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Scirtothrips dorsalis Hood (Thysanoptera: Thripidae) is an invasive, early-season pest of strawberry in Florida, causing feeding injury to young foliage that results in stunted plant growth and yield loss. Spinetoram, an effective insecticide for thrips pests with up to 3 applications per season permitted in strawberry, is often applied repeatedly during the early-season (Oct–Nov) to manage S. dorsalis , leaving few or no applications for flower thrips pests later in the season (Dec–Mar). Therefore, new strategies are needed to manage S. dorsalis with less insecticide, with the hypothesis that the first insecticide application can be delayed because young strawberry plants can compensate for minor feeding injury without compromising strawberry yield. Experiments conducted in strawberry field plots in Balm, FL, during 2018 and 2019 showed that delaying a spinetoram application for 14 days after infesting a plant with zero, 5, 10, or 20 S. dorsalis adults did not reduce the plant vigor and yield compared to spinetoram application after 4 days. Furthermore, young plants recovered from injury (10–30% bronzing injury on leaf veins and petioles) due to 1 or 2 S. dorsalis adults or larvae per trifoliate. A strategy of delaying the first spinetoram application when plants have 4–5 trifoliates should help reduce the number of insecticide applications needed for S. dorsalis management and reserve spinetoram applications for later in the season. Lower input costs in Florida strawberry without compromising yields due to thrips damage will improve the economics and sustainability of production systems. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) imagers are active microwave sensors that could overcome many challenges of passive optical bathymetry inversion, yet their capacity to yield accurate high-resolution bathymetric mapping is not studied sufficiently. In this study, we evaluate the feasibility of applying fast Fourier transform (FFT) to SAR data in coastal nearshore bathymetry derivation in Florida's coastal waters. The study aims to develop a robust SAR bathymetry inversion framework across extensive spatial scales to address the dearth of bathymetric data in deeper nearshore coastal regions. By leveraging the Sentinel-1 datasets as a rich source of training data, our method yields high-resolution and accurate depth extraction up to 80 m. A comprehensive workflow to determine both the wavelength and peak wave period is associated with the proposed automated model compilation. A novel contour geometry-based spectral analysis technique for wavelength retrieval is presented that enables an efficient and scalable SAR bathymetry model. Multi-date SAR images were used to assess the robustness of the proposed depth-retrieval model. An accuracy assessment against the GMRT data demonstrated the high efficacy of the proposed approach, achieving a coefficient of determination ( R 2 ) above 0.95, a root-mean-square error (RMSE) of 1.56–10.20 m, and relative errors of 3.56–11.08% in automatically extracting the underwater terrain at every 50 m interval. A sensitivity analysis was conducted to estimate the uncertainty associated with our method. Overall, this study highlights the potential of SAR technology to produce updated, cost-effective, and accurate bathymetry maps of high resolution and to fill bathymetric data gaps worldwide. The code and datasets are made publicly available. [ABSTRACT FROM AUTHOR]

To overcome spatial, spectral and temporal constraints of different remote sensing products, data fusion is a good technique to improve the prediction capability of soil prediction models. However, few studies have analyzed the effects of image fusion on digital soil mapping (DSM) models. This research fused multispectral (MS) and panchromatic Landsat 8 (L8) bands, and MS Sentinel 2 (S2) and panchromatic L8 bands using the Brovey, Intensity–Hue–Saturation and Gram–Schmidt methods in an agricultural area in Yellow River Basin, China. To analyze the effects of image fusion on DSM models, various SOC prediction models derived from remote sensing image datasets were established by the random forest method. Soil salinity indices and spectral reflectance from all the remote sensing data had relatively strong negative correlations with SOC, and vegetation indices and water indices from all the remote sensing data had relatively strong positive correlations with SOC. Soil moisture and vegetation were the main controlling factors of the SOC spatial pattern in the study area. More spectral indices derived from pansharpened L8 and fused S2–L8 images by all three image fusion methods had stronger relationships with SOC compared with those from MS L8 and MS S2, respectively. All the SOC models established by pansharpened L8 and fused S2–L8 images had higher prediction accuracy than those established by MS L8 and MS S2, respectively. The fusion between S2 and L8 bands had stronger effects on enhancing the prediction accuracy of SOC models compared with the fusion between panchromatic and MS L8 bands. It is concluded that digital soil mapping and image fusion can be utilized to increase the prediction performance of SOC spatial prediction models. [ABSTRACT FROM AUTHOR]

Biomass is a key biophysical parameter for precision agriculture and plant breeding. Fast, accurate and non-destructive monitoring of biomass enables various applications related to crop growth. In this paper, strawberry dry biomass weight was modeled using 4 canopy geometric parameters (area, average height, volume, standard deviation of height) and 25 spectral variables (5 band original reflectance values and 20 vegetation indices (VIs)) extracted from the Unmanned Aerial Vehicle (UAV) multispectral imagery. Six regression techniques—multiple linear regression (MLR), random forest (RF), support vector machine (SVM), multivariate adaptive regression splines (MARS), eXtreme Gradient Boosting (XGBoost) and artificial neural network (ANN)—were employed and evaluated for biomass prediction. The ANN had the highest accuracy in a five-fold cross-validation, with R2 of 0.89~0.93, RMSE of 7.16~8.98 g and MAE of 5.06~6.29 g. As for the other five models, the addition of VIs increased the R2 from 0.77~0.80 to 0.83~0.86, and reduced the RMSE from 8.89~9.58 to 7.35~8.09 g and the MAE from 6.30~6.70 to 5.25~5.47 g, respectively. Red-edge-related VIs, including the normalized difference red-edge index (NDRE), simple ratio vegetation index red-edge (SRRedEdge), modified simple ratio red-edge (MSRRedEdge) and chlorophyll index red and red-edge (CIred&RE), were the most influential VIs for biomass modeling. In conclusion, the combination of canopy geometric parameters and VIs obtained from the UAV imagery was effective for strawberry dry biomass estimation using machine learning models. [ABSTRACT FROM AUTHOR]

High-throughput phenotyping is an emerging approach in plant science, but thus far only a few applications have been made in horticultural crop breeding. Remote sensing of leaf or canopy spectral reflectance can help breeders rapidly measure traits, increase selection accuracy, and thereby improve response to selection. In the present study, we evaluated the integration of spectral analysis of canopy reflectance and genomic information for the prediction of strawberry (Fragaria × ananassa) powdery mildew disease. Two multi-parental breeding populations of strawberry comprising a total of 340 and 464 pedigree-connected seedlings were evaluated in two separate seasons. A single-trait Bayesian prediction method using 1001 spectral wavebands in the ultraviolet–visible–near infrared region (350–1350 nm wavelength) combined with 8552 single nucleotide polymorphism markers showed up to 2-fold increase in predictive ability over models using markers alone. The integration of high-throughput phenotyping was further validated independently across years/trials with improved response to selection of up to 90%. We also conducted Bayesian multi-trait analysis using the estimated vegetative indices as secondary traits. Three vegetative indices (Datt3, REP_Li, and Vogelmann2) had high genetic correlations (r A) with powdery mildew visual ratings with average r A values of 0.76, 0.71, and 0.71, respectively. Increasing training population sizes by incorporating individuals with only vegetative index information yielded substantial increases in predictive ability. These results strongly indicate the use of vegetative indices as secondary traits for indirect selection. Overall, combining spectrometry and genome-wide prediction improved selection accuracy and response to selection for powdery mildew resistance, demonstrating the power of an integrated phenomics–genomics approach in strawberry breeding. [ABSTRACT FROM AUTHOR]

Identifying and mitigating sources of measurement error is a critical task in geomatics research and the geospatial industry as a whole. In pursuit of such error, accuracy assessments of lidar data have revealed a range bias in low-cost scanners. This phenomenon is a temporally correlated instability in the lidar scanner where the measured distance between target and sensor changes over time while both are held stationary. This research presents an assessment of two low-cost lidar scanners, the Velodyne® HDL–32E and Livox® Mid–40, in which their temporal stability is analyzed and methods to mitigate systematic error are implemented. By immobilizing each scanner as it observes a stationary target surface over the course of multiple hours, trends in scanner precision are identified. Scanner accuracy is then determined using a terrestrial lidar scanner, the Riegl® VZ-400, to observe both subject scanner and target, and extracting the distances between scanner origin and observed surface. Patterns identified in each scanner's distance measurements indicate temporal autocorrelation, and, by exploiting the high linear correlation between scanner internal temperature and measured distance in the HDL–32E, it is possible to mitigate the resulting error. Application of the proposed solution lowers the Velodyne® scanner's measurement RMSE by over 60%, providing levels of measurement accuracy comparable to more expensive lidar systems. [ABSTRACT FROM AUTHOR]

The recent developments of new deep learning architectures create opportunities to accurately classify high-resolution unoccupied aerial system (UAS) images of natural coastal systems and mandate continuous evaluation of algorithm performance. We evaluated the performance of the U-Net and DeepLabv3 deep convolutional network architectures and two traditional machine learning techniques (support vector machine (SVM) and random forest (RF)) applied to seventeen coastal land cover types in west Florida using UAS multispectral aerial imagery and canopy height models (CHM). Twelve combinations of spectral bands and CHMs were used. Our results using the spectral bands showed that the U-Net (83.80–85.27% overall accuracy) and the DeepLabV3 (75.20–83.50% overall accuracy) deep learning techniques outperformed the SVM (60.50–71.10% overall accuracy) and the RF (57.40–71.0%) machine learning algorithms. The addition of the CHM to the spectral bands slightly increased the overall accuracy as a whole in the deep learning models, while the addition of a CHM notably improved the SVM and RF results. Similarly, using bands outside the three spectral bands, namely, near-infrared and red edge, increased the performance of the machine learning classifiers but had minimal impact on the deep learning classification results. The difference in the overall accuracies produced by using UAS-based lidar and SfM point clouds, as supplementary geometrical information, in the classification process was minimal across all classification techniques. Our results highlight the advantage of using deep learning networks to classify high-resolution UAS images in highly diverse coastal landscapes. We also found that low-cost, three-visible-band imagery produces results comparable to multispectral imagery that do not risk a significant reduction in classification accuracy when adopting deep learning models. [ABSTRACT FROM AUTHOR]

Salinity is a widespread soil and underground water contaminant threatening food security and economic stability. Phytoremediation is an efficient and environmental-friendly solution to mitigate salinity impacts. The present study was conducted to evaluate the phytoremediation potential of five multipurpose trees: Vachellia nilotica, Concorpus erectus, Syzygium cumini, Tamarix aphylla and Eucalyptus cammaldulensis under four salinity treatments: Control, 10, 20 and 30 dS m−1. Salinity negatively impacted all the tested species. However, E. cammaldulensis and T. aphylla exhibited the lowest reduction (28%) and (35%) in plant height respectively along with a minimal reduction in leaf gas exchange while V. nilotica, S. cumini and C. erectus showed severe dieback. Similarly, the antioxidant enzymes increased significantly in E. cammaldulensis and T. aphylla as Superoxide Dismutase (87% and 79%), Catalase (66% and 67%) and Peroxidase (89% and 81%), respectively. Furthermore, both of these species maintained optimum Na/K ratio reducing the highest levels of soil ECe and SAR, suggesting the best phytoremediation potential. The present study identifies that E. cammaldulensis and T. aphylla showed effective tolerance mechanisms and the highest salt sequestration; therefore, may be used for phyto-amelioration of salinity impacted lands. Novelty statement Although previous studies evaluated the tolerance potential of many tree species, comparative and physiochemical evaluation of multipurpose tree species has been remained unexplored. In this scenario, eco-physiological characterization of multipurpose tree species may inform tree species for phytoremediation of saline soils according to the level of salinity. Optimizing tree species selection also improves the success of wood for energy and revenue generation while restoring degraded soils. [ABSTRACT FROM AUTHOR]

Natural ecosystems are characterized as dynamic systems that evolve through natural patterns of disturbance. Land managers can work within this system of natural disturbance by applying the concept of natural variability to their management plans. Land management challenges typically stem from infrequent and severe disturbances, like hurricanes, where resource management and conservation implications are a result of significant changes to a landscape's patch dynamics. A frequently used strategy for overcoming these challenges is co-management. Since cooperative management requires collaboration across sectors, institutional and organization integration must be approached strategically. The importance of developing agreement on shared issues makes finding an effective consensus-building process critical. This study utilized the Delphi technique to develop consensus and identify the most significant post-hurricane impacts experienced by land managers following Hurricane Irma. We purposively selected a panel of 24 land managers that represented multiple sectors including county government, non-governmental organizations, and state and federal agencies. The study used a series of three online surveys, which were validated using an expert panel of evaluators, land managers, survey designers and natural resource educators. The panel achieved consensus on 5 post-hurricane impacts that they identified to have a significant effect on achieving their land management goals. The Delphi technique demonstrated to be a valuable process for gaining input from a diverse set of experts in the identification of agreed upon challenges to land management that resulted from extreme weather events. Our results serve as a foundation for discussions on management strategies to address extreme natural disturbances. [ABSTRACT FROM AUTHOR]

In this article, we reflect on more than 40 years of geomatics/surveying education experience at the University of Florida (UF) with a focus on key programmatic changes over the last decade. This includes key challenges faced by the program and subsequent adaptive actions. We summarize continuous efforts made by the UF's administration, Geomatics program faculty' and staff, and collaboration with stakeholders in the state of Florida to address strategic issues faced by the program through the years. In the article, we show that carefully considered expansion to address geographically disadvantaged, "place-bound" student populations and adoption of distance education is one way to address current geomatics education issues (e.g., low enrollment). In addition, diversification of the geomatics education portfolio through degree and certification programs that address industry demand can have a positive impact on increasing both Geomatics program exposure and the level of service to the surveying and mapping profession. [ABSTRACT FROM AUTHOR]

Graphical abstract (A) A general representation of the camera positioning – showing image overlap in red; (B) photos were captured in a parallel-manner to the measured trees within the sampled windbreak and right-of-ways – note the control point target within multiple images; (C) processing the photos created a 3D model of each study site; (D) stem diameter was measured from 'sliced' point cloud data by selecting two points from opposite sides of each stem. Highlights • Tree stems were photographed to create 3D structure from motion (SfM) photogrammetric models. • Diameters at breast height measurements from photogrammetric models were highly correlated with physical measurements. • Photogrammetric point clouds can be used to measure stem DBH, and the point clouds can provide a 3D record the modeled stem. Abstract 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.). [ABSTRACT FROM AUTHOR]

The depletion of forests has become a serious environmental issue in Pakistan. To ensure forest sustainability, a sound monitoring of deforestation is crucial. The current study was designed to quantify the change in Murree Forests, Pakistan using Landsat Imagery for time series of 2000, 2010 and 2019. The acquired images were inter-calibrated and classified using the maximum-likelihood classification. The findings revealed the distribution of the above classes in 2000, 2010 and 2019 as pine forests (45%, 38% and 35%), scrub vegetation (20%, 19% and 19%), rangelands (11%, 8% and 10%), settlements (8%, 20% and 17%), agricultural lands (10%, 13% and 12%), and barren lands (6%, 9% and 12%) respectively. This study identifies that extensive Forest cover changes have occurred in the study area due to pressure of anthropogenic activities. Current findings alarm that deforestation should be reduced through implementing appropriate forest conservation policies to ensure forest sustainability. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The purpose of this study is to examine the use of multi-resolution object-based classification methods for the classification of Unmanned Aircraft Systems (UAS) images of wetland vegetation and to compare its performance with pixel-based classification approaches. Three types of classifiers (Support Vector Machine, Artificial Neural Network and Maximum Likelihood) were utilized to classify the object-based images, the original 8-cm UAS images and the down-sampled (30 cm) version of the image. The results of the object-based and two pixel-based classifications were evaluated and compared. Object-based classification produced higher accuracy than pixel-based classifications if the same type of classifier is used. Our results also showed that under the same classification scheme (i.e. object or pixel), the Support Vector Machine classifier performed slightly better than Artificial Neural Network, which often yielded better results than Maximum Likelihood. With an overall accuracy of 70.78%, object-based classification using Support Vector Machine showed the best performance. This study also concludes that while UAS has the potential to provide flexible and feasible solutions for wetland mapping, some issues related to image quality still need to be addressed in order to improve the classification performance. [ABSTRACT FROM PUBLISHER]

Florida is the fourth most vulnerable coastal state in the USA to sea level rise (SLR). Studies predict that a 1.20 m rise translates into the displacement of almost five million people and destroys about 2.6 million homes. The only solution to reducing the vulnerability of Florida's coastline is the creation and implementation of coastal policies, including a reduction in armoring and the adoption of policies such as rolling easements. This paper advances a SLR inundation computer model that estimates the costs of applying rolling easement policy through three outcomes: property value loss, property area loss and conservation easement payments to home owners. The GIS computer model is modular which allows new policy components, datasets, or ArcGIS tools to easily be added to the model. The results show that property land inundation and real property losses are primarily linear while rolling easement compensation payments are substantial during the first three scenarios then are largely stable for the remaining SLR steps. [ABSTRACT FROM AUTHOR]

Periurban forests are key to offsetting anthropogenic carbon emissions, but they are under constant threat from urbanization. In particular, secondary Neotropical forest types in Andean periurban areas have a high potential to store carbon, but are currently poorly characterized. To address this lack of information, we developed a method to estimate periurban aboveground biomass (AGB)—a proxy for multiple ecosystem services—of secondary Andean forests near Bogotá, Colombia, based on very high resolution (VHR) GeoEye-1, Pleiades-1A imagery and field-measured plot data. Specifically, we tested a series of different pre-processing workflows to derive six vegetation indices that were regressed against in situ estimates of AGB. Overall, the coupling of linear models and the Ratio Vegetation Index produced the most satisfactory results. Atmospheric and topographic correction proved to be key in improving model fit, especially in high aerosol and rugged terrain such as the Andes. Methods and findings provide baseline AGB and carbon stock information for little studied periurban Andean secondary forests. The methodological approach can also be used for integrating limited forest monitoring plot AGB data with very high resolution imagery for cost-effective modelling of ecosystem service provision from forests, monitoring reforestation and forest cover change, and for carbon offset assessments. [ABSTRACT FROM AUTHOR]

Developing spectral models of soil properties is an important frontier in remote sensing and soil science. Several studies have focused on modeling soil properties such as total pools of soil organic matter and carbon in bare soils. We extended this effort to model soil parameters in areas densely covered with coastal vegetation. Moreover, we investigated soil properties indicative of soil functions such as nutrient and organic matter turnover and storage. These properties include the partitioning of mineral and organic soil between particulate (>53 μm) and fine size classes, and the partitioning of soil carbon and nitrogen pools between stable and labile fractions. Soil samples were obtained from Avicennia germinans mangrove forest and Juncus roemerianus salt marsh plots on the west coast of central Florida. Spectra corresponding to field plot locations from Hyperion hyperspectral image were extracted and analyzed. The spectral information was regressed against the soil variables to determine the best single bands and optimal band combinations for the simple ratio (SR) and normalized difference index (NDI) indices. The regression analysis yielded levels of correlation for soil variables with R 2 values ranging from 0.21 to 0.47 for best individual bands, 0.28 to 0.81 for two-band indices, and 0.53 to 0.96 for partial least-squares (PLS) regressions for the Hyperion image data. Spectral models using Hyperion data adequately (RPD > 1.4) predicted particulate organic matter (POM), silt + clay, labile carbon (C), and labile nitrogen (N) (where RPD = ratio of standard deviation to root mean square error of cross-validation [RMSECV]). The SR (0.53 μm, 2.11 μm) model of labile N with R 2 = 0.81, RMSECV= 0.28, and RPD = 1.94 produced the best results in this study. Our results provide optimism that remote-sensing spectral models can successfully predict soil properties indicative of ecosystem nutrient and organic matter turnover and storage, and do so in areas with dense canopy cover. [ABSTRACT FROM AUTHOR]

Monitoring aquatic vegetation is an important component of water resource management due to the ecological services provided by these habitats. Spectrally-rich hyperspectral imagery can be an efficient tool for mapping and classifying macrophyte communities. Identification of submerged vegetation in aquatic regions is complicated by variations in optical properties of water constituents, sun-water-sensor geometry, water depth and the spectral/structural complexity of the plants. Many studies have attempted to detect aquatic vegetation in coastal waters; however, few studies have targeted shallow, black-water rivers tainted with chromophoric dissolved organic matter (CDOM). This study investigates methods to analyze airborne hyperspectral imagery and detect and classify aquatic vegetation in a black-water riverine system. Images were normalized to account for reflectance from the water surface and varying water depth before being analyzed by the Maximum Likelihood (ML) and three other non-parametric classifiers: Artificial Neural Network (ANN), Support Vector Machine (SVM) and Spectral Angular Mapper (SAM). Quality assessment analysis indicated a general classification and detection accuracy improvement when non-parametric classifiers were applied on the normalized and depth invariant images. A maximum classification accuracy of about 69% was achieved when the ANN classifier was applied on the normalized images, and maximum detection accuracies of 93% and 92% were obtained when the SAM and the SVM classifiers were applied on depth invariant images, respectively. [ABSTRACT FROM AUTHOR]

Managing Pinus elliotii forests for timber production and/or carbon sequestration is a common management objective, but can negatively affect water yield due to high losses from evapotranspiration. Thus, understanding the trade-offs and potential synergies among multiple ecosystem goods services, as well as the drivers influencing these interactions, can help identify effective forest management practices. We used available data from 377 permanent plots from the USDA Forest Service Forest Inventory and Analysis Program for 2002-2011, and a forest water yield model to quantify provision levels and spatial distribution and patterns of carbon sequestration, timber volume and water yield for Pinus elliotii ecosystems in North Florida, USA. A ranking-classification framework and statistical analyses were used to better understand the interactions among ecosystem services and the effect of biophysical drivers on ecosystem service bundles. Results indicate that increased biomass reduced water yield but this trade-off varied across space. Specific synergies, or acceptable provision levels, among carbon sequestration, timber volume and water yield were identified and mapped. Additionally, stand age, silvicultural treatment, and site quality significantly affected the provision level of, and interactions among, the three ecosystem goods and services. The framework developed in this study can be used to assess, map, and manage subtropical forests for optimal provision of ecosystem services. [ABSTRACT FROM AUTHOR]

The image-based modeling and simulation of plant growth have numerous and diverse applications. In this study, we used image-based and manual field measurements to develop and validate a methodology to simulate strawberry (Fragaria × ananassa Duch.) plant canopies throughout the Florida strawberry growing season. The simulated plants were used to create a synthetic image using radiative transfer modeling. Observed canopy properties were incorporated into an L-system simulator, and a series of strawberry canopies corresponding to specific weekly observation dates were created. The simulated canopies were compared visually with actual plant images and quantitatively with in-situ leaf area throughout the strawberry season. A simple regression model with L-system-derived and in-situ total leaf areas had an A d j   R 2 value of 0.78. The L-system simulated canopies were used to derive information needed for image simulation, such as leaf area and leaf angle distribution. Spectral and plant canopy information were used to create synthetic high spatial resolution multispectral images using the Discrete Anisotropic Radiative Transfer (DART) software. Vegetation spectral indices were extracted from the simulated image and used to develop multiple regression models of in-situ biophysical parameters (leaf area and dry biomass), achieving A d j   R 2 values of 0.63 and 0.50, respectively. The Normalized Difference Vegetation Index (NDVI) and the Red Edge Simple Ratio (SRre) vegetation indices, which utilize the red, red edge, and near infrared bands of the spectrum, were identified as statistically significant variables (p < 0.10). This study showed that both geometric (canopy seize metrics) and spectral variables were successful in modeling in-situ biomass and leaf area. Combining the geometric and spectral variables, however, only slightly improved the prediction model. These results show the feasibility of simulating strawberry canopies and images with inherent geometrical, topological, and spectral properties of real strawberry plants. The simulated canopies and images can be used in applications beyond creating realistic computer graphics for quantitative applications requiring the depiction of vegetation biological processes, such as stress modeling and remote sensing mission planning. [ABSTRACT FROM AUTHOR]

In order to monitor natural and anthropogenic disturbance effects to wetland ecosystems, it is necessary to employ both accurate and rapid mapping of wet graminoid/sedge communities. Thus, it is desirable to utilize automated classification algorithms so that the monitoring can be done regularly and in an efficient manner. This study developed a classification and accuracy assessment method for wetland mapping of at-risk plant communities in marl prairie and marsh areas of the Everglades National Park. Maximum likelihood (ML) and Support Vector Machine (SVM) classifiers were tested using 30.5 cm aerial imagery, the normalized difference vegetation index (NDVI), first and second order texture features and ancillary data. Additionally, appropriate window sizes for different texture features were estimated using semivariogram analysis. Findings show that the addition of NDVI and texture features increased classification accuracy from 66.2% using the ML classifier (spectral bands only) to 83.71% using the SVM classifier (spectral bands, NDVI and first order texture features). [ABSTRACT FROM AUTHOR]

Florida is threatened by sea level rise (SLR) because of its low elevation and populous coastlines. With only a 0.3 m future water level rise, most of Florida's natural beaches will disappear; with a 1.2 m rise, 2.4 million people will be displaced and 730,000 hectares of land lost. The only way to combat this threat is through coastal policy making. Currently, counties in Florida have no way to choose "good" policies due to the lack of needed information. Using high-resolution digital elevation models (DEM) derived from airborne light detection and ranging (LiDAR) measurements, parcel data, and beach transects from the Army Corps of Engineers, the effects of Sea Level Rise (SLR) on two study areas in Key West (Monroe County) and Pinellas County were analyzed in this study, under three policy scenarios: armoring prohibition, armoring, and relocation (rolling easements). To better address the SLR uncertainty, a range of SLR estimates from 0.15 m to 1.35 m, in 0.15 m increments, was used to simulate the three policy options. Each policy scenario was considered in view of selected primary criteria for each policy, obtained from literature and simulating them using ArcGIS. The results show that Key West would be rapidly inundated by rising waters, leaving little room for "relocation" but the mainland of Pinellas would be inundated much more slowly, allowing for progressive policy options to be implemented. [ABSTRACT FROM AUTHOR]

This research provides a performance test of terrestrial light detection and ranging (LIDAR) and multibeam echo sounder scanners integrated with Global Navigation Satellite System/Inertial Navigation System positioning and orientation on a survey vessel platform. To measure the accuracy of the data, experiments were designed to allow the LIDAR and sonar scanners to acquire scans of a control target that extended above and below the water surface. The scans were acquired under normal and induced conditions expected in a marine survey environment, such as variations in speed, range, motion, and orientation. SD, root-mean-square error (RMSE), and mean were computed across all data sets for each experiment. Horizontal RMSE values of 0.06 and 0.03 m were achieved for the LIDAR and sonar data, respectively. Vertical RMSE results of 0.04 m were found for both data types. These results were comparable with previous mobile mapping research involving similar systems. Contributing uncertainty and error sources were analyzed and discussed. [ABSTRACT FROM AUTHOR]

Pine flatwoods forests in the southeastern US have experienced severe wildfires over the past few decades, often attributed to fuel load build-up. These forest communities are fire dependent and require regular burning for ecosystem maintenance and health. Although prescribed fire has been used to reduce wildfire risk and maintain ecosystem integrity, managers are still working to reintroduce fire to long unburned areas. Common perception holds that reintroduction of fire in long unburned forests will produce severe fire effects, resulting in a reluctance to prescribe fire without first using expensive mechanical fuels reduction techniques. To inform prioritization and timing of future fire use, we apply remote sensing analysis to examine the set of conditions most likely to result in high burn severity effects, in relation to vegetation, years since the previous fire, and historical fire frequency. We analyze Landsat imagery-based differenced Normalized Burn Ratios (dNBR) to model the relationships between previous and future burn severity to better predict areas of potential high severity. Our results show that remote sensing techniques are useful for modeling the relationship between elevated risk of high burn severity and the amount of time between fires, the type of fire (wildfire or prescribed burn), and the historical frequency of fires in pine flatwoods forests. [ABSTRACT FROM AUTHOR]

This study evaluates a methodology for using IKONOS stereo imagery to determine the height and position of buildings in dense residential areas. The method was tested on three selected sites in an area of 8.5 km long by 7 km wide and covered by two overlapping (97% overlap) IKONOS images. The images were oriented using rational function models in addition to ground control points. Buildings were identified using an algorithm that utilized the Digital Surface Model (DSM) extracted from the images in addition to the image spectral properties. A digital terrain model was used with the DSM created from the IKONOS stereo imagery to compute building heights. Positional accuracy and building heights were evaluated using corner coordinates extracted from topographic maps and surveyed building heights. The results showed that the average building detection percentage for the test area was 82.6% with an average missing factor of 0.16. When the image rational polynomial coefficients were used to build the image model, results showed a horizontal accuracy of 2.42 and 2.39 m Root Mean Square Error (RMSE) for the easting and northing coordinates, respectively. When ground control points were used, the results improved to the sub-meter level. Differences between building heights extracted from the image model and the corresponding heights obtained through traditional ground surveying had a RMSE of 1.05 m. [ABSTRACT FROM AUTHOR]