Remote sensing of scattered Natura 2000 habitats using a one-class classifier

Mapping of habitats with relevance for nature conservation involves the identification of patches of target habitats in a complex mosaic of vegetation types not relevant for conservation planning. Limiting the necessary ground reference to a small sample of target habitats would greatly reduce and therefore support the field mapping effort. We thus aim to answer in this study the question: can semi-automated remote sensing methods help to map such patches without the need of ground references from sites not relevant for nature conservation? Approaches able to fulfill this task may help to improve the efficiency of large scale mapping and monitoring programs such as requested for the European Habitat Directive.In the present study, we used the maximum-entropy based classification approach Maxent to map four habitat types across a patchy landscape of 1000 km² near Munich, Germany. This task was conducted using the low number of 125 ground reference points only along with easily available multi-seasonal RapidEye satellite imagery. Encountered problems include the non-stationarity of habitat reflectance due to different phenological development across space, continuous transitions between the habitats and the need for improved methods for detailed validation.The result of the tested approach is a habitat map with an overall accuracy of 70%. The rather simple and affordable approach can thus be recommended for a first survey of previously unmapped areas, as a tool for identifying potential gaps in existing habitat inventories and as a first check for changes in the distribution of habitats.     

Towards continuous long-term measurements of suspended particulate matter (SPM) in turbid coastal waters

The dynamics of sediment transport in the East Frisian Wadden Sea are important for the coastal zone and for ecosystem functioning. The tidal inlets between the East Frisian islands connect the back-barrier intertidal flats to the North Sea. Here, concentrations of suspended particulate matter (SPM) in the water column are highly variable, depending on weather conditions and tides. In order to estimate the nature and quantity of sediment transport, in situ measurements were carried out at a Time Series Station in the tidal inlet between the islands of Spiekeroog and Langeoog. This study shows the suitability of multispectral transmissometry (MST) for obtaining long-term SPM measurements with high resolution. The comparability of this technique to the standard filter method and the laser diffraction method [laser in situ scattering and transmissometry (LISST)] is demonstrated. In addition, the Junge coefficients derived from both MST and LISST measurements are compared. A time series of SPM data covering nearly 4 months is presented. As a major result, the data reveal that a single storm surge can have less impact on SPM dynamics than longer-lasting gales. This high-resolution long-term data set is very valuable for modelling suspended matter flux. It also provides background information for studying the influence of SPM dynamics on coastal sediments.     

基于遥感影像研究宁波地区断裂活动性

利用遥感影像多分辨率、 多光谱以及多传感器相结合的方法, 对浙江宁波地区约1900 km²范围的断裂活动性进行分析判读。 根据断裂的影像特征, 将所判读的断裂分为晚更新世活动, 早、 中更新世活动及第四纪活动不明显3类, 同时对主要断裂的活动性进行了详细分析。 宁波地区主要发育NNE向、 NE向和NW向三组活动构造。 其中NNE向育王山山前断裂, 在TM、 MSS和SPOT影像上都表现出清晰的断层形迹, 切过三处冲积洪积扇, 晚更新世有活动。 走向20°的老鹰山与走向55°的清凉山截然相接显示的线性影像反映出NE向算山-曹隘断裂的形迹, 断裂向宁波盆地延伸, 为宁波盆地规模最大的隐伏断裂。 多时相MSS影像上, 显示出NW向新乐—宝幢和宁波—莫枝活动断层的形迹, 它是深部构造活动在地表的反映, 与走向45°的算山—曹隘断裂, 可能是一组地壳破裂网络。     

多光谱浅海水深提取方法研究

利用我国南海某岛礁的TM数据和实测水深资料,试验性地研究了一种在不同底质反射条件下多光谱定量提取水深信息的方法,计算了浅海岛礁水深,取得了较好的应用效果和较高的测深精度.     

基于局部光谱空间分析的多尺度边缘检测

分析了传统边缘检测算法在多光谱影像应用中的局限性，提出了一种基于局部区域的光谱空间平均半径测度的边缘检测方法，该方法扩展了原有的单波段影像边缘检测的概念，将像元的灰度信息转化为光谱矢量，提高了检测的可靠性，并给出了多尺度边缘组合的算法。通过MAIS成像光谱仪对鄱阳湖30个波段数据试验，证明了此方法对边缘检测的有效性。     

用多光谱卫星信息分析白昼云天条件下的湿度场

TOVS资料的晴空湿度场反演已进行多年 ,如何利用卫星遥感信息实现云天条件下的湿度场反演 ,仍是一个值得研究的问题。多光谱卫星图像上不同云类的云层高低分布蕴涵着三维湿度场的分布信息 ,文中用相关分析、最小二乘拟合分析和多元线性回归分析分别讨论了多光谱卫星信息与各标准层相对湿度的关系。标准回归分析的复相关系数R在地面最小 ,约为 0 6 4,随高度增加 ,R逐层增大 ,70 0hPa以上大于 0 7,2 5 0hPa以上大于 0 8。相应显著性检验的结果F值从地面的 30 71,也逐层增大 ,至 2 0 0hPa时达 119 36。逐步回归分析在剔除了不显著的变量因子后 ,回归自变量减少到 2～ 4个 ,反映显著水平的F值有较大提高。在此基础上建立了一组云天条件下的多元线性回归方程 ,由此可得到具有卫星图像像素分辨率 (8～ 10km)的 9层湿度场。     

Efficacy of multi-season Sentinel-2 imagery for compositional vegetation classification

Vegetation maps are essential tools for the conservation and management of landscapes as they contain essential information for informing conservation decisions. Traditionally, maps have been created using field-based approaches which, due to limitations in costs and time, restrict the size of the area for which they can be created and frequency at which they can be updated. With the increasing availability of satellite sensors providing multi-spectral imagery with high temporal frequency, new methods for efficient and accurate vegetation mapping have been developed. The objective of this study was to investigate to what extent multi-seasonal Sentinel-2 imagery can assist in mapping complex compositional classifications at fine spatial scales. We deliberately chose a challenging case study, namely a visually and structurally homogenous scrub vegetation (known as kwongan) of Western Australia. The classification scheme consists of 24 target classes and a random 60/40 split was used for model building and validation. We compared several multi-temporal (seasonal) feature sets, consisting of numerous combinations of spectral bands, vegetation indices as well as principal component and tasselled cap transformations, as input to four machine learning classifiers (Support Vector Machines; SVM, Nearest Neighbour; NN, Random Forests; RF, and Classification Trees; CT) to separate target classes. The results show that a multi-temporal feature set combining autumn and spring images sufficiently captured the phenological differences between the classes and produced the best results, with SVM (74%) and NN (72%) classifiers returning statistically superior results compared to RF (65%) and CT (50%). The SWIR spectral bands captured during spring, the greenness indices captured during spring and the tasselled cap transformations derived from the autumn image emerged as most informative, which suggests that ecological factors (e.g. shared species, patch dynamics) occurring at a sub-pixel level likely had the biggest impact on class confusion. However, despite these challenges, the results are auspicious and suggest that seasonal Sentinel-2 imagery has the potential to predict compositional vegetation classes with high accuracy. Further work is needed to determine whether these results are replicable in other vegetation types and regions.     

Detecting nutrient deficiency in spruce forests using multispectral satellite imagery

Nutrient deficiency in forest stands has a negative impact on timber production. Although there are numerous studies investigating nutrient deficiency in forests using remote sensing, research has usually focused on extracting nutrient/pigment concentrations using hyperspectral imagery. Results of studies using this method of assessment are uncertain at the canopy level. This study proposes using freely available multispectral imagery to identify nutrient deficiency in commercially managed forest plantations. A classification map of nutrient deficient, healthy, and a third class, other, for State spruce forests in the Republic of Ireland was constructed using multispectral Sentinel 2 images from Spring and a Random Forest model. The forest area of interest (AOI) was Sitka spruce or Norway spruce plantations greater than 12 years old. Results showed that the overall accuracy was 89% and the associated Kappa Index of agreement was 79%. An unbiased area estimator was vital for an accurate estimate of the scale of nutrient deficiency, which concluded that 23% of the AOI was nutrient deficient. Early detection of nutrient deficiency is crucial to mitigate negative impacts on productivity so a time series analysis of the spectral response of healthy and nutrient deficient classes using Google Earth Engine's Landsat 5, 7, and 8 archive was carried out. A control of known nutrient deficient sites, as identified through foliar analysis, was used for comparison with the nutrient deficient and healthy training data. The spectral response showed a decrease through time for all of the foliar analysis and training data using the green (520–600 nm), red (630–690 nm), and SWIR spectra (1550–1700 nm) during Spring. This decreasing trend is due to the growth of foliage, with the difference in spectral response between nutrient deficient and healthy stands being attributed to the presence of chlorosis in stands suffering from nutrient deficiency. Spectral thresholds using digital numbers for nutrient deficient stands were identified for an operational optimum age cohort of between 10–12 years old which will be used for early detection.     

Hierarchical classification with subsequent aggregation of heathland habitats using an intra-annual RapidEye time-series

Mapping heathland habitats is generally challenging due to fine-scale habitats as well as spectral ambiguities between different classes. A multi-seasonal time-series of multispectral RapidEye data from several phenological stages was analysed towards the classification of different vegetation communities.A 3-level hierarchical dependent classification using Import Vector Machines was tested, based on the assumption that a probabilistic output per class would help the mapping. The first level of the hierarchical classification was related to the moisture gradient, which was derived from Ellenberg’s moisture indicative value. The second level aimed to separate plant alliances; the third level differentiated individual plant associations.For the final integration of the three classification levels, two approaches were implemented: (i) the F1-score and (ii) the maximum classification probability. The overall classification accuracies of both methods were found to be similar, around 0.7.Nevertheless, based on our expert knowledge we found the probabilistic approach to provide a more realistic picture and to be more practical compared to the result using the F1-score from the management point of view. In addition, the overall performance of the maximum probabilistic approach is better in the sense that the same accuracy of 0.7 was achieved with a differentiation of 33 classes instead of only 13 classes for the F1-score, meaning that the method is able to separate more spectral classes at a more detailed level providing the same accuracy.     

Remote spectral imaging with simultaneous extraction of 3D topography for historical wall paintings

PRISMS (Portable Remote Imaging System for Multispectral Scanning) is designed for in situ, simultaneous high resolution spectral and 3D topographic imaging of wall paintings and other large surfaces. In particular, it can image at transverse resolutions of tens of microns remotely from distances of tens of metres, making high resolution imaging possible from a fixed position on the ground for areas at heights that is difficult to access. The spectral imaging system is fully automated giving 3D topographic mapping at millimetre accuracy as a by-product of the image focusing process. PRISMS is the first imaging device capable of both 3D mapping and spectral imaging simultaneously without additional distance measuring devices. Examples from applications of PRISMS to wall paintings at a UNESCO site in the Gobi desert are presented to demonstrate the potential of the instrument for large scale 3D spectral imaging, revealing faded writing and material identification.