多宝山矿田的地面波谱研究及其在铜矿预测中的应用

根据矿田内岩矿地质波谱与ＴＭ图象亮度的线性相关关系，论述了多宝山铜矿田内矿床的地面波谱特征，地空相关模型及ＴＭ数据的地空相关分析方法，在此基础上讨论了其在铜矿预测中的应用效果。     

A New Model for Quantifying Anisotropic Scale Invariance and for Decomposition of Mixing Patterns

A new power–law function has been derived to represent the relationship between area of the set consisting of wave numbers with spectral energy density above S (A(>S)) on the two-dimensional frequency plane and S. The power–law relation holds if the field concerned possessing isotropic scale invariance or generalized scaling invariance involves rotational and ratio-scale changing transforms. The equation is valid for dealing with common exploration geophysical and geochemical fields encountered in mineral exploration and environmental assessment. This power–law function not only provides a new model for characterizing anisotropic scaling invariance for generalized scaling field, for example, estimating the power exponent of power spectrum of generalized scale invariance measure in frequency domain, but also forms a theoretical base for the S–A filtering technique developed for decomposing a mixing field into components on the basis of distinct scaling properties in the frequency domain. It is demonstrated that the method has potential to become a general technique for image processing and pattern recognition.     

Leaf area index retrieval based on canopy reflectance and vegetation index in eastern China

The aim of this paper is to investigate the feasibility of using Landsat TM data to retrieve leaf area index (LAI). To get a LAI retrieval model based ground reflectance and vegetation index, detailed field data were collected in the study area of eastern China, dominated by bamboo, tea plant and greengage. Plant canopy reflectance of Landsat TM wavelength bands has been inversed using software of 6S. LAI is an important ecological parameter. In this paper, atmospheric corrected Landsat TM imagery was utilized to calculate different vegetation indices (VI), such as simple ratio vegetation index (SR), shortwave infrared modified simple ratio (MSR), and normalized difference vegetation index (NDVI). Data of 53 samples of LAI were measured by LAI-2000 (LI-COR) in the study area. LAI was modeled based on different reflectances of bands and different vegetation indices from Landsat TM and LAI samples data. There are certainly correlations between LAI and the reflectance of Tm3, TM4, TM5 and TM7. The best model through analyzing the results is LAI = 1.2097*MSR + 0.4741 using the method of regression analysis. The result shows that the correlation coefficient R2 is 0.5157, and average accuracy is 85.75%. However, whether the model of this paper is suitable for application in subtropics needs to be verified in the future.     

Pixel‐oriented land use classification in energy balance modelling

Mass and energy transfer between soil, vegetation and atmosphere is the process that allows to maintain an adequate energy and water balance in the earth–atmosphere system. However, the evaluation of the energy balance components, such as the net radiation and the sensible and latent heat fluxes, is characterized by significant uncertainties related to both the dynamic nature of heat transfer processes and surfaces heterogeneity. Therefore, a detailed land use classification and an accurate evaluation of vegetation spatial distribution are required for an accurate estimation of these variables. For this purpose, in the present article, a pixel‐oriented supervised classification was applied to obtain land use maps of the Basilicata region in Southern Italy by processing three Landsat TM and ETM+ satellite images. An accuracy analysis based on the overall accuracy index and the agreement Khat of Cohen coefficient showed a good performance of the applied classification methodology and a good quality of the obtained maps. Subsequently, these maps were used in the application of a simplified two‐source energy balance model for estimating the actual evapotranspiration at a regional scale. The comparison between the simulations made by applying the simplified two‐source energy balance model and the measurements of evapotranspiration at a lysimetric station located in the study area showed the applicability and the validity of the proposed methodology. Copyright © 2012 John Wiley & Sons, Ltd.     

Exploration of remotely sensed forest structure and ultrasonic range sensor metrics to improve empirical snow models

Current methods to estimate snow accumulation and ablation at the plot and watershed levels can be improved as new technologies offer alternative approaches to more accurately monitor snow dynamics and their drivers. Here we conduct a meta‐analysis of snow and vegetation data collected in British Columbia to explore the relationships between a wide range of forest structure variables – obtained from Light Detection and Ranging (LiDAR), hemispherical photography (HP) and Landsat Thematic Mapper – and several indicators of snow accumulation and ablation estimated from manual snow surveys and ultrasonic range sensors. By merging and standardizing all the ground plot information available in the study area, we demonstrate how LiDAR‐derived forest cover above 0.5 m was the variable explaining the highest percentage of absolute peak snow water equivalent (SWE) (33%), while HP‐derived leaf area index and gap fraction (45° angle of view) were the best potential predictors of snow ablation rate (explaining 57% of variance). This study reveals how continuous SWE data from ultrasonic sensors are fundamental to obtain statistically significant relationships between snow indicators and structural metrics by increasing mean r² by 20% when compared to manual surveys. The relationships between vegetation and spectral indices from Landsat and snow indicators, not explored before, were almost as high as those shown by LiDAR or HP and thus point towards a new line of research with important practical implications. While the use of different data sources from two snow seasons prevented us from developing models with predictive capacity, a large sample size helped to identify outliers that weakened the relationships and suggest improvements for future research. A concise overview of the limitations of this and previous studies is provided along with propositions to consistently improve experimental designs to take advantage of remote sensing technologies, and better represent spatial and temporal variations of snow. Copyright © 2013 John Wiley & Sons, Ltd.     

Remote estimation of terrestrial evapotranspiration by Landsat 5 TM and the SEBAL model in cold and high‐altitude regions: a case study of the upper reach of the Shule River Basin,China

Evapotranspiration (ET) is an important expenditure in water and energy balances, especially on cold and high‐altitude land surfaces. Daily ET of the upper reach of the Shule River Basin was estimated using Landsat 5 TM data and the Surface Energy Balance Algorithm for Land (SEBAL) model. Based on observations made at the Suli station, the algorithms of land surface temperature and soil heat flux in SEBAL were modified. Land surface temperature was retrieved and compared with ground truth via three methods: the radiative transfer equation method, the mono‐window algorithm, and the single‐channel method. We selected the best of these methods, mono‐window algorithm, for estimating ET. The average error of daily ET estimated by the modified SEBAL model and measured by the eddy covariance system was 16.4%, with a root‐mean‐square error of 0.52 mm d^?1. The estimated ET means were 3.09, 2.48, and 1.48 mm d^?1 on June 9 (DOY 160), June 25 (DOY 176), and July 27 (DOY 208) of the year 2010, respectively. The average estimated ET on the glacier surface of all days was more than 3 mm d^?1, a measurement that is difficult to capture in‐situ and has rarely been reported. This study will improve the understanding of water balance in cold, high‐altitude regions. Copyright © 2016 John Wiley & Sons, Ltd.     

基于光谱角匹配算法的水体信息提取研究

针对水体信息提取时,传统算法不能较好地解决山体阴影被误提为水体的问题,引入多用于高光谱数据处理的光谱角匹配算法,以Land-sat8 OLI(band1-band7)为数据源,以黄河小浪底水库周边区域为实验区,开展水体信息提取研究,结果显示:水体指数法和波段关系提取的结果中有大量的山体阴影信息,提取精度不超过30%,而光谱角匹配法提取结果受阴影的影响较小,提取精度在99%以上。     

Selection and quality assessment of Landsat data for the North American forest dynamics forest history maps of the US

Using the NASA Earth Exchange platform, the North American Forest Dynamics (NAFD) project mapped forest history wall-to-wall, annually for the contiguous US (1986–2010) using the Vegetation Change Tracker algorithm. As with any effort to identify real changes in remotely sensed time-series, data gaps, shifts in seasonality, misregistration, inconsistent radiometry and cloud contamination can be sources of error. We discuss the NAFD image selection and processing stream (NISPS) that was designed to minimize these sources of error. The NISPS image quality assessments highlighted issues with the Landsat archive and metadata including inadequate georegistration, unreliability of the pre-2009 L5 cloud cover assessments algorithm, missing growing-season imagery and paucity of clear views. Assessment maps of Landsat 5–7 image quantities and qualities are presented that offer novel perspectives on the growing-season archive considered for this study. Over 150,000+ Landsat images were considered for the NAFD project. Optimally, one high quality cloud-free image in each year or a total of 12,152 images would be used. However, to accommodate data gaps and cloud/shadow contamination 23,338 images were needed. In 220 specific path-row image years no acceptable images were found resulting in data gaps in the annual national map products.     

Evaluation of a rule-based compositing technique for Landsat-5 TM and Landsat-7 ETM+ images

Image compositing is a multi-objective optimization process. Its goal is to produce a seamless cloud and artefact-free artificial image. This is achieved by aggregating image observations and by replacing poor and cloudy data with good observations from imagery acquired within the timeframe of interest. This compositing process aims to minimise the visual artefacts which could result from different radiometric properties, caused by atmospheric conditions, phenologic patterns and land cover changes. It has the following requirements: (1) image compositing must be cloud free, which requires the detection of clouds and shadows, and (2) the image composite must be seamless, minimizing artefacts and visible across inter image seams. This study proposes a new rule-based compositing technique (RBC) that combines the strengths of several existing methods. A quantitative and qualitative evaluation is made of the RBC technique by comparing it to the maximum NDVI (MaxNDVI), minimum red (MinRed) and maximum ratio (MaxRatio) compositing techniques. A total of 174 Landsat TM and ETM+ images, covering three study sites and three different timeframes for each site, are used in the evaluation. A new set of quantitative/qualitative evaluation techniques for compositing quality measurement was developed and showed that the RBC technique outperformed all other techniques, with MaxRatio, MaxNDVI, and MinRed techniques in order of performance from best to worst.     

Study of high temperature targets identification and temperature retrieval experimental model in SWIR remote sensing based Landsat8

For surface features in short-wave infrared (SWIR, 1.3–3.0 μm) in remote sensing imagery, pixel values depict the total energy including reflection and emission. For surface features at normal temperature in SWIR band, emission energy can be ignored. While for surface features at high temperature in SWIR band, emission energy is equal to or even higher than the reflection energy. So remote sensing imagery of SWIR band can be used to separate emission and reflection energy as well as to realize temperature retrieval of high temperature targets. In this study, the seventh band (SWIR band) of Landsat8 OLI remote sensing imagery is used to perform the theoretical model research for temperature retrieval of high temperature targets. In the meantime, it is also used with the corresponding observation experiment of synchronization satellite to check the theoretical model. The result shows that the radiant flux density for mixed pixels with high temperature targets is higher than adjacent pixels without high temperature targets. Thus, the high temperature pixels can be identified in SWIR band. The retrieval results of temperature and fractional area for high temperature targets are consistent with reality. In the study, the result illustrates that it is effective to identify high temperature targets in remote sensing imagery of SWIR band and the model is appropriate for temperature retrieval use.