Remote Sensing, Image Processing & Analysis: Remote sensing of the environment pertains to the capture of data about earth surface features and phenomena without being in contact with them, normally with sensors on aircraft and satellites. Image and other remotely sensed data can be corrected, enhanced, and interpreted with the aid of computer image processing systems. Human interpreters and computer-assisted image analysis routines are capable of extracting information from remotely sensed data, often in the forms of maps.
Image-based Change Detection: Remotely sensed images captured over time by sensors on aircraft or satellites enable information about changes in earth surface features and phenomena to be extracted. This is facilitated by image processing routines that co-align these multi-temporal images and then extract change information, or enable updating of maps and GIS layers.
Unmanned Aerial Vehicle: Also known as unmanned aircraft systems (UAS) or drones, unmanned aerial vehicles (UAV) enable flexible, low altitude imaging of earth surface features. In particular, UAV images captured with large amounts of overlap between adjacent images enable 3-D models of both ground and vertical feature (e.g., structures and plants) surfaces.
Development of a Remote Sensing Network for Time-Sensitive Detection of Fine Scale Damage to Transportation Infrastructure
The focus of this study is on assessing damage to transportation infrastructure following a major hazard event. The premise is that some transportation infrastructure (such as bridges which impact transportation options), are so critical to saving human lives and supporting emergency response actions that near real-time information on the damage status of such infrastructure is essential and yet may be difficult to ascertain in a timely manner with conventional, ground observations and sensor networks. We hypothesize that the solution to this post-hazard information access challenge is to design flexible, ready-to-deploy, time-sensitive remote sensing systems (TSRSS) based on a network of airborne platforms and digital cameras. The key is to determine which transportation infrastructure types and damage are truly “critical” and then to design and pre-plan low-cost TSRSS that meets maximum time to delivery and minimum information reliability requirements of decision makers. Team members are interacting with and surveying transportation infrastructure managers to determine these timeliness and reliability requirements, as well as estimating and attempting to optimize time to delivery and reliability characteristics of the TSRSS through tool development, simulation and empirical testing of the components of end-to-end TSRSS.
The Urban Transition in Ghana and Its Relation to Land Cover and Land Use Change Through Analysis of Multi-scale and Multi-temporal Satellite Image Data
The objectives of this proposed project were: (1) to identify, map, and quantify land cover and land use change (LCLUC) within an extensive study area in Ghana over the past 10 or 11 years (2000 through 2010), (2) to understand the regional impacts of LCLUC associated with rural-to-urban migration in driving these changes, and (3) to assess LCULC and its effect on demographic and quality of life factors for four major urban centers during this time period. Our team, consists of remote sensing and social scientists from San Diego State University (lead), The George Washington University, University of California Santa Barbara, and University of Ghana Legon.
We mapped and quantified LCLUC at two spatial scales: (1) inter-regional scale for the Greater Accra, Central, and Ashanti regions of southern and central Ghana, and (2) intra-urban scale for Accra, Kumasi, Cape Coast and Obuasi, the four major cities within the study area. Inter-regional identification of LCLUC will be based on moderate spatial resolution, multi-temporal image data from Landsat ETM+ and ERS-2 synthetic aperture radar (SAR) satellite systems. High spatial resolution image data from QuickBird and IKONOS commercial satellite systems were utilized primarily for intra-urban mapping of LCLUC. The c. 2000 through 2010 time frame coincides well with a period of available demographic and health data for Ghana. We utilized quantitative spatial analysis techniques to examine relationships between LCLUC and magnitudes and changes of demographic, socioeconomic, and health variables. Quantitative analysis tools included generalized linear and multi-level regression models, multinomial logit models, regression tree analysis, and agent based models. We examined relationships between satellite-derived LCLUC and its impacts on demographic and socio-economic over four regions (states) in Ghana. In addition, we assessed the effects of LCLUC on quality of life indicators such as child mortality, slum indices, and food security, within four of the major cities of Ghana.
Optimization of Remote Sensing Networks for Time-sensitive Detection of Fine Scale Damage to Critical Infrastructure Leave geography site
The focus of this study is on assessing damage to infrastructure following a major hazard event using airborne remote sensing. The premise is that some infrastructure, particular in cities, is so critical to saving human lives and supporting emergency response actions that near real-time information on the damage status of such infrastructure is essential and yet may be difficult to ascertain with conventional, ground observations and sensor networks. We hypothesize that the solution to this post-hazard information access challenge is to design flexible, ready-to-deploy, time-sensitive remote sensing systems (TSRSS) based on a network of airborne platforms and digital cameras. Our team is collaborating on research pertaining to important elements of end-to-end TSRSS that supports post-disaster assessment of damage to critical infrastructure and allocation of emergency response resources.
Mapping and Monitoring Vegetation on San Clemente Island
Research and development projects pertaining to mapping vegetation community types and monitoring shrub cover for the entirety of San Clemente Island (SCI) are being supported by the US Navy, who operates and mostly utilizes the island for military training purposes. The goal of a now concluded vegetation mapping project was to create an alliance-level vegetation classification scheme, and develop and implement semi-automatic image-based procedures for vegetation community type mapping. The classification scheme and map were designed to meet the needs of wildlife biologists and botanists who study and manage natural resources on the island. We integrated very high spatial resolution image and lidar point cloud data with an object-based image analysis (OBIA) approach to generate the alliance-level vegetation map. A current project emphasizes time-sequential mapping of shrub cover based on multi-temporal airborne multispectral imagery and elevation point clouds derived from lidar and aerial imagery processed by Structure from Motion techniques. The goal of the current project is to determine how reliably shrub cover can be monitored over time, as shrubs are key plant growth forms types when habitats of rare and endangered bird species on the island.
Theory and techniques in watershed analysis. Use of GIS and statistical programming for analyses of geomorphology, hydrology, and water quality data.
Acquiring and interpreting remotely sensed data of environment. Electromagnetic radiation processes, aerial and satellite imaging systems and imagery. Geographic analysis of selected human, terrestrial, and marine processes and resources.
Practical exercises, introductory processing, visual interpretation and mapping of remotely sensed imagery.
Digital image processing. Thermal infrared and microwave imaging systems and image interpretation principles. Geographic analysis of selected human, terrestrial, oceanographic, and atmospheric processes and resources.
Digital image processing, visual interpretation, mapping of thermal infrared, and microwave imagery.
Sensor systems, image interpretation and geographic applications in thermal infrared and microwave remote sensing. Principles of digital image processing.
Processing and analysis of remotely sensed data. Laboratory training in sensor systems and digital image-processing methods including thermal infrared and microwave data analysis.
Spatial analytic techniques from image processing, remote sensing, geographic information systems, cartography or quantitative methods. May be repeated with new content. See Class Schedule for specific content.