LORACS: JAVA software for modeling landscape connectivity and matrix permeability

GIS tools and remote sensing products have opened the possibility to model the impacts of matrix permeability on a broad range of ecological phenomena. LORACS (Landscape ORganization and Connectivity Survey) is JAVA software containing a GUI interface and an API for easy extensibility. The user inputs maps with the relative costs to move within each pixel and the location of source and target patches. The software outputs Multiple Shortest Paths and Conditional Minimum Transit Cost maps. One key feature is the derivation of uncertainty estimates around path length, cost, and spatial distribution. We use data from the Brazilian Atlantic Forest to illustrate how LORACS can be used to translate assumptions about habitat quality into landscape connectivity patterns.     

面向生态系统评估的多源数据融合体系

基于生态系统服务功能的生态系统评估是识别生态环境问题、开展生态系统恢复和生物多样性保护、建立生态补偿机制的重要基础,也是保障国家生态安全、推进生态文明建设的重要环节。生态系统评估涉及生态系统多个方面,需要多要素、多类型、多尺度的生态系统观测数据作为支撑。地面观测数据和遥感数据是生态系统评估的两大数据源,但是其在使用时常存在观测标准不一、观测要素不全面、时间连续性不足、尺度不匹配等问题,给生态系统评估增加了极大的不确定性。如何融合不同尺度的观测数据量化生态系统服务功能是实现生态系统准确评估的关键。为此,从观测尺度出发,阐述了地面观测数据、近地面遥感数据、机载遥感数据和卫星遥感数据的特点及其在问题,并综述了这几类数据源进行融合的常用方法,并以生产力、固碳能力、生物多样性几个关键生态参数为例介绍了“基于多源数据融合的生态系统评估技术及其应用研究”项目的多源数据融合体系。最后,总结面向生态系统评估的多源数据融合体系,并指出了该研究的未来发展方向。     

用间接遥感方法探测大熊猫栖息地竹林分布

竹子是野生大熊猫赖以生存的唯一食物。探测大熊猫栖息地内的竹林分布状况,有助于深入了解大熊猫及其栖息地的空间分布格局与特点,并为评估其栖息地适宜性、破碎化程度和生态承载力提供科学依据。由于大熊猫的主食竹大都生长于林下,直接通过遥感影像解译的方法很难实现对其分布密度的探测。以佛坪自然保护区的两大优势竹种——巴山木竹和秦岭箭竹为例,在运用遥感和GIS方法获取空间连续的环境变量时,引入了林上和林下的光照条件,通过分析不同竹种与各环境要素之间的关系,建立竹子密度的预测模型,最后在GIS空间分析技术的支持下实现了对林下竹子密度的绘制。研究结果显示:该方法能够比较准确地预测出林下竹子的分布状态,对两种竹子的密度预测精度均可达到70%以上。     

Integration of ground survey and remote sensing derived data: Producing robust indicators of habitat extent and condition

The availability of suitable habitat is a key predictor of the changing status of biodiversity. Quantifying habitat availability over large spatial scales is, however, challenging. Although remote sensing techniques have high spatial coverage, there is uncertainty associated with these estimates due to errors in classification. Alternatively, the extent of habitats can be estimated from ground‐based field survey. Financial and logistical constraints mean that on‐the‐ground surveys have much lower coverage, but they can produce much higher quality estimates of habitat extent in the areas that are surveyed. Here, we demonstrate a new combined model which uses both types of data to produce unified national estimates of the extent of four key habitats across Great Britain based on Countryside Survey and Land Cover Map. This approach considers that the true proportion of habitat per km² (Z_i) is unobserved, but both ground survey and remote sensing can be used to estimate Z_i. The model allows the relationship between remote sensing data and Z_i to be spatially biased while ground survey is assumed to be unbiased. Taking a statistical model‐based approach to integrating field survey and remote sensing data allows for information on bias and precision to be captured and propagated such that estimates produced and parameters estimated are robust and interpretable. A simulation study shows that the combined model should perform best when error in the ground survey data is low. We use repeat surveys to parameterize the variance of ground survey data and demonstrate that error in this data source is small. The model produced revised national estimates of broadleaved woodland, arable land, bog, and fen, marsh and swamp extent across Britain in 2007.     

Remote Sensing Derived Fire Frequency,Soil Moisture and Ecosystem Productivity Explain Regional Movements in Emu over Australia

Species distribution modeling has been widely used in studying habitat relationships and for conservation purposes. However, neglecting ecological knowledge about species, e.g. their seasonal movements, and ignoring the proper environmental factors that can explain key elements for species survival (shelter, food and water) increase model uncertainty. This study exemplifies how these ecological gaps in species distribution modeling can be addressed by modeling the distribution of the emu (Dromaius novaehollandiae) in Australia. Emus cover a large area during the austral winter. However, their habitat shrinks during the summer months. We show evidence of emu summer habitat shrinkage due to higher fire frequency, and low water and food availability in northern regions. Our findings indicate that emus prefer areas with higher vegetation productivity and low fire recurrence, while their distribution is linked to an optimal intermediate (~0.12 m³ m^-3) soil moisture range. We propose that the application of three geospatial data products derived from satellite remote sensing, namely fire frequency, ecosystem productivity, and soil water content, provides an effective representation of emu general habitat requirements, and substantially improves species distribution modeling and representation of the species’ ecological habitat niche across Australia.     

Spatial Uncertainty and Ecological Models

Applied ecological models that are used to understand and manage natural systems often rely on spatial data as input. Spatial uncertainty in these data can propagate into model predictions. Uncertainty analysis, sensitivity analysis, error analysis, error budget analysis, spatial decision analysis, and hypothesis testing using neutral models are all techniques designed to explore the relationship between variation in model inputs and variation in model predictions. Although similar methods can be used to answer them, these approaches address different questions. These approaches differ in (a) whether the focus is forward or backward (forward to evaluate the magnitude of variation in model predictions propagated or backward to rank input parameters by their influence); (b) whether the question involves model robustness to large variations in spatial pattern or to small deviations from a reference map; and (c) whether processes that generate input uncertainty (for example, cartographic error) are of interest. In this commentary, we propose a taxonomy of approaches, all of which clarify the relationship between spatial uncertainty and the predictions of ecological models. We describe existing techniques and indicate a few areas where research is needed.     

基于斑块的红树林空间演变机理分析方法

现有红树林空间动态分析多是从整体的角度分析其面积的变化情况及其影响因素,着重于面积变化的定量分析和影响因素的定性分析,对其变化的发生途径缺乏深入分析,也不研究其斑块数量的变化动态。由于一定区域范围内的红树林由成百上千甚至更多空间上相互分离的斑块组成,各斑块边界和面积变化动态构成了区域红树林整体的空间分布变化动态,因此,只有深入摸清各个斑块的变化情况及其影响因素,才能对区域红树林整体变化情况作出全面、详细和准确的分析评估。提出了基于斑块的红树林空间演变机理分析方法,首先通过两期高空间分辨率遥感图像提取红树林空间分布信息,在GIS支持下采用叠置分析方法,根据前、后两期各个斑块的空间位置、形状和面积变化情况以及图像表征,逐一分析确定每个斑块变化的主要驱动因子(即变化原因,包括自然过程、围垦、养殖塘和盐田建设、工程建设和人工造林5种)和变化途径(即变化类型,包括稳定、扩张、萎缩、碎化、消失和新增6种),在此基础上构建斑块数量和面积变化的驱动因子-变化途径状态矩阵,通过总驱动量、总驱动率、净驱动量、净驱动率、趋势驱动率、总流量、总流率、净流量、净流率、趋势净流率和作用力等系列指标定量地评估红树林斑块数量和面积的变化动态。该方法不但能够定量地表达了各个驱动因子对红树林斑块数量和面积变化的影响程度,而且能够准确地阐明了红树林斑块数量和面积发生变化的途径,并且还能够准确地反映了每个驱动因子通过何种途径影响斑块数量和面积的变化,实现了红树林空间动态变化分析的定位化、定量化和精确化。     

Mapping Tension: Remote Sensing and the Production of a Statewide Land Cover Map

This article examines the process by which remotely sensed land cover maps work to both simplify and complicate landscapes. The central argument is not merely that the construction of land cover maps is complex, but that the points of complexity often arise through the process of trying to simplify. In other words, the forces of complexity are intimately connected to the forces of simplicity and vice versa. This article takes as a case study the production of WISCLAND, (Wisconsin Initiative for Statewide Cooperation on Landscape Analysis and Data), a statewide land cover map of Wisconsin derived from remote sensing data and GIS (Geographic Information Systems) technologies and proceeds by analysis of mapping methodology, practice, and representation. In addition to the development of a more nuanced critique of the use of land cover maps, it facilitates the possibility for a constructive dialogue between remote sensing practitioners and the critical GIS community.     

基于遥感和GIS的城乡交错带景观演变研究——以上海西南地区为例

以上海西南城乡交错带1984、1989和1994年3个时相的航空遥感图像为主要信息源,在ARCIINFO地理信息系统软件的支持下,建立上海西南城乡交错带景观数据库。将城乡交错带划分出5个一级景观类型,12个二级景观类型。运用景观格局分析指数对1984—1994年上海西南城乡交错带的景观格局特征及其动态进行研究。通过分析,可以看到1984—1989年和1989—1994年两个时期各种景观的结构变化特征都有明显的差异。城乡交错带作为城市化发展的一个过程,区域发展常常受政策性因素的作用较大,不合理的景观格局常常是城市生态问题产生的症结所在。虽然景观类型个体受人为控制较强,但整体却由于缺乏协调而导致一定程度的无序性,这将造成今后景观结构调整和生态建设的难点。因此今后有必要确立城乡交错带在城市发展中的地位和作用,建立合理的景观结构,注重保护开敞空间,确立城市自然生态系统发展体系,提高区域生态功能。     

中国陆地植被净初级生产力遥感估算

该文在综合分析已有光能利用率模型的基础上,构建了一个净初级生产力(NPP)遥感估算模型,该模型体现了3方面的特色:1)将植被覆盖分类引入模型,并考虑植被覆盖分类精度对NPP估算的影响,由它们共同决定不同植被覆盖类型的归一化植被指数(NDVI)最大值;2)根据误差最小的原则,利用中国的NPP实测数据,模拟出各植被类型的最大光能利用率,使之更符合中国的实际情况;3)根据区域蒸散模型来模拟水分胁迫因子,与土壤水分子模型相比,这在一定程度上对有关参数实行了简化,使其实际的可操作性得到加强。模拟结果表明,1989~1993年中国陆地植被NPP平均值为3.12 Pg C (1 Pg=10¹⁵ g),NPP模拟值与观测值比较接近,690个实测点的平均相对误差为4.5%;进一步与其它模型模拟结果以及前人研究结果的比较表明,该文所构建的NPP遥感估算模型具有一定的可靠性,说明在区域及全球尺度上,利用地理信息系统技术将遥感数据和各种观测数据集成在一起,并对NPP模型进行参数校正,基本上可以实现全球范围不同生态系统NPP的动态监测。     

北京市农业景观生态与美学质量空间评价

选择了重点反映农业景观生态与美学功能的生态系统功能、自然性、开阔与多样性、污染概率和整洁度等10个单一空间显性指数,基于田间调查和专家系统赋予其不同权重,构建了农业景观质量综合评价指标,并基于地理信息系统支持下的土地利用数据和利用遥感获得的植被指数,对北京市农业景观质量及其空间差异进行了评价.结果表明：北京市农业景观质量的区域差别较大,城区及小城镇边缘的农业景观质量最差,近郊外围及远郊区较好;北京市农业景观的优劣主要与地形和人为压力有关.基于空间信息技术和景观指数,在大尺度上构建的综合指标体系,对评价农业景观质量的空间差异具有重要意义.     

生物多样性遥感研究方法浅议

概括了遥感在生物多样性研究方面的的优势及在各种尺度为生物多样性评价提供信息的能力;讨论了生物多样性信息系统应具备的功能和应包含的内容;分析了生物多样性遥感研究中数学模型与地理信息系统的耦合问题。     

Review of forest landscape models: Types, methods, development and applications

Forest landscape models simulate forest change through time using spatially referenced data across a broad spatial scale (i.e. landscape scale) generally larger than a single forest stand. Spatial interactions between forest stands are a key component of such models. These models can incorporate other spatio-temporal processes such as natural disturbances (e.g. wildfires, hurricanes, outbreaks of native and exotic invasive pests and diseases) and human influences (e.g. harvesting and commercial thinning, planting, fire suppression). The models are increasingly used as tools for studying forest management, ecological assessment, restoration planning, and climate change. In this paper, we define forest landscape models and discuss development, components, and types of the models. We also review commonly used methods and approaches of modeling forest landscapes, their application, and their strengths and weaknesses. New developments in computer sciences, geographic information systems (GIS), remote sensing technologies, decision-support systems, and geo-spatial statistics have provided opportunities for developing a new generation of forest landscape models that are increasingly valuable for ecological research, restoration planning and resource management.     

Review of forest landscape models: Types, methods, development and applications

Forest landscape models simulate forest change through time using spatially referenced data across a broad spatial scale (i.e. landscape scale) generally larger than a single forest stand. Spatial interactions between forest stands are a key component of such models. These models can incorporate other spatio-temporal processes such as natural disturbances (e.g. wildfires, hurricanes, outbreaks of native and exotic invasive pests and diseases) and human influences (e.g. harvesting and commercial thinning, planting, fire suppression). The models are increasingly used as tools for studying forest management, ecological assessment, restoration planning, and climate change. In this paper, we define forest landscape models and discuss development, components, and types of the models. We also review commonly used methods and approaches of modeling forest landscapes, their application, and their strengths and weaknesses. New developments in computer sciences, geographic information systems (GIS), remote sensing technologies, decision-support systems, and geo-spatial statistics have provided opportunities for developing a new generation of forest landscape models that are increasingly valuable for ecological research, restoration planning and resource management.     

陆地生物地球化学模型的应用和发展

以TEM和DNDC模型为例,在分析国外生物地球化学模型发展基础上,按照模拟方法,应用目的,元素类型,生态系统类型和空间尺度等对现有的生物地球化学模型进行了分类,对生物地球化学模型的基本框架（植物,大气和土壤3个组分及植物－大气,植物－土壤和土壤－大气界面等3个界面）,以及内部基本过程（物理的,化学的和生物的过程）进行了总结分析,对目前生物地球化学模型建立中的几个问题（如跨尺度问题,地理信息系统（GIS）和遥感技术结合,考虑人类活动的影响和生物地球化学模型的比较研究）的研究动态进行了评价。     

Object-oriented satellite image time series analysis using a graph-based representation

Nowadays, remote sensing technologies produce huge amounts of satellite images that can be helpful to monitor geographical areas over time. A satellite image time series (SITS) usually contains spatio-temporal phenomena that are complex and difficult to understand. Conceiving new data mining tools for SITS analysis is challenging since we need to simultaneously manage the spatial and the temporal dimensions at the same time. In this work, we propose a new clustering framework specifically designed for SITS data. Our method firstly detects spatio-temporal entities, then it characterizes their evolutions by mean of a graph-based representation, and finally it produces clusters of spatio-temporal entities sharing similar temporal behaviors. Unlike previous approaches, which mainly work at pixel-level, our framework exploits a purely object-based representation to perform the clustering task. Object-based analysis involves a segmentation step where segments (objects) are extracted from an image and constitute the element of analysis. We experimentally validate our method on two real world SITS datasets by comparing it with standard techniques employed in remote sensing analysis. We also use a qualitative analysis to highlight the interpretability of the results obtained.     

Ground-Truthing Sahelian Greening: Ethnographic and Spatial Evidence from Burkina Faso

Historically, the Sahel of West Africa has been considered synonymous with desertification. In recent decades, however, satellite images reveal patterns of enhanced vegetation termed the “greening of the Sahel.” This greening is well-documented but its mechanisms remain poorly understood. The Sahel is also a region emerging from a 30 year period of reduced rainfall in which several severe droughts occurred. As a response to droughts and land degradation, farmers have rehabilitated thousands of hectares of degraded soils by constructing low barriers of rock through widespread soil and water conservation (SWC) development projects. Remote sensing analyses suggest that these extensive soil conservation projects may explain greening in northern Burkina Faso. This study combines ethnographic fieldwork with the analysis of Geographic Information System (GIS) and remote sensing (RS) data to test whether SWC investments contribute to greening. Ethnographic data reveal a tension between the perceptions of rural producers who feel that their SWC efforts contribute to greening and those of state officials who contend that SWC has only local impacts and that the regional landscape continues to degrade. Our analysis of GIS and RS data suggest that both perspectives are valid but contingent on the particular spatial and temporal scale used for analysis.     

Spatial heterogeneity of global forest aboveground carbon stocks and fluxes constrained by spaceborne lidar data and mechanistic modeling

Forest carbon is a large and uncertain component of the global carbon cycle. An important source of complexity is the spatial heterogeneity of vegetation vertical structure and extent, which results from variations in climate, soils, and disturbances and influences both contemporary carbon stocks and fluxes. Recent advances in remote sensing and ecosystem modeling have the potential to significantly improve the characterization of vegetation structure and its resulting influence on carbon. Here, we used novel remote sensing observations of tree canopy height collected by two NASA spaceborne lidar missions, Global Ecosystem Dynamics Investigation and ICE, Cloud, and Land Elevation Satellite 2, together with a newly developed global Ecosystem Demography model (v3.0) to characterize the spatial heterogeneity of global forest structure and quantify the corresponding implications for forest carbon stocks and fluxes. Multiple-scale evaluations suggested favorable results relative to other estimates including field inventory, remote sensing-based products, and national statistics. However, this approach utilized several orders of magnitude more data (3.77 billion lidar samples) on vegetation structure than used previously and enabled a qualitative increase in the spatial resolution of model estimates achievable (0.25° to 0.01°). At this resolution, process-based models are now able to capture detailed spatial patterns of forest structure previously unattainable, including patterns of natural and anthropogenic disturbance and recovery. Through the novel integration of new remote sensing data and ecosystem modeling, this study bridges the gap between existing empirically based remote sensing approaches and process-based modeling approaches. This study more generally demonstrates the promising value of spaceborne lidar observations for advancing carbon modeling at a global scale.     

遥感技术在昆虫生态学中的应用途径与进展

结合雷达、航空和卫星遥感本身的特点 ,从害虫本身、害虫所造成的危害、影响害虫发展的环境因子三方面介绍了遥感技术在区域性害虫的早期监测及预测中的应用途径与最新进展。针对害虫本身的个体大小、可动性和种群所在空间尺度的影响 ,作者强调应发挥不同遥感系统各自独特的优势 ,同时 ,综合应用“3S”技术和时空模型方法 ,才能够实现害虫动态的可视化、立体化、实时化和精确化监测及预测。     

The spatial distribution of cereal bioenergy potential in China

Biomass energy that exists in crop residues can be used for electricity generation and fuel production. However, its spatial distribution has formed a bottleneck in its utilization. This study introduces a data fusion method that uses the Net Primary Productivity (NPP) product of the Moderate‐resolution Imaging Spectroradiometer (MODIS) data as a weighting factor to downscale crop statistics from a county scale to a 1 km² spatial resolution using GIS to accurately map the spatial distribution of cereal bioenergy potential in China. The study demonstrates that the combination of remote sensing and statistical methods improves both spatial resolution and accuracy of the results, and resolves errors and uncertainties stemming from remote sensing processes. The results of the study will allow better decision making for siting biomass power plants, which will in turn reduce the cost of transportation of materials and increase the use of bioenergy.