Using neural networks and GIS to forecast land use changes: a Land Transformation Model

The Land Transformation Model (LTM), which couples geographic information systems (GIS) with artificial neural networks (ANNs) to forecast land use changes, is presented here. A variety of social, political, and environmental factors contribute to the model's predictor variables of land use change. This paper presents a version of the LTM parameterized for Michigan's Grand Traverse Bay Watershed and explores how factors such as roads, highways, residential streets, rivers, Great Lakes coastlines, recreational facilities, inland lakes, agricultural density, and quality of views can influence urbanization patterns in this coastal watershed. ANNs are used to learn the patterns of development in the region and test the predictive capacity of the model, while GIS is used to develop the spatial, predictor drivers and perform spatial analysis on the results. The predictive ability of the model improved at larger scales when assessed using a moving scalable window metric. Finally, the individual contribution of each predictor variable was examined and shown to vary across spatial scales. At the smallest scales, quality views were the strongest predictor variable. We interpreted the multi-scale influences of land use change, illustrating the relative influences of site (e.g. quality of views, residential streets) and situation (e.g. highways and county roads) variables at different scales.     

基于Landsat TM 数据的若尔盖县LUCC 时空特征研究

若尔盖县是世界著名若尔盖湿地的主要组成部分, 是青藏高原高寒生态系统的典型代表。基于1989 年、1997 年和2004 年3 期Landsat TM 影像的土地利用ö土地覆被分类结果, 运用地理信息系统空间分析方法和数理统计学方法, 深入分析了四川省若尔盖县近15 年来各土地利用/覆被类型尤其是草地和沼泽的数量和空间变化特征。结果表明: ①研究区主要土地利用与土地覆被类型为草地、沼泽、林地和裸地, 其中草地与沼泽面积逐步减少, 而裸地面积成倍增长。②通过建立研究区LU CC 幅度、LU CC 数量和空间变化模型以及趋势与状态指数模型, 很好的表现了研究区LU CC 的时空特征。从整个区域来看, 前期综合LU CC 趋势和状态指数为0. 37, 处于准平衡状态;后期小于前期, 为0. 23, 处于平衡状态, 整个时段其指数为0. 35, 为准平衡状态, 呈现双向转换态势。③定位分析了研究区LU CC 情况, 发现区域草地和沼泽退化相当严重, 而且前后两期退化区在空间上有所转移。     

Comparison of methods for LUCC monitoring over 50 years from aerial photographs and satellite images in a Sahelian catchment

Land use/cover change (LUCC) is a major indicator of the impact of climate change and human activity, particularly in the Sahel, where the land cover has changed greatly over the past 50 years. Aerial and satellite sensors have been taking images of the Earth's surface for several decades. These data have been widely used to monitor LUCC, but many questions remain concerning what type of pre-processing should be carried out on image resolutions and which methods are most appropriate for successfully mapping patterns and dynamics in both croplands and natural vegetation. This study considers these methodological questions. It uses multi-source imagery from 1952 to 2003 (aerial photographs, Corona, Landsat Multispectral Scanner (MSS), Landsat Thematic Mapper (TM) and Satellite Pour l'Observation de la Terre (SPOT) 5 images) and pursues two objectives: (i) to implement and compare a number of processing chains on the basis of multi-sensor data, in order (ii) to accurately track and quantify LUCC in a 100 km² Sahelian catchment over 50 years. The heterogeneity of the spatial and spectral resolution of the images led us to compare post-classification methods aimed at producing coherent diachronic maps based on a common land-cover nomenclature. Three main approaches were tested: pixel-based classification, vector grid-based on-screen interpretation and object-oriented classification. Within the automated approaches, we also examined the influence of spectral synthesis and spatial homogenization of the data through the use of composite bands (principal component analysis (PCA) and indices) and by resampling images at a common resolution. Classification accuracy was estimated by computing confusion matrices, by analysing overall change in the relative areas of land use/cover types and by studying the geographical coherence of the changes. These analyses indicate that on-screen interpretation is the most suitable approach for providing coherent, valid results from the multi-source images available over the study period. However, satisfactory classifications are obtained with the pixel-based and object-oriented approaches. The results also show significant sensitivity, depending on the method considered, to the combinations of bands used and to resampling. Lastly, the 50-year trends in LUCC point out a large increase in croplands and erosional surfaces with sparse vegetation and a drastic reduction in woody covers.     

Characterizing urban sprawl using multi-stage remote sensing images and landscape metrics

This study intends to explore the spatial analytical methods to identify both general trends and more subtle patterns of urban land changes. Landsat imagery of metropolitan Kansas City, USA was used to generate time series of land cover data over the past three decades. Based on remotely sensed land cover data, landscape metrics were calculated. Both the remotely sensed data and landscape metrics were used to characterize long-term trends and patterns of urban sprawl. Land cover change analyses at the metropolitan, county, and city levels reveal that over the past three decades the significant increase of built-up land in the study area was mainly at the expense of non-forest vegetation cover. The spatial and temporal heterogeneity of the land cover changes allowed the identification of fast and slow sprawling areas. The landscape metrics were analyzed across jurisdictional levels to understand the effects of the built-up expansion on the forestland and non-forest vegetation cover. The results of the analysis suggest that at the metropolitan level both the areas of non-forest vegetation and the forestland became more fragmented due to development while large forest patches were less affected. Metrics statistics show that this landscape effect occurred moderately at the county level, while it could be only weakly identified at the city level, suggesting a scale effect that the landscape response of urbanization can be better revealed within larger spatial units (e.g., a metropolitan area or a county as compared to a city). The interpretation of the built-up patch density metrics helped identify different stages of urbanization in two major urban sprawl directions of the metropolitan area. Land consumption indices (LCI) were devised to relate the remotely sensed built-up growth to changes in housing and commercial constructions as major driving factors, providing an effective measure to compare and characterize urban sprawl across jurisdictional boundaries and time periods.     

Evaluating Hyperion capability for land cover mapping in a fragmented ecosystem: Pollino National Park, Italy

We analyze the capability of Hyperion spaceborne hyperspectral data for discriminating land cover in a complex natural ecosystem according to the structure of the currently used European standard classification system (CORINE Land Cover 2000). For this purpose, we used Hyperion imagery acquired over Pollino National Park (Italy).Hyperion pre-processed data (30 m spatial resolution) were classified at the pixel level using common parametric supervised classification methods. The algorithms' performance and class level accuracy were compared with those obtained for the same area using airborne hyperspectral MIVIS data (7 m spatial resolution).Moreover, in selected test areas characterized by heterogeneous land cover (as mapped by MIVIS classification) a Linear Spectral Unmixing (LSU) technique was applied to Hyperion data to derive the abundance fractions of land cover endmembers. The accuracy of the LSU analysis was evaluated using the Residual Error parameter, by comparing Hyperion LSU results with land cover fractional abundances achieved from reference data (i.e., MIVIS and air-photo classification).The results show the potential of Hyperion spaceborne hyperspectral imagery in mapping land cover and vegetation diversity up to the 4th level of the CORINE legend, even at the sub-pixel level, within a fragmented ecosystem such as that of Pollino National Park. Moreover, we defined a criterion for evaluating the Hyperion accuracy in retrieving land cover abundances at the sub-pixel scale. Sub-pixel analysis allowed us to determine the optimal threshold to select the areas on which consistent fractional land cover monitoring can be achieved using the Hyperion sensor.     

HiTempo: a platform for time-series analysis of remote-sensing satellite data in a high-performance computing environment

Course resolution earth observation satellites offer large data sets with daily observations at global scales. These data sets represent a rich resource that, because of the high acquisition rate, allows the application of time-series analysis methods. To research the application of these time-series analysis methods to large data sets, it is necessary to turn to high-performance computing (HPC) resources and software designs. This article presents an overview of the development of the HiTempo platform, which was designed to facilitate research into time-series analysis of hyper-temporal sequences of satellite image data. The platform is designed to facilitate the exhaustive evaluation and comparison of algorithms, while ensuring that experiments are reproducible. Early results obtained using applications built within the platform are presented. A sample model-based change detection algorithm based on the extended Kalman filter has been shown to achieve a 97% detection success rate on simulated data sets constructed from MODIS time series. This algorithm has also been parallelized to illustrate that an entire sequence of MODIS tiles (415 tiles over 9 years) can be processed in under 19 minutes using 32 processors.     

A modernized version of a 1D soil vegetation atmosphere transfer model for improving its future use in land surface interactions studies

SimSphere is a land biosphere model that provides a mathematical representation of vertical ‘views’ of the physical mechanisms controlling Earth's energy and mass transfers in the soil/vegetation/atmosphere continuum. Herein, we present recent advancements introduced to SimSphere code, aiming at making its use more integrated to the automation of processes within High Performance Computing (HPC) that allows using the model at large scale. In particular, a new interface to the model is presented, so-called “SimSphere-SOA” which forms a command line land biosphere tool, a Web Service interface and a parameters verification facade that offers a standardised environment for specification execution and result retrieval of a typical model simulation based on Service Oriented Architecture (SOA). SimSphere-SOA library can now execute various simulations in parallel. This allows exploitation of the tool in a simple and efficient way in comparison to the currently distributed approach. In SimSphere-SOA, an Application Programming Interface (API) is also provided to execute simulations that can be publicly consumed. Finally this API is exported as a Web Service for remotely executing simulations through web based tools. This way a simulation by the model can be executed efficiently and subsequently the model simulation outputs may be used in any kind of relevant analysis required.The use of these new functionalities offered by SimSphere-SOA is also demonstrated using a “real world” simulation configuration file. The inclusion of those new functions in SimSphere are of considerable importance in the light of the model's expanding use worldwide as an educational and research tool.     

Assessing the effects of human-induced land degradation in the former homelands of northern South Africa with a 1 km AVHRR NDVI time-series

There is a pressing need for an objective, repeatable, systematic and spatially explicit measure of land degradation. In northeastern South Africa (SA), there are large areas of the former homelands that are widely regarded as degraded. A time-series of seasonally integrated 1 km, Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data was used to compare degraded rangelands [mapped by the National Land Cover (NLC) using Landsat Thematic Mapper (TM) imagery] to nondegraded rangelands within the same land capability units (LCUs). Nondegraded and degraded areas in the same LCU (paired areas) were compared by: (i) testing for differences in spatial mean ∑NDVI values, (ii) calculating the relative degradation impact (RDI) as the difference between the spatial mean ∑NDVI values of paired areas expressed as a percentage of nondegraded mean value, (iii) investigating the relationship between RDI and rainfall and (iv) comparing the resilience and stability of paired areas in response to natural variations in rainfall. The ∑NDVI of degraded areas was significantly lower for most of the LCUs. Relative degradation impacts (RDI) across all LCUs ranged from 1% to 20% with an average of 9%. Although ∑NDVI was related to rainfall, RDI was not. Degraded areas were no less stable or resilient than nondegraded. However, the productivity of degraded areas, i.e., the forage production per unit rainfall, was consistently lower than nondegraded areas, even within years of above normal rainfall. The results indicate that there has not been a catastrophic reduction in ecosystem function within degraded areas. Instead, degradation impacts were reflected as reductions in productivity that varied along a continuum from slight to severe, depending on the specific LCU.     

基于遥感影像多尺度分析技术的LUCC研究

基于遥感和地理信息系统技术研究土地利用／覆被变化（LUCC）目前普遍采用的方法，其中研究区不同时期遥感影像土地利用／覆被信息的获取是研究的关键，而传统的单一尺度的信息自动提取方式很难满足高精度LUCC研究的需要。为此，提出了以多尺度分析技术来提取土地利用／覆被信息的方法，并以江津市的LUCC研究为例，显示出这种方法的优越性。     

区域遥感蒸散发模型方法研究

遥感技术为大面积区域陆面水分蒸散发量估算提供了一种新的手段,分析了国内外应用较好的遥感监测陆面水分蒸发的几种方法,主要包括地表热量平衡方法、互补相关陆面蒸散发、Penman-Montieth模型和区域蒸散发的气候学方法,并给出了可供参考的地面参数(地面反照率、NDVI、陆面温度等)的遥感获取方法,还阐述了各种方法的特点及彼此之间的联系。     

Landsat continuity: Issues and opportunities for land cover monitoring

Initiated in 1972, the Landsat program has provided a continuous record of earth observation for 35 years. The assemblage of Landsat spatial, spectral, and temporal resolutions, over a reasonably sized image extent, results in imagery that can be processed to represent land cover over large areas with an amount of spatial detail that is absolutely unique and indispensable for monitoring, management, and scientific activities. Recent technical problems with the two existing Landsat satellites, and delays in the development and launch of a successor, increase the likelihood that a gap in Landsat continuity may occur. In this communication, we identify the key features of the Landsat program that have resulted in the extensive use of Landsat data for large area land cover mapping and monitoring. We then augment this list of key features by examining the data needs of existing large area land cover monitoring programs. Subsequently, we use this list as a basis for reviewing the current constellation of earth observation satellites to identify potential alternative data sources for large area land cover applications. Notions of a virtual constellation of satellites to meet large area land cover mapping and monitoring needs are also presented. Finally, research priorities that would facilitate the integration of these alternative data sources into existing large area land cover monitoring programs are identified. Continuity of the Landsat program and the measurements provided are critical for scientific, environmental, economic, and social purposes. It is difficult to overstate the importance of Landsat; there are no other systems in orbit, or planned for launch in the short-term, that can duplicate or approach replication, of the measurements and information conferred by Landsat. While technical and political options are being pursued, there is no satellite image data stream poised to enter the National Satellite Land Remote Sensing Data Archive should system failures occur to Landsat-5 and -7.     

Modelling the impacts of land system dynamics on human well-being: Using an agent-based approach to cope with data limitations in Koper,Slovenia

To cope with data limitations and to provide insight into the dynamics of LUCC for local stakeholders in the Municipality of Koper, Slovenia, we constructed an ABM (loosely defined) that integrates utility theory, logistic regression, and cellular automaton-like rules to represent the decision-making strategies of different agents. The model is used to evaluate the impact of LUCC on human well-being, as represented by the provision of highly productive agricultural soil, the extent of noise pollution, and quality-of-life measurements. Results for the Municipality of Koper show that, under a range of model assumptions, (1) high quality agricultural soils are disproportionately affected by urban growth, (2) aggregate resident quality of life increases non-linearly with a change in development density, (3) some drivers of residential settlement produce non-linear preference responses, and (4) clustering industrial development had a beneficial impact on human well-being. Additional novel contributions include the incorporation of noise pollution feedbacks and an approach to empirically inform agent preferences using a conjoint analysis of social survey data.     

Analysis of Landscape Characteristics of Urban Heat /Sink Islandin Hangzhou based on ASTER Remote Sensing Imagery

Rapid urbanization has significant contributions to the Surface Urban Heat Island (SUHI).Analyzing the SUHI distribution and its impact factors using remote sensing data has received increasing attentions in the past decades,whereas few study has investigated that of the surface Urban Heat Sink Island (SUHI).The paper selects Hangzhou metropolis as a case study to explore SUHI/SUHS spatial patterns and its causes.We first retrieve the Land Surface Temperature (LST) using ASTER thermal infrared remote sensing imagery and extract the region of SUHI/SUHS using the Mean\|Standard deviation method.Landsat8 OLI data is used to classify land use and extract both impervious surface and vegetation information.After that,different landscape patterns within SUHI/SUHS area are analyzed and quantified by using several selected landscape index.The largest impact factors in SUHI/SUHS areas are identified.Finally,we analyze the spatial characteristics of LST using the spatial gradient analysis method,and reveal its relationship with vegetation and impervious surface.The results show that：(1) a large landscape pattern difference exists within SUHI/SUHS area;the impervious surface has the greatest impact on LST of the SUHI area,whereas the vegetation has more obviously cooling effect on LST of the SUHS area than the water body;(2) with the increasing distance from the city center,the same trend was found between the mean LST values and the impervious surface density (positive correlations),whereas the opposite trend between the mean LST values and the vegetation density (negative correlations).And the warming effect of impervious surface is greater than the cooling effect of vegetation in Hangzhou.     

Parallel Implementation of a Class of Adaptive Signal Processing Applications

Recently, High Performance Computing (HPC) platforms have been employed to realize many computationally demanding applications in signal and image processing. These applications require real-time performance constraints to be met. These constraints include latency as well as throughput. In order to meet these performance requirements, efficient parallel algorithms are needed. These algorithms must be engineered to exploit the computational characteristics of such applications. In this paper we present a methodology for mapping a class of adaptive signal processing applications onto HPC platforms such that the throughput performance is optimized. We first define a new task model using the salient computational characteristics of a class of adaptive signal processing applications. Based on this task model, we propose a new execution model. In the earlier linear pipelined execution model, the task mapping choices were restricted. The new model permits flexible task mapping choices, leading to improved throughput performance compared with the previous model. Using the new model, a three-step task mapping methodology is developed. It consists of (1) a data remapping step, (2) a coarse resource allocation step, and (3) a fine performance tuning step. The methodology is demonstrated by designing parallel algorithms for modern radar and sonar signal processing applications. These are implemented on IBM SP2 and Cray T3E, state-of-the-art HPC platforms, to show the effectiveness of our approach. Experimental results show significant performance improvement over those obtained by previous approaches. Our code is written using C and the Message Passing Interface (MPI). Thus, it is portable across various HPC platforms. Received April 8, 1998; revised February 2, 1999.     

基于GIS的城市土地利用分析与建模框架研究

本文针对城市土地利用数据的时空特性,依托地理信息系统(GIS)丰富的空间分析工具以及对海量空间数据的高性能计算优势,围绕城市土地利用研究有关数据的处理、分析、建模等方面问题设计了一个基于GIS的城市土地利用分析与建模框架;框架主体结构中有关城市土地利用变化的驱动力机制建模方法选取逻辑回归模型,对地理数据的空间自相关性处理则根据Getis自相关系数构建滤波模型;具体应用则结合深圳市国土资源局的"城市土地利用虚拟政策实验室"项目,取得良好效果     

基于知识的山东丘陵区土地利用/覆盖分类研究

土地利用/覆盖信息的获取是土地利用/覆盖变化研究的前提和基础, 传统的基于光谱信息的分类无法克服地物光谱特征相似造成的混淆。以龙口市为例, 探讨了综合应用高程、坡度等地学专家知识和地物的光谱知识, 对山东丘陵地区土地利用/覆盖进行自动分类的方法。实验证明, 基于知识的土地利用ö覆盖分类方法消除了单纯利用光谱信息的不足, 达到了90. 24% 的分类精度, 远高于最大似然法分类。     

Visualization workflows for level-12 HUC scales: Towards an expert system for watershed analysis in a distributed computing environment

Visualization workflows are important services for expert users to analyze watersheds when using our HydroTerre end-to-end workflows. Analysis is an interactive and iterative process and we demonstrate that the expert user can focus on model results, not data preparation, by using a web application to rapidly create, tune, and calibrate hydrological models anywhere in the continental USA (CONUS). The HydroTerre system captures user interaction for provenance and reproducibility to share modeling strategies with modelers. Our end-to-end workflow consists of four workflows. The first is data workflows using Essential Terrestrial Variables (ETV) data sets that we demonstrated to construct watershed models anywhere in the CONUS (Leonard and Duffy, 2013). The second is data-model workflows that transform the data workflow results to model inputs. The model inputs are consumed in the third workflow, model workflows (Leonard and Duffy, 2014a) that handle distribution of data and model within High Performance Computing (HPC) environments. This article focuses on our fourth workflow, visualization workflows, which consume the first three workflows to form an end-to-end system to create and share hydrological model results efficiently for analysis and peer review. We show how visualization workflows are incorporated into the HydroTerre infrastructure design and demonstrate the efficiency and robustness for an expert modeler to produce, analyze, and share new hydrological models using CONUS national datasets.     

Land Surface Temperature Data Fusion of Landsat ETM and MODISby Combining TsHARP and STITFM Model

Land Surface Temperature (LST) is an important parameter that describes energy balance of substance and energy exchange between the surface and the atmosphere,and LST has widely used in the fields of urban heat island effect,soil moisture and surface radiative flux.Currently,no satellite sensor can deliver thermal infrared data at both high temporal resolution and spatial resolution,which strongly limits the wide application of thermal infrared data.Based on the MODIS land surface temperature product and Landsat ETM+image,a temporal and spatial fusion method is proposed by combining the TsHARP (Thermal sHARPening) model with the STITFM (Spatio\|Temporal Integrated Temperature Fusion Model) algorithm,defined as CTsSTITFM model in this study.The TsHARP method is used to downscale the 1 km MODIS land surface temperature image to LST data at spatial resolution of 250 m.Then the accuracy is verified by the retrieval LST from Landsat ETM+ image at the same time.Land surface temperature image at 30 m spatial scale is predicted by fusing Landsat ETM+ and downscaling MODIS data using STITFM model.The fusion LST image is validated by the estimated LST from Landsat ETM+ data for the same predicted.The results show that the proposed method has a better precision comparing to the STITFM algorithm.Under the default parameter setting,the predicted LST values using CTsSTITFM fusion method have a root mean square error (RMSE) less than 1.33 K.By adjusting the window size of CTsSTITFM fusion method,the fusion results in the selected areas show some regularity with the increasing of the window.In general,a reasonable window size set may slightly improve the effects of LST fusion.The CTsSTITFM fusion method can solve the problem of mixed pixels caused by coarse\|scale MODIS surface temperature images to some degree.     

A multi-objective Markov Chain Monte Carlo cellular automata model: Simulating multi-density urban expansion in NYC

Cellular automata (CA) models have increasingly been used to simulate land use/cover changes (LUCC). Metaheuristic optimization algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA) have been recently introduced into CA frameworks to generate more accurate simulations. Although Markov Chain Monte Carlo (MCMC) is simpler than PSO and GA, it is rarely used to calibrate CA models. In this article, we introduce a novel multi-chain multi-objective MCMC (mc-MO-MCMC) CA model to simulate LUCC. Unlike the classical MCMC, the proposed mc-MO-MCMC is a multiple chains method that imports crossover operation from classical evolutionary optimization algorithms. In each new chain, after the initial one, the crossover operator generates the initial solution. The selection of solutions to be crossed over are made according to their fitness score. In this paper, we chose the example of New York City (USA) to apply our model to simulate three conflicting objectives of changes from non-urban to low-, medium- or high-density urban between 2001 and 2016 using USA National Land Cover Database (NLCD). Elevation, slope, Euclidean distance to highways and local roads, population volume and average household income are used as LUCC causative factors. Furthermore, to demonstrate the efficiency of our proposed model, we compare it with the multi-objective genetic algorithm (MO-GA) and standard single-chain multi-objective MCMC (sc-MO-MCMC). Our results demonstrate that mc-MO-MCMC produces accurate simulations of land use dynamics featured by faster convergence to the Pareto frontier comparing to MO-GA and sc-MO-MCMC. The proposed multi-objective cellular automata model should efficiently help to simulate a trade-off among multiple and, possibly, conflicting land use change dynamics at once.