A time-integrated MODIS burn severity assessment using the multi-temporal differenced normalized burn ratio (dNBRMT)

Burn severity is an important parameter in post-fire management. It incorporates both the direct fire impact (vegetation depletion) and ecosystem responses (vegetation regeneration). From a remote sensing perspective, burn severity is traditionally estimated using Landsat's differenced normalized burn ratio (dNBR). In this case study of the large 2007 Peloponnese (Greece) wildfires, Landsat dNBR estimates correlated reasonably well with Geo composite burn index (GeoCBI) field data of severity (R² = 0.56). The usage of Landsat imagery is, however, restricted by cloud cover and image-to-image normalization constraints. Therefore a multi-temporal burn severity approach based on coarse spatial, high temporal resolution moderate resolution imaging spectroradiometer (MODIS) imagery is presented in this study. The multi-temporal dNBR (dNBR_MT) is defined as the 1-year integrated difference between burned pixels and their unique control pixels. These control pixels were selected based on time series similarity and spatial context and reflect how burned pixels would have behaved in the case no fire had occurred. Linear regression between downsampled Landsat dNBR and dNBR_MT estimates resulted in a moderate-high coefficient of determination R² = 0.54. dNBR_MT estimates are indicative for the change in vegetation productivity due to the fire. This change is considerably higher for forests than for more sparsely vegetated areas like shrub lands. Although Landsat dNBR is superior for spatial detail, MODIS-derived dNBR_MT estimates present a valuable alternative for burn severity mapping at continental to global scale without image availability constraints. This is beneficial to compare trends in burn severity across regions and time. Moreover, thanks to MODIS's repeated temporal sampling, the dNBR_MT accounts for both first- and second-order fire effects.     

Integrating TM and ancillary geographical data with classification trees for land cover classification of marsh area

The main objective of this research is to determine the capacity of land cover classification combining spectral and textural features of Landsat TM imagery with ancillary geographical data in wetlands of the Sanjiang Plain, Heilongjiang Province, China. Semi-variograms and Z-test value were calculated to assess the separability of grey-level co-occurrence texture measures to maximize the difference between land cover types. The degree of spatial autocorrelation showed that window sizes of 3×3 pixels and 11×11 pixels were most appropriate for Landsat TM image texture calculations. The texture analysis showed that co-occurrence entropy, dissimilarity, and variance texture measures, derived from the Landsat TM spectrum bands and vegetation indices provided the most significant statistical differentiation between land cover types. Subsequently, a Classification and Regression Tree (CART) algorithm was applied to three different combinations of predictors: 1) TM imagery alone (TM-only); 2) TM imagery plus image texture (TM+TXT model); and 3) all predictors including TM imagery, image texture and additional ancillary GIS information (TM+TXT+GIS model). Compared with traditional Maximum Likelihood Classification (MLC) supervised classification, three classification trees predictive models reduced the overall error rate significantly. Image texture measures and ancillary geographical variables depressed the speckle noise effectively and reduced classification error rate of marsh obviously. For classification trees model making use of all available predictors, omission error rate was 12.90% and commission error rate was 10.99% for marsh. The developed method is portable, relatively easy to implement and should be applicable in other settings and over larger extents.     

IRS - P6卫星LISS3图像数据质量评价

利用辐射精度、清晰度、信息量、信噪比、几何精度、地面分辨率6个数据质量评价指标,对LISS3与TM图像进行对比分析。研究结果表明,LISS3图像在地面分辨率和信噪比方面有显著提高,几何精度(均方根误差为0.28)小于1/2个像元,在绿(0.52~0.59μm)、红(0.62~0.68μm)和近红外(0.77~0.86μm)波段其辐射精度、清晰度、信息量均高于TM图像,在短波红外波段(1.55~1.70μm)相应指标略低于TM图像。分析LISS3在短波红外波段图像质量指标降低的原因,提出增加LISS3第5波段宽度、新增波长2.2~2.5μm的短波红外波段以改进LISS3传感器的波段参数。     

江苏海岸王港地区盐沼植被变化的TM图像分析

通过对2002~2003年度3个时相的Landsat TM图像进行监督分类处理,研究了盐沼植被的分布和变化。结果表明,目前盐蒿和大米草(Spartina anglica)占据的面积很小,年际变化不大,由于滩涂围垦的影响其面积还在进一步缩小。互花米草(Spartina alterniflora)成为优势种,占据了大部分的盐沼面积,向海蔓延显著,扩张的趋势还在持续,其覆盖面积的年增长速率可达18%。     

HJ-1A星辐射定标及近海水体大气校正的精度验证

以应用HJ-1A星数据进行近海水体定量反演为目的,对HJ-1A星CCD的星上定标系数的精度进行验证,并基于Landsat-5TM数据对其进行交叉定标。在此基础上反演近海水体的反射率,将反演结果与实测水体反射率进行比较发现:对HJ-1A星CCD数据进行交叉定标能在一定程度上能提高近海水体反射率反演的精度。结果表明,HJ-1A星CCD影像的星上定标系数不够精确,在利用该数据进行近海水体的定量反演研究时,需要利用交叉定标对星上定标参数进行修正。     

Satellite Remote Sensing as a Reconnaissance Tool for Assessing Nautical Chart Adequacy and Completeness

National hydrographic offices need a better means of assessing the adequacy of existing nautical charts in order to plan and prioritize future hydrographic surveys. The ability to derive bathymetry from multispectral satellite imagery is a topic that has received considerable attention in scientific literature. However, published studies have not addressed the ability of satellite-derived bathymetry to meet specific hydrographic survey requirements. Specifically, the bathymetry needs to be referenced to a chart datum and statistical uncertainty estimates of the bathymetry should be provided. Ideally, the procedure should be based on readily-available, low-cost software, tools, and data. This paper describes the development and testing of a procedure using publicly-available, multispectral satellite imagery to map and portray shallow-water bathymetry in a GIS environment for three study sites: Northeast United States, Nigeria, and Belize. Landsat imagery and published algorithms were used to derive estimates of the bathymetry in shallow waters, and uncertainty of the satellite-derived bathymetry was then assessed using a Monte Carlo method. Results indicate that the practical procedures developed in this study are suitable for use by national hydrographic offices.     

海岸带滩涂地貌演变遥感方法研究

遥感技术是进行海岸带资源调查的有效技术手段,对水利工程规划、防灾减灾、滩涂开发利用等方面有较大的实用价值。在海岸带资源调查中,传统的地面调查方法不仅工作量大,而且实施起来非常困难;遥感技术由于具有感测范围大、获取信息快、更新周期短的特点,为海岸带资源调查和地貌演变的分析和研究提供了较好的方法和手段。文章探讨了遥感技术在江苏省安台线(海安、东台之间海域界线)勘界工作中的运用,采用较新的卫星遥感影像作为工作底图,同时利用1988—2003年之间不同时相的七景影像监测滩涂的演变,在提供了准确的滩涂地形、地貌资料的基础上,通过监测潮沟的变化推断出滩涂的演变,为海域勘界工作提供了有力而可靠的依据。     

基于遥感影像的浙江省三门湾近40年海岸线及海岸带时空变化分析

根据1985、1995、2005、2013和2020年的Landsat卫星图像,采用人机交互手段将三门湾海岸线分为基岩岸线、砂质岸线、淤泥质岸线、人工岸线和河口岸线。采用交互式监督分类将三门湾海岸带的土地利用分为水体、林地、裸地、建设用地和农业用地。研究结果表明：近40年三门湾人工岸线和基岩岸线变化最大,分别增加了80.3 km和减少了79.3 km;近40年三门湾海岸带面积累计增加了130 km2,建设用地和水体是围填海新增土地的主要利用类型,主要的土地转移形式为裸地向建设用地、农业用地的转变以及林地转为建设用地;近40年三门湾岸线分形维数由1.22减小至1.201,人工岸线比例和建设用地占比分别上升了13%和22.57%,总体上呈现向海推进、人工化加强的趋势。人类活动是造成三门湾岸线及海岸带变化的主要原因,随着对海岸带保护与合理利用的推进,三门湾海岸带开发利用格局正逐渐稳中向好。     

绿潮Lansat影像滑动窗口自适应阈值全自动检测方法

Since the atmospheric correction is a necessary preprocessing step of remote sensing image before detecting green tide, the introduced error directly affects the detection precision. Therefore, the detection method of green tide is presented from Landsat TM/ETM plus image which needs not the atmospheric correction. In order to achieve an automatic detection of green tide, a linear relationship(y =0.723 x+0.504) between detection threshold y and subtraction x(x=λ_(nir)–λ_(red)) is found from the comparing Landsat TM/ETM plus image with the field surveys.Using this relationship, green tide patches can be detected automatically from Landsat TM/ETM plus image.Considering there is brightness difference between different regions in an image, the image will be divided into a plurality of windows(sub-images) with a same size firstly, and then each window will be detected using an adaptive detection threshold determined according to the discovered linear relationship. It is found that big errors will appear in some windows, such as those covered by clouds seriously. To solve this problem, the moving step k of windows is proposed to be less than the window width n. Using this mechanism, most pixels will be detected[n/k]×[n/k] times except the boundary pixels, then every pixel will be assigned the final class(green tide or sea water) according to majority rule voting strategy. It can be seen from the experiments, the proposed detection method using multi-windows and their adaptive thresholds can detect green tide from Landsat TM/ETM plus image automatically. Meanwhile, it avoids the reliance on the accurate atmospheric correction.     

基于遥感数据的渤海海冰面积提取订正模型研究

为了获取准确的海冰信息,利用高空间分辨率和高时间分辨率数据相结合的方法来测算海冰面积,在假设TM海冰面积是比较准确的前提下,用TM海冰面积来订正MODIS和NOAA／AVHRR海冰面积,以提高这两种高时间分辨率遥感资料对海冰面积的判别精度。选取2003年2月5日的TM、MODIS和NOAA／AVHRR数据,在这3个图像上分别选择20个样本,然后进行了海冰面积的线性回归分析,得到的海冰面积提取订正模型。并分别以此两种模型为基础,对2002年12月8日至2003年2月26日和2005年2月1日至2月11日冬季渤海海冰面积进行修正,并将由MODIS和NOAA／AVHRR提取的海冰面积、用模型订正后和国家海洋环境预报中心给出的海冰面积做了详细的比较。结果表明：线性回归可以提高海冰信息的提取精度。此研究为海冰防灾减灾和海冰资源量估算提供依据。