协同多时相波谱特征的不透水面信息级联提取

不透水面作为反应城市表征变化和区域城镇化的重要技术指标,其位置、图斑大小、空间分布等信息在地表水热循环和能量平衡等领域被广泛需求。传统方法大都基于单一时相信息提取不透水面,而忽略多时相所蕴含的丰富信息。因此,本文提出多时相信息融合的不透水面级联提取方法,利用Landsat-8 OLI遥感影像分析归一化植被指数（Normalized Difference Vegetation Index, NDVI）、改进的归一化水体指数（Modified Normalized Difference Water Index, MNDWI）和归一化建筑指数（Normalized Difference Building Index, NDBI）年内时序变化特点和典型地物间多时相波谱曲线的协同特征,并归纳不透水面多时相变化规律;再根据先验知识所获取的有效地表信息,进行多时相分级提取不透水面信息。此外,基于实地考察数据和同期2 m GF-1遥感影像屏幕数字化生成30 m不透水面图斑,进行精度验证、分析和对比单时相、四季相及多时相3种时序情况下的提取精度。结果表明：单时相提取不透水面总精度最低,四季相提取精度优于单时相,而多时相提取精度最高（精度可达93.66%,Kappa系数为0.81）。本方法在偏远城镇不透水面的有效识别中显露潜在优势,可为不透水面提取方法融合时序波谱特征提供新思路。     

A Landsat-based evaluation of lake water clarity in Maine lakes

Temporal trends in water clarity and land-use/land-cover (LULC), as well as the relationship between changes in water clarity and LULC, were analyzed using water clarity values extracted from Landsat images from 1986 to 2008, acquired for east-central Maine. Of 40 lakes identified using satellite imagery, our analysis found one lake with a significant decrease in water clarity. In a second data-set, with 99 lakes, we identified two lakes with a significant increase in water clarity. Analyses of the relationship between temporal changes in the water clarity and LULC did not identify any clear, consistent, relationships between changes in the water quality variables and LULC. Overall, the results of this study aid in the identification of the relationship between water clarity and LULC, and identify temporal changes in water clarity. The findings of this study support the previous research that demonstrates the ability of satellite imagery to be used in assessments of water clarity, thus enabling evaluation at broader spatial scales and longer temporal scales than assessments that rely solely on ground-based data.     

Mapping and monitoring of land use changes in post-Soviet western Ukraine using remote sensing data

While agriculture is expanded and intensified in many parts of the world, decreases in land use intensity and farmland abandonment take place in other parts. Eastern Europe experienced widespread changes of agricultural land use after the collapse of the Soviet Union in 1991, however, rates and patterns of these changes are still not well understood. Our objective was to map and analyze changes of land management regimes, including large-scale cropland, small-scale cropland, and abandoned farmland. Monitoring land management regimes is a promising avenue to better understand the temporal and spatial patterns of land use intensity changes. For mapping and change detection, we used an object-based approach with Superpixel segmentation for delineating objects and a Random Forest classifier. We applied this approach to Landsat and ERS SAR data for the years 1986, 1993, 1999, 2006, and 2010 to estimate change trajectories for this time period in western Ukraine. The first period during the 1990s was characterized by post-socialist transition processes including farmland abandonment and substantial subsistence agriculture. Later on, recultivation processes and the recurrence of industrial, large-scale farming were triggered by global food prices that have led to a growing interest in this region.     

Topographic and spectral data resolve land cover misclassification to distinguish and monitor wetlands in western Uganda

Wetlands provide vital wildlife habitat and ecosystem services, but changes in human land use has made them one of the world’s most threatened ecosystems. Although wetlands are generally protected by law, growing human populations increasingly drain and clear them to provide agricultural land, especially in tropical Africa. Managing and conserving wetlands requires accurately monitoring their spatial and temporal extent, often using remote sensing, but distinguishing wetlands from other land covers can be difficult. Here, we report on a method to separate wetlands dominated by papyrus (Cyperus papyrus L.) from spectrally similar grasslands dominated by elephant grass (Pennisetum purpureum Schumach.). We tested whether topographic, spectral, and temperature data improved land cover classification within and around Kibale National Park, a priority conservation area in densely populated western Uganda. Slope and reflectance in the mid-IR range best separated the combined papyrus/elephant grass pixels (average accuracy: 86%). Using a time series of satellite images, we quantified changes in six land covers across the landscape from 1984 to 2008 (papyrus, elephant grass, forest, mixed agriculture/bare soil/short grass, mixed tea/shrub, and water). We found stark differences in how land cover changed inside versus outside the park, with particularly sharp changes next to the park boundary. Inside the park, changes in land cover varied with location and management history: elephant grass areas decreased by 52% through forest regeneration but there was no net difference in papyrus areas. Outside the park, elephant grass and papyrus areas decreased by 61% and 39%, mostly converted to agriculture. Our method and findings are particularly relevant in light of social, biotic, and abiotic changes in western Uganda, as interactions between climate change, infectious disease, and changing human population demographics and distribution are predicted to intensify existing agricultural pressure on natural areas.     

Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data

Land cover classification of finer resolution remote sensing data is always difficult to acquire high-frequency time series data which contains temporal features for improving classification accuracy. This paper proposed a method of land cover classification with finer resolution remote sensing data integrating temporal features extracted from time series coarser resolution data. The coarser resolution vegetation index data is first fused with finer resolution data to obtain time series finer resolution data. Temporal features are extracted from the fused data and added to improve classification accuracy. The result indicates that temporal features extracted from coarser resolution data have significant effect on improving classification accuracy of finer resolution data, especially for vegetation types. The overall classification accuracy is significantly improved approximately 4% from 90.4% to 94.6% and 89.0% to 93.7% for using Landsat 8 and Landsat 5 data, respectively. The user and producer accuracies for all land cover types have been improved.     

Characterization of aboveground biomass in an unmanaged boreal forest using Landsat temporal segmentation metrics

Canada is dominated by forested ecosystems which are subject to various inventory and management practices, with more northern boreal forests subject to neither. Our objectives were to measure the capacity of temporal trajectory metrics for estimating selected forest attributes in a northern Canadian boreal forest context using Landsat imagery and investigate the importance of different types of temporal trajectory metrics. Results indicated that Wetness was the best Tasseled Cap (TC) component for aboveground biomass estimation (R² = 50%, RMSE% = 56%), and the combination of simple and complex metrics from all TC components produced the highest R² (62%) and lowest RMSE% (49%). Using a similar combination of variables, other forest attributes were estimated equally reliably with lower RMSE% values. The most important temporal trajectory metrics were simple and described TC component values at each point of change in the temporal trajectory, however the most important variables overall were environmental variables.     

基于遥感影像的黄河内蒙古段冰-水分类研究

及时获取凌汛期河冰和水体的空间分布特征,对于预测冰凌灾害、提高防凌信息化管理水平有重要意义。遥感技术是当前获取河冰和水体空间分布的最主要手段之一。但是,黄河水体有大量悬浮泥沙,这给基于遥感技术的高精度冰-水分类带来了挑战。以黄河内蒙古段为例,基于Landsat 8 OLI遥感影像数据,在利用归一化积雪指数（NDSI）及河道矢量数据排除无关地物的基础上,对比了近红外波段反射率值、归一化差异水体指数（NDWI）、归一化积雪指数（NDSI）、改进的归一化积雪指数（MNDSI）以及归一化差异未封冻水体指数（NDUWI）在黄河内蒙古段典型河道河冰、水体分类中的表现,计算各指标总体分类精度及Kappa系数并进行阈值稳定性分析。结果表明：在利用NDSI和高清历史影像排除河道外无关地物后,NDUWI在各子段影像中的总体分类精度和Kappa系数均达到90.00%及0.90以上,其河冰、水体最优区分阈值大体分布于阈值中值附近。研究结果可为凌汛期黄河冰凌监测方法的选取以及冰上爆破位置的拟定提供依据。     

An ensemble pansharpening approach for finer-scale mapping of sugarcane with Landsat 8 imagery

We tested the effects of three fast pansharpening methods – Intensity-Hue-Saturation (IHS), Brovey Transform (BT), and Additive Wavelet Transform (AWT) – on sugarcane classification in a Landsat 8 image (bands 1–7), and proposed two ensemble pansharpening approaches (band stacking and band averaging) which combine the pixel-level information of multiple pansharpened images for classification. To test the proposed ensemble pansharpening approaches, we classified “sugarcane” and “other” land cover in the unsharpened Landsat multispectral image, the individual pansharpened images, and the band-stacked and band-averaged ensemble images using Support Vector Machines (SVM), and assessed the classification accuracy of each image. Of the individual pansharpened images, the AWT image achieved higher classification accuracy than the unsharpened image, while the IHS and BT images did not. The band-stacked ensemble images achieved higher classification accuracies than the unsharpened and individual pansharpened images, with the IHS-BT-AWT band-stacked image producing the most accurate classification result, followed by the IHS-BT band-stacked image. The ensemble images containing averaged pixel values from multiple pansharpened images achieved lower classification accuracies than the band-stacked ensemble images, but most still had higher accuracies than the unsharpened and individual pansharpened results. Our results indicate that ensemble pansharpening approaches have the potential to increase classification accuracy, at least for relatively simple classification tasks. Based on the results of the study, we recommend further investigation of ensemble pansharpening for image analysis (e.g. classification and regression tasks) in agricultural and non-agricultural environments.     

基于 Landsat 8的城市热岛效应研究初探--以厦门市为例

以厦门市为研究区域,以2013年的Landsat 8为研究数据,研究亮度温度反演和地表温度反演算法,通过ENVI/IDL软件编程实现,并且利用厦门市气象局实测温度验证地表反演结果的有效性。同时,还分析了当前厦门市热岛效应空间分布情况,以期为厦门市城市规划和建设旅行城市等工作提供重要参考意义。     

北京地区Landsat 8 OLI高空间分辨率气溶胶光学厚度反演

卫星气溶胶光学厚度(AOD)反演中,传统暗目标方法在反射率较低的水体、浓密植被覆盖区域取得了较好效果,在反射率较高且结构复杂的高反射地表上空目前多采用深蓝算法,但存在空间分辨率较低,对细节分布描述性较差等问题。为解决这一问题,本文首先以5年(2008年—2012年)长时间序列MODIS地表反射率产品为基础,采用最小值合成法建立500 m分辨率逐月地表反射率产品数据集,然后利用地物波谱库中典型地物波谱数据,分析建立MODIS与Landsat 8 OLI传感器蓝光波段反射率转换模型,最后北京地区AERONET地基观测数据确定了气溶胶光学物理参数,并反演获取了北京地区上空500 m分辨率的AOD分布。为验证反演算法的精度,分别将反演结果同AERONET及MODIS/Terra气溶胶产品(MOD04)进行交叉对比,同时利用相关系数R,均方根误差RMSE,平均绝对误差MAE以及MODIS AOD产品预期误差EE共4个指标进行衡量。结果表明:算法反演获取的AOD与AERONET观测值具有较高的一致性,各指标分别为R=0.963,RMSE=0.156,MAE=0.097,EE=85.3%,稍优于MOD04产品(R=0.962,RMSE=0.158,MAE=0.101,EE=75.8%),并且有效的对比点数也高于MOD04。通过与地基观测相比,卫星遥感获取的高分辨率城市地区AOD精度可作为定量评估城市空气质量的有效依据。