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21.
The chloroplast and mitochondrion of red algae (Phylum Rhodophyta) may have originated from different endosymbiosis. In this study, we carried out phylogenomic analysis to distinguish their evolutionary lin-eages by using red algal RNA-seq datasets of the 1 000 Plants (1KP) Project and publicly available complete genomes of mitochondria and chloroplasts of Rhodophyta. We have found that red algae were divided into three clades of orders, Florideophyceae, Bangiophyceae and Cyanidiophyceae. Taxonomy resolution for Class Florideophyceae showed that Order Gigartinales was close to Order Halymeniales, while Order Graci-lariales was in a clade of Order Ceramials. We confirmed Prionitis divaricata (Family Halymeniaceae) was closely related to the clade of Order Gracilariales, rather than to genus Grateloupia of Order Halymeniales as reported before. Furthermore, we found both mitochondrial and chloroplastic genes in Rhodophyta under negative selection (Ka/Ks〈1), suggesting that red algae, as one primitive group of eukaryotic algae, might share joint evolutionary history with these two organelles for a long time, although we identified some dif-ferences in their phylogenetic trees. Our analysis provided the basic phylogenetic relationships of red algae, and demonstrated their potential ability to study endosymbiotic events.  相似文献   
22.
TerraSAR-X satellite acquires very high spatial resolution data with potential for detailed land cover mapping. A known problem with synthetic aperture radar (SAR) data is the lack of spectral information. Fusion of SAR and multispectral data provides opportunities for better image interpretation and information extraction. The aim of this study was to investigate the fusion between TerraSAR-X and Landsat ETM+ for protected area mapping using high pass filtering (HPF), principal component analysis with band substitution (PCA) and principal component with wavelet transform (WPCA). A total of thirteen land cover classes were identified for classification using a non-parametric C 4.5 decision tree classifier. Overall classification accuracies of 74.99%, 83.12% and 85.38% and kappa indices of 0.7220, 0.8100 and 0.8369 were obtained for HPF, PCA and WPCA fusion approaches respectively. These results indicate a high potential for a combined use of TerraSAR-X and Landsat ETM+ data for protected area mapping in Uganda.  相似文献   
23.
基于地面激光扫描数据的单木特征因子提取与分析   总被引:1,自引:0,他引:1  
田金苓  王佳  易正晖  冯仲科 《测绘科学》2012,37(5):179-180,189
本文利用三维激光扫描仪对树木进行扫描获取树点云数据,经过格式转换、分离、提取后,对树木各测量因子包括胸径、树高、树冠、材积量进行测定与测量方法与意义的分析。通过实验分析,可以得出:树冠测定因子通过测定树冠的叶面积指数来更精确地反映树冠的生理学意义;通过不规则三角网构建的多面体计算的树干体积较以平均断面积、中央断面积求树干材积更为准确与便捷。  相似文献   
24.
The aim of this study is to present an automatic approach for olive tree dendrometric parameter estimation from airborne laser scanning (ALS) data. The proposed method is based on a unique combination of the alpha-shape algorithm applied to normalized point cloud and principal component analysis. A key issue of the alpha-shape algorithm is to define the α parameter, as it directly affects the crown delineation results. We propose to adjust this parameter based on a group of representative trees in an orchard for which the classical field measurements were performed. The best value of the α parameter is one whose correlation coefficient of dendrometric parameters between field measurements and estimated values is the highest. We determined crown diameters as principal components of ALS points representing a delineated crown. The method was applied to a test area of an olive orchard in Spain. The tree dendrometric parameters estimated from ALS data were compared with field measurements to assess the quality of the developed approach. We found the method to be equally good or even superior to previously investigated semi-automatic methods. The average error is 19% for tree height, 53% for crown base height, and 13% and 9% for the length of the longer diameter and perpendicular diameter, respectively.  相似文献   
25.
Crop diversity (e.g. the number of agricultural crop types and the level of evenness in area distribution) in the agricultural systems of arid Central Asia has recently been increased mainly to achieve food security of the rural population, however, not throughout the irrigation system. Site-specific factors that promote or hamper crop diversification after the dissolvent of the Soviet Union have hardly been assessed yet. While tapping the potential of remote sensing, the objective was to map and explain spatial patterns of current crop diversity by the example of the irrigated agricultural landscapes of the Fergana Valley, Uzbekistan. Multi-temporal Landsat and RapidEye satellite data formed the basis for creating annual and multi-annual crop maps for 2010–2012 while using supervised classifications. Applying the Simpson index of diversity (SID) to circular buffers with radii of 1.5 and 5 km elucidated the spatial distribution of crop diversity at both the local and landscape spatial scales. A variable importance analysis, rooted in the conditional forest algorithm, investigated potential environmental and socio-economic drivers of the spatial patterns of crop diversity. Overall accuracy of the annual crop maps ranged from 0.84 to 0.86 whilst the SID varied between 0.1 and 0.85. The findings confirmed the existence of areas under monocultures as well as of crop diverse patches. Higher crop diversity occurred in the more distal parts of the irrigation system and sparsely settled areas, especially due to orchards. In contrast, in water-secure and densely settled areas, cotton-wheat rotations dominated due to the state interventions in crop cultivation. Distances to irrigation infrastructure, settlements and the road network influenced crop diversity the most. Spatial explicit information on crop diversity per se has the potential to support policymaking and spatial planning towards crop diversification. Driver analysis as exemplified at the study region in Uzbekistan can help reaching the declared policy to increase crop diversity throughout the country and even beyond.  相似文献   
26.
The endemic argan woodlands cover large parts of South Morocco and create a characteristic landscape with areas of sparsely vegetated and bare soil surfaces between single trees. This unique ecosystem has been under extensive agrosilvopastoral management for centuries and is now at risk of degradation caused by overgrazing and increasing scarcity and variability of rainfall. To investigate susceptibility to wind erosion, we conducted an experimental–empirical study including wind tunnel tests and a drone-generated digital elevation model and quantified wind-erodible material on five different associated surface types by means of sediment catchers. The highest emission flux was measured on freshly ploughed surfaces (1875 g m–2 h–1), while older ploughed areas with a re-established crust produced a much lower emission flux (795 g m–2 h–1). Extensive tillage may have been a sustainable practice for generations, but increasing drought and uncertainty of rainfall now lead to an acute risk of severe soil erosion and dust production. The typical crusted surfaces characterized by residual rock fragment accumulation and wash processes produced the second highest emission flux (1,354 g m–2 h–1). Material collected from tree-shaded areas (933 g m–2 h–1) was revealed to be a considerable source of organic material, possibly affecting substrate conditions positively on a larger regional scale. The lowest flux was measured on rock fragment-covered surfaces (301 g m–2 h–1). The data show that open argan woodland may be a considerable source for wind erosion and dust production, depending on surface characteristics strongly related to management. An adapted management must include the conservation of argan trees to offer a promising approach to prevent severe wind erosion and dust production and mitigate possible impacts of land-use change and climate change related shifts in wind and rainfall patterns. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd  相似文献   
27.
Grain size properties and the variation of organic matter in coastal beach and dune environments are assumed to be controlled by the intensity of aeolian processes, time and the sediment source. However, assumptions are based on relatively limited empirical studies. In this study, we examined which environmental variables are the main predictors of multiple topsoil properties. To achieve this, we analysed an extensive dataset systematically collected across all beach zones and a large geographical area at the Finnish Baltic Sea coast characterized by post‐glacial land uplift. We included a comprehensive set of predictors in the analysis and applied boosted regression trees, a modern modelling technique particularly suited for analysis without prior assumptions of the data model. The results suggest that mean grain size and sorting are mainly determined by northing and fetch. Northing, disturbance and fetch predicted the variation of soil organic matter while litter cover was strongly related to disturbance. Based on the analyses, we were able to identify the main drivers of multiple topsoil properties on land uplift beaches. Parent material is suggested to determine sediment textural properties, which largely masks the effects of transient processes. Mean grain size and sorting are highly interdependent: grains become finer and sorting improves with increasing shore exposure. The intensity of momentary geomorphic processes controls the accumulation of litter whereas the slower accumulation of organic matter in the soil is influenced also by the static exposure setting. Skewness and kurtosis of the grain size distribution are mainly influenced by unmeasured processes, potentially relating to the geomorphological origin of the sediment. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   
28.
This paper presents an application of Airborne Laser Scanning (ALS) data in conjunction with an IRS LISS-III image for mapping forest fuel types. For two study areas of 165 km2 and 487 km2 in Sicily (Italy), 16,761 plots of size 30-m × 30-m were distributed using a tessellation-based stratified sampling scheme. ALS metrics and spectral signatures from IRS extracted for each plot were used as predictors to classify forest fuel types observed and identified by photointerpretation and fieldwork. Following use of traditional parametric methods that produced unsatisfactory results, three non-parametric classification approaches were tested: (i) classification and regression tree (CART), (ii) the CART bagging method called Random Forests, and (iii) the CART bagging/boosting stochastic gradient boosting (SGB) approach. This contribution summarizes previous experiences using ALS data for estimating forest variables useful for fire management in general and for fuel type mapping, in particular. It summarizes characteristics of classification and regression trees, presents the pre-processing operation, the classification algorithms, and the achieved results. The results demonstrated superiority of the SGB method with overall accuracy of 84%. The most relevant ALS metric was canopy cover, defined as the percent of non-ground returns. Other relevant metrics included the spectral information from IRS and several other ALS metrics such as percentiles of the height distribution, the mean height of all returns, and the number of returns.  相似文献   
29.
Image classification using multispectral sensors has shown good performance in detecting macrophytes at the species level. However, species level classification often does not utilize the texture information provided by high resolution images. This study investigated whether image texture provides useful vector(s) for the discrimination of monospecific stands of three floating macrophyte species in Quickbird imagery of the South Nation River. Semivariograms indicated that window sizes of 5 × 5 and 13 × 13 pixels were the most appropriate spatial scales for calculation of the grey level co-occurrence matrix and subsequent texture attributes from the multispectral and panchromatic bands. Of the 214 investigated vectors (13 Haralick texture attributes * 15 bands + 9 spectral bands + 10 transformations/indices), feature selection determined which combination of spectral and textural vectors had the greatest class separability based on the Mann–Whitney U-test and Jefferies–Matusita distance. While multispectral red and near infrared (NIR) performed satisfactorily, the addition of panchromatic-dissimilarity slightly improved class separability and the accuracy of a decision tree classifier (Kappa: red/NIR/panchromatic-dissimilarity – 93.2% versus red/NIR – 90.4%). Class separability improved by incorporating a second texture attribute, but resulted in a decrease in classification accuracy. The results suggest that incorporating image texture may be beneficial for separating stands with high spatial heterogeneity. However, the benefits may be limited and must be weighed against the increased complexity of the classifier.  相似文献   
30.
Hyperspectral remote sensing research was conducted to document the biophysical and biochemical characteristics of controlled forest plots subjected to various nutrient and irrigation treatments. The experimental plots were located on the Savannah River Site near Aiken, SC. AISA hyperspectral imagery were analysed using three approaches, including: (1) normalized difference vegetation index based simple linear regression (NSLR), (2) partial least squares regression (PLSR) and (3) machine-learning regression trees (MLRT) to predict the biophysical and biochemical characteristics of the crops (leaf area index, stem biomass and five leaf nutrients concentrations). The calibration and cross-validation results were compared between the three techniques. The PLSR approach generally resulted in good predictive performance. The MLRT approach appeared to be a useful method to predict characteristics in a complex environment (i.e. many tree species and numerous fertilization and/or irrigation treatments) due to its powerful adaptability.  相似文献   
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