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.     

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.     

Temporal data mining using genetic algorithm and neural network —A case study of air pollutant forecasts

This paper integrates genetic algorithm and neural network techniques to build new temporal predicting analysis tools for geographic information system (GIS). These new GIS tools can be readily applied in a practical and appropriate manner in spatial and temporal research to patch the gaps in GIS data mining and knowledge discovery functions. The specific achievement here is the integration of related artificial intelligent technologies into GIS software to establish a conceptual spatial and temporal analysis framework. And, by using this framework to develop an artificial intelligent spatial and temporal information analyst (ASIA) system which then is fully utilized in the existing GIS package. This study of air pollutants forecasting provides a geographical practical case to prove the rationalization and justness of the conceptual temporal analysis framework.     

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 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.     

Monitoring of salt affected soils — A case study using aerial photographs,salyut‐7 space photographs,and landsat TM data

Soils of part of Ukai‐kakarapar Command area, Gujarat (India) have been mapped at 1:25, 000 scale using aerial photographs of December 1977. It was observed that about 36.3% of the area was affected by soil salinity/alkalinity. The test area has been remapped using Salyut‐7 space photographs taken during Indo‐Soviet joint flight in April, 1984. The area affected by soil salinity/alkalinity was found to be substantially higher (80.3%). The earlier mapping using aerial photographs was done when the soil surface was compartively moist (December 1977) as compared to date of Salyut‐7 photography (April 1984), when the soil surface was likely to be devoid of moisture and the salts moved to the surface. To have easy comparision with the map prepared by using aerial photographs, Landsat TM data of December, 1985 was used in which 45.7% of the total area was mapped as salt affected. The extent of area delineated using Landsat TM was higher than that of 1977 but much lesser than the area delineated using Salyut‐7 (MKF‐6M) photographs. This indicated that the increase in the extent of salt affected area in the map prepared using the MKF‐6M photographs might be partly due to actual increase in the salinity/alkalinity and partly due to the seasonal affects. Among the various bands of MKF‐6M, band ‐4 was found to be the best for delineating the salt affected soils. The boundaries were sharper in the FCC and band No.4 of MKF‐6M than in the aerial photographs.     

Hydrogeomorphological mapping in assessing ground water by using remote sensing data — A case study in lehra gaga block, sangrur district, Punjab

The paper presents the results of hydrogeomorphological mapping using IRS-IB LISS II data and evaluation of ground water prospects of each hydrogeomorphological unit in the Lehra Gaga block of Sangrur district, Punjab. The major geomorphic units identified in the area are, alluvial plain, sand dunes, palaeo channels and the Ghagar flood plain. The study area being part of alluvial plain has good to excellent ground water prospects. Field observations showed that ground water occurs under both confined and unconfined conditions with water table at shallow depth. The area on either side of the Ghagar river and along the major canals (about 46% of the total geographical area in the block) have good quality of ground water and is suitable for irrigation, whereas the water quality is marginal (sodic) in 52 per cent area of the block..     

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.     

Feasibility of estimating heavy metal contaminations in floodplain soils using laboratory-based hyperspectral data—A case study along Le’an River,China

It is necessary to estimate heavy metal concentrations within soils for understanding heavy metal contaminations and for keeping the sustainable developments of ecosystems. This study, with the floodplain along Le’an River and its two branches in Jiangxi Province of China as a case study, aimed to explore the feasibility of estimating concentrations of heavy metal lead (Pb), copper (Cu) and zinc (Zn) within soils using laboratory-based hyperspectral data. Thirty soil samples were collected, and their hyperspectral data, soil organic matters and Pb, Cu and Zn concentrations were measured in the laboratory. The potential relations among hyperspectral data, soil organic matter and Pb, Cu and Zn concentrations were explored and further used to estimate Pb, Cu and Zn concentrations from hyperspectral data with soil organic matter as a bridge. The results showed that the ratio of the first-order derivatives of spectral absorbance at wavelengths 624 and 564 nm could explain 52% of the variation of soil organic matter; the soil organic matter could explain 59%, 51% and 50% of the variation of Pb, Cu and Zn concentrations with estimated standard errors of 1.41, 48.27 and 45.15 mg·kg?; and the absolute estimation errors were 8%–56%, 12%–118% and 2%–22%, and 50%, 67% and 100% of them were less than 25% for Pb, Cu and Zn concentration estimations. We concluded that the laboratory-based hyperspectral data hold potentials in estimating concentrations of heavy metal Pb, Cu and Zn in soils. More sampling points or other potential linear and non-linear regression methods should be used for improving the stabilities and accuracies of the estimation models.     

Groundwater assessment through hydrogeomorphological and geophysical survey — A case study in Godavari sub-watershed, Giridih, Bihar

Groundwater assessment has been carried out through hydrogeomorphological and geophysical survey. Remotely sensed data has been used to delineate the hydrogeomorphological units. Each hydrogeomorphological unit has been further sub-divided based on the tone, texture and pattern. These units its have been categorised based on depth of weathering, local geomorphological association, recharge characteristics etc. The top layer resistivity of hydrogeomorphological units has been analysed by electrical resistivity method. Correlation between different sub-units of same hydrogeomorphic unit and top soil resistivity has been attempted. The correlation and integration of resistivity map with geomorphological map were carried out through. ‘GRAM’ Geographic information System, This has helped in better understanding the surface resistivity pattern. These sub divisions have different geohydrological and recharge characteristics. Resistivity zonation map prepared on the basis of limited field data anfd through interpolation/extrapolation can be modified/corrected with the help of information derived from remotely sensed data to obtain more realistic picture.     

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.     

Assessing gaps in irrigated agricultural productivity through satellite earth observations—A case study of the Fergana Valley,Central Asia

Improving crop area and/or crop yields in agricultural regions is one of the foremost scientific challenges for the next decades. This is especially true in irrigated areas because sustainable intensification of irrigated crop production is virtually the sole means to enhance food supply and contribute to meeting food demands of a growing population. Yet, irrigated crop production worldwide is suffering from soil degradation and salinity, reduced soil fertility, and water scarcity rendering the performance of irrigation schemes often below potential. On the other hand, the scope for improving irrigated agricultural productivity remains obscure also due to the lack of spatial data on agricultural production (e.g. crop acreage and yield). To fill this gap, satellite earth observations and a replicable methodology were used to estimate crop yields at the field level for the period 2010/2014 in the Fergana Valley, Central Asia, to understand the response of agricultural productivity to factors related to the irrigation and drainage infrastructure and environment. The results showed that cropping pattern, i.e. the presence or absence of multi-annual crop rotations, and spatial diversity of crops had the most persistent effects on crop yields across observation years suggesting the need for introducing sustainable cropping systems. On the other hand, areas with a lower crop diversity or abundance of crop rotation tended to have lower crop yields, with differences of partly more than one t/ha yield. It is argued that factors related to the infrastructure, for example, the distance of farms to the next settlement or the density of roads, had a persistent effect on crop yield dynamics over time. The improvement potential of cotton and wheat yields were estimated at 5%, compared to crop yields of farms in the direct vicinity of settlements or roads. In this study it is highlighted how remotely sensed estimates of crop production in combination with geospatial technologies provide a unique perspective that, when combined with field surveys, can support planners to identify management priorities for improving regional production and/or reducing environmental impacts.     

A terrain evaluation using remote sensing and gis—case study of neyyar wild life sanctuary, Kerala

A multi-thematic analysis based on different physical factors has been adopted to generate integrated maps on erosion proneness as well as on critical slope under a GIS platform for terrain evaluation. This spatial data on erosion proneness has demonstrated that 17.62 km² area, out of the 140 km² in the catchment zone, needs careful attention for ecorestoration. In the critical slope map four land stability classes have been demarcated. The area represented as Unstable and Moderately-stable is found to be important for slope stability problems. Temporal change in the drainage network over a period of four decades as well as the extent of loss in the perennial status of tributaries have been recorded to evaluate the landform changes. A catchment treatment plan has also been suggested.     

CART and IDC – based classification of irrigated agricultural fields using multi-source satellite data

To understand water productivity of crops cultivated in the Eastern Province of Saudi Arabia, this study was conducted to generate a reliable crop type map using a multi-temporal satellite data (ASTER, Landsat-8 and MODIS) and crop phenology. Classification And Regression Tree (CART) and ISO-DATA Cluster (IDC) classification techniques were utilized for the identification of crops. The Ideal Crop Spectral Curves were generated and utilized for the formulation of CART decision rules. For IDC, the stacked images of the phenology-integrated Normalized Difference Vegetation Index were utilized for the classification. The overall accuracy of the classified maps of CART was 76, 77 and 81% for ASTER, MODIS and Landsat-8, respectively. For IDC, the accuracy was determined at 67, 63 and 60% for ASTER, MODIS and Landsat-8, respectively. The developed decision rules can be efficiently used for mapping of crop types for the same agro-climatic region of the study area.     

Analysis of seasonal variation in the Niger River interior delta using satellite data∗

The Niger River is one of the most important sources of water supply for human consumption and agriculture in Western Africa. Two Landsat‐5 Multispectral Scanner (MSS) images, corresponding to the dry and wet seasons, over a selected area of the Niger River interior delta were classified to produce a land cover/land use map that reflects the geo‐hydrological units of this area. To classify the satellite data, training statistics were generated using a clustering algorithm with parameter values that maximize the separability among spectral classes. Both dry and wet season images are required to obtain an accurate classification for evaluation of hydrological parameters. The spatial resolution of the MSS proved to be adequate for this kind of work, since all the major cover types and geographic features were correctly recognized.     

Climate change mitigation in Canada’s forest sector: a spatially explicit case study for two regions

Background

We determine the potential of forests and the forest sector to mitigate greenhouse gas (GHG) emissions by changes in management practices and wood use for two regions within Canada’s managed forest from 2018 to 2050. Our modeling frameworks include the Carbon Budget Model of the Canadian Forest Sector, a framework for harvested wood products that estimates emissions based on product half-life decay times, and an account of marginal emission substitution benefits from the changes in use of wood products and bioenergy. Using a spatially explicit forest inventory with 16 ha pixels, we examine mitigation scenarios relating to forest management and wood use: increased harvesting efficiency; residue management for bioenergy; reduced harvest; reduced slashburning, and more longer-lived wood products. The primary reason for the spatially explicit approach at this coarse resolution was to estimate transportation distances associated with delivering harvest residues for heat and/or electricity production for local communities.

Results

Results demonstrated large differences among alternative scenarios, and from alternative assumptions about substitution benefits for fossil fuel-based energy and products which changed scenario rankings. Combining forest management activities with a wood-use scenario that generated more longer-lived products had the highest mitigation potential.

Conclusions

The use of harvest residues to meet local energy demands in place of burning fossil fuels was found to be an effective scenario to reduce GHG emissions, along with scenarios that increased the utilization level for harvest, and increased the longevity of wood products. Substitution benefits from avoiding fossil fuels or emissions-intensive products were dependent on local circumstances for energy demand and fuel mix, and the assumed wood use for products. As projected future demand for biomass use in national GHG mitigation strategies could exceed sustainable biomass supply, analyses such as this can help identify biomass sources that achieve the greatest mitigation benefits.

     

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.     

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.     

Calibration of ASTER and ETM + imagery using empirical line method—A case study of north-east of Hajjah, Yemen

This study is aimed at using the Empirical Line Method (ELM) to eliminate atmospheric effects with respect to visible and near infrared bands of advanced spaceborne thermal emission and reflection radiometer (ASTER) and enhanced thematic mapper plus (ETM+) data. Two targets (Amran limestone as light target and quartz-biotite-sericite-graphite schists as dark target), which were widely exposed and easy to identify in the imagery were selected. The accuracy of the atmospheric correction method was evaluated from three targets (vegetation cover, Amran limestone and Akbra shale) of the surface reflectance. Analytical spectral devices (ASD) FieldSpec3 was used to measure the spectra of target samples. ETM+ data were less influenced by the atmospheric effect when compared to ASTER data. Normalized differences vegetation indices (NDVI) displayed good results with reflectance data when compared with digital number (DN) data because it is highly sensitive to ground truth reflectance (GTR). Most of the differences observed before and after calibration of satellite images (ASTER and ETM+) were absorbed in the SWIR region.      

Multi-scale assessment of invasive plant species diversity using Pléiades 1A,RapidEye and Landsat-8 data

We used a full remote sensing-based approach to assess plant species diversity in large and inaccessible areas affected by Lantana camara L., a common invasive species within the deciduous forests of Western Himalayan region of India, using spectral heterogeneity information extracted from optical data. The spread of L. camara was precisely mapped by Pléiades 1A data, followed by comparing Pléiades 1A, RapidEye and Landsat-8 OLI – assessed plant species diversities in invaded areas. The single plant species analysis was improved by Pléiades 1A-based diversity analysis, and higher species diversity values were observed for mixed vegetation cover. Furthermore, lower Coefficient of Variation and Renyi diversity values were observed where L. camara was the only species, while higher variations were observed in areas with a mixed spectral reflectance. This study was concluded to add a crucial baseline to the previous studies on remote sensing-based solutions for rapid estimation of biodiversity attributes.