中国区域滨海湿地固碳能力研究现状与提升

对我国滨海湿地的固碳能力进行了评估,同时,对其固碳能力的遥感监测和评估方法进行了总结和分析,最后提出了滨海湿地固碳能力的提升技术。全球看来,我国滨海湿地的固碳能力总体上不高。国内看来,在北方泥炭地和植被覆盖率高,生长茂盛的区域,尤其是有红树林生长的区域固碳能力相对较高。滨海湿地主要分布在潮间带,人为监测困难,卫星遥感的出现为滨海湿地的监测带来很大方便,遥感监测已成为评估湿地固碳能力的一种高时效方法。滨海湿地固碳蕴藏着巨大潜力,提升滨海湿地的固碳能力可以大大降低全球CO2含量,对于改善全球温室效应有不可替代的作用。     

基于T213集合预报的延伸期产品释用方法及初步试验

基于T213集合预报系统2008年1月中国及附近区域500 hPa高度和850 hPa温度的1~15 d预报资料,构建延伸期产品释用方法,通过对逐日11~15 d预报资料做集合平均和后向衰减权重系数滑动平均,进而得到延伸期(11~15 d)候平均和候距平预报,并对预报效果进行检验,结果表明:对11~15 d预报场做集合平均和后向衰减权重系数滑动平均均能降低预报误差,改善整体预报效果。由此得到的500 hPa高度场和850 hPa温度场11~1 5 d候平均预报误差与逐日控制预报第5d的水平相当,候距平相关系数均接近0.6,整体而言效果较好,具备一定的应用价值。500 hPa高度场和850 hPa温度场11~15 d候距平预报在中国大陆地区位相准确率均较高,东南沿海和东北部分地区稍差,且850 hPa温度场的位相准确率整体高于500 hPa高度场。候距平预报对延伸期(11~15 d)的大范围持续性异常距平具有较强的捕捉能力,对异常距平出现的范围和分布、强距平中心的位置的预报均较好,但强度整体偏弱。     

Soil respiration mapped by exclusively use of MODIS data for forest landscapes of Saskatchewan,Canada

Soil respiration (R_s) is of great importance to the global carbon balance. Remote sensing of R_s is challenging because of (1) the lack of long-term R_s data for model development and (2) limited knowledge of using satellite-based products to estimate R_s. Using 8-years (2002–2009) of continuous R_s measurements with nonsteady-state automated chamber systems at a Canadian boreal black spruce stand (SK-OBS), we found that R_s was strongly correlated with the product of the normalized difference vegetation index (NDVI) and the nighttime land surface temperature (LSTn) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The coefficients of the linear regression equation of this correlation between R_s and NDVI × LSTn could be further calibrated using the MODIS leaf area index (LAI) product, resulting in an algorithm that is driven solely by remote sensing observations. Modeled R_s closely tracked the seasonal patterns of measured R_s and explained 74–92% of the variance in R_s with a root mean square error (RMSE) less than 1.0 g C/m²/d. Further validation of the model from SK-OBS site at another two independent sites (SK-OA and SK-OJP, old aspen and old jack pine, respectively) showed that the algorithm can produce good estimates of R_s with an overall R² of 0.78 (p < 0.001) for data of these two sites. Consequently, we mapped R_s of forest landscapes of Saskatchewan using entirely MODIS observations for 2003 and spatial and temporal patterns of R_s were well modeled. These results point to a strong relationship between the soil respiratory process and canopy photosynthesis as indicated from the greenness index (i.e., NDVI), thereby implying the potential of remote sensing data for detecting variations in R_s. A combination of both biological and environmental variables estimated from remote sensing in this analysis may be valuable in future investigations of spatial and temporal characteristics of R_s.     

Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing

As a widely used approach for feature extraction and data reduction, Principal Components Analysis (PCA) suffers from high computational cost, large memory requirement and low efficacy in dealing with large dimensional datasets such as Hyperspectral Imaging (HSI). Consequently, a novel Folded-PCA is proposed, where the spectral vector is folded into a matrix to allow the covariance matrix to be determined more efficiently. With this matrix-based representation, both global and local structures are extracted to provide additional information for data classification. Moreover, both the computational cost and the memory requirement have been significantly reduced. Using Support Vector Machine (SVM) for classification on two well-known HSI datasets and one Synthetic Aperture Radar (SAR) dataset in remote sensing, quantitative results are generated for objective evaluations. Comprehensive results have indicated that the proposed Folded-PCA approach not only outperforms the conventional PCA but also the baseline approach where the whole feature sets are used.     

Optimising three-band spectral indices to assess aerial N concentration,N uptake and aboveground biomass of winter wheat remotely in China and Germany

Remotely and accurately quantifying the canopy nitrogen status in crops is essential for regional studies of N budgets and N balances. In this study, we optimised three-band spectral algorithms to estimate the N status of winter wheat. This study extends previous work to optimise the band combinations further and identifies the optimised central bands and suitable bandwidths of the three-band nitrogen planar domain index (NPDI) for estimating the aerial N uptake, N concentration and aboveground biomass. Analysis of the influence of bandwidth change on the accuracy of estimating the canopy N status and aboveground biomass indicated that the suitable bandwidths for optimised central bands were 37 nm at 846 nm, 13 nm at 738 nm and 57 nm at 560 nm for assessing the aerial N uptake and were 37 nm at 958 nm, 21 nm at 696 nm and 73 nm at 578 nm for the assessment of the aerial N concentration and were 49 nm at 806 nm, 17 nm at 738 nm and 57 nm at 560 nm for the estimation of aboveground biomass. The optimised three-band NPDI could consistently and stably estimate the aerial N uptake and aboveground biomass of winter wheat in the vegetative stage and the aerial N concentration in the reproductive stage compared to the fixed band combinations. With suitable bandwidths, the broadband NPDI demonstrated excellent performance in estimating the aerial N concentration, N uptake and biomass. We conclude that the band-optimised algorithm represents a promising tool to measure the improved performance of the NPDI in estimating the aerial N uptake and biomass in the vegetative stage and the aerial N concentration in the reproductive stage, which will be useful for designing improved nitrogen diagnosis systems and for enhancing the applications of ground- and satellite-based sensors.     

Effects of green space spatial pattern on land surface temperature: Implications for sustainable urban planning and climate change adaptation

The urban heat island (UHI) refers to the phenomenon of higher atmospheric and surface temperatures occurring in urban areas than in the surrounding rural areas. Mitigation of the UHI effects via the configuration of green spaces and sustainable design of urban environments has become an issue of increasing concern under changing climate. In this paper, the effects of the composition and configuration of green space on land surface temperatures (LST) were explored using landscape metrics including percentage of landscape (PLAND), edge density (ED) and patch density (PD). An oasis city of Aksu in Northwestern China was used as a case study. The metrics were calculated by moving window method based on a green space map derived from Landsat Thematic Mapper (TM) imagery, and LST data were retrieved from Landsat TM thermal band. A normalized mutual information measure was employed to investigate the relationship between LST and the spatial pattern of green space. The results showed that while the PLAND is the most important variable that elicits LST dynamics, spatial configuration of green space also has significant effect on LST. Though, the highest normalized mutual information measure was with the PLAND (0.71), it was found that the ED and PD combination is the most deterministic factors of LST than the unique effects of a single variable or the joint effects of PLAND and PD or PLAND and ED. Normalized mutual information measure estimations between LST and PLAND and ED, PLAND and PD and ED and PD were 0.7679, 0.7650 and 0.7832, respectively. A combination of the three factors PLAND, PD and ED explained much of the variance of LST with a normalized mutual information measure of 0.8694. Results from this study can expand our understanding of the relationship between LST and street trees and vegetation, and provide insights for sustainable urban planning and management under changing climate.     

The potential of the greenness and radiation (GR) model to interpret 8-day gross primary production of vegetation

Remote sensing of vegetation gross primary production (GPP) is an important step to analyze terrestrial carbon (C) cycles in response to changing climate. The availability of global networks of C flux measurements provides a valuable opportunity to develop remote sensing based GPP algorithms and test their performances across diverse regions and plant functional types (PFTs). Using 70 global C flux measurements including 24 non-forest (NF), 17 deciduous forest (DF) and 29 evergreen forest (EF), we present the evaluation of an upscaled remote sensing based greenness and radiation (GR) model for GPP estimation. This model is developed using enhanced vegetation index (EVI) and land surface temperature (LST) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and global course resolution radiation data from the National Center for Environmental Prediction (NCEP). Model calibration was achieved using statistical parameters of both EVI and LST fitted for different PFTs. Our results indicate that compared to the standard MODIS GPP product, the calibrated GR model improved the GPP accuracy by reducing the root mean square errors (RMSE) by 16%, 30% and 11% for the NF, DF and EF sites, respectively. The standard MODIS and GR model intercomparisons at individual sites for GPP estimation also showed that GR model performs better in terms of model accuracy and stability. This evaluation demonstrates the potential use of the GR model in capturing short-term GPP variations in areas lacking ground measurements for most of vegetated ecosystems globally.     

A GIHS-based spectral preservation fusion method for remote sensing images using edge restored spectral modulation

High spatial resolution and spectral fidelity are basic standards for evaluating an image fusion algorithm. Numerous fusion methods for remote sensing images have been developed. Some of these methods are based on the intensity–hue–saturation (IHS) transform and the generalized IHS (GIHS), which may cause serious spectral distortion. Spectral distortion in the GIHS is proven to result from changes in saturation during fusion. Therefore, reducing such changes can achieve high spectral fidelity. A GIHS-based spectral preservation fusion method that can theoretically reduce spectral distortion is proposed in this study. The proposed algorithm consists of two steps. The first step is spectral modulation (SM), which uses the Gaussian function to extract spatial details and conduct SM of multispectral (MS) images. This method yields a desirable visual effect without requiring histogram matching between the panchromatic image and the intensity of the MS image. The second step uses the Gaussian convolution function to restore lost edge details during SM. The proposed method is proven effective and shown to provide better results compared with other GIHS-based methods.     

A simple two-band semi-analytical model for retrieval of specific absorption coefficients in coastal waters

A non-linear iterative method is used to replace the traditional spectral slope technique in initializing the total absorption decomposition model. Based on comparison of absorption coefficient by QAA and two-band semi-analytical model (TSAA) models with field measurements collected from the West Florida Shelf waters and Bohai Sea, it is shown that both models are effective in estimating absorption coefficients from the West Florida Shelf waters, but the TSAA model is superior to the QAA model. Use of the TSAA model in estimating absorption coefficient in the West Florida Shelf and Bohai Sea decreases the uncertainty of estimation by 1.3–74.7% from the QAA model. The TSAA model’s sensitivity to the input parameters was evaluated by varying one parameter and keeping the others fixed at their default values. Our results indicate that the TSAA model has quite a strong noise tolerance to addressing the field data of the total absorption coefficient.     

Multi-class geospatial object detection and geographic image classification based on collection of part detectors

The rapid development of remote sensing technology has facilitated us the acquisition of remote sensing images with higher and higher spatial resolution, but how to automatically understand the image contents is still a big challenge. In this paper, we develop a practical and rotation-invariant framework for multi-class geospatial object detection and geographic image classification based on collection of part detectors (COPD). The COPD is composed of a set of representative and discriminative part detectors, where each part detector is a linear support vector machine (SVM) classifier used for the detection of objects or recurring spatial patterns within a certain range of orientation. Specifically, when performing multi-class geospatial object detection, we learn a set of seed-based part detectors where each part detector corresponds to a particular viewpoint of an object class, so the collection of them provides a solution for rotation-invariant detection of multi-class objects. When performing geographic image classification, we utilize a large number of pre-trained part detectors to discovery distinctive visual parts from images and use them as attributes to represent the images. Comprehensive evaluations on two remote sensing image databases and comparisons with some state-of-the-art approaches demonstrate the effectiveness and superiority of the developed framework.