Investigation of Uncertainties of Establishment Schemes in Dynamic Global Vegetation Models简

In Dynamic Global Vegetation Models （DGVMs）, the establishment of woody vegetation refers to flowering, fertiliza- tion, seed production, germination, and the growth of tree seedlings. It determines not only the population densities but also other important ecosystem structural variables. In current DGVMs, establishments of woody plant functional types （PFTs） are assumed to be either the same in the same grid cell, or largely stochastic. We investigated the uncertainties in the competition of establishment among coexisting woody PFTs from three aspects： the dependence of PFT establishments on vegetation states; background establishment; and relative establishment potentials of different PFTs. Sensitivity experi- ments showed that the dependence of establishment rate on the fractional coverage of a PFT favored the dominant PFT by increasing its share in establishment. While a small background establishment rate had little impact on equilibrium states of the ecosystem, it did change the timescale required for the establishment of alien species in pre-existing forest due to their disadvantage in seed competition during the early stage of invasion. Meanwhile, establishment purely fiom background （the scheme commonly used in current DGVMs） led to inconsistent behavior in response to the change in PFT specification （e.g., number of PFTs and their specification）. Furthermore, the results also indicated that trade-off between irtdividual growth and reproduction/colonization has significant influences on the competition of establishment. Hence, further development of es- tablishment parameterization in DGVMs is essential in reducing the uncertainties in simulations of both ecosystem structures and successions.     

Response of BITS (a benthic index based on taxonomic sufficiency) to water and sedimentary variables and comparison with other indices in three Adriatic lagoons

The European Water Framework Directive (WFD, 2000/60/EC) recommends the development of biotic indices for assessing the ecological quality status of water bodies. The Benthic Index based on Taxonomic Sufficiency (BITS) was specifically developed for lagoonal systems, according to the tolerant/opportunistic approach. Macrobenthic data, variables indicative of eutrophication and variables indicative of sedimentary organic matter quality were collected during 18 surveys carried out between 2004 and 2005 in three Adriatic coastal lagoons (Venice Lagoon, Sacca di Goro, Lesina Lagoon). The relationship between environmental variables and biotic indices (AMBI, BENTIX, BITS, BOPA and FINE) was tested using multivariate analyses. Indices based on species classification level appeared to relate better with the variables describing organic matter quality, whereas BITS, which is based on family classification level, seemed related to eutrophication indicative variables, such as sulphide, ammonium, dissolved oxygen and orthophosphate concentrations. The BITS approach reduces the costs associated with sorting and identification of organisms. Providing a rapid assessment of ecological quality, and producing an ecologically relevant classification, BITS seems a promising tool for monitoring programs of Adriatic lagoonal ecosystems. Its sensitivity in reflecting the field conditions elsewhere, however, remains to be assessed.     

生态干扰度:一种评价植被天然性程度的方法

地球上现存的植物或森林群落都是植被与其环境长期适应及人类长期干扰影响的结果 ,因此 ,现实的植被反映着历史上人类对它们不同程度的影响。‘生态干扰’的概念常常被用来描述历史上人类对植被或森林影响的大小 ,即‘生态干扰度’;或用来表示现实植被离开它的‘天然植被’的距离 ,即现实植被的‘天然性程度’。如此的‘天然植被’可以是 :( 1 )历史上从未受过人类任何干扰的‘原始植被’;( 2 )基于当前立地的‘潜在的自然植被’。然而 ,成百上千年以来人类对自然的利用干扰 ,致使真正的‘原始植被’现在几乎无处可寻。因此 ,‘潜在的自然植被’被用来作为生态干扰度估计或植被天然性程度评价的参照系。作为一种很实用的生态分析手段 ,生态干扰度的方法已被广泛地应用在农、林、水、景观、城市及自然保护等诸多领域的生态评价上。为此 ,本文论述了生态干扰度的概念、原理、方法和应用实例。     

Reducing background effects in orchards through spectral vegetation index correction

Satellite remote sensing provides an alternative to time-consuming and labor intensive in situ measurements of biophysical variables in agricultural crops required for precision agriculture applications. In orchards, however, the spatial resolution causes mixtures of canopies and background (i.e. soil, grass and shadow), hampering the estimation of these biophysical variables. Furthermore, variable background mixtures obstruct meaningful comparisons between different orchard blocks, rows or within each row. Current correction methodologies use spectral differences between canopies and background, but struggle with a vegetated orchard floor. This background influence and the lack of a generic solution are addressed in this study.Firstly, the problem was demonstrated in a controlled environment for vegetation indices sensitive to chlorophyll content, water content and leaf area index. Afterwards, traditional background correction methods (i.e. soil-adjusted vegetation indices and signal unmixing) were compared to the proposed vegetation index correction. This correction was based on the mixing degree of each pixel (i.e. tree cover fraction) to rescale the vegetation indices accordingly and was applied to synthetic and WorldView-2 satellite imagery. Through the correction, the effect of background admixture for vegetation indices was reduced, and the estimation of biophysical variables was improved (ΔR² = 0.2–0.31).     

运用德尔菲调查—灰色统计法确立水库鱼产力综合评价中的指标权重体系

本文运用德尔菲方法对水库鱼产力综合评价中指标权重的合理分配问题作了专家调查,并采用灰色统计法对调查结果进行归纳处理,从而确立了一个水库鱼产力影响因素诸层次各方面的评价指标权重体系,可供今后的评价工作参考使用。     

Mapping of aggregated floodplain plant communities using image fusion of CASI and LiDAR data

Combined optical and laser altimeter data offer the potential to map and monitor plant communities based on their spectral and structural characteristics. A problem unresolved is, however, that narrowly defined plant communities, i.e. plant communities at a low hierarchical level of classification in the Braun-Blanquet system, often cannot be linked directly to remote sensing data for vegetation mapping. We studied whether and how a floristic dataset can be aggregated into a few major discrete, mappable classes without substantial loss of ecological meaning. Multi-source airborne data (CASI and LiDAR) and floristic field data were collected for a floodplain along the river Waal in the Netherlands. Mapping results based on floristic similarity alone did not achieve highest levels of accuracy. Ordination of floristic data showed that terrain elevation and soil moisture were the main underlying environmental drivers shaping the floodplain vegetation, but grouping of plant communities based on their position in the ordination space is not always obvious. Combined ordination-based grouping with floristic similarity clustering led to syntaxonomically relevant aggregated plant assemblages and yielded highest mapping accuracies.     

应用MSS数字图像处理进行森林资源动态监测的探讨

本文探讨了应用多时相Landsat Mss图像进行森林动态监测的方法。研究了差值图像法、比值植被指数差值法、归一化植被指数差值法、多时相主分量分析法(垂直植被指数法)和分类比较法对提取森林动态变化信息的作用。结果表明,3种植被指数法都能较好地提取植被动态信息,可用于森林面积消长的动态监测。     

A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling

During the last 50 years, the management of agroecosystems has been undergoing major changes to meet the growing demand for food, timber, fibre and fuel. As a result of this intensified use, the ecological status of many agroecosystems has been severely deteriorated. Modeling the behavior of agroecosystems is, therefore, of great help since it allows the definition of management strategies that maximize (crop) production while minimizing the environmental impacts. Remote sensing can support such modeling by offering information on the spatial and temporal variation of important canopy state variables which would be very difficult to obtain otherwise.In this paper, we present an overview of different methods that can be used to derive biophysical and biochemical canopy state variables from optical remote sensing data in the VNIR-SWIR regions. The overview is based on an extensive literature review where both statistical–empirical and physically based methods are discussed. Subsequently, the prevailing techniques of assimilating remote sensing data into agroecosystem models are outlined. The increasing complexity of data assimilation methods and of models describing agroecosystem functioning has significantly increased computational demands. For this reason, we include a short section on the potential of parallel processing to deal with the complex and computationally intensive algorithms described in the preceding sections.The studied literature reveals that many valuable techniques have been developed both for the retrieval of canopy state variables from reflective remote sensing data as for assimilating the retrieved variables in agroecosystem models. However, for agroecosystem modeling and remote sensing data assimilation to be commonly employed on a global operational basis, emphasis will have to be put on bridging the mismatch between data availability and accuracy on one hand, and model and user requirements on the other. This could be achieved by integrating imagery with different spatial, temporal, spectral, and angular resolutions, and the fusion of optical data with data of different origin, such as LIDAR and radar/microwave.     

Evaluation of fuzzy-based classifiers for cotton crop identification

In this study, an evaluation of fuzzy-based classifiers for specific crop identification using multi-spectral temporal data spanning over one growing season has been carried out. The temporal data sets have been georeferenced with 0.3 pixel rms error. Temporal information of cotton crop has been incorporated through the following five indices: simple ratio (SR), normalized difference vegetation index (NDVI), transformed normalized difference vegetation index (TNDVI), soil-adjusted vegetation index (SAVI) and triangular vegetation index (TVI), to study the effect of indices on classified output. For this purpose, a comparative study between two fuzzy-based soft classification approaches, possibilistic c-means (PCM) and noise classifier (NC), was undertaken. In this study, advanced wide field sensor (AWiFS) data for soft classification and linear imaging self scanner sensor (LISS III) data for soft testing purpose from Resourcesat-1 (IRS-P6) satellite were used. It has been observed that NC fuzzy classifier using TNDVI temporal index – dataset 2, which comprises four temporal images performs better than PCM classifier giving highest fuzzy overall accuracy of 96.03%.     

Integrating environmental variables and WorldView-2 image data to improve the prediction and mapping of Thaumastocoris peregrinus (bronze bug) damage in plantation forests

This study integrated environmental variables together with high spectral resolution WorldView-2 imagery to detect and map Thaumastocoris peregrinus damage in Eucalypt plantation forests in KwaZulu-Natal, South Africa. The WorldView-2 bands, vegetation indices and environmental variables were entered separately into PLS regression models to predict T. peregrinus damage. The datasets were then integrated to test the collective strength in predicting T. peregrinus damage. Important variables were identified by variable importance (VIP) scores and were re-entered into a PLS regression model. The VIP model was then extrapolated to map the severity of damage and predicted T. peregrinus damage with an R² value of 0.71 and a RMSE of 3.26% on an independent test dataset. The red edge and near-infrared bands of the WorldView-2 sensor together with the temperature dataset were identified as important variables in predicting T. peregrinus damage. The results indicate the potential of integrating WorldView-2 data and environmental variables to improve the mapping and monitoring of insect outbreaks in plantation forests. The result is critical for plantation health monitoring using a new sensor which contains important vegetation wavelengths.