Estimation and analysis of the ratio of transpiration to evapotranspiration in forest ecosystems along the North-South Transect of East China

Journal of Geographical Sciences - The ratio of transpiration to evapotranspiration (T/ET) is a key parameter for quantifying water use efficiency of ecosystems and understanding the interaction...     

Stem radial growth indicate the options of species,topography and stand management for artificial forests in the western Loess Plateau,China

An understanding of the differences in artificial forest between tree species,slope aspects,and management options in arid environments is fundamentally important for efficient management of these artificial systems;however,few studies have quantified the spatial and temporal differences in stem radial growth of trees in the arid western Loess Plateau of China.Using dendrochronology,we assessed the growth of three woody species(the native shrub Reaumuria soongorica,the exotic shrub Tamarix ramosissima and tree Platycladus orientalis)by measuring the annual stem radial increment.We also describe the long-term growth trends and responses to climatic factors on slopes with different aspects during periods with and without irrigation.We found that precipitation during the main growing season was significantly positively correlated with ring growth for all three species and both slope aspects.In addition,supplemental water(e.g.,irrigation,rainwater harvesting)greatly relieved drought stress and promoted radial growth.Our results suggest that as the main afforestation species in the Loess Plateau used for soil and water conservation,P.orientalis is more suitable than T.ramosissima under rain-fed conditions.However,a landscape that combined a tree(P.orientalis)with a shrub(R.soongorica)and grassland appears likely to represent the best means of ecological restoration in the arid western Loess Plateau.     

Reflectance variation in boreal landscape during the snow melting period using airborne imaging spectroscopy

We aim a better understanding of the effect of spring-time snow melt on the remotely sensed scene reflectance by using an extensive amount of optical spectral data obtained from an airborne hyperspectral campaign in Northern Finland. We investigate the behaviour of thin snow reflectance for different land cover types, such as open areas, boreal forests and treeless fells. Our results not only confirm the generally known fact that the reflectance of a melting thin snow layer is considerably lower than that of a thick snow layer, but we also present analyses of the reflectance variation over different land covers and in boreal forests as a function of canopy coverage. According to common knowledge, the highly variating reflectance spectra of partially transparent, most likely also contaminated thin snow pack weakens the performance of snow detection algorithms, in particular in the mapping of Fractional Snow Cover (FSC) during the end of the melting period. The obtained results directly support further development of the SCAmod algorithm for FSC retrieval, and can be likewise applied to develop other algorithms for optical satellite data (e.g. spectral unmixing methods), and to perform accuracy assessments for snow detection algorithms.A useful part of this work is the investigation of the competence of Normalized Difference Snow Index (NDSI) in snow detection in late spring, since it is widely used in snow mapping. We conclude, based on the spectral data analysis, that the NDSI -based snow mapping is more accurate in open areas than in forests. However, at the very end of the snow melting period the behavior of the NDSI becomes more unstable and unpredictable in non-forests with shallow snow, increasing the inaccuracy also in non-forested areas. For instance in peatbogs covered by melting snow layer (snow depth < 30 cm) the mean NDSI -0.6 was observed, having coefficient of variation as high as 70%, whereas for deeper snow packs the mean NDSI shows positive values.     

Performance of GNSS-R GLORI data for biomass estimation over the Landes forest

The Above-Ground Biomass (AGB) is a key parameter used for the modeling of the carbon cycle. The aim of this study is to make an experimental assessment of the sensitivity of Global Navigation Satellite System (GNSS) reflected signals to forest AGB. This is based on the analysis of the data recorded during several GLORI airborne campaigns in June and July 2015, over the Landes Forest (France). Ground truth measurements of tree height, density and diameter at breast height (DBH), as well as AGB, were carried out for 100 maritime pine forest plots of various ages. The GNSS-R data were used to obtain the right-left (Γ_RL) and right-right (Γ_RR) reflectivity observables, which are geo-referenced in accordance with the known positions of relevant GPS satellites and the airborne receiver. The correlations between forest AGB and the GNSS-R observables yield the highest sensitivity at high elevation angles (70°-90°). In this case, for (Γ_RL) and the reflectivity polarization ratio (PR = Γ_RL/Γ_RR) estimated with a coherent integration time Tc = 20 ms, the coefficients of determination R² are equal to 0.67 and 0.51, with a sensitivity of −0.051 dB/[10⁶g (Mg) ha⁻¹], and −0.053 dB/[Mg ha⁻¹], respectively. The relationships between AGB and the observables are confirmed through the use of a 5-fold cross validation approach, with several different coherent integration times.     

基于随机森林的FY-2G云检测方法

根据遥感影像中云检测原理,提出了基于随机森林的遥感影像云检测方法,并将其应用于FY-2G影像。结合国家气象卫星中心（NSMC）的云检测产品数据进行了算法的精度检验,云检测个例的精度检验结果显,最高命中率（POD）为88.32%,最低误报率（FAR）为9.36%,临界成功指数（CSI）为80.14%。结果表明,该方法有效地提高了云检测精度,同时能正确标识NSMC中部分误判的情况。     

Data-moderate assessments of Cape monkfish Lophius vomerinus and west coast sole Austroglossus microlepis in Namibian waters

There is global interest in providing scientific advice on optimal harvesting of all commercially exploited fish stocks. Nevertheless, many commercially important stocks lack analytical assessments. Therefore, we evaluate a data-moderate stock assessment method: the stochastic surplus production model in continuous time (SPiCT). The method was applied to two Namibian stocks: (i) the data-rich Cape monkfish Lophius vomerinus, where results are compared to a new data-rich assessment using a state–space assessment model (SAM); and (ii) the data-moderate west coast sole Austroglossus microlepis, which is an important bycatch species in the Cape monkfish fishery, but currently unassessed. The information available to the data-moderate assessment is total commercial catch, commercial catch per unit effort (CPUE), and survey CPUE. SPiCT and SAM gave largely consistent estimates of relative fishing mortality (F/F_MSY) and relative exploitable biomass (B/B_MSY) for the Cape monkfish stock, although with some discrepancies. Differences in the biomass estimates between the two assessments suggest that further investigation is required to understand the cause, and that some caution is necessary when considering the biomass of the stock. SPiCT shows that the west coast sole may be overexploited, although the confidence bounds were too wide for a firm conclusion. Similarity in the estimates of F/F_MSY for Cape monkfish in recent years, using SPiCT relative to SAM, likewise indicates the suitability of SPiCT for managing west coast sole.     

河西防护林体系的持续高效发展

祁连山水源涵养林和河西走廊防风固沙林的持续高效发展,必须通过社会林业和优化林业内部结构来实现。     

基于SMOTE-RF算法的村庄发展类型识别方法研究

准确把握区域发展规律,定量、客观地认识村庄发展类型,对“因地制宜、分类推进”乡村振兴具有非常重要的现实意义。针对区域村庄发展类型自动、准确识别问题,研究提出了一种基于SMOTE-RF算法的村庄发展类型识别模型。研究首先从地形、区位、社会经济、农业生产和生态环境等方面提出了面向村庄发展多维特征表达的指标体系。在此基础上,针对村庄样本不平衡分布特点,利用SMOTE过采样技术对少数类样本进行分析和模拟,合成平衡化的村庄分类样本集;进而利用随机森林算法自动构建村庄发展的多维属性特征与村庄类型之间的非线性关系,形成可用于区域村庄发展类型自动识别的智能分类器。为验证模型的有效性,研究选取山东招远市作为试验区开展了实证研究。实验结果表明,耦合SMOTE过采样技术的随机森林分类模型有效保障了村庄分类结果的可靠性和准确度。在试验区,模型自动识别结果与规划专家分类结果的一致性达88.27%,Kappa系数为0.78,整体一致性良好。相对于人工分类,基于SMOTE-RF方法的村庄类型自动识别方法减少了依赖人工经验分类带来的不确定性,保障了分类结果的一致性,能够为国土空间规划和乡村振兴专项规划决策提供可靠的决...     

Unsupervised hydrologic classification of rivers: Watershed controls on natural and anthropogenic flow regimes,Alabama, USA

Watershed hydrology has often focused on modelling studies of individual watersheds, which consider each river system as unique. Classification is an alternative approach that instead focuses on the similarities among different watersheds. Although both supervised and unsupervised hydrologic classifications have been developed, few previous studies have used classification to assess the degree of anthropogenic modification of hydrologic regime. Here, we conducted an unsupervised hydrologic classification of 189 U.S. Geological Survey gages, including 41 minimally impacted gages from the Hydro‐Climatic Data Network (HCDN), in the five major interstate river basins in the U.S. state of Alabama. For the natural classification, the most significant predictor variables for cluster membership were related to compressive strength of bedrock, bedrock depth, hydraulic conductivity, elevation, temperature, and soil texture, and several land‐cover variables were also significant in the anthropogenic classification. We then developed two random‐forest models: one based on all 189 gages using both natural and anthropogenic variables from the Stream‐Catchment (StreamCat) dataset and one based on the 41 HCDN gages using natural StreamCat variables only. We used the random‐forest models to predict natural and anthropogenic normative hydrologic class for over 158,000 National Hydrography Dataset Plus catchments in the study area. Catchments that changed their class between the natural and anthropogenic classifications can be identified as those that have a large amount of anthropogenic influences on their hydrologic regime, including many catchments on the coast, in the north‐western Coastal Plain, in the Interior Low Plateaus, and in the Piedmont. Using unsupervised hydrologic classifications is a promising approach for uncovering the physical processes that affect hydrologic regime. There are also potential applications in river management, including predicting the hydrologic behaviour of ungaged watersheds, identifying relatively unimpaired rivers to serve as conservation and restoration targets, and regionalization of environmental instream flow standards and climate‐change impacts.     

Characterization of the subsurface architecture and identification of potential groundwater paths in a clay-rich floodplain using multi-electrode resistivity imaging

The interaction between surface water and groundwater in clay-rich fluvial environments can be complex and is generally poorly understood. Airborne electromagnetic surveys are often used for characterizing regional groundwater systems, but they are constrained by the resolution of the method. A resistivity imaging survey has been carried out in the Macquarie Marshes (New South Wales, Australia) in combination with water chemical sampling. The results have enabled the identification of buried palaeochannels and the location of potential recharge points. The data have been compared with previously published airborne electromagnetic data in the same area. Deeper less conductive features suggest that there is a potential connection between the Great Artesian Basin and groundwater contained within the shallow sand aquifer. Even though the chemistry of the groundwater samples does not indicate interaction with the Great Artesian Basin, the observed discontinuity in the saprolite implies potential for this to happen in other locations.