Invasive species detection in Hawaiian rainforests using airborne imaging spectroscopy and LiDAR

Remote sensing of invasive species is a critical component of conservation and management efforts, but reliable methods for the detection of invaders have not been widely established. In Hawaiian forests, we recently found that invasive trees often have hyperspectral signatures unique from that of native trees, but mapping based on spectral reflectance properties alone is confounded by issues of canopy senescence and mortality, intra- and inter-canopy gaps and shadowing, and terrain variability. We deployed a new hybrid airborne system combining the Carnegie Airborne Observatory (CAO) small-footprint light detection and ranging (LiDAR) system with the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) to map the three-dimensional spectral and structural properties of Hawaiian forests. The CAO-AVIRIS systems and data were fully integrated using in-flight and post-flight fusion techniques, facilitating an analysis of forest canopy properties to determine the presence and abundance of three highly invasive tree species in Hawaiian rainforests.

The LiDAR sub-system was used to model forest canopy height and top-of-canopy surfaces; these structural data allowed for automated masking of forest gaps, intra- and inter-canopy shadows, and minimum vegetation height in the AVIRIS images. The remaining sunlit canopy spectra were analyzed using spatially-constrained spectral mixture analysis. The results of the combined LiDAR-spectroscopic analysis highlighted the location and fractional abundance of each invasive tree species throughout the rainforest sites. Field validation studies demonstrated < 6.8% and < 18.6% error rates in the detection of invasive tree species at  7 m² and  2 m² minimum canopy cover thresholds. Our results show that full integration of imaging spectroscopy and LiDAR measurements provides enormous flexibility and analytical potential for studies of terrestrial ecosystems and the species contained within them.     

Using k-nn and discriminant analyses to classify rain forest types in a Landsat TM image over northern Costa Rica

Conservation and land use planning in humid tropical lowland forests urgently need accurate remote sensing techniques to distinguish among floristically different forest types. We investigated the degree to which floristically and structurally defined Costa Rican lowland rain forest types can be accurately discriminated by a non-parametric k nearest neighbors (k-nn) classifier or linear discriminant analysis. Pixel values of Landsat Thematic Mapper (TM) image and Shuttle Radar Topography Mission (SRTM) elevation model extracted from segments or from 5 × 5 pixel windows were employed in the classifications. 104 field plots were classified into three floristic and one structural type of forest (regrowth forest). Three floristically defined forest types were formed through clustering the old-growth forest plots (n = 52) by their species specific importance values. An error assessment of the image classification was conducted via cross-validation and error matrices, and overall percent accuracy and Kappa scores were used as measures of accuracy. Image classification of the four forest types did not adequately distinguish two old-growth forest classes, so they were merged into a single forest class. The resulting three forest classes were most accurately classified by the k-nn classifier using segmented image data (overall accuracy 91%). The second best method, with respect to accuracy, was the k-nn with 5 × 5 pixel windows data (89% accuracy), followed by the canonical discriminant analysis using the 5 × 5 pixel window data (86%) and the segment data (82%). We conclude the k-nn classifier can accurately distinguish floristically and structurally different rain forest types. The classification accuracies were higher for the k-nn classifier than for the canonical discriminant analysis, but the differences in Kappa scores were not statistically significant. The segmentation did not increase classification accuracy in this study.     

在盐湖地区暴露48个月纯锌的腐蚀行为

采用X射线衍射(XRD)、扫描电子显微镜(SEM)、金相显微镜和电化学阻抗等手段研究了锌在青海盐湖大气环境(富盐干旱型大气环境)中的腐蚀行为。结果表明,在青海盐湖大气环境中锌的腐蚀规律遵循经验公式m=Atn。锌的向地面比向天面腐蚀严重,向天面暴晒48个月出现锈层脱落现象。两个表面的腐蚀产物均由Zn5(OH)8Cl2·H2O,Zn5(CO)3(OH)6和Zn4SO4(OH)6·3H2O组成,锈层均富含SiO2。电化学结果表明,随着暴晒时间的延长向地面的极化电阻Rp逐渐增大,而向天面的Rp逐渐增加,而暴晒48个月时减小。锈层有抑制基体腐蚀的作用,对两个表面锈层的保护性随暴晒时间的延长而增大,而向天面暴晒48个月时保护性减弱。     

察尔汗盐湖资源与我国钾肥工业发展

概述了察尔汗盐湖的自然条件、地质条件与资源状况,阐明了我国钾肥工业的现状以及察尔汗盐湖资源开发对发展我国钾肥工业的重要作用。     

Hydrodynamic effects of reconnecting lake group with Yangtze River in China

The hydrodynamic effects of reconnecting a lake group with the Yangtze River were simulated using a three-dimensional hydrodynamic model. The model was calibrated and validated using the measured water temperature and total phosphorous. The circulation patterns, water temperature, and water exchange conditions between sub-lakes were simulated under two conditions: (1) the present condition, in which the lake group is isolated from the Yangtze River; and (2) the future condition, with a proposed improvement ...     

SODA: A new method of in-scene atmospheric water vapor estimation and post-flight spectral recalibration for hyperspectral sensors: Application to the HyMap sensor at two locations

A new method of estimating per-pixel atmospheric column water vapor (ACWV) and potential differences in the reported band center wavelengths of the HyMap sensor has been developed. The new method uses variations of a second order derivative algorithm (SODA) to assess the impact of atmospheric residual features on calculated surface reflectance spectra after atmospheric compensation. The SODA method provides an alternative to the current band ratio techniques of ACWV estimation and also allows the same form of algorithm to be used for the estimation of possible band shifts. A comparison of in-situ measured surface reflectance at two field sites in Western Australia demonstrates improvement in the resulting spectra when post-flight updates are made to the reported HyMap band center wavelengths and applied during the atmospheric compensation process. The same SODA methodology was varied to estimate the ACWV on a per-pixel basis and found to significantly reduce the appearance of the underlying surface structure on the resulting ACWV images as well as improve the overall accuracy of the estimation. The ACWV estimated from the HyMap imagery at the two field sites was found to agree with in-situ atmospheric ACWV measurements to within 2% and represented a two fold increase in accuracy over a 3 band ratio Continuum Interpolated Band Ratio (CIBR) technique of ACWV estimation.     

Water property monitoring and assessment for China's inland Lake Taihu from MODIS-Aqua measurements

We provide results of quantitative measurements and characterization for inland freshwater Lake Taihu from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellite Aqua. China's Lake Taihu, which is located in the Yangtze River delta in one of the world's most urbanized and heavily populated areas, contains consistently highly turbid waters in addition to frequent large seasonal algae blooms in various lake regions. Thus, satellite data processing requires use of the shortwave infrared (SWIR) atmospheric correction algorithm. Specifically for Lake Taihu, an iterative SWIR-based atmospheric correction algorithm has been developed and proven to provide reasonably accurate water-leaving radiance spectra data. Using MODIS-Aqua measurements, the blue-green algae bloom in Lake Taihu in 2007 has been studied in detail, demonstrating the importance and usefulness of satellite water color remote sensing for effectively monitoring and managing a bloom event.Seasonal and interannual variability, as well as spatial distributions, of lake water properties were studied and assessed using the MODIS-Aqua measurements from 2002 to 2008. Results show that overall waters in Lake Taihu are consistently highly turbid all year round, with the winter and summer as the most and least turbid seasons in the lake, respectively. Extremely turbid waters in the winter are primarily attributed to strong winter winds that lead to significant amounts of total suspended sediment (TSS) in the water column. In addition, MODIS-Aqua-measured water-leaving radiance at the blue band is consistently low in various bay regions in Lake Taihu, indicating high algae concentration in these regions. Climatological water property maps, including normalized water-leaving radiance spectra nL_w(λ), chlorophyll-a concentration, and water diffuse attenuation coefficient at the wavelength of 490 nm (K_d(490)), are derived from all MODIS-Aqua data from 2002 to 2008 for Lake Taihu, showing overall spatial distribution features for the lake water property.     

Anthropogenic and climatic influences over the past three centuries on characteristics of an Adirondack lake,Eastern North America

Fourth Lake is a drainage lake at 43°N, 74°W, from which a 37‐cm long mud‐water interface core was recovered. ²¹⁰Pb dating indicates the core spans ≈340 years, from the Little Ice Age through modern global warming. Diatom accumulation responds to anthropogenic watershed disturbances, declining slightly up‐core until a peak in the late‐1800s attributable to sediment and nutrient influx from logging and enlargement of the outlet dam. A dramatic decrease occurs ≈1900 as logging and lake filling ceased, and a smaller peak ≈1960 accompanies residential development. Similar changes occur in organic carbon accumulation, which ranges from 0.0038–0.024 mg cm^?2 year^?1, with generally decreasing values up‐core, punctuated by maximum values in the late‐1800s. Expressing diatoms as concentration, however, reveals a doubling up‐core that positively correlates with changes extending beyond the watershed, including Northern Hemisphere temperature, atmospheric CO₂ concentration and solar irradiance (R = 0.627, 0.675 and 0.400, respectively). A >50% increase in % organic carbon, from 3.8% to 5.9%, also positively correlates with these larger‐scale environmental conditions (R = 0.828, 0.830 and 0.832), while negative correlations with the extrabasinal records are exhibited by magnetic susceptibility (R = ?0.654, ?0.496, and ?0.660) and clay (R = ?0.770, ?0.762, and ?0.737). These changes are consistent with decreased sediment influx and reduced dilution of biogenous sedimentary components. In contrast to total diatoms, the accumulation of planktonic genus Asterionella displays a long‐term increase up‐core. Potential explanations include increasing duration of the ice‐free season or a shift in the timing of the spring bloom and a mismatch with abundance of predator(s). Asterionella also increases as a percentage of total diatoms, being positively correlated with extrabasinal conditions (R = 0.827, 0.774 and 0.674). This change occurs at the expense of many benthic genera and, over the past century, at the expense of tychoplanktonic genus, Aulacosiera. Heavily silicified, Aulacosiera requires strong mixing to remain within the epilimnion. Thus, its decline might result from increasing stratification caused by warming.     

基于指标权重的湖库营养状态识别

针对湖库营养状态识别未考虑指标权重及营养状态分级较少的不足,基于熵值法与概率神经网络（PNN）基本原理,提出熵值PNN湖库营养状态识别模型,以全国24个湖库营养状态识别为例进行分析。利用熵值法确定识别指标权重,依据我国湖库富营养化评价标准,将湖库营养状态划分为极贫营养一极重度富营养11个等级,提出基于改进的湖库营养状态识别等级标准,在等级标准域值间采用随机内插的方法生成样本对PNN模型进行训练和检验,利用正确识别率和运行时间对PNN模型性能进行评价。最后,基于两种方案对全国24个湖库营养状态进行识别。结果表明：@PNN模型对于随机生成的训练样本和检验样本的正确识别率分别达到98．9％、98．6％（5次平均）,模型用于湖库营养状态识别是合理可行的。②湖库识别结果存在差异。比较而言,基于指标权重考虑的识别结果更能科学、客观地反映湖库营养状态。     

江苏太湖流域降雨径流年际变化分析

以江苏太湖流域（1956—2012）降雨为基础,分析江苏太湖流域径流及时空变化特征,研究该流域降雨径流的年际年内变化及两者的变化关系。结果表明：降雨径流都呈增长趋势。降雨平均每10年增加29mm,径流平均每10年增加26mm;降雨在年内季节分配上,夏季、冬季呈加大趋势,春季、秋季呈减小趋势;加大的降水年内分配的不均导致径流在年内分配的更加不均匀,径流集中在汛期,汛期径流量约达年径流量的84．8％,加大了某季节内的洪水、干旱风险。