modeled BTs are biased high with respect to the AVHRR BTs. The “Model minus Observation” (M − O) bias ranges from about 0 to 2 K, depending upon spectral band, view zenith angle, and sea and atmosphere state at the retrieval point. The bias asymptotically decreases towards confidently clear-sky conditions, but it never vanishes and invariably shows channel-specific dependencies on view zenith angle and geophysical conditions (e.g., column water vapor and sea-air temperature difference). Fuller exploration of the potential of the current RTM (e.g., adding global vertical aerosol profiles) or improvements to its input (NCEP SST and atmospheric profiles) may reduce this bias, but they cannot fully reconcile its spectral and angular structure. The fact that the M − O biases are closely reproducible for five AVHRR sensors flown onboard different platforms adds confidence in the validation approach employed in this study. We emphasize the need for establishing a globally adequate forward RTM for the use in SST modeling and retrievals. A first test of the RTM adequacy is its ability, when used in conjunction with the global fields from the numerical weather prediction models, to reproduce the TOA clear-sky radiances measured by satellite sensors.     

Assessing the coupling between surface albedo derived from MODIS and the fraction of diffuse skylight over spatially-characterized landscapes

In this effort, the MODerate Resolution Imaging Spectroradiometer (MODIS) (Collection V005) Bidirectional Reflectance Distribution Function (BRDF)/Albedo algorithm is used to retrieve instantaneous surface albedo at a point in time and under specific atmospheric conditions. These retrievals are then used to study the role that the fraction of diffuse skylight plays under realistic scenarios of anisotropic diffuse illumination and multiple scattering between the surface and atmosphere. Simulations of the sky radiance using the MODTRAN®5.1 radiative transfer model were performed under different aerosol optical properties, illumination conditions, and surface characteristics to describe these effects on surface albedo retrievals from MODIS. This technique was examined using a validation scheme over four measurement sites with varied aerosol levels and landscapes, ranging from croplands to tundra ecosystems, and over extended time periods. Furthermore, a series of geostatistical analyses were performed to examine the types of spatial patterns observed at each measurement site. In particular, Enhanced Thematic Mapper Plus (ETM+) retrievals of surface albedo were acquired to analyze the change in variogram model parameters as a function of increased window-size. Results were then used to assess the degree to which a given point measurement is able to capture the intrinsic variability at the scale of MODIS observations. Assessments of MODIS instantaneous albedos that account for anisotropic multiple scattering, over snow-free and snow-covered lands and at all diurnal solar zenith angles, show a slight improvement over the albedo formulations that treat the downwelling diffuse radiation as isotropic. Comparisons with field measurements show biases improving by 0.004-0.013 absolute units (root-mean-squared error) or 0.1%-2.0% relative error.     

基于Landsat 5 TM数据反演地表温度

利用Landsat 5第6波段数据,根据单窗算法反演合肥市地表温度。针对在夏季大气中水汽含量比较大的问题,利用Modtran中纬度夏季模式模拟出在大气水汽含量位于0.4 -5.4 区间时,大气透过率与大气水汽含量之间的关系。利用模拟出的结果及MODIS水汽产品对TM图像进行大气校正并反演地表温度,选择均匀区的反演温度值与MODIS温度产品比较,比较结果显示植被均匀区的温差在1K以内,城镇建筑均匀区的温差在1.1K以内,故可用此方法对TM图像进行大气校正进而反演地表温度。     

典型大气条件下的目标-背景对比度的计算分析

大气中目标—背景对比度是进行目标探测识别的一个重要参量,本文从辐射传输理论 出发,利用辐射传输软件包MODTRAN ,计算了具体大气状况下的目标—背景对比度,并且计算了典型大气条件下可见光到红外波段上( 0. 4～14μm) 的目标—背景对比度,分析了目标背景对比度对波长的依赖关系。