Characterizing 3D vegetation structure from space: Mission requirements

Human and natural forces are rapidly modifying the global distribution and structure of terrestrial ecosystems on which all of life depends, altering the global carbon cycle, affecting our climate now and for the foreseeable future, causing steep reductions in species diversity, and endangering Earth's sustainability.To understand changes and trends in terrestrial ecosystems and their functioning as carbon sources and sinks, and to characterize the impact of their changes on climate, habitat and biodiversity, new space assets are urgently needed to produce high spatial resolution global maps of the three-dimensional (3D) structure of vegetation, its biomass above ground, the carbon stored within and the implications for atmospheric green house gas concentrations and climate. These needs were articulated in a 2007 National Research Council (NRC) report (NRC, 2007) recommending a new satellite mission, DESDynI, carrying an L-band Polarized Synthetic Aperture Radar (Pol-SAR) and a multi-beam lidar (Light RAnging And Detection) operating at 1064 nm. The objectives of this paper are to articulate the importance of these new, multi-year, 3D vegetation structure and biomass measurements, to briefly review the feasibility of radar and lidar remote sensing technology to meet these requirements, to define the data products and measurement requirements, and to consider implications of mission durations. The paper addresses these objectives by synthesizing research results and other input from a broad community of terrestrial ecology, carbon cycle, and remote sensing scientists and working groups. We conclude that:

(1)

Current global biomass and 3-D vegetation structure information is unsuitable for both science and management and policy. The only existing global datasets of biomass are approximations based on combining land cover type and representative carbon values, instead of measurements of actual biomass. Current measurement attempts based on radar and multispectral data have low explanatory power outside low biomass areas. There is no current capability for repeatable disturbance and regrowth estimates.

(2)

The science and policy needs for information on vegetation 3D structure can be successfully addressed by a mission capable of producing (i) a first global inventory of forest biomass with a spatial resolution 1 km or finer and unprecedented accuracy (ii) annual global disturbance maps at a spatial resolution of 1 ha with subsequent biomass accumulation rates at resolutions of 1 km or finer, and (iii) transects of vertical and horizontal forest structure with 30 m along-transect measurements globally at 25 m spatial resolution, essential for habitat characterization.

We also show from the literature that lidar profile samples together with wall-to-wall L-band quad-pol-SAR imagery and ecosystem dynamics models can work together to satisfy these vegetation 3D structure and biomass measurement requirements. Finally we argue that the technology readiness levels of combined pol-SAR and lidar instruments are adequate for space flight. Remaining to be worked out, are the particulars of a lidar/pol-SAR mission design that is feasible and at a minimum satisfies the information and measurement requirement articulated herein.     

Characterization of terrestrial water dynamics in the Congo Basin using GRACE and satellite radar altimetry

The Congo Basin is the world's third largest in size (~ 3.7 million km²), and second only to the Amazon River in discharge (~ 40,200 m³ s^− 1 annual average). However, the hydrological dynamics of seasonally flooded wetlands and floodplains remains poorly quantified. Here, we separate the Congo wetland into four 3° × 3° regions, and use remote sensing measurements (i.e., GRACE, satellite radar altimeter, GPCP, JERS-1, SRTM, and MODIS) to estimate the amounts of water filling and draining from the Congo wetland, and to determine the source of the water. We find that the amount of water annually filling and draining the Congo wetlands is 111 km³, which is about one-third the size of the water volumes found on the mainstem Amazon floodplain. Based on amplitude comparisons among the water volume changes and timing comparisons among their fluxes, we conclude that the local upland runoff is the main source of the Congo wetland water, not the fluvial process of river-floodplain water exchange as in the Amazon. Our hydraulic analysis using altimeter measurements also supports our conclusion by demonstrating that water surface elevations in the wetlands are consistently higher than the adjacent river water levels. Our research highlights differences in the hydrology and hydrodynamics between the Congo wetland and the mainstem Amazon floodplain.     

A fast and robust bulk-loading algorithm for indexing very large digital elevation datasets: I. Algorithm

Digital elevation models (DEMs) constitute a valuable source of data for a number of geoscience-related applications. The Shuttle Radar Topography Mission (SRTM) collected and made available to the public the world's largest DEM (composed of billions of points) until that date. The SRTM DEM is stored on the NASA repository as a well-organized collection of flat files. The retrieval of this stored topographic information about a region of interest involves one selection of a proper list of files, their downloading, data filtering in the desired region, and their processing according to user needs. With the aim to provide an easier and faster access to this data by improving its further analysis and processing, we have indexed the SRTM DEM by means of a spatial indexing based on the kd-tree data structure, called the Q-tree. This paper is the first in a two-part series that describes the method followed to build an index on such huge amounts of data, minimizing the number of insert operations. We demonstrate that our method can build a very efficient space-partitioning index, with good performance in both point and range queries on the spatial data. To the best of our knowledge, this is the only successful spatial indexing proposal in the literature that deals with such a huge volume of data.     

高性能SAR成像程序的并行粒度选择技术

高性能并行仿真程序必须采用针对性的并行优化技术以获得高效的性能加速。而根据程序特性,确定适合的并行粒度是设计并行优化技术的基础。因此,以典型合成孔径雷达(SAR)成像程序--距离-多普勒(RD)程序为代表,研究了高性能SAR成像程序的并行粒度选择技术,重点针对并行粒度的基本准则和RD算法的并行粒度选择方法进行了研究。实验结果证明,经过并行粒度优化选择的SAR成像程序能够获得显著的性能提升,并且具有良好的可扩展性。     

三聚氰胺装置中液位计的选用

通过对V-3131助滤剂槽上使用的吹气式液位计LT-36113的测量原理及使用情况分析,证明吹气式液位计在使用中的缺陷,从而说明使用雷达液位计的必要性,并表明雷达液位计将被广泛应用。     

Gamma mixture models for target recognition

This paper considers a mixture model approach to automatic target recognition using high-resolution radar measurements. The mixture model approach is motivated from several perspectives including requirements that the target classifier is robust to uncertainty in amplitude scaling, rotation and translation of the target. Estimation of the model parameters is achieved using the expectation-maximisation (EM) algorithm. Gamma mixtures are introduced and the re-estimation equations derived. The models are applied to the classification of high-resolution radar range profiles of ships and results compared with a previously published self-organising map approach.     

空载SAR发展状况

空载SAR具有提供高分辨率成像的能力,这种能力不受气象条件和太阳照射的限制,其成像范围不受国界、地域和地理自然条件的限制,受到广泛重视,空载SAR经过20多年的发展,尽管数量有限,所提供的信息量却是巨大的,并被广泛应用,为人类提供了大量鲜为人知的信息,概述了空载SAR的用途及发展过程,说明空载SAR的现状和其主要性能,介绍正在发展中的空载SAR项目,分析空载SAR的发展趋势。     

保水剂对草坪土壤持水特性影响研究

为了探明保水剂对草地种植条件下土壤水分特征曲线的影响,基于王全九等开发的入渗特性法进行了土壤水平入渗试验,得出了在草坪土壤中施用不同浓度保水剂的Brooks-Corey土壤水分特征曲线。结果表明：在低吸力段,相同水势下土壤含水量随保水剂施用浓度增大而增大,在高吸力段,相同水势下土壤含水量随保水剂施用浓度增大而减小;在0~1 bar范围内,施用保水剂处理的土壤水分特征曲线均匀降低,表明施用保水剂能够稳定而有效地供给植物所需水分。本研究可以为制定城市绿地草坪合理的灌溉制度和在草坪种植中合理推广使用保水剂提供理论依据。     

地质雷达在水电站坝基工程中的应用研究

岩溶地区水电站工程建设中对坝基岩体完整程度要求较高,为了探明坝基岩体中是否存在溶洞、溶隙和破碎带等不良地质体,应用地质雷达较准确地判断出了坝基下不良地质体的走向和规模,取得了良好的效果,为处理岩溶地质问题和工程安全施工提供了依据.     

探地雷达在水利工程安全检测中的应用

以陕西神木采兔沟水库工程为实例,对探地雷达的工作原理及实际应用进行了介绍。结合采兔沟泄洪洞洞身的质量检测对具体的测线布置、探测方法及参数选择进行了阐述,在探测中,针对泄洪洞具体形状,在不同部位共布置了17条测线,经对检测结果分析,发现导流泄洪洞底板下方虽存在疏松区域,但不存在空洞,其他部位亦无大的缺陷,不影响安全运行。