Evaluation of directional normalization methods for Landsat TM/ETM+ over primary Amazonian lowland forests

Biodiversity mapping in extensive tropical forest areas poses a major challenge for the interpretation of Landsat images, because floristically clearly distinct forest types may show little difference in reflectance. In such cases, the effects of the bidirectional reflection distribution function (BRDF) can be sufficiently strong to cause erroneous image interpretation and classification. Since the opening of the Landsat archive in 2008, several BRDF normalization methods for Landsat have been developed. The simplest of these consist of an empirical view angle normalization, whereas more complex approaches apply the semi-empirical Ross–Li BRDF model and the MODIS MCD43-series of products to normalize directional Landsat reflectance to standard view and solar angles. Here we quantify the effect of surface anisotropy on Landsat TM/ETM+ images over old-growth Amazonian forests, and evaluate five angular normalization approaches. Even for the narrow swath of the Landsat sensors, we observed directional effects in all spectral bands. Those normalization methods that are based on removing the surface reflectance gradient as observed in each image were adequate to normalize TM/ETM+ imagery to nadir viewing, but were less suitable for multitemporal analysis when the solar vector varied strongly among images. Approaches based on the MODIS BRDF model parameters successfully reduced directional effects in the visible bands, but removed only half of the systematic errors in the infrared bands. The best results were obtained when the semi-empirical BRDF model was calibrated using pairs of Landsat observation. This method produces a single set of BRDF parameters, which can then be used to operationally normalize Landsat TM/ETM+ imagery over Amazonian forests to nadir viewing and a standard solar configuration.     

Validation of MODIS integrated water vapor product against reference GPS data at the Iberian Peninsula

In this work, the water vapor product from MODIS (MODerate-resolution Imaging Spectroradiometer) instrument, on-board Aqua and Terra satellites, is compared against GPS water vapor data from 21 stations in the Iberian Peninsula as reference. GPS water vapor data is obtained from ground-based receiver stations which measure the delay caused by water vapor in the GPS microwave signals. The study period extends from 2007 until 2012. Regression analysis in every GPS station show that MODIS overestimates low integrated water vapor (IWV) data and tends to underestimate high IWV data. R² shows a fair agreement, between 0.38 and 0.71. Inter-quartile range (IQR) in every station is around 30–45%. The dependence on several parameters was also analyzed. IWV dependence showed that low IWV are highly overestimated by MODIS, with high IQR (low precision), sharply decreasing as IWV increases. Regarding dependence on solar zenith angle (SZA), performance of MODIS IWV data decreases between 50° and 90°, while night-time MODIS data (infrared) are quite stable. The seasonal cycles of IWV and SZA cause a seasonal dependence on MODIS performance. In summer and winter, MODIS IWV tends to overestimate the reference IWV value, while in spring and autumn the tendency is to underestimate. Low IWV from coastal stations is highly overestimated (∼60%) and quite imprecise (IQR around 60%). On the contrary, high IWV data show very little dependence along seasons. Cloud-fraction (CF) dependence was also studied, showing that clouds display a negligible impact on IWV over/underestimation. However, IQR increases with CF, except in night-time satellite values, which are quite stable.     

Evaluating land cover changes in Eastern and Southern Africa from 2000 to 2010 using validated Landsat and MODIS data

In this study, we assessed land cover land use (LCLU) changes and their potential environmental drivers (i.e., precipitation, temperature) in five countries in Eastern & Southern (E&S) Africa (Rwanda, Botswana, Tanzania, Malawi and Namibia) between 2000 and 2010. Landsat-derived LCLU products developed by the Regional Centre for Mapping of Resources for Development (RCMRD) through the SERVIR (Spanish for “to serve”) program, a joint initiative of NASA and USAID, and NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to evaluate and quantify the LCLU changes in these five countries. Given that the original development of the MODIS land cover type standard products included limited training sites in Africa, we performed a two-level verification/validation of the MODIS land cover product in these five countries. Precipitation data from CHIRPS dataset were used to evaluate and quantify the precipitation changes in these countries and see if it was a significant driver behind some of these LCLU changes. MODIS Land Surface Temperature (LST) data were also used to see if temperature was a main driver too.Our validation analysis revealed that the overall accuracies of the regional MODIS LCLU product for this African region alone were lower than that of the global MODIS LCLU product overall accuracy (63–66% vs. 75%). However, for countries with uniform or homogenous land cover, the overall accuracy was much higher than the global accuracy and as high as 87% and 78% for Botswana and Namibia, respectively. In addition, the wetland and grassland classes had the highest user’s accuracies in most of the countries (89%–99%), which are the ones with the highest number of MODIS land cover classification algorithm training sites.Our LCLU change analysis revealed that Botswana’s most significant changes were the net reforestation, net grass loss and net wetland expansion. For Rwanda, although there have been significant forest, grass and crop expansions in some areas, there also have been significant forest, grass and crop loss in other areas that resulted in very minimal net changes. As for Tanzania, its most significant changes were the net deforestation and net crop expansion. Malawi’s most significant changes were the net deforestation, net crop expansion, net grass expansion and net wetland loss. Finally, Namibia’s most significant changes were the net deforestation and net grass expansion.The only noticeable environmental driver was in Malawi, which had a significant net wetland loss and could be due to the fact that it was the only country that had a reduction in total precipitation between the periods when the LCLU maps were developed. Not only that, but Malawi also happened to have a slight increase in temperature, which would cause more evaporation and net decrease in wetlands if the precipitation didn’t increase as was the case in that country. In addition, within our studied countries, forestland expansion and loss as well as crop expansion and loss were happening in the same country almost equally in some cases. All of that implies that non-environmental factors, such as socioeconomics and governmental policies, could have been the main drivers of these LCLU changes in many of these countries in E&S Africa. It will be important to further study in the future the detailed effects of such drivers on these LCLU changes in this part of the world.     

How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?

Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.     

Validation of ship manoeuvring models using metamodels

This paper describes how simplified auxiliary models—metamodels—can be used to create benchmarks for validating ship manoeuvring simulation models. A metamodel represents ship performance for a limited range of parameters, such as rudder angles and surge velocity. In contrast to traditional system identification methods, metamodels are identified from multiple trial recordings, each containing data on the ship’s inherent dynamics (similar for all trials) and random disturbances such as environmental effects and slightly different loading conditions. Thus, metamodels can be used to obtain these essential data, where simple averaging is not possible. In addition, metamodels are used to represent a ship’s behaviour and not to obtain physical insights into ship dynamics. The experimental trials used for the identification of metamodels can be found in in-service recorded data. After the metamodel is identified, it is used to simulate trials without substantial deviations from the ship state parameters used for the identification. Subsequently, the predictions of the metamodels are compared with the predictions of a tested manoeuvring simulation model. We present two case studies to demonstrate the application of metamodels for moderate turning motions of two ships.     

GEOCHEMICAL RECOGNITION OF SPILLED SEDIMENTS USED IN NUMERICAL MODEL VALIDATION

A fixed link (tunnel and bridge, in total 16 km) was constructed between Sweden and Denmark during 1995-2000. As part of the work, approximately 16 million tonnes of seabed materials (limestone and clay till) were dredged, and about 0.6 million tonnes of these were spilled in the water. Modelling of the spreading and sedimentation of the spilled sediments took place as part of the environmental monitoring of the construction activities. In order to verify the results of the numerical modelling of sediment spreading and sedimentation, a new method with the purpose of distinguishing between the spilled sediments and the naturally occurring sediments was developed. Because the spilled sediments tend to accumulate at the seabed in areas with natural sediments of the same size, it is difficult to separate these based purely on the physical properties. The new method is based on the geo-chemical differences between the natural sediment in the area and the spill. The basic propertiesused are the higher content of calcium carbonate material in the spill as compared to the natural sediments and the higher Ca/Sr ratio in the spill compared to shell fragments dominating the natural calcium carbonate deposition in the area. The reason for these differences is that carbonate derived from recent shell debris can be discriminated from Danien limestone, which is the material in which the majority of the dredging took place, on the basis of the Ca/Sr ratio being 488 in Danien Limestone and 237 in shell debris. The geochemical recognition of the origin of the sediments proved useful in separating the spilled from the naturally occurring sediments. Without this separation, validation of the modelling of accumulation of spilled sediments would not have been possible. The method has general validity and can be used in many situations where the origin of a given sediment is sought.     

Pilot‐scale field validation of the long electrode electrical resistivity tomography method

A validation experiment, carried out in a scaled field setting, was attempted for the long electrode electrical resistivity tomography method in order to demonstrate the performance of the technique in imaging a simple buried target. The experiment was an approximately 1/17 scale mock‐up of a region encompassing a buried nuclear waste tank on the Hanford site. The target of focus was constructed by manually forming a simulated plume within the vadose zone using a tank waste simulant. The long electrode results were compared to results from conventional point electrodes on the surface and buried within the survey domain. Using a pole‐pole array, both point and long electrode imaging techniques identified the lateral extents of the pre‐formed plume with reasonable fidelity but the long electrode method was handicapped in reconstructing vertical boundaries. The pole‐dipole and dipole‐dipole arrays were also tested with the long electrode method and were shown to have the least favourable target properties, including the position of the reconstructed plume relative to the known plume and the intensity of false positive targets. The poor performance of the pole‐dipole and dipole‐dipole arrays was attributed to an inexhaustive and non‐optimal coverage of data at key electrodes, as well as an increased noise for electrode combinations with high geometric factors. However, when comparing the model resolution matrix among the different acquisition strategies, the pole‐dipole and dipole‐dipole arrays using long electrodes were shown to have significantly higher average and maximum values within the matrix than any pole‐pole array. The model resolution describes how well the inversion model resolves the subsurface. Given the model resolution performance of the pole‐dipole and dipole‐dipole arrays, it may be worth investing in tools to understand the optimum subset of randomly distributed electrode pairs to produce maximum performance from the inversion model.     

Further developments of a fuzzy set map comparison approach

Fuzzy set map comparison offers a novel approach to map comparison.The approach is specifically aimed at categorical raster maps and applies fuzzy set techniques, accounting for fuzziness of location and fuzziness of category, to create a similarity map as well as an overall similarity statistic: the Fuzzy Kappa. To date, the calculation of the Fuzzy Kappa (or K-fuzzy) has not been formally derived, and the documented procedure was only valid for cases without fuzziness of category. Furthermore, it required an infinitely large, edgeless map. This paper presents the full derivation of the Fuzzy Kappa; the method is now valid for comparisons considering fuzziness of both location and category and does not require further assumptions. This theoretical completion opens opportunities for use of the technique that surpass the original intentions. In particular, the categorical similarity matrix can be applied to highlight or disregard differences pertaining to selected categories or groups of categories and to distinguish between differences due to omission and commission.     

Next generation of global land cover characterization,mapping, and monitoring

Land cover change is increasingly affecting the biophysics, biogeochemistry, and biogeography of the Earth's surface and the atmosphere, with far-reaching consequences to human well-being. However, our scientific understanding of the distribution and dynamics of land cover and land cover change (LCLCC) is limited. Previous global land cover assessments performed using coarse spatial resolution (300 m–1 km) satellite data did not provide enough thematic detail or change information for global change studies and for resource management. High resolution (∼30 m) land cover characterization and monitoring is needed that permits detection of land change at the scale of most human activity and offers the increased flexibility of environmental model parameterization needed for global change studies. However, there are a number of challenges to overcome before producing such data sets including unavailability of consistent global coverage of satellite data, sheer volume of data, unavailability of timely and accurate training and validation data, difficulties in preparing image mosaics, and high performance computing requirements. Integration of remote sensing and information technology is needed for process automation and high-performance computing needs. Recent developments in these areas have created an opportunity for operational high resolution land cover mapping, and monitoring of the world. Here, we report and discuss these advancements and opportunities in producing the next generations of global land cover characterization, mapping, and monitoring at 30-m spatial resolution primarily in the context of United States, Group on Earth Observations Global 30 m land cover initiative (UGLC).     

黑河流域生态—水文过程综合遥感观测联合试验总体设计

介绍了"黑河流域生态—水文过程综合遥感观测联合试验"的背景、科学目标、试验组成和试验方案。试验的总体目标是显著提升对流域生态和水文过程的观测能力,建立国际领先的流域观测系统,提高遥感在流域生态—水文集成研究和水资源管理中的应用能力。由基础试验、专题试验、应用试验、产品与方法研究和信息系统组成。其中,①基础试验:搭载微波辐射计、成像光谱仪、热像仪、激光雷达等航空遥感设备,开展一系列关键生态和水文参量的观测;发展遥感正向模型及反演和估算方法。形成覆盖全流域的水文气象综合观测网,为流域生态—水文模型研究提供更有代表性的模型参数、驱动数据及更高精度的验证数据。构建无线传感器网络,度量生态水文模型所需的若干关键的驱动、参数和模型状态的空间异质性。开展航空遥感定标和地基遥感试验。依托传感器网络,并辅之以地面同步和加密观测,开展遥感产品真实性检验。②专题试验:开展"非均匀下垫面多尺度地表蒸散发观测试验",采用密集的涡动相关仪、大孔径闪烁仪与自动气象站的观测矩阵,为揭示地表蒸散发的空间异质性,实现非均匀下垫面地表蒸散发的尺度扩展,发展和验证蒸散发模型提供基础数据。③应用试验:在流域上、中、下游分别开展针对积雪和冻土水文、灌溉水平衡和作物生长、生态耗水的综合观测试验,将观测数据和遥感产品用于上游分布式水文模型、中游地表水—地下水—农作物生长耦合模型、下游生态耗水模型,通过实证研究提升遥感在流域生态—水文集成研究和水资源管理中的应用能力。加强试验将在2012年5月起按中游、上游、下游的顺序展开,全流域持续观测期为2013—2015年。在各类试验的支持下,开展全流域生态—水文关键参量遥感产品生产,发展尺度转换方法,建立多源遥感数据同化系统。