Source: UNIVERSITY OF FLORIDA submitted to NRP
DEVELOPING A MULTI-PLATFORM MULTI-SENSOR FRAMEWORK FOR MONITORING FOREST PRODUCTIVITY, CHEMISTRY AND WATER STRESS
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
COMPLETE
Funding Source
MCINTIRE-STENNIS
Reporting Frequency
Annual
Accession No.
1011658
Grant No.
(N/A)
Cumulative Award Amt.
(N/A)
Proposal No.
(N/A)
Multistate No.
(N/A)
Project Start Date
Oct 4, 2017
Project End Date
Sep 14, 2022
Grant Year
(N/A)
Program Code
[(N/A)]- (N/A)
Recipient Organization
UNIVERSITY OF FLORIDA
G022 MCCARTY HALL
GAINESVILLE,FL 32611
Performing Department
Forest Resources and Conservation
Non Technical Summary
Forestry in Florida produces a significant income, but is under growing threat from invasive pests and drought and hurricanes. New techniques are needed to successfully monitor these forests. Recent advances in UAS may serve to fulfill this need, but methods to collect and analyze UAS data are lacking, and in particular in the context of integrating this data into ongoing data collection efforts. We propose to develop and apply such a framework using the NEON facilities. Subsequently, we propose to test and refine these methods for use in tropical forests where similar issues exist.
Animal Health Component
20%
Research Effort Categories
Basic
40%
Applied
20%
Developmental
40%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
1027210107025%
1020499107025%
1230611107025%
1220499107010%
1250699107015%
Knowledge Area
102 - Soil, Plant, Water, Nutrient Relationships; 125 - Agroforestry; 122 - Management and Control of Forest and Range Fires; 123 - Management and Sustainability of Forest Resources;

Subject Of Investigation
0611 - Conifer forests of the South; 0499 - Atmosphere, general/other; 0699 - Trees, forests, and forest products, general; 7210 - Remote sensing equipment and technology;

Field Of Science
1070 - Ecology;
Goals / Objectives
(1) To develop and test sensor fusion approaches incorporating airborne and unmanned airborne systems (UAS) and hyperspectral, LiDAR, thermal, and RGB sensors. Specifically focusing on approaches that advance research questions related to forest and ecosystem ecology and the management of forests.(2) To develop a protocol to integrate UAS, airborne, and National Ecological Observatory Network (NEON) tower data into comparable integrated datasets, using the OSBS as a case study, but with consideration of applicability to scaling up across all NEON tower sites.(3) Test fusion approach to monitor tropical forest structure and diversity and rates of disturbance of secondary and primary forests in Costa Rica.
Project Methods
(1) We will employ a new sensor suite and flight platforms built in collaboration with the University of Florida termed the GatorEye Unpiloted Flying Laboratory (GE-UFL), and for which the PI Broadbent is the director. The sensor package includes a 270 spectral band hyperspectral sensor, a dual-return LiDAR sensor capable of 600,000 returns per second, and a high-res RGB camera. This suite is geolocated to < 1 cm using a dual frequency GNSS and post-processed kinematic algorithms relative to a base station, and with orientation obtained from a tactical grade Inertial Movement Unit. The flight platform is hexacopter capable of 15-minute flight times and with live video and telemetry feed. All UAV missions will be conducted in close collaboration with appropriate regulatory authorities in all study regions and be conducted to adhere to flight and mission regulations. Mission pilots have appropriate FAA credentials, including a Remote Pilot Certificate, and conduct rigorous pre- and inflight safety checks.(2) NEON AOP data is freely available and will be provided by NEON on an external hard drive following collection and preprocessing. AOP data was collected for OSBS early October 2016. The PI Broadbent has a workstation (Dell T7910) and software (ENVI + IDL and ENVI LiDAR) necessary to allow for geostatistical integration of GatorEye UAS and AOP data. Specific objectives include comparing the digital surface and elevation models derived from GatorEye UAS and AOP data, and hyperspectral indices of forest productivity and species foliar signatures. Furthermore, Broadbent is working closely with NEON AOP directors to coordinate simultaneous missions for optimal sensor and algorithm comparison and calibration. These are expected to be in Fall 2017.(3) NEON eddy flux data and tower footprint calculations will be conducted following downloading the data from the NEON website. We will use open source eddy flux software, combined with EddyPro SmartFlux 2 software to assess average hourly-daily footprints and CO2 and H2O flux rates. This software package is available freely for download from LiCOR on their website. It is capable of calculating fluxes and estimating footprints as required for this project.(4) We will fully integrate the data derived from steps 2-4 by taking the footprints calculated in step 4, and extracting the forest structure and productivity information provided by the GatorEye. Comparison of footprint structure, composition, and productivity will be made by extracting the mean and standard deviation of the daily footprint from the UAS data. We will make comparisons between the ability to produce meaningful correlations between the UAS and tower data, and the between the AOP and tower data. We will also compare how time lags between UAS data and tower data impact their correlations.(5) We will test the applicability of hardware and sensor fusion algorithms for quantifying and monitoring tropical forest structure, diversity and stress by applying the approaches developed and described in step 1 to a network of plots covering young secondary to old-growth tropical forests located in Costa Rica. We have worked in and developed this plot network for this purpose over the last 2 years, including having conducted detailed field surveys and mapping. In addition to direct science output, results will provide proof of concept for studies of forest ecology and sustainable use globally.

Progress 10/01/20 to 09/30/21

Outputs
Target Audience:The GatorEye program conducted projectsthroughout Florida, with various biological stations and groups, for both data collection and capacity building. It also conducted fieldwork, outreach, and capacity-building efforts in Costa Rica, Trinidad and Tobago, St. Lucia, Dominica, St. Lucia. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?We conducted outreach and demonstrations using GatorEye systems across the Caribbean in 2021, as well as for school children in Costa Rica. How have the results been disseminated to communities of interest?Demonstrations and discussions of technology and applications, typically in outdoor settings. What do you plan to do during the next reporting period to accomplish the goals?Continue pushing the frontier of sensor fusion, in particular, new algorithms for efficient lidar + hyperspectral integration, and also a new long-term priority is developing the ability to extract all textural and pattern information from tiles of high-resolution visual data for species identification. One key goal is developing hardware and software capabilities to conduct very large-area data collection and cross-calibration, which is ongoing currently here in Florida.

Impacts
What was accomplished under these goals? (1) We conducted data collection efforts in 5 + countries, resulting in over 15 GatorEye related papers in 2021-22, and which all serve to advance Major Goal 1. (2) We have developed and tested multiple new GatorEye systems, including the GatorEye Lite, GatorEye v2, and GatorEye XL. We are now working on papers further integrating and comparing data from these systems with aircraft, satellite and tower collected data. (3) We conducted the largest biodiversity transect GatorEye data collection globally across a ridge-to-reef transect in Southern Costa Rica, covering a large variety of tropical forests. We anticipate follow-up data collection efforts and are working on multiple papers from this effort with a variety of collaborators.

Publications

  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Leite, R.V., Silva, C.A., Broadbent, E.N., Do Amaral, C.H., Liesenberg, V., De Almeida, D.R.A., Mohan, M., Godinho, S., Cardil, A., Hamamura, C. and De Faria, B.L., 2022. Large scale multi-layer fuel load characterization in tropical savanna using GEDI spaceborne lidar data. Remote Sensing of Environment, 268, p.112764.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: De Almeida, D.R.A., Broadbent, E.N., Ferreira, M.P., Meli, P., Zambrano, A.M.A., Gorgens, E.B., Resende, A.F., de Almeida, C.T., Do Amaral, C.H., Dalla Corte, A.P. and Silva, C.A., 2021. Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion. Remote Sensing of Environment, 264, p.112582.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Schnitzer, S.A., DeFilippis, D.M., Visser, M., Estrada?Villegas, S., Rivera?Cama�a, R., Bernal, B., Per�z, S., Vald�z, A., Vald�z, S., Aguilar, A. and Dalling, J.W., 2021. Local canopy disturbance as an explanation for long?term increases in liana abundance. Ecology Letters.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: de Almeida, D.R.A., Broadbent, E., Zambrano, A.M.A., Ferreira, M.P. and Brancalion, P.H.S., 2021, July. Fusion of Lidar and Hyperspectral Data from Drones for Ecological Questions: The Gatoreye Atlantic Forest Restoration Case Study. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 714-715). IEEE.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Schroder, W., Murtha, T., Broadbent, E.N. and Almeyda Zambrano, A.M., 2021. A confluence of communities: households and land use at the junction of the Upper Usumacinta and Lacant�n Rivers, Chiapas, Mexico. World Archaeology, pp.1-28.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: da Costa, M.B.T., Silva, C.A., Broadbent, E.N., Leite, R.V., Mohan, M., Liesenberg, V., Stoddart, J., do Amaral, C.H., de Almeida, D.R.A., da Silva, A.L. and Goya, L.R.R.Y., 2021. Beyond trees: Mapping total aboveground biomass density in the Brazilian savanna using high-density UAV-lidar data. Forest Ecology and Management, 491, p.119155.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Johnson, D.J., Magee, L., Pandit, K., Bourdon, J., Broadbent, E.N., Glenn, K., Kaddoura, Y., Machado, S., Nieves, J., Wilkinson, B.E. and Zambrano, A.M.A., 2021. Canopy tree density and species influence tree regeneration patterns and woody species diversity in a longleaf pine forest. Forest Ecology and Management, 490, p.119082.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: da Cunha Neto, E.M., Rex, F.E., Veras, H.F.P., Moura, M.M., Sanquetta, C.R., K�fer, P.S., Sanquetta, M.N., Zambrano, A.M.A., Broadbent, E.N. and Dalla Corte, A.P., 2021. Using high-density UAV-Lidar for deriving tree height of Araucaria Angustifolia in an Urban Atlantic Rain Forest. Urban Forestry & Urban Greening, p.127197.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Merrick, T., Pau, S., Detto, M., Broadbent, E.N., Bohlman, S., Still, C.J. and Almeyda Zambrano, A.M., 2021. Unveiling spatial and temporal heterogeneity of a tropical forest canopy using high-resolution NIRv, FCVI, and NIRvrad from UAS observations. Biogeosciences Discussions, pp.1-20.
  • Type: Other Status: Published Year Published: 2021 Citation: Silva, C.A., Hudak, A., Vierling, L., Valbuena, R., Cardil, A., Mohan, M., de Almeida, D.R.A., Broadbent, E.N., Zambrano, A., Wilkinson, B. and Sharma, A., 2021. Package treetop.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Visser, M.D., Detto, M., Meunier, F., Wu, J., Bongalov, B., Coomes, D., Nunes, M.H., Sanchez-Azofeifa, A., Foster, J., Broadbent, E. and Verbeeck, H., 2021. Why can we detect lianas from space?. bioRxiv.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Mohan, M., Leite, R.V., Broadbent, E.N., Jaafar, W.S.W.M., Srinivasan, S., Bajaj, S., Dalla Corte, A.P., do Amaral, C.H., Gopan, G., Saad, S.N.M. and Kamarulzaman, A.M.M., 2021. Individual tree detection using UAV-lidar and UAV-SfM data: A tutorial for beginners. Open Geosciences, 13(1), pp.1028-1039.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Meunier, F., Visser, M.D., Shiklomanov, A., Dietze, M.C., Guzman, J.A., Sanchez-Azofeifa, A., De Deurwaerder, H.P., Moorty, S.M.K., Schnitzer, S.A., Marvin, D.C. and Longo, M., 2021. Liana optical traits increase tropical forest albedo and reduce ecosystem productivity. bioRxiv.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Shrestha, M., Broadbent, E.N. and Vogel, J.G., 2021. Using GatorEye UAV-Borne LiDAR to Quantify the Spatial and Temporal Effects of a Prescribed Fire on Understory Height and Biomass in a Pine Savanna. Forests, 12(1), p.38.


Progress 10/01/19 to 09/30/20

Outputs
Target Audience:Research using the GatorEye system, coupled withaircraft and satellite sensors,supported by the McIntire-Stennis program was conducted throughout the United States, including New Hampshire, Vermont, Virginia, Washington DC, Alabama, and Florida. Separate but related thematically international work was conducted in Mexico, Costa Rica, Panama, Peru, and Brazil. We worked directly with a wide group of collaborators, including undergraduate, graduate, post-docs, and scientists. We also conducted general outreach with park guards and managers, and the general public. Our research was featured on multiple television stations and received wide coverage nationally and internationally. Currently, we have a Discovery TV channel segment being developed about some of our research in Florida that was supported by this program. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?We have incorporated demonstrations into various data collection periods resulting in training opportunities for the GatorEye system and sensor fusion. We have led workshops nationally and internationally on using less advanced drone systems for general monitoring of environmental degradation, and are working with FAMU university to develop and mini-workshop on both topics. How have the results been disseminated to communities of interest?I have presented as the keynote speaker on the system and our research at fourmajor conferences internationally and nationally during this period. We have published in peer-reviewed journals 5 articles, and have many more in development. What do you plan to do during the next reporting period to accomplish the goals?(1) I plan to continue advancing workflows to expand our sensor fusion capabilities, including detailed 3D tree segmentation algorithms, and integration of the hyperspectral sensors into the lidar to map and monitor species richness and ecological health and integrity. (2) Continue collecting long-term datasets at NEON sites, including adding to our growing 'big-plot' network. (3) Collect data in Costa Rica in the Osa Peninsula, and the Guanacaste Peninsula in support of our goals to enhance GatorEye and data fusion capabilities for measuring key forest parameters in primary and secondary forests.

Impacts
What was accomplished under these goals? (1) We developed and built the generation 3 GatorEye system, which now incorporates dual hyperspectral sensors, radiometric thermal, high-res visual, and significantly increased power LiDAR system. We then developed suite of new workflows that are now being used for publications in various status levels, from already published, to in review, to in press. (2) We are now working with a new ForestGeo plot situated at the OSBS NEON site, and which has resulted in a paper just submitted and under review. We have now collected data at 4 NEON sites located across New England. We have developed several proposal concepts, and just completed a major NSF proposal which has been situated and is under review via the NSF Macrosystems NEON call. (3) We have been collecting data in Panama and Costa Rica, including secondary and primary forest areas, which have resulted in a series of publications with post-docs based in our lab, and with post-docs in other labs, as well as collaborations on this topic with scientists internationally, and well as at national universities including Princeton and others.

Publications

  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Dalla Corte, A.P., Souza, D.V., Rex, F.E., Sanquetta, C.R., Mohan, M., Silva, C.A., Zambrano, A.M.A., Prata, G., de Almeida, D.R.A., Trautenm�ller, J.W. and Klauberg, C., 2020. Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes. Computers and Electronics in Agriculture, 179, p.105815.
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: de Almeida, D.R.A., Almeyda Zambrano, A.M., Broadbent, E.N., Wendt, A.L., Foster, P., Wilkinson, B.E., Salk, C., Papa, D.D.A., Stark, S.C., Valbuena, R. and Gorgens, E.B., 2020. Detecting successional changes in tropical forest structure using GatorEye drone?borne lidar. Biotropica.
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: dOliveira, M.V., Broadbent, E.N., Oliveira, L.C., Almeida, D.R., Papa, D.A., Ferreira, M.E., Zambrano, A.M.A., Silva, C.A., Avino, F.S., Prata, G.A. and Mello, R.A., 2020. Aboveground Biomass Estimation in Amazonian Tropical Forests: a Comparison of Aircraft-and GatorEye UAV-borne LiDAR Data in the Chico Mendes Extractive Reserve in Acre, Brazil. Remote Sensing, 12(11), p.1754.
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Dalla Corte, A.P., Rex, F.E., Almeida, D.R.A.D., Sanquetta, C.R., Silva, C.A., Moura, M.M., Wilkinson, B., Zambrano, A.M.A., Cunha Neto, E.M.D., Veras, H.F. and Moraes, A.D., 2020. Measuring individual tree diameter and height using GatorEye High-Density UAV-Lidar in an integrated crop-livestock-forest system. Remote Sensing, 12(5), p.863.
  • Type: Journal Articles Status: Under Review Year Published: 2020 Citation: Shresth, M., Broadbent, E., Vogel, J. In review. Using GatorEye LiDAR to estimate understory biomass dynamics from fire in a longleaf pine forest.


Progress 10/01/18 to 09/30/19

Outputs
Target Audience:Our target audience was forest management professionals and scientists. We worked closely with people from the National Ecological Observatory Network, the US Forest Service, management and geospatial scientists at Florida protected areas, and also participated in international activities aimed at capacity building and training of institutions in sensor fusion and monitoring in Costa Rica, Peru, and Brazil Changes/Problems:No major changes. We are proceeding as we had anticipated, and actually I see our progress as accomplishing our best-case scenario, so I am pleased and hope to maintain momentum throughout the upcoming year and beyond. What opportunities for training and professional development has the project provided?We have developed and given drone training workshops in Bolivia, Peru, and Brazil, specifically focused on training indigenous groups in drone use for monitoring. We have given workshops related to sensor fusion in Peru and Brazil using the GatorEye system. We now have multiple undergraduates, graduate students, post-docs, and visiting international faculty members working on sensor fusion using our GatorEye system data. How have the results been disseminated to communities of interest?(a) Workshops. (b) Conferences. I was an invited keynote speaker on this topic at 3 international conferences in 2019, and 1 national conference, and have more upcoming. (c) Demos. Including an upcoming demo and discussion on this topic in January 2020 to the Director of the Forest Service, the Undersecretary of the Environment, where I will be discussing drone-borne sensor fusion, as well as Forest 4.0 technology in general. (d) Peer-reviewed publications. In 2019, I have currently published 14 peer-reviewed articles, with 5 of them focused on drone-borne sensor fusion applications and engineering. What do you plan to do during the next reporting period to accomplish the goals?Continue similar activities and approaches as in 2019. Our upcoming focus will be heavily on development, calibration, and validation of a full sensor integration / fusion workflow, including radiometric thermal, visual, hyperspectral, and visual, for various applications.

Impacts
What was accomplished under these goals? (1) The hardware required to meet objective one has been conceptualized and funding was raised for the upgrade. This is occurring currently and the system will be undergoing tests and subsequently software development in the upcoming months. (2) A study is ongoing, funded through the University of Florida IFAS, linking the GatorEye data with NEON AOP (aircraft borne), and tower sensors, which is the proposed goal here. We are now working closely with Phenocam scientists, and maintain close collaboration with NEON program. I currently serve on the NEON AOP scientific advisory committee. (3) We have completed and submitted our first paper on this theme, which is now under review for publication in thepeer-reviewed journal Biotropica.

Publications

  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Almeida, DRA; Broadbent, Eben N; Zambrano, Angelica M Almeyda; Wilkinson, Benjamin E; Ferreira, Manuel Eduardo; Chazdon, R; Meli, P; Gorgens, EB; Silva, Carlos Alberto; Stark, Scott C; Monitoring the structure of forest restoration plantations with a drone-lidar system International Journal of Applied Earth Observation and Geoinformation 79 192-198 2019 Elsevier
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Murtha, Timothy M; Broadbent, Eben N; Golden, Charles; Scherer, Andrew; Schroder, Whittaker; Wilkinson, Ben; Zambrano, Angélica Almeyda; Drone-Mounted Lidar Survey of Maya Settlement and Landscape Latin American Antiquity 30 3 630-636 2019 Cambridge University Press
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Ferreira, Manuel Eduardo; Alves, Leomar R; Albuquerque, Rafael W; Broadbent, Eben; de Almeida, Danilo RA; Avino, Felipe Spina; Cezare, Cassio HG; Zambrano, Angelica M Almeyda; Wilkinson, Ben; Oliveira-da-Costa, Marcelo; Monitoring The Brazilian Savanna with lidar and RGB Sensors Onboard Remotely Piloted Aircraft Systems IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium 9240-9243 2019 IEEE
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: de Almeida, Danilo Roberti Alves; Stark, Scott Christopher; Valbuena, Ruben; Broadbent, Eben North; Silva, Thiago Sanna Freire; de Resende, Angelica Faria; Ferreira, Matheus Pinheiro; Cardil, Adrián; Silva, Carlos Alberto; Amazonas, Nino; A new era in forest restoration monitoring Restoration Ecology 2019 John Wiley & Sons, Ltd (10.1111)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Merrick, Trina L; Pau, Stephanie; Detto, Matteo; Broadbent, Eben N; Bohlman, Stephanie; Zambrano, Angelica M Almeyda; Bennartz, Ralf; Multi-scale analysis of tropical forest canopy productivity and function using heterogeneous remote sensing data on Barro Colorado Island, Panama AGU Fall Meeting 2019 2019 AGU
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Barbour, Terry E; Sassaman, Kenneth E; Zambrano, Angelica Maria Almeyda; Broadbent, Eben North; Wilkinson, Ben; Kanaski, Richard; Rare pre-Columbian settlement on the Florida Gulf Coast revealed through high-resolution drone LiDAR Proceedings of the National Academy of Sciences 116 47 23493-23498 2019 National Academy of Sciences


Progress 10/04/17 to 09/30/18

Outputs
Target Audience:Our target audience was forest management professionals and scientists. We worked closely with people from the National Ecological Observatory Network, the US Forest Service, management and geospatial scientists at Florida protected areas, and also participated in international activities aimed at capacity building and training of institutions in sensor fusion and monitoring in Costa Rica, Peru, and Brazil. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The GatorEye is actively collecting data in Peru, Brazil, Panama, Costa Rica, Mexico, and throughout the US.Projects include collaborators, e.g., USFS, from over 20 institutions through the Americas. We have conducted training activities related to sensor fusion in Brazil, Peru, and Costa Rica, as well as in Florida. How have the results been disseminated to communities of interest?We have provided a number of demonstration flights and workshops related to the GatorEye system and how tower, drone, aircraft and satellite data can be fused and integrated to address the core objectives of this project. What do you plan to do during the next reporting period to accomplish the goals?The primary objective is to complete publications, with many ongoing or near submission or in review, related to the GatorEye system and the integration framework. We also intend to submit for external competitive grants leveraging the capacity we have developed through this project.

Impacts
What was accomplished under these goals? The GatorEye Unmanned Flying Laboratory is a key element of the University of Florida, School of Forest Resource and Conservation (SFRC), Spatial Ecology and Conservation (SPEC) Lab's McIntire-Stennis project developing a Multi-Platform Multi-Sensor Framework for Monitoring Forest Productivity, Chemistry And Water Stress. Threats to forests, nationally and internationally, are numerous, scaling from individual leaves to entire counties. They can have easily identifiable outcomes, such as degradation from hurricanes or tornadoes, or be more subtle and difficult to detect, such as water stress or leaf discoloration from insect pathogens. Today, the volume and types of data available to locate, map and diagnosis impacts is unprecedented. For example, we now have access to daily global high-res satellite images, continental networks of tower bioclimatic data, aircraft, and now drones with advanced sensor suites. Overall, our program, initiated in 2017, seeks to develop a framework to integrate this multitude of new geospatial data sources to efficiently identify, map and monitor our forests - now and into the future. Supporting this effort, SFRC's SPEC Lab has led the development of the GatorEye UFL, which combines a lidar (lasers), hyperspectral, and visual sensors to collect unprecedented volumes of data measuring forest dynamics and health, and in the process is pushing the frontier of ecological remote sensing data fusion and analysis. The system has been incorporated into over $550,000 in funded proposals.The GatorEye is actively collecting data in Peru, Brazil, Panama, Costa Rica, Mexico, and throughout the US. Projects include collaborators, e.g., USFS, from over 20 institutions through the Americas.

Publications

  • Type: Theses/Dissertations Status: Other Year Published: 2020 Citation: Youssef Kaddoura, PhD Dissertation.
  • Type: Journal Articles Status: Other Year Published: 2019 Citation: Broadbent et al. In development. Soundmapper4D R package for fusion of acoustic data for geospatial mapping of bird species.
  • Type: Journal Articles Status: Other Year Published: 2019 Citation: Mabel CesarinaBaez,Ang�lica Mar�a Almeyda Zambrano, Beatriz LopezGutierrez, Jaime Chavez, Pamela Montero-Alvarez, Ana Oliveira Fiorini, Diego Garcia Olaechea, Gretchen Stokes, Ben Wilkinson, Eben Broadbent. In preparation. Comparing high-resolution satellite and drone LiDAR data for trail mapping in mixed pine and oak forests in central Florida using a participatory approach.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2019 Citation: Stokes G et al. In preparation. Mapping of lake submerged aquatic vegetation using drone-borne hyperspectral data.