Effect of synoptic meteorological conditions on aerosol properties over urban environment: A study over tropical urban region of Hyderabad,India

Aerosol particle size distribution and chemical properties are important in studies related to human health and climate. The present study describes an analysis of aerosol mass loading, Aerosol Optical Depth (AOD), black carbon aerosol mass concentration and carbon monoxide over tropical urban region of Hyderabad, India, during March 2006, coinciding with active forest fires season over India. Aerosol optical depth, particulate matter mass loading and carbon monoxide were observed to be high on days with air mass coming from north of the study area. Spatial occurrence of forest fires was analysed using MODIS daytime data and DMSP-OLS nighttime data sets. Aerosol optical depth measured using Microtops-II sunphotometer correlated well with MODIS derived AOD values. Results of the study suggested that synoptic meteorological conditions play an important role in the observed aerosol properties over the study area during the forest fire season.     

采用土壤水利用技术的冬小麦田土壤过氧化氢酶活性分析

本文以河北衡水试验场土壤水利用试验为例,讨论不同处理下土壤过氧化氢酶活性的时空变化规律。初步分析得出:(1)冬小麦拔节期,采用不同土壤水利用技术的冬小麦田浅层土壤过氧化氢酶活性差异明显,以沟播不盖秸秆处理的过氧化氢酶活性最强,缩行密植处理的最弱;(2)土壤过氧化氢酶活性随深度的变化趋势大体一致,呈反S形;(3)沟播不盖秸秆处理沟中土壤过氧化氢酶活性明显高于垄上;(4)土壤过氧化氢酶对于田间微生态环境具有指示作用,在一定程度上反映了土壤水利用技术对微生态环境的影响。     

北京市杨镇湿地植物群落多样性及优势种重要值的研究

通过研究杨镇湿地的植被分布、植物群落、种类及地上部分的生物量，对湿地建群种及部分优势种在整个湿地植被的重要值和湿地群落多样性进行了分析。结果表明杨镇湿地区的植物种类有55科、158属、246种(或变种)；湿地的植被可分为34个群落，包括25个沼生群落和9个水生群落；湿地优势种重要值的季节性变化明显。湿地的植物多样性资源丰富，是个潜在的物种基因库。如国家三级渐危植物鹿藿(Glycine soja野大豆)，稀见的芡实(Euryale ferox鸡头米)等。由于近来北京地区持续干旱等原因使湿地面积呈减少的趋势，导致旱生植物葎草(Humulus scandens)等大量入侵，正在迅速改变着湿地的原始景观状况。     

Evaluating uncertainty in Landsat-derived postfire recovery metrics due to terrain,soil, and shrub type variations in southern California

ABSTRACT

Temporal trajectories of apparent vegetation abundance based on the multi-decadal Landsat image series provide valuable information on the postfire recovery of chaparral shrublands, which tend to mature within one decade. Signals of change in fractional shrub cover (FSC) extracted from time-sequential Normalized Difference Vegetation Index (NDVI) data can be systematically biased due to spatial variation in shrub type, soil substrate, or illumination differences associated with topography. We evaluate the effects of these variables in Landsat-derived metrics of FSC and postfire recovery, based upon three chaparral sites in southern California which contain shrub community ecotones, complex terrain, and soil variations. Detailed validations of prefire and postfire FSC are based on high spatial resolution ortho-imagery; cross-stratified random sampling is used for variable control. We find that differences in the composition and structure of shrubs (inferred from ortho-imagery) can substantially influence FSC-NDVI relations and impact recovery metrics. Differences in soil type have a moderate effect on the FSC-NDVI relation in one of the study sites, while no substantial effects were observed due to variation of terrain illumination among the study sites. Arithmetic difference recovery metrics – based on NDVI values that were not normalized with unburned control plots – correlate in a moderate but significant manner with a change in FSC (R ² values range 0.47–0.59 at two sites). Similar regression coefficients resulted from using Landsat visible reflectance data alone. The lowest correlations to FSC resulted from Soil-Adjusted Vegetation Index (SAVI) and are attributed to the effects of the soil-adjustment factor in sparsely vegetated areas. The Normalized Burn Ratio and Normalized Burn Ratio 2 showed a moderate correlation to FSC. This study confirms the utility of Landsat NDVI data for postfire recovery evaluation and implies a need for stratified analysis of postfire recovery in some chaparral landscapes.     

Evaluation of machine learning models for rice dry biomass estimation and mapping using quad-source optical imagery

ABSTRACT

Several machine learning regression models have been advanced for the estimation of crop biophysical parameters with optical satellite imagery. However, literature on the comparative performances of such models is still limited in range and scope, especially under multiple data sources, despite the potential of multi-source imagery to improving crop monitoring in cloudy areas. To fill in this knowledge gap, this study explored the synergistic use of Landsat-8, Sentinel-2A, China’s environment and disaster monitoring and forecasting satellites (HJ-1 A and B) and Gaofen-1 (GF-1) data to evaluate four machine learning regression models that include Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and Gradient Boosting Decision Tree (GBDT), for rice dry biomass estimation and mapping. Taking a major rice cultivation area in southeast China as case study during the 2016 and 2017 growing seasons, a cross-calibrated time series of the Enhanced Vegetation Index (EVI) was obtained from the quad-source optical imagery and on which the aforementioned models were applied, respectively. Results indicate that in the before rice heading scenario, the most accurate dry biomass estimates were obtained by the GBDT model (R² of 0.82 and RMSE of 191.8 g/m²) followed by the RF model (R² of 0.79 and RMSE of 197.8 g/m²). After heading, the k-NN model performed best (R² of 0.43 and RMSE of 452.1 g/m²) followed by the RF model (R² of 0.42 and RMSE of 464.7 g/m²). Whist the k-NN model performed least in the before heading scenario, SVM performed least in the after heading scenario. These findings may suggest that machine learning regression models based on an ensemble of decision trees (RF and GBDT) are more suitable for the estimation of rice dry biomass, at least with optical satellite imagery. Studies that would extend the evaluation of these machine learning models, to other parameters like leaf area index, and to microwave imagery, are hereby recommended.     

Wetland vegetation biomass estimation and mapping from Landsat ETM data: a case study of poyang lake

Poyang Lake is the largest freshwater lake in China. This paper conducted a digital and rapid investigation of the lake’s wetland vegetation biomass using Landsat ETM data acquired on April 16, 2000. First, utilizing the false color composite derived from the ETM data as one of the main references, the authors designed a reasonable sampling route for field measurement of the biomass, and carried it out on April 18–28, 2000. Then after both the sampling data and the ETM data were geometrically corrected to an equal-area projection of Albers, linear relationships among the sampling data and some transformed data derived from the ETM data and the ETM 4 were calculated. The results show that the sampling data is best relative to the band 4 data with a high correlation coefficient of 0.86, followed by the DVI and NDVI data with 0.83 and 0.80 respectively. Therefore, a linear regression model, which was based on the field data and band 4 data, was used to estimate the total biomass of entire Poyang Lake, and then the map of the biomass distribution was compiled.     

南极长城湾及其附近水域浮游动物的种类组成和数量变化

初步鉴定南极长城湾及其附近水域浮游动物 3 5种。南极种挠足类 Calanus propinquus、Calanoidesacutus、Metrdia gerlachei和南极磷虾幼体为调查区的优势种。结果表明 ,调查水域浮游动物种类单纯 ,符合南极水域浮游动物分布的一般规律。调查水域浮游动物总生物量和个体数量具明显的季节变化。夏季主要有 Calanioidisacatus和南极磷虾幼体组成 ,冬季主要有 Metr-dia gerlacher、Calanus propinquus、Oithon similis、O.frigda及南极磷虾幼体组成。挠足类拟成体的数量在各月份中占相当大的比例。水温是制约浮游动物生物量的重要因素。     

Resin diterpenoids as tracers for biomass combustion aerosols

Diterpenoids present in atmospheric particles produced by slash burns were analyzed by capillary gas chromatography-mass spectrometry. This suite of compounds, originating in the resinous higher plants which were combusted, was comprised of reaction intermediates in the thermal transformation sequence of components such as abietic acid and dehydroabietin to retene. This sequence parallels the diagenetic alteration/decomposition processes noted in sedimentary records. Since global transport of combustion aerosols has been well documented, it is likely that the thermally-altered diterpenoids elucidated in this study can be deposited directly to sedimentary sinks.     

High Ash Content in Net-Plankton Samples from Shallow Coastal Water: Possible Source of Error in Dry Weight Measurement of Zooplankton Biomass

Annual examination of net-plankton biomass in dry weight, ash-free dry weight, organic carbon, and nitrogen weight at the Manazuru harbor, central Japan revealed that net-plankton samples from shallow coastal water contained considerable amount of ash (53±13% of dry weight) which would be caused by contamination of inorganic materials from re-suspendion of sediments, terrestrial runoff and chain-forming diatoms. Therefore, in coastal water, dry weight is inadequate fro determination of zooplankton biomass in volving the possibility of over-estimation. Practical estimation of net-plankton biomass in shallow coastal waters, ash-free dry weight, organic carbon, and/or nitrogen are more adequate.