A framework for a microscale flood damage assessment and visualization for a building using BIM–GIS integration

Flood damage assessment (FDA) is a key component of risk-based method for flood management. In the current FDA approaches, generally the uniqueness of the building is disregarded in the analysis. Therefore, they are unfit for detailed applications in which case-by-case analysis of building damage is an essential requirement. This limitation is compounded by the use of incomplete and often low-quality data inputs about the building and the assumptions and approximations made regarding the geometry and materials of its components. Such shortcomings may result in incomplete and uncertain outcomes. Considering the benefits and increasing use of three-dimensional (3D) urban modeling and Building Information Model in various urban management processes, in this paper, an integrated framework for utilization of detailed 3D building models for the assessment and 3D visualization of flood damage to building according to its distinct behavior against flood is presented. A proof-of-concept demonstration of the framework in a case study underlined the feasibility of implementation of the framework, which can potentially benefit a variety of users and be used as a complementary approach to the current FDA methods for improving the resilience of the community toward floods and their adverse impacts.     

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.     

Stratification approach for forest cover type and landuse mapping using IRS-1A LISS-II data — A case study

Indian Remote Sensing Satellite-1A (IRS-1A) LISS-II data of 24th Nov., 1988 was analysed digitally to differentiate three density classes viz. dense/closed forest, open forest and degraded forest within each vegetation type in the district, Jalpaiguri, West Bengal. Stratification approach was used to classify separately forest cover into pure sal forests, mixed forests, riverine forests along with man-made sal/teak plantations. In this approach the forested and non-forested areas were classified separately through supervised classification techniques using maximum likelihood algorithm using VAX 11/780 based VIPS-32 Image Processing software. Later the two classified outputs were composited to provide entire area of the district. The forest cover of the district was 1420.89 sq. km, (22.82 percent). Other broad landuse/landcover dominant in the district include agricultural areas.(45.20 percent) and tea gardens (10.49 percent). The accuracy of the classified output was estimated to be 90 percent for forested areas and 85 percent in case of other landuse/landcover classes.     

Image matching as a data source for forest inventory – Comparison of Semi-Global Matching and Next-Generation Automatic Terrain Extraction algorithms in a typical managed boreal forest environment

Image matching is emerging as a compelling alternative to airborne laser scanning (ALS) as a data source for forest inventory and management. There is currently an open discussion in the forest inventory community about whether, and to what extent, the new method can be applied to practical inventory campaigns. This paper aims to contribute to this discussion by comparing two different image matching algorithms (Semi-Global Matching [SGM] and Next-Generation Automatic Terrain Extraction [NGATE]) and ALS in a typical managed boreal forest environment in southern Finland. Spectral features from unrectified aerial images were included in the modeling and the potential of image matching in areas without a high resolution digital terrain model (DTM) was also explored. Plot level predictions for total volume, stem number, basal area, height of basal area median tree and diameter of basal area median tree were modeled using an area-based approach. Plot level dominant tree species were predicted using a random forest algorithm, also using an area-based approach. The statistical difference between the error rates from different datasets was evaluated using a bootstrap method.Results showed that ALS outperformed image matching with every forest attribute, even when a high resolution DTM was used for height normalization and spectral information from images was included. Dominant tree species classification with image matching achieved accuracy levels similar to ALS regardless of the resolution of the DTM when spectral metrics were used. Neither of the image matching algorithms consistently outperformed the other, but there were noticeably different error rates depending on the parameter configuration, spectral band, resolution of DTM, or response variable. This study showed that image matching provides reasonable point cloud data for forest inventory purposes, especially when a high resolution DTM is available and information from the understory is redundant.     

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.     

Landuse/land cover mapping and change detection using satellite data — a case study of dehlon block, district Ludhiana, Punjab

The land use information collected for Dehlon block of Ludhiana district, Punjab from the analysis of the IRS-1B LISS-II data for the year 1993 and IRS PAN data for the year 1997 and SOI topographical maps for 1964 revealed a large change in the area of different land use categories during the period from 1964 to 1997. The agricultural land covering an area of about 94.14 per cent in 1964 reduced to 90.26 per cent in 1997. while the area under rural settlements increased from 312 ha in 1964 to 1162 ha in 1997. An extra area of about 169 ha under waste land was added during the period under study making total waste land area to about 400 ha in 1997. However, the block lacks the forest cover of the required limit. Considerable change in living environment was observed in the block. Number of persons per unit settlement area (ha) being 213.3 in 1964 reduced to 97.1 in 1991; it indicate that the living standard of the people of the block has improved with the changed cropping pattern and increased agricultural production during the period from 1964 to 1991.     

A local spatial decision support system for developing countries based on MCA,fuzzy sets and OWA – case study of a municipality in Cuba

This paper presents a Spatial Decision Support System for local governments of developing countries. It allows municipality government, enterprises, scientific community and civil society to address decision problems using GIS. The framework is supported by four modules of information technologies: Environmental Decision Support Database, Data Manipulation, Decision Support, and Mapping. A case study is presented covering the implementation of this framework in one municipality of Cuba. An example of land suitability planning for coconut crops is used to evaluate the system performance and usability. Results show local municipalities are able to use this framework to solve local decision problems using state of the art decision making even with low infrastructure development.     

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.     

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.