森林资源监测中GF-2卫星影像波段配准误差分析

通过对GF-2卫星影像正射校正及波段模拟配准误差试验,分析GF-2卫星正射校正方法的选择以及不同配准误差下对GF-2卫星影像自动分类结果的影响;最后介绍GF-2遥感影像在森林资源监测应用中的初步测试。研究结果表明:正射校正时,当校正精度要求控制在RMS2时,控制点数量选择范围在85~95间较为合理,且控制点数在90个时,RMS值最小;经有理函数模型与卫片模型比较后,卫片模型校正精度较高;以目视判读为主时,实践中建议使用三次卷积重采样法输出结果最好;波段模拟配准误差试验中,配准误差与各地类面积变化间存在显著的线性关系;对于森林面积监测时,配准误差应小于0.3个像元。此研究可为新型国产卫星数据在森林资源监测中的应用提供参考。     

新冠病毒疫情期间复工复产卫星遥感监测

本文利用美国新一代极轨卫星NPP搭载的VIIRS载荷数据反演获得的热点数据,以及美国NASA的Aura卫星搭载的OMI载荷的二氧化氮对流层柱浓度资料,对中国地区2020-01—02及2019年同期,以及抗击疫情期间和后期复工复产的情况进行监测。监测结果表明：（1）2020-02较1月份及2019年同期相比,工业企业的耗能水平却出现显著降低,NO₂浓度呈现显著下降趋势。（2）全国工业热源点数和分布范围及密度自2020-02-03正式复工后稳步提升。湖北省除武汉、黄石和鄂州地区仍可监测到少数热点外,其他地区2月份均未再监测到任何热点,也证明了湖北基本没有复工迹象。（3）疫情得到基本控制之后,京津冀地区工业热源点有显著增加,但新增区域一般仍处于低能耗水平。从全国大气NO₂柱浓度监测来看,复工开始前后与2019年同期相比,污染物浓度均呈现明显下降趋势,说明复工强度有限。（4）到3月初工业热源点及其能耗水平出现显著升高,中国大部分地区的热源企业虽已逐步复工生产,说明工业产能较2月有显著提升,但生产规模或产能并未完全恢复。     

Sentinel-2卫星落叶松林龄信息反演

林龄结构信息能够有效反映区域森林群落不同生长阶段的固碳能力,对于评估森林生态系统的健康状况具有重要意义。本研究以中国温带典型优势树种落叶松林为研究对象,分别选择其芽萌动期、展叶期和落叶期时段的Sentinel-2影像,采用多元线性回归（MLR）、随机森林（RF）、支持向量机回归（SVR）、前馈反向传播神经网络（BP）以及多元自适应回归样条（MARS）等5种方法依次构建落叶松林龄反演模型。通过相关性分析首先确定最佳遥感反演物候期,并在此基础上根据相关性差异筛选出5个最优特征变量用于模型反演,分别为冠层含水量（CWC）,归一化水体指数（NDWI）,叶面积指数（LAI）,光合有效辐射吸收率（FAPAR）和植被覆盖度（FVC）。研究结果表明,展叶期为落叶松林最佳遥感反演物候期。除植被衰减指数（PSRI）以及落叶期的NDVI、RVI外,落叶松林龄与各指标之间均呈负相关关系,其中与冠层含水量（CWC）的相关性最高,pearson相关系数达到-0.74（p<0.01）。此外,不同模型反演结果表明,随机森林模型（RF）为最佳落叶松林龄估测模型,其平均决定系数R²和平均均方根误差RMSE分别为0.89和2.91 a;多元线性回归模型（MLR）的林龄估测结果最差,其平均决定系数R²和平均均方根误差RMSE仅为0.57和5.69 a,非线性模型能更好的解释林龄与建模变量之间的关系。     

Targeting rare earth element bearing mine tailings on Bangka Island,Indonesia, with Sentinel-2 MSI

A laboratory study on rare earth element bearing mine tailings, collected from Bangka Island, Indonesia, reported a new spectral absorption feature at 674 nm associated with Erbium. The present study aims to evaluate the capability of the European Space Agency’s Sentinel-2 MSI sensors to detect this absorption feature from space. An arithmetic band operation is performed on selected visible and near-infrared spectral bands of a Sentinel-2 image. The results show that Sentinel-2 MSI is capable of detecting the 674 nm Erbium-related absorption feature within the particular environmental setting of the study area.     

Winter cover crops in Dutch maize fields: Variability in quality and its drivers assessed from multi-temporal Sentinel-2 imagery

Planting a cover crop between the main cropping seasons is an agricultural management measure with multiple potential benefits for sustainable food production. In the maize production system of the Netherlands, an effective establishment of a winter cover crop is important for reducing nitrogen leaching to groundwater. Cover crop establishment after maize cultivation is obliged by law for sandy soils and consequently implemented on nearly all maize fields, but the winter-time vegetative ground cover varies significantly between fields. The objectives of this study are to assess the variability in winter vegetative cover and evaluate to what extent this variability can be explained by the timing of cover crop establishment and weather conditions in two growing seasons (2017–2018). We used Sentinel-2 satellite imagery to construct NDVI time series for fields known to be cultivated with maize within the province of Overijssel. We fitted piecewise logistic functions to the time series in order to estimate cover crop sowing date and retrieve the fitted NDVI value for 1 December (NDVI_Dec). We used NDVI_Dec to represent the quality of cover crop establishment at the start of the winter season. The Sentinel-2 estimated sowing dates compared reasonably with ground reference data for eight fields (RMSE = 6.6 days). The two analysed years differed considerably, with 2018 being much drier and warmer during summer. This drought resulted in an earlier estimated cover crop sowing date (on average 19 days) and an NDVI_Dec value that was 0.2 higher than in 2017. Combining both years and all fields, we found that Sentinel-2 retrieved sowing dates could explain 55% of the NDVI_Dec variability. This corresponded to a positive relationship (R² = 0.50) between NDVI_Dec and the cumulative growing degree days (GDD) between sowing date and 1 December until reaching 400 GDD. Based on cumulative GDD derived from two weather stations within Overijssel, we found that on average for the past three decades a sowing date of 19 September (± 7 days) allowed to attain these 400 GDD; this provides support for the current legislation that states that from 2019 onwards a cover crop should be sown before 1 October. To meet this deadline, while simultaneously ascertaining a harvest-ready main crop, in practice implies that undersowing of the cover crop during spring will gain importance. Our results show that Sentinel-2 NDVI time series can assess the effectiveness and timing of cover crop growth for small agricultural fields, and as such has potential to inform regulatory frameworks as well as farmers with actionable information that may help to reduce nitrogen leaching.     

不动产空间数据二三维一体化存储设计与应用探讨

通过对不动产空间数据和ArcGIS的GeoDatabase数据模型的分析,设计了不动产登记空间数据成果二三维数据统一存储的规则和方法,从而为不动产登记成果的二三维系统应用提供了数据支撑。     

BDS-2/BDS-3伪距单点定位精度分析

北斗三号(BDS-3)基本系统于2018年底开始提供全球服务．通过处理37个全球GNSS服务组织(IGS)多模实验跟踪网(MGEX)观测站90天北斗数据,评估了北斗二号(BDS-2)和BDS-3在全球范围内的可见卫星数、几何精度衰减因子(GDOP)和单频伪距单点定位精度,分析了BDS-2／BDS-3组合对BDS-2、BDS-3单系统空间几何构型、伪距单点定位(SPP)精度的改善程度．结果表明,BDS-3的空间几何构型较BDS-2有明显的提升,定位精度在东方向、北方向和高程方向分别为1.490、2.610、5.238 m(RMS),相较于BDS-2分别提高了58％、1％、24％．BDS-2／BDS-3组合在东方向、北方向和高程方向分别为1.45、2.36、4.90 m(RMS),较BDS-2与BDS-3单系统分别提高了59％、11％、29％,以及3％、10％、6％．并且BDS-2／BDS-3组合明显削弱了BDS-2定位精度与地理经度相关的边缘效应.      

Self-organised clustering for road extraction in classified imagery

The extraction of road networks from digital imagery is a fundamental image analysis operation. Common problems encountered in automated road extraction include high sensitivity to typical scene clutter in high-resolution imagery, and inefficiency to meaningfully exploit multispectral imagery (MSI). With a ground sample distance (GSD) of less than 2 m per pixel, roads can be broadly described as elongated regions. We propose an approach of elongated region-based analysis for 2D road extraction from high-resolution imagery, which is suitable for MSI, and is insensitive to conventional edge definition. A self-organising road map (SORM) algorithm is presented, inspired from a specialised variation of Kohonen's self-organising map (SOM) neural network algorithm. A spectrally classified high-resolution image is assumed to be the input for our analysis. Our approach proceeds by performing spatial cluster analysis as a mid-level processing technique. This allows us to improve tolerance to road clutter in high-resolution images, and to minimise the effect on road extraction of common classification errors. This approach is designed in consideration of the emerging trend towards high-resolution multispectral sensors. Preliminary results demonstrate robust road extraction ability due to the non-local approach, when presented with noisy input.     

河口表层悬浮泥沙气象卫星遥感定量模式研究

分析了HRIS光谱图像帧序列的相关性 ,并针对这一特性 ,提出了利用D2 PCM方法 ,降低光谱图像序列的谱相关和空间相关 ,减少图像中的冗余 ,从而实现光谱图像的压缩。该方法提高了压缩比和压缩效率 ,算法运算量小 ,快速。