基于DEM的山地地理可照时数空间分布估算统计模型——以重庆为例

为了建立合理、准确且便于估算和应用的山地地理可照时数空间分布模型,本文以重庆为例,首先利用分布式模型估算了30 a平均年和各月可照时数,并利用基于气象行业标准得到的理论值进行了验证;其次,以栅格单元为统计样本,建立了基于地形尺度的可照时数估算的统计模型。结果表明:(1)基于分布式模型的模拟结果具有较高可信度,除10月外,其他各月和年的相对误差均小于10%,且东北和东南部的相对误差较中西部高。(2)西部方山丘陵区的可照时数高,中部平行岭谷区次之,东北和东南部低。冬季可照时数的空间异质性最大,夏季次之,春秋季接近,均较小。(3)各月和年可照时数与主要地理和地形因子之间的相关关系均表现为极显著性,各月复相关系数在0.7929~0.8277,年复相关系数为0.8522。(4)重庆可照时数估算统计模型在一定程度上简化了分布式模型的计算步骤和计算量,便于其应用,并可为其他地区估算可照时数提供方法参考。     

A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling

During the last 50 years, the management of agroecosystems has been undergoing major changes to meet the growing demand for food, timber, fibre and fuel. As a result of this intensified use, the ecological status of many agroecosystems has been severely deteriorated. Modeling the behavior of agroecosystems is, therefore, of great help since it allows the definition of management strategies that maximize (crop) production while minimizing the environmental impacts. Remote sensing can support such modeling by offering information on the spatial and temporal variation of important canopy state variables which would be very difficult to obtain otherwise.In this paper, we present an overview of different methods that can be used to derive biophysical and biochemical canopy state variables from optical remote sensing data in the VNIR-SWIR regions. The overview is based on an extensive literature review where both statistical–empirical and physically based methods are discussed. Subsequently, the prevailing techniques of assimilating remote sensing data into agroecosystem models are outlined. The increasing complexity of data assimilation methods and of models describing agroecosystem functioning has significantly increased computational demands. For this reason, we include a short section on the potential of parallel processing to deal with the complex and computationally intensive algorithms described in the preceding sections.The studied literature reveals that many valuable techniques have been developed both for the retrieval of canopy state variables from reflective remote sensing data as for assimilating the retrieved variables in agroecosystem models. However, for agroecosystem modeling and remote sensing data assimilation to be commonly employed on a global operational basis, emphasis will have to be put on bridging the mismatch between data availability and accuracy on one hand, and model and user requirements on the other. This could be achieved by integrating imagery with different spatial, temporal, spectral, and angular resolutions, and the fusion of optical data with data of different origin, such as LIDAR and radar/microwave.     

Landscape Controls on Structural Variation in Eucalypt Vegetation Communities: Woronora Plateau,Australia

Quantification of landscape-based vegetation structural variation and pattern is a significant goal for a variety of ecological, monitoring and biodiversity studies. Vegetation structural metrics, derived from airborne laser scanning (ALS or aerial light detection and ranging—LiDAR) and QuickBird satellite imagery, were used to establish the degree of plot-based vegetation variation at a hillslope scale. Topographic position is an indicator of energy and water availability, and was quantified using DEM-based insolation and topographic wetness, respectively, stratifying areas into hot-warm-cold and wet-moist-dry topographic classes. A range of vegetation metrics—maximum and modal canopy height, crown cover, foliage cover, NDVI and semivariance—were compared among randomly selected plots from each topographic class. NDVI increases with increasing landscape wetness, whereas ALS-derived foliage cover decreases with increasing insolation. Foliage cover is well correlated with crown cover (R ² =0.65), and since foliage cover is readily calculable for whole-of-landscape application, it will provide valuable and complementary information to NDVI. Between-plot heterogeneity increases with increasing wetness and decreasing insolation, indicating that more sampling is required in these locations to capture the full range of landscape-based variability. Pattern analysis in landscape ecology is one of the fundamental requirements of landscape ecology, and the methods described here offer statistically significant, quantifiable and repeatable means to realise that goal at a fine spatial grain.     

砒砂岩区地貌形态三维分形特征量化及空间变异

针对现有地貌形态三维分形模型结构存在的不足,构建一个新的地貌形态三维分形维数测算模型。基于该模型对砒砂岩区274个小流域的地貌形态三维分形维数进行计算并分析其空间变异规律。研究表明：① 基于该模型计算的分形维数能更准确地反映地貌形态复杂度信息;② 砒砂岩区小流域地貌形态三维分形维数介于1.683 6~1.948 6之间;③ 地貌形态三维分形维数整体上覆土砒砂岩区（均值为1.765 9）<裸露砒砂岩强度侵蚀区（均值为1.785 4）和剧烈侵蚀区（均值为1.774 8）<覆沙砒砂岩区（均值为1.796 6）。由于地表覆盖物、砒砂岩裸露程度和土壤侵蚀机理的差异而形成的不同地貌特征是该区地貌形态分形特征空间变异的主要原因。     

Measuring the significance of a divide to local drainage patterns

This article presents a framework for estimating a new topographic attribute derived from digital elevation models (DEMs) called maximum branch length (B _max). Branch length is defined as the distance travelled along a flow path initiated at one grid cell to the confluence with the flow path passing through a second cell. B _max is the longest branch length measured for a grid cell and its eight neighbours. The index provides a physically meaningful method for assessing the relative significance of drainage divides to the dispersion of materials and energy across a landscape, that is, it is a measure of ‘divide size’. B _max is particularly useful for studying divide network structure, for mapping drainage divides, and in landform classification applications. Sensitivity analyses were performed to evaluate the robustness of estimates of B _max to the algorithm used to estimate flow lengths and the prevalence of edge effects resulting from inadequate DEM extent. The findings suggest that the index is insensitive to the specific flow algorithm used but that edge effects can result in significant underestimation along major divides. Edge contamination can, however, be avoided by using an appropriately extensive DEM.     

Greenness Index from Phenocams Performs Well in Linking Climatic Factors and Monitoring Grass Phenology in a Temperate Prairie Ecosystem

Near-surface remote sensing (e.g., digital cameras) has played an important role in capturing plant phenological metrics at either a focal or landscape scale. Exploring the relationship of the digital image-based greenness index (e.g., Gcc, green chromatic coordinate) with that derived from satellites is critical for land surface process research. Moreover, our understanding of how well Gcc time series associate with environmental variables at field stations in North American prairies remains limited. This paper investigated the response of grass Gcc to daily environmental factors in 2018, such as soil moisture (temperature), air temperature, and solar radiation. Thereafter, using a derivative-based phenology extraction method, we evaluated the correspondence between key phenological events (mainly including start, end and length of growing season, and date with maximum greenness value) derived from Gcc, MODIS and VIIRS NDVI (EVI) for the period 2015-2018. The results showed that daily Gcc was in good agreement with ground-level environmental variables. Additionally, multivariate regression analysis identified that the grass growth in the study area was mainly affected by soil temperature and solar radiation, but not by air temperature. High frequency Gcc time series can respond immediately to precipitation events. In the same year, the phenological metrics retrieved from digital cameras and multiple satellites are similar, with spring phenology having a larger relative difference. There are distinct divergences between changing rates in the greenup and senescence stages. Gcc also shows a close relationship with growing degree days (GDD) derived from air temperature. This study evaluated the performance of a digital camera for monitoring vegetation phenological metrics and related climatic factors. This research will enable multiscale modeling of plant phenology and grassland resource management of temperate prairie ecosystems.     

中国西北地区石羊河流域农业生态系统能值评价

通过能值方法分析了石羊河流域农业生态系统的资源环境基础和经济特征。结果表明,石羊河流域中下游凉州区和民勤绿洲的能值总投入呈降低趋势,能值投资率都偏高,生产成本较大;能值产出率低,对购买能值的利用效率不高;但能值自给率有所提高;环境载荷率高,农业生态环境所受压力较大,系统可持续发展指标表明两个绿洲经济系统极为不发达,属于消费型经济系统,并且人均可用能值和能值密度都呈降低趋势。     

塔里木河荒漠植被光谱可分性模拟

以塔里木河典型植被为研究对象,分析胡杨、芦苇叶片及柽柳冠层的可分性,并计算背景的影响。首先用ASD光谱仪测新鲜叶片光谱,找出光谱特征点;然后模拟EO-1高光谱数据和TM多光谱数据;最后植被与土壤光谱按比例混合,分析背景的影响。以上三步分别计算植被指数（VI）。结果显示:叶片光谱特征位置430 nm、670 nm、750 nm附近,黄边斜率和红外平台平均高度,1 080~1 280 nm、1 430~1 650 nm能够区分塔里木河流域3个主要植被类型。模拟的EO-1波谱保持了控制波形的10个特征,TM 只有绿反射峰和红吸收谷、近红外1个反射峰3个特征,大部分特征都消失了。植被指数显示(R680-R500)/R750、(R680-R550)/R705、R1430+\:+R1650、D712/D688能够区分3类,且指数值差异较大,为绿峰、红谷和近红外波峰的组合;模拟的EO-1数据(R680-R500)/R750、(R680-R550)/R705、R1430+\:+R1650能分别区分植被,TM多波谱数据不能有效区分植被。     

转型期上海社会空间分异研究

采用2000年第五次全国人口普查数据库中居民委员会尺度的数据,对转型期上海城市空间重构与分异展开研究。存在6类社会区：计划经济时代建设的工人居住区、外来人口集中居住区、白领集中居住区、农民居住区、新建普通住宅居住区、离退休人员集中居住区。通过计算分异指数,发现当前上海存在严重的住房分异;但并不存在明显的以社会经济属性为基础的社会空间分异。造成这一现象的原因在于计划经济时代的历史以及仍然存在的大型企事业单位对住房的影响。中国城市目前的社会空间分异在程度上与西方城市还有根本的差异。     

地形在高大金字塔沙山形成发育过程中的作用——以敦煌鸣沙山-月牙泉地区为例

地形在金字塔沙山形成发育过程中起着重要的作用。金字塔沙山通常形成于山前地带,应属于地形屏障影响下形成的一种沙丘类型。首先,上升气流的发育是山前风阻区气流的主要特征,地形屏障是上升气流发生发展的主要原因。沙山的坡脚、坡中及坡顶分别是上升气流的启动区、发育区及衰退区。上升气流是沙山增高增大发育的主要机制。其次,金字塔沙山多发育于局地环流发育较强的地带,局地环流与区域风况配置是形成复杂沙丘类型的重要因素。本区局地环流偏南风不仅持续时间长,且受到鸣沙山微地形的影响,下坡气流较强。实地观测结果破解了常规气候台观测数据难以揭示上升气流及局地环流对金字塔沙丘形成发育的影响。第三,下附地形在沙山形成发育过程中决定着沙丘发育的“临界尺度”,即丘体达到“临界尺度”的时候,坡面上升气流及风速放大作用逐渐显现,促使丘体增高增大发育,随着沙丘形态与上升气流的互馈作用进一步增强,金字塔沙丘逐渐形成演化为高大沙山。实地观测进一步证实了金字塔沙山是纵向（横向）沙丘形变的一种形式。并提出了金字塔沙山在地形条件下形成演化的一种新模式。