Geographic information technology usage in developing countries – A case study in Mozambique

ABSTRACT

Geographic information technologies (GIT) have matured, have become pervasive throughout many types of organizations over recent decades and gained a wide range of attention in diverse sectors of society. The goal of this paper is to identify the main drivers and uses of GIT in organizations and to give an overview of GIT uses in developing countries with a focus on a case study developed for Mozambique by analyzing the results of a survey of 123 Mozambican organizations that are current GIT users. The results show that the main drivers behind are compatibility, complexity, competitive pressure, donor pressure, government policy, intention to adopt, innovation, relative advantage, security, and technology competence. Organizations of the public and private sectors use GIT for customer/resource/risk management, decision support, education, research, monitoring, prediction, simulation, suitability analysis, sustainability, use and access.     

NDVI dynamics as reflected in climatic variables: spatial and temporal trends – a case study of Hungary

Understanding climate change and revealing its future paths on a local level is a great challenge for the future. Beside the expanding sets of available climatic data, satellite images provide a valuable source of information. In our study we aimed to reveal whether satellite data are an appropriate way to identify global trends, given their shorter available time range. We used the CARPATCLIM (CC) database (1961–2010) and the MODIS NDVI images (2000–2016) and evaluated the time period covered by both (2000–2010). We performed a regression analysis between the NDVI and CC variables, and a time series analysis for the 1961–2008 and 2000–2008 periods at all data points. The results justified the belief that maximum temperature (TMAX), potential evapotranspiration and aridity all have a strong correlation with the NDVI; furthermore, the short period trend of TMAX can be described with a functional connection with its long period trend. Consequently, TMAX is an appropriate tool as an explanatory variable for NDVI spatial and temporal variance. Spatial pattern analysis revealed that with regression coefficients, macro-regions reflected topography (plains, hills and mountains), while in the case of time series regression slopes, it justified a decreasing trend from western areas (Transdanubia) to eastern ones (The Great Hungarian Plain). This is an important consideration for future agricultural and land use planning; i.e. that western areas have to allow for greater effects of climate change.     

A quantitative method for storm surge vulnerability assessment – a case study of Weihai city

ABSTRACT

In traditional vulnerability assessments, a synthetic index method is usually used to select all types of social and economic indexes so that more aspects can be covered; however, the requisite social and economic data are not always available or are not highly relevant to the studied geographical space, which makes it difficult to conduct quantitative calculations. In this paper, a spatial value density assessment method was developed to improve the hazard of place model. First, a three-dimensional (3D) model of a coastal city was obtained using oblique airborne photogrammetry and image-based 3D reconstruction and then, architecture footprints were employed to extract the geometric information of each individual building. Additionally, a vulnerability assessment system was established to quantitatively account for the aggregate economic value of a selected set of urban surface features. Using geographic information system (GIS) techniques, the aggregate value of these urban features within each geographic unit can be accurately calculated to quantify the exposure and vulnerability of coastal cities to storm surge. A vulnerability assessment was conducted using Weihai city as an example. The study shows that vulnerability assessment accuracy was greatly improved by downscaling the assessment granularity from county-level administrative districts to a 1-km grid.     

Channel pattern transformation of the Burhi‐Gandak river,Bihar, India: A study based on multidata sets

The Burhi‐Gandak river is one of the major tributaries of the Ganga River in the middle Ganga basin. The river is i known to have an oscillatory character and has changed its course in Recent times. In the present study, a variety of remote sensing data combined with surface and subsurface data have been used to delineate the fluvial palaeofeatures of this river. The palaeofeatures of the Burhi‐Gandak indicate that the river has shifted from N to S over a distance of 30 km. Further, its is observed that Burhi‐Gandak river had a larger channel width and higher discharge on earlier times and was very active. On detailed examination, a past link between the now northerly flowing Baghmati and Burhi‐Gandak rivers is detected. Based on the above features, three distinct stages of migration of the river have been identified and a summary of fluvial palaeohistory is presented.     

Integrated approach of using ASTER-derived emissivity and pixel temperature for delineating different granitoids – a case study in parts of Dharwar Craton,India

We have delineated different granitoids based on variation in emissivity and relative surface temperature recorded in thermal bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor of EO-1 satellite. In this regard, we have used emissivity normalization algorithm to derive broadband emissivity from thermal bands of ASTER sensor to delineate different lithounits of the granitoid family. We have compared emissivity and radiance image composites in terms of delineation of different granitoids. We have also used false colour composite (FCC) image derived using two emissivity bands and temperature (derived using emissivity normalisation method) bands to delineate different granitoids. We could differentiate different granitoids in the three-dimensional (3D) data space of ASTER-derived emissivity bands (second and third bands) and temperature bands. Based on the analysis of 3D scatter plot, we also proposed a ternary diagram of emissivity and temperature, which can be used to delineate different granitoids.     

Analyzing the impact of thermal stress on vegetation health and agricultural drought – a case study from Gujarat,India

Although poor precipitation due to delayed arrival and/or early retreat of the southwest monsoon is considered the chief architect of drought in India, heat waves may also play a crucial role in the intensification of droughts. In the Indian subcontinent, occurrence of heat waves during the pre-monsoon and high air-temperature in the subsequent monsoon season imparts thermal stress on vegetation causing degradation of vegetation health (VH). In the present study, various vegetation indices and land-use/land-cover data derived from multi-sensor satellite have been used to assess VH and agricultural drought in Gujarat during 1981–2010. This Geographical Information Systems-based study has also used heat wave and temperature data to analyze the adverse effects of high temperature on VH. The time series of Vegetation Condition Index and Temperature Condition Index (TCI) has shown that the combined influence of moisture-stress and thermal stress determines the occurrence and severity of drought, which is reflected in the Vegetation Health Index (VHI). A strong correlation among aboveground air-temperature, the TCI and the VHI indicates definite influence of thermal stress on VH. Further, a systematic variation and strong resemblance between temperature, crop yield, TCI and VHI has established the impact of thermal stress on agricultural productivity.     

Changing water resources study using GIS and spatial model — A case study of Bhatar Block,district Burdwan,West Bengal,India

Markov chains have been used to model spatial changes in a variety of spheres. Changes in social situations, economic standards, natural resource availability, and even weather conditions have been explored and predicted using Markov Random Function (MRF) and Markov Random Chains (MRC). In this section, we try to use data of Mahata village of Bhatar Block, extracted from GIS based maps/images in a MRC to obtain present transition probabilities and predict future changes. The village is facing the problem of decreasing the water table and at the same time the number of surface water bodies is also decreasing. This is a serious situation for the development of the agricultural activities in general and at the same time it poses threat to the human habitation of the village in the long run. The average depth of the ground water table from ground level increased from 8 meter to 15 meter within the last 10 years. The threat is coming from the changes in land use and land cover, especially due to substantial extension of agricultural activities which is expanding at a very fast rate. Increasing population is also demanding more lands for settlement and industrial uses. The surface water bodies i.e. the ponds etc. are used for such intensive irrigation purposes. As a result the surface water bodies depletes before the onset of summer. The cultivators use those dried up ponds or surface water bodies for agricultural purposes also. There is thus a serious trend to convert the surface water bodies into the agricultural land. It is estimated using MRC, that in next 25 years, the number of surface water bodies will deplete by 50% in the agriculturally active Bhatar PS at the current rates of depletion. Shifting to less water needy crops, prevention of LULC conversion, and water harvesting would provide some solace to the situation.     

Enhanced change detection index for disaster response,recovery assessment and monitoring of buildings and critical facilities—A case study for Muzzaffarabad,Pakistan

The availability of Very High Resolution (VHR) optical sensors and a growing image archive that is frequently updated, allows the use of change detection in post-disaster recovery and monitoring for robust and rapid results. The proposed semi-automated GIS object-based method uses readily available pre-disaster GIS data and adds existing knowledge into the processing to enhance change detection. It also allows targeting specific types of changes pertaining to similar man-made objects such as buildings and critical facilities. The change detection method is based on pre/post normalized index, gradient of intensity, texture and edge similarity filters within the object and a set of training data. More emphasis is put on the building edges to capture the structural damage in quantifying change after disaster. Once the change is quantified, based on training data, the method can be used automatically to detect change in order to observe recovery over time in potentially large areas. Analysis over time can also contribute to obtaining a full picture of the recovery and development after disaster, thereby giving managers a better understanding of productive management and recovery practices. The recovery and monitoring can be analyzed using the index in zones extending from to epicentre of disaster or administrative boundaries over time.     

An unsupervised two-stage clustering approach for forest structure classification based on X-band InSAR data — A case study in complex temperate forest stands

Forest structure at stand level plays a key role for sustainable forest management, since the biodiversity, productivity, growth and stability of the forest can be positively influenced by managing its structural diversity. In contrast to field-based measurements, remote sensing techniques offer a cost-efficient opportunity to collect area-wide information about forest stand structure with high spatial and temporal resolution. Especially Interferometric Synthetic Aperture Radar (InSAR), which facilitates worldwide acquisition of 3d information independent from weather conditions and illumination, is convenient to capture forest stand structure. This study purposes an unsupervised two-stage clustering approach for forest structure classification based on height information derived from interferometric X-band SAR data which was performed in complex temperate forest stands of Traunstein forest (South Germany). In particular, a four dimensional input data set composed of first-order height statistics was non-linearly projected on a two-dimensional Self-Organizing Map, spatially ordered according to similarity (based on the Euclidean distance) in the first stage and classified using the k-means algorithm in the second stage. The study demonstrated that X-band InSAR data exhibits considerable capabilities for forest structure classification. Moreover, the unsupervised classification approach achieved meaningful and reasonable results by means of comparison to aerial imagery and LiDAR data.     

Spatial mashup technology and real time data integration in geo-web application using open source GIS – a case study for disaster management

The Web 2.0 technologies and standards enable web as a platform by allowing the user participation in web application. In the realization of Web 2.0, new knowledge and services are created by combining information and services from different sources which are known as ‘mashups'. The present study focused on spatial mashup solution for disaster management using open source GIS, mobile applications, web services in web 2.0, Geo-RDBMS and XML which are in the central of intelligent geo web services. The geo-web application is developed to generate the actionable GIS products at user end during disaster event by consuming various data and information services from web and central server system and also real time ground observation data collected through a mobile device. The technological solution developed in this study is successfully demonstrated for disaster management in the Assam State of India during the floods in 2010.     

Selection of suitable sites for water harvesting structures in Soankhad watershed,Punjab using remote sensing and geographical information system (RS&;GIS) approach— A case study

A case study has been conducted to identify suitable sites for water harvesting structures in Soankhad watershed, Punjab using information technologies such as Remote Sensing and Geographical Information System (RS-GIS). The IRS-1C, P6 satellite imagery of the Soankhad watershed was used. The various Thematic maps such as land use map, hydrological soil group map, slope map and DEM map were prepared for selecting suitable site for construction of water harvesting structures. The suitable sites were not found for nala bunding and farm ponds due to steep slope, less soil thickness and high runoff velocity. Fourteen check dams and six percolation tanks were proposed for the construction as per Integrated Mission for Sustainable Development (IMSD) guidelines. The water balance study of the Soankhad watershed was also computed with monthly mean temperature and rainfall data using TM model. The average runoff for the wet season (July–September) 1996 was computed to be about 1543.82 mm and the total runoff volume from the Soankhad watershed was estimated to be about 143.52 Mm³.     

Estimating the starting time and identifying the type of urbanization based on dense time series of landsat-derived Vegetation-Impervious-Soil (V-I-S) maps – A case study of North Taiwan from 1990 to 2015

Land cover and land use change (LCLUC) is a global phenomenon, and LCLUC in urbanizing regions has substantial impacts on humans and their environments. In this paper, a semi-automatic approach to identifying the type and starting time of urbanization was developed and tested based on dense time series of Vegetation-Impervious-Soil (V-I-S) maps derived from Landsat surface reflectance imagery. The accuracy of modeled V-I-S fractions and the estimated time of initial change in impervious cover were assessed. North Taiwan, one of the regions of the island of Taiwan that experienced the greatest urban LCLUC, was chosen as a test area, and the study period is 1990 to 2015, a period of substantial urbanization. In total, 295 dates of Landsat imagery were used to create 295 V-I-S fraction maps that were used to construct fractional cover time series for each pixel. Root Mean Square Error (RMSE)s for the modeled Vegetation, Impervious, and Soil were 25 %, 22 %, 24 % respectively. The time of Urban Expansion is estimated by logistic regression applied to Impervious cover time series, while the time of change for Urban Renewal is determined by the period of brief Soil exposure. The identified location and estimated time for newly urbanized lands were generally accurate, with 80% of Urban Expansion estimated within ±2.4 years. However, the accuracy of identified Urban Renewal was relatively low. Our approach to identifying Urban Expansion with dense time series of Landsat imagery is shown to be reliable, while Urban Renewal identification is not.     

Mapping irrigated agriculture in complex landscapes using SPOT6 imagery and object-based image analysis – A case study in the Central Rift Valley,Ethiopia –

Irrigation infrastructure development for smallholder farmers in developing countries increasingly gains attention in the light of domestic food security and poverty alleviation. However, these complex landscapes with small cultivated plots pose a challenge with regard to mapping and monitoring irrigated agriculture. This study presents an object-based approach to map irrigated agriculture in an area in the Central Rift Valley in Ethiopia using SPOT6 imagery. The study is a proof-of-concept that the use of shape, texture, neighbour and location information next to spectral information is beneficial for the classification of irrigated agriculture. The underlying assumption is that the application of irrigation has a positive effect on crop growth throughout the field, following the field's borders, which is detectable in an object-based approach. The type of agricultural system was also mapped, distinguishing smallholder farming and modern large-scale agriculture. Irrigated agriculture was mapped with an overall accuracy of 94% and a kappa coefficient of 0.85. Producer's and user's accuracies were on average 90.6% and 84.2% respectively. The distinction between smallholder farming and large-scale agriculture was identified with an overall accuracy of 95% and a kappa coefficient of 0.88. The classifications were performed at the field level, since the segmentation was able to adequately delineate individual fields. The additional use of object features proved essential for the identification of cropland plots, irrigation period and type of agricultural system. This method is independent of expert knowledge on crop phenology and absolute spectral values. The proposed method is useful for the assessment of spatio-temporal dynamics of irrigated (smallholder) agriculture in complex landscapes and yields a basis for land and water managers on agricultural water use.     

Compactness,connectivity, and walking accessibility on the neighborhood level according to sustainability certifications: improvement or downgrade? A case study of Cairo,Egypt

Urban sustainability certifications (USCs) urge developers to exceed the local norms and regulatory requirements to attain sustainability. USCs are gaining international recognition as planning and policy support tools. This study aims to assess the relevance of four USCs (LEED for Neighborhood Development, BREAAM communities, CASBEE for Urban Development, and Pearl Community Rating System) in contexts outside their country of origin using Cairo Governorate as a case study. The study focuses on compactness, street connectivity, and walking accessibility as prominent components for sustainable mobility and urban form at the neighborhood level. The study examines 202 neighborhoods in Cairo in terms of compactness and then focuses on eight urban areas in different locations and with different characteristics to assess their connectivity and walking accessibility. Different analyses were performed with ArcGIS software using data about neighborhoods’ population, residential units, street networks, established buildings, buildings’ outlines and heights, and detailed uses. Results show that USCs’ indicators and thresholds are generally lenient and insensitive to the context of formal areas in Cairo Governorate, which are significantly more compact, mixed (horizontally and vertically), and connected. This study adds to the currently limited empirical evidence refuting the use of some USCs as global tools and questioning their utilization in different contexts either as they are or even through an adaptation process.     

Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almería (Spain)

This paper shows the first comparison between data from Sentinel-2 (S2) Multi Spectral Instrument (MSI) and Landsat 8 (L8) Operational Land Imager (OLI) headed up to greenhouse detection. Two closely related in time scenes, one for each sensor, were classified by using Object Based Image Analysis and Random Forest (RF). The RF input consisted of several object-based features computed from spectral bands and including mean values, spectral indices and textural features. S2 and L8 data comparisons were also extended using a common segmentation dataset extracted form VHR World-View 2 (WV2) imagery to test differences only due to their specific spectral contribution. The best band combinations to perform segmentation were found through a modified version of the Euclidian Distance 2 index. Four different RF classifications schemes were considered achieving 89.1%, 91.3%, 90.9% and 93.4% as the best overall accuracies respectively, evaluated over the whole study area.     

Techniques of classification for landuse/landcover with special reference to forest type mapping in Jaldapara Wildlife Sanctuary, Jalpaiguri District, West Bengal—a case study

The accuracy of three classification techniques namely Maximum likelihood, contextual and neural network for landuse/landcover with special emphasis on forest type mapping was evaluated in Jaldapara Wildlife Sanctuary area using IRS-1B LISS II data of Dec. 1994. The area was segregated into ten categories by using all the three classification techniques taking same set of training areas. The classification accuracy was evaluated from the error matrix of same set of training and validating pixels. The analysis showed that the neural net work achieved maximum accuracy of 95 percent, maximum likelihood algorithm with 91.06 percent and contextual classifier with 87.42 percent. It is concluded that the neural network classifier works better in heterogeneous and contextual in homogenous forestlands whereas the maximum likelihood is the best in both the conditions.