REMOTE SENSING FOREST BIOMASS: AN EVALUATION USING HIGH RESOLUTION REMOTE SENSOR DATA AND LOBLOLLY PINE PLOTS

Eight-year-old Loblolly pine (Pinus taeda) plots located in South Carolina were evaluated using high resolution aircraft multispectral scanner data to determine if biomass could be accurately measured. Sixteen pine plots located on both sandy and clay soils were treated with 0, 180, or 360 kg. (approximately 0, 400, or 800 lb.) of nitrogen per plot. Indices of biomass from remote sensing data were significantly correlated with in situ biomass measurements made in each plot. A ratio of infrared (.9–1.1 μm) and red (.65–.70 μm) channels yielded the best correlation. The indices were not sensitive to differences in soil type (sandy or clay).     

The biomass of macro- and interstitial fauna on clean and wrack-covered beaches in Western Australia

The benthic faunal spectrum including bacteria, protozoans, meiofauna, wrack epifauna and macrofauna, was quantitatively surveyed on two modally reflective, moderate energy, Western Australian beaches. The more exposed beach had coarser sand, no intertidal macrofauna and a poor interstitial fauna. The less exposed beach had a large deposit of wrack totalling 161 kg m^?1 dry mass concentrated on the lower shore. The amphipod Allorchestes compressa was abundant in the fresh wrack comprising most of the macrofauna. There were also fairly abundant small epifauna on the wrack. Dry biomass of macrofauna, epifauna, meiofauna, protozoans and bacteria was 0, 0, 15, 4 and 180 g m^?1 on the more exposed beach and 160, 3, 112, 9 and 901 g m^?1 on the less exposed beach with wrack. On the latter beach there was an inverse correlation between meiofaunal densities and the densities of protozoans and bacteria, suggesting grazing by the former on the latter. On both beaches meiofauna was concentrated in the mid- to upper beach, protozoans near the surface and bacteria in the mid- to lower beach. It is estimated that bacteria are responsible for most of the secondary production on both beaches.     

Hydrodynamic control of phytoplankton in low salinity waters of the James River estuary,Virginia, U.S.A.

Autotrophic biomass and productivity as well as nutrient distributions and phytoplankton cell populations in the James River estuary, Virginia, were quantified both spatially and temporally over a 17-month period. Emphasis was placed on the very low salinity region of the estuary in order to gain information on the fate of freshwater phytoplankters. Differing amounts of freshwater plant biomass are advected into the estuary as living material, DOC or POC and the demonstrated variability of this input must play an important role in marine biogeochemical cycling.Late summer and fall maxima in both chlorophyll a and the photosynthetic production of particulate organic carbon in very low salinity regions were inversely correlated with river discharge.During periods of low river discharge greater than 50% of the chlorophyll a biomass measured at 0‰ disappeared within a narrow range of salinity (0–2‰). Cell enumeration data suggest that species introduced from the freshwater end-member tend to comprise the bulk of the biomass removed. Confounding factors, which may contribute to the regulation of both the abundance and species of phytoplankters mid-river, include the flocculation of colloidal material with phytoplankton cells, the presence of the turbidity maximum and the growth of endemic phytoplankton populations.An inverse relationship exists between the phytoplankton abundance in very low salinity waters and the abundance of biomass measured in the lower portion of the river (estuary). Thus, autotrophic production in the fresh and very low salinity areas may indirectly regulate the onset on the spring bloom in the estuary by controlling the amount of nutrients available.     

Nutrient budgets and trophic state in a hypersaline coastal lagoon: Lagoa de Araruama, Brazil

Lagoa de Araruama in the state of Rio de Janeiro, Brazil, is a hypersaline lagoon with salinity varying spatially from 45 to 56. We collected water samples during monthly cruises throughout the lagoon, and along the streams feeding the system, from April 1991 to March 1992. Nutrients and other water quality parameters exhibited great spatial and temporal variations. Mass balance calculations indicate large amounts of anthropogenic nutrient inputs. The data indicate that the lagoon currently is oligotrophic but is in a state of transition to become a mesotrophic system. Molar dissolved inorganic nitrogen:dissolved inorganic phosphorus (DIN/DIP) varied between 2.2:1 and 659:1 with a volume-weighted average of 22:1. The high DIN/DIP ratio contrasts with that found in nearby lagoons, suggesting that phytoplankton primary production is limited by phosphorus in Lagoa de Araruama. The major loss of DIP is apparently driven by biological assimilation and diagenic reactions in the sediments. Calculations indicate that the lagoon is slightly net autotrophic at +0.9 mol C m⁻² yr⁻¹. This suggests that the biomass of the primary producers is restricted by phosphorus availability. Phosphorus retention in the sediment and the hypersaline state of the lagoon prevent changes in autotrophic communities and the formation of eutrophic conditions.     

全站仪交汇法在高速公路路面沉降监测中的应用

文章介绍了全站仪交会法沉降监测,方便简洁地解决了测量人员不能进入的高速公路等特殊位置的测量问题,并通过对该方法的精度分析,论证了其可行性和有效性,对以后类似的测量工作提供经验和依据。     

数字测图技术在阜新市地理数据库更新中的应用

采用GPS和全站仪联合作业的数字测图方式进行了外业快速数据采集。并利用南方CASS软件对数据进行内业成图工作,最后利用大型的地理信息系统软件ArcGIS进行地理数据的入库,从而实现了阜新市区地理数据库的更新,此方式具有速度快、效率高和精度高等优点,可以借鉴到同类的地区。     

电离层电子浓度总含量梯度对GPS差分定位精度的影响

针对GPS定位测量的信号传播路径误差,分析了电离层电子浓度总含量梯度对差分定位精度的影响,利用L1载波重点讨论了1999年-2000年太阳活动期间低纬度赤道异常地区GPS差分定位精度的问题,同时验证对于长基线采用GPS广域差分技术可以使电离层定位误差得到明显提高。     

可解释性长短期记忆模型用于预测湖泊总磷浓度变化

对湖泊总磷的变化预测和来源识别对水资源调度和流域生态治理有着重要的意义,然而复杂的生化反应和水动力条件导致的非平稳性给湖泊总磷浓度的准确预测带来极大的困难。为克服这一挑战,本文引入了基于加权回归的季节趋势分解(seasonal and trend decomposition using Loess,STL)技术和夏普利加法(SHapley additive exPlanations,SHAP)结合长短期记忆网络(long short-term memory neural network,LSTM)和门控循环单元(gated recurrent unit,GRU)构建了一个可解释的预测框架,以增强对湖泊总磷浓度演变的预测并提高其可解释性。研究表明:(1)在骆马湖总磷浓度的预测中,该框架拥有较好的预报精度(R²=0.878),优于LSTM和卷积长短期记忆模型(convolutional neural networks and long short term memory network,CNN-LSTM)。当预测时间步长增加到8 h时,该框架有效提高了总磷浓度的预测精度,平均相对误差和均方根误差分别降低了47.1%和33.3%。从预测趋势来看,骆马湖在汛期的总磷平均浓度为0.158 mg/L,相较于非汛期的平均浓度,增加了202.1%。(2)运河来水是骆马湖总磷浓度最重要的影响因素,贡献权重为60.0%,并且不同断面(三湾、三场)的污染源受水动力、气象等因素的影响存在显著的时空差异。本文凸显了神经网络模型在预警水体污染方面的可实施性,并且为提高传统神经网络的学习能力和可解释性的开发与验证提供了重要方向。     

Seasonal change of ice algal and phytoplankton assemblages in the Nella Fjord near Zhongshan Station, East Antarctica

The ice algal and phytoplankton assemblages were studied from Nella Fjord near Zhongshan Station, East Antarctica from April 12 to December 30, 1992. Algal blooms occurred about 3 cm thick on the bottom of sea ice in late April and mid November to early December respectively, and a phytoplankton bloom appeared in the underlying surface water in mid December following the spring ice algal bloom. The biomass in ice bottom was 1 to 3 orders of magnitude higher than that of surface water. Amphiprora kjellmanii, Berkeleya sp., Navicula glaciei, Nitzschia barkelyi, N. cylindrus /N. curta, N. lecointei and Nitzschia sp. were common in the sea ice temporarily or throughout the study period. The biomass in a certain ice segment was decreased gradually and the dominant species were usually succeeded as the season went on. Nitzschia sublineata and Dactyliosolen antarctica were two seasonal dominant species only observed in underlying water column. The assemblages between bottom of ice and underlying surface water were different except when spring ice algae bloomed. The evidence shows that the ice algal blooms occurred mainly by in situ growth of ice algae, and the phytoplankton bloom was mostly caused by the release of ice algae.