Estimating the starting time and identifying the type of urbanization based on dense time series of landsat-derived Vegetation-Impervious-Soil (V-I-S) maps – A case study of North Taiwan from 1990 to 2015

Land cover and land use change (LCLUC) is a global phenomenon, and LCLUC in urbanizing regions has substantial impacts on humans and their environments. In this paper, a semi-automatic approach to identifying the type and starting time of urbanization was developed and tested based on dense time series of Vegetation-Impervious-Soil (V-I-S) maps derived from Landsat surface reflectance imagery. The accuracy of modeled V-I-S fractions and the estimated time of initial change in impervious cover were assessed. North Taiwan, one of the regions of the island of Taiwan that experienced the greatest urban LCLUC, was chosen as a test area, and the study period is 1990 to 2015, a period of substantial urbanization. In total, 295 dates of Landsat imagery were used to create 295 V-I-S fraction maps that were used to construct fractional cover time series for each pixel. Root Mean Square Error (RMSE)s for the modeled Vegetation, Impervious, and Soil were 25 %, 22 %, 24 % respectively. The time of Urban Expansion is estimated by logistic regression applied to Impervious cover time series, while the time of change for Urban Renewal is determined by the period of brief Soil exposure. The identified location and estimated time for newly urbanized lands were generally accurate, with 80% of Urban Expansion estimated within ±2.4 years. However, the accuracy of identified Urban Renewal was relatively low. Our approach to identifying Urban Expansion with dense time series of Landsat imagery is shown to be reliable, while Urban Renewal identification is not.     

Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data

Most studies have the achieved rapid and accurate determination of soil organic carbon (SOC) using laboratory spectroscopy; however, it remains difficult to map the spatial distribution of SOC. To predict and map SOC at a regional scale, we obtained fourteen hyperspectral images from the Gaofen-5 (GF-5) satellite and decomposed and reconstructed the original reflectance (OR) and the first derivative reflectance (FDR) using discrete wavelet transform (DWT) at different scales. At these different scales, as inputs, we selected the 3 optimal bands with the highest weight coefficient using principal component analysis and chose the normalized difference index (NDI), ratio index (RI) and difference index (DI) with the strongest correlation with the SOC content using a contour map method. These inputs were then used to build regional-scale SOC prediction models using random forest (RF), support vector machine (SVM) and back-propagation neural network (BPNN) algorithms. The results indicated that: 1) at a low decomposition scale, DWT can effectively eliminate the noise in satellite hyperspectral data, and the FDR combined with DWT can improve the SOC prediction accuracy significantly; 2) the method of selecting inputs using principal component analysis and a contour map can eliminate the redundancy of hyperspectral data while retaining the physical meaning of the inputs. For the model with the highest prediction accuracy, the inputs were all derived from the wavelength range of SOC variations; 3) the differences in prediction accuracy among the different prediction models are small; and 4) the SOC prediction accuracy using hyperspectral satellite data is greatly improved compared with that of previous SOC prediction studies using multispectral satellite data. This study provides a highly robust and accurate method for predicting and mapping regional SOC contents.     

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.     

基于遥感技术的近30年中亚地区主要湖泊变化

在全球气候变暖的背景下,研究中亚干旱半干旱地区主要湖泊的变化不仅对内陆水资源管理和可持续发展有着重要意义,也为进一步研究湖泊变化对气候变化和人类活动的响应提供基础。获取1978年MSS、1989年TM、1998年TM及2010年ETM的同季相4期遥感图像数据,通过人工解译提取中亚地区7个湖泊信息,获得近30 a的湖泊面积变化;利用T/P和Envisat雷达高度计提取1992—2012年的湖泊水位信息;基于湖泊面积和水位的时空变化特征分析了湖泊变化的影响因素。结果表明:近30 a来,由于湖泊类型的不同,除萨雷卡梅什湖外的其他6个湖泊均出现不同程度的面积缩减;其中,平原尾闾湖面积变化最显著,高山封闭湖相对平稳,吞吐湖泊的面积变化相对复杂;湖泊的水位变化模式因流域和湖泊类型而异;高山封闭湖泊受气候的影响较大,而吞吐湖泊受人为因素影响显著。     

核密度估计法支持下的网络空间POI点可视化与分析

城市空间POI点的分布模式、分布密度在基础设施规划、城市空间分析中具有重要意义,表达该特征的核密度法(kernel density estimation)由于顾及了地理学第一定律的区位影响,比其他密度表达方法(如样方密度、基于Voronoi图密度)占优。然而,传统的核密度计算方法往往基于二维延展的欧氏空间,忽略了城市网络空间中设施点的服务功能及相互联系发生于网络路径距离而非欧氏距离的事实。本研究针对该缺陷,给出了网络空间核密度计算模型,分析了核密度方法在置入网络结构中受多种约束条件的扩展模式,讨论了衰减阈值及高度极值对核密度特征表达的影响。通过实际多种POI点分布模式(随机型、稀疏型、区域密集型、线状密集型)下的核密度分析试验,讨论了POI基础设施在城市区域中的分布特征、影响因素、服务功能。     

顾及地形起伏的无人机影像覆盖分析方法

使用无人机实施测绘航空摄影时,由于无人机相对航高较低,地面起伏会对无人机影像的分辨率、覆盖范围、重叠度造成较大的影响,影像成果会出现分辨率不足、重叠度不够、覆盖漏洞等缺陷。针对这一情况,本文提出了一种利用数字微分正解法的计算方法,借助DEM准确计算每张影像的覆盖范围,并使用FME软件高效生成全部影像的覆盖范围。经过实际使用,验证了该方法可以在航线设计阶段准确预测并分析全部影像的覆盖范围、重叠度,因此可及时发现设计问题并调整航线。该方法可以有效减少因地形起伏造成的影像覆盖缺陷,减少返工现象,从整体上提高了作业效率。     

利用BDS与无人船进行污染源分析及处理

在大数据的背景下,充分利用北斗卫星导航系统（BDS）的定位功能,以无人船作为用户端,在水质数据采集和污染源位置的问题中积极探索新的实践方案,迅速、准确地找到污染源,减少河流污染物污染的时间,且无人船上装有净化模块,在发现污染时可作简易处理。在无人船上安装水质分析仪,水质分析仪的传输模块中安装经给防水处理的北斗卫星定位芯片和WiFi数据传输器,进而对河流的污染物种类及浓度进行分析,并采用大数据的计算方法,计算出污染源位置,向云平台反映污染源位置分析结果与污染物处理方法。通过BDS,将数据按照位置区域划分,并将能到达污染源的最短路径发送到处理人的移动设备上。本文通过对基于BDS定位的水质污染监测可视化系统进行分析,以期迅速找出污染源,减少水质污染现象的发生。     

基于PS-InSAR技术的黄土大厚度挖方区回弹变形规律分析

以陕北某湿陷性黄土大厚度挖方地基工程为研究对象,采用PS-InSAR技术对2018-10—2019-11间获取的16景TerraSAR-X卫星影像进行处理,获取了湿陷性黄土挖方区回弹变形信息,总结了大厚度挖方区时序回弹变形特征。结果表明,由于上部土体应力卸载,在开挖区域存在地基土回弹变形现象,选取的高密度PS点变形信息较好地反映了研究区的真实变形情况,回弹变形范围与开挖边界吻合,另外挖方厚度越大,土体开挖引起的回弹变形越大;在开挖完成后的1年监测时间内,回弹区变形量随时间呈线性变化,在最大开挖厚度处,产生最大回弹量为29.3 mm;此外,PS-InSAR技术监测到的变形量与实地水准结果吻合性较好,表明该技术在黄土大厚度挖方区回弹变形监测中具有较好的应用效果。     

资源三号和天绘一号卫星影像无控联合平差精度分析

近年来国产卫星传感器类型日益丰富,使用自主可控的国产卫星影像进行生产和研究的需求越来越大,多源国产卫星影像联合平差的精度分析显得尤为重要。本文针对在立体测图生产中,资源三号卫星影像在时相较旧、噪声较大、云覆盖和摄影漏洞等特殊困难的条件下,利用天绘一号卫星影像进行补漏生产的情况,最终分析并验证了两者无控联合平差的可行性和成果精度。     

路网约束条件下热力规则设计与试验

热力图是一种能直观准确展示空间观测值的有效工具,在多个领域具有广泛应用。本文在分析设定不同道路层权重、道路技术等级分类的基础上,以反距离权重、直方图均衡化、密度补偿、参数多次迭代等方法,研究构建了以热力方式展示道路网分布及发达程度的相关规则体系。热力规则通过道路赋权、路线曲面化、密度协调、图面综合等多套规则的有机结合,实现了道路网总体发达水平信息的提取与展示,并以全球地理信息资源建设项目路网成果中的亚洲和非洲部分国家数据为例,进行了信息提取与热力分布试验。