University of New Mexico UNM Digital Repository Geography ETDs Electronic Theses and Dissertations Summer 7-30-2016 Modeling the Timeliness of Airborne Remote Sensing Data Andrew Loerch Follow this and additional works at:https://digitalrepository.unm.edu/geog_etds Part of theEnvironmental Sciences Commons Recommended Citation Loerch, Andrew. "Modeling the Timeliness of Airborne Remote Sensing Data." (2016).https://digitalrepository.unm.edu/ geog_etds/31 This Thesis is brought to you for free and open access by the Electronic Theses and Dissertations at UNM Digital Repository. It has been accepted for inclusion in Geography ETDs by an authorized administrator of UNM Digital Repository. For more information, please [email protected]. i Andrew C. Loerch Candidate Geography and Environmental Studies Department This thesis is approved, and it is acceptable in quality and form for publication: Approved by the Thesis Committee: Dr. Christopher Lippitt , Chairperson Dr. Caitlin Lippitt Dr. Danqinq Xiao ii MODELING THE TIMELINESS OF AIRBORNE REMOTE SENSING DATA by ANDREW C. LOERCH B.S., GEOGRAPHY, UNIVERSITY OF NEW MEXICO, 2014 B.A., GERMAN, UNIVERSITY OF NEW MEXICO, 2014 THESIS Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science Geography The University of New Mexico Albuquerque, New Mexico December 2016 iii MODELING THE TIMELINESS OF AIRBORNE REMOTE SENSING DATA by ANDREW C. LOERCH B.S., GEOGRAPHY, UNIVERSITY OF NEW MEXICO, 2014 B.A., GERMAN, UNIVERSITY OF NEW MEXICO, 2014 M.S., GEOGRAPHY, UNIVERSITY OF NEW MEXICO, 2016 ABSTRACT The model, “Remote Sensing Communication Model” (RSCM), which permits the estimation of the timeliness of remote sensing systems (RSS) is tested (Lippitt, Stow, & Clarke, 2014). This model conceptualizes RSS as having capacities that determine the timeliness of the systems, where a system is comprised of three segments, each with a capacity that determines the timeliness of that segment: acquisition capacity, transmission capacity, and receiver capacity (i.e., the capacity of a human and/or machine analyst to produce information) (Lippitt et al., 2014). Acquisition and transmission capacity analyses are run to aid in the optimization of a flexible time-sensitive remote sensing system being designed for emergency response in Bernalillo County, NM. Modeled timeliness is validated using empirical tests of airborne acquisitions, the model modified to improve fit, and then used for a variety of manned and unmanned platform and sensor combinations to infer the timeliness of data delivery to emergency managers, based on both currently available and potential airborne assets. In doing so, this research assesses the accuracy of capacity based estimates of timeliness for airborne RSSs and demonstrate a method for the optimization of platform, sensor, and transmission configurations for emergency response. iv Table of Contents 1. INTRODUCTION .............................................................................................................. 1 2. BACKGROUND ................................................................................................................ 3 2.1 The Disaster Management Framework and User Needs ................................................ 3 2.2 Remote Sensing Technologies for Disaster Response .................................................... 5 2.2.1 S-UAS for Hazard Response ....................................................................... 7 2.3 Estimating the Timeliness Capacity of Remote Sensing Systems ................................... 9 2.3.1 Acquisition Capacity .................................................................................. 11 2.3.2 Transmission Capacity .............................................................................. 12 2.3.3 Analyst Capacity ........................................................................................ 13 3. METHODS ..................................................................................................................... 15 3.1 Data ............................................................................................................................... 15 3.1.1 Field Data ................................................................................................... 15 3.1.2 Survey Data ................................................................................................ 16 3.1.3 UAS Data ................................................................................................... 18 3.1.4 Targeted Regions/Sites .............................................................................. 19 3.2 Analysis ......................................................................................................................... 20 3.2.1 Implementation of the Model in Code ....................................................... 20 3.2.2 Model Validation ....................................................................................... 21 3.2.3 Estimating Timeliness for Extant Platform/Sensor Combinations ............ 25 3.2.4 Estimating Timeliness for S-UAS Platform/Sensor Combinations ........... 26 4. RESULTS AND DISCUSSION ........................................................................................... 28 4.1 Model Validation .......................................................................................................... 28 v 4.1.1 Acquisition Capacity .................................................................................. 29 4.1.2 Transmission Capacity ............................................................................... 32 4.2 Aerial Survey Firm Results by Platform/Sensor Combination and Site ......................... 33 4.3 S-UAS Results by Site .................................................................................................... 35 4.4 Limitations of These Results ......................................................................................... 36 5. CONCLUSIONS .............................................................................................................. 38 6. BIBLIOGRAPHY ............................................................................................................. 41 7. TABLES ......................................................................................................................... 46 8. FIGURES ....................................................................................................................... 53 1 1. INTRODUCTION Remote sensing is a critical hazard response technology and the timeliness of its information is critical for its effective use in hazard response (Bruzewicz, 2003; Cutter, 2003). Timeliness is defined by Lippitt as “the time between information request and the use of that information to inform a decision” (C. D. Lippitt, Stow, & Clarke, 2014). While we can predict the timeliness of image acquisitions from static systems with known temporal resolutions, such as satellites, it is far more difficult to predict the timeliness of acquisitions from aircraft and unmanned aerial systems. Predicting the timeliness of remote sensing systems prior to operational deployment is a requirement for time-sensitive remote sensing (Lippitt et al. 2014). When compared to satellites, aircraft acquisitions have additional factors affecting timeliness that makes it challenging to incorporate them into the standard operating procedures of emergency management organizations (C. Lippitt, Stow, & Coulter, 2015). The number of aircraft, their locations relative to areas at high risk for disasters, the types of aircraft, and the imaging sensors they operate, all affect the timeliness of data acquisition and delivery. As the literature review elucidates, accurately estimated timeliness of data delivery from airborne remote sensing systems is not currently incorporated into emergency managers’ standard operating procedures, and this has resulted in limited use of remote sensing in response and recovery efforts (C. D. Lippitt et al., 2014). This research tests a model called “Remote Sensing Communication Model” (RSCM), which permits the estimation of the timeliness of remote sensing systems (RSS) (Lippitt, Stow, & Clarke, 2014), for the estimation of timeliness for airborne RSSs. The 2 RSCM conceptualizes RSSs as having capacities that determine the timeliness of the systems, where a system is conceptualized as having three segments (sensor, channels, and receivers), each with a capacity that determines the timeliness of that segment based on the data volume to be acquired: acquisition capacity, transmission capacity, and receiver capacity (i.e., the capacity of a human and/or machine analyst to produce information) (Lippitt et al., 2014). To validate portions of the RSCM and to aid in the optimization of a flexible time-sensitive remote system being designed for emergency response in Bernalillo County, NM and San Diego County, CA this research performs and validates an acquisition and transmission capacity analysis. Modeled timeliness is validated using empirical tests of actual airborne acquisitions, the model is modified to improve fit, and used with a variety of extant platform and sensor combinations, operated by local aerial survey companies, to infer the timeliness of data delivery to emergency managers for six potential critical infrastructure sites based upon both currently available and potential manned and unmanned airborne assets. This research therefore assesses the accuracy of capacity based estimates of timeliness for airborne RSSs and demonstrates a method for the on demand optimization of platform, sensor, and transmission configuration for emergency response. The questions, “How accurately can the timeliness of airborne data acquisition and delivery be estimated, using the Remote Sensing Communication Model Capacity Analysis?”, “Using RSCM Capacity Analysis, what is the estimated data delivery timeliness of extant aerial survey firms in support of a remote sensing system for hazard response in New Mexico and San Diego County, CA?”, and “How will the introduction of UAS affect data delivery timeliness?” are answered in this thesis. 3 2. BACKGROUND Before, during, and after a hazard event, there is an obvious desire to have and utilize best available information in an effort to prevent damage to property and loss of life. Emergency managers and disaster responders have previously used combinations of on the ground assessments, satellite imagery, and airborne imagery, to collect this information, with varying degrees of success (Cutter, 2003; Ehrlich, Guo, Molch, Ma, & Pesaresi, 2009). Disasters and hazards can be local, regional, and global in their effects on places and people. Often, there is little time to prepare for an impending disaster, and once such an event occurs, there is often a short window of hours-days available in which to rescue survivors and assess damaged critical infrastructure (Cutter, 2003; C. Lippitt et al., 2015). For these reasons, the types of technologies and information necessary for improving disaster prevention and response across spatial and temporal scales continues to be an active research area. This review looks at how emergency managers utilize remotely sensed data, models for estimating the acquisition and delivery timeliness of imagery, and the potential benefits of small unmanned airborne systems (S-UAS) to image acquisition and delivery timeliness. 2.1 The Disaster Management Framework and User Needs Determining the data needs of emergency managers and disaster responders first requires an understanding of the types of groups that provide disaster management, the frameworks in which they operate, and how they use various types of remotely sensed data. 4 Modern disaster management regimes exist at scales that range from neighborhood communities and local governments, up to national and global levels. These regimes also consist of formal and informal institutions (Cutter, 2003). Formal institutions are generally governmental agencies and well-established non-profit organizations, and informal institutions can be individuals, volunteer groups, impromptu aid and donation organizations and funds, the media, etc. Often, disaster management involves coordination between these agencies and networks to share financial, technological, and personnel resources, as well as information. Most formal disaster management institutions, which are the primary focus of this research, recognize and use a structured framework called the “Disaster Management Cycle” (Gitas, Polychronaki, Katagis, & Mallinis, 2008; Laben, 2002). This cycle, which is also referenced in the majority of literature reviewed in this thesis, consists of phases. The pre-disaster and inter-disaster phases are: Reconstruction, Mitigation, and Preparedness. The post-disaster phases are: Rescue, Relief, and Recovery. These six phases are often condensed as Mitigation, preparedness, response, and recovery (Laben, 2002). It is helpful to think of disaster and hazard management in these phases as most remote sensing technologies have varying degrees of usefulness depending on the phase. The current trend shows that satellite and airborne remote sensing systems are being adopted heavily for Reconstruction, Mitigation, Preparedness and Recovery, but not in the Rescue and Relief phases (Cutter, 2003; Laben, 2002). The Reconstruction, Mitigation, Preparedness, and Recovery phases generally span longer time frames, which makes them more likely to be compatible with the slow acquisition, processing,