An enhanced spatiotemporal fusion method – Implications for coal fire monitoring using satellite imagery

Many real-world applications require remotely sensed images at both high spatial and temporal resolutions. This requirement, however, is generally not met by single satellite system. A number of spatiotemporal fusion models have been developed to overcome this constraint. Landsat and Visible Infrared Imaging Radiometer Suite (VIIRS) data have been extensively used for detection and monitoring of active fires at different scales. Fusing the data obtained from these sensors will, therefore, significantly contribute to the satellite-based monitoring of fires. Among the available spatiotemporal fusion methods, the spatial and temporal adaptive reflectance fusion model (STARFM) and enhanced STARFM (ESTARFM) algorithms have been widely used for studying the land surface dynamics in the homogeneous and heterogeneous regions. The present study explores the applicability of STARFM and ESTARFM algorithms for fusing the high spatial resolution Landsat-8 OLI data with high temporal resolution VIIRS data in the context of active surface coal fire monitoring. Further, a modified version of ESTARFM algorithm, referred as modified-ESTARFM, is developed to improve the performance of the fusion model. Jharia coalfield (India), known for widespread occurrences of coal fires, is taken as the study area. The qualitative and quantitative assessments of the predicted (synthetic) Landsat-like images from different algorithms (STARFM, modified-STARFM, ESTARFM, modified-ESTARFM) indicate that the modified-ESTARFM outperforms the other fusion approaches used in this study. Considering the advantages, limitations and performance of the algorithms used, modified-ESTARFM along with STARFM can be used for surface coal fire monitoring. The study will not only contribute to remote sensing based coal fire studies but also to other applications, such as forest fires, crop residue burning, land cover and land use change, vegetation phenology, etc.     

基于Terra和NPP卫星的NDVI产品在呼伦贝尔地区植被监测中的对比

为了探索基于多源卫星产品组建长时间序列NDVI并开展生态监测与灾害评估的可能性,利用相关系数、均方根误差、标准差和平均偏差等方法,结合泰勒图,对呼伦贝尔地区2012—2021年基于Terra和NPP卫星逐16 d的NDVI产品进行比较。结果表明：NPP/NDVI的多年均值略高于Terra/NDVI,两者在草原植被类型下的一致性最优,其次为耕地,森林植被的一致性较差。在呼伦贝尔非植被生长季,Terra/NDVI与NPP/NDVI的偏差主要集中在森林,且以Terra/NDVI偏高为主。在植被生长季初期2种NDVI产品的偏差呈现零散、随机分布,中期的偏差相对较小,生长季后期则以Terra/NDVI小于NPP/NDVI的负偏差为主。虽然2种卫星产品的总体偏差不大,但在开展NDVI的距平分析或计算VCI等指数时,较小的偏差会被放大。呼伦贝尔草地在考虑两种NDVI产品偏差纠正的前提下基本可替代使用,而耕地、林地植被的协同应用仍需谨慎。     

中南半岛旱季VIIRS活跃火的空间特征与国别差异

热带是全球活跃火(active fire)的集中发生区,客观认识其空间特征、国别差异及其动态变化对评估区域生物质燃烧及其碳排放等具有重要意义。作为热带季风气候典型区,中南半岛旱季活跃火发生发展空间特征及其动态变化仍缺乏清晰认识。为此,论文利用可见光红外成像辐射仪(VIIRS) S-NPP 2012—2019年活跃火矢量数据,基于核密度与空间自相关评价了中南半岛及国别旱季尤其是其特征月份(2—4月)活跃火发生发展的密集程度、集聚特征及其动态变化。结果表明：① 中南半岛活跃火核密度低值区占比最大(79%),高值区最小(4%);柬埔寨、缅甸、老挝等经济落后国家的核密度均值明显高于泰国和越南;2012—2019年核密度高值区具有朝高海拔、向内陆与趋边境等分布特征,且柬埔寨东北部长居高值区。② 活跃火核密度中值区变化集中在1—4月,且多分布在低、高值区周围;高值区变化集中在2—4月,由柬埔寨东北部逐渐向缅甸东/西部、泰国西北部以及老挝北/南部转移。③ 半岛与5国活跃火核密度在旱季具有显著空间正相关性,空间集聚类型以“高—高”型和“低—低”型集聚为主,越南、柬埔寨等国局部自相关性强于泰国和老挝。     

大都市城乡融合区空间演进及内在关联性测度——基于武汉市夜间灯光数据

大都市城乡融合区时空演变及其内在空间关联性研究对避免城市低效蔓延式发展、实现空间结构协同和精明增长有重大意义。论文以武汉市城乡融合区为研究对象,基于NPP/VIIRS夜间灯光强度表征都市要素配置及运行效率和效益,利用引力模型测度2016—2018年各城乡融合区空间关联强度,分析各城乡融合区空间联系势能时空演变趋势和社会经济区位度变化。研究结果表明：① 武汉市城乡融合区面积在2016—2018年间扩大了28.10%,小斑块区域逐渐整合,区域发展连续性增强,总体向西北方向扩展最为显著;② 武汉市城乡融合区之间的空间联系网络结构整体上趋向“多中心”分布,这一过程源于各城乡融合区的经济发展水平、城镇化发展规模和交通通达性变化,东湖新技术开发区、中法武汉生态示范城、武汉临空经济区等在此过程中是各城乡融合区的区域发展引擎;③ 武汉市城乡融合区空间联系强度整体上逐年上升,空间联系整体格局从武洪区和江夏区的“大小中心”结构过渡到江夏区—武洪区—蔡汉区—江黄区—江硚东区的“外围圈层式”结构,但洪山区和江夏区作为武汉市城乡融合区中心区域,未能形成多方向空间辐射力和吸引力,对其他地区的带动明显不足;④ 各城乡融合区社会经济区位度变化显示,现行武汉市都市发展区规划实施成效在北部、西部和东北部城乡融合区较为显著。     

利用NPP/VIIRS微光数据反演夜间气溶胶光学厚度

大气气溶胶具有气候环境和健康效应,且具有明显的时空和昼夜变化特征。因此探测夜间气溶胶光学厚度（AOD）具有重要的意义。NPP/VIIRS上的昼夜通道DNB（Day Night Band）为夜间反演AOD提供了有效的数据。基于大气辐射传输理论对夜间AOD进行反演,首先,利用“背景合成法”获取夜间城市灯光真实辐射值;然后,利用改进的“消光法”反演夜间AOD。选取华北地区作为研究对象,获得了2016-03—2017-02新月日期的夜间城市灯光真实值,选择其中的2016年7月和10月的2次污染天气过程,反演了夜间AOD分布。通过北京、天津和郑州的太阳光度计地基观测结果和北京市环境质量监测站空气质量指数（AQI）对反演结果进行验证,结果表明反演结果和观测结果有较好的一致性,展示了卫星微光数据在夜间城市污染空间分布监测中的应用潜力。     

基于VIIRS数据的南黄海区域激光测深性能评估

机载激光测深技术因其特有的优势备受关注,但现阶段其探测能力有限,为解决其适用范围问题,本文利用VIIRS(Visible Infrared Imaging Radiometer Suite)数据对南黄海区域的激光测深性能进行评估,首先利用外业实测数据和卫星影像反演南黄海区域的490 nm波段漫衰减系数,其次建立490 nm和532 nm波段漫衰减系数之间的线性关系,最后以CZMIL(Coastal Zone Mapping and Imaging LiDAR)系统为例评估了南黄海区域激光测深能力,并给出了江苏沿岸海域CZMIL系统的适用范围。研究结果表明:CZMIL系统在南黄海区域的最大测深能力范围为0.5~40 m;理论最低潮面时,江苏沿海海域CZMIL系统的适用范围约为4 700 km²。     

Landsat/OLI与夜间灯光数据在提取城市不透水面中的精度差异分析

不透水面作为反映城市发展程度和表征城市生态环境的重要指标,在城市化研究中成为重要的数据源。当前,不透水面信息的获取通常基于遥感数据来开展,包括不同分辨率的遥感数据。这些遥感数据在高精度提取城市不透水面的能力具有较大的差异,会因尺度不同而带来提取精度的偏差。因此,理解不同遥感数据源在不透水面提取上的差异尤为重要。本文利用Landsat/OLI光谱数据和VIIRS/DNB夜间灯光数据分别采用线性光谱混合分析法和大尺度不透水面指数法提取珠江三角洲研究区的不透水面信息,并从不透水面总体精度、不同密度精度对比分析2类数据源提取不透水面的差异。结果表明：① Landsat/OLI和VIIRS/DNB两者提取不透水面的总体精度差异不大,Landsat/OLI提取不透水面的精度总体上略高于VIIRS/DNB。2种数据提取不透水面的均方根误差RMSE分别是0.18和0.21,系统误差SE分别是0.12和0.13,决定系数R ²分别是0.76和0.67。② Landsat/OLI和VIIRS/DNB数据对不同密度不透水面分布区域的提取能力不同：VIIRS/DNB在低密度不透水面区域提取精度高于Landsat/OLI;而Landsat/OLI在中、高密度不透水面区域提取精度均高于VIIRS/DNB。通过2种数据提取精度差异的对比,以期为不同密度的不透水面分布区域提取找到最佳尺度的数据源,提高不透水面提取的效率和精度。     

NPOESS/VIIRS及其云图产品的应用

美国国家极轨业务环境卫星系统计划(NPOESs)第1颗卫星预计于2010年发射,可见光红外成像辐射仪(VIIRS)是NPOESS卫星计划的首要关键载荷,该仪器继承、发展和集成了3颗现有卫星有效载荷的功能,其预期云图产晶在整个NPOESS计划应用产品中处于最高优先级地位.从简要介绍NPOESS/VIIRS仪器特征人手,从云图解释的物理基础出发,利用现有卫星载荷类似通道云图实例,对VIIRS预期云图产品进行了概要分析,给出该载荷云图产品在改进和提高云分析与预报水平方面的预期应用潜力和价值.     

A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery

ABSTRACT

Accurate crime prediction can help allocate police resources for crime reduction and prevention. There are two popular approaches to predict criminal activities: one is based on historical crime, and the other is based on environmental variables correlated with criminal patterns. Previous research on geo-statistical modeling mainly considered one type of data in space-time domain, and few sought to blend multi-source data. In this research, we proposed a spatio-temporal Cokriging algorithm to integrate historical crime data and urban transitional zones for more accurate crime prediction. Time-series historical crime data were used as the primary variable, while urban transitional zones identified from the VIIRS nightlight imagery were used as the secondary co-variable. The algorithm has been applied to predict weekly-based street crime and hotspots in Cincinnati, Ohio. Statistical tests and Predictive Accuracy Index (PAI) and Predictive Efficiency Index (PEI) tests were used to validate predictions in comparison with those of the control group without using the co-variable. The validation results demonstrate that the proposed algorithm with historical crime data and urban transitional zones increased the correlation coefficient by 5.4% for weekdays and by 12.3% for weekends in statistical tests, and gained higher hit rates measured by PAI/PEI in the hotspots test.     

“Ghost cities” identification using multi-source remote sensing datasets: A case study in Yangtze River Delta

Drastic urbanization has taken place in China during the last two decades. Recently, a considerable number of “ghost cities” have emerged due to the extensive and unreasonable urban expansion which far exceeds the practical demand. In order to investigate “ghost cities”, we proposed a feasible framework by utilizing multi-source remote sensing datasets, including nighttime light imagery, land cover type products and population grid. After eliminating blooming effect of nighttime imagery by a proposed modified optimal threshold method (MOTM) and extracting built-up area from land cover type products, we developed a “ghost city” index (GCI) to quantify and evaluate the intensity of “ghost city” phenomenon in Yangtze River Delta at county/district level. The GCI was established according to the intrinsic features of “ghost cities”, comprising three criteria: consistency of lit area and built-up area, illumination intensity and population density. Then, we explored the spatial pattern of “ghost cities” of different GCI categories and the ternary contour was applied to demonstrate the key factor among three criteria. Our finding implies that “ghost cities” are prominently spatial clustered. Meanwhile, counties and new development zones have higher risk of suffering from the phenomenon, while capital cities and municipal cities have an alleviative effect for ambient regions. Besides, regions with higher intensity of the phenomenon tend to have less balanced composition among three criteria. Our results show good consistency with previous reports and studies, providing a more objective and spatial explicit insight into the “ghost city” phenomenon. Our findings do not only prove the capability of monitoring “ghost cities” using remote sensing data, but would also be beneficial to urban planning and regional sustainable development.