生态系统服务功能价值评估—以锡林河流域天然草地生态系统为例

时空尺度和生态系统的健康状况是影响生态系统服务功能的重要因素。锡林河流域天然草地关键的地理位置和独特的生境条件使其具有十分重要的生态意义和经济价值。评估锡林河流域天然草地在不同时空尺度及生态系统健康状况的生态系统服务功能的价值，不仅能够全面客观的评价该流域天然草地的生态服务功能，而且还能够为当地经济政策的制定，经济的建设发展提供科学依据。 本研究以锡林河流域天然草地为例，通过对草地生态系统服务功能价值评估和计算机情景模拟，研究了不同空间尺度及放牧影响锡林河流域天然草地生态系统服务功能价值。对于不同空间尺度下草地生态系统服务价值的研究，根据草地的产草量和退化程度将锡林河流域天然草地划分成5个级别，利用Costanza等的基本思路和方法，进行了价值评估。结果表明锡林河流域草地生态系统每年的服务价值为76.154×108元。受各级草场的生产量和放牧强度的综合影响，健康程度不同的各等级天然草地的单位面积生态系统服务功能的价值存在着很大差异，从1级草地到5级草地，单位面积服务价值比重从38.1%下降到4.8%。参考国际、国内和锡林浩特当地三种不同的生态系统服务单价，计算得出这三种不同空间尺度下锡林河流域天然草场生态系统服务价值分别为：88.199×108元/年、76.154×108元/年、14.236×108元/年，并且各项服务功能在总价值中所占的比例也随空间尺度变化。以气体调节为例，服务价值的比重分别占3.7%、11.0%和7.9%，这说明生态系统服务功能的价值与空间尺度有关。 通过草地生态系统服务功能当量因子得到不同类型草地生态系统单位面积服务价值，研究放牧影响下各类型草地生态系统单位面积服务价值以及年平均累积服务价值。草地生态系统服务功能当量因子说明生态系统各项服务功能不仅与生态系统的生物量有关，同时受到生态系统内环境因子、生物因子、生态系统过程多方面因素的共同作用。最大直接经济价值放牧率大于生态系统服务功能最大时的放牧率，但是获得经济利润小于生态系统服务功能最大时的价值。同时，在各类草地生态系统中单位面积服务价值以贝加尔针茅草原最高为2242.347元/公顷，克氏针茅草原最低为1655.413元/公顷。草地生态系统的累积服务价值在时间范围一定时随放牧率变化明显，并且时间范围越大变化越大。重牧状态下的草地生态系统累积服务价值最低，且随时间的增加而减少;无牧、轻牧和中牧状态下草地生态系统的累积服务价值则随时间的延续而增长。以上结果说明放牧对草地生态系统服务功能有显著影响，以经济利益为目的获得最大利润的放牧方式带来的经济价值是短期的，从长远看，只有科学管理，合理放牧，才能使草地生态系统长期稳定的提供最佳的生态系统服务功能。

Coarse-level object recognition using interlevel products of complex wavelets

This paper introduces the Interlevel Product (ILP) which is a transform based upon the Dual-Tree Complex Wavelet. Coefficients of the ILP have complex values whose magnitudes indicate the amplitude of multilevel features, and whose phases indicate the nature of these features (e.g. ridges vs. edges). In particular, the phases of ILP coefficients are approximately invariant to small shifts in the original images. We accordingly introduce this transform as a solution to coarse scale template matching, where alignment concerns between decimation of a target and decimation of a larger search image can be mitigated, and computational efficiency can be maintained. Furthermore, template matching with ILP coefficients can provide several intuitive "near-matches" that may be of interest in image retrieval applications. © 2005 IEEE.

Semantic Discriminant mapping for classification and browsing of remote sensing textures and objects

We present a new approach based on Discriminant Analysis to map a high dimensional image feature space onto a subspace which has the following advantages: 1. each dimension corresponds to a semantic likelihood, 2. an efficient and simple multiclass classifier is proposed and 3. it is low dimensional. This mapping is learnt from a given set of labeled images with a class groundtruth. In the new space a classifier is naturally derived which performs as well as a linear SVM. We will show that projecting images in this new space provides a database browsing tool which is meaningful to the user. Results are presented on a remote sensing database with eight classes, made available online. The output semantic space is a low dimensional feature space which opens perspectives for other recognition tasks. © 2005 IEEE.

榕母管蓟马生物学特性研究

研究了榕母管蓟马（ＧｙｎａｉｋｏｔｈｒｉｐｓｕｚｅｌｉＺｉｍｍｅｒｍａｎ）生物学特性，结果表明：在温度２７、３０℃时，正常羽化率达９０．０％～９５．０％，雌虫产卵量达７５～８０粒，卵的孵化率达９０．０％～１００％。在２４℃温度范围内，成虫寿命长达３０～５６ｄ。在云南景谷１年发生８代，世代重叠。

Restoration of images and 3D data to higher resolution by deconvolution with sparsity regularization

Image convolution is conventionally approximated by the LTI discrete model. It is well recognized that the higher the sampling rate, the better is the approximation. However sometimes images or 3D data are only available at a lower sampling rate due to physical constraints of the imaging system. In this paper, we model the under-sampled observation as the result of combining convolution and subsampling. Because the wavelet coefficients of piecewise smooth images tend to be sparse and well modelled by tree-like structures, we propose the L0 reweighted-L2 minimization (L0RL2 ) algorithm to solve this problem. This promotes model-based sparsity by minimizing the reweighted L2 norm, which approximates the L0 norm, and by enforcing a tree model over the weights. We test the algorithm on 3 examples: a simple ring, the cameraman image and a 3D microscope dataset; and show that good results can be obtained. © 2010 IEEE.

Convex approaches to model wavelet sparsity patterns

Statistical dependencies among wavelet coefficients are commonly represented by graphical models such as hidden Markov trees (HMTs). However, in linear inverse problems such as deconvolution, tomography, and compressed sensing, the presence of a sensing or observation matrix produces a linear mixing of the simple Markovian dependency structure. This leads to reconstruction problems that are non-convex optimizations. Past work has dealt with this issue by resorting to greedy or suboptimal iterative reconstruction methods. In this paper, we propose new modeling approaches based on group-sparsity penalties that leads to convex optimizations that can be solved exactly and efficiently. We show that the methods we develop perform significantly better in de-convolution and compressed sensing applications, while being as computationally efficient as standard coefficient-wise approaches such as lasso. © 2011 IEEE.

Biologically-inspired object recognition system with features from complex wavelets

In this paper, a novel cortex-inspired feed-forward hierarchical object recognition system based on complex wavelets is proposed and tested. Complex wavelets contain three key properties for object representation: shift invariance, which enables the extraction of stable local features; good directional selectivity, which simplifies the determination of image orientations; and limited redundancy, which allows for efficient signal analysis using the multi-resolution decomposition offered by complex wavelets. In this paper, we propose a complete cortex-inspired object recognition system based on complex wavelets. We find that the implementation of the HMAX model for object recognition in [1, 2] is rather over-complete and includes too much redundant information and processing. We have optimized the structure of the model to make it more efficient. Specifically, we have used the Caltech 5 standard dataset to compare with Serre's model in [2] (which employs Gabor filter bands). Results demonstrate that the complex wavelet model achieves a speed improvement of about 4 times over the Serre model and gives comparable recognition performance. © 2011 IEEE.

Co-occurrence flow for pedestrian detection

The last few years have seen considerable progress in pedestrian detection. Recent work has established a combination of oriented gradients and optic flow as effective features although the detection rates are still unsatisfactory for practical use. This paper introduces a new type of motion feature, the co-occurrence flow (CoF). The advance is to capture relative movements of different parts of the entire body, unlike existing motion features which extract internal motion in a local fashion. Through evaluations on the TUD-Brussels pedestrian dataset, we show that our motion feature based on co-occurrence flow contributes to boost the performance of existing methods. © 2011 IEEE.