1996—2015年黄河源区植被覆盖度提取和时空变化分析

研究黄河源区植被覆盖度时空变化对于深入理解青藏高原多年冻土区在气候变化和人类活动双重作用下的植被响应,以及为黄河源区生态环境保护和治理提供决策具有重要的意义。以陆地卫星（Landsat）影像为主要数据源,利用多端元混合像元分解模型（Multiple Endmember Spectral Mixture Analysis,MESMA）,完成了1996—2015年黄河源区4.4万km²、7个时相的植被覆盖度提取,并基于转移矩阵和一元线性回归趋势法分析了植被覆盖度变化情况。结果表明：黄河源区东南部植被覆盖度较高,西北部植被覆盖度较低,且植被覆盖度在空间上由东南向西北呈递减趋势。1996—2004年植被覆盖度整体呈下降趋势,2004—2015年植被覆盖度呈增加趋势。1996—2015年植被覆盖度呈增加趋势的区域占57.25%,基本不变的区域占16.02%,植被覆盖度呈下降趋势的区域占26.73%。植被覆盖度下降的主要原因是黄河源头及一些河谷地带、环湖地区受人类影响较大,且东南部海拔较低地区受到过度放牧的影响。尽管黄河源区1996—2015年植被覆盖度总体呈改善趋势,但毒杂草的面积也由1996年的16 060 km²增加到2015年的22 942 km²,20年间增加了6 882 km²,毒杂草面积的增加对黄河源区局部地区畜牧业的发展有不利影响。     

Multiscale quantification of urban composition from EO-1/Hyperion data using object-based spectral unmixing

Quantification of the urban composition is important in urban planning and management. Previous research has primarily focused on unmixing medium-spatial resolution multispectral imagery using spectral mixture analysis (SMA) in order to estimate the abundance of urban components. For this study an object-based multiple endmember spectral mixture analysis (MESMA) approach was applied to unmix the 30-m Earth Observing-1 (EO-1)/Hyperion hyperspectral imagery. The abundance of two physical urban components (vegetation and impervious surface) was estimated and mapped at multiple scales and two defined geographic zones. The estimation results were validated by a reference dataset generated from fine spatial resolution aerial photography. The object-based MESMA approach was compared with its corresponding pixel-based one, and EO-1/Hyperion hyperspectral data was compared with the simulated EO-1/Advanced Land Imager (ALI) multispectral data in the unmixing modeling. The pros and cons of the object-based MESMA were evaluated. The result illustrates that the object-based MESMA is promising for unmixing the medium-spatial resolution hyperspectral imagery to quantify the urban composition, and it is an attractive alternative to the traditional pixel-based mixture analysis for various applications.     

Advanced image processing methods as a tool to map and quantify different types of biological soil crust

Biological soil crusts (BSCs) modify numerous soil surface properties and affect many key ecosystem processes. As BSCs are considered one of the most important components of semiarid ecosystems, accurate characterisation of their spatial distribution is increasingly in demand. This paper describes a novel methodology for identifying the areas dominated by different types of BSCs and quantifying their relative cover at subpixel scale in a semiarid ecosystem of SE Spain. The approach consists of two consecutive steps: (i) First, Support Vector Machine (SVM) classification to identify the main ground units, dominated by homogenous surface cover (bare soil, cyanobacteria BSC, lichen BSC, green and dry vegetation), which are of strong ecological relevance. (ii) Spectral mixture analysis (SMA) of the ground units to quantify the proportion of each type of surface cover within each pixel, to correctly characterize the complex spatial heterogeneity inherent to semiarid ecosystems. SVM classification showed very good results with a Kappa coefficient of 0.93%, discriminating among areas dominated by bare soil, cyanobacteria BSC, lichen BSC, green and dry vegetation. Subpixel relative abundance images achieved relatively high accuracy for both types of BSCs (about 80%), whereas general overestimation of vegetation was observed. Our results open the possibility of introducing the effect of presence and of relative cover of BSCs in spatially distributed hydrological and ecological models, and assessment and monitoring aimed at reducing degradation in these areas.     

Ecosystem-based marine spatial management: Review of concepts, policies, tools, and critical issues

Conventional sectoral management and piecemeal governance are considered less and less appropriate in pursuit of sustainable development. Ecosystem based marine spatial management (EB-MSM) is an approach that recognizes the full array of interactions within an ecosystem, including human uses, rather than considering single issues, species, or ecosystem services in isolation. Marine spatial planning and ocean zoning are emerging concepts that can support EB-MSM. EB-MSM is driven by high-level goals that managers aim to achieve through the implementation of measures. High-level goals and objectives need to be translated into more operational objectives before specific targets, limits and measures can be elaborated.Monitoring, evaluation and adaptation are necessary to ensure that marine management measures are both effective and efficient. Solid monitoring frameworks are the foundation of adaptive management, as they provide the necessary information to evaluate performance and the effectiveness of management actions. Marine protected areas (MPAs) - possibly set up in networks - constitute a key component in EB-MSM policies and practises and have been applied as a cornerstone in conservation of marine biodiversity, management of fish populations, development of coastal tourism, etc. Moreover, MPA experiences have provided methods and concepts (such as zoning) to a wider EB-MSM context. The assignment of values to biophysical features of the marine environment allows the direct assessment of related management choices and may assist EB-MSM.A range of monetary valuation techniques have been proposed to reduce attributes of goods and services to a single metric. However, in the marine environment such an approach is often over simplistic, and thus less reductive techniques may be necessary. Rather than producing a single metric, the results of non-monetary assessments guide policy allowing weight to be given as necessary to potential areas of conflict and consensus.Strategies to take into account climate change effects and geohazard risks in EB-MSM have been applied or proposed worldwide. EB-MSM regimes must be alert to such risks and flexible to account for changes.     

Estimating fractional land cover in semi-arid central Kalahari: the impact of mapping method (spectral unmixing vs. object-based image analysis) and vegetation morphology

Focusing on the central Kalahari, this study utilized fractional cover of photosynthetic vegetation (f_PV), non-photosynthetic vegetation (f_NPV) and bare soil (f_BS), derived in situ and estimated from GeoEye-1 imagery using Multiple Endmember Spectral Mixture Analysis (MESMA) and object-based image analysis (OBIA) to determine superior method for fractional cover estimation and the impact of vegetation morphology on the estimation accuracy. MESMA mapped fractional cover by testing endmember models of varying complexity. Based on OBIA, image was segmented at five segmentation scales followed by classification. MESMA provided more accurate fractional cover estimates than OBIA. The increasing segmentation scale in OBIA resulted in a consistent increase in error. Different vegetation morphology types showed varied responses to the changing segmentation scale, reflecting their unique ecology and physiognomy. While areas under woody cover produced lower error even at coarse segmentation scales, those with herbaceous cover provided low error only at the fine segmentation scale.     

Spectral mixture analysis to assess post-fire vegetation regeneration using Landsat Thematic Mapper imagery: Accounting for soil brightness variation

Post-fire vegetation cover is a crucial parameter in rangeland management. This study aims to assess the post-fire vegetation recovery 3 years after the large 2007 Peloponnese (Greece) wildfires. Post-fire recovery landscapes typically are mixed vegetation-substrate environments which makes spectral mixture analysis (SMA) a very effective tool to derive fractional vegetation cover maps. Using a combination of field and simulation techniques this study aimed to account for the impact of background brightness variability on SMA model performance. The field data consisted out of a spectral library of in situ measured reflectance signals of vegetation and substrate and 78 line transect plots. In addition, a Landsat Thematic Mapper (TM) scene was employed in the study. A simple SMA, in which each constituting terrain feature is represented by its mean spectral signature, a multiple endmember SMA (MESMA) and a segmented SMA, which accounts for soil brightness variations by forcing the substrate endmember choice based on ancillary data (lithological map), were applied. In the study area two main spectrally different lithological units were present: relatively bright limestone and relatively dark flysch (sand-siltstone). Although the simple SMA model resulted in reasonable regression fits for the flysch and limestones subsets separately (coefficient of determination R² of respectively 0.67 and 0.72 between field and TM data), the performance of the regression model on the pooled dataset was considerably weaker (R² = 0.65). Moreover, the regression lines significantly diverged among the different subsets leading to systematic over-or underestimations of the vegetative fraction depending on the substrate type. MESMA did not solve the endmember variability issue. The MESMA model did not manage to select the proper substrate spectrum on a reliable basis due to the lack of shape differences between the flysch and limestone spectra,. The segmented SMA model which accounts for soil brightness variations minimized the variability problems. Compared to the simple SMA and MESMA models, the segmented SMA resulted in a higher overall correlation (R² = 0.70), its regression slope and intercept were more similar among the different substrate types and its resulting regression lines more closely resembled the expected one-one line. This paper demonstrates the improvement of a segmented approach in accounting for soil brightness variations in estimating vegetative cover using SMA. However, further research is required to evaluate the model's performance for other soil types, with other image data and at different post-fire timings.     

Spectral mixture analysis to monitor defoliation in mixed-aged Eucalyptus globulus Labill plantations in southern Australia using Landsat5-TM and EO-1Hyperion data

Defoliation is a key parameter of forest health and is associated with reduced productivity and tree mortality. Assessing the health of forests requires regular observations over large areas. Satellite remote sensing provides a cost-effective alternative to traditional ground-based assessment of forest health, but assessing defoliation can be difficult due to mixed pixels where vegetation cover is low or fragmented. In this study we apply a novel spectral unmixing technique, referred to as weighted Multiple Endmember Spectral Mixture Analysis (wMESMA), to Landsat 5-TM and EO-1 Hyperion data acquired over a Eucalyptus globulus (Labill.) plantation in southern Australia. This technique combines an iterative mixture analysis cycle allowing endmembers to vary on a per pixel basis (MESMA) and a weighting algorithm that prioritizes wavebands based on their robustness against endmember variability. Spectral mixture analysis provides an estimate of the physically interpretable canopy cover, which is not necessarily correlated with defoliation in mixed-aged plantations due to natural variation in canopy cover as stands age. There is considerable variability in the degree of defoliation as well as in stand age among sites and in this study we found that results were significantly improved by the inclusion of an age correction algorithm for both the multi-spectral (R²_{no age correction} = 0.55 vs R²_{age correction} = 0.73 for Landsat) and hyperspectral (R²_{no age correction} = 0.12 vs R²_{age correction} = 0.50 for Hyperion) image data. The improved accuracy obtained from Landsat compared to the Hyperion data illustrates the potential of applying SMA techniques for analysis of multi-spectral datasets such as MODIS and SPOT-VEGETATION.     

Assessing environmental impacts of urban growth using remote sensing

ABSTRACT

This paper provides a study of the changes in land use in urban environments in two cities, Wuhan, China and western Sydney in Australia. Since mixed pixels are a characteristic of medium resolution images such as Landsat, when used for the classification of urban areas, due to changes in urban ground cover within a pixel, Multiple Endmember Spectral Mixture Analysis (MESMA) together with Super-Resolution Mapping (SRM) are employed to derive class fractions to generate classification maps at a higher spatial resolution using an Artificial Neural Network (ANN) predicted Wavelet method. Landsat images over the two cities for a 30-year period, are classified in terms of vegetation, buildings, soil and water. The classifications are then processed using Indifrag software to assess the levels of fragmentation caused by changes in the areas of buildings, vegetation, water and soil over the 30 years. The extents of fragmentation of vegetation, buildings, water and soil for the two cities are compared, while the percentages of vegetation are compared with recommended percentages of green space for urban areas for the benefit of health and well-being of inhabitants. Changes in Ecosystem Service Values (ESVs) resulting from the urbanization have been assessed for Wuhan and Sydney. The UN Sustainable Development Goals (SDG) for urban areas are being assessed by researchers to better understand how to achieve the sustainability of cities.     

Enhancing endmember selection in multiple endmember spectral mixture analysis (MESMA) for urban impervious surface area mapping using spectral angle and spectral distance parameters

Successful retrieval of urban impervious surface area is achieved with remote sensing data using the multiple endmember spectral mixture analysis (MESMA). MESMA is well suited for studying the urban impervious surface area because it allows the number and types of the endmembers to vary on a per-pixel basis, thereby, allowing the control of the large spectral variability. However, MESMA must calculate all potential endmember combinations of each pixel to determine the best-fit one. Therefore, it is a time-consuming and inefficient unmixing technology, especially for hyperspectral images because these images have more complicated endmember categories. Hence, in this paper, we design an improved MESMA (SASD-MESMA: spectral angle and spectral distance MESMA) to enhance the computational efficiency of conventional MESMA, and we validate this new method by analyzing the Hyperion image (Jan-2011) and the field-spectra data of Guangzhou (China). In SASD-MESMA, the parameters of spectral angle (SA) and spectral distance (SD) are used to evaluate the similarity degree between library spectra and image spectra in order to identify the most representative endmember combination for each pixel. Results demonstrate that the SA and SD parameters are useful to reduce misjudgment in selecting candidate endmembers and effective for determining the appropriate endmembers in one pixel. Meanwhile, this research indicates that the proposed SASD-MESMA performs very well in retrieving impervious surface area, forest, grass and soil distributions on the sub-pixel level (the overall root mean square error (RMSE) is 0.15 and the correlation coefficient of determination (R²) is 0.68).