Evaluation of Goddard’s LiDAR,hyperspectral, and thermal data products for mapping urban land-cover types

Goddard’s LiDAR (Light Detection And Ranging), hyperspectral and thermal (G-LiHT) airborne imager is a new system to advance concepts of data fusion for worldwide applications. A recent G-LiHT mission conducted in June 2016 over an urban area opens a new opportunity to assess the G-LiHT products for urban land-cover mapping. In this study, the G-LiHT hyperspectral and LiDAR-canopy height model (LiDAR-CHM) products were evaluated to map five broad land-cover types. A feature/decision-level fusion strategy was developed to integrate two products. Contemporary data processing techniques were applied, including object-based image analysis, machine-learning algorithms, and ensemble analysis. Evaluation focused on the capability of G-LiHT hyperspectral products compared with multispectral data with similar spatial resolution, the contribution of LiDAR-CHM, and the potential of ensemble analysis in land-cover mapping. The results showed that there was no significant difference between the application of the G-LiHT hyperspectral product and simulated Quickbird data in the classification. A synthesis of G-LiHT hyperspectral and LiDAR-CHM products achieved the best result with an overall accuracy of 96.3% and a Kappa value of 0.95 when ensemble analysis was applied. Ensemble analysis of the three classifiers not only increased the classification accuracy but also generated an uncertainty map to show regions with a robust classification as well as areas where classification errors were most likely to occur. Ensemble analysis is a promising tool for land-cover classification.     

Pleiades遥感数据融合在地理国情调查中的应用

地理国情调查工作中,数据源的选取和预处理在工程过程中占重要地位,由于航空影像数据获取时相受到一定的限制,因此,航天遥感数据作为影像有效补充。普利亚(Pleiades)航天数据以其优越的数据质量在地理国情调查工作中得到有效应用。本文从普利亚(Pleiades)地理国情调查应用出发,以探索全波段与多波段数据融合方法,提高数据可用性为目的,研究了遥感数据的几种融合方法,并对融合后的数据进行定量评价,从中选取最优方案,从而提高普利亚(Pleiades)数据的空间分辨率,达到数据分割,解译准确、精确的目标。     

Mapping abandoned agricultural land in Kyzyl-Orda,Kazakhstan using satellite remote sensing

In many regions worldwide, cropland abandonment is growing, which has strong and known environmental and socio-economic consequences. Yet, spatially explicit information on the spatial pattern of abandonment is sparse, particularly in post-Soviet countries of Central Asia. When thriving reaching for key Millennium Development Goals such as food security and poverty reduction, the issue of cropland abandonment is critical and therefore must be monitored and limited, or land use transformed into an alternative one. Central Asia experienced large changes of its agricultural system after the collapse of the Soviet Union in 1991. Land degradation, which started already before independence, and cropland abandonment is growing in extent, but their spatial pattern remains ill-understood. The objective of this study was to map and analyse agricultural land use in the irrigated areas of Kyzyl-Orda, southern Kazakhstan, Central Asia. For mapping land use and identifying abandoned agricultural land, an object-based classification approach was applied. Random forest (RF) and support vector machines (SVM) algorithms permitted classifying Landsat and RapidEye data from 2009 to 2014. Overlaying these maps with information about irrigated land parcels, installed during the Soviet period, allowed indicating abandoned fields. Fusing the results of the two approaches, RF and SVM, resulted in classification accuracies of up to 97%. This was statistically significantly higher than with RF or SVM alone. Through the analysis of the land use trajectories, abandoned agricultural fields and a clear indication of abandoned land were identified on almost 50% of all fields in Kyzyl-Orda with an accuracy of approximately 80%. The outputs of this study may provide valuable information for planners, policy- and decision-makers to support better-informed decision-making like reducing possible environmental impacts of land abandonment, or identifying areas for sustainable intensification or re-cultivation.     

基于高分一号卫星影像的多种融合方法比较

以扬州地区为试验区,利用高分一号(GF-1)卫星全色及多光谱影像数据开展融合试验,将PCA、GS、Pansharping 3种传统融合方法与Elbakary、Proxy-sharpening和SFIM等以全色波段为尺度因子的融合方法进行对比,从光谱继承性和空间融入度两方面分析融合效果,寻找适合GF-1号卫星影像的较佳融合方法。结果表明,传统融合方法在光谱继承性和空间融入度两方面的评价结果中保持一致,且Pansharping方法在定性和定量分析中都为最优;以全色波段为尺度因子的融合方法其空间融入度随着光谱继承性的降低而增加;基于不同移动窗口大小的SFIM方法其光谱继承度和空间融入度均属最优,其中,移动窗口为5×5和7×7时,整体融合精度高于其他方法。     

熔融制样X射线荧光光谱法测定碳化钨及其废料中的钨

碳化钨被广泛应用于硬质合金生产材料,其钨的含量关系碳化钨的性能及货物结算,生产过程中因配比错误、打磨等原因还会产生大量的含钨废料,从废料中回收钨能弥补钨资源的不足,因此测定碳化钨及其废料中的钨具有重要意义。目前碳化钨中钨含量的测定通常参照国家标准采用辛可宁重量法,准确度较高,但只适用于检测三氧化钨含量大于50%的样品,且过程繁琐,工作量大。本文根据碳化钨在高温下易氧化的特点,借鉴国际标准ASTM B890-07,提出将碳化钨转化为三氧化钨,采用熔融制样-X射线荧光光谱(XRF)测定钨含量的方法。即碳化钨先在马弗炉中灼烧至钨完全转化为三氧化钨,再加入五氧化二钽作内标,四硼酸钠-四硼酸锂为混合熔剂,在1150℃下熔融制成试样片,用XRF法测定钨的含量。结果表明,最佳的灼烧温度为800℃,在此温度下碳化钨完全转化成三氧化钨,有效地解决了碳化钨熔融制样的难点;方法精密度(RSD)为0.2%,实际样品的测定值与国家标准方法(辛可宁重量法)一致,可测量三氧化钨含量的范围为30%~100%,且分析时间大大缩短,可满足大量实际样品检测的需要。     

一种拓展的半物理时空融合算法及其初步应用

Landsat 5卫星较低的时间分辨率(16天)使得其很难获得大区域的、时相一致的清晰影像数据集。本文发展了一种基于半物理模型的时空融合算法-即乘性调制融合算法,并借助多时序的MODIS反射率数据来生成多时相的Landsat TM/ETM+反射率合成影像,经镶嵌后得到区域尺度的高时空分辨率地表反射率数据集(Landsat TM/ETM+)。本文利用吉林省2006年—2011年的Landsat 5 TM地表反射率数据以及500 m的MOD09A1反射率产品来生成3个时相的Landsat 5 TM反射率合成数据,从而获得研究区在上述时相下地表反射率数据的镶嵌图。初步分析表明,所生成的Landsat 5 TM反射率数据的光谱分布特征与MOD09A1反射率数据较为一致,且图像在整体上光谱特征的连续性较好。     

电感耦合等离子体发射光谱法同时测定锡矿石中锡钨钼铜铅锌

锡矿石是难分解的矿物,共生与伴生元素多,其中的锡钨钼在单一盐酸溶液中易沉淀,准确测定锡矿石中的主次量元素一直是分析技术难点。本文以过氧化钠为熔剂,高温熔融样品,在酒石酸-盐酸-双氧水体系中进行酸化,选用该矿种中仅含有少量的钴作为内标,建立了电感耦合等离子体发射光谱同时测定锡矿石中锡钨钼铜铅锌的分析方法。方法线性范围为0.00~40.0 mg/L;方法检出限为锡10 mg/kg,钨30 mg/kg,钼3.3 mg/kg,铜12 mg/kg,铅15 mg/kg,锌40 mg/kg;方法精密度(n = 9)小于5.0%,实际样品的测定值与传统化学方法及国家标准方法的测定值吻合较好。本方法采用过氧化钠碱熔锡矿石,溶样彻底,并省去了氢氟酸挥发硅的蒸酸过程,节约了样品处理时间;采用酒石酸-双氧水-盐酸体系溶解熔融物,有利于溶液中的锡钨钼形成稳定的络合物,避免了单纯盐酸体系下产生钨酸、钼酸和锡酸沉淀导致测定结果偏低的问题。     

蓝晶石矿中氟钠镁铝硅铁钛钾钙元素的X射线荧光光谱分析

蓝晶石的分析通常采用碱熔体系,重量、容量、比色、原子吸收光谱法、离子选择电极法等多种分析手段进行单独测定,对含有刚玉、金红石矿物的难熔性蓝晶石样品,这种分析方法常因熔矿不完全而导致测定结果偏低,而且分析手续冗长,操作复杂,不能满足地质测试的需要。本文采用玻璃熔融法制样,建立了X射线荧光光谱法同时测定蓝晶石矿中F、Na、Mg、Al、Si、Fe、Ti等主量元素的分析方法。以不同矿种的标准物质和自制含多种矿物组分的蓝晶石管理样拟合校准曲线,对玻璃熔融法的稀释比、熔矿温度及对F元素的影响因素等测定条件进行优化,确定选择样品与四硼酸锂-偏硼酸锂混合熔剂的稀释比为1:10,在1050℃温度下实际样品熔矿完全,各元素的分析结果与化学分析法的测定值基本吻合。方法检出限小于0.05%,方法精密度(RSD, n=7)小于4.5%。本法操作简单,重现性好,准确可靠,解决了难熔样品的熔矿问题,同时也很好地解决了传统方法费时、耗材、不能同时测定多元素的问题。     

电感耦合等离子体发射光谱法测定锆钛砂矿中铪钛锆

锆钛砂矿是一种极难消解的矿物,除氢氟酸外,几乎不溶于所有的酸,由于矿物中铪、钛、锆含量高,而铪、钛、锆又易水解形成难溶的偏铪酸、偏钛酸、偏锆酸析出,样品前处理给定量分析带来很大困难。传统的化学法繁琐费时,且只能进行锆(铪)合量的分析。本文建立了电感耦合等离子体发射光谱法(ICP-AES)测定锆钛砂矿样品中铪、钛、锆的方法。通过筛选四种溶矿方法,确定在刚玉坩埚中用过氧化钠于700℃时熔融样品,硝酸-EDTA浸取盐分前处理矿物,利用EDTA的强络合性质可使铪、钛、锆形成稳定的可溶络合物,制备出有代表性的样品溶液;在ICP-AES分析中,采用Re作为内标和大的高频功率消除了基体效应的影响。方法的精密度(RSD,n=11)低于1.3%,Hf、Ti、Zr的检出限分别为0.97 μg/g、0.86 μg/g、0.33 μg/g。实际样品的测定值与化学分析方法和X射线荧光光谱法的测定结果基本吻合。。本方法采用刚玉坩埚熔矿,提高了样品处理数量,降低了分析成本,适用于难熔锆钛砂矿样品的快速定量分析。     

Ground peak identification in dense shrub areas using large footprint waveform LiDAR and Landsat images

Large footprint waveform LiDAR data have been widely used to extract tree heights. These heights are typically estimated by subtracting the top height from the ground. Compared to the top height detection, the identification of the ground peak in a waveform is more challenging. This is particularly evident in ground detection in shrub areas, where the reflection of the shrub canopy may significantly overlap with the ground reflection. To tackle this problem, a novel method based on Partial Curve-Fitting (PCF) of the shrub peak was developed to detect the ground peak. Results indicated that the PCF method improves ground identification by 32–42%, compared to existing methods. To offer further improvement, a Multi-Algorithm Integration Classifier (MAIC) was built to fuse multiple ground peak algorithms and selectively apply the best method for each waveform plot. The PCF ground peak identification method along with the MAIC-based fusion is expected to significantly improve ground detection and shrub height estimation, thus assisting biodiversity, forest succession, and carbon sequestration studies, while offering an early example of future multiple algorithm integration.