A. Projects Related to Web and Mobile
1. Developing an Emergency and Disaster Response Mobile Application
Natural disaster can occur anytime, anywhere. Every person should be prepared in case he/she is in trouble. The best way to be prepared is by the use of the mobile application because in present context every person has a mobile phone with him. Also among various mobile phones, Smartphones are popular among people nowadays. These Smartphones are GPS integrated. So location of a person can be known very easily in smart phones. As the number of users of smart phone is high, the number of the application user will also be high due to which there is high chance that the response to the requester in disaster can be delivered in real time. It also can be used to alert high numbers of users about the disaster. There are various ways to respond by using smart phone applications. Most of the applications that had been developed are used in alerting people or sending the notification to specific people only. But in our project, we are going to develop such an application that alert or send notification/SMS to the people that are near to the disaster by using the technique called geofencing. We are going to develop the application for the android platform. For developing a complete system incorporating geofencing and notification/alert system we need to develop a n android application, central database system and a server setup. This helps to provide quick response to the people in the need of help. Applications like Disaster Alert, FEMA have been developed and used in android platform for disaster alert and response system. But these applications have not employed the concept of geofencing in the field of disaster response system. However, the concept of geofencing has been widely used in the European and US business centers to promote their business and products by sending the alert notifications about their services to people entering their geofence. If we can use the concept of geofencing in the android application for disaster response system, then it would be a new as well as a very effective tool for a requestor in a post disaster situation to send help request to people in the near regions and also alerting other people about the disaster and suggesting them to apply precautions when they enter into the geofence of disaster zones.
Team member: Suresh Manandhar, Shashwat Kafle ,Dipesh Suwal
2. Disaster Aid
Location Based Services (LBS) is a recent concept that denotes applications integrating geographic location with the general notion of services. Extraction of location information of users carrying GPS embedded cell phones and connected to the internet through GPRS or Wi-Fi can be useful in providing users with numerous services that require their locations. Here, we would like to discuss on the development of such a location based service that assists in disaster risk reduction, preparedness and management. The exploitation of location information of the people (especially victims), a reliable map of the city and use of cellular services enabling internet connection (gprs, wifi,etc.) can be integrated to produce a system enabling victims to broadcast their position and call for help as well as the emergency service providers to reach that spot through the shortest route in minimum time. We also aim in enabling the trend of collaborative information sharing for disaster management in the system. It means that instead of relying on a single source and uploading data from a single source we aim to enable the mass to upload data themselves confirm the uploaded data or dismiss it. We have named this system “Disaster Aid”.
Team Member: Arun Bhandari .Nishanta Khanal
3. Fast Relief Ambulance
Fast Relief Ambulance is a routing application that is intended to provide and efficient ambulance service to the people. The purpose of the system is to provide the nearest ambulance service to the people. When someone calls for ambulance service to the hospital, the hospital operator will check the available ambulance services near the area. Then, the ambulance operator will send the message to the ambulance driver to reach the concerned locality. Then the ambulance driver will start to move towards the concerned locality. Thus, the operator can track the ambulances along the way.
The use of the system can thus help us to manage the emergency services well. Since, users will also be provided with list of the numbers of hospitals, they can easily call the hospital nearest to their locality. And the hospital operator can also locate the nearest ambulance nearby and send notification for the immediate relief.
Team Member: Aawesh Man Shrestha, Anil Rai , Ayam Pokhrel, Kripesh Shiwakoti
B. Projects Related to GIS and Remote Sensing
1. Impact Assessment of Mikania Micrantha in Chitwan National Park and
Determinationof Potential Invasion Sites
Invasive Alien Plant Species (IAPS) are species, native to one area or region, that have been introduced into an area outside their normal distribution, either by accident or on purpose, and which have colonized or invaded their new home, threatening biological diversity, ecosystems and habitats, and human wellbeing. Mikania micrantha is one of such IAPS which is colonizing at an alarming rate in Chitwan National Park and its buffer zone community forests. This project is concerned with assessment of impacts caused by the notorious weed M. micrantha, commonly called as Lahare Banmara, in buffer zone community forest of Chitwan National Park in Meghauli and Bachhauli VDCs in which factors such as distribution, trends and impacts on landcover pertaining to the weed will be considered. Sampling sites will be selected using unbiased sampling strategy and primary as well as secondary data obtained will be used for statistical and Geographic Information System (GIS) analysis. Primary data for the project will be presence data and digital topographic and meteorological data will serve as secondary data. Finally, Maxent model based on maximum entropy method will be used for determination of potential invasion sites along with validation and the map will be published on the web using GeoServer.
Team Member: Ruby Adhikari , Tina Baidar , Rita Ranjit ,Anu Bhalu Shrestha
2. Change Detection and Predictive Vegetation Mapping Of Shivapuri Nagarjun
National Park Using Remote Sensing And GIS”
Assessing and monitoring the state of the earth surface is a key requirement for global change research (NRC, 1999; LAMBIN et al., 2001; JUNG et al., 2006; XIE, 2008). Classifying and mapping vegetation is an important technical task for managing natural resources as vegetation provides a base for all living beings and plays an essential role in affecting global climate change (XIAO et al., 2004; XIE, 2008).The forest area has been decreasing all over the world. So there is need to detect loss of vegetation cover and to analyse the assessment of the impact of human activities and biodiversity. The change detection is the measure of different data and thematic change that provides tangible insights into underlying process involved land cover and land use change. Normalized Difference Vegetation Index (NDVI) is a technique from Remote Sensing and GIS technology to detect spatiotemporal change of vegetation cover on the earth surface. Understanding of change detection is useful in policy making process, regulatory actions and subsequent land use activities.
Predictive vegetation mapping can be defined as predicting the geographic distribution of the vegetation composition across a landscape from mapped environmental variables (Janet Franklin, 1992).The predictive vegetation mapping has been used for the biodiversity conservation planning, ecological restoration planning and assessing the impacts of environmental change in the vegetation. Currie (1991) and Pierce et al., (2005) found solar radiation and potential evapotranspiration strongly related with plant diversity. Therefore environmental data e.g. slope, aspect, elevation, solar radiation, soil moisture, etc. has great potential to explain the variation in species pattern and diversity (Zhang et al., 2007; Vogiatzakis et al., 2006). Predictive vegetation mapping always start with the development of model, followed by the application of that model to the geographic data base and the realization of model. Artificial Neural Network (ANN) model is a feed-forward model with a back propagation learning algorithm in which the model improves itself by making corrections to its internal structure based on the amount of error at the output.
Change detection and prediction of vegetation plays important role in wildlife management, fire management, Successional studies and trend assessment and providing framework for similar research activities.
Team member: Maheshwor Karki, David Nhemaphuki, Bibek Karki
3. Climate Wizard Nepal
Climate Wizard Nepal is a web based visualization of maps depicting climate change for different time periods such that the all level of audiences can understand the present scenario of climate change.
Climate Wizard Nepal will also allow users to drill down in both time and space and see the climate change trend in the past and the probability that might occur in the future. This will provide a direction and information about risks and uncertainties to city planners, conservationists, agriculturist and almost everyone to identify the critical areas of climate change before undertaking any significant chore. Climate Wizard Nepal allows non-technical audience to benefit from the extension built to prepare thematic maps automatic using their data in hand, view them and even download them.
Team member: Archana K.C, Jyoti Dhakal, Megha Shrestha, Sushmita Timilsina
4. Spatial-temporal Urban Change Extraction and Modelling
Urbanization is the phenomenon of formation and growth of cities and urban area due to the movement of people from rural to urban. Haphazard and unmanaged urbanization adversely affect sustainable development. Population and urban growth require advanced methods for city planners and managers to support sustainable development in urban areas. Although urban growth is an inescapable process, efforts can be made to protect natural resources, reduce natural hazards such as flooding and improve the livelihoods of urban residents through proper way of urban planning and management (Soffianian, 2010). However, the available information on the city growth and evolution is insufficient and outdated which makes the decision making process less efficient and less transparent.
Hence quantification of urban growth processes and patterns is crucial to monitor urbanization and its impact on environment over time. The procedure for quantification begins with the remote sensing image classification followed by the spatial-temporal analysis of the spatial metrics and SLEUTH modeling for the urban growth forecasting. Landsat images of four different time series covering the entire study area will be used to produce the land cover map by using the maximum likelihood classification algorithm in ENVI/ERDAS imagine and post classification will be done to create growth or change map which are then overlaid on ArcGIS using analysis tools and then the spatial extent and urban growths will be analyzed. Further the classified image is used as an input for FRAGSTATS to quantify the changing patterns and processes. Finally the SLEUTH modeling is performed to predict the urban growth pattern.
Team member: Janak Parajuli, Dhurba Poudel, Kamal Singh Thakuri
5. Generating High Resolution DSM using UAV technology
Generating High Resolution DSM often demands highly accurate datasets. Among the range of terrestrial and aerial methods available to produce such a dataset, this project tests the utility of images acquired by a fixed wing, low cost Unmanned Aerial Vehicle (UAV).The data processing of UAV images have been carried out using the algorithms ranging from Classical Photogrammetry to modern Computer Vision (CV).
Nowadays, the use of UAV has increased to offer fast, easy and cost effective way to capture high resolution images for a comparatively smaller area .In this paper, we present a state-of-the-art photogrammetry and image processing techniques provided by different software and their algorithms. The key element in a UAV photogrammetric data processing of the images which have been obtained with large variation in its geometry is the accurate georeferencing.
Twenty seven high resolution (2.4 cm average spatial resolution) UAV-acquired images of a sand mine at Tielt-Winge, Belgium have been used for this project. All the images have been acquired by a Sony Nex-5R digital camera mounted on a Trimble UX5 Imaging Rover, a fixed wing UAV. Although three software: LPS, AgiSoft PhotoScan and PIX4D were used for image processing, the identified algorithms and limitations in processing are valid for most other commercial photogrammetric software available on the market.
Highly accurate ground control points have been used to quantify the accuracy of output DSM in terms of RMSE and it shows centimeter level accuracy have been obtained in the final result. Moreover the comparison of output DSM has also been performed through calculation of difference of DSM, and visual inspection of individual DSM. All these observations have shown that SIFT algorithm and Dense Stereo matching provide better result for UAV data compared to traditional tie point generation using Forstner interest operator and traditional digital image matching algorithms.
Team members: Uttam Pudasaini, Biplov Bhandari, Niroj Pant, Upendra Oli