Special Issue "She Maps"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Discoveries in Remote Sensing".

Deadline for manuscript submissions: closed (31 August 2020).

Special Issue Editors

Dr. Karen Joyce
E-Mail Website
Guest Editor
College of Science and Engineering, James Cook University, Cairns, QLD 4870, Australia
Interests: Remote Sensing; Coral Reefs; Unmanned Airborne Systems
Special Issues and Collections in MDPI journals
Dr. Renee Bartolo
E-Mail Website
Guest Editor
Environmental Research Institute of the Supervising Scientist, Department of Environment and Energy, Darwin, NT 0820, Australia
Interests: remote sensing; landscape ecology; drones; ecological restoration; ecological risk assessment; climate change impacts on tropical wetlands; mangrove mapping; tropical wetlands
Special Issues and Collections in MDPI journals
Ms. Sylvia Michael
E-Mail
Guest Editor
Independent Researcher, Australia
Interests: remote sensing; satellite imagery; applications of remotely sensed imagery
Dr. Karen Anderson
E-Mail Website
Guest Editor
Environment and Sustainability Institute, University of Exeter, Penryn Campus, Penryn, Cornwall, TR10 9FE, UK
Interests: Remote and proximal sensing; Laser scanning and waveform LiDAR; Field spectroscopy; Drone sensing; Structure-from-motion photogrammetry; Eco-hydrology; Vegetation structure; Mountain hydrology; Ecosystem services
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The United Nations recognises that all of their Sustainable Development Goals rely on achieving gender equality. It is a big call. But it is also something that will not happen without actively seeking to attain it.

One small aspect on the pathway to achieving equality is to recognise that despite having no innate cognitive differences, women are underrepresented in many scientific and technical fields, particularly in leadership roles. Furthermore, male authorship continues to dominate peer-reviewed literature. These two facts are intrinsically linked, as the volume of peer-reviewed publications plays an important role in career progression.

Rather than feeling overwhelmed by the enormity of changing the global statistics, let us change the trajectory within our own discipline. We know that publications within the Remote Sensing journal follow a broader pattern, with female authorship estimated at less than 25%. So, let us act on this and highlight the latest research in remote sensing theory and applications conducted by women around the world, reported on by our female experts. We can work towards changing the statistics of the journal, while also promoting the women who contribute to the science. This may seem like a small gesture, but from little things, big things grow.

We invite contributions with female lead-authors and encourage 50% female authorship, considering remote sensing applications, technology, theory, ethics, and science. We will use an inclusive definition of female to mean everyone who identifies as a woman, regardless of sex assigned at birth, as well as those who identify as non-binary.

Dr. Karen Joyce
Dr. Renee Bartolo
Ms. Sylvia Michael
Dr. Karen Anderson
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (13 papers)

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Open AccessArticle
How Far Can We Classify Macroalgae Remotely? An Example Using a New Spectral Library of Species from the South West Atlantic (Argentine Patagonia)
Remote Sens. 2020, 12(23), 3870; https://doi.org/10.3390/rs12233870 - 26 Nov 2020
Viewed by 498
Abstract
Macroalgae have attracted the interest of remote sensing as targets to study coastal marine ecosystems because of their key ecological role. The goal of this paper is to analyze a new spectral library, including 28 macroalgae from the South-West Atlantic coast, in order [...] Read more.
Macroalgae have attracted the interest of remote sensing as targets to study coastal marine ecosystems because of their key ecological role. The goal of this paper is to analyze a new spectral library, including 28 macroalgae from the South-West Atlantic coast, in order to assess its use in hyperspectral remote sensing. The library includes species collected in the Atlantic Patagonian coast (Argentina) with representatives of brown, red, and green algae, being 22 of the species included in a spectral library for the first time. The spectra of these main groups are described, and the intraspecific variability is also assessed, considering kelp differentiated tissues and depth range, discussing them from the point of view of their effects on spectral features. A classification and an independent component analysis using the spectral range and simulated bands of two state-of-the-art drone-borne hyperspectral sensors were performed. The results show spectral features and clusters identifying further algae taxonomic groups, showing the potential applications of this spectral library for drone-based mapping of this ecological and economical asset of our coastal marine ecosystems. Full article
(This article belongs to the Special Issue She Maps)
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Open AccessArticle
Application of UAV Imagery to Detect and Quantify Submerged Filamentous Algae and Rooted Macrophytes in a Non-Wadeable River
Remote Sens. 2020, 12(20), 3332; https://doi.org/10.3390/rs12203332 - 13 Oct 2020
Cited by 2 | Viewed by 778
Abstract
Imagery from unoccupied aerial vehicles (UAVs) is useful for mapping floating and emerged primary producers, as well as single taxa of submerged primary producers in shallow, clear lakes and streams. However, there is little research on the effectiveness of UAV imagery-based detection and [...] Read more.
Imagery from unoccupied aerial vehicles (UAVs) is useful for mapping floating and emerged primary producers, as well as single taxa of submerged primary producers in shallow, clear lakes and streams. However, there is little research on the effectiveness of UAV imagery-based detection and quantification of submerged filamentous algae and rooted macrophytes in deeper rivers using a standard red-green-blue (RGB) camera. This study provides a novel application of UAV imagery analysis for monitoring a non-wadeable river, the Klamath River in northern California, USA. River depth and solar angle during flight were analyzed to understand their effects on benthic primary producer detection. A supervised, pixel-based Random Trees classifier was utilized as a detection mechanism to estimate the percent cover of submerged filamentous algae and rooted macrophytes from aerial photos within 32 sites along the river in June and July 2019. In-situ surveys conducted via wading and snorkeling were used to validate these data. Overall accuracy was 82% for all sites and the highest overall accuracy of classified UAV images was associated with solar angles between 47.5 and 58.72° (10:04 a.m. to 11:21 a.m.). Benthic algae were detected at depths of 1.9 m underwater and submerged macrophytes were detected down to 1.2 m (river depth) via the UAV imagery in this relatively clear river (Secchi depth > 2 m). Percent cover reached a maximum of 31% for rooted macrophytes and 39% for filamentous algae within all sites. Macrophytes dominated the upstream reaches, while filamentous algae dominated the downstream reaches closer to the Pacific Ocean. In upcoming years, four proposed dam removals are expected to alter the species composition and abundance of benthic filamentous algae and rooted macrophytes, and aerial imagery provides an effective method to monitor these changes. Full article
(This article belongs to the Special Issue She Maps)
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Open AccessArticle
Evaluating Post-Fire Vegetation Recovery in Cajander Larch Forests in Northeastern Siberia Using UAV Derived Vegetation Indices
Remote Sens. 2020, 12(18), 2970; https://doi.org/10.3390/rs12182970 - 12 Sep 2020
Cited by 2 | Viewed by 1272
Abstract
The ability to monitor post-fire ecological responses and associated vegetation cover change is crucial to understanding how boreal forests respond to wildfire under changing climate conditions. Uncrewed aerial vehicles (UAVs) offer an affordable means of monitoring post-fire vegetation recovery for boreal ecosystems where [...] Read more.
The ability to monitor post-fire ecological responses and associated vegetation cover change is crucial to understanding how boreal forests respond to wildfire under changing climate conditions. Uncrewed aerial vehicles (UAVs) offer an affordable means of monitoring post-fire vegetation recovery for boreal ecosystems where field campaigns are spatially limited, and available satellite data are reduced by short growing seasons and frequent cloud cover. UAV data could be particularly useful across data-limited regions like the Cajander larch (Larix cajanderi Mayr.) forests of northeastern Siberia that are susceptible to amplified climate warming. Cajander larch forests require fire for regeneration but are also slow to accumulate biomass post-fire; thus, tall shrubs and other understory vegetation including grasses, mosses, and lichens dominate for several decades post-fire. Here we aim to evaluate the ability of two vegetation indices, one based on the visible spectrum (GCC; Green Chromatic Coordinate) and one using multispectral data (NDVI; Normalized Difference Vegetation Index), to predict field-based vegetation measures collected across post-fire landscapes of high-latitude Cajander larch forests. GCC and NDVI showed stronger linkages with each other at coarser spatial resolutions e.g., pixel aggregated means with 3-m, 5-m and 10-m radii compared to finer resolutions (e.g., 1-m or less). NDVI was a stronger predictor of aboveground carbon biomass and tree basal area than GCC. NDVI showed a stronger decline with increasing distance from the unburned edge into the burned forest. Our results show NDVI tended to be a stronger predictor of some field-based measures and while GCC showed similar relationships with the data, it was generally a weaker predictor of field-based measures for this region. Our findings show distinguishable edge effects and differentiation between burned and unburned forests several decades post-fire, which corresponds to the relatively slow accumulation of biomass for this ecosystem post-fire. These findings show the utility of UAV data for NDVI in this region as a tool for quantifying and monitoring the post-fire vegetation dynamics in Cajander larch forests. Full article
(This article belongs to the Special Issue She Maps)
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Open AccessArticle
Comparing the Spatial Accuracy of Digital Surface Models from Four Unoccupied Aerial Systems: Photogrammetry Versus LiDAR
Remote Sens. 2020, 12(17), 2806; https://doi.org/10.3390/rs12172806 - 29 Aug 2020
Cited by 4 | Viewed by 1615
Abstract
The technological growth and accessibility of Unoccupied Aerial Systems (UAS) have revolutionized the way geographic data are collected. Digital Surface Models (DSMs) are an integral component of geospatial analyses and are now easily produced at a high resolution from UAS images and photogrammetric [...] Read more.
The technological growth and accessibility of Unoccupied Aerial Systems (UAS) have revolutionized the way geographic data are collected. Digital Surface Models (DSMs) are an integral component of geospatial analyses and are now easily produced at a high resolution from UAS images and photogrammetric software. Systematic testing is required to understand the strengths and weaknesses of DSMs produced from various UAS. Thus, in this study, we used photogrammetry to create DSMs using four UAS (DJI Inspire 1, DJI Phantom 4 Pro, DJI Mavic Pro, and DJI Matrice 210) to test the overall accuracy of DSM outputs across a mixed land cover study area. The accuracy and spatial variability of these DSMs were determined by comparing them to (1) 12 high-precision GPS targets (checkpoints) in the field, and (2) a DSM created from Light Detection and Ranging (LiDAR) (Velodyne VLP-16 Puck Lite) on a fifth UAS, a DJI Matrice 600 Pro. Data were collected on July 20, 2018 over a site with mixed land cover near Middleton, NS, Canada. The study site comprised an area of eight hectares (~20 acres) with land cover types including forest, vines, dirt road, bare soil, long grass, and mowed grass. The LiDAR point cloud was used to create a 0.10 m DSM which had an overall Root Mean Square Error (RMSE) accuracy of ±0.04 m compared to 12 checkpoints spread throughout the study area. UAS were flown three times each and DSMs were created with the use of Ground Control Points (GCPs), also at 0.10 m resolution. The overall RMSE values of UAS DSMs ranged from ±0.03 to ±0.06 m compared to 12 checkpoints. Next, DSMs of Difference (DoDs) compared UAS DSMs to the LiDAR DSM, with results ranging from ±1.97 m to ±2.09 m overall. Upon further investigation over respective land covers, high discrepancies occurred over vegetated terrain and in areas outside the extent of GCPs. This indicated LiDAR’s superiority in mapping complex vegetation surfaces and stressed the importance of a complete GCP network spanning the entirety of the study area. While UAS DSMs and LiDAR DSM were of comparable high quality when evaluated based on checkpoints, further examination of the DoDs exposed critical discrepancies across the study site, namely in vegetated areas. Each of the four test UAS performed consistently well, with P4P as the clear front runner in overall ranking. Full article
(This article belongs to the Special Issue She Maps)
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Open AccessArticle
An UAV and Satellite Multispectral Data Approach to Monitor Water Quality in Small Reservoirs
Remote Sens. 2020, 12(9), 1514; https://doi.org/10.3390/rs12091514 - 09 May 2020
Cited by 6 | Viewed by 1516
Abstract
A multi-sensor and multi-scale monitoring tool for the spatially explicit and periodic monitoring of eutrophication in a small drinking water reservoir is presented. The tool was built with freely available satellite and in situ data combined with Unmanned Aerial Vehicle (UAV)-based technology. The [...] Read more.
A multi-sensor and multi-scale monitoring tool for the spatially explicit and periodic monitoring of eutrophication in a small drinking water reservoir is presented. The tool was built with freely available satellite and in situ data combined with Unmanned Aerial Vehicle (UAV)-based technology. The goal is to evaluate the performance of a multi-platform approach for the trophic state monitoring with images obtained with MultiSpectral Sensors on board satellites Sentinel 2 (S2A and S2B), Landsat 8 (L8) and UAV. We assessed the performance of three different sensors (MultiSpectral Instrument (MSI), Operational Land Imager (OLI) and Rededge Micasense) for retrieving the pigment chlorophyll-a (chl-a), as a quantitative descriptor of phytoplankton biomass and trophic level. The study was conducted in a waterbody affected by cyanobacterial blooms, one of the most important eutrophication-derived risks for human health. Different empirical models and band indices were evaluated. Spectral band combinations using red and near-infrared (NIR) bands were the most suitable for retrieving chl-a concentration (especially 2 band algorithm (2BDA), the Surface Algal Bloom Index (SABI) and 3 band algorithm (3BDA)) even though blue and green bands were useful to classify UAV images into two chl-a ranges. The results show a moderately good agreement among the three sensors at different spatial resolutions (10 m., 30 m. and 8 cm.), indicating a high potential for the development of a multi-platform and multi-sensor approach for the eutrophication monitoring of small reservoirs. Full article
(This article belongs to the Special Issue She Maps)
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Open AccessEditor’s ChoiceArticle
Integrating National Ecological Observatory Network (NEON) Airborne Remote Sensing and In-Situ Data for Optimal Tree Species Classification
Remote Sens. 2020, 12(9), 1414; https://doi.org/10.3390/rs12091414 - 30 Apr 2020
Cited by 3 | Viewed by 1871
Abstract
Accurately mapping tree species composition and diversity is a critical step towards spatially explicit and species-specific ecological understanding. The National Ecological Observatory Network (NEON) is a valuable source of open ecological data across the United States. Freely available NEON data include in-situ measurements [...] Read more.
Accurately mapping tree species composition and diversity is a critical step towards spatially explicit and species-specific ecological understanding. The National Ecological Observatory Network (NEON) is a valuable source of open ecological data across the United States. Freely available NEON data include in-situ measurements of individual trees, including stem locations, species, and crown diameter, along with the NEON Airborne Observation Platform (AOP) airborne remote sensing imagery, including hyperspectral, multispectral, and light detection and ranging (LiDAR) data products. An important aspect of predicting species using remote sensing data is creating high-quality training sets for optimal classification purposes. Ultimately, manually creating training data is an expensive and time-consuming task that relies on human analyst decisions and may require external data sets or information. We combine in-situ and airborne remote sensing NEON data to evaluate the impact of automated training set preparation and a novel data preprocessing workflow on classifying the four dominant subalpine coniferous tree species at the Niwot Ridge Mountain Research Station forested NEON site in Colorado, USA. We trained pixel-based Random Forest (RF) machine learning models using a series of training data sets along with remote sensing raster data as descriptive features. The highest classification accuracies, 69% and 60% based on internal RF error assessment and an independent validation set, respectively, were obtained using circular tree crown polygons created with half the maximum crown diameter per tree. LiDAR-derived data products were the most important features for species classification, followed by vegetation indices. This work contributes to the open development of well-labeled training data sets for forest composition mapping using openly available NEON data without requiring external data collection, manual delineation steps, or site-specific parameters. Full article
(This article belongs to the Special Issue She Maps)
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Open AccessArticle
Counting Mixed Breeding Aggregations of Animal Species Using Drones: Lessons from Waterbirds on Semi-Automation
Remote Sens. 2020, 12(7), 1185; https://doi.org/10.3390/rs12071185 - 07 Apr 2020
Cited by 9 | Viewed by 1749
Abstract
Using drones to count wildlife saves time and resources and allows access to difficult or dangerous areas. We collected drone imagery of breeding waterbirds at colonies in the Okavango Delta (Botswana) and Lowbidgee floodplain (Australia). We developed a semi-automated counting method, using machine [...] Read more.
Using drones to count wildlife saves time and resources and allows access to difficult or dangerous areas. We collected drone imagery of breeding waterbirds at colonies in the Okavango Delta (Botswana) and Lowbidgee floodplain (Australia). We developed a semi-automated counting method, using machine learning, and compared effectiveness of freeware and payware in identifying and counting waterbird species (targets) in the Okavango Delta. We tested transferability to the Australian breeding colony. Our detection accuracy (targets), between the training and test data, was 91% for the Okavango Delta colony and 98% for the Lowbidgee floodplain colony. These estimates were within 1–5%, whether using freeware or payware for the different colonies. Our semi-automated method was 26% quicker, including development, and 500% quicker without development, than manual counting. Drone data of waterbird colonies can be collected quickly, allowing later counting with minimal disturbance. Our semi-automated methods efficiently provided accurate estimates of nesting species of waterbirds, even with complex backgrounds. This could be used to track breeding waterbird populations around the world, indicators of river and wetland health, with general applicability for monitoring other taxa. Full article
(This article belongs to the Special Issue She Maps)
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Open AccessArticle
Synthetic Aperture Radar Remote Sensing of Operational Platform Produced Water Releases
Remote Sens. 2019, 11(23), 2882; https://doi.org/10.3390/rs11232882 - 03 Dec 2019
Cited by 2 | Viewed by 1163
Abstract
Oil spill detection services based on satellite synthetic aperture radar (SAR) frequently detect oil slicks close to platforms due to legal releases of produced water. Separating these slicks from larger releases, e.g., due to accidental leakage is challenging. The aim of this work [...] Read more.
Oil spill detection services based on satellite synthetic aperture radar (SAR) frequently detect oil slicks close to platforms due to legal releases of produced water. Separating these slicks from larger releases, e.g., due to accidental leakage is challenging. The aim of this work is to investigate the SAR characteristics of produced water, including the typical appearance in HH/VV data, possible variations with oil volume, and limitations on detectability. The study is based on dual-polarization TerraSAR-X data collected with constant imaging geometry over one platform in the North Sea. Despite the low oil content (volume percentage of 0.001%–0.002% in this data set), produced water is clearly detectable, with median damping ratios around 3–9 dB. Produced water is detected here in wind speeds of 2–12 m/s, with reduced detectability above ca 9 m/s. Hourly average release volumes with an oil component as low as 0.003 m 3 are detected. The damping ratio, polarization difference, and co-polarization power ratio are investigated and show no clear correlation with released oil volume. However, some indications of trends such as increasing signal damping with oil volume should be further investigated when data over larger release volumes are available. When comparing the properties of the entire slick with the most recently released part, similar or slightly higher damping ratios were found in the full slick case. Full article
(This article belongs to the Special Issue She Maps)
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Open AccessTechnical Note
A Semi-Automated Method for Estimating Adélie Penguin Colony Abundance from a Fusion of Multispectral and Thermal Imagery Collected with Unoccupied Aircraft Systems
Remote Sens. 2020, 12(22), 3692; https://doi.org/10.3390/rs12223692 - 11 Nov 2020
Cited by 1 | Viewed by 1230
Abstract
Monitoring Adélie penguin (Pygoscelis adeliae) populations on the Western Antarctic Peninsula (WAP) provides information about the health of the species and the WAP marine ecosystem itself. In January 2017, surveys of Adélie penguin colonies at Avian Island and Torgersen Island off the [...] Read more.
Monitoring Adélie penguin (Pygoscelis adeliae) populations on the Western Antarctic Peninsula (WAP) provides information about the health of the species and the WAP marine ecosystem itself. In January 2017, surveys of Adélie penguin colonies at Avian Island and Torgersen Island off the WAP were conducted via unoccupied aircraft systems (UAS) collecting optical Red Green Blue (RGB), thermal, and multispectral imagery. A semi-automated workflow to count individual penguins using a fusion of multispectral and thermal imagery was developed and combined into an ArcGIS workflow. This workflow isolates colonies using multispectral imagery and detects and counts individuals by thermal signatures. Two analysts conducted manual counts from synoptic RGB UAS imagery. The automated system deviated from analyst counts by −3.96% on Avian Island and by 17.83% on Torgersen Island. However, colony-by-colony comparisons revealed that the greatest deviations occurred at larger colonies. Matched pairs analysis revealed no significant differences between automated and manual counts at both locations (p > 0.31) and linear regressions of colony sizes from both methods revealed significant positive relationships approaching unity (p < 0.0002. R2 = 0.91). These results indicate that combining UAS surveys with sensor fusion techniques and semi-automated workflows provide efficient and accurate methods for monitoring seabird colonies in remote environments. Full article
(This article belongs to the Special Issue She Maps)
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Open AccessTechnical Note
Modeling Salt Marsh Vegetation Height Using Unoccupied Aircraft Systems and Structure from Motion
Remote Sens. 2020, 12(14), 2333; https://doi.org/10.3390/rs12142333 - 21 Jul 2020
Cited by 4 | Viewed by 1956
Abstract
Salt marshes provide important services to coastal ecosystems in the southeastern United States. In many locations, salt marsh habitats are threatened by coastal development and erosion, necessitating large-scale monitoring. Assessing vegetation height across the extent of a marsh can provide a comprehensive analysis [...] Read more.
Salt marshes provide important services to coastal ecosystems in the southeastern United States. In many locations, salt marsh habitats are threatened by coastal development and erosion, necessitating large-scale monitoring. Assessing vegetation height across the extent of a marsh can provide a comprehensive analysis of its health, as vegetation height is associated with Above Ground Biomass (AGB) and can be used to track degradation or growth over time. Traditional methods to do this, however, rely on manual measurements of stem heights that can cause harm to the marsh ecosystem. Moreover, manual measurements are limited in scale and are often time and labor intensive. Unoccupied Aircraft Systems (UAS) can provide an alternative to manual measurements and generate continuous results across a large spatial extent in a short period of time. In this study, a multirotor UAS equipped with optical Red Green Blue (RGB) and multispectral sensors was used to survey five salt marshes in Beaufort, North Carolina. Structure-from-Motion (SfM) photogrammetry of the resultant imagery allowed for continuous modeling of the entire marsh ecosystem in a three-dimensional space. From these models, vegetation height was extracted and compared to ground-based manual measurements. Vegetation heights generated from UAS data consistently under-predicted true vegetation height proportionally and a transformation was developed to predict true vegetation height. Vegetation height may be used as a proxy for Above Ground Biomass (AGB) and contribute to blue carbon estimates, which describe the carbon sequestered in marine ecosystems. Employing this transformation, our results indicate that UAS and SfM are capable of producing accurate assessments of salt marsh health via consistent and accurate vegetation height measurements. Full article
(This article belongs to the Special Issue She Maps)
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Open AccessLetter
Comparison of Smartphone and Drone Lidar Methods for Characterizing Spatial Variation in PAI in a Tropical Forest
Remote Sens. 2020, 12(11), 1765; https://doi.org/10.3390/rs12111765 - 30 May 2020
Cited by 1 | Viewed by 1106
Abstract
Estimating leaf area index (LAI) and assessing spatial variation in LAI across a landscape is crucial to many ecological studies. Several direct and indirect methods of LAI estimation have been developed and compared; however, many of these methods are prohibitively expensive and/or time [...] Read more.
Estimating leaf area index (LAI) and assessing spatial variation in LAI across a landscape is crucial to many ecological studies. Several direct and indirect methods of LAI estimation have been developed and compared; however, many of these methods are prohibitively expensive and/or time consuming. Here, we examine the feasibility of using the free image processing software CAN-EYE to estimate effective plant area index (PAIeff) from hemispherical canopy images taken with an extremely inexpensive smartphone clip-on fisheye lens. We evaluate the effectiveness of this inexpensive method by comparing CAN-EYE smartphone PAIeff estimates to those from drone lidar over a lowland tropical forest at La Selva Biological Station, Costa Rica. We estimated PAIeff from drone lidar using a method based in radiative transfer theory that has been previously validated using simulated data; we consider this a conservative test of smartphone PAIeff reliability because above-canopy lidar estimates share few assumptions with understory image methods. Smartphone PAIeff varied from 0.1 to 4.4 throughout our study area and we found a significant correlation (r = 0.62, n = 42, p < 0.001) between smartphone and lidar PAIeff, which was robust to image processing analytical options and smartphone model. When old growth and secondary forests are assumed to have different leaf angle distributions for the lidar PAIeff algorithm (spherical and planophile, respectively) this relationship is further improved (r = 0.77, n = 42, p < 0.001). However, we found deviations in the magnitude of the PAIeff estimations depending on image analytical options. Our results suggest that smartphone images can be used to characterize spatial variation in PAIeff in a complex, heterogenous tropical forest canopy, with only small reductions in explanatory power compared to true digital hemispherical photography. Full article
(This article belongs to the Special Issue She Maps)
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Open AccessTechnical Note
Automated Inundation Mapping Over Large Areas Using Landsat Data and Google Earth Engine
Remote Sens. 2020, 12(8), 1348; https://doi.org/10.3390/rs12081348 - 24 Apr 2020
Cited by 4 | Viewed by 2167
Abstract
Accurate inundation maps for flooded wetlands and rivers are a critical resource for their management and conservation. In this paper, we automate a method (thresholding of the short-wave infrared band) for classifying peak inundation in the Okavango Delta, northern Botswana, using Landsat imagery [...] Read more.
Accurate inundation maps for flooded wetlands and rivers are a critical resource for their management and conservation. In this paper, we automate a method (thresholding of the short-wave infrared band) for classifying peak inundation in the Okavango Delta, northern Botswana, using Landsat imagery and Google Earth Engine. Inundation classification in the Okavango Delta is complex owing to the spectral overlap between inundated areas covered with aquatic vegetation and dryland vegetation classes on satellite imagery, and classifications have predominately been implemented on broad spatial resolution imagery. We present the longest time series to date (1990–2019) of inundation maps for the peak flood season at a high spatial resolution (30 m) for the Okavango Delta. We validated the maps using image-based and in situ data accuracy assessments, with overall accuracy ranging from 91.5% to 98.1%. Use of Landsat imagery resulted in consistently lower (on average, 692 km2) estimates of inundation extent than previous studies that used Moderate Resolution Imaging Spectroradiometer (MODIS) and National Oceanic and Atmospheric Administration Advanced Very-High-Resolution Radiometer (NOAA AVHRR) imagery, likely owing to the increased number of mixed pixels that occur when using broad spatial resolution imagery, which can lead to overestimations of the size of inundated areas. We provide the inundation maps and Google Earth Engine code for public use. This classification method can likely be adapted for inundation mapping in other regions. Full article
(This article belongs to the Special Issue She Maps)
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Open AccessTechnical Note
Rapid and Accurate Monitoring of Intertidal Oyster Reef Habitat Using Unoccupied Aircraft Systems and Structure from Motion
Remote Sens. 2019, 11(20), 2394; https://doi.org/10.3390/rs11202394 - 16 Oct 2019
Cited by 6 | Viewed by 2142
Abstract
Oysters support an economically important fishery in many locations in the United States and provide benefits to the surrounding environment by filtering water, providing habitat for fish, and stabilizing shorelines. Changes in oyster reef health reflect variations in factors such as recreational and [...] Read more.
Oysters support an economically important fishery in many locations in the United States and provide benefits to the surrounding environment by filtering water, providing habitat for fish, and stabilizing shorelines. Changes in oyster reef health reflect variations in factors such as recreational and commercial harvests, predation, disease, storms, and broader anthropogenic influences, such as climate change. Consistent measurements of reef area and morphology can help effectively monitor oyster habitat across locations. However, traditional approaches to acquiring these data are time-consuming and can be costly. Unoccupied aircraft systems (UAS) present a rapid and reliable method for assessing oyster habitat that may overcome these limitations, although little information on the accuracy of platforms and processing techniques is available. In the present study, oyster reefs ranging in size from 30 m2 to 300 m2 were surveyed using both fixed-wing and multirotor UAS and compared with ground-based surveys of each reef conducted with a real-time kinematic global positioning system (RTK-GPS). Survey images from UAS were processed using structure from motion (SfM) stereo photogrammetry techniques, with and without the use of ground control point (GCP) correction, to create reef-scale measures of area and morphology for comparison to ground-based measures. UAS-based estimates of both reef area and morphology were consistently lower than ground-based estimates, and the results of matched pairs analyses revealed that differences in reef area did not vary significantly by aircraft or the use of GCPs. However, the use of GCPs increased the accuracy of UAS-based reef morphology measurements, particularly in areas with the presence of water and/or homogeneous spectral characteristics. Our results indicate that both fixed-wing and multirotor UAS can be used to accurately monitor intertidal oyster reefs over time and that proper ground control techniques will improve measurements of reef morphology. These non-destructive methods help modernize oyster habitat monitoring by providing useful and accurate knowledge about the structure and health of oyster reefs ecosystems. Full article
(This article belongs to the Special Issue She Maps)
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