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      Using Unmanned Aerial Systems (UAS) to assay mangrove estuaries on the Pacific coast of Costa Rica

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          Abstract

          Mangrove forests, one of the world’s most endangered ecosystems, are also some of the most difficult to access. This is especially true along the Pacific coast of Costa Rica, where 99% of the country’s mangroves occur. Unmanned Aerial Systems (UAS), or drones, have become a convenient tool for natural area assessment, and offer a solution to the problems of remote mangrove monitoring. This study is the first to use UAS to analyze the structure of a mangrove forests within Central America. Our goals were to (1) determine the forest structure of two estuaries in northwestern Costa Rica through traditional ground measurements, (2) assess the accuracy of UAS measurements of canopy height and percent coverage and (3) determine whether the normalized difference vegetation index (NDVI) could discriminate between the most abundant mangrove species. We flew a UAS equipped with a single NDVI sensor during the peak wet (Sept–Nov) and dry (Jan–Feb) seasons. The structure and species composition of the estuaries showed a possible transition between the wet mangroves of southern Costa Rica and the drier northern mangroves. UAS-derived measurements at 100 cm/pixel resolution of percent canopy coverage and maximum and mean canopy height were not statistically different from ground measurements (p > 0.05). However, there were differences in mean canopy height at 10 cm/pixel resolution (p = 0.043), indicating diminished returns in accuracy as resolution becomes extremely fine. Mean NDVI values of Avicennia germinans (most abundant species) changed significantly between seasons (p < 0.001). Mean NDVI of Rhizophora racemosa (second most abundant species) was significantly different from A. germinans and dry forest dominant plots during the dry season (p < 0.001), demonstrating NDVI’s capability of discriminating mangrove species. This study provides the first structural assessment of the studied estuaries and a framework for future studies of mangroves using UAS.

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          Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature

          Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error, and thus the MAE would be a better metric for that purpose. While some concerns over using RMSE raised by Willmott and Matsuura (2005) and Willmott et al. (2009) are valid, the proposed avoidance of RMSE in favor of MAE is not the solution. Citing the aforementioned papers, many researchers chose MAE over RMSE to present their model evaluation statistics when presenting or adding the RMSE measures could be more beneficial. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric, whereas Willmott et al. (2009) indicated that the sums-of-squares-based statistics do not satisfy this rule. In the end, we discussed some circumstances where using the RMSE will be more beneficial. However, we do not contend that the RMSE is superior over the MAE. Instead, a combination of metrics, including but certainly not limited to RMSEs and MAEs, are often required to assess model performance.
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            On the relation between NDVI, fractional vegetation cover, and leaf area index

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              Ecology of Tropical Dry Forest

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                5 June 2019
                2019
                : 14
                : 6
                : e0217310
                Affiliations
                [1 ] Purdue University Fort Wayne, Fort Wayne, Indiana, United States of America
                [2 ] The Leatherback Trust, Goldring-Gund Marine Biology Station, Playa Grande, Costa Rica
                Texas Tech University, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-1538-664X
                Article
                PONE-D-18-36642
                10.1371/journal.pone.0217310
                6550448
                31166979
                456f3d72-e1a5-4b6b-9457-7b57a578185b
                © 2019 Yaney-Keller et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 22 December 2018
                : 8 May 2019
                Page count
                Figures: 5, Tables: 4, Pages: 20
                Funding
                Funding for this study was provided to FVP by The Leatherback Trust, ( https://www.leatherback.org), Fort Wayne Children's Zoo, ( https://kidszoo.org/), Mary Margaret Stucky Testament Trust, ( https://www.manta.com/c/mr4rpd3/mary-margaret-stucky-testament-trust), under Purdue Fort Wayne Grant #8000078286. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Ecology and Environmental Sciences
                Aquatic Environments
                Marine Environments
                Coasts
                Mangrove Swamps
                Earth Sciences
                Marine and Aquatic Sciences
                Aquatic Environments
                Marine Environments
                Coasts
                Mangrove Swamps
                Earth Sciences
                Marine and Aquatic Sciences
                Bodies of Water
                Estuaries
                Biology and Life Sciences
                Ecology
                Ecosystems
                Forests
                Ecology and Environmental Sciences
                Ecology
                Ecosystems
                Forests
                Ecology and Environmental Sciences
                Terrestrial Environments
                Forests
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Support Vector Machines
                Research and Analysis Methods
                Research Design
                Survey Research
                Surveys
                People and places
                Geographical locations
                North America
                Central America
                Costa Rica
                Biology and Life Sciences
                Ecology
                Forest Ecology
                Ecology and Environmental Sciences
                Ecology
                Forest Ecology
                Biology and Life Sciences
                Plant Science
                Plant Anatomy
                Leaves
                Custom metadata
                All tree measurement data used to make the conclusions from this study are available from the Open Science Framework database (DOI 10.17605/OSF.IO/34KRH).

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                Uncategorized

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