Updating the 2001 National Land Cover Database Impervious Surface Products to 2006 using Landsat Imagery Change Detection Methods

A prototype method was developed to update the U.S. Geological Survey (USGS) National Land Cover Database (NLCD) 2001 to a nominal date of 2006. NLCD 2001 is widely used as a baseline for national land cover and impervious cover conditions. To enable the updating of this database in an optimal manner, methods are designed to be accomplished by individual Landsat scene. Using conservative change thresholds based on land cover classes, areas of change and no-change were segregated from change vectors calculated from normalized Landsat scenes from 2001 and 2006. By sampling from NLCD 2001 impervious surface in unchanged areas, impervious surface predictions were estimated for changed areas within an urban extent defined by a companion land cover classification. Methods were developed and tested for national application across six study sites containing a variety of urban impervious surface. Results show the vast majority of impervious surface change associated with urban development was captured, with overall RMSE from 6.86 to 13.12% for these areas. Changes of urban development density were also evaluated by characterizing the categories of change by percentile for impervious surface. This prototype method provides a relatively low cost, flexible approach to generate updated impervious surface using NLCD 2001 as the baseline.     

基于Radarsat2与Landsat8协同反演植被覆盖地表土壤水分的一种新方法

主动微波遥感与被动光学遥感在反演地表土壤水分方面分别具有各自的优缺点,为了将这两者的优势结合弥补缺点,提出了一种基于Radarsat 2与Landsat 8数据协同反演植被覆盖地表土壤水分的半经验耦合模型.该模型基于水云模型,将光学遥感反演得到的植被冠层含水量作为水云模型的关键输入参数,并同时考虑植被冠层与土壤以及其之间的部分对雷达后向散射系数的影响,以此来去除雷达回波中的植被部分.最后选用内蒙古呼伦贝尔市额尔古纳市大兴安岭西侧研究区的Radarsat 2与Landsat 8遥感数据,利用新的耦合模型反演得到植被覆盖区土壤水分含量,并利用地面测量数据对模型进行验证.结果表明:利用Landsat 8数据反演植被含水量算法精度较高(R2=0.89),论文提出的耦合模型反演植被覆盖地表土壤水分精度比之前算法也有了较大的提高,其中HH极化效果最好,R2由0.27提高至0.65.这表明该耦合模型具有较好的反演精度,可以应用于植被覆盖区土壤水分含量的反演.     

最小二乘法联合光学与雷达遥感数据估算玉米叶面积指数

针对单源数据经验模型估算精度较低等问题,提出采用最小二乘法联合光学和雷达遥感数据构建联合估算模型,以中国科学院河北怀来遥感综合实验站为研究区,以夏季玉米为研究对象,利用Landsat8和Radarsat2影像实现研究区叶面积指数估算:首先分别建立了多光谱数据和雷达数据与实测叶面积指数之间的回归模型,然后利用最小二乘算法联合不同数据间的回归模型构建估算模型,最后利用迭代法估算叶面积指数并通过验证数据对估算结果进行评价分析,同时与单源数据经验模型、多源数据加权平均模型和基于物理模型查找表估算结果进行对比。通过对研究区59个样本点数据分析表明:基于最小二乘算法联合光学与雷达遥感数据能够提高叶面积指数的估算精度(R2=0.5442,RMSE=0.81),优于单源遥感数据拟合经验模型(DVI经验模型:(R2=0.485,RMSE=1.27))、基于权重的光学微波联合模型(R2=0.447,RMSE=1.36)和物理模型查找表法(R2=0.333,RMSE=1.36),并当叶面积指数大于3时,对其由于信息饱和或误差引起的低估或高估现象具有一定的抑制作用。     

HJ-1B和Landsat卫星蓝藻水华监测能力评估——以洱海为例

蓝藻水华是富营养化湖泊共同面临的问题。遥感技术为快速、大范围水华监测提供了可能,选取遥感数据应首先明确不同卫星的水华监测能力。以洱海为例,对比分析HJ-1B和Landsat卫星在内陆中小湖泊水华监测的时间和空间监测能力,评价两者在水华监测中的适用性及优势。结果表明:两者均能有效识别水华,提取水华分布细节信息,相比MODIS更适合用于中小湖泊水华监测;进一步分析表明,综合两者数据监测蓝藻水华,可以更加客观统计水华时间特征,描述水华空间分布发展规律,对于其它中小湖泊利用遥感手段辅助水华监测具有参考意义。     

Characterizing the state and processes of change in a dynamic forest environment using hierarchical spatio-temporal segmentation

Discrete changes in forest abundance, distribution, and productivity are readily detectable using a number of remotely sensed data sources; however, continuous changes such as growth and succession processes are more difficult to monitor. In this research we explore the potential of spectral trajectories generated from a 35-year (1973-2008) time-series of Landsat imagery to characterize change processes in a dynamic forest environment in northwestern Alberta, Canada. We propose a method of hierarchical spatio-temporal segmentation that enables the characterization of change processes that are spatially diffuse and temporally imprecise. Calibrated imagery from Landsat sensors are radiometrically normalized and two metrics derived from the Tasseled Cap Transformation components, greenness and brightness, are used to generate the Tasseled Cap Angle (TCA). The TCA is a measure of the proportion of vegetation to non-vegetation (the occupation state), and its derivative, the Process Indicator (PI), is a measure of change in this proportion through time. These indices condense information from the visible and near-infrared wavelengths, and facilitate lengthy time series analysis of forest landscape change using data from all Landsat sensors.A combination of the original TCA and its derivative sequence are input to a three level hierarchical segmentation process with the highest and lowest levels defining homogeneous objects at the initial and final date, and the intermediate level identifying trajectories with similar change processes. The development through time of the TCA and PI are described, and the spatial and temporal associations of processes are statistically assessed using the Moran's Index.A full range of change types were identified on the landscape, from stand replacing disturbances to more subtle growth and succession processes. Results indicate that the study area is in a constant state of change, and maintains a high average proportion of vegetation to non-vegetation. The amount of total landscape modified per decade increased from 18% and 14% in the 1970s and 1980s respectively, to more than 30% and 33% in the 1990s and 2000s. On average, the proportion of vegetation to non-vegetation was increasing prior to 1981, decreasing between 1981 and 1997, and increasing post-1997. There was a high degree of spatial autocorrelation amongst change processes, with a maximum Moran's I of 0.79 in 1973; landscape change became more spatially disperse and widespread after 1981. Temporal correlation of change processes was observed locally, with the period 1990-1995 having the most persistent change.     

Evaluation of integrated data sets—Four examples

Several large data sets have been integrated and utilized for rapid evaluation on a reconnaissance scale for the Montrose 1 × 2° Quadrangle, Colorado. The data sets include Landsat imagery, hydrogeochemical and stream sediment analyses, airborne geophysical data, known mineral occurrences, and a geologic map. All data sets were registered to a 179 × 119 rectangular grid and projected onto Universal Transverse Mercator coordinates. A grid resolution of 1 km was used. All possible combinations of three, for most data sets, were examined for general geologic correlations by utilizing a color microfilm output. In addition, gray-level pictures of statistical output, for example factor analysis, have been employed to aid evaluations. Examples for the data sets dysprosium-calcium, lead-copper-zinc, and equivalent uranium-uranium in water-uranium in sediment are described with respect to geologic applications, base-metal regimes, and geochemical associations.     

Extension of retrospective datasets using multiple sensors. An approach to radiometric intercalibration of Landsat TM and MSS data

The establishment of monitoring frameworks for environmental problems is frequently based on retrospective, multi-temporal series of satellite images. The derivation of concise conclusions from these series largely depends on their quantitative consistency; hence, a completely standardized analysis of multi-temporal time series is mandatory. In the context of the required radiometric rectification, sensor calibration is an essential component, the accuracy of which largely determines the overall result. While for newer sensor systems extensive documentation on the development of the sensor's sensitivity exists, older systems often lack the corresponding information.A methodology has been developed, tested and validated to radiometrically intercalibrate different sensor systems based on the precondition of simultaneous image acquisition, which is for example provided by the Landsat TM and MSS sensor systems hosted on Landsat 5. Different interpolation and processing steps within the procedure have been analyzed, and an intercalibration scheme is proposed to derive Landsat MSS calibration factors from Landsat TM, which is used as a calibrated reference. It supports a full radiometric rectification of Landsat MSS data and enables their incorporation into satellite image time series, which can thus be significantly extended by a maximum of 12 years.     

Comparison of burned area estimates derived from SPOT-VEGETATION and Landsat ETM+ data in Africa: Influence of spatial pattern and vegetation type

An algorithm for burned area mapping in Africa based on classification trees was developed using SPOT-VEGETATION (VGT) imagery. The derived 1 km spatial resolution burned area maps were compared with 30 m spatial resolution maps obtained with 13 Landsat ETM+ scenes, through linear regression analysis. The procedure quantifies the bias in burned area estimation present in the low spatial resolution burned area map. Good correspondence was observed for seven sites, with values of the coefficient of determination (R²) ranging from 0.787 to 0.983. Poorer agreement was observed in four sites (R² values between 0.257 and 0.417), and intermediate values of R² (0.670 and 0.613) were obtained for two sites. The observed variation in the level of agreement between the Landsat and VGT estimates of area burned results from differences in the spatial pattern and size distribution of burns in the different fire regimes encompassed by our analysis. Small and fragmented burned areas result in large underestimation at 1 km spatial resolution. When large and compact burned areas dominate the landscape, VGT estimates of burned area are accurate, although in certain situations there is some overestimation. Accuracy of VGT burned area estimates also depends on vegetation type. Results showed that in forest ecosystems VGT maps underestimate substantially the amount of burned area. The most accurate estimates were obtained for woodlands and grasslands. An overall linear regression fitted with the data from the 13 comparison sites revealed that there is a strong relationship between VGT and Landsat estimates of burned area, with a value of R² of 0.754 and a slope of 0.803. Our findings indicate that burned area mapping based on 1 km spatial resolution VGT data provides adequate regional information.     

Assessment of estuarine water-quality indicators using MODIS medium-resolution bands: Initial results from Tampa Bay, FL

Using Tampa Bay, FL as an example, we explored the potential for using MODIS medium-resolution bands (250- and 500-m data at 469-, 555-, and 645-nm) for estuarine monitoring. Field surveys during 21-22 October 2003 showed that Tampa Bay has Case-II waters, in that for the salinity range of 24-32 psu, (a) chlorophyll concentration (11 to 23 mg m⁻³), (b) colored dissolved organic matter (CDOM) absorption coefficient at 400 nm (0.9 to 2.5 m⁻¹), and (c) total suspended sediment concentration (TSS: 2 to 11 mg L⁻¹) often do not co-vary. CDOM is the only constituent that showed a linear, inverse relationship with surface salinity, although the slope of the relationship changed with location within the bay. The MODIS medium-resolution bands, although designed for land use, are 4-5 times more sensitive than Landsat-7/ETM+ data and are comparable to or higher than those of CZCS. Several approaches were used to derive synoptic maps of water constituents from concurrent MODIS medium-resolution data. We found that application of various atmospheric-correction algorithms yielded no significant differences, due primarily to uncertainties in the sensor radiometric calibration and other sensor artifacts. However, where each scene could be groundtruthed, simple regressions between in situ observations of constituents and at-sensor radiances provided reasonable synoptic maps. We address the need for improvements of sensor calibration/characterization, atmospheric correction, and bio-optical algorithms to make operational and quantitative use of these medium-resolution bands.