Evaluation of satellite data efficiency in identification of plant groups

Application of remote sensing (RS) and geographical information system (GIS) techniques has been increased in natural sciences. In fact, it is inevitable applying of these techniques in vegetation studies due to the existence of some problems in traditional methods (e.g. sampling, calculation, analysis and so on). On this scope, scientists must have sufficient information about the efficiency of these techniques as a useful tool in their studies. This study aims to evaluate the IRS-P6 LISS III and Landsat ETM⁺ efficiency in plant groups’ identification. In order to this purpose, 143 training samples were collected from areas that showed homogenous composition of plant species in at least area of 3600 m² (60 × 60 m). Coordinates of these training samples were recorded using a GPS device and transferred to a GIS database. Also, ENVI 4.2 package has used to process and analyze the satellites data. Several methods of processing such as; spectral separability, supervised classification and assessment of classification accuracy were used in order to gain a satisfy evaluation of the data efficiency. The results indicated that net farming of alfalfa and Juniperus polycarpus–Artemisia kopetdaghensisi community have the most separability on the satellite images (1.99 for Landsat and 2 for IRS). Against, the least separabilities on the Landsat data were between Ju. polycarpus–Onobrychis cornuta and Ju. polycarpus–Ar. kopetdaghensis communities (1.57) and between Ju. polycarpus–Ar. kopetdaghensis and Ju. polycarpus–Agropyron intermedium (1.53) on the IRS data. According to these results, it is concluded that the satellite data are somedeal able to identify plant groups when vegetation communities are sufficiently homogenous, abundant and spectrally and ecologically separable.     

植物物候研究进展

植物物候直接反映了气候变化的影响,是植被动态模拟的关键.在遥感和模型技术的推动下,植物物候与全球变化关系的研究日益受到人们的关注.文中从植物物候与环境因子的相互关系、植物物候对全球变化的响应以及植物物候的遥感监测方面,综合论述了植物物候的研究进展,找出植被物候研究的不足,进而提出未来植被物候的研究方向.     

Review of optical-based remote sensing for plant trait mapping

Plant trait data have been used in various studies related to ecosystem functioning, community ecology, and assessment of ecosystem services. Evidences are that plant scientists agree on a set of key plant traits, which are relatively easy to measure and have a stable and strong predictive response to ecosystem functions. However, the field measurements of plant trait data are still limited to small area, to a certain moment in time and to certain number of species only. Therefore, remote sensing (RS) offers potential to complement or even replace field measurements of some plant traits. It offers instantaneous spatially contiguous information, covers larger areas and in case of satellite observations profits from their revisit capacity.In this review, we first introduce RS concepts of light–vegetation interactions, RS instruments for vegetation studies, RS methods, and scaling between field and RS observations. Further we discuss in detail current achievements and challenges of optical RS for mapping of key plant traits. We concentrate our discussion on three categorical plant traits (plant growth and life forms, flammability properties and photosynthetic pathways and activity) and on five continuous plant traits (plant height, leaf phenology, leaf mass per area, nitrogen and phosphorous concentration or content). We review existing literature to determine the retrieval accuracy of the continuous plant traits. The relative estimation error using RS ranged between 10% and 45% of measured mean value, i.e. around 10% for plant height of tall canopies, 20% for plant height of short canopies, 15% for plant nitrogen, 25% for plant phosphorus content/concentration, and 45% for leaf mass per area estimates.The potential of RS to map plant traits is particularly high when traits are related to leaf biochemistry, photosynthetic processes and canopy structure. There are also other plant traits, i.e. leaf chlorophyll content, water content and leaf area index, which can be retrieved from optical RS well and can be of importance for plant scientists.We underline the need that future assessments of ecosystem functioning using RS should require comprehensive and integrated measurements of various plant traits together with leaf and canopy spectral properties. By doing so, the interplay between plant structural, physiological, biochemical, phenological and spectral properties can be better understood.     

基于多源遥感数据的植物物种分类与识别: 研究进展与展望

物种分类与识别是生物多样性监测的基础, 明确物种的类别及其分布是解决几乎所有生态学问题的前提。为深入了解基于多源遥感数据的植物物种分类与识别相关研究的发展现状和存在的问题, 本文对2000年以来该领域的研究进行了总结分析, 发现: 当前大多数研究集中在欧洲和北美地区的温带或北方森林以及南非的热带稀树草原; 使用最多的遥感数据是机载高光谱数据, 而激光雷达作为补充数据, 通过单木分割及提供单木的三维垂直结构信息, 显著提高了分类精度; 支持向量机和随机森林作为应用最广的非参数分类算法, 平均分类精度达80%; 随着计算机技术及机器学习领域的不断成熟, 人工神经网络在物种识别领域得以迅速发展。基于此, 本文对目前基于遥感数据的植物物种分类与识别中在分类对象复杂性、多源遥感数据整合、植物物候与纹理特征整合和分类算法技术等方面面临的挑战进行了总结, 并建议通过整合多时相监测数据、高光谱和激光雷达数据、短波红外等特定波谱信息、采用深度学习等方法来提高分类精度。     

Developing a denoising filter for electron microscopy and tomography data in the cloud

The low radiation conditions and the predominantly phase-object image formation of cryo-electron microscopy (cryo-EM) result in extremely high noise levels and low contrast in the recorded micrographs. The process of single particle or tomographic 3D reconstruction does not completely eliminate this noise and is even capable of introducing new sources of noise during alignment or when correcting for instrument parameters. The recently developed Digital Paths Supervised Variance (DPSV) denoising filter uses local variance information to control regional noise in a robust and adaptive manner. The performance of the DPSV filter was evaluated in this review qualitatively and quantitatively using simulated and experimental data from cryo-EM and tomography in two and three dimensions. We also assessed the benefit of filtering experimental reconstructions for visualization purposes and for enhancing the accuracy of feature detection. The DPSV filter eliminates high-frequency noise artifacts (density gaps), which would normally preclude the accurate segmentation of tomography reconstructions or the detection of alpha-helices in single-particle reconstructions. This collaborative software development project was carried out entirely by virtual interactions among the authors using publicly available development and file sharing tools.     

Floristic groups and plant communities of southeastern Santa Fe,Argentina

This paper is a survey of the vegetation of the southeastern departments in the Province of Santa Fe (Argentina). The vegetation was analyzed following Braun-Blanquet's approach modified by Mueller-Dombois & Ellenberg (1974). The most relevant species of the region were placed in 25 groups according to their requirements or general behaviour. Most of the communities are herbaceous, and apart from the woody and some other miscellaneous ones they were grouped into three ecologically and floristically defined sets. The first set, the Stipa grasslands and related communities, which are characterized by the more or less abundant presence of Stipa hyalina, Stipa neesiana and Stipa papposa, comprises five different communities. The second, the halophilous communities, comprises five communities, the two Spartina ssp. grasslands, the halophilous prairies of Distichlis spicata, the short sedge Scirpus americanus communities and the ‘pela-dales’. The third, the hygrophilous communities, comprises nine communities which are not so well defined as the ones in the other sets. Besides, two further communities have been included, the Paspalum quadrifarium and the Melica macra tall grasslands.     

Mapping species density of trees,shrubs and vines in a tropical forest,using field measurements,satellite multiespectral imagery and spatial interpolation

We estimated the number of species in a tropical forest landscape in Quintana Roo, Mexico, based on the relationship between reflectance values of satellite imagery and field measurements of plant species density (mean number of species per plot). Total species density as well as that of tree, shrub and vine species were identified from 141 sampling quadrats (16543 individuals sampled). Spatial prediction of plant diversity was performed using universal kriging. This approach considered the linear relationship between plant species density and reflectance values of Thematic Mapper™, as well as the spatial dependence of the observations. We explored the linear relationships between spectral properties of TM bands and the species density of trees, shrubs and vines, using regression analysis. We employed Akaike Information Criterion (AIC) to select a set of candidate models. Based on Akaike weights, we calculated model-averaged parameters. Linear regression between number of species and reflectance values of TM bands yielded regression residuals. We used variogram analysis to analyze the spatial structure of these residuals. Results show that accounting for spatial autocorrelation in the residual variation improved model R² from 0.57 to 0.66 for number of all species, from 0.58 to 0.65 for number of tree species, from 0.26 to 0.41 for number of shrub species and from 0.13 to 0.17 for species density of vines. The empirical models we developed can be used to predict landscape-level species density in the Yucatan Peninsula, helping to guide and evaluate management and conservation strategies.     

Regression Tree Analysis of satellite and terrain data to guide vegetation sampling and surveys

Abstract. Monitoring of regional vegetation and surface biophysical properties is tightly constrained by both the quantity and quality of ground data. Stratified sampling is often used to increase sampling efficiency, but its effectiveness hinges on appropriate classification of the land surface. A good classification must be sufficiently detailed to include the important sources of spatial variability, but at the same time it should be as parsimonious as possible to conserve scarce and expensive degrees of freedom in ground data. As part of the First ISLSCP (International Satellite Land Surface Climatology Program) Field Experiment (FIFE), we used Regression Tree Analysis to derive an ecological classification of a tall grass prairie landscape. The classification is derived from digital terrain, land use, and land cover data and is based on their association with spectral vegetation indices calculated from single-date and multi-temporal satellite imagery. The regression tree analysis produced a site stratification that is similar to the a priori scheme actually used in FIFE, but is simpler and considerably more effective in reducing sample variance in surface measurements of variables such as biomass, soil moisture and Bowen Ratio. More generally, regression tree analysis is a useful technique for identifying and estimating complex hierarchical relationships in multivariate data sets.     

Using satellite and real-time weather data to predict maize production

 Large-scale assessments of crop conditions prior to harvest are critical for providing early estimates of production. Satellite and weather information provide the opportunity for near real-time crop monitoring. The objective of this research was to develop an operational assessment system for crop production utilizing data from these sources. Maize (Zea mays) production was assessed in 42 Crop Reporting Districts (CRDs) across the United States Corn Belt, which produce 60% of all maize grown in the United States. Satellite, climatolocal, and agricultural data were collected for 8 years, 1985–1992, and aggregated into CRDs. A model predicting the normalized maize yields for each CRD was developed that included as independent variables a satellite data variable, the Vegetation Condition Index, and a climatological variable, the Crop Moisture Index. This model explained approximately three-quarters (R ²=0.73) of the variation observed in the normalized yields, and was examined both for its accuracy and its timeliness in providing production estimates. Predicted seasonal yields were summed to provide a maize production estimate for the entire Corn Belt study region. Production estimates deviated from the final USDA statistics, which become available several months after harvest, by less than 10% for all eight growing seasons. In addition, the production estimates were available approximately 2 months prior to the completion of the maize harvest. This system has the potential for providing timely in-formation to organizations monitoring regional or global agricultural production for humanitarian or economic benefits. Received: 23 July 1997 / Accepted: 17 February 1998     

The potential of the satellite derived green chlorophyll index for estimating midday light use efficiency in maize, coniferous forest and grassland

Light use efficiency (LUE) is an important variable in carbon cycle and climate change research. We present an investigation of remotely estimating midday LUE using the green chlorophyll index (CI_green) derived from the cloud-free Moderate Resolution Imaging Spectroradiometer (MODIS) images in maize, coniferous forest and grassland. Similar temporal patterns are observed in both canopy chlorophyll content and midday LUE which indicates that the chlorophyll content in the maize canopy servers as a proxy of midday LUE (R² = 0.736, p < 0.001). Therefore, the CI_green, tested as a good indicator of canopy chlorophyll content (R² = 0.840, p < 0.001), has been demonstrated to be a reliable candidate in providing reasonable estimates of midday LUE with determination coefficient R² equals to 0.820 and a root mean square error (RMSE) of 0.002 mol CO₂ per mol incident photosynthetic photon flux density (PPFD). Further validation of the prediction model derived from the maize site demonstrates that the CI_green has potential to be applied in the coniferous forest and grassland ecosystems with RMSE of 0.005 and 0.004 mol CO₂ mol⁻¹ PPFD, respectively. A comparison analysis between different vegetation types is included and these results could be helpful in the development of future LUE and terrestrial models.     

国内8款常用植物识别软件的识别能力评价

随着智能手机和人工智能技术的发展, 以手机app为载体的植物识别软件慢慢走进公众生活、科普活动和科研活动的各个方面。植物识别app的识别正确率是决定其使用价值和用户体验的关键因素。目前, 国内应用市场上有许多植物识别app, 它们的开发目的和应用范围各异, 软件本身的关注点、数据库来源、算法、硬件要求也存在很大差异。对于不同人群, 植物识别app有不同的意义, 如对于科研人员来说, 识别能力强的app是提高效率的一大工具; 对植物爱好者来说, 具一定准确率的识别app可以作为入门的工具。因此, 对各app的识别能力进行分析与评价显得尤为重要。本文选取了8款常用的app, 分别对400张已准确鉴定的植物图片进行识别, 其中干旱半干旱区、温带、热带和亚热带4个区各选取100张。这些图片共计122科164属340种, 涵盖了乔木、灌木、草本、草质藤本和木质藤本5种生长型, 包含23种国家级保护植物。种、属、科准确识别正确分别计4分、2分、1分, 以此标准对软件识别能力按总得分进行排序, 正确率得分由高到低依次为花帮主、百度识图、花伴侣、形色、花卉识别、植物识别、发现识花、微软识花。     

Evidence for Megalake Chad, north-central Africa, during the late Quaternary from satellite data

The existence of a very large Lake Chad during the late Quaternary, Megalake Chad, has long been questioned. A Megalake Chad would present strong evidence for climatic fluctuations of great magnitude during the Holocene in tropical Africa. In this paper we used satellite data from Landsat and Modis sensors to collect and analyse new information on landforms in a 2 000 000 km² region of the Lake Chad Basin. We detected 2300 km of remains marking the ancient shoreline of Megalake Chad. The satellite data also indicated many Saharan rivers and relict deltas leading to the long paleoshoreline. Large dunefield flattenings were observed and interpreted as the result of wave-cut erosion by the paleolake. Similarities were noticed between the landforms observed along the paleoshoreline of Megalake Chad and that of the former Aral Sea. This finding has significant consequences for reconstructing paleohydrology and paleoenvironments through the Lake Chad basin, and continental climate change.     

miRPlant: an integrated tool for identification of plant miRNA from RNA sequencing data

Background

Small RNA sequencing is commonly used to identify novel miRNAs and to determine their expression levels in plants. There are several miRNA identification tools for animals such as miRDeep, miRDeep2 and miRDeep*. miRDeep-P was developed to identify plant miRNA using miRDeep’s probabilistic model of miRNA biogenesis, but it depends on several third party tools and lacks a user-friendly interface. The objective of our miRPlant program is to predict novel plant miRNA, while providing a user-friendly interface with improved accuracy of prediction.

Result

We have developed a user-friendly plant miRNA prediction tool called miRPlant. We show using 16 plant miRNA datasets from four different plant species that miRPlant has at least a 10% improvement in accuracy compared to miRDeep-P, which is the most popular plant miRNA prediction tool. Furthermore, miRPlant uses a Graphical User Interface for data input and output, and identified miRNA are shown with all RNAseq reads in a hairpin diagram.

Conclusions

We have developed miRPlant which extends miRDeep* to various plant species by adopting suitable strategies to identify hairpin excision regions and hairpin structure filtering for plants. miRPlant does not require any third party tools such as mapping or RNA secondary structure prediction tools. miRPlant is also the first plant miRNA prediction tool that dynamically plots miRNA hairpin structure with small reads for identified novel miRNAs. This feature will enable biologists to visualize novel pre-miRNA structure and the location of small RNA reads relative to the hairpin. Moreover, miRPlant can be easily used by biologists with limited bioinformatics skills.miRPlant and its manual are freely available at http://www.australianprostatecentre.org/research/software/mirplant or http://sourceforge.net/projects/mirplant/.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-275) contains supplementary material, which is available to authorized users.     

基于能量属性的植物功能群划分方法探索——以内蒙古锡林河流域草原植物群落为例

植物功能群(plant functional groups, PFGs) 是具有确定的植物功能特征的一系列植物的组合,是生态学家为研究植被对气候变化和干扰的响应而引入的生态学概念.目前功能群研究中最核心的问题仍在于决定植物功能群划分的植物特征的选择上.以内蒙古锡林河流域草原植物群落为例,选取3个草原类型(羊草草原、大针茅草原和羊草草甸草原)及其退化梯度系列(未退化、轻度退化、中度退化、重度退化),在对植物热值进行分析测定的基础上,依据植物的能量属性-单位重量干物质在完全燃烧后所释放出来的热量值,采用人为分段的方法对草原植物进行了能量功能群的划分(高能值植物功能群、中能值植物功能群和低能值植物功能群).并探讨了这种能量功能群划分方法在草原植被动态研究中的客观性与可行性.     

Fire monitoring in savanna ecosystems using MODIS data: a case study of Kruger National Park, South Africa

The heterogeneity of savanna ecosystems is guaranteed by disturbance events like fires, droughts, floods and browsing and grazing by herbivores. For conservation areas with limited space to preserve biodiversity, fire monitoring is crucial. Long periods of satellite remotely sensed data provide an alternative solution to estimate the distribution of different vegetation types and fire-affected patches over time. This study focusses on the application of MODIS data to detect, identify and delineate fire-affected areas in Kruger National Park (KNP), South Africa, for the period 2001–2003. Fire scars on KNP’s savanna were identified using threshold and supervised classification methods on moderate-resolution imaging spectroradiometer (MODIS) with 500-m resolution and 32-day global composites using a combination of band 1 (red), 2 (NIR, near infrared), 4 (green) and 6 (SWIR, short wave infrared). On identified fire scars, the spectral indexes of albedo, normalised difference infrared index (NDII) and normalised difference vegetation index (NDVI) were extracted. The following four broad habitat types were used for this analysis: riparian woodland, dense woodland, mixed woodland and open-tree savanna. The values of albedo, NDII and NDVI during the dry season (June to October) for different years are lower on fire-affected patches. Mixed woodland is the largest habitat burned with 21%, 43% and 2% of the KNP area affected by fire in 2001, 2002 and 2003, respectively. Riparian woodland is the least affected by fire. The supervised classification method has a greater accuracy for fire scars detection in KNP savannas during the dry season. We conclude that MODIS data can be used successfully for fire monitoring in savanna ecosystems.