Recipient Organization
UNIVERSITY OF FLORIDA
G022 MCCARTY HALL
GAINESVILLE,FL 32611
Performing Department
Gulf Coast Research and Education Center
Non Technical Summary
With the rapid technological advances in remote sensing imaging technologies, an increasing need for methods to integrate such technologies and maximize their use in natural resource management and precision agriculture applications is heightened. Multi-sensor technologies can provide different sources of data to account for the tradeoffs of each data type and fill in the needs of specific type of applications. Spectral analysis of different land cover types has been the subject of a considerable amount of research in the last few decades. For example, hyperspectral imagery has been used in applications that involve studying plant stress, estimating water quality parameters of both open oceans and turbid inland waters, agricultural crop classification and yield estimation, and characterization of ecosystems.Variations in the amount of emitted, absorbed, and reflected electromagnetic energy arise from spectral characteristics of each plant species, plant condition and spatial distribution, sensor specifications, and atmospheric condition. Each of these factors contributes to a plant's spectra to form a unique pattern, or signature. On the other hand, the typical low spatial resolution of hyperspectral imagery and the need for image calibration techniques affect data quality and suitability for targeting specific applications that require precise object characterization. Some of these issues can be resolved by integrating different data types such as high spatial resolution imagery captured by lower spectral resolution sensors or Lidar point clouds. This type of data can provide textural and structural information due to their high spatial resolution and their potential for three-dimensional analysis.The need to establish the relationship between plant status and spectral measurements is of interest to many state and national agencies as well as private industry. Being able to utilize spectral data for the early identification of invasive plant species or optimizing the use of fertilization in agricultural lands could save the economy billions of dollars, not to mention the positive impact on human health and the environment. Fortunately, recent advances in spatial data acquisition technologies including multispectral and hyperspectral imagery and their acquisition platforms (e.g. introducing Unmanned Air Systems (UAS)) made the acquisition and use of such technologies more feasible and promising. However, in order to facilitate the use of such technologies to answer specific research questions, the research community needs to develop the methodologies required for data acquisition system integration, experimental design, and analysis algorithms.Improving remote sensing data acquisition tools, methodologies, and analysis algorithms for land cover characterization continues to be the main focus of this research effort. In this context, the proposed develop and use integrated multi-sensor spatial data acquisition techniques that include hyperspectral, multispectral, positioning and attitude sensors, etc. to analyze vegetation response. This research will help close the gap in understanding and quantifying spatial changes of, for example, vegetation, soils, and water quality. Furthermore, successful development could lead to integrated multi-sensor platform that provide a suite of data and enables applied and basic research opportunities in the precision agriculture and natural resource management fields.
Animal Health Component
85%
Research Effort Categories
Basic
0%
Applied
85%
Developmental
15%
Goals / Objectives
This proposal aims at integrating and developing spatial data sources, methodologies and algorithms to study the relationship between remote sensing data and object signatures. In this context, the proposed develop and use integrated multi-sensor spatial data acquisition techniques that include hyperspectral, multispectral, positioning and attitude sensors, etc. to analyze landcover response and classification. This research will help close the gap in understanding and quantifying spatial changes of, for example, vegetation, soils, and water quality. Furthermore, successful development could lead to integrated multi-sensor platform that provide a suite of data and enables applied and basic research opportunities in the precision agriculture and natural resource management fields. In achieving these goals, the following objectives are identified:I. Develop, calibrate and test spectral and navigation measuring systemsII. Design experiments and develop methods to collect and analyze spectral and field measurementsIII. Use spatial data acquisition sensors and techniques (including multispectral and hyperspectral imagery) to study land cover characteristics
Project Methods
The following is project methods organized by project objectives: I. Develop, calibrate and test spectral and navigation measuring systemsSystems that combine hyperspectral, multispectral and navigation sensors are subject to continuous improvement. Tighter integration of the system components for accurate georeferencing and adding more sensors is proposed. This process involves acquiring the sensors, developing new integration techniques, implementing system calibration and testing steps.System integration involves integrating different remote sensing and navigation data acquisition sensors. For example, across platform integration involves utilizing ground-based system in combination with sensors onboard UAS. Such integration overcome many of the limitations associated with each system leading to wide scale implementation and potential commercialization.II. Design experiments and develop methods to collect and analyze spectral and field measurementsThis objective aims at designing field experiments to collect spatial data using onboard sensors. The data should be augmented by establishing ground control locations and spectral calibration techniques. Field spectrometer measurements and multi-sensor imaging systems will be collected and analyzed. The design includes identifying management procedures, frequency of observations and accompanied reference lab measurements. It is envisioned that the results of ground based remote sensing leads to efficient and wider scale implementation using aerial systems (e.g. using UAS).III. Use spatial data acquisition sensors and techniques (including multispectral and hyperspectral imagery) to study land cover characteristicsDifferent types of remote sensing imagery will be analyzed. Image correction algorithms will be implemented. Hyperspectral image analysis techniques such as dimensionality reduction using Minimum Noise Fraction Transform algorithm (Chen, et al., 2003; Pande-chettri & Abd-Elrahman, 2013), statistical modeling, object based analysis will be implemented. The results will be analyzed and compared to ground truth data for modeling and assessment. It is envisioned that this objective will lead not only to better methods for land cover (e.g. vegetation, soils, water quality, etc.) characterization, but also to the introduction of more efficient techniques to achieve these results.Techniques and results achieved by applying remote sensing techniques at certain scale (e.g. ground based) can be a great asset for a different scale implementation. For example, determining specific bands capable of revealing specific water quality parameters can be the basis of larger scale water sampling by mounting a light weight sensor capable of sensing only the few bands relevant to specific water quality parameters on an UAS. This process involves developing appropriate sensors, calibration and analysis techniques in addition to extensive testing and assessment.