Retrospective mapping of burnt areas in Central Siberia using a modification of the normalised difference water index

The boreal forest contains almost half the total carbon pool of world forest ecosystems, and so has a very significant role in global biogeochemical cycles. The flux of greenhouse gases in and out of these forests is influenced strongly by disturbances such as diseases, logging and predominantly fire. It is important to quantify these disturbances to enable the modelling of major greenhouse gases. However, because of the remoteness and vastness of the boreal forest, little data is available on the type, extent, frequency and severity of these disturbances in Siberia. For burnt areas, two of the more responsive wavelengths are the short wave infra-red (SWIR) and the near infra-red (NIR). These produce a vegetation index, the normalised difference SWIR (NDSWIR) capable of detecting retrospective disturbances. Here we combine the NDSWIR from MODIS imagery acquired in the summer of 2003 with thermal anomaly data from 1992 to 2003 to detect and date areas which burnt at some point between 1992 and 2003. The semi-automated method is called SWIR and Thermal ANomalies for Detecting Disturbances (STANDD) and is complemented by an Normalised Difference Vegetation Index (NDVI) differencing method using MODIS 2002 and 2003 imagery to ensure reliable detection of area burnt in the year of image acquisition (i.e. 2003). The area of this study covers approximately 3 million km² stretching from Lake Baikal in the south to the Laptev Sea in the north, above the Arctic Circle. Landsat ETM+ images were used to validate the shape and areal extent of the burnt areas resulting in an 81% overall accuracy with a kappa coefficient of agreement of 0.63.     

基于PCA和SVM的遥感影像水体提取方法及验证

针对内陆湖泊水质及光谱特性空间差异性大、支流水系结构复杂而导致的遥感影像水体提取精度低的问题,提出了结合光谱主成分分析(PCA)及支持向量机(SVM)的PCA-SVM水体提取算法。基于GF-1卫星遥感影像,对原始影像光谱波段特征进行PCA降维,从中优选熵、方差、差异性纹理特征向量,结合原始波段及归一化差分水体指数(NDWI),构建了8维特征向量,并基于SVM算法提取湖泊水体。以巢湖洪水期与非洪水期影像为研究实例,分别采用NDWI法、传统SVM算法及PCA-SVM算法对水体进行提取,并进一步基于PCA-SVM算法对2020年汛期巢湖洪水期淹没演变过程进行反演和跟踪,定量解析特征向量组合及SVM惩罚系数C对水体提取性能的影响。结果表明：PCA-SVM算法提取的湖泊完整、支流连续,显著改善了含蓝藻水体漏提、建筑物误提等问题;洪水期和非洪水期提取结果的F1分数分别为95.08%和97.95%,虚警率分别为5.43%和1.13%,提取精度显著高于NDWI法和SVM算法。     

基于FY-3C MERSI影像的洱海水体提取研究

遥感技术在水体监测方面已有成熟应用,风云三号是我国自主研发卫星,时间分辨率高、覆盖范围广,能短时间内完整监测大范围水体。针对国产FY-3系列中分辨率卫星在水体信息自动化提取中的应用问题,以FY-3C MERSI影像为数据源,采用归一化差异水体指数(NDWI)结合大津算法(Otsu)进行阈值分割,提取出洱海水体边界,通过与采用相同方法的邻近时期Landsat 8 OLI影像水体提取结果的对比分析,发现二者水体边界整体拟合较好,面积误差为1.53%,FY-3C MERSI影像较为快速、准确地提取出了洱海水体。表明MERSI数据在水体信息自动化提取方面具有较高的应用价值和潜力,可用于较大面积湖泊的水体提取。     

Satellite based lake bed elevation model of Lake Urmia using time series of Landsat imagery

This study presents a lake bed elevation model of Lake Urmia. In the course of model generation, a time series of the extent of the lake surface was derived from 129 satellite images with different acquisition dates based on the Landsat sensors Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI). Due to the rapid shrinking of the lake during the last two decades, lake surface areas ranging from 890 km² to 6125 km² could be covered. The water edge of the various lake extents was then linked to the observed water level on the day of the satellite image acquisition. The resulting contour lines, covering water levels between 1270.04 m and 1278.42 m a.s.l. and thus representing the lakebed morphology in its shallow parts, were merged with existing data (deeper parts) and interpolated to generate a lake bed elevation model. Finally, Lake Urmia’s Level-Area-Volume relationships were derived from the lake bed elevation model and compared to bathymetric data previously published.     

Integrating Remote Sensing and GIS Techniques with Groundwater Flow Modeling for Assessment of Waterlogged Areas

False Colour Composites (FCC's) of IRS-1A LISS-II sensor pertaining to the dates 9th April 1989 and 7th December 1989 are used to delineate the pre-monsoon and post-monsoon surface waterlogged areas in a region around Habibpur sub-distributary bounded by Vaishali branch canal and Gandak river in North Bihar, India for the year 1989 using visual interpretation technique. Also, digital data of IRS-1C LISS-III sensor pertaining to the dates 7th December 1998 and 6th April 1999 are analyzed in a digital image processing software – ERDAS Imagine 8.3.1, to delineate the pre-monsoon and post-monsoon surface waterlogged areas for the year 1998–1999. Further, for the study area, the waterlogging conditions are delineated for the year 1991–1992 using the groundwater flow modeling software package, MODFLOW. The results obtained using satellite remote sensing data and groundwater flow modeling are integrated in a GIS environment in ERDAS Imagine for assessment of the waterlogging areas.     

Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data

Accurate estimates of vegetation biophysical variables are valuable as input to models describing the exchange of carbon dioxide and energy between the land surface and the atmosphere and important for a wide range of applications related to vegetation monitoring, weather prediction, and climate change. The present study explores the benefits of combining vegetation index and physically based approaches for the spatial and temporal mapping of green leaf area index (LAI), total chlorophyll content (TC_ab), and total vegetation water content (VWC). A numerical optimization method was employed for the inversion of a canopy reflectance model using Terra and Aqua MODIS multi-spectral, multi-temporal, and multi-angle reflectance observations to aid the determination of vegetation-specific physiological and structural canopy parameters. Land cover and site-specific inversion modeling was applied to a restricted number of pixels to build multiple species- and environmentally dependent formulations relating the three biophysical properties of interest to a number of selected simpler spectral vegetation indices (VI). While inversions generally are computationally slow, the coupling with the simple and computationally efficient VI approach makes the combined retrieval scheme for LAI, TC_ab, and VWC suitable for large-scale mapping operations. In order to facilitate application of the canopy reflectance model to heterogeneous forested areas, a simple correction scheme was elaborated, which was found to improve forest LAI predictions significantly and also provided more realistic values of leaf chlorophyll contents.The inversion scheme was designed to enable biophysical parameter retrievals for land cover classes characterized by contrasting canopy architectures, leaf inclination angles, and leaf biochemical constituents without utilizing calibration measurements. Preliminary LAI validation results for the Island of Zealand, Denmark (57°N, 12°E) provided confidence in the approach with root mean square (RMS) deviations between estimates and in-situ measurements of 0.62, 0.46, and 0.63 for barley, wheat, and deciduous forest sites, respectively. Despite the independence on site-specific in-situ measurements, the RMS deviations of the automated approach are in the same range as those established in other studies employing field-based empirical calibration.Being completely automated and image-based and independent on extensive and impractical surface measurements, the retrieval scheme has potential for operational use and can quite easily be implemented for other regions. More validation studies are needed to evaluate the usefulness and limitations of the approach for other environments and species compositions.     

Foliar nutrient and water content in subtropical tree islands: A new chemohydrodynamic link between satellite vegetation indices and foliar δN values

We examined the relationships between two satellite-derived vegetation indices and foliar δ¹⁵N values obtained from dominant canopy species in a set of tree islands located in the Everglades National Park in South Florida, USA. These tree islands constitute important nutrient hotspots in an otherwise P-limited wetland environment. Foliar δ¹⁵N values obtained from a previous study of 17 tree islands in both slough (perennially wet) and prairie (seasonally wet) locations served as a proxy of P availability at the stand level. We utilized five cloud-free SPOT 4 multispectral images (20 m spatial resolution) from different times of the seasonal cycle to derive two atmospherically corrected vegetation indices: the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI), averaged for each tree island. NDWI, which incorporates a shortwave infrared (SWIR) band that provides information on leaf water content, showed consistently higher linear fits with island foliar δ¹⁵N values than did NDVI. In addition, NDWI showed greater variation throughout the seasonal cycle than did NDVI, and was significantly correlated with average water stage, which suggests that the SWIR band captures important information on seasonally variable water status. Tree islands in slough locations showed higher NDWI than prairie islands during the dry season, which is consistent with higher levels of transpiration and nutrient harvesting and accumulation for perennially wet locations. Overall, the results suggest that water availability is closely related to P availability in subtropical tree islands, and that NDWI may provide a robust indicator of community-level water and nutrient status.     

博斯腾湖湖岸线时空变化特征

利用2006-2016年内的5期landsat遥感数据影像来初步研究博斯腾湖湖岸线在时间和空间两个维度的变化特征以及博斯腾湖湖岸线的变化趋势。通过运用水体指数找出博斯腾湖的水体,提取这5期遥感数据中博斯腾湖的面积和周长,进而计算岸线发育系数、形状复杂程度和圆形度来表征岸线的变化特征。研究结果表明:博斯腾湖湖岸线呈阶段性的变化趋势,2006-2013年间博斯腾湖湖岸线的面积从990. 78 km~2减少到902. 25 km~2,2013-2016年期间博斯腾湖湖岸线面积从902. 25 km~2增加到963. 48 km~2。博斯腾湖湖岸线的面积呈现先减少后增加的过程;博斯腾湖湖岸线的周长也呈现阶段性变化,从2006年的325. 32 km减小到2013年269. 33 km,而2013-2016年为增加趋势,从2013年269. 33 km增加到2016年的331. 93 km。在空间上博斯腾湖的西北角的湿地在2006-2011年向东南变化剧烈,2011-2016年表现稳定,在博斯腾湖的南岸和东南角也出现了周期性的变化,岸线2006-2013年呈现向北部缩小的特征,2013-2016年则向南变化,且变化幅度较大。     

An integrated flood management system based on linking environmental models and disaster-related data

The flood disaster management system (FDMS) is a platform developed to provide ongoing disaster reduction capabilities that cover the entire process of flood management. The ontology-based approach links environmental models with disaster-related data to support FDMS-constructed workflows with suitable models and recommend appropriate datasets as model input automatically. This automated activity for model selection and data binding reduces the time-consuming and unreliable operations involved in traditional management techniques, which rely on manual retrieval through simple metadata indices—typically when flood management personnel are overwhelmed with large quantities of observed data. The OpenMI-based modular design used in the system unifies interfaces and data exchange to provide flexible and extensible architecture. Subsequent 3D visualization improves the interpretability of disaster data and the effectiveness of decision-making processes. This paper presents an overview of the design and capabilities of FDMS that provides one-stop management for flood disasters.     

Determination of phenological dates in boreal regions using normalized difference water index

Monitoring and understanding plant phenology are important in the context of studies of terrestrial productivity and global change. Vegetation phenology, such as dates of onsets of greening up and leaf senescence, has been determined by remote sensing using mainly the normalized difference vegetation index (NDVI). In boreal regions, the results suffer from significant uncertainties because of the effect of snow on NDVI. In this paper, SPOT VEGETATION S10 data over Siberia have been analysed to define a more appropriate method. The analysis of time series of NDVI, normalized difference snow index (NDSI), and normalized difference water index (NDWI), together with an analysis of in situ phenological records in Siberia, shows that the vegetation phenology can be detected using NDWI, with small effect of snow. In spring, the date of onset of greening up is taken as the date at which NDWI starts increasing, since NDWI decreases with snowmelt and increases with greening up. In the fall, the date of onset of leaf coloring is taken as the date at which NDWI starts decreasing, since NDWI decreases with senescence and increases with snow accumulation. The results are compared to the results obtained using NDVI-based methods, taking in situ phenological records as the reference. NDWI gives better estimations of the start of greening up than NDVI (reduced RMSE, bias and dispersions, and higher correlation), whereas it does not improve the determination of the start of leaf coloring. A map of greening up dates in central Siberia obtained from NDWI is shown for year 2002 and the reliability of the method is discussed.