Mapping spatio-temporal flood inundation dynamics at large river basin scale using time-series flow data and MODIS imagery

Flood inundation is crucial to the survival and prosperity of flora and fauna communities in floodplain and wetland ecosystems. This study tried to map flood inundation characteristics in the Murray-Darling Basin, Australia, utilizing hydrological and remotely sensed data. It integrated river flow time series and Moderate Resolution Imaging Spectroradiometer (MODIS) images to map inundation dynamics over the study area on both temporal and spatial dimensions. Flow data were analyzed to derive flow peaks and Annual Exceedance Probabilities (AEPs) using the annual flood series method. The peaks were linked with MODIS images for inundation detection. Ten annual maximum inundation maps were generated for water years 2001–2010, which were then overlaid to derive an inundation frequency map. AEPs were also combined with the annual maximum inundation maps to derive an inundation probability map. The resultant maps revealed spatial and temporal patterns of flood inundation in the basin, which will benefit ecological and environmental studies when considering response of floodplain and wetland ecosystems to flood inundation.     

基于哨兵数据相干性的洪水淹没范围确定方法

洪涝灾害会造成农田淹没、居民住宅损毁等危害,因此对洪水淹没范围进行实时、准确监测可有效进行灾后治理。利用光学传感器提取洪水淹没范围时,不能穿透云层,因此无法获取有效地面信息;而SAR使用微波波段,不受天气影响,在夜间也能成像。因此,SAR成为洪水灾害灾情评估的有力工具。本文利用2021年9月23日、10月5日、10月17日3景SAR雷达影像Sentinel-1A数据,计算相干性系数,设置阈值为0.2,提取水体淹没范围,分析其扩张范围及变化趋势,并根据生成的形变图分析水位抬升变化,验证了基于雷达数据的相干系数阈值提取方法监测洪水淹没范围,以及采用InSAR技术准确提取水体边界与分析水位上升趋势的可行性。     

Land use intensity trajectories on Amazonian pastures derived from Landsat time series

Monitoring changes in land use intensity of grazing systems in the Amazon is an important prerequisite to study the complex political and socio-economic forces driving Amazonian deforestation. Remote sensing offers the potential to map pasture vegetation over large areas, but mapping pasture conditions consistently through time is not a trivial task because of seasonal changes associated with phenology and data gaps from clouds and cloud shadows. In this study, we tested spectral-temporal metrics derived from intra-annual Landsat time series to distinguish between grass-dominated and woody pastures. The abundance of woody vegetation on pastures is an indicator for management intensity, since the duration and intensity of land use steer secondary succession rates, apart from climate and soil conditions. We used the developed Landsat-based metrics to analyze pasture intensity trajectories between 1985 and 2012 in Novo Progresso, Brazil, finding that woody vegetation cover generally decreased after four to ten years of grazing activity. Pastures established in the 80s and early 90s showed a higher fraction of woody vegetation during their initial land use history than pastures established in the early 2000s. Historic intensity trajectories suggested a trend towards more intensive land use in the last decade, which aligns well with regional environmental policies and market dynamics. This study demonstrates the potential of dense Landsat time series to monitor land-use intensification on Amazonian pastures.     

时序Sentinel-1A数据支持的长江中下游汛情动态监测

合成孔径雷达（SAR）因其对地观测全天候、全天时优势,成为多云多雨天气限制下洪水动态监测中不可或缺的数据来源之一。由于GEE（Google Earth Engine）云计算平台的兴起和短重访Sentinel-1数据的可获取性,洪水监测与灾害评估目前正面向动态化、广域化快速发展。顾及洪水淹没区土地覆盖变化的复杂性和发生时间的不确定性,基于时序Sentinel-1A卫星数据提出了针对大尺度范围、连续长期的汛情自动检测及动态监测方法。该方法首先,利用图像二值化分割时序SAR数据实现水体时空分布粗制图,逐像素计算时间序列中被识别为水体候选点的频率。然后,利用Sentinel-2光学影像对精度较粗的初期SAR水体提取结果进行校正,得到精细的水体分布图。最后,针对不同频率区间的淹没特点,采用差异化的时序异常检测策略识别淹没范围：对低频覆水区利用欧氏距离检测时序断点,以提取扰动强度大、淹没时间短的洪涝灾害区;对高频覆水区利用标准分数（Z-Score）检测时序断点,以提取季节性水体覆盖区。在GEE平台上利用该方法,实现了2020-05—10长江中下游地区全域洪水淹没范围时空信息的自动、快速、有效监测,揭示了不同区域汛情发展模式的差异性。本文提出的洪水快速监测方法对大尺度下的汛情动态监测、灾害定量评估和快速预警响应具有重要的现实意义。     

利用天宫一号和Landsat7对地观测数据的森林变化检测

由于自然演替和一些干扰因素的影响,森林覆盖处在不断的变化中.结合云南省西双版纳地区的天宫一号高光谱数据以及Landsat影像,研究了热带森林覆盖制图与变化检测的自动化识别方法.首先分析了每景影像中红光波段的光谱属性,依据直方图提取出纯净森林像元,然后计算影像中各像元与纯净森林像元之间的光谱相似性,从而得到森林指数并以此为依据提取出每景影像对应的森林覆盖图,将多期的森林覆盖专题图进行叠加分析即可得到森林变化专题图.结果表明:(1)使用天宫一号高光谱影像可以进行森林覆盖自动化提取,生成的森林覆盖图合理地反映了森林分布状况;(2)与多期遥感影像结合进行森林变化信息提取,提取结果很好地体现了森林减少和森林恢复情况,对新恢复的未郁闭森林也可以进行有效检测.     

Assessment of radargrammetric DSMs from TerraSAR-X Stripmap images in a mountainous relief area of the Amazon region

The Brazilian Amazon is a vast territory with an enormous need for mapping and monitoring of renewable and non-renewable resources. Due to the adverse environmental condition (rain, cloud, dense vegetation) and difficult access, topographic information is still poor, and when available needs to be updated or re-mapped. In this paper, the feasibility of using Digital Surface Models (DSMs) extracted from TerraSAR-X Stripmap stereo-pair images for detailed topographic mapping was investigated for a mountainous area in the Carajás Mineral Province, located on the easternmost border of the Brazilian Amazon. The quality of the radargrammetric DSMs was evaluated regarding field altimetric measurements. Precise topographic field information acquired from a Global Positioning System (GPS) was used as Ground Control Points (GCPs) for the modeling of the stereoscopic DSMs and as Independent Check Points (ICPs) for the calculation of elevation accuracies. The analysis was performed following two ways: (1) the use of Root Mean Square Error (RMSE) and (2) calculations of systematic error (bias) and precision. The test for significant systematic error was based on the Student’s-t distribution and the test of precision was based on the Chi-squared distribution. The investigation has shown that the accuracy of the TerraSAR-X Stripmap DSMs met the requirements for 1:50,000 map (Class A) as requested by the Brazilian Standard for Cartographic Accuracy. Thus, the use of TerraSAR-X Stripmap images can be considered a promising alternative for detailed topographic mapping in similar environments of the Amazon region, where available topographic information is rare or presents low quality.     

Inundation Extent and Flood Frequency Mapping Using LANDSAT Imagery and Digital Elevation Models

We modeled the extent of inundation around Poyang Lake, China using 13 Landsat images and two digital elevation models (DEMs). Boundaries of the observed inundation extents were (a) labeled with lake-level measurements taken at a representative hydrological station and (b) interpolated to create a Water Line DEM (WL-DEM) that was used to map inundation frequency. A 30 m contour-based DEM produced slightly better results than the Shuttle Radar Topography Mission DEM, but neither DEM was accurate for medium and low lake levels. The WL-DEM exhibited improved accuracy at medium lake levels, but had relatively high errors at low lake levels.     

SuperMap在日本防灾建设上的典型应用

以日本防灾建设为背景,介绍了SuperMap在日本防灾信息系统开发中的几个典型应用,主要包括灾害地图门户网站、家物倒坏危险区域瓦片制作工具群、洪水推定区域地图数据下载网站、落石防灾管理系统和浸水模拟检索系统.     

Consistency of vegetation index seasonality across the Amazon rainforest

Vegetation indices (VIs) calculated from remotely sensed reflectance are widely used tools for characterizing the extent and status of vegetated areas. Recently, however, their capability to monitor the Amazon forest phenology has been intensely scrutinized. In this study, we analyze the consistency of VIs seasonal patterns obtained from two MODIS products: the Collection 5 BRDF product (MCD43) and the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC). The spatio-temporal patterns of the VIs were also compared with field measured leaf litterfall, gross ecosystem productivity and active microwave data. Our results show that significant seasonal patterns are observed in all VIs after the removal of view-illumination effects and cloud contamination. However, we demonstrate inconsistencies in the characteristics of seasonal patterns between different VIs and MODIS products. We demonstrate that differences in the original reflectance band values form a major source of discrepancy between MODIS VI products. The MAIAC atmospheric correction algorithm significantly reduces noise signals in the red and blue bands. Another important source of discrepancy is caused by differences in the availability of clear-sky data, as the MAIAC product allows increased availability of valid pixels in the equatorial Amazon. Finally, differences in VIs seasonal patterns were also caused by MODIS collection 5 calibration degradation. The correlation of remote sensing and field data also varied spatially, leading to different temporal offsets between VIs, active microwave and field measured data. We conclude that recent improvements in the MAIAC product have led to changes in the characteristics of spatio-temporal patterns of VIs seasonality across the Amazon forest, when compared to the MCD43 product. Nevertheless, despite improved quality and reduced uncertainties in the MAIAC product, a robust biophysical interpretation of VIs seasonality is still missing.     

Application of Remote Sensing in Monitoring Unsustainable Wetlands: Case Study Hamun Wetland

Monitoring wetland as one of the important parts of the global ecosystem is necessary for conservational programs. But, usually, collecting in situ data is restricted in these areas because of their remote locations, vast area and dynamic conditions. Remote sensing provides a cost effective tool to investigate hydrological patterns and the seasonal trend of changes in wetlands. In this paper, Land-use/land-cover change during water inundation period of Hamun wetland was investigated in order to determine change trend during this period. Hamun wetland is an unsustainable ecosystem, and monitoring this wetland is essential for conservation goals. This trend is critical for decision makers in order to plan the conservational scheme in all unsustainable ecosystems. To reach this objective, the land-use/land-cover maps during inundation period of Hamun were produced using Landsat 8 time series images. The results of accuracy assessment showed the classification of water and vegetation have the highest accuracy (94% and 93%, respectively). And the accuracy of plants in the water classes was the lowest (water–veg?=?89.9%, veg–water 1?=?88.8%, veg–water 2?=?87.6%). This means the higher misclassification is in determining the vegetation in the water. Then, the changes in the land-cover classes in relation to wetland inundation were investigated. Results of land-use/land-cover change illustrate the regions that were suitable for water birds but lost their suitability when the wetland dried out. These areas are crucial for water bird’s conservation. Satellite data determined these areas with acceptable accuracy.     

Flood inundation mapping in a part of Mahanadi river basin

Man has had to live with floods since the very inception of his existence. Fury of flood causes heavy damages to both life and property. Accurate flood inundation data is one of the essential requirements for effective management of flood problem. Remote Sensing methods are well suited for acquiring flood inundation data because of synoptic, repetitive coverage of the satellite data. This paper briefs on an attempt to map flood inundated areas in a part of Mahanadi river basin using remote sensing data.     

Integrating stream gage data and Landsat imagery to complete time-series of surface water extents in Central Valley,California

Accurate monitoring of surface water location and extent is critical for the management of diverse water resource phenomena. The multi-decadal archive of Landsat satellite imagery is punctuated by missing data due to cloud cover during acquisition times, hindering the assembly of a continuous time series of inundation dynamics. This study investigated whether streamflow volume measurements could be integrated with satellite data to fill gaps in monthly surface water chronologies for the Central Valley region of California, USA, from 1984 to 2015. We aggregated measurements of maximum monthly water extent within each of the study area’s 50 8-digit hydrologic unit code (HUC) watersheds from two Landsat-derived datasets: the European Commission’s Joint Research Centre (JRC) Monthly Water History and the U.S. Geological Survey Dynamic Surface Water Extent (DSWE). We calculated Spearman rank correlation coefficients between water extent values in each HUC and streamflow discharge data. Linear regression fits of the water extent/streamflow data pairs with the highest correlations served as the basis for interpolation of missing imagery surface water values on a HUC-wise basis. Results show strong (ρ > 0.7) maximum correlations in 11 (22.4%) and 25 (51.0%) HUCs for the DSWE and JRC time series, respectively, when comparisons were restricted to imagery and gages co-located in each HUC. Strong maximum correlations occurred in 39 (79.6%; DSWE) and 42 (85.7%; JRC) HUCs when imagery was paired with discharge data from any study area gage, providing a solid basis for reconstruction of water extent values. We generated continuous time series of 30+ years in 35 HUCs, demonstrating that this technique can provide quantitative estimates of historical surface water extents and elucidate flooding or drought events over the period of data collection. Results of a non-parametric trend analysis of the long-term time series on an annual, seasonal, and monthly basis varied among HUCs, though most trends indicate an increase in surface water over the past 30 years.     

地图代数在洪水淹没模拟与分析中的应用

扩展了地图代数系统的功能 ,提出了运用地图代数进行洪水淹没模拟与分析的方法 ,建立了洪水淹没模型 ,并给出了洪水淹没模拟与分析的实例     

Visual and Statistical Comparisons of Surface Modeling Techniques for Point-based Environmental Data

Existing studies on spatial interpolation tend to overplay statistical perspective, paying little attention the locality and the visual performance of generated surface models. In an attempt to bridge these gaps in literatures, the authors compared the performance of five surface modelling methods, using a set of integrative criteria including absolute and relative statistical accuracy, visual pleasantness and faithfulness of generated surface models, sensitivity to changing sample size and search conditions, and computational intensity. The modeling methods used were: inverse distance, kriging, linear triangulation, minimum curvature, and radial basis functions. Because terrain relief is one of few environmental attributes whose continuous surfaces can be directly observed through appropriate procedures, we used as input data two sets of elevation points sampled irregularly from a USGS 1:24,000 topographical map covering a hilly area. We found that surface modeling methods, even if statistically accurate, may not always ensure a graphically faithful representation of the reality. The surprising result of this study was that the surface models generated from a larger sample were less statistically accurate than those generated from a smaller sample.     

Deducing Rice Crop Dynamics and Cultural Types of Bangladesh Using Geospatial Techniques

Rice crop occupies an important aspect of food security and also contributes to global warming via GHGs emission. Characterizing rice crop using spatial technologies holds the key for addressing issues of global warming and food security as different rice ecosystems respond differently to the changed climatic conditions. Remote sensing has become an important tool for assessing seasonal vegetation dynamics at regional and global scale. Bangladesh is one of the major rice growing countries in South Asia. In present study we have used remote sensing data along with GIS and ancillary map inputs in combination to derive seasonal rice maps, rice phenology and rice cultural types of Bangladesh. The SPOT VGT S10 NDVI data spanning Aus, Aman and Boro crop season (1^st May 2008 to 30^th April 2009) were used, first for generating the non-agriculture mask through ISODATA clustering and then to generate seasonal rice maps during second classification. The spectral rice profiles were modelled and phenological parameters were derived. NDVI growth profiles were modelled and crop calendar was derived. To segregate the rice cultural types of Bangladesh into IPCC rice categories, we used elevation, irrigated area, interpolated rainfall maps and flood map through logical modelling in GIS. The results indicated that the remote sensing derived rice area was 9.99 million ha as against the reported area of 11.28 million ha. The wet and dry seasons accounted for 64% and 36 % of the rice area, respectively. The flood prone, drought prone and deep water categories account for 7.5%, 5.56% and 2.03%, respectively. The novelty of current findings lies in the spatial outcome in form of seasonal and rice cultural type maps of Bangladesh which are helpful for variety of applications.     

遥感云计算平台相关文献计量可视化分析

在遥感大数据时代背景下,遥感云计算平台的出现改变了遥感数据处理和分析的传统模式,极大地提高了运算效率,使得全球尺度的快速分析成为可能.国内外已有众多学者利用遥感云计算平台开展研究,然而相对缺乏对遥感云计算平台发展和应用的客观性综述.本文基于Web of Science (WoS)和中国知网CNKI(China Nati...     

Urban event detection with big data of taxi OD trips: A time series decomposition approach

Big urban mobility data, such as taxi trips, cell phone records, and geo‐social media check‐ins, offer great opportunities for analyzing the dynamics, events, and spatiotemporal trends of the urban social landscape. In this article, we present a new approach to the detection of urban events based on location‐specific time series decomposition and outlier detection. The approach first extracts long‐term temporal trends and seasonal periodicity patterns. Events are defined as anomalies that deviate significantly from the prediction with the discovered temporal patterns, i.e., trend and periodicity. Specifically, we adopt the STL approach, i.e., seasonal and trend decomposition using LOESS (locally weighted scatterplot smoothing), to decompose the time series for each location into three components: long‐term trend, seasonal periodicity, and the remainder. Events are extracted from the remainder component for each location with an outlier detection method. We analyze over a billion taxi trips for over seven years in Manhattan (New York City) to detect and map urban events at different temporal resolutions. Results show that the approach is effective and robust in detecting events and revealing urban dynamics with both holistic understandings and location‐specific interpretations.     

A new 500-m resolution map of canopy height for Amazon forest using spaceborne LiDAR and cloud-free MODIS imagery

In the present study, we aimed to map canopy heights in the Brazilian Amazon mainly on the basis of spaceborne LiDAR and cloud-free MODIS imagery with a new method (the Self-Organizing Relationships method) for spatial modeling of the LiDAR footprint. To evaluate the general versatility, we compared the created canopy height map with two different canopy height estimates on the basis of our original field study plots (799 plots located in eight study sites) and a previously developed canopy height map. The compared canopy height estimates were obtained by: (1) a stem diameter at breast height (D) – tree height (H) relationship specific to each site on the basis of our original field study, (2) a previously developed D–H model involving environmental and structural factors as explanatory variables (Feldpausch et al., 2011), and (3) a previously developed canopy height map derived from the spaceborne LiDAR data with different spatial modeling method and explanatory variables (Simard et al., 2011). As a result, our canopy height map successfully detected a spatial distribution pattern in canopy height estimates based on our original field study data (r = 0.845, p = 8.31 × 10⁻³) though our canopy height map showed a poor correlation (r = 0.563, p = 0.146) with the canopy height estimate based on a previously developed model by Feldpausch et al. (2011). We also confirmed that the created canopy height map showed a similar pattern with the previously developed canopy height map by Simard et al. (2011). It was concluded that the use of the spaceborne LiDAR data provides a sufficient accuracy in estimating the canopy height at regional scale.     

Use of WorldView-2 time series to establish a wetland monitoring program for potential offsite impacts of mine site rehabilitation

The Ramsar-listed wetlands of the Magela Creek floodplain, situated in the World Heritage Kakadu National Park, in northern Australia are recognised for their biodiversity and cultural values. The floodplain is also a downstream receiving environment for Ranger uranium mine, which is entering closure and rehabilitation phases. Vegetation on the floodplain is spatially and temporally variable which is related to the hydrology of the region, primarily the extent and level of inundation and available soil moisture. Time-series mapping of the floodplain vegetation will provide a contemporary baseline of annual vegetation dynamics to assist with determining whether change is natural or a result of the potential impacts of mine closure activities such as increased suspended sediment moving downstream. The research described here used geographic object-based image analysis (GEOBIA) to classify the upper Magela Creek floodplain vegetation from WorldView-2 imagery captured over four years (2010–2013) and ancillary data including a canopy height model. A step-wise rule set was used to implement a decision tree classification. The resulting maps showed the 12 major vegetation communities that exist on the Magela Creek floodplain and their distribution for May 2010, May 2011, June 2012 and June 2013 with overall accuracies of over 80% for each map. Most of the error appears to be associated with confusion between vegetation classes that are spectrally similar such as the classes dominated by grasses. Object-based change detection was then applied to the maps to analyse change between dates. Results indicate that change between dates was detected for large areas of the floodplain. Most of the change is associated with the amount of surface water present, indicating that although imagery was captured at the same time of year, the imagery represents different stages of the seasonal cycle of the floodplain.     

Identification and analysis of groundwater potential zones in Ken–Betwa river linking area using remote sensing and geographic information system

The use of remote sensing data with other ancillary data in a geographic information system (GIS) environment is useful to delineate groundwater potential zonation map of Ken–Betwa river linking area of Bundelkhand. Various themes of information such as geomorphology, land use/land cover, lineament extracted from digital processing of Landsat (ETM+) satellite data of the year 2005 and drainage map were extracted from survey of India topographic sheets, and elevation, slope data were generated from shuttle radar topography mission (SRTM) digital elevation model (DEM). These themes were overlaid to generate groundwater potential zonation (GWPZ) map of the area. The final map of the area shows different zones of groundwater prospects, viz., good (5.22% of the area), moderate (65.83% of the area) poor (15.31% of the area) and very poor (13.64% of area).