A novel approach to leveraging social media for rapid flood mapping: a case study of the 2015 South Carolina floods

Rapid flood mapping is critical for local authorities and emergency responders to identify areas in need of immediate attention. However, traditional data collection practices such as remote sensing and field surveying often fail to offer timely information during or right after a flooding event. Social media such as Twitter have emerged as a new data source for disaster management and flood mapping. Using the 2015 South Carolina floods as the study case, this paper introduces a novel approach to mapping the flood in near real time by leveraging Twitter data in geospatial processes. Specifically, in this study, we first analyzed the spatiotemporal patterns of flood-related tweets using quantitative methods to better understand how Twitter activity is related to flood phenomena. Then, a kernel-based flood mapping model was developed to map the flooding possibility for the study area based on the water height points derived from tweets and stream gauges. The identified patterns of Twitter activity were used to assign the weights of flood model parameters. The feasibility and accuracy of the model was evaluated by comparing the model output with official inundation maps. Results show that the proposed approach could provide a consistent and comparable estimation of the flood situation in near real time, which is essential for improving the situational awareness during a flooding event to support decision-making.     

An integrated remote sensing and GIS approach for monitoring areas affected by selective logging: A case study in northern Mato Grosso,Brazilian Amazon

Forest cover disturbances due to processes such as logging and forest fires are a widespread issue especially in the tropics, and have heavily affected forest biomass and functioning in the Brazilian Amazon in the past decades. Satellite remote sensing has played a key role for assessing logging activities in this region; however, there are still remaining challenges regarding the quantification and monitoring of these processes affecting forested lands. In this study, we propose a new method for monitoring areas affected by selective logging in one of the hotspots of Mato Grosso state in the Brazilian Amazon, based on a combination of object-based and pixel-based classification approaches applied on remote sensing data. Logging intensity and changes over time are assessed within grid cells of 300 m × 300 m spatial resolution. Our method encompassed three main steps: (1) mapping forest/non-forest areas through an object-based classification approach applied to a temporal series of Landsat images during the period 2000–2015, (2) mapping yearly logging activities from soil fraction images on the same Landsat data series, and (3) integrating information from previous steps within a regular grid-cell of 300 m × 300 m in order to monitor disturbance intensities over this 15-years period. The overall accuracy of the baseline forest/non-forest mask (year 2000) and of the undisturbed vs disturbed forest (for selected years) were 93% and 84% respectively. Our results indicate that annual forest disturbance rates, mainly due to logging activities, were higher than annual deforestation rates during the whole period of study. The deforested areas correspond to circa 25% of the areas affected by forest disturbances. Deforestation rates were highest from 2001 to 2005 and then decreased considerably after 2006. In contrast, the annual forest disturbance rates show high temporal variability with a slow decrease over the 15-year period, resulting in a significant increase of the ratio between disturbed and deforested areas. Although the majority of the areas, which have been affected by selective logging during the period 2000–2014, were not deforested by 2015, more than 70% of the deforested areas in 2015 had been at least once identified as disturbed forest during that period.     

A method for groundwater prospect zonation in data poor areas using remote sensing and GIS: a case study in Kalikavu Panchayath of Malappuram district,Kerala, India

Abstract

The present study was an attempt to delineate potential groundwater zones in Kalikavu Panchayat of Malappuram district, Kerala, India. The geo-spatial database on geomorphology, landuse, geology, slope and drainage network was generated in a geographic information system (GIS) environment from satellite data, Survey of India topographic sheets and field observations. To understand the movement and occurrence of groundwater, the geology, geomorphology, structural set-up and recharging conditions have to be well understood. In the present study, the potential recharge areas are delineated in terms of geology, geomorphology, land use, slope, drainage pattern, etc. Various thematic data generated were integrated using a heuristic method in the GIS domain to generate maps showing potential groundwater zones. The composite output map scores were reclassified into different zones using a decision rule. The final output map shows different zones of groundwater prospect, viz., very good (15.57% of the area), good (43.74%), moderate (28.38%) and poor (12.31%). Geomorphic units such as valley plains, valley fills and alluvial terraces were identified as good to excellent prospect zones, while the gently sloping lateritic uplands were identified as good to moderate zones. Steeply sloping hilly terrains underlain by hard rocks were identified as poor groundwater prospect zones.     

The Geography,Structure, and Evolution of the GIS Research Community in the US: A Network Analysis from 1992 to 2011

The research community of Geographic Information Systems and Science (GIS) has been growing rapidly during the last two decades. Little attention has been paid to understanding its geography, structure and evolution. Taking a new organizational perspective, this article aims to fill the knowledge gap by analyzing collaboration and citation networks between GIS research organizations, including academic institutions, government agencies, businesses, and others. These two networks are analyzed in geographical and bibliographical spaces, respectively, to discover characteristic distributions and structures. The results show an uneven geographic distribution of GIS research organizations, and clustered spatial interactions between them. Both collaboration and citation networks exhibit typical “scale‐free” structures, which came into being around the year 2000 and have remained to the present. Further, the GIS research community is composed of 11 cohesive sub‐groups, with each having a clear hub‐spoke structure and a few highly connected organizations as leaders. These results shed light on the overall picture of the GIS research community, and offer a reference system that stimulates further exploration.     

移动GIS在传染病防控方面的应用——以COVID-19为例

新型冠状病毒肺炎（COVID-19）疫情的出现和暴发流行,给社会、经济及人群健康提出巨大的挑战,已经成为重大公共卫生事件和社会问题。作为一种新发传染病,提早发现、迅速采用有效应对举措,是防止病毒蔓延扩散的重要环节。地理信息系统（GIS）在传染病的控制、预防、预警中有举足轻重的地位,移动GIS（Mobile GIS）作为GIS技术的发展,进一步提高了我国卫生部门应对突发传染病的能力。本文以COVID-19防控为例,重点介绍了移动GIS技术在传染病防控中的应用。     

Application of gis,MASMAP in particular,in the study of urban areas — A case study of Rohini,West of Delhi

The increasing awareness in computer aided technology for mapping and analysis is advancing in developing countries. There are many systems available in this field which links graphical data and attribute data using relational data base management system to meet the needs of various users. MASMAP/MIMER is one such system of fourth generation for rapid mapping and designing. Taking into account the MASMAP as a tool for the application in management of sewerage network, a case study of Rohini, located in west of Delhi, has been discussed in this paper.     

Observed biases in the conterminous U.S. AVHRR satellite data set: A North Carolina case study

The USGS EROS Data Center has produced a national data set for long‐term ecological monitoring termed the “Conterminous U.S. AVHRR (Advanced Very High Resolution Radiometer) Data Set” that includes biweekly maximum‐value composite (MVC) images of NDVI (Normalized Difference Vegetation Index) values, original five channels of calibrated AVHRR satellite data, image viewing and illumination geometry, date of observation, and ancillary data sets pertaining to landcover and political boundaries (Loveland et al., 1991; Eidenshink, 1992).

The basic intent of the study was to evaluate the potential of the data set for broad‐scale, multitemporal landscape mapping by assessing the quality and sensitivity of the data set to support such applications. Potential biases existing in the data set were identified and analytical procedures suggested to deal with such biases. Results from analyses within the State of North Carolina suggest that the time series of the NDVI values is influenced by sensitivities to residual cloud contamination, preceeding climatic events, temporal and spatial scales of analyses, and the composition and spatial organization of the study area. Spatial and temporal discontinuities within and between images, irregular space‐time semivariograms, and statistical summaries of the data show the existence of biases in the data set for North Carolina. Possible adjustments to reduce this level of uncertainty include the generation of NDVI composites over longer time periods, exclusion of suspected contaminated data, or the use of spatial and temporal interpolations of contaminated values to reduce their relative impact on each composite image. Regional variations in NDVI responses to viewing and illumination geometry may also be important factors for users to consider.     

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).     

The accessibility analysis of multiple public facilities in the central urban areas based on multi-transportation modes: A study in Chengdu

With the rapid development of social economy in Chengdu, it is of great significance to analyze the accessibility of a public facilities resources, and services in respective regions for the spatial planning of public facilities and the optimization of urban construction in Chengdu. Most existing studies only consider the accessibility of a single public facility under a single transportation mode studying facilities at the same level. In this research, with the consideration of all these factors mentioned above, an improved 3-step floating catchment area (3SFCA) model of multi-transportation modes is introduced to analyze the accessibility and spatial distribution of multiple types of public facilities under multiple traffic modes. The new model classifies the grade of public facilities and calculates the service accessibility of public facilities under the driving, public transport, cycling, and walking modes in the five dimensions of education, medical care, commerce, transportation, and leisure in the central urban areas of Chengdu. Through the comparative analysis of multiple models, we found that the improved 3SFCA can more accurately evaluate the accessibility of all dimensions of public facilities in central urban areas of Chengdu. The accessibility of compound public facilities in the central city is excellent. With the gradual expansion to the outward areas, the diversity and accessibility of compound public facilities gradually decreases. Furthermore, the accessibility distribution of public facilities in the study area is not balanced. The accessibility between the central urban areas and the marginal areas differs greatly and the spatial differentiation is substantial.     

灌溉区与雨养区作物长势差异比较分析——以美国内布拉斯加为例

以美国内布拉斯加为例,按照耕地灌溉比例0%—30%,30%—60%,60%—100%将农业区分为雨养农业区、混合农业区与灌溉农业区,同时筛选丰水年(2008年)、平水年(2005年)、枯水年(2012年),比较相同年份雨养农业区、混合农业区与灌溉农业区的作物长势的峰值特征差异,以及相同农业区在丰水年、平水年、枯水年的长势过程线的相似性,并定量分析作物长势随灌溉百分比的变化规律与趋势。研究表明:(1)相同年份,灌溉农业区作物长势好于混合农业区,混合农业区的作物长势好于雨养农业区,耕地灌溉比例越高,作物长势越好;(2)不同年份的灌溉农业区作物长势差异最小,混合农业区次之,雨养农业区长势差异最大,即耕地灌溉比例越高,作物长势越稳定;(3)枯水年雨养农业区的作物长势过程线与降水过程线同增同减,受灌溉与降水的双重影响,灌溉农业区的作物长势过程线的峰值滞后于降水峰值;丰水年,作物水分胁迫减弱,灌溉农业区、混合农业区与雨养农业区作物长势过程线与降水过程线变化趋势基本一致;(4)作物长势增幅与灌溉百分比之间呈现显著的分段二次函数变化关系,当灌溉百分比增幅小于60%时,作物长势增长幅度逐步加快,当灌溉百分比大于60%时,作物长势增速逐步放缓,在枯水年时,长势随灌溉百分比增加而增长的幅度高于丰水年与枯水年。鉴于不同农业区作物长势差异,作物长势的定量监测需要进一步区分灌溉与雨养农业。     

Methods and applications of a geomorphological GIS: a case study in the Ordos region of China

An information system designed for geomorphology has been established employing GIS tools, statistics, fuzzy mathematics, digital image processing and finite element analysis. In addition, we have reorganized and proposed a series of quantitative geomorphic analysis modules (quantitative analysis, information compound, division and assessment, dynamic simulation and prediction), and developed new technology in computer-aided geomorphic mapping.Then, by the methods mentioned above, we have made a quantitative analysis in a case study and worked out a map series which consists of about 100 maps on morphotectonics in the Ordos region in China.This work contributes to the development of geomorphology, the theory and applications of GIS, and research on regional morphotectonics, which shows that a GIS-based approach to geomorphic modelling has distinct advantages over traditional methods.     

山区地形效应对微波辐射特征与土壤水分反演的影响—以青藏高原地区为例

采用星载微波辐射计AMSR-E的低频C波段(6.925GHz),改进了山区微波辐射传输方程,以中国青藏高原地区为例,研究山区可能产生的多种地形效应对微波辐射特征以及土壤水分反演的影响。结果表明,地形效应使得垂直极化亮温最多衰减达到16K,水平极化的亮温最大增强了18K,土壤水分在地形的影响下将被高估超过最大允许误差4%。最后,利用地形效应模拟模型计算的山区地表有效发射率,为山区土壤水分的反演提出了可行的地形校正方法。     

基于RapidEye影像的农村居民地遥感监测——以江西省泰和县为例

农村居民地遥感信息获取对于监测农村居民地时空变化、服务"三农"和国土资源管理具有重要意义。以Rapid Eye卫星影像数据为数据源,江西省泰和县为研究区,利用最大似然法进行居民地等土地覆盖类别的分类提取与精度评价及分析。结果表明:分类总体精度达到84.33%,其中农村居民地的制图精度和用户精度分别为76.01%和82.28%,与第二次全国土地调查中的居民地数据对比,其一致性达到71.0%。结合实地验证,对本分类精度的误差原因进行分析。本研究表明利用5 m分辨率的Rapid Eye影像进行县级农村居民地监测是可行的,可为后续同类研究提供技术参考。     

基于缓冲区分析的城市化与地表水质关系研究——以烟台沿海区县为例

将城市化过程中的人为土地定义为建设用地,利用2004年TM影像得到烟台沿海区县建设用地数据。用缓冲区分析提取乡镇水平上建设用地比率和人口密度数据,缓冲半径从100m到2000m,利用ArcView空间分析功能对建设用地比率、人口密度、河网等级和水质进行叠置分析,并结合逐步回归分析方法揭示烟台沿海区县28个水质监测站各水质参量的空间变化情况。结果显示,通过建设用地比率和人口密度共同作用可以解释85%以上的水质参量模型,反映城市化的建设用地比率比人口密度能更好地解释水质参量的空间变化,大多数水质参量的最佳模型出现在2000m的缓冲半径上,表明建设用地比率和人口密度是影响研究区域水质的首要因素。同时,运用缓冲区分析等GIS空间分析方法为区域城市化进程与水环境质量的研究提供了新的途径。     

A GIS numerical framework to study the process basis of scaling statistics in river networks

A new geographic information system (GIS) numerical framework (NF), called CUENCAS, for flows in river networks is presented. The networks are extracted from digital elevation models (DEMs). The program automatically partitions a basin into hillslopes and channel links that are required to correspond to these features in an actual terrain. To investigate the appropriate DEM resolution for this correspondence, we take a high-resolution DEM at 10-m pixel size, and create DEMs at eight different resolutions in increments of 10 m by averaging. The extracted networks from 10-30 m remain about the same, even though there is a tenfold reduction in the number of pixels. By contrast, the extracted networks show increasing distortions of the original network from 40-90 m DEMs. We show the presence of statistical self-similarity (scaling) in the probability distributions of drainage areas in a Horton-Strahler framework using CUENCAS. The NF for flows takes advantage of the hillslope-link decomposition of an actual terrain and specifies mass and momentum balance equations and physical parameterizations at this scale. These equations are numerically solved. An application of NF is given to test different physical assumptions that produce statistical self-similarity in spatial peak flow statistics in a Horton-Strahler framework.     

Spatial analysis of public health facilities in Riyadh Governorate,Saudi Arabia: a GIS-based study to assess geographic variations of service provision and accessibility

Much attention has been given to address public health policy in Saudi Arabia, particularly for the management, quality, and coverage issues. However, assessment of spatial patterns, distribution, and provision of public health services has been neglected. This paper analyzes the availability and accessibility of public health facilities across Riyadh Governorate, Saudi Arabia at the subnational level. Spatial and attribute data of public health facilities potentially have been analyzed using GIS to produce accurate measure of accessibility. The spatial pattern of service distribution was examined using average nearest neighbor. Distances from demand points (populations) to providers (facilities) were calculated for each district using near analysis. In addition, the ratios of public health facility to population were calculated to identify underserved and overserved areas. The findings clearly indicate that the spatial pattern of the distribution of public health facilities was significantly clustered (p value < 0.001) with Z-score of ?10.9. Several districts within the central parts of the governorate were identified as having a higher density of facilities, while most of districts that are located in the marginal parts exhibit very low density of health facilities. Overall, there was a considerable variation in the average distance from district centroids to health facilities. Substantially, less than half of the population (45.4%) living in 61 districts has access to public health facility within less than 1-km distance. In contrast, the greatest increase in distances was observed for 6% of population living in 38 districts. People that live in such districts need to travel long distances for public health care. The output of this analysis can assist policy-makers and authorities of Riyadh Governorate in planning public health delivery.     

Groundwater potential modelling using remote sensing and GIS: a case study of the Al Dhaid area,United Arab Emirates

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM and LANDSAT images of spatial resolution 30?m were used to construct groundwater potential zones (GPZ) map by integrating geological fractures, drainage network, slope and relief, and convergence index maps of the study area. Weight and score of each map were developed according to their level of contribution toward groundwater accumulation and spatial distribution of groundwater wells. The area that has very high potential for groundwater is located at the foot of Oman Mountains and Al Dhaid Depression covering an area of about 59.33?km², which is 4.40% of the study area. Further hydrological map and data on hydraulic properties of shallow aquifer, as recorded from observation wells in the regions, have been used to validate the produced GPZ map. The validation result showed sufficient agreement between the produced GPZ map.     

Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: A case study in the St. Lucia wetlands,South Africa

Wetlands have been determined as one of the most valuable ecosystems on Earth and are currently being lost at alarming rates. Large-scale monitoring of wetlands is of high importance, but also challenging. The Sentinel-1 and -2 satellite missions for the first time provide radar and optical data at high spatial and temporal detail, and with this a unique opportunity for more accurate wetland mapping from space arises. Recent studies already used Sentinel-1 and -2 data to map specific wetland types or characteristics, but for comprehensive wetland characterisations the potential of the data has not been researched yet. The aim of our research was to study the use of the high-resolution and temporally dense Sentinel-1 and -2 data for wetland mapping in multiple levels of characterisation. The use of the data was assessed by applying Random Forests for multiple classification levels including general wetland delineation, wetland vegetation types and surface water dynamics. The results for the St. Lucia wetlands in South Africa showed that combining Sentinel-1 and -2 led to significantly higher classification accuracies than for using the systems separately. Accuracies were relatively poor for classifications in high-vegetated wetlands, as subcanopy flooding could not be detected with Sentinel-1’s C-band sensors operating in VV/VH mode. When excluding high-vegetated areas, overall accuracies were reached of 88.5% for general wetland delineation, 90.7% for mapping wetland vegetation types and 87.1% for mapping surface water dynamics. Sentinel-2 was particularly of value for general wetland delineation, while Sentinel-1 showed more value for mapping wetland vegetation types. Overlaid maps of all classification levels obtained overall accuracies of 69.1% and 76.4% for classifying ten and seven wetland classes respectively.     

Development of an image based integrated method for determining and mapping aerosol optical thickness (AOT) over urban areas using the darkest pixel atmospheric correction method,RT equation and GIS: A case study of the Limassol area in Cyprus

This paper presents the development of an image-based integrated method for determining and mapping aerosol optical thickness (AOT). Using the radiative transfer (RT) equation, a methodology was developed to create a Geographical Information System (GIS) model that can visually display the AOT distribution over urban areas. In this paper, the model was applied to eleven Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) satellite images of Limassol, Cyprus during 2010 and 2011 to determine the AOT levels in Limassol Cyprus during satellite overpass. The study is innovative and unique in that the RT equation, satellite images, the darkest pixel (DP) method of atmospheric correction and GIS were integrated to derive AOT from satellite images and display the AOT distribution over an urban area without the input of any meteorological or atmospheric parameters. The accuracy of the algorithm was verified through statistical analysis by the strong agreement between the AOT values derived using the algorithm and the in situ AOT values from the ground-based sensors.