Development of large-area land cover and forest change indicators using multi-sensor Landsat imagery: Application to the Humber River Basin,Canada

Monitoring ecological indicators is important for assessing impacts of human activities on ecosystems. A means of identifying and applying appropriate indicators is a prerequisite for: environmental assessment; better assessment and understanding of ecosystem health; elucidation of biogeochemical trends; and more accurate predictions of future responses to global change, particularly those due to anthropogenic disturbance. The challenge is to derive meaningful indicators of change that capture the complexities of ecosystems yet can be monitored consistently over large areas and across time. In this study, methods for monitoring indicators of land cover (LC) and forest change were developed using multi-sensor Landsat imagery. Mapping and updating procedures were applied to the Humber River Basin (HRB) in Newfoundland and Labrador, one of four test sites in Canada selected for testing the development of national-scale methods. Procedures involved unsupervised clustering and labeling of baseline imagery, followed by image-to-image spectral clustering to derive binary change masks within which new LC types were classified for non-baseline imagery. Updated maps were compatible with the baseline map and reflected change in LC for three time periods: 1976–1990, 1990–2001, and 2001–2007. From the LC products, several change indicators were quantified including: forest depletion, forest regeneration, forest change, net forest change, and annual rates of change. The procedures were validated using field plots to assess the accuracy of the 2007 LC product (74.2% for 10 LC classes) and change classes observed from 2001 to 2007 (87.8% for four change classes: depletion, regeneration, non-treed class no change, and treed class no change). Methods were considered to be highly efficient and operationally feasible over large areas spanning multiple Landsat scenes. Specific results for the test site provided trend information supporting land and resource management in the HRB region.     

Demonstration of a virtual active hyperspectral LiDAR in automated point cloud classification

In this paper, a measurement system for the acquisition of a virtual hyperspectral LiDAR dataset is presented. As commercial hyperspectral LiDARs are not yet available, the system provides a novel type of data for the testing and developing of future hyperspectral LiDAR algorithms. The measurement system consists of two parts: first, backscattered reflectance spectra are collected using a spectrometer and a cutting-edge technology, white-light supercontinuum laser source; second, a commercial monochromatic LiDAR system is used for ranging. A virtual hyperspectral LiDAR dataset is produced by data fusion. Such a dataset was collected on a Norway spruce (Picea abies) sample. The performance of classification was tested using an experimental hyperspectral algorithm based on a novel combination of the Spectral Correlation Mapper and a region growing algorithm. The classifier was able to automatically distinguish between needles, branches and background, in other words, perform a difficult task using only traditional TLS data.     

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.     

全球尺度下遥感与地理信息系统一体化软件平台研建进展

地理信息科学与技术在全球变化和地球系统科学研究中,正发挥越来越重要的作用。而在全球尺度下开展分析,必须发展用基于3维坐标进行分析,而且要具备全球尺度上的地表信息提取能力。并支持全球尺度的空间分析与应用的软件。介绍了在Google Earth基础上,开发的遥感和地理信息系统一体化软件平台：全球制图者（Global Mapper,GM）和全球分析者（Global Analyst,GA）。该平台除具备GE强大的空间数据浏览功能之外,还建立了全球地表信息采集与管理、建库与地图叠加、全球图像处理、可视化展示与球面空间分析等技术。该软件突破了传统的2维坐标分析,改变了数据获取和数据管理的方式方法,将在地球系统科学与全球变化研究中发挥重要作用。     

基于ArcGIS和Global Mapper软件的三维地形可视化技术的应用

以某项目的地形测绘为例,阐述基于ArcGIS和Global Mapper软件实现三维地形可视化与测绘的方法及流程,重点论述了数据的采集与处理,三维地形的模拟,海量数据的实时显示,三维地形应用,为用户提供更为直观、实时量测。实践证明：此方法建立的三维地形符合工程应用的要求,具有良好的可行性。     

Biophysical and Spectral Characteristics of Cool- and Warm-Season Grasslands under Three Land Management Practices in Eastern Kansas

Grasslands are the largest of the Earth's four major vegetation types and are among the most agriculturally productive lands. Grassland management practices alter biophysical factors, such as plant species composition, soil structure and chemistry, and primary productivity. Large-scale changes in land management practices can have significant impacts on available natural resources, therefore impacting a regional socioeconomics. For these reasons, an improved understanding of the geographic distribution of changing land management practices and how these changes affect grassland biophysical characteristics of an area is critical to improve land use planning and practices. This study investigated the spectral characteristics, as measured using satellite remotelysensed data, of three land management practices [grazing, haying, and management under Conservation Reserve Program (CRP)] on areas dominated by cool- and warm-season grasslands. The satellite imagery was acquired in May, July, and September in 1997 over Douglas County, Kansas. Biomass, vegetation cover, and species richness data were collected around the satellite overflight periods. Our results indicate that the biophysical characteristics and spectral reflectances are significantly different among the three management practices over cool- and warm-season grasslands. Interestingly, sites grazed by livestock had the highest species richness and the highest forb cover, although the total cover is almost the same as the other practices, and they have the highest near-infrared reflectance throughout the growing season.     

月球东海盆地的矿物光谱特征及遥感探测

东海盆地是月球上最年轻的大型撞击盆地之一,其地形地貌和矿物与岩石类型分布将有助于我们深入理解月球撞击盆地形成过程和地质演化历史。LOLA高程数据揭示东海盆地为保存完好的多环撞击盆地。基于月球矿物绘图仪（M3）反射率数据,在东海盆地发现了尖晶石、辉石、结晶斜长石、橄榄石等矿物,采用修正高斯模型（MGM）进行混合矿物光谱分解获取了矿物端员,利用光谱角分类方法（SAM）作出了Maunder 撞击坑的主要矿物分布图。发现的纯结晶斜长石矿物与最近其他月球探测（如Kaguya)相吻合,为月球岩浆洋模型提供了新约束条件。在Lowell撞击坑中央峰发现尖晶石分布,并利用多光谱成像仪（MI）数据进行了验证;利用MGM方法,在Maunder建造上发现结晶斜长石与尖晶石的混合矿物,我们通过分析认为东海盆地的尖晶石在外卢克山脉上可能有更广泛的分布。     

Google Earth 影像在地理国情普查中的补充使用

介绍了利用Google Earth数据作为地理国情普查数据的补充资料的3种方法,总结Goole Earth数据以及Global Mapper软件在地理国情普查工作的应用的一些经验和体会。     

基于混合光谱理论的太湖水体叶绿素a浓度提取

TM图象多波段数据作为遥感监测水体叶绿素a浓度的数据源,已有多种遥感定量模型与之对应,但主要还是以经验模型为主。利用TM数据首先采用特征波段比值方法,建立太湖水体合适的叶绿素a浓度反演的遥感定量模型。由于经验模型的缺陷性,还提出了基于TM数据的水体混合光谱分析模型,同时分析了端元光谱提取方法对模型求解的影响。通过计算叶绿素a浓度模型估算结果与实地测量数据的相关系数和均方根误差(RMSE),可以发现混合光谱分析模型也是水体叶绿素a浓度遥感估算的另一条佳径。