基于SPAN与NDVI的全极化SAR数据喀斯特地区土地类型划分

地貌复杂性、地物多样性等特征使得全极化SAR（Synthetic Aperture Radar，SAR）数据的散射机制和散射强度相互交织，从而导致基于传统Wishart-H/α的全极化SAR数据难以实现喀斯特地区土地类型的有效划分。针对此问题，本文先用复Wishart距离测度对研究区土地类型样本进行聚类，同时利用H/α平面对研究区进行超盒聚类，然后根据超盒聚类结果平均相干矩阵与样本聚类结果平均相干矩阵间的复Wishart距离进行半监督分类，获得研究区土地类型划分的初步结果。在此基础上利用对建筑物与裸岩地敏感的极化总功率（Polarimetric-Total-Power，SPAN）和对林地、草地与耕地敏感的归一化植被指数（Normalized Differential Vegetation Index，NDVI）对初步结果继续进行划分，最终将研究区土地类型划分为水体、林地、草地、耕地、建筑地和裸岩地，总体分类精度为81.45%；采用另一地势相对平缓、地形相对单一的典型喀斯特地区全极化SAR数据进行验证，在实现该地区土地类型划分的同时总体分类精度为85.66%。说明gai该研究方法能够实现喀斯特地区土地类型的准确划分。     

基于Sentinel-1A影像和一维CNN的中国南方生长季早期作物种类识别

作物的早期识别对粮食安全至关重要。在以往的研究中,中国南方作物早期识别面临的主要挑战包括:1)云层覆盖时间长、地块尺寸小且作物类型丰富;2)缺少高时空分辨率合成孔径雷达(synthetic aperture radar,SAR)数据。欧洲航天局Sentinel-1A(S1A)卫星提供的SAR图像具有12 d的重访周期,空间分辨率达10 m,为中国南方作物早期识别提供了新的机遇。为在作物早期识别中充分利用S1A影像的时间特征,本研究提出一维卷积神经网络(one-dimensional convolutional neural network,1D CNN)的增量训练方法:首先利用生长季内全时间序列数据来训练1D CNN的超参数,称为分类器;然后从生长季内第一次S1A影像获取开始,在每个数据获取时间点输入该点之前(包括该点)生长季内所有数据训练分类器在该点的其他参数。以中国湛江地区2017年生长季为研究实例,分别基于VV、VH和VH+VV,评估不同极化数据在该地区的作物分类效果。为验证该方法的有效性,本研究同时应用经典的随机森林(random forest,RF)模型对研究区进行试验。结果表明:1)基于VH+VV、VH和VV极化数据的分类精度依次降低,其中,基于VH+VV后向散射系数时间序列1D CNN和RF测试结果的Kappa系数最大值分别为0.924和0.916,说明S1A时间序列数据在该地区作物分类任务中有效;2)在研究区域内2017年生长季早期,基于1D CNN和RF的5种作物的F-measure均达到0.85及以上,说明本文所构建的1D CNN在该地区主要作物早期分类任务中有效。研究结果证明,针对中国南方作物早期分类,本研究提出的1D CNN训练方案可行。研究结果可为深度学习在作物早期分类任务中的应用提供参考。     

Marinopyrrole derivatives as potential antibiotic agents against methicillin-resistant Staphylococcus aureus (I)

Infections caused by drug-resistant pathogens are on the rise. The ongoing spread of methicillin-resistant Staphylococcus aureus (MRSA) strains exemplifies the urgent need for new antibiotics. The marine natural product, marinopyrrole A, was previously shown to have potent antibiotic activity against Gram-positive pathogens, including MRSA. However, its minimum inhibitory concentration (MIC) against MRSA was increased by >500 fold in the presence of 20% human serum, thus greatly limiting therapeutic potential. Here we report our discovery of a novel derivative of marinopyrrole A, designated 1a, featuring a 2-4 fold improved MIC against MRSA and significantly less susceptibility to serum inhibition. Importantly, compound 1a displayed rapid and concentration-dependent killing of MRSA. Compared to the natural product counterpart, compound 1a provides an important natural product based scaffold for further Structure Activity Relationship (SAR) and optimization.     

Hordenine is responsible for plant defense response through jasmonate-dependent defense pathway

Organic agriculture does not rely on synthetic chemical fungicides. An alternative pest management strategy to chemical fungicides is the use of bioactive natural compounds. Hordenine [4-(2-dimethylaminoethyl)] is a phenethylamine alkaloid found in barley. Although hordenine has various pharmacological effects, including antibiotic activity against microorganisms, no studies have been carried out to investigate the inhibitory effects of hordenine on phytopathogenic fungal infection in host plants. Both grape downy mildew and strawberry anthracnose were suppressed by hordenine treatment. Hordenine had no effect on mycelial growth of phytopathogenic fungi, whereas plant defense response through the jasmonate-dependent defense pathway was enhanced in hordenine-treated plants. The concern over environmental pollution has led to the introduction of new pesticides, including bioactive natural compound based pesticide. Hordenine may be used in organic agriculture as an innovative elicitor of plant defense response to downy mildew and anthracnose.     

Comparison between TanDEM-X- and ALS-based estimation of aboveground biomass and tree height in boreal forests

Interferometric Synthetic Aperture Radar (InSAR) data from TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X) were used to estimate aboveground biomass (AGB) and tree height with linear regression models. These were compared to models based on airborne laser scanning (ALS) data at two Swedish boreal forest test sites, Krycklan (64°N19°E) and Remningstorp (58°N13°E). The predictions were validated using field data at the stand-level (0.5–26.1 ha) and at the plot-level (10 m radius). Additionally, the ALS metrics percentile 99 (p99) and vegetation ratio, commonly used to estimate AGB and tree height, were estimated in order to investigate the feasibility of replacing ALS data with TanDEM-X InSAR data. Both AGB and tree height could be estimated with about the same accuracy at the stand-level from both TanDEM-X- and ALS-based data. The AGB was estimated with 17.2% and 14.6% root mean square error (RMSE) and the tree height with 7.6% and 4.1% RMSE from TanDEM-X data at the stand-level at the two test sites Krycklan and Remningstorp. The Pearson correlation coefficients between the TanDEM-X height and the ALS height p99 were r?=?.98 and r?=?.95 at the two test sites. The TanDEM-X height contains information related to both tree height and forest density, which was validated from several estimation models.     

Predicting the Classes and Distribution of Salt-Affected Soils in Northwest Libya

Sodicity and salinity can adversely affect soil structure and are common constraints to plant growth in arid regions. Current remote sensing techniques cannot distinguish between the various classes of salt-affected soils. Field and laboratory measurements of salt-affected soils are time-consuming and expensive. Mapping of the salt-affected soils can be used in soil conservation planning to identify regions with different degrees of limitations. There is a need to use existing field and laboratory measurements to create maps of classes of salt-affected soils. The objectives of this study are to classify salt-affected soils, use existing field data to interpolate and validate geospatial predictions of the classes of salt-affected soils using Geographic Information Systems (GIS), and create maps showing the different classes and distribution of salt-affected soils. The classification framework for salt-affected soils is based on electrical conductivity (ECe), soil pH and the sodium adsorption ratio (SAR), and provides four degrees of limitations to salt-affected soils: slight (normal soils), moderate (saline soils), severe (sodic soils), and extreme (saline-sodic soils). Spatial interpolation of the field data from northwestern Libya was verified by cross-validation, and maps of the salt-affected soils in the region were created. The majority of soils in this region of Libya are normal (slight degree of limitation). Twenty percent of the topsoil is saline-sodic (extreme degree of limitation). Land use recommendations and rehabilitation strategies can be developed from such maps of salt-affected soil classes. The methodology followed in this study can be applied to other arid regions around the world, particularly in developing countries where budgetary constraints limit detailed field and laboratory measurements of sodicity and salinity.