塔里木盆地沙尘暴卫星云图特征分析

利用美国ＮＯＡＡ－Ｎ系列卫星１９７８—１９８７年的云图资料和近几年的遥感图像资料，对塔里木盆地沙尘暴的卫星云图特征进行分析，对沙漠沙尘暴的时空分布特征进行探讨。     

多步骤分类法在上海市闵行区土地利用/覆盖信息提取中的应用

遥感图像分类是进行土地利用/覆盖变化研究的基础。采用多步骤分类法对上海市闵行区4期遥感图像进行分类，总分类精度达到了85%。     

农牧交错带土地沙化遥感监测

以内蒙古多伦县为研究对象,作时隔5a的两次遥感调查,对内蒙古东南部农牧业交错带的土地沙化进行监测。建立了土地沙化分类体系,把土地沙化与土地利用紧密联系,依据植被指数概念,采用了一组比值组合,有效地将土地沙化类别分层分离。通过第一次遥感调查,了解到多伦县由于过度耕种和过度放牧,致使土地沙化十分严重。经第二次调查对比分析,退耕还林、退牧还草的治沙效果显著,土地沙化发展趋势得到遏制,遥感监测为地方政府的防沙治沙起了重要作用。     

遥感技术在浙江东部穿山半岛地区活动断裂调查中的应用

在区域遥感影像图制作和初步地质解译的基础上,结合相关地质资料,建立了浙江东部穿山半岛地区活动断裂的判译标志,对区域解译出的断裂进行了详细的研究,成功地厘定了穿山半岛地区的活动断裂。     

Compositional interpretation of PFS/MEx and TES/MGS thermal infrared spectra of Phobos

The origin of the Martian satellites presents a puzzle of long standing. Addressing the composition of Phobos will help constrain theories of its formation. Visible and near-infrared spectra of Phobos lack deep absorption features, making the compositional interpretation a tricky task. PFS/MEx and TES/MGS observations in the thermal infrared show several spectral features that can be used to investigate the composition of the surface. Our results show that the majority of the spectra are consistent with the presence of phyllosilicates, particularly in the area northeast of Stickney. This area corresponds to the “blue” region as defined by Murchie et al. (1999). Analysis of PFS and TES observations in the “red” region defined by Murchie et al. (1999) are consistent with tectosilicates, especially feldspars/feldspathoids. We discuss several physical and chemical mechanisms that can act to eliminate or reduce the strength of bands in the VIS/NIR spectra, with possibly little or no effect in the mid-IR. Comparison of the TES and PFS data to the meteorites shows that no class of chondritic meteorites provide significant agreement with the spectral features observed. The lack of consistency of the PFS and TES spectra to analogs of ultraprimitive materials (organic residues) suggests that an origin via capture of a transneptunian object is not supported by these observations, although it cannot be completely ruled out. Derived surface temperatures from PFS and TES data are in very good agreement with brightness temperatures derived from Viking orbiter measurements, Earth-based observations, and values predicted by numerical models. Our results show that the surface temperature of Phobos varies with solar incidence angle and heliocentric distance, reconciling the different results.We collect and summarize the compositional clues for the origin of Phobos discussed in this paper, including our results. Currently, the most likely scenario is the in-situ formation of Phobos, although a capture of achrondrite-like meteorites is not ruled out.     

非合作目标分光路激光测距中距离值的确定方法

摘要：卫星激光测距是目前空间大地测量的主要手段之一,在地球动力学和大地测量学等领域有广泛的应用。云南天文台即将开展非合作目标分光路激光测距试验,即激光接收和发射光路系统分别由相邻较近的两台望远镜完成。针对分光路非合作目标激光测距系统,提出了一种非合作目标分光路激光测距中测距值的确定方法,并给出了相应的计算公式,为分光路激光测距数据处理提供参考。     

Modelling the spectral response of the desert tree Prosopis tamarugo to water stress

In this paper, we carried out a laboratory experiment to study changes in canopy reflectance of Tamarugo plants under controlled water stress. Tamarugo (Prosopis tamarugo Phil.) is an endemic and endangered tree species adapted to the hyper-arid conditions of the Atacama Desert, Northern Chile. Observed variation in reflectance during the day (due to leaf movements) as well as changes over the experimental period (due to water stress) were successfully modelled by using the Soil-Leaf-Canopy (SLC) radiative transfer model. Empirical canopy reflectance changes were mostly explained by the parameters leaf area index (LAI), leaf inclination distribution function (LIDF) and equivalent water thickness (EWT) as shown by the SLC simulations. Diurnal leaf movements observed in Tamarugo plants (as adaptation to decrease direct solar irradiation at the hottest time of the day) had an important effect on canopy reflectance and were explained by the LIDF parameter. The results suggest that remote sensing based assessment of this desert tree should consider LAI and canopy water content (CWC) as water stress indicators. Consequently, we tested fifteen different vegetation indices and spectral absorption features proposed in literature for detecting changes of LAI and CWC, considering the effect of LIDF variations. A sensitivity analysis was carried out using SLC simulations with a broad range of LAI, LIDF and EWT values. The Water Index was the most sensitive remote sensing feature for estimating CWC for values less than 0.036 g/cm², while the area under the curve for the spectral range 910–1070 nm was most sensitive for values higher than 0.036 g/cm². The red-edge chlorophyll index (CI_red-edge) performed the best for estimating LAI. Diurnal leaf movements had an effect on all remote sensing features tested, particularly on those for detecting changes in CWC.     

Winter wheat yield forecasting in Ukraine based on Earth observation,meteorological data and biophysical models

Ukraine is one of the most developed agriculture countries and one of the biggest crop producers in the world. Timely and accurate crop yield forecasts for Ukraine at regional level become a key element in providing support to policy makers in food security. In this paper, feasibility and relative efficiency of using moderate resolution satellite data to winter wheat forecasting in Ukraine at oblast level is assessed. Oblast is a sub-national administrative unit that corresponds to the NUTS2 level of the Nomenclature of Territorial Units for Statistics (NUTS) of the European Union. NDVI values were derived from the MODIS sensor at the 250 m spatial resolution. For each oblast NDVI values were averaged for a cropland map (Rainfed croplands class) derived from the ESA GlobCover map, and were used as predictors in the regression models. Using a leave-one-out cross-validation procedure, the best time for making reliable yield forecasts in terms of root mean square error was identified. For most oblasts, NDVI values taken in April–May provided the minimum RMSE value when comparing to the official statistics, thus enabling forecasts 2–3 months prior to harvest. The NDVI-based approach was compared to the following approaches: empirical model based on meteorological observations (with forecasts in April–May that provide minimum RMSE value) and WOFOST crop growth simulation model implemented in the CGMS system (with forecasts in June that provide minimum RMSE value). All three approaches were run to produce winter wheat yield forecasts for independent datasets for 2010 and 2011, i.e. on data that were not used within model calibration process. The most accurate predictions for 2010 were achieved using the CGMS system with the RMSE value of 0.3 t ha⁻¹ in June and 0.4 t ha⁻¹ in April, while performance of three approaches for 2011 was almost the same (0.5–0.6 t ha⁻¹ in April). Both NDVI-based approach and CGMS system overestimated winter wheat yield comparing to official statistics in 2010, and underestimated it in 2011. Therefore, we can conclude that performance of empirical NDVI-based regression model was similar to meteorological and CGMS models when producing winter wheat yield forecasts at oblast level in Ukraine 2–3 months prior to harvest, while providing minimum requirements to input datasets.     

A comprehensive review of earthquake-induced building damage detection with remote sensing techniques

Earthquakes are among the most catastrophic natural disasters to affect mankind. One of the critical problems after an earthquake is building damage assessment. The area, amount, rate, and type of the damage are essential information for rescue, humanitarian and reconstruction operations in the disaster area. Remote sensing techniques play an important role in obtaining building damage information because of their non-contact, low cost, wide field of view, and fast response capacities. Now that more and diverse types of remote sensing data become available, various methods are designed and reported for building damage assessment. This paper provides a comprehensive review of these methods in two categories: multi-temporal techniques that evaluate the changes between the pre- and post-event data and mono-temporal techniques that interpret only the post-event data. Both categories of methods are discussed and evaluated in detail in terms of the type of remote sensing data utilized, including optical, LiDAR and SAR data. Performances of the methods and future efforts are drawn from this extensive evaluation.     

Uncertainty analysis of remote sensing of colored dissolved organic matter: Evaluations and comparisons for three rivers in North America

The uncertainties involved in remote sensing inversion of CDOM (Colored Dissolved Organic Matter) were analyzed in estuarine and coastal regions of three North American rivers: Mississippi, Hudson, and Neponset. Water optical and biogeochemical properties, including CDOM absorption and above-surface spectra, were collected in very high resolution. CDOM’s concentrations (a_g(440), absorption coefficient at 440 nm) were inverted from EO-1 Hyperion images, using a quasi-analytical algorithm for CDOM (QAA-CDOM). Uncertainties are classified to five levels, in which the underwater measurement uncertainty (level 1), image preprocessing uncertainty (level 4) and inverse model uncertainty (level 5) were evaluated. Results indicate that at level 1, in situ CDOM measurement is significant with 0.1 in the unit of QSU and 0.01 in the unit of a_g(440) (m⁻¹). At level 4, surface wave is a potential uncertainty source for high-resolution images in estuarine and coastal regions. The remote sensing reflectance of wavy water is about 10 times of the truth. At level 5, the overall uncertainty of QAA-CDOM inversion is 0.006 m⁻¹, with accuracy R² = 0.77, k = 1.1 and RMSE_log = 0.33 m⁻¹. The correlations between uncertainties and other water properties indicate that the large uncertainty in some rivers, such as the Neponset and Atchafalaya, might be caused by high-concentration chlorophyll or sediments. The relationships among the three level uncertainties show that the level 1 uncertainty generally does not propagate into level 4 and 5, but the large uncertainty at level 4 usually introduce large uncertainty at level 5.