The relationship between cyanobacteria and environmental factors in the Bering Sea

During the first Chinese Scientific Expedition to the Arctic in July - September 1999, cyanobacteria in the Bering Sea were measured by epifluorescence microscopy. Cyanobacterial abundance varied from 0 to 7. 93 × 103 cell/ml and decreased along a northerly directed latitudinal gradient in horizontal distribution. Cyanobacteria did not occur at station Bl - 12 (north of 60 °N). Vertically, high cya-nobacterial abundance appeared in the upper 25 - 50 m and decreased rapidly below 50 m. There were no cyanobacteria at the 150 m. Seawater temperature and NH4+ -N are suggested to affect the distribution of cyanobacteria.     

Abundance and Biomass of Benthic Bacteria in Jiaozhou Bay, China

The abundance and biomass of benthic heterotrophic bacteria were investigated for the 4 typical sampling stations in the northern muddy part of Jiaozhou Bay, estuary of the Dagu River, raft culturing and nearby areas of Huangdao in March, June, August and December, 2002. The abundance and biomass range from 0.98×10⁷ to 16.87×10⁷ cells g⁻¹ sediment and 0.45 to 7.08 μg C g⁻¹ sediment, respectively. Correlation analysis showed that heterotrophic bacterial abundance and biomass are significantly correlated to water temperature (R=0.79 and 0.83, respectively,P<0.01).     

基于MODIS数据的小麦生物量估算模型研究

地表生物量对农作物估产、植被长势评估具有很重要的意义。随着遥感技术的发展与应用,遥感为生物量估算提供了一种新的手段。本文以唐山市为例,利用小麦种植区的MODIS遥感影像数据和同期野外调查获得的16组32个生物量数据,对比分析了归一化植被指数(NDVI)、增强型植被指数(EVI)与小麦生物量多个回归方程的相关系数,进而建立了NDVI、EVI与小麦生物量的线性回归模型。结果显示,使用MODIS数据的植被指数能够很好地对研究区地上生物量进行估算,其中使用EVI的三次函数模型拟合精度最高,并且对每组数据进行平均处理会使模型精度进一步提高。     

A radiative transfer model-based method for the estimation of grassland aboveground biomass

This paper presents a novel method to derive grassland aboveground biomass (AGB) based on the PROSAILH (PROSPECT + SAILH) radiative transfer model (RTM). Two variables, leaf area index (LAI, m²m⁻², defined as a one-side leaf area per unit of horizontal ground area) and dry matter content (DMC, gcm⁻², defined as the dry matter per leaf area), were retrieved using PROSAILH and reflectance data from Landsat 8 OLI product. The result of LAI × DMC was regarded as the estimated grassland AGB according to their definitions. The well-known ill-posed inversion problem when inverting PROSAILH was alleviated using ecological criteria to constrain the simulation scenario and therefore the number of simulated spectra. A case study of the presented method was applied to a plateau grassland in China to estimate its AGB. The results were compared to those obtained using an exponential regression, a partial least squares regression (PLSR) and an artificial neural networks (ANN). The RTM-based method offered higher accuracy (R² = 0.64 and RMSE = 42.67 gm⁻²) than the exponential regression (R² = 0.48 and RMSE = 41.65 gm⁻²) and the ANN (R² = 0.43 and RMSE = 46.26 gm⁻²). However, the proposed method offered similar performance than PLSR as presented better determination coefficient than PLSR (R² = 0.55) but higher RMSE (RMSE = 37.79 gm⁻²). Although it is still necessary to test these methodologies in other areas, the RTM-based method offers greater robustness and reproducibility to estimate grassland AGB at large scale without the need to collect field measurements and therefore is considered the most promising methodology.     

Assessing the influence of return density on estimation of lidar-based aboveground biomass in tropical peat swamp forests of Kalimantan,Indonesia

The airborne lidar system (ALS) provides a means to efficiently monitor the status of remote tropical forests and continues to be the subject of intense evaluation. However, the cost of ALS acquisition can vary significantly depending on the acquisition parameters, particularly the return density (i.e., spatial resolution) of the lidar point cloud. This study assessed the effect of lidar return density on the accuracy of lidar metrics and regression models for estimating aboveground biomass (AGB) and basal area (BA) in tropical peat swamp forests (PSF) in Kalimantan, Indonesia. A large dataset of ALS covering an area of 123,000 ha was used in this study. This study found that cumulative return proportion (CRP) variables represent a better accumulation of AGB over tree heights than height-related variables. The CRP variables in power models explained 80.9% and 90.9% of the BA and AGB variations, respectively. Further, it was found that low-density (and low-cost) lidar should be considered as a feasible option for assessing AGB and BA in vast areas of flat, lowland PSF. The performance of the models generated using reduced return densities as low as 1/9 returns per m² also yielded strong agreement with the original high-density data. The use model-based statistical inferences enabled relatively precise estimates of the mean AGB at the landscape scale to be obtained with a fairly low-density of 1/4 returns per m², with less than 10% standard error (SE). Further, even when very low-density lidar data was used (i.e., 1/49 returns per m²) the bias of the mean AGB estimates were still less than 10% with a SE of approximately 15%. This study also investigated the influence of different DTM resolutions for normalizing the elevation during the generation of forest-related lidar metrics using various return densities point cloud. We found that the high-resolution digital terrain model (DTM) had little effect on the accuracy of lidar metrics calculation in PSF. The accuracy of low-density lidar metrics in PSF was more influenced by the density of aboveground returns, rather than the last return. This is due to the flat topography of the study area. The results of this study will be valuable for future economical and feasible assessments of forest metrics over large areas of tropical peat swamp ecosystems.     

Estimation of damage to agriculture biomass due to Hudhud cyclone and carbon stock assessment in cyclone affected areas using Landsat-8

Developing countries are vulnerable to tropical cyclones due to climatic variability and the frequency and magnitude of some extreme weather and disaster events is likely to increase. Cities and towns situated along the coastal belt of Visakhapatnam district experienced severe damage because of Hudhub cyclone (12 October 2014). The main objective of this research was to identify and quantify the damage to agriculture and vegetation caused by Hudhud cyclone. In this study, landsat-8 satellite data-sets acquired before and after the cyclone have been used; image processing techniques have been carried out to assess the changes of pre- and post-cyclone condition. Economic loss of agriculture crops has been assessed using crop production loss per hectare and total economic loss for agriculture crops in the study area was calculated. Classification results and land use land cover change analysis show that 13.25% of agriculture-Kharif and 31.1% of vegetation was damaged. Crop Biomass was estimated with aid of crop conversion factor for pre- and post-cyclone conditions. Total ‘Above ground biomass’ of the agriculture crop area was estimated at 31.57 t/ha and total loss of biomass was assessed to be 4.2 t/ha. Carbon stock was found to be varying from 0.3 to 8.3 t.C/ha in specific agriculture crops. From the results, it was concluded that Hudhud has done significant damage to the rural and urban areas of Visakhapatnam. The outcome of this study can be used by decision-makers for the release of post disaster relief fund to affected areas.     

A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems

Remote sensing-based methods of aboveground biomass (AGB) estimation in forest ecosystems have gained increased attention, and substantial research has been conducted in the past three decades. This paper provides a survey of current biomass estimation methods using remote sensing data and discusses four critical issues – collection of field-based biomass reference data, extraction and selection of suitable variables from remote sensing data, identification of proper algorithms to develop biomass estimation models, and uncertainty analysis to refine the estimation procedure. Additionally, we discuss the impacts of scales on biomass estimation performance and describe a general biomass estimation procedure. Although optical sensor and radar data have been primary sources for AGB estimation, data saturation is an important factor resulting in estimation uncertainty. LIght Detection and Ranging (lidar) can remove data saturation, but limited availability of lidar data prevents its extensive application. This literature survey has indicated the limitations of using single-sensor data for biomass estimation and the importance of integrating multi-sensor/scale remote sensing data to produce accurate estimates over large areas. More research is needed to extract a vertical vegetation structure (e.g. canopy height) from interferometry synthetic aperture radar (InSAR) or optical stereo images to incorporate it into horizontal structures (e.g. canopy cover) in biomass estimation modeling.     

南黄海网采浮游植物碳含量及群落结构的季节变化

根据2011—2012年4、6、8、10月和11月共5个航次的网采浮游植物样品,以细胞体积转换碳含量为基础,分析了南黄海海域网采浮游植物在春、夏、秋三个季节的变化规律。结果表明:浮游植物总碳含量及各类群碳含量均表现出明显的季节差异,春季和秋季浮游植物在空间分布上纬度差异较小,呈现由近岸到外海降低的特点;夏季海域南部浮游植物生物量远远高于南黄海北部及中部区域。浮游植物总碳含量最高值出现在夏季,为(4.62±11.79)×104μg C/m~3,最低值出现在秋季,在1000μg C/m~3以下。甲藻在浮游植物中所占比例在夏季8月最低,为14.05%,在初春和秋末较高,约为48%;甲藻的比重在南部海域明显较低。温度和盐度是影响南黄海浮游植物群落的重要环境因子,而浮游植物与营养盐的关系呈现不同的季节特点。圆筛藻属(Coscinodiscus)和角藻属(Ceratium)在各调查月份均为南黄海海域浮游植物群落的优势种属。本文从浮游植物碳含量的角度为南黄海海域的生态状况研究提供了基础资料。     

Model selection in Bayesian framework to identify the best WorldView-2 based vegetation index in predicting green biomass of salt marshes in the northern Gulf of Mexico

Coastal wetlands are among the most productive ecosystems globally but have experienced dramatic degradation and loss within the past several decades. Vegetation biomass of coastal wetlands is not only the key component of blue carbon storage but also plays an important role in vertical accretion, important for maintaining these habitats under relative sea-level rise. Remote sensing offers a cost-effective approach to study vegetation biomass at a broad spatial scale. We developed statistical models to predict peak aboveground green biomass of Spartina alterniflora and Juncus roemerianus, two dominant species of salt marshes using WorldView-2 satellite imagery at the Grand Bay National Estuarine Research Reserve (NERR) on the Mississippi coast in the northern Gulf of Mexico. The model accounted for nested data structures in the sampled biomass, assimilated uncertainties from data, parameters and model structures, and helped determine the best vegetation index among a variety of commonly-used indices to predict aboveground green biomass. We developed a series of mixed-effects models, which included different combinations of fixed effect(s), random intercept, and random slope(s). The fixed effects were species and one of the 60 vegetation indices derived from a WorldView-2 image obtained on 6 October 2012. The random effect used was site. We implemented the models in a Bayesian framework and selected the best model structure and vegetation index based on minimum posterior predictive loss and deviance information criterion. The results showed that the best vegetation index to predict peak green biomass was the green chlorophyll index derived from the reflectance values of band 8 (near-infrared) and band 3 (green), and its effect on biomass prediction varied among sites. The inclusion of species as a fixed effect improved the model prediction. The study demonstrated the need to account for spatial dependence of data in developing a robust model, and the importance of the second WorldView-2 near-infrared band (860–1040 nm) in predicting aboveground green biomass for the Grand Bay NERR. The analysis using mixed-effects modeling in Bayesian inference which coherently combined field and WorldView-2 data with uncertainties accounted for provides a robust and nondestructive tool for resource managers to monitor the status of coastal wetlands at a high spatial resolution in a timely manner. Through this study, we hope to emphasize the importance of appropriately accounting for nested data structures using mixed-effects models and promote wider application of Bayesian inference to facilitate assimilation of uncertainties in remote sensing applications.     

How the capacity of bedrock to collect dust and produce soil affects phosphorus bioavailability in the northern Appalachian Mountains of Pennsylvania

More above-ground biomass (kg m⁻²) grows in the northern Appalachian Mountains (USA) in forests on shale than on sandstone at all landscape positions other than ridgetops. This has been tentatively attributed to physical (rather than chemical) attributes of the substrates, such as elevation, particle size, and water capacity. However, shales have generally similar phosphorus (P) concentrations to sandstones and, in the Valley and Ridge province, they erode more quickly. This led us to hypothesize that faster replenishment of the lithogenic nutrient P in shale soils through erosion + soil production could instead control the differences in biomass. To test this, soils and foliage from 10 sites on shales and sandstones in the northern Appalachians from roughly the same elevation and aspect were analysed. We discovered that, when controlling for location, concentrations of bioavailable P in soils and P in foliage were higher and P resorbed from senescing red oak leaves was lower on slower-eroding sandstone than on faster-eroding shale. Lower resorption generally can be attributed to lower P limitation for trees. Further investigation of weathering and erosion on one of the sandstone–shale pairs within a larger, paired watershed study revealed that the differences in P concentrations in biomass and foliage between lithologies likely developed because sandstones act as ‘collectors’ that trap nutrients from residual and exogenous sources, while shales erode quickly and thus promote production of soil from bedrock that releases P to ecosystems. We concluded that the combined effects of differential rates of dust collection and erosion results in roughly equal biomass growing on sandstone and shale ridgetops. This work emphasizes the balance between a landscape's capacity to collect dust versus produce soil in controlling bioavailability of nutrients.