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Amr H. Abd-Elrahman
University of Florida, School of Forest, Fisheries, and Geomatic Sciences, Gulf Coast Research and Education Center, 1200 North Park Road, Plant City, FL 33563, USA

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Journal article
Published: 29 July 2021 in Science of The Total Environment
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In the subtropics, climate change is pushing woody mangrove forests into herbaceous saltmarshes, altering soil carbon (C) and nitrogen (N) pools, with implications for coastal wetland productivity and C and N exports. We quantified total C and N pools, and mobile fractions including extractable mineral N, extractable organic C and N, and active (aerobically mineralizable) C and N, in surface soils (top 7.6 cm) of adjacent mangrove (primarily Avicennia germinans) and saltmarsh (Juncus roemerianus) vegetation zones in tidal wetlands of west-central Florida (USA). We tested whether surface-soil accumulations of C, N, and their potentially mobile fractions are greater in mangrove than in saltmarsh owing to greater accumulations in the mangrove zone of soil organic matter (SOM) and fine mineral particles (C- and N-retaining soil constituents). Extractable organic fractions were 39–45% more concentrated in mangrove than in saltmarsh surface soil, and they scaled steeply and positively with SOM and fine mineral particle (silt + clay) concentrations, which themselves were likewise greater in mangrove soil. Elevation may drive this linkage. Mangrove locations were generally at lower elevations, which tended to have greater fine particle content in the surface soil. Active C and extractable mineral N were marginally (p < 0.1) greater in mangrove soil, while active N, total N, and total C showed no statistical differences between zones. Extractable organic C and N fractions composed greater shares of total C and N pools in mangrove than in saltmarsh surface soils, which is meaningful for ecosystem function, as persistent leaching of this fraction can perpetuate nutrient limitation. The active (mineralizable) C and N fractions we observed constituted a relatively small component of total C and N pools, suggesting that mangrove surface soils may export less C and N than would be expected from their large total C and N pools.

ACS Style

David Bruce Lewis; Kristine L. Jimenez; Amr Abd-Elrahman; Michael G. Andreu; Shawn M. Landry; Robert J. Northrop; Cassandra Campbell; Hilary Flower; Mark C. Rains; Christina L. Richards. Carbon and nitrogen pools and mobile fractions in surface soils across a mangrove saltmarsh ecotone. Science of The Total Environment 2021, 798, 149328 .

AMA Style

David Bruce Lewis, Kristine L. Jimenez, Amr Abd-Elrahman, Michael G. Andreu, Shawn M. Landry, Robert J. Northrop, Cassandra Campbell, Hilary Flower, Mark C. Rains, Christina L. Richards. Carbon and nitrogen pools and mobile fractions in surface soils across a mangrove saltmarsh ecotone. Science of The Total Environment. 2021; 798 ():149328.

Chicago/Turabian Style

David Bruce Lewis; Kristine L. Jimenez; Amr Abd-Elrahman; Michael G. Andreu; Shawn M. Landry; Robert J. Northrop; Cassandra Campbell; Hilary Flower; Mark C. Rains; Christina L. Richards. 2021. "Carbon and nitrogen pools and mobile fractions in surface soils across a mangrove saltmarsh ecotone." Science of The Total Environment 798, no. : 149328.

Journal article
Published: 07 April 2021 in ISPRS International Journal of Geo-Information
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Strawberries (Fragaria × ananassa Duch.) are highly perishable fruit. Timely prediction of yield is crucial for labor management and marketing decision-making. This study demonstrates the use of high-resolution ground-based imagery, in addition to previous yield and weather information, for yield prediction throughout the season at different intervals (3–4 days, 1 week, and 3 weeks pre-harvest). Flower and fruit counts, yield, and high-resolution imagery data were collected 31 times for two cultivars (‘Florida Radiance’ and ‘Florida Beauty’) throughout the growing season. Orthorectified mosaics and digital surface models were created to extract canopy size variables (canopy area, average canopy height, canopy height standard deviation, and canopy volume) and visually count flower and fruit number. Data collected at the plot level (6 plots per cultivar, 24 plants per plot) were used to develop prediction models. Using image-based counts and canopy variables, flower and fruit counts were predicted with percentage prediction errors of 26.3% and 25.7%, respectively. Furthermore, by adding image-derived variables to the models, the accuracy of predicting out-of-sample yields at different time intervals was increased by 10–29% compared to those models without image-derived variables. These results suggest that close-range high-resolution images can contribute to yield prediction and could assist the industry with decision making by changing growers’ prediction practices.

ACS Style

Amr Abd-Elrahman; Feng Wu; Shinsuke Agehara; Katie Britt. Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches. ISPRS International Journal of Geo-Information 2021, 10, 239 .

AMA Style

Amr Abd-Elrahman, Feng Wu, Shinsuke Agehara, Katie Britt. Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches. ISPRS International Journal of Geo-Information. 2021; 10 (4):239.

Chicago/Turabian Style

Amr Abd-Elrahman; Feng Wu; Shinsuke Agehara; Katie Britt. 2021. "Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches." ISPRS International Journal of Geo-Information 10, no. 4: 239.

Journal article
Published: 23 February 2021 in Remote Sensing
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This study investigates the use of unmanned aerial systems (UAS) mapping for monitoring the efficacy of invasive aquatic vegetation (AV) management on a floating-leaved AV species, Nymphoides cristata (CFH). The study site consists of 48 treatment plots (TPs). Based on six unique flights over two days at three different flight altitudes while using both a multispectral and RGB sensor, accuracy assessment of the final object-based image analysis (OBIA)-derived classified images yielded overall accuracies ranging from 89.6% to 95.4%. The multispectral sensor was significantly more accurate than the RGB sensor at measuring CFH areal coverage within each TP only with the highest multispectral, spatial resolution (2.7 cm/pix at 40 m altitude). When measuring response in the AV community area between the day of treatment and two weeks after treatment, there was no significant difference between the temporal area change from the reference datasets and the area changes derived from either the RGB or multispectral sensor. Thus, water resource managers need to weigh small gains in accuracy from using multispectral sensors against other operational considerations such as the additional processing time due to increased file sizes, higher financial costs for equipment procurements, and longer flight durations in the field when operating multispectral sensors.

ACS Style

Adam Benjamin; Amr Abd-Elrahman; Lyn Gettys; Hartwig Hochmair; Kyle Thayer. Monitoring the Efficacy of Crested Floatingheart (Nymphoides cristata) Management with Object-Based Image Analysis of UAS Imagery. Remote Sensing 2021, 13, 830 .

AMA Style

Adam Benjamin, Amr Abd-Elrahman, Lyn Gettys, Hartwig Hochmair, Kyle Thayer. Monitoring the Efficacy of Crested Floatingheart (Nymphoides cristata) Management with Object-Based Image Analysis of UAS Imagery. Remote Sensing. 2021; 13 (4):830.

Chicago/Turabian Style

Adam Benjamin; Amr Abd-Elrahman; Lyn Gettys; Hartwig Hochmair; Kyle Thayer. 2021. "Monitoring the Efficacy of Crested Floatingheart (Nymphoides cristata) Management with Object-Based Image Analysis of UAS Imagery." Remote Sensing 13, no. 4: 830.

Review
Published: 02 February 2021 in Remote Sensing
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Measurement of plant characteristics is still the primary bottleneck in both plant breeding and crop management. Rapid and accurate acquisition of information about large plant populations is critical for monitoring plant health and dissecting the underlying genetic traits. In recent years, high-throughput phenotyping technology has benefitted immensely from both remote sensing and machine learning. Simultaneous use of multiple sensors (e.g., high-resolution RGB, multispectral, hyperspectral, chlorophyll fluorescence, and light detection and ranging (LiDAR)) allows a range of spatial and spectral resolutions depending on the trait in question. Meanwhile, computer vision and machine learning methodology have emerged as powerful tools for extracting useful biological information from image data. Together, these tools allow the evaluation of various morphological, structural, biophysical, and biochemical traits. In this review, we focus on the recent development of phenomics approaches in strawberry farming, particularly those utilizing remote sensing and machine learning, with an eye toward future prospects for strawberries in precision agriculture. The research discussed is broadly categorized according to strawberry traits related to (1) fruit/flower detection, fruit maturity, fruit quality, internal fruit attributes, fruit shape, and yield prediction; (2) leaf and canopy attributes; (3) water stress; and (4) pest and disease detection. Finally, we present a synthesis of the potential research opportunities and directions that could further promote the use of remote sensing and machine learning in strawberry farming.

ACS Style

Caiwang Zheng; Amr Abd-Elrahman; Vance Whitaker. Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming. Remote Sensing 2021, 13, 531 .

AMA Style

Caiwang Zheng, Amr Abd-Elrahman, Vance Whitaker. Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming. Remote Sensing. 2021; 13 (3):531.

Chicago/Turabian Style

Caiwang Zheng; Amr Abd-Elrahman; Vance Whitaker. 2021. "Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming." Remote Sensing 13, no. 3: 531.

Journal article
Published: 21 December 2020 in Sensors
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Many lightweight lidar sensors employed for UAS lidar mapping feature a fan-style laser emitter-detector configuration which results in a non-uniform pattern of laser pulse returns. As the role of UAS lidar mapping grows in both research and industry, it is imperative to understand the behavior of the fan-style lidar sensor to ensure proper mission planning. This study introduces sensor modeling software for scanning simulation and analytical equations developed in-house to characterize the non-uniform return density (i.e., scan pattern) of the fan-style sensor, with special focus given to a popular fan-style sensor, the Velodyne VLP-16 laser scanner. The results indicate that, despite the high pulse frequency of modern scanners, areas of poor laser pulse coverage are often present along the scanning path under typical mission parameters. These areas of poor coverage appear in a variety of shapes and sizes which do not necessarily correspond to the forward speed of the scanner or the height of the scanner above the ground, highlighting the importance of scan simulation for proper mission planning when using a fan-style sensor.

ACS Style

H. Andrew Lassiter; Travis Whitley; Benjamin Wilkinson; Amr Abd-Elrahman. Scan Pattern Characterization of Velodyne VLP-16 Lidar Sensor for UAS Laser Scanning. Sensors 2020, 20, 7351 .

AMA Style

H. Andrew Lassiter, Travis Whitley, Benjamin Wilkinson, Amr Abd-Elrahman. Scan Pattern Characterization of Velodyne VLP-16 Lidar Sensor for UAS Laser Scanning. Sensors. 2020; 20 (24):7351.

Chicago/Turabian Style

H. Andrew Lassiter; Travis Whitley; Benjamin Wilkinson; Amr Abd-Elrahman. 2020. "Scan Pattern Characterization of Velodyne VLP-16 Lidar Sensor for UAS Laser Scanning." Sensors 20, no. 24: 7351.

Journal article
Published: 05 November 2020 in Remote Sensing
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Capturing high spatial resolution imagery is becoming a standard operation in many agricultural applications. The increased capacity for image capture necessitates corresponding advances in analysis algorithms. This study introduces automated raster geoprocessing methods to automatically extract strawberry (Fragaria × ananassa) canopy size metrics using raster image analysis and utilize the extracted metrics in statistical modeling of strawberry dry weight. Automated canopy delineation and canopy size metrics extraction models were developed and implemented using ArcMap software v 10.7 and made available by the authors. The workflows were demonstrated using high spatial resolution (1 mm resolution) orthoimages and digital surface models (2 mm) of 34 strawberry plots (each containing 17 different plant genotypes) planted on raised beds. The images were captured on a weekly basis throughout the strawberry growing season (16 weeks) between early November and late February. The results of extracting four canopy size metrics (area, volume, average height, and height standard deviation) using automatically delineated and visually interpreted canopies were compared. The trends observed in the differences between canopy metrics extracted using the automatically delineated and visually interpreted canopies showed no significant differences. The R2 values of the models were 0.77 and 0.76 for the two datasets and the leave-one-out (LOO) cross validation root mean square error (RMSE) of the two models were 9.2 g and 9.4 g, respectively. The results show the feasibility of using automated methods for canopy delineation and canopy metric extraction to support plant phenotyping applications.

ACS Style

Amr Abd-Elrahman; Zhen Guan; Cheryl Dalid; Vance Whitaker; Katherine Britt; Benjamin Wilkinson; Ali Gonzalez. Automated Canopy Delineation and Size Metrics Extraction for Strawberry Dry Weight Modeling Using Raster Analysis of High-Resolution Imagery. Remote Sensing 2020, 12, 3632 .

AMA Style

Amr Abd-Elrahman, Zhen Guan, Cheryl Dalid, Vance Whitaker, Katherine Britt, Benjamin Wilkinson, Ali Gonzalez. Automated Canopy Delineation and Size Metrics Extraction for Strawberry Dry Weight Modeling Using Raster Analysis of High-Resolution Imagery. Remote Sensing. 2020; 12 (21):3632.

Chicago/Turabian Style

Amr Abd-Elrahman; Zhen Guan; Cheryl Dalid; Vance Whitaker; Katherine Britt; Benjamin Wilkinson; Ali Gonzalez. 2020. "Automated Canopy Delineation and Size Metrics Extraction for Strawberry Dry Weight Modeling Using Raster Analysis of High-Resolution Imagery." Remote Sensing 12, no. 21: 3632.

Journal article
Published: 02 November 2020 in EDIS
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This new 13-page article combines canopy coverage data from all of Florida's metropolitan and micropolitan areas with ecological models developed by the USDA Forest Service to calculate several key benefits of urban trees and an approximation of their monetary value. Benefits of urban trees include carbon sequestration/storage, air pollution filtration, and stormwater mitigation. Written by Drew C. McLean, Andrew K. Koeser, Deborah R. Hilbert, Shawn Landry, Amr Abd-Elrahman, Katie Britt, Mary Lusk, Michael G. Andreu, and Robert J. Northrop, and published by the UF/IFAS Environmental Horticulture Department.https://edis.ifas.ufl.edu/ep595

ACS Style

Drew C. McLean; Andrew Koeser; Deborah R. Hilbert; Shawn Landry; Amr Abd-Elrahman; Katie Britt; Mary Lusk; Michael Andreu; Robert Northrop. Florida’s Urban Forest: A Valuation of Benefits. EDIS 2020, 2020, 1 .

AMA Style

Drew C. McLean, Andrew Koeser, Deborah R. Hilbert, Shawn Landry, Amr Abd-Elrahman, Katie Britt, Mary Lusk, Michael Andreu, Robert Northrop. Florida’s Urban Forest: A Valuation of Benefits. EDIS. 2020; 2020 (6):1.

Chicago/Turabian Style

Drew C. McLean; Andrew Koeser; Deborah R. Hilbert; Shawn Landry; Amr Abd-Elrahman; Katie Britt; Mary Lusk; Michael Andreu; Robert Northrop. 2020. "Florida’s Urban Forest: A Valuation of Benefits." EDIS 2020, no. 6: 1.

Journal article
Published: 14 December 2019 in Remote Sensing
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Lidar from small unoccupied aerial systems (UAS) is a viable method for collecting geospatial data associated with a wide variety of applications. Point clouds from UAS lidar require a means for accuracy assessment, calibration, and adjustment. In order to carry out these procedures, specific locations within the point cloud must be precisely found. To do this, artificial targets may be used for rural settings, or anywhere there is a lack of identifiable and measurable features in the scene. This paper presents the design of lidar targets for precise location based on geometric structure. The targets and associated mensuration algorithm were tested in two scenarios to investigate their performance under different point densities, and different levels of algorithmic rigor. The results show that the targets can be accurately located within point clouds from typical scanning parameters to

ACS Style

Benjamin Wilkinson; H. Andrew Lassiter; Amr Abd-Elrahman; Raymond R. Carthy; Peter Ifju; Eben Broadbent; Nathan Grimes. Geometric Targets for UAS Lidar. Remote Sensing 2019, 11, 3019 .

AMA Style

Benjamin Wilkinson, H. Andrew Lassiter, Amr Abd-Elrahman, Raymond R. Carthy, Peter Ifju, Eben Broadbent, Nathan Grimes. Geometric Targets for UAS Lidar. Remote Sensing. 2019; 11 (24):3019.

Chicago/Turabian Style

Benjamin Wilkinson; H. Andrew Lassiter; Amr Abd-Elrahman; Raymond R. Carthy; Peter Ifju; Eben Broadbent; Nathan Grimes. 2019. "Geometric Targets for UAS Lidar." Remote Sensing 11, no. 24: 3019.

Journal article
Published: 19 August 2019 in Forests
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Urban forests are often heavily populated by street trees along right-of-ways (ROW), and monitoring efforts can enhance municipal tree management. Terrestrial photogrammetric techniques have been used to measure tree biometry, but have typically used images from various angles around individual trees or forest plots to capture the entire stem while also utilizing local coordinate systems (i.e., non-georeferenced data). We proposed the mobile collection of georeferenced imagery along 100 m sections of urban roadway to create photogrammetric point cloud datasets suitable for measuring stem diameters and attaining positional x and y coordinates of street trees. In a comparison between stationary and mobile photogrammetry, diameter measurements of urban street trees (N = 88) showed a slightly lower error (RMSE = 8.02%) relative to non-mobile stem measurements (RMSE = 10.37%). Tree Y-coordinates throughout urban sites for mobile photogrammetric data showed a lower standard deviation of 1.70 m relative to 2.38 m for a handheld GPS, which was similar for X-coordinates where photogrammetry and handheld GPS coordinates showed standard deviations of 1.59 m and the handheld GPS 2.36 m, respectively—suggesting higher precision for the mobile photogrammetric models. The mobile photogrammetric system used in this study to create georeferenced models for measuring stem diameters and mapping tree positions can also be potentially expanded for more wide-scale applications related to tree inventory and monitoring of roadside infrastructure.

ACS Style

John Roberts; Andrew Koeser; Amr Abd-Elrahman; Benjamin Wilkinson; Gail Hansen; Shawn Landry; Ali Perez. Mobile Terrestrial Photogrammetry for Street Tree Mapping and Measurements. Forests 2019, 10, 701 .

AMA Style

John Roberts, Andrew Koeser, Amr Abd-Elrahman, Benjamin Wilkinson, Gail Hansen, Shawn Landry, Ali Perez. Mobile Terrestrial Photogrammetry for Street Tree Mapping and Measurements. Forests. 2019; 10 (8):701.

Chicago/Turabian Style

John Roberts; Andrew Koeser; Amr Abd-Elrahman; Benjamin Wilkinson; Gail Hansen; Shawn Landry; Ali Perez. 2019. "Mobile Terrestrial Photogrammetry for Street Tree Mapping and Measurements." Forests 10, no. 8: 701.

Journal article
Published: 13 December 2018 in Computers and Electronics in Agriculture
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Early and accurate disease detection is essential for implementing timely disease management practices. Current disease detection tactics, like visual detection through scouting, are labor intensive, expensive, requires a level of expertise in pest identification, and, may result in subjective disease identification. Diagnosis based on visual symptoms is often compromised by the inability to differentiate between similar symptoms caused by different biotic and abiotic factors. In this paper, an automated early disease detection technique for avocado trees is presented and evaluated. This remote sensing technique can detect an important avocado disease, the laurel wilt (Lw) disease, and differentiate it from healthy trees (H), trees infected by phytophthora root rot (Prr), and trees with iron (Fe) and nitrogen (N) deficiencies. Detection of Lw disease in avocado trees, in early stage, is very difficult, because it has similar symptoms with other stress factors such as nutrient deficiency, salt damage, phytophthora root rot, etc. The proposed disease detection procedure contains several steps including image acquisition, image pre-processing, image segmentation, feature extraction and classification. For image acquisition, two cameras were utilized and evaluated: (i) a Tetracamera (6 bands Tetracam) and (ii) a modified Canon camera (3 bands); and two classification methods were studied: (a) neural network multilayer perceptron (MLP), and (ii) K- nearest neighbors, to detect Lw in asymptomatic stage and in late (symptomatic) stage. Additionally, two segmentation methods, region of interest (OVROI) and polygon region of interest (PROI), were utilized. The MLP classification method with the Tetracam was able to successfully detect Lw with an accuracy of 99% in asymptomatic (early) stage. Hence, low-cost remote technique can be utilized to differentiate healthy and unhealthy plants.

ACS Style

Jaafar Abdulridha; Reza Ehsani; Amr Abd-Elrahman; Yiannis Ampatzidis. A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses. Computers and Electronics in Agriculture 2018, 156, 549 -557.

AMA Style

Jaafar Abdulridha, Reza Ehsani, Amr Abd-Elrahman, Yiannis Ampatzidis. A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses. Computers and Electronics in Agriculture. 2018; 156 ():549-557.

Chicago/Turabian Style

Jaafar Abdulridha; Reza Ehsani; Amr Abd-Elrahman; Yiannis Ampatzidis. 2018. "A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses." Computers and Electronics in Agriculture 156, no. : 549-557.

Short communication
Published: 20 July 2018 in Urban Forestry & Urban Greening
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Much of forest science is dependent on accurate stem measurements, and relatively new photogrammetric techniques may be suitable for modeling stems from the terrestrial perspective. From imagery taken along a windbreak and urban roadways we tested the viability of photogrammetric modeling for producing accurate diameter at breast height measurements. Treatments for different point cloud models differed based on intervals between control points (i.e., every 5 m, 10 m, 25 m, and an absence of target control points) and site conditions (i.e., urban mixed species vs. a windbreak of Pinus taeda) over 100 m sections in the Tampa Bay, FL area. Stem diameter measurements from both the windbreak (n = 53) and the urban sites (n = 93) showed high conformity between field-derived and point cloud model measurements (linear regression showed R2 values >0.9 and RMSE values ranging from 7.04 − 12.35%) with the number of control point targets having little influence on modeled DBH accuracy. Modeled stems of larger trees had greater associated error relative to DBH tape measurements, which can be attributed, in part, to problems with estimating diameter from non-circular stems of certain urban species (i.e., Quercus virginiana). Future work will focus on georeferencing these datasets and extracting data on other aspects of stem biometry (e.g., lean angle of stem, stem volume, etc.).

ACS Style

John W. Roberts; Andrew K. Koeser; Amr H. Abd-Elrahman; Gail Hansen; Shawn M. Landry; Benjamin E. Wilkinson. Terrestrial photogrammetric stem mensuration for street trees. Urban Forestry & Urban Greening 2018, 35, 66 -71.

AMA Style

John W. Roberts, Andrew K. Koeser, Amr H. Abd-Elrahman, Gail Hansen, Shawn M. Landry, Benjamin E. Wilkinson. Terrestrial photogrammetric stem mensuration for street trees. Urban Forestry & Urban Greening. 2018; 35 ():66-71.

Chicago/Turabian Style

John W. Roberts; Andrew K. Koeser; Amr H. Abd-Elrahman; Gail Hansen; Shawn M. Landry; Benjamin E. Wilkinson. 2018. "Terrestrial photogrammetric stem mensuration for street trees." Urban Forestry & Urban Greening 35, no. : 66-71.

Journal article
Published: 13 July 2018 in Remote Sensing of Environment
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Context information is rarely used in the object-based landcover classification. Previous models that attempted to utilize this information usually required the user to input empirical values for critical model parameters, leading to less optimal performance. Multi-view image information is useful for improving classification accuracy, but the methods to assimilate multi-view information to make it usable for context driven models have not been explored in the literature. Here we propose a novel method to exploit the multi-view information for generating class membership probability. Moreover, we develop a new conditional random field model to integrate multi-view information and context information to further improve landcover classification accuracy. This model does not require the user to manually input parameters because all parameters in the Conditional Random Field (CRF) model are fully learned from the training dataset using the gradient descent approach. Using multi-view data extracted from small Unmanned Aerial Systems (UASs), we experimented with Gaussian Mixed Model (GMM), Random Forest (RF), Support Vector Machine (SVM) and Deep Convolutional Neural Networks (DCNN) classifiers to test model performance. The results showed that our model improved average overall accuracies from 58.3% to 74.7% for the GMM classifier, 75.8% to 87.3% for the RF classifier, 75.0% to 84.4% for the SVM classifier and 80.3% to 86.3% for the DCNN classifier. Although the degree of improvement may depend on the specific classifier respectively, the proposed model can significantly improve classification accuracy irrespective of classifier type.

ACS Style

Tao Liu; Amr Abd-Elrahman; Alina Zare; Bon A. Dewitt; Luke Flory; Scot E. Smith. A fully learnable context-driven object-based model for mapping land cover using multi-view data from unmanned aircraft systems. Remote Sensing of Environment 2018, 216, 328 -344.

AMA Style

Tao Liu, Amr Abd-Elrahman, Alina Zare, Bon A. Dewitt, Luke Flory, Scot E. Smith. A fully learnable context-driven object-based model for mapping land cover using multi-view data from unmanned aircraft systems. Remote Sensing of Environment. 2018; 216 ():328-344.

Chicago/Turabian Style

Tao Liu; Amr Abd-Elrahman; Alina Zare; Bon A. Dewitt; Luke Flory; Scot E. Smith. 2018. "A fully learnable context-driven object-based model for mapping land cover using multi-view data from unmanned aircraft systems." Remote Sensing of Environment 216, no. : 328-344.

Journal article
Published: 12 July 2018 in GIScience & Remote Sensing
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Unmanned Aerial Systems (UASs) have the potential to provide multi-view data, but the approaches used to extract the multi-view data from UAS and investigation of their use in image classification are currently unavailable in publications to our best knowledge. This study presents a method that combines collinearity equations and a two-phase optimization procedure to automatically project a point from real world coordinate system of an orthoimage to UAS image coordinate system (row and column numbers) to be used in multi-view data extraction. The results show average errors for the computed UAS column and row numbers were 1.6 and 1.8 pixels respectively evaluated with leave-one-out method. Based on this algorithm, it’s also for the first time that object-based multi-view data were extracted and presented, and the potential of using the multi-view data to aid Geographic Object-Based Image Analysis(GEOBIA) through bidirectional reflectance distribution function (BRDF) modelling was evaluated with two representatives of BRDFs, the Rahman-Pinty-Verstraete(RPV) and Ross-Thick-LiSparse (RTLS). Our results indicate the RPV model tends to overestimate the bidirectional reflectance for land cover types with high reflectance, while perform well for those with relatively low reflectance in our study area. To test the impact of using multi-view data on image classification, we extracted parameters from BRDF models and used these parameters as object features for object-based classification. The 10-fold cross validation results show that the 3-parameter RTLS significantly improved overall accuracy compared to the classifications relying only on the orthoimage features, while other BRDF models did not show significant improvements, raising the needs to develop new methods to better utilize the multi-view information in GEOIBA in the future.

ACS Style

Tao Liu; Amr Abd-Elrahman; Bon Dewitt; Scot Smith; Jon Morton; Victor L. Wilhelm. Evaluating the potential of multi-view data extraction from small Unmanned Aerial Systems (UASs) for object-based classification for Wetland land covers. GIScience & Remote Sensing 2018, 56, 130 -159.

AMA Style

Tao Liu, Amr Abd-Elrahman, Bon Dewitt, Scot Smith, Jon Morton, Victor L. Wilhelm. Evaluating the potential of multi-view data extraction from small Unmanned Aerial Systems (UASs) for object-based classification for Wetland land covers. GIScience & Remote Sensing. 2018; 56 (1):130-159.

Chicago/Turabian Style

Tao Liu; Amr Abd-Elrahman; Bon Dewitt; Scot Smith; Jon Morton; Victor L. Wilhelm. 2018. "Evaluating the potential of multi-view data extraction from small Unmanned Aerial Systems (UASs) for object-based classification for Wetland land covers." GIScience & Remote Sensing 56, no. 1: 130-159.

Journal article
Published: 04 July 2018 in Remote Sensing of Environment
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Traditionally, the multiple images collected by cameras mounted on Unmanned Aircraft Systems (UAS) are mosaicked into a single orthophoto on which Object-Based Image Analysis (OBIA) is conducted. This approach does not take advantage of the Multi-View (MV) information of the individual images. In this study, we introduce a new OBIA approach utilizing multi-view information of original UAS images and compare its performance with that of traditional OBIA, which uses only the orthophoto (Ortho-OBIA). The proposed approach, called multi-view object-based image analysis (MV-OBIA), classifies multi-view object instances on UAS images corresponding to each orthophoto object and utilizes a voting procedure to assign a final label to the orthophoto object. The proposed MV-OBIA is also compared with the classification approaches based on Bidirectional Reflectance Distribution Function (BRDF) simulation. Finally, to reduce the computational burden of multi-view object-based data generation for MV-OBIA and make the proposed approach more operational in practice, this study proposes two window-based implementations of MV-OBIA that utilize a window positioned at the geometric centroid of the object instance, instead of the object instance itself, to extract features. The first window-based MV-OBIA adopts a fixed window size (denoted as FWMV-OBIA), while the second window-based MV-OBIA uses an adaptive window size (denoted as AWMV-OBIA). Our results show that the MV-OBIA substantially improves the overall accuracy compared with Ortho-OBIA, regardless of the features used for classification and types of wetland land covers in our study site. Furthermore, the MV-OBIA also demonstrates a much higher efficiency in utilizing the multi-view information for classification based on its considerably higher overall accuracy compared with BRDF-based methods. Lastly, FWMV-OBIA and AWMV-OBIA both show potential in generating an equal if not higher overall accuracy compared with MV-OBIA at substantially reduced computational costs.

ACS Style

Tao Liu; Amr Abd-Elrahman. Multi-view object-based classification of wetland land covers using unmanned aircraft system images. Remote Sensing of Environment 2018, 216, 122 -138.

AMA Style

Tao Liu, Amr Abd-Elrahman. Multi-view object-based classification of wetland land covers using unmanned aircraft system images. Remote Sensing of Environment. 2018; 216 ():122-138.

Chicago/Turabian Style

Tao Liu; Amr Abd-Elrahman. 2018. "Multi-view object-based classification of wetland land covers using unmanned aircraft system images." Remote Sensing of Environment 216, no. : 122-138.

Journal article
Published: 01 June 2018 in Geoderma
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ACS Style

Yiming Xu; Scot E. Smith; Sabine Grunwald; Amr Abd-Elrahman; Suhas P. Wani. Effects of image pansharpening on soil total nitrogen prediction models in South India. Geoderma 2018, 320, 52 -66.

AMA Style

Yiming Xu, Scot E. Smith, Sabine Grunwald, Amr Abd-Elrahman, Suhas P. Wani. Effects of image pansharpening on soil total nitrogen prediction models in South India. Geoderma. 2018; 320 ():52-66.

Chicago/Turabian Style

Yiming Xu; Scot E. Smith; Sabine Grunwald; Amr Abd-Elrahman; Suhas P. Wani. 2018. "Effects of image pansharpening on soil total nitrogen prediction models in South India." Geoderma 320, no. : 52-66.

Journal article
Published: 01 April 2018 in CATENA
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Mapping soil nutrients can help smallholder farmers identify soil nutrient status and implement site-specific soil management schemes. In the past, Digital Soil Mapping has seldom been utilized to guide soil nutrient management in smallholder farm settings in South India. The objective of this research was to analyze the spatial resolution effects of different remote sensing images on soil total nitrogen (TN) prediction models in two smallholder villages, Kothapally and Masuti in South India. Regression kriging (RK) was used to characterize the spatial pattern of TN in the topsoil (0–15 cm) by incorporating spectral indices with different spatial resolutions. The results suggested that soil moisture, vegetation, and soil crusts can contribute to the conservation of soil TN in both study areas. Soil prediction models with different spatial resolutions showed a similar spatial pattern of soil TN. The results also demonstrated that the effect of very fine spatial remote sensing spectral data inputs does not always lead to an increase of soil prediction model performance. A RapidEye-based (5 m) soil TN prediction model had lower prediction accuracy than a Landsat 8-based (30 m) soil TN prediction model in Masuti. WorldView-2/GeoEye-1/Pleiades-1A-based (2 m) soil TN prediction models had the highest prediction accuracy in both study areas. The spectral indices based on new bands of WorldView-2 such as coastal, yellow, red edge, and new near infrared bands had relatively strong correlations with soil TN. The utilization of Very High Spatial resolution images such as WorldView-2 in Digital Soil Mapping could improve soil model performance and spatial characterization. Remote sensing-based soil prediction models have high potential to be widely applied in smallholder farm settings.

ACS Style

Yiming Xu; Scot E. Smith; Sabine Grunwald; Amr Abd-Elrahman; Suhas P. Wani; Vimala D. Nair. Estimating soil total nitrogen in smallholder farm settings using remote sensing spectral indices and regression kriging. CATENA 2018, 163, 111 -122.

AMA Style

Yiming Xu, Scot E. Smith, Sabine Grunwald, Amr Abd-Elrahman, Suhas P. Wani, Vimala D. Nair. Estimating soil total nitrogen in smallholder farm settings using remote sensing spectral indices and regression kriging. CATENA. 2018; 163 ():111-122.

Chicago/Turabian Style

Yiming Xu; Scot E. Smith; Sabine Grunwald; Amr Abd-Elrahman; Suhas P. Wani; Vimala D. Nair. 2018. "Estimating soil total nitrogen in smallholder farm settings using remote sensing spectral indices and regression kriging." CATENA 163, no. : 111-122.

Journal article
Published: 18 March 2018 in ISPRS Journal of Photogrammetry and Remote Sensing
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Deep convolutional neural network (DCNN) requires massive training datasets to trigger its image classification power, while collecting training samples for remote sensing application is usually an expensive process. When DCNN is simply implemented with traditional object-based image analysis (OBIA) for classification of Unmanned Aerial systems (UAS) orthoimage, its power may be undermined if the number training samples is relatively small. This research aims to develop a novel OBIA classification approach that can take advantage of DCNN by enriching the training dataset automatically using multi-view data. Specifically, this study introduces a Multi-View Object-based classification using Deep convolutional neural network (MODe) method to process UAS images for land cover classification. MODe conducts the classification on multi-view UAS images instead of directly on the orthoimage, and gets the final results via a voting procedure. 10-fold cross validation results show the mean overall classification accuracy increasing substantially from 65.32%, when DCNN was applied on the orthoimage to 82.08% achieved when MODe was implemented. This study also compared the performances of the support vector machine (SVM) and random forest (RF) classifiers with DCNN under traditional OBIA and the proposed multi-view OBIA frameworks. The results indicate that the advantage of DCNN over traditional classifiers in terms of accuracy is more obvious when these classifiers were applied with the proposed multi-view OBIA framework than when these classifiers were applied within the traditional OBIA framework.

ACS Style

Tao Liu; Amr Abd-Elrahman. Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification. ISPRS Journal of Photogrammetry and Remote Sensing 2018, 139, 154 -170.

AMA Style

Tao Liu, Amr Abd-Elrahman. Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification. ISPRS Journal of Photogrammetry and Remote Sensing. 2018; 139 ():154-170.

Chicago/Turabian Style

Tao Liu; Amr Abd-Elrahman. 2018. "Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification." ISPRS Journal of Photogrammetry and Remote Sensing 139, no. : 154-170.

Journal article
Published: 14 March 2018 in Remote Sensing
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Fully Convolutional Networks (FCN) has shown better performance than other classifiers like Random Forest (RF), Support Vector Machine (SVM) and patch-based Deep Convolutional Neural Network (DCNN), for object-based classification using orthoimage only in previous studies; however, for further improving deep learning algorithm performance, multi-view data should be considered for training data enrichment, which has not been investigated for FCN. The present study developed a novel OBIA classification using FCN and multi-view data extracted from small Unmanned Aerial System (UAS) for mapping landcovers. Specifically, this study proposed three methods to automatically generate multi-view training samples from orthoimage training datasets to conduct multi-view object-based classification using FCN, and compared their performances with each other and also with RF, SVM, and DCNN classifiers. The first method does not consider the object surrounding information, while the other two utilized object context information. We demonstrated that all the three versions of FCN multi-view object-based classification outperformed their counterparts utilizing orthoimage data only. Furthermore, the results also showed that when multi-view training samples were prepared with consideration of object surroundings, FCN trained with these samples gave much better accuracy than FCN classification trained without context information. Similar accuracies were achieved from the two methods utilizing object surrounding information, although sample preparation was conducted using two different ways. When comparing FCN with RF, SVM, DCNN implies that FCN generally produced better accuracy than the other classifiers, regardless of using orthoimage or multi-view data.

ACS Style

Tao Liu; Amr Abd-Elrahman. An Object-Based Image Analysis Method for Enhancing Classification of Land Covers Using Fully Convolutional Networks and Multi-View Images of Small Unmanned Aerial System. Remote Sensing 2018, 10, 457 .

AMA Style

Tao Liu, Amr Abd-Elrahman. An Object-Based Image Analysis Method for Enhancing Classification of Land Covers Using Fully Convolutional Networks and Multi-View Images of Small Unmanned Aerial System. Remote Sensing. 2018; 10 (3):457.

Chicago/Turabian Style

Tao Liu; Amr Abd-Elrahman. 2018. "An Object-Based Image Analysis Method for Enhancing Classification of Land Covers Using Fully Convolutional Networks and Multi-View Images of Small Unmanned Aerial System." Remote Sensing 10, no. 3: 457.

Original articles
Published: 19 January 2018 in GIScience & Remote Sensing
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Deep learning networks have shown great success in several computer vision applications, but its implementation in natural land cover mapping in the context of object-based image analysis (OBIA) is rarely explored area especially in terms of the impact of training sample size on the performance comparison. In this study, two representatives of deep learning networks including fully convolutional networks (FCN) and patch-based deep convolutional neural networks (DCNN), and two conventional classifiers including random forest and support vector machine were implemented within the framework of OBIA to classify seven natural land cover types. We assessed the deep learning classifiers using different training sample sizes and compared their performance with traditional classifiers. FCN was implemented using two types of training samples to investigate its ability to utilize object surrounding information. Our results indicate that DCNN may produce inferior performance compared to conventional classifiers when the training sample size is small, but it tends to show substantially higher accuracy than the conventional classifiers when the training sample size becomes large. The results also imply that FCN is more efficient in utilizing the information in the training sample than DCNN and conventional classifiers, with higher if not similar achieved accuracy regardless of sample size. DCNN and FCN tend to show similar performance for the large sample size when the training samples used for training the FCN do not contain object surrounding label information. However, with the ability of utilizing surrounding label information, FCN always achieved much higher accuracy than all the other classification methods regardless of the number of training samples.

ACS Style

Tao Liu; Amr Abd-Elrahman; Jon Morton; Victor L. Wilhelm. Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system. GIScience & Remote Sensing 2018, 55, 243 -264.

AMA Style

Tao Liu, Amr Abd-Elrahman, Jon Morton, Victor L. Wilhelm. Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system. GIScience & Remote Sensing. 2018; 55 (2):243-264.

Chicago/Turabian Style

Tao Liu; Amr Abd-Elrahman; Jon Morton; Victor L. Wilhelm. 2018. "Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system." GIScience & Remote Sensing 55, no. 2: 243-264.

Article
Published: 11 September 2017 in Environmental Monitoring and Assessment
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Digital soil mapping (DSM) is gaining momentum as a technique to help smallholder farmers secure soil security and food security in developing regions. However, communications of the digital soil mapping information between diverse audiences become problematic due to the inconsistent scale of DSM information. Spatial downscaling can make use of accessible soil information at relatively coarse spatial resolution to provide valuable soil information at relatively fine spatial resolution. The objective of this research was to disaggregate the coarse spatial resolution soil exchangeable potassium (Kex) and soil total nitrogen (TN) base map into fine spatial resolution soil downscaled map using weighted generalized additive models (GAMs) in two smallholder villages in South India. By incorporating fine spatial resolution spectral indices in the downscaling process, the soil downscaled maps not only conserve the spatial information of coarse spatial resolution soil maps but also depict the spatial details of soil properties at fine spatial resolution. The results of this study demonstrated difference between the fine spatial resolution downscaled maps and fine spatial resolution base maps is smaller than the difference between coarse spatial resolution base maps and fine spatial resolution base maps. The appropriate and economical strategy to promote the DSM technique in smallholder farms is to develop the relatively coarse spatial resolution soil prediction maps or utilize available coarse spatial resolution soil maps at the regional scale and to disaggregate these maps to the fine spatial resolution downscaled soil maps at farm scale.

ACS Style

Yiming Xu; Scot E. Smith; Sabine Grunwald; Amr Abd-Elrahman; Suhas P. Wani; Vimala D. Nair. Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings. Environmental Monitoring and Assessment 2017, 189, 1 .

AMA Style

Yiming Xu, Scot E. Smith, Sabine Grunwald, Amr Abd-Elrahman, Suhas P. Wani, Vimala D. Nair. Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings. Environmental Monitoring and Assessment. 2017; 189 (10):1.

Chicago/Turabian Style

Yiming Xu; Scot E. Smith; Sabine Grunwald; Amr Abd-Elrahman; Suhas P. Wani; Vimala D. Nair. 2017. "Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings." Environmental Monitoring and Assessment 189, no. 10: 1.