Modeling spatial autocorrelation in spatial interaction data: empirical evidence from 2002 Germany journey-to-work flows

Since before the inception of work by Okabe, the intermingling of spatial autocorrelation (i.e., local distance and configuration) and distance decay (i.e., global distance) effects has been suspected in spatial interaction data. This convolution was first treated conceptually because technology and methodology did not exist at the time to easily or fully address spatial autocorrelation effects within spatial interaction model specifications. Today, however, sufficient computer power coupled with eigenfunction-based spatial filtering offers a means for accommodating spatial autocorrelation effects within a spatial interaction model for modest-sized problems. In keeping with Okabe’s more recent efforts to dissemination spatial analysis tools, this paper summarizes how to implement the methodology utilized to analyze a particular empirical flows dataset.

A time-integrated MODIS burn severity assessment using the multi-temporal differenced normalized burn ratio (dNBRMT)

Burn severity is an important parameter in post-fire management. It incorporates both the direct fire impact (vegetation depletion) and ecosystem responses (vegetation regeneration). From a remote sensing perspective, burn severity is traditionally estimated using Landsat's differenced normalized burn ratio (dNBR). In this case study of the large 2007 Peloponnese (Greece) wildfires, Landsat dNBR estimates correlated reasonably well with Geo composite burn index (GeoCBI) field data of severity (R² = 0.56). The usage of Landsat imagery is, however, restricted by cloud cover and image-to-image normalization constraints. Therefore a multi-temporal burn severity approach based on coarse spatial, high temporal resolution moderate resolution imaging spectroradiometer (MODIS) imagery is presented in this study. The multi-temporal dNBR (dNBR_MT) is defined as the 1-year integrated difference between burned pixels and their unique control pixels. These control pixels were selected based on time series similarity and spatial context and reflect how burned pixels would have behaved in the case no fire had occurred. Linear regression between downsampled Landsat dNBR and dNBR_MT estimates resulted in a moderate-high coefficient of determination R² = 0.54. dNBR_MT estimates are indicative for the change in vegetation productivity due to the fire. This change is considerably higher for forests than for more sparsely vegetated areas like shrub lands. Although Landsat dNBR is superior for spatial detail, MODIS-derived dNBR_MT estimates present a valuable alternative for burn severity mapping at continental to global scale without image availability constraints. This is beneficial to compare trends in burn severity across regions and time. Moreover, thanks to MODIS's repeated temporal sampling, the dNBR_MT accounts for both first- and second-order fire effects.     

Remotely sensed characterization of forest fuel types by using satellite ASTER data

The characterization of fuel types is very important for computing spatial fire hazard and risk and simulating fire growth and intensity across a landscape. However, due to the complex nature of fuel characteristic a fuel map is considered one of the most difficult thematic layers to build up. The advent of sensors with increased spatial resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate the accuracy and utility of imagery from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. In order to ascertain how well ASTER data can provide an exhaustive classification of fuel properties a sample area characterized by mixed vegetation covers was analysed. The selected sample areas has an extension at around 60 km² and is located inside the Sila plateau in the Calabria Region (South of Italy). Fieldwork fuel type recognitions, performed before, after and during the acquisition of remote sensing ASTER data, were used as ground-truth dataset to assess the results obtained for the considered test area. The method comprised the following three steps: (I) adaptation of Prometheus fuel types for obtaining a standardization system useful for remotely sensed classification of fuel types and properties in the considered Mediterranean ecosystems; (II) model construction for the spectral characterization and mapping of fuel types based on a maximum likelihood (ML) classification algorithm; (III) accuracy assessment for the performance evaluation based on the comparison of ASTER-based results with ground-truth. Results from our analysis showed that the use ASTER data provided a valuable characterization and mapping of fuel types being that the achieved classification accuracy was higher than 90%.     

局部空间自相关统计洪水灾害影像分析

针对洪水灾害分析在速度、准确性和及时性等方面的需求,该文在研究空间自相关分析的基础上,提出了一种基于局部自相关统计的洪水灾害影像分析方法。首先,对影像进行掩膜处理,去除云层干扰;其次,采用局部空间统计的方法对影像进行统计分析;然后,通过密度分割的方法提取水体,将影像分为水体和陆地两类;最后,将3幅影像分类的结果进行空间叠加分析,分析洪水灾害影响情况。以2013年嫩江流域3个时期的影像为实验数据,设计了仿真实验。实验结果表明,该方法可以较准确地对大面积洪水影响区域进行分析。     

Remote gully erosion mapping using aster data and geomorphologic analysis in the Main Ethiopian Rift

The Main Ethiopian Rift(MER)is an area of extreme topography underlain by post-Miocene volcanic rocks,Jurassic limestone and a Precambrian basement.A prime concern is the rapid expansion of wide gullies that are impinging on agricultural land.We investigate the potential contribution of Advanced Space-borne Thermal Emission and Reflection Radiometer(ASTER)data and geomorphologic parameters to discern patterns and features of gully erosion in the MER.Maximum Likelihood Classifica-tion(MLC),Support Vector Machine(SVM),and Minimum Distance(MD)classifiers are used to extract different gully shapes and patterns.Several spatial textures based on Grey Level Co-occurrence Matrices(GLCMs)are then generated.Afterwards,the same classifiers are applied to the ASTER data combined with the spatial texture information.We used geomorphologic parameters ex-tracted from SRTM and ASTER DEMs to describe the geomorphologic setting and the gullies’ shapes.The classifications show accuracies varying between 67% and 89%.Maps derived from this quantitative analysis allow the monitoring and mapping of land degradation as a direct result of gully-widening.This study reveals the utility of combining ASTER data and spatial textural infor-mation in discerning areas affected by gully erosion.     

Remote gully erosion mapping using aster data and geomorphologic analysis in the Main Ethiopian Rift

The Main Ethiopian Rift (MER) is an area of extreme topography underlain by post-Miocene volcanic rocks, Jurassic limestone and a Precambrian basement. A prime concern is the rapid expansion of wide gullies that are impinging on agricultural land. We investigate the potential contribution of Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) data and geomorphologic parameters to discern patterns and features of gully erosion in the MER. Maximum Likelihood Classification (MLC), Support Vector Machine (SVM), and Minimum Distance (MD) classifiers are used to extract different gully shapes and patterns. Several spatial textures based on Grey Level Co-occurrence Matrices (GLCMs) are then generated. Afterwards, the same classifiers are applied to the ASTER data combined with the spatial texture information. We used geomorphologic parameters extracted from SRTM and ASTER DEMs to describe the geomorphologic setting and the gullies’ shapes. The classifications show accuracies varying between 67% and 89%. Maps derived from this quantitative analysis allow the monitoring and mapping of land degradation as a direct result of gully-widening. This study reveals the utility of combining ASTER data and spatial textural information in discerning areas affected by gully erosion.     

Developing local measures of spatial association for categorical data

This paper describes a procedure for extending local statistics to categorical spatial data. The approach is based on the notion that there are two fundamental characteristics of categorical spatial data; composition and configuration. Further, it is argued that, when considered locally, the latter should be measured conditionally with respect to the former. These ideas are developed for binary, gridded data. Local composition is measured by counting the numbers of cells of a particular type, while local configuration is measured by join counts. The approach is illustrated using a small, empirical data set and an ad hoc procedure is developed to deal with the impact of global spatial autocorrelation on the local statistics.The author gratefully acknowledges financial support from the GEOIDE Network of Centres of Excellence (ENV #4) and the helpful comments of three anonymous reviewers.     

Modeling of multi-strata forest fire severity using Landsat TM Data

Most of fire severity studies use field measures of composite burn index (CBI) to represent forest fire severity and fit the relationships between CBI and Landsat imagery derived differenced normalized burn ratio (dNBR) to predict and map fire severity at unsampled locations. However, less attention has been paid on the multi-strata forest fire severity, which represents fire activities and ecological responses at different forest layers. In this study, using field measured fire severity across five forest strata of dominant tree, intermediate-sized tree, shrub, herb, substrate layers, and the aggregated measure of CBI as response variables, we fit statistical models with predictors of Landsat TM bands, Landsat derived NBR or dNBR, image differencing, and image ratioing data. We model multi-strata forest fire in the historical recorded largest wildfire in California, the Big Sur Basin Complex fire. We explore the potential contributions of the post-fire Landsat bands, image differencing, image ratioing to fire severity modeling and compare with the widely used NBR and dNBR. Models using combinations of post-fire Landsat bands perform much better than NBR, dNBR, image differencing, and image ratioing. We predict and map multi-strata forest fire severity across the whole Big Sur fire areas, and find that the overall measure CBI is not optimal to represent multi-strata forest fire severity.     

Influences of forest roads and their edge effects on the spatial pattern of burn severity

Previous research has shown that forest roads are an important feature in many landscapes and have significant effects on wildfire ignition and cessation. However, forest road effects on burn severity have not been studied at the landscape level. Therefore, the overarching goal of our study is to identify the influences of road edge effects on the spatial patterns of burn severity. We analyzed six fires within the Okanogan–Wenatchee National Forest on the eastern slope of the Cascades mountain range of central Washington.We generated two categories for assessing road variables: (1) Primary Road Effect Zone (area within 150 m of the nearest road) and (2) Secondary Road Effect Zone (area from 150 m to 300 m to the nearest road). A regular sampling grid including one out of every 9 cells was created for each fire.These grids were intersected with burn severity data in the form of the Relative Differenced Normalized Burn Ratio (RdNBR), road distance category, stream distance, elevation, slope, terrain shape index, heat load index, canopy cover, and fuel type. We fit spatial regression models with RdNBR as the dependent variable.We found that high burn severity is less likely to occur in the Primary Road Effect Zone for most fires, although one fire exhibited the opposite relationship. Forest road edge effects were hypothesized to be an important determinant of burn severity because fragmentation created by roads alters the roadside fuel profile and environment and because road corridors create barriers to fire spread. Recognizing roadside effects on burn severity patterns highlights the need for further study of the range of effects that roads have on fuels and the fire environment and the potential for incorporating road effects into landscape-level assessments of fire risk.     

Predictive fire occurrence modelling to improve burned area estimation at a regional scale: A case study in East Caprivi,Namibia

Fires threaten human lives, property and natural resources in Southern African savannas. Due to warming climate, fire occurrence may increase and fires become more intense. It is crucial, therefore, to understand the complexity of spatiotemporal and probabilistic characteristics of fires. This study scrutinizes spatiotemporal characteristics of fires and the role played by abiotic, biotic and anthropogenic factors for fire probability modelling in a semiarid Southern African savanna environment. The MODIS fire products: fire hot spots (MOD14A2 and MYD14A2) and burned area product MODIS (MCD45A1), and GIS derived data were used in analysis. Fire hot spots occurrence was first analysed, and spatial autocorrelation for fires investigated, using Moran's I correlograms. Fire probability models were created using generalized linear models (GLMs). Separate models were produced for abiotic, biotic, anthropogenic and combined factors and an autocovariate variable was tested for model improvement. The hierarchical partitioning method was used to determine independent effects of explanatory variables. The discriminating ability of models was evaluated using area under the curve (AUC) from the receiver operating characteristic (ROC) plot. The results showed that 19.2–24.4% of East Caprivi burned when detected using MODIS hot spots fire data and these fires were strongly spatially autocorrelated. Therefore, the autocovariate variable significantly improved fire probability models when added to them. For autologistic models, i.e. models accounting for spatial autocorrelation, discrimination was good to excellent (AUC 0.858–0.942). For models not counting spatial autocorrelation, prediction success was poor to moderate (AUC 0.542–0.745). The results of this study clearly showed that spatial autocorrelation has to be taken in to account in the fire probability model building process when using remotely sensed and GIS derived data. This study also showed that fire probability models accounting for spatial autocorrelation proved to be superior in regional scale burned area estimation when compared with MODIS burned area product (MCD45A1).     

Winter wheat biomass estimation using high temporal and spatial resolution satellite data combined with a light use efficiency model

Winter wheat biomass was estimated using HJ CCD and MODIS data, combined with a radiation use efficiency model. Results were validated with ground measurement data. Winter wheat biomass estimated with HJ CCD data correlated well with observed biomass in different experiments (coefficients of determination R² of 0.507, 0.556 and 0.499; n?=?48). In addition, R² values between MODIS estimated and observed biomass are 0.420, 0.502 and 0.633. Even if we downscaled biomass estimated using HJ CCD data to MODIS pixel size (9?×?9 HJ CCD pixels to approximate that MODIS pixel), R² values between estimated and observed biomass were still higher than those from MODIS. We conclude that estimation with remote sensing data, such as the HJ CCD data with high spatial resolution and shorter revisit cycle, can show more detail in spatial pattern and improve the application of remote sensing on a local scale. There is also potential for applying the approach to many other studies, including agricultural production estimation, crop growth monitoring and agricultural ecosystem carbon cycle studies.     

应用迁移学习的林火烈度初始评估研究

林火发生后,开展森林生态系统烈度信息的初始评估,能够为灾后生态修复管理措施的快速实施提供定量依据。为了改善传统林火烈度评估模型的时效性,本研究利用历史过火区域的实地调查数据,构建基于迁移学习的烈度评估模型,并将其应用于2020年3月30日发生的西昌泸山森林大火烈度初始评估研究中。研究结果表明：迁移学习算法能够将源区域和目标区域的遥感影像光谱转换为多个新的特征变量,在这些新特征变量构成的投影空间中,源区域和目标区域样本具有相似的特征分布。在此基础上,基于源区域历史实地调查数据构建的烈度评估模型,能够迁移应用于目标区域的烈度评估。在本研究林火烈度的初始评估中,基于迁移学习的烈度评估模型精度较高,总体精度为71.20%,Kappa系数为0.64。与该模型对比,未进行迁移学习的支持向量回归模型精度较低,其总体精度为58.00%,Kappa系数为0.48。同时,基于dNDVI、dLST和dNBR指数的经验回归模型精度最低,其总体精度分别为：20.80%、34.8%和24.80%,Kappa系数分别为：0.01、0.19和0.06。本研究可为林火灾后管理措施的快速响应,提供一种新的思路和参考。     

Hydromorphogeological mapping of Panwari area,Hamirpur district,Uttar Pradesh using satellite data

The area around Panwari town, Hamirpur district, Uttar Pradesh, faces acute water scarcity and chronically drought prone. The groundwater resources in the area have not been fully exploited. The present study was undertaken to evaluate the groundwater prospective zones. Landsat TM and IRS-1A LISS-II data have been used to differentiate different hydromorphogeological units and to delineate the major trends of lineaments. The digitally enhanced False Colour Composite, Principal Component Analysis and Edge Detections were useful for better correlation. The digital enhancement was helpful with identification of faint lineaments. In addition, the boundaries of various lands forms were better discriminable on the digitally enhanced products. The deeply and moderately weathered buried pediplains are the most potential zones for groundwater targeting. Occurrence of lineaments in such zones is also a favourable indicator. A number of promising groundwater well sites have been located in the pediplains.     

Characterization of spatial and temporal variation of suspended sediments in the Yellow and East China Seas using satellite ocean color data

Total suspended sediment (TSS) data concentrations are retrieved from two sets of satellite ocean color data (the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua and the Korean Geostationary Ocean Color Imager (GOCI)) using an existing regional model to characterize spatial and temporal variation of TSS in the Yellow and East China Seas. MODIS-derived TSS maps show that TSS concentrations are, in general, high along the Korean and Chinese coasts including the Bohai Sea and the Yangtz River estuary, and lower in the middle of the Yellow Sea and the southeastern area of the East China Sea. The monthly average of 10-year MODIS data reveals that TSS values are highest during winter (January to February) and lowest in summer (July to August). Short-term TSS concentrations retrieved from GOCI data showed the dominant influence of semi-diurnal tidal changes on sediment dynamics through temporal (hourly) and spatial distribution in coastal zones of the Yellow sea. The results presented here demonstrate that the satellite-derived TSS products can be utilized as an application tool for future studies on long- and short-term sediment dynamics of turbid coastal waters. In particular, GOCI observations provide unique important capabilities to characterize and quantify the water properties at high temporal (hourly) and spatial (0.5 km) resolutions in the turbid coastal waters of the Yellow Sea and its vicinities.     

基于机载MASTER数据的果园叶面积指数遥感反演

叶面积指数(leaf area index,LAI)是描述植被冠层结构的重要参数,准确获取果树的LAI对果树长势监测和果树估产均有重要作用。以美国加州中部的果园为研究区,基于沿太阳主平面飞行成像的机载MODIS/ASTER模拟传感器(MODIS/ASTER airborne simulator,MASTER)数据,利用实测LAI数据与归一化差值植被指数(normalized difference vegetation index,NDVI)、归一化差值红外指数(normalized difference infrared index,NDII)和归一化差值水体指数(normalized difference water index,NDWI)分别建立回归模型,并选取NDWI进行研究区LAI的反演。结果表明:由于地物的二向性反射,垂直太阳主平面飞行获取的遥感数据具有明显的亮度梯度现象,而沿太阳主平面飞行获取的遥感数据几乎不受亮度梯度的影响;NDVI在高植被覆盖区容易达到饱和,而NDWI比NDVI和NDII具有更高的拟合度和更小的均方根误差,更加适合研究区LAI的遥感反演;该研究结果可以丰富LAI反演理论,也可以为研究LAI尺度问题提供理论和数据支持。     

Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series

Satellite data holds considerable potential as a source of information on rice crop growth which can be used to inform agronomy. However, given the typical field sizes in many rice-growing countries such as China, data from coarse spatial resolution satellite systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are inadequate for resolving crop growth variability at the field scale. Nevertheless, systems such as MODIS do provide images with sufficient frequency to be able to capture the detail of rice crop growth trajectories throughout a growing season. In order to generate high spatial and temporal resolution data suitable for mapping rice crop phenology, this study fused MODIS data with lower frequency, higher spatial resolution Landsat data. An overall workflow was developed which began with image preprocessing, calculation of multi-temporal normalized difference vegetation index (NDVI) images, and spatiotemporal fusion of data from the two sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model was used to effectively downscale the MODIS data to deliver a time-series of 30 m spatial resolution NDVI data at 8-day intervals throughout the rice-growing season. Zonal statistical analysis was used to extract NDVI time-series for individual fields and signal filtering was applied to the time-series to generate rice phenology curves. The downscaled MODIS NDVI products were able to characterize the development of paddy rice at fine spatial and temporal resolutions, across wide spatial extents over multiple growing seasons. These data permitted the extraction of key crop seasonality parameters that quantified inter-annual growth variability for a whole agricultural region and enabled mapping of the variability in crop performance between and within fields. Hence, this approach can provide rice crop growth data that is suitable for informing agronomic policy and practice across a wide range of scales.     

Service-oriented infrastructure for flood mapping using optical and SAR satellite data

In this paper, we present the service-oriented infrastructure within the Wide Area Grid project that was carried out within the Working Group on Information Systems and Services of the Committee on Earth Observation Satellites. The developed infrastructure integrates services and computational resources of several regional and national Grid systems: Ukrainian Academician Grid (with satellite data processing Grid segment, UASpaceGrid) and Grid system at the Center on Earth Observation and Digital Earth of Chinese Academy of Sciences. The study focuses on integrating geo-information services on flood mapping provided by Ukrainian and Chinese entities to benefit from information acquired from multiple sources. We also describe services for workflow automation and management in Grid environment and provide an example of workflow automation for generating flood maps from optical and synthetic-aperture radar satellite imagery. We also discuss issues of enabling trust for the infrastructure using certificates and reputation-based model. Applications of utilizing the developed infrastructure for operational flood mapping in Ukraine and China are given as well.     

Semi-automatic mapping of anthropogenic impervious surfaces in an urban/suburban area using Landsat 8 satellite data

Impervious surfaces have a significant impact on urban runoff, groundwater, base flow, water quality, and climate. Increase in Anthropogenic Impervious Surfaces (AIS) for a region is a true representation of urban expansion. Monitoring of AIS in an urban region is helpful for better urban planning and resource management. Cost effective and efficient maps of AIS can be obtained for larger areas using remote sensing techniques. In the present study, extraction of AIS has been carried out using Double window Flexible Pace Search (DFPS) from a new index named as Normalized Difference Impervious Surface Index (NDAISI). NDAISI is developed by enhancing Biophysical Composition Index (BCI) in two stages using a new Modified Normalized Difference Soil Index (MNDSI). MNDSI has been developed from Band 7 and Band 8 (PAN) of Landsat 8 data. In comparison to existing impervious surface extraction methods, the new NDAISI approach is able to improve Spectral Discrimination Index (SDI) for bare soil and AIS significantly. Overall accuracy of mapping of AIS, using NDAISI approach has been found to be increased by nearly 23% when compared with existing impervious surface extraction methods.     

HJ-1卫星CCD数据的大气校正及其效果分析

利用MODTRAN 4模型对地表-大气-遥感器之间的辐射传输过程进行模拟,模拟出对应大气参数下的表观辐亮度;利用模拟生成的大气校正查找表,逐像元对HJ-1卫星CCD数据进行大气校正;并从光谱曲线、与MODIS地表反射率产品比较、归一化植被指数(NDVI)三个方面,探讨了HJ-1卫星CCD数据大气校正效果.结果表明:(1...