应用计算机图像技术识别间日疟原虫的初步研究

目的应用数字图像技术识别间日疟原虫。方法基于图像预处理、图像分割、特征提取、判决分类等步骤,分别对间日疟原虫薄血膜的裂殖体图像进行处理,观察其识别效率和准确度。结果边缘检测和图像灰度值检测两种方法均能够识别间日疟原虫裂殖体,而二者的联合方法可以得到较好的识别效果。结论初步探索采用边缘和图像灰度联合检测的方法能够识别间日疟原虫薄血膜的裂殖体。     

Spectral unmixing of coral reef benthos under ideal conditions

Hyperspectral remote sensing has shown promise for detailed discrimination of coral reef substratum types, but, by necessity, it samples at pixel scales larger than reef substratum patch sizes. Spectral unmixing techniques have been successful in resolving subpixel areal cover in terrestrial environments. However, the application of spectral unmixing on coral reefs is fundamentally challenging, due not only to the water column, but also to the potentially large number of spectrally similar and ecologically significant end-member (substratum) classes involved. A controlled ex-situ experiment was conducted using field-spectrometer data to assess the accuracy of spectral unmixing techniques to estimate the areal cover of small-scale (<0.25 m²) assemblages of reef substrata (e.g., changes in cover between massive corals, branching corals, bleached corals, macroalgae, and coralline red algae). Mixture compositions were obtained precisely by analysis of digital images collected by a camera calibrated to the field of view of the spectrometer. Linear unmixing techniques were applied to derive estimates of substratum proportions using the full spectral resolution data and various transformations of it, including derivatives and down sampling (merging adjacent wavelengths into broader spectral bands). Comparison of actual and estimated substratum proportions indicate that spectral unmixing may be a practical approach for estimating subpixel-scale cover of coral reef substrata. In the most accurate treatment, coefficients of determination across all mixture sets were high for most end-member classes (37 of 52 cases with r ² >0.64, i.e. r >0.8). The most successful analyses were based on derivatives of down-sampled data, implying that spectral unmixing benefits more from spectral smoothing and judicious choice of band locations than from high spectral resolution per se. Although these results show that changes in coral and algal cover can be determined by unmixing their spectra, the method is not yet an operational remote sensing tool. Primary empirical research is needed before taking the next step, which is to incorporate a water column, of variable depth, above the sea bed.     

A Pixel-Encoder Retinal Ganglion Cell with Spatially Offset Excitatory and Inhibitory Receptive Fields

1. Download high-res image (145KB)
2. Download full-size image

     

大批量测定植物(互花米草)叶面积的一种新方法

叶面积的准确测定是评价生态系统能流、碳流和水分利用的重要基础.目前常用的叶面积测定方法都有其局限性,有些方法快捷、准确,但仅适用于小批量测定;大多数方法很难在较短时间内准确进行大批量测定.本文以互花米草叶面积测定为例,探讨了以扫描仪和叶片干重为基础,结合PHOTOSHOP图像处理技术,大批量、快捷、准确测定植物叶面积的方法--扫描-干重法.结果表明:虚拟正方形叶片边长与叶子像素成幂函数曲线关系,公式为419.85x^1.9693,R≈1.00;用此公式验证扫描-干重法,相对误差仅为3.651%.用扫描-干重法测得的结果与方格法相比无显著差异(P=0.473),表明该方法是可行的.     

应用NOAA/AVHRR资料监测松毛虫危害研究初探

探讨了利用气象卫星定量监测松毛虫危害程度的可能性.以针叶被害率代表松毛虫的危害程度,轻度、中度、重度危害分别定义为针叶被害率<30%、30%～60%和>60%.根据地面光谱观测资料,建立了归一化植被指数与针叶被害率的相关方程,无松毛虫危害时NDVI为0.8823;为了消除大气等因子影响,利用松毛虫危害与未被危害的植被指数相对值表示松毛虫轻、中、重危害程度的遥感监测指标,无危害为1,0.78～1为轻度危害,0.57～0.78为中度危害,<0.57为重度危害.监测危害面积时,利用线性可加垂直植被指数进行混合象元分解.并分别对严重、中度、轻度3种类型发生年进行了定量监测分析,结果表明,AVHRR资料对中等以上松毛虫危害可进行定量监测分析,监测受灾面积比用同期的陆地卫星TM资料监测的受灾面积小12.1%～14.3%;对于轻度危害区域,采用气象卫星不易分辨,主要是由于不同下垫面和大气影响的差异,以及气象卫星空间分辨率较低.     

高原河谷城镇化对植被覆盖度的影响

以典型的高原河谷城市——西宁市为例，定量分析了2000-2015年研究区景观格局及植被覆盖度的时空演变，揭示了高原河谷地区的城镇建设对植被覆盖度的干扰过程及生态环境质量的影响。结果表明：（1）2000-2015年间，西宁市城镇面积增幅为25.99%，总体城镇化率为4.76%，各区县城镇化水平差异较大；（2）西宁市平均植被覆盖度呈增加趋势但不显著（P < 0.57）。植被覆盖度以中高和高植被覆盖度为主，空间上呈现"东西低-南北高"的分布特征，退耕（牧）还林（草）区的植被覆盖度显著增加（P < 0.01），说明生态恢复工程效果显著；（3）区县尺度上，城镇化速率与中高覆盖植被面积变化呈显著负相关（P < 0.01），区县内生态质量有所下降，城镇发展程度和区位条件的差异是影响中高覆盖度植被变化的主要因素；（4）格网尺度上，建成区及其周边区域的低覆盖植被面积呈显著增加趋势（P < 0.01），表明城镇内部区域生态质量有所改善。研究结果对保护和提高西宁市生态环境质量及合理推进城镇建设等方面具有重要意义。     

基于LSMM和改进的FCM提取城市植被覆盖度——以北京市海淀区为例

植被是城市生态系统的重要组成部分,及时获取植被覆盖信息对城市生态环境监测具有重要意义。利用中分辨率Landsat TM遥感数据,采用线性光谱分解模型(LSMM)开展城市植被覆盖度提取;同时,通过改进训练样本选择方式,在最小噪声变换(MNF)、像元纯净指数分析(PPI)、N维可视化分析基础上得到端元样本,再运用模糊C-均值(FCM)获取植被覆盖度;最后以高分辨率SPOT5遥感数据对两种方式的提取结果进行精度检验。结果显示,基于LSMM和改进的FCM提取的城市植被覆盖度与检验数据相关系数分别为0.8252和0.9381,后者可以较好地处理其他要素的非线性影响,因而具有较高精度。     

A hyperspectral indicator system for rangeland degradation on the Tibetan Plateau: A case study towards spaceborne monitoring

The Tibetan Plateau suffers from progressive degradation caused by over-grazing due to improper livestock management, global climate change and herbivory from small mammals. Therefore, a robust indicator system for rangeland degradation has to be developed and tested. This paper investigates local patterns of degradation at two sites (Lake Namco and Mt. Kailash) in Xizang province (China) that are covered by vegetation types typical of a large portion of the plateau. The suitability of a two-indicator system is analysed using hyperspectral field measurements, and its transferability to spaceborne data is tested. The indicators are (1) land-cover fractions derived from linear spectral unmixing and (2) chlorophyll content as a proxy for nutrient and water availability calculated using hyperspectral vegetation indices and partial least squares regression. Because cattle remain near settlements overnight in the local semi-nomadic pastoral system, it can be expected that grazing intensity is highest near the settlement and declines with increasing distance. Therefore, we tested the effect of distance on both indicators using a Spearman correlation analysis. The predicted chlorophyll content and land cover fractions of the indicator system were in good agreement with field observations (correlation coefficients between 0.70 and 0.98). High correlations between distance from settlements and land-cover fractions at both study sites demonstrated that the land-cover fraction is a reliable indicator for degradation. A positive correlation between distance from settlements and photosynthetically active vegetation (PV) revealed over-grazing patterns at the first site. Furthermore, the chlorophyll indicator was proven suitable because chlorophyll concentration declined with increasing distance from settlements. This underlines the over-grazing pattern because cattle excrement was the only external source of nutrients in the ecosystem and it was positively correlated with grazing intensity. However, at the second site, we found a significant positive effect of distance on the amount of photosynthetically non-active vegetation; no effect of distance on PV and chlorophyll content was found. Therefore, no evidence of pasture degradation was detected at the second site. Regarding the potential use of satellite data for degradation monitoring, we found that (1) the land-cover indicator derived from multispectral data was more robust than using noise-filtered hyperspectral information and (2) the chlorophyll amount indicator was estimated from simulated EnMAP data with low error rates. Because the proposed two-indicator system can be derived from multi- and hyperspectral satellite data and combines site conditions and local plant cover, it provides a time-saving and robust method to measure pasture degradation across large areas, assuming that respective satellite data are available.     

Estimators of tissue proportions from X-ray CT images

Estimators are derived of tissue proportions from X-ray computed tomography (CT) images. These take into account that many pixels in such images are responses to mixtures of tissue types. The problem is motivated by an application involving estimation of sheep tissue weights. The standard estimator, a count of the number of pixels in a particular range of values, is compared with the maximum likelihood fit of a mixed-pixel distribution and a moment-based estimator. Both simulations and the application show the moment estimator to be best.     

2006-2016年岷江上游植被覆盖度时空变化及驱动力

基于MODIS NDVI遥感数据,采用像元二分模型估算岷江上游植被覆盖度,运用一元线性回归分析和稳定性分析方法,研究2006-2016年岷江上游植被覆盖度时空变化格局及稳定性,并分段讨论2008年"5.12汶川地震"对岷江上游植被的破坏程度以及震后植被恢复情况,利用地理探测器模型对岷江上游植被覆盖度影响因子及影响力进行探测,分析岷江上游植被覆盖度变化驱动力。结果表明：（1）2006-2016年岷江上游植被覆盖整体状况良好,植被覆盖总体情况较为稳定,多年平均植被覆盖度为0.79,植被覆盖度大于0.8的区域占整个岷江上游地区面积的69%。（2）2008年"5.12汶川地震"给整个岷江上游植被造成了严重的破坏,植被覆盖度退化区域面积为14013.41 km²,占整个岷江上游面积的57%,2008-2016年岷江上游植被恢复状况良好,植被覆盖度改善区域面积为17390.69 km²,占整个岷江上游面积的71%,岷江上游植被覆盖度已经超过震前水平。（3）岷江上游植被覆盖度主要受海拔、气温、土壤类型、降水4个因子的影响,其解释力均在40%以上;地貌类型、植被类型的解释力在20%-40%之间;坡度、坡向的解释力均小于1%。