Landscape characterization of Sariska National Park (India) and its surroundings

Landscape characterization gives an overall information on the status of Land Use and Land Cover(LULC),changes in its composition and the impact of natural and human influences operating at different spatial and temporal scales.This information can be used to monitor changes in natural forest resources and protected areas,delineate potential conservation areas and can serve in effective management of ecologically fragile landscapes.In the present study,geo-spatial tools were used to characterize the landscape of Sariska National Park and its surroundings.Satellite data was used to prepare LULC maps for 1989 and 2000,change detection analysis and computation of landscape metrics.Climatic data,field records and modeling tools were used to map the po-tential spread of two invasive species,Prosopis juliflora and Adhatoda vasica.The results show that the forest area increased from 1989 to 2000,indicating better management practices.Landscape metrics(PAFRAC,PLADJ and AI)also support this argument.Improvements in the degraded forest can further enhance this effect.The entire reserve however is suitable for the invasion of P.juliflora and A.vasica but is more pronounced in Boswellia serrata and Anogeissus pendula-Acacia catechu(open)forests.A detailed landscape characterization map can help forest managers to make important policy decisions concerning issues such as in-vasive species.     

基于GIS的丘陵区耕地景观格局时空演变特征分析

以四川省绵阳市涪城区为例,运用景观格局原理与GIS空间分析方法,分析1996--2009年期间耕地景观时空格局及其演变机理（驱动力）。结果表明：13年间,研究区耕地面积呈急剧减少的趋势,变化率明显高于省内丘陵区平均水平;耕地景观空间格局稳定性逐步降低,格局时空变化的地形分异特征显著;变化趋势受坡度、新增建设用地、人口密度、高程、城镇化水平、GDP、起伏度等负向驱动力与灌溉条件、等级公路水平、土地整理程度等正向驱动力因子共同影响。     

基于物联网与GIS的智慧园林系统设计与实现

传统的园林管理存在数据信息多、测量手法多,无法实时在线监测等问题,针对园林管理过程中存在的这些问题,提出了智慧园林的解决方案。并根据设计理念,对各个子模块的功能进行设计和实现,给出实现方式。结合空间信息集成技术、可视化技术、物联网、GIS等信息技术构建园林资源信息的动态监测、数据时空分析、多维分析、在线服务平台。给园林绿化规划、虫害分析预报、养护管理、名树古木保护等提供科学管理依据,建立了较为完备的监测管理与实时动态分析系统。     

A spatial panel ordered-response model with application to the analysis of urban land-use development intensity patterns

This paper proposes and estimates a spatial panel ordered-response probit model with temporal autoregressive error terms to analyze changes in urban land development intensity levels over time. Such a model structure maintains a close linkage between the land owner’s decision (unobserved to the analyst) and the land development intensity level (observed by the analyst) and accommodates spatial interactions between land owners that lead to spatial spillover effects. In addition, the model structure incorporates spatial heterogeneity as well as spatial heteroscedasticity. The resulting model is estimated using a composite marginal likelihood (CML) approach that does not require any simulation machinery and that can be applied to data sets of any size. A simulation exercise indicates that the CML approach recovers the model parameters very well, even in the presence of high spatial and temporal dependence. In addition, the simulation results demonstrate that ignoring spatial dependency and spatial heterogeneity when both are actually present will lead to bias in parameter estimation. A demonstration exercise applies the proposed model to examine urban land development intensity levels using parcel-level data from Austin, Texas.     

Performance evaluation of land change simulation models using landscape metrics

This study proposes a landscape metrics-based method for model performance evaluation of land change simulation models. To quantify model performance at both landscape and class levels, a set of composition- and configuration-based metrics including number of patches, class area, landscape shape index, mean patch area and mean Euclidean nearest neighbour distance were employed. These landscape metrics provided detailed information on simulation success of a cellular automata-Markov chain (CA-Markov) model standpoint of spatial arrangement of the simulated map versus the corresponding reference layer. As a measure of model simulation success, mean relative error (MRE) of the metrics was calculated. At both landscape and class levels, the MRE values were accounted for 22.73 and 10.2%, respectively, which are further categorised into qualitative measurements of model simulation performance for simple and quick comparison of the results. Findings of the present study depict a hierarchical and multi spatial level assessment of model performance.     

Application of landscape ecology and remote sensing for assessment,monitoring and conservation of biodiversity

Landscape ecology, inter alia, addresses the question as to how altered landscape patterns affect the distribution, persistence, and abundance of a species. Landscape ecology plays an important role in integrating the different scales of biodiversity from habitat patch to biome level. Satellite remote sensing technology with multi-sensor capabilities offers multi-scale information on landscape composition and configuration. Advances in geospatial analytical tools and spatial statistics have improved the capability to quantify spatial heterogeneity. Globally, landscape level characterization of biodiversity has become an important discipline of science. Considering the vast extent, heterogeneity, and ecological and economic importance of forest landscapes, significant efforts have been made in India during the past decade to strengthen landscape level biodiversity characterization. The generic frame work of studies comprises preparation of national databases providing information on composition and configuration of different landscapes using multi-scale remote sensing techniques, understanding the landscape patterns using geospatial models to elicit disturbance and diversity patterns and application of this information for bioprospecting and conservation purposes. Studies on hierarchical linkage of multi-scale information to study the processes of change, landscape function, dynamics of habitat fragmentation, invasion, development of network of conservation areas based on the understanding of multi-species responses to landscape mosaics, macro-ecological studies to understand environment and species richness, habitat and species transitions and losses, landscape level solutions to adaptation and mitigation strategies to climate change are a few of the future challenges. The paper presents the current experiences and, analyses in conjunction with international scenario and identifies future challenges of Indian landscape level biodiversity studies.     

Multi‐temporal AVHRR digital data: An approach for landcover mapping of heterogeneous landscapes

A multi‐temporal sequence of seven NOAA‐n, Advanced Very High Resolution Radiometer (AVHRR) satellite scenes (April 10, May 18, June 6, June 29, July 20, and August 18, 1987) were composited to derive cover‐type information in the heterogeneous landscape of University Lake Watershed, North Carolina, U.S.A. The Normalized Difference Vegetation Index (NDVI) was calculated for each scene and merged into a seven‐dimensional dataset, representing each time period sampled. An unsupervised classification was performed on the multi‐temporal composite to derive five cover‐type classes. Similar classifications were generated on single scene information. Ground control information was derived from an unsupervised classification of one kilometer grid compositional percentages initially derived from photo‐interpreted landcover information. The multi‐temporal NDVI classification more consistently characterized phenologic responses on a spatially dissected landscape than single scene clustering. Sub‐pixel information showed how the algorithm separated compositional information between classes. Temporal vectors were plotted to illustrate differentiation on the basis of NDVI profiles.     

A Spatio‐Temporal Graph Model for Marine Dune Dynamics Analysis and Representation

Defining a model for the representation and the analysis of spatio‐temporal dynamics remains an open domain in geographical information sciences. In this article we investigate a spatio‐temporal graph‐based model dedicated to managing and extracting sets of geographical entities related in space and time. The approach is based on spatial and temporal local relations between neighboring entities during consecutive times. The model allows us to extract sets of connected entities distant in time and space over long periods and large spaces. From GIS concepts and qualitative reasoning on space and time, we combine the graph model with a dedicated spatial database. It includes information on geometry and geomorphometric parameters, and on spatial and temporal relations. This allows us to extend classical measurements of spatial parameters, with comparisons of entities linked by complex relations in space and time. As a case study, we show how the model suggests an efficient representation of dunes dynamics on a nautical chart for safe navigation.     

'Situating' Simulation to Model Human Spatio-Temporal Interactions: An Example Using Crime Events

Many social phenomena have a spatio‐temporal dimension and involve dynamic decisions made by individuals. In the past, researchers have often turned to geographic information systems (GIS) to model these interactions. Although GIS provide a powerful tool for examining the spatial aspects of these interactions, they are unable to model the dynamic, individual‐level interactions across time and space. In an attempt to address these issues, some researchers have begun to use simulation models. But these models rely on artificial landscapes that do not take into account the environment in which humans move and interact. This research presents the methodology for ‘situating’ simulation through the use of a new modeling tool, Agent Analyst, which integrates agent‐based modeling (ABM) and GIS. Three versions of a model of street robbery are presented to illustrate the importance of using ‘real’ data to inform agent activity spaces and movement. The successful implementation of this model demonstrates that: (1) agents can move along existing street networks; (2) land use patterns can be used to realistically distribute agent's homes and activities across a city; and (3) the incidence and pattern of street robberies is significantly different when ‘real’ data are used.     

A reliable traffic prediction approach for bike‐sharing system by exploiting rich information with temporal link prediction strategy

Bike‐sharing systems have been widely used in major cities across the world. As bike borrowing and return at different stations in different periods are not balanced, the bikes in a bike‐sharing system need to be redistributed frequently to rebalance the system. Therefore, traffic flow forecasting of the bike‐sharing system is an important issue, as this is conducive to achieving rebalancing of the bike system. In this article, we present a new traffic flow prediction approach based on the temporal links in dynamic traffic flow networks. A station clustering algorithm is first introduced to cluster stations into groups. A temporal link prediction method based on the dynamic traffic flow network method (STW+M) is then proposed to predict the traffic flow between stations. In our method, the non‐negative tensor decomposition and time‐series analysis model capture the rich information (temporal variabilities, spatial characteristics, and weather information) of the across‐clusters transition. Then, a temporal link prediction strategy is used to forecast potential links and weights in the traffic flow network by investigating both the network structure and the results of tensor computations. In order to assess the methods proposed in this article, we have used the data of bike‐sharing systems in New York and Washington, DC to conduct bike traffic prediction and the experimental results have shown that our method produces the lowest root mean square error (RMSE) and mean square error (MSE). Compared to four prediction methods from the literature, our RMSE and MSE of the two datasets have been lowered by an average of 2.55 (Washington, DC) and 2.41 (New York) and 3.35 (Washington, DC) and 2.96 (New York), respectively. The results show that the proposed approach improves predictions of traffic flow.     

A Multiscale Approach for Spatio-Temporal Outlier Detection

A spatial outlier is a spatially referenced object whose thematic attribute values are significantly different from those of other spatially referenced objects in its spatial neighborhood. It represents an object that is significantly different from its neighbourhoods even though it may not be significantly different from the entire population. Here we extend this concept to the spatio‐temporal domain and define a spatial‐temporal outlier (ST‐outlier) to be a spatial‐temporal object whose thematic attribute values are significantly different from those of other spatially and temporally referenced objects in its spatial or/and temporal neighbourhoods. Identification of ST‐outliers can lead to the discovery of unexpected, interesting, and implicit knowledge, such as local instability or deformation. Many methods have been recently proposed to detect spatial outliers, but how to detect the temporal outliers or spatial‐temporal outliers has been seldom discussed. In this paper we propose a multiscale approach to detect ST‐outliers by evaluating the change between consecutive spatial and temporal scales. A four‐step procedure consisting of classification, aggregation, comparison and verification is put forward to address the semantic and dynamic properties of geographic phenomena for ST‐outlier detection. The effectiveness of the approach is illustrated by a practical coastal geomorphic study.     

基于C5.0决策树算法的元胞自动机的洪河湿地演化模拟

以洪河自然保护区1992年、2001年、2010年三期TM遥感影像为数据源,利用C5.0决策树算法从已有的数据及其影响因子数据中挖掘出洪河湿地的演变规则,并将获得的转换规则应用到元胞自动机模型中进行洪河湿地演变的动态模拟与预测,分析和探讨了元胞自动机模型在湿地景观模拟和预测中的重要作用。结果表明,在现有的空间变量和条件不变的情况下,在未来的洪河自然保护区湿地面积将减小,洪河自然保护区干旱化将加重。通过对湿地景观的动态变化模拟和预测研究,能够较好地反映湿地景观的动态变化情况。     

Simulation Modelling Applied to Runoff Modelling Using MapScript

Dynamic spatial analysis addresses computational aspects of space–time processing. This paper describes the development of a spatial analysis tool and modelling framework that together offer a solution for simulating landscape processes. A better approach to integrating landscape spatial analysis with Geographical Information Systems is advocated in this paper. Enhancements include special spatial operators and map algebra language constructs to handle dispersal and advective flows over landscape surfaces. These functional components to landscape modelling are developed in a modular way and are linked together in a modelling framework that performs dynamic simulation. The concepts and modelling framework are demonstrated using a hydrological modelling example. The approach provides a modelling environment for scientists and land resource managers to write and to visualize spatial process models with ease.     

CAUSTA: Clifford Algebra‐based Unified Spatio‐Temporal Analysis

Introducing Clifford algebra as the mathematical foundation, a unified spatio‐temporal data model and hierarchical spatio‐temporal index are constructed by linking basic data objects, like pointclouds and Spatio‐Temporal Hyper Cubes of different dimensions, within the multivector structure of Clifford algebra. The transformation from geographic space into homogeneous and conformal space means that geometric, metric and many other kinds of operators of Clifford algebra can be implemented and we then design the shortest path, high‐dimensional Voronoi and unified spatial‐temporal process analyses with spacetime algebra. Tests with real world data suggest these traditional GIS analysis algorithms can be extended and constructed under Clifford Algebra framework, which can accommodate multiple dimensions. The prototype software system CAUSTA (Clifford Algebra based Unified Spatial‐Temporal Analysis) provides a useful tool for investigating and modeling the distribution characteristics and dynamic process of complex geographical phenomena under the unified spatio‐temporal structure.     

时态信息可视化模型研究及实现

时间问题是地理学中一个基本的研究问题。随着空间技术和现代信息基础设施的飞速发展,使得空间信息具备了较强的时态特性,传统静态地图中的可视化表达方式已无法满足时态信息的动态表示需求,如何在空间格局上和基于时间维的动态演化过程中,提供时态信息模拟结果的可视化分析和表达,是不可忽缺的一个环节。针对当前时空数据模型较多,而可视化研究相对较少的现状,在充分研究时态信息特点的基础上,研究了可视化动态过程所涉及的相关模型,包括概念模型、时空耦合模型以及预测模型等。     

基于植被分布特征的森林景观建模方法

在虚拟地理环境中如何快速地生成具有特定空间格局的森林景观是一个具有挑战的问题。现有的研究主要以模拟或随机的方式确定树木的位置,但难以反映每个树种的分布特征。本文提出了一种基于图的中性景观模型,根据指定的景观指数生成特定组成结构和空间配置的森林景观。模型将格网随机划分为若干不规则的区域,并将其转化为对应的区域邻接图,通过源节点的受限生长生成特定大小和形状的图斑。相比其他中性景观模型,该模型在可控性和灵活性上具有优势,能对每种类型的景观特征分别进行表达。最后根据模型生成的森林景观确定树木的位置与种类。本方法不仅能为植被场景的可视化提供合理的植被分布,而且能反映森林景观的空间分布特征,为虚拟森林环境中的林业规划决策以及植被分布规律认知提供一定的支持。     

基于GIS的长白山景观格局演化信息图谱分析

景观格局是若干生态系统的综合体,受地貌、气候和其他外界多种因素的干扰,是模拟和预测地理要素变化趋势的基础。地学信息图谱理论为景观格局研究提供新的方法。基于RS和GIS技术,分析了20世纪70年代后期以来长白山地区景观格局图谱的时空变化规律。利用1977年MSS、1985年、1999年TM影像时间系列的遥感数据,建立空间信息库,选取景观/斑块类型面积、斑块所占景观面积的比例、平均斑块面积、斑块个数、破碎度指数、分维数、优势度指数等指标,对长白山景观空间格局的动态演变进行了分析。分析了长白山地区近30年来的景观结构变化,建立长白山景观格局演化的信息图谱。景观格局与过程的变化表明,人为活动对景观格局产生的影响,已成为景观格局变化的主要驱动因子。     

A filtering approach for estimating lake water quality from remote sensing data

In this paper we consider the estimation of lake water quality constituent distributions from hyperspectral remote sensing data. We present a computational approach that can be used to assimilate information from mathematical evolution models into data processing. The method is based on a reduced order iterated extended Kalman filter, and a convection–diffusion model is used to describe the movement of the water quality constituents. The performance of the technique is evaluated in a simulation study. The results show that the filter approach with an appropriate evolution model yields estimates that have better spatial and temporal resolutions than those obtained with conventional methods. Furthermore, the use of a feasible evolution model may make it possible to obtain information also on the concentrations in the lower parts of the lake.     

A Framework and Software Tool to Support Collaborative Landscape Analysis: Fitting Square Pegs into Square Holes

For landscape models to be applied successfully in management situations, models must address appropriate questions, include relevant processes and interactions, be perceived as credible and involve people affected by decisions. We propose a framework for collaborative model building that can address these issues, and has its roots in adaptive management, computer‐supported collaborative work and landscape ecology. Models built through this framework integrate a variety of information sources, address relevant questions, and are customized for the particular landscape and policy environment under study. Participants are involved in the process from the start, and because their input is incorporated, they feel ownership of the resulting models, increasing the chance of model acceptance and application. There are two requirements for success: a tool that supports rapid model prototyping and modification, that makes a clear link between a conceptual and implemented model, and that has the ability to implement a wide range of model types; and a core team with skills in communication, research and analysis, and knowledge of ecology and forestry in addition to modelling. SELES (Spatially Explicit Landscape Event Simulator) is a tool for building and running models of landscape dynamics. It combines discrete event simulation with a spatial database and a relatively simple modelling language to allow rapid development of landscape simulations, and provides a high‐level means of specifying complex model behaviours ranging from management actions to natural disturbance and succession. We have applied our framework in several forest modelling projects in British Columbia, Canada. We have found that this framework increases the interest by local experts and decision‐makers to participate actively in the model building process. The workshop process and resulting models have efficiently provided insight into the dynamics of large landscapes over long time frames. The use of SELES has facilitated this process by providing a flexible, transparent environment in which models can be rapidly implemented and refined. As a result, model findings may be more readily incorporated into decision‐support systems designed to assist resource managers in making informed decisions.     

基于地理事件时变序列的时空数据模型研究与实现

首先分析比较了已有的时空数据模型的特点，针对其存在的不足，提出并讨论了一种基于地理事件时变序列的时空数据模型，阐述了该模型的时空语义概念和时空拓扑关系，并针对具体的实际应用，在现有成熟的GIS平台上进行扩展，验证了该数据模型的有效性。