Tropical Dry Forest Succession and the Contribution of Lianas to Wood Area Index (WAI)

The transmission and interception of light through the canopy is an important indicator of forest productivity in tropical forest ecosystems, and the amount of light that eventually reaches the forest floor is influenced by its interactions with leaves, branches, fruits, and flowers among many different canopy elements. While most studies of forest canopy light interception focus on leaf area index (LAI), very few studies have examined wood area index (WAI), which may account for a substantial component of light interception in tropical forests. The influence of lianas on the interception of light and their overall contribution to WAI is a potentially important factor, but it is generally overlooked because of its difficulty to assess. In this paper we evaluate the relative contribution that lianas have to the overall WAI and canopy openness as function of successional stage via a latitudinal comparison of sites across the Americas (Mexico, Costa Rica and Brazil). Our results suggest that lianas significantly increase WAI and decreases canopy openness. However, lianas were absent at all of our study sites where canopy openness exceeded 60%. Our data are the first to explicitly document the role of lianas in the estimation of WAI and, overall, they will contribute to better estimations of ecosystem level LAI in tropical environments, where there is a lack of data on WAI

Conservation and trade of the endangered Hypancistrus zebra (Siluriformes, Loricariidae), one of the most trafficked Brazilian fish

Abstract Hypancistrus zebra, also known as the zebra pleco, is a small sucker-mouth catfish endemic to the Xingu River in Brazil where its survival in the wild is threatened by habitat modification and overfishing for the ornamental fish industry. It is a highly sought-after freshwater ornamental species and one of the most commonly trafficked from Brazil. To date, little is known about its global legal and illicit supply chains within the ornamental fish trade. Through a mixed methods approach (i.e., online survey, key informant interviews and web scraping), we examined the trade and trafficking of this species as well as the awareness of the international aquarist community and local and international stakeholders regarding its conservation. We also establish the historical timeline of zebra pleco keeping and breeding in captivity and assess whether commercial captive breeding can play an important role in the conservation of this species. The retail price of the zebra pleco increased worldwide after an export ban in 2004 but have since decreased to an average of $US 155 (+/-$US 23 based on geographical location) per fish. Fishermen have been consistently paid relatively little ($US 7–60) for each specimen compared to the average wholesale price of$US 100 (+/- $US 94 over time). We conservatively estimate ~100,000 specimens are trafficked out of Brazil annually, of which half or more die in transport, and only a small fraction is seized by law enforcement in Brazil or internationally. The fishes are primarily smuggled from Brazil to Peru and Colombia and then exported internationally with the majority sent to China. The majority of aquarists surveyed (representing 35 countries) were aware the zebra pleco is both endangered and highly endemic. There was less awareness that buying wild caught specimens shipped from Peru, Colombia or elsewhere implies supporting wildlife trafficking. Nevertheless, nearly three quarters of respondents preferred aquarium bred specimens, if available. The zebra pleco is being bred in captivity in high numbers in several countries, yet in Brazil it remains illegal to keep in private aquaria or to commercially breed them. Given the large success of hobby and commercial breeders around the world, H. zebra is well suited for indoor breeding facilities. We argue that implementing regulated local breeding facilities in Brazil to increase the already large numbers reproduced in captivity worldwide, could decrease the demand for trafficked specimens, one of the primary factors threatening its survival. Given its iconic status among freshwater fishes it should be recognized as a flagship species of the Xingu River's conservation

Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing

There is fine-scale spatial heterogeneity in key vegetation properties including leaf-area index (LAI) and biomass in treeless northern peatlands, and hyperspectral drone data with high spatial and spectral resolution could detect the spatial patterns with high accuracy. However, the advantage of hyperspectral drone data has not been tested in a multi-source remote sensing approach (i.e. inclusion of multiple different remote sensing datatypes); and overall, sub-meter-level leaf-area index (LAI) and biomass maps have largely been absent. We evaluated the detectability of LAI and biomass patterns at a northern boreal fen (Halssiaapa) in northern Finland with multi-temporal and multi-source remote sensing data and assessed the benefit of hyperspectral drone data. We measured vascular plant percentage cover and height as well as moss cover in 140 field plots and connected the structural information to measured aboveground vascular LAI and biomass and moss biomass with linear regressions. We predicted both total and plant functional type (PFT) specific LAI and biomass patterns with random forests regressions with predictors including RGB and hyperspectral drone (28 bands in a spectral range of 500-900 nm), aerial and satellite imagery as well as topography and vegetation height information derived from structure-from-motion drone photogrammetry and aerial lidar data. The modeling performance was between moderate and good for total LAI and biomass (mean explained variance between 49.8 and 66.5%) and variable for PFTs (0.3-61.6%). Hyperspectral data increased model performance in most of the regressions, usually relatively little, but in some of the regressions, the inclusion of hyperspectral data even decreased model performance (change in mean explained variance between -14.5 and 9.1%-points). The most important features in regressions included drone topography, vegetation height, hyperspectral and RGB features. The spatial patterns and landscape estimates of LAI and biomass were quite similar in regressions with or without hyperspectral data, in particular for moss and total biomass. The results suggest that the fine-scale spatial patterns of peatland LAI and biomass can be detected with multi-source remote sensing data, vegetation mapping should include both spectral and topographic predictors at sub-meter-level spatial resolution and that hyperspectral imagery gives only slight benefits.Peer reviewe

Assessing the impact of illumination on UAV pushbroom hyperspectral imagery collected under various cloud cover conditions

The recent development of small form-factor (<6 kg), full range (400–2500 nm) pushbroom hyperspectral imaging systems (HSI) for unmanned aerial vehicles (UAV) poses a new range of opportunities for passive remote sensing applications. The flexible deployment of these UAV-HSI systems have the potential to expand the data acquisition window to acceptable (though non-ideal) atmospheric conditions. This is an important consideration for time-sensitive applications (e.g. phenology) in areas with persistent cloud cover. Since the majority of UAV studies have focused on applications with ideal illumination conditions (e.g. minimal or non-cloud cover), little is known to what extent UAV-HSI data are affected by changes in illumination conditions due to variable cloud cover. In this study, we acquired UAV pushbroom HSI (400–2500 nm) over three consecutive days with various illumination conditions (i.e. cloud cover), which were complemented with downwelling irradiance data to characterize illumination conditions and in-situ and laboratory reference panel measurements across a range of reflectivity (i.e. 2%, 10%, 18% and 50%) used to evaluate reflectance products. Using these data we address four fundamental aspects for UAV-HSI acquired under various conditions ranging from high (624.6 ± 16.63 W·m2) to low (2.5 ± 0.9 W·m2) direct irradiance: atmospheric compensation, signal-to-noise ratio (SNR), spectral vegetation indices and endmembers extraction. For instance, two atmospheric compensation methods were applied, a radiative transfer model suitable for high direct irradiance, and an Empirical Line Model (ELM) for diffuse irradiance conditions. SNR results for two distinctive vegetation classes (i.e. tree canopy vs herbaceous vegetation) reveal wavelength dependent attenuation by cloud cover, with higher SNR under high direct irradiance for canopy vegetation. Spectral vegetation index (SVIs) results revealed high variability and index dependent effects. For example, NDVI had significant differences (p < 0.05) across illumination conditions, while NDWI appeared insensitive at the canopy level. Finally, often neglected diffuse illumination conditions may be beneficial for revealing spectral features in vegetation that are obscured by the predominantly non-Lambertian reflectance encountered under high direct illumination. To our knowledge, our study is the first to use a full range pushbroom UAV sensor (400–2500 nm) for assessing illumination effects on the aforementioned variables. Our findings pave the way for understanding the advantages and limitations of ultra-high spatial resolution full range high fidelity UAV-HSI for ecological and other applications

Advances in geocomputation and geospatial artificial intelligence (GeoAI) for mapping

Geocomputation and geospatial artificial intelligence (GeoAI) have essential roles in advancing geographic information science (GIS) and Earth observation to a new stage. GeoAI has enhanced traditional geospatial analysis and mapping, altering the methods for understanding and managing complex human–natural systems. However, there are still challenges in various aspects of geospatial applications related to natural, built, and social environments, and in integrating unique geospatial features into GeoAI models. Meanwhile, geospatial and Earth data are critical components in geocomputation and GeoAI studies, as they can effectively reveal geospatial patterns, factors, relationships, and decision-making processes. This editorial provides a comprehensive overview of geocomputation and GeoAI applications in mapping, classifying them into four categories: (i) buildings and infrastructure, (ii) land use analysis, (iii) natural environment and hazards, and (iv) social issues and human activities. In addition, the editorial summarizes geospatial and Earth data in case studies into seven categories, including in-situ data, geospatial datasets, crowdsourced geospatial data (i.e., geospatial big data), remote sensing data, photogrammetry data, LiDAR, and statistical data. Finally, the editorial presents challenges and opportunities for future research

Multi-Temporal Surface Water Classification for Four Major Rivers from the Peruvian Amazon

We describe a new minimum extent, persistent surface water classification for reaches of four major rivers in the Peruvian Amazon (i.e., Amazon, Napo, Pastaza, Ucayali). These data were generated by the Peruvian Amazon Rural Livelihoods and Poverty (PARLAP) Project which aims to better understand the nexus between livelihoods (e.g., fishing, agriculture, forest use, trade), poverty, and conservation in the Peruvian Amazon over a 35,000 km river network. Previous surface water datasets do not adequately capture the temporal changes in the course of the rivers, nor discriminate between primary main channel and non-main channel (e.g., oxbow lakes) water. We generated the surface water classifications in Google Earth Engine from Landsat TM 5, 7 ETM+, and 8 OLI satellite imagery for time periods from circa 1989, 2000, and 2015 using a hierarchical logical binary classification predominantly based on a modified Normalized Difference Water Index (mNDWI) and shortwave infrared surface reflectance. We included surface reflectance in the blue band and brightness temperature to minimize misclassification. High accuracies were achieved for all time periods (&gt;90%)

A High-Accuracy GNSS Dataset of Ground Truth Points Collected within Îles-de-Boucherville National Park, Quebec, Canada

A new ground truth dataset generated with high-accuracy Global Navigation Satellite Systems (GNSS) positional data of the invasive reed Phragmites australis subsp. australis within Îles-de-Boucherville National Park (Quebec, Canada) is described. The park is one of five study sites for the Canadian Airborne Biodiversity Observatory (CABO) and has stands of invasive P. australis spread throughout the park. Previously, within the context of CABO, no ground truth data had been collected within the park consolidating the locations of P. australis. This dataset was collected to serve as training and validation data for CABO airborne hyperspectral imagery acquired in 2019 to assist with the detection and mapping of P. australis. The locations of the ground truth points were found to be accurate within one pixel of the hyperspectral imagery. Overall, 320 ground truth points were collected, representing 158 locations where P. australis was present and 162 locations where it was absent. Auxiliary data includes field photographs and digitized field notes that provide context for each point

Foliar spectra and traits of bog plants across nitrogen deposition gradients

Bogs, as nutrient-poor ecosystems, are particularly sensitive to atmospheric nitrogen (N) deposition. Nitrogen deposition alters bog plant community composition and can limit their ability to sequester carbon (C). Spectroscopy is a promising approach for studying how N deposition affects bogs because of its ability to remotely determine changes in plant species composition in the long term as well as shorter-term changes in foliar chemistry. However, there is limited knowledge on the extent to which bog plants differ in their foliar spectral properties, how N deposition might affect those properties, and whether subtle inter- or intraspecific changes in foliar traits can be spectrally detected. The objective of the study was to assess the effect of N deposition on foliar traits and spectra. Usinganintegratingspherefittedtoafieldspectrometer,wemeasuredspectralpropertiesof leavesfromthefourmostcommonvascularplantspecies(Chamaedaphnecalyculata,Kalmiaangustifolia, RhododendrongroenlandicumandEriophorumvaginatum)inthreebogsinsouthernQuébecandOntario, Canada, exposed to different atmospheric N deposition levels, including one subjected to a 18-year N fertilization experiment. We also measured chemical and morphological properties of those leaves. We found detectable intraspecific changes in leaf structural traits and chemistry (namely chlorophyll b and N concentrations) with increasing N deposition and identified spectral regions that helped distinguish the site-specific populations within each species. Most of the variation in leaf spectral, chemical, and morphological properties was among species. As such, species had distinct spectral foliar signatures, allowing us to identify them with high accuracy with partial least squares discriminant analyses (PLSDA). Predictions of foliar traits from spectra using partial least squares regression (PLSR) were generally accurate, particularly for the concentrations of N and C, soluble C, leafwater,anddrymattercontent(<10%RMSEP).However,thesemulti-speciesPLSRmodelswerenot accuratewithinspecies,wheretherangeofvalueswasnarrow. Toimprovethedetectionofshort-term intraspecific changes in functional traits, models should be trained with more species-specific data. Our field study showing clear differences in foliar spectra and traits among species, and some within-speciesdifferencesduetoNdeposition,suggestthatspectroscopyisapromisingapproachfor assessing long-term vegetation changes in bogs subject to atmospheric pollution

Comparing UAS LiDAR and Structure-from-Motion Photogrammetry for Peatland Mapping and Virtual Reality (VR) Visualization

The mapping of peatland microtopography (e.g., hummocks and hollows) is key for understanding and modeling complex hydrological and biochemical processes. Here we compare unmanned aerial system (UAS) derived structure-from-motion (SfM) photogrammetry and LiDAR point clouds and digital surface models of an ombrotrophic bog, and we assess the utility of these technologies in terms of payload, efficiency, and end product quality (e.g., point density, microform representation, etc.). In addition, given their generally poor accessibility and fragility, peatlands provide an ideal model to test the usability of virtual reality (VR) and augmented reality (AR) visualizations. As an integrated system, the LiDAR implementation was found to be more straightforward, with fewer points of potential failure (e.g., hardware interactions). It was also more efficient for data collection (10 vs. 18 min for 1.17 ha) and produced considerably smaller file sizes (e.g., 51 MB vs. 1 GB). However, SfM provided higher spatial detail of the microforms due to its greater point density (570.4 vs. 19.4 pts/m2). Our VR/AR assessment revealed that the most immersive user experience was achieved from the Oculus Quest 2 compared to Google Cardboard VR viewers or mobile AR, showcasing the potential of VR for natural sciences in different environments. We expect VR implementations in environmental sciences to become more popular, as evaluations such as the one shown in our study are carried out for different ecosystems

The Correlation Coefficient as a Simple Tool for the Localization of Errors in Spectroscopic Imaging Data

The correlation coefficient (CC) was substantiated as a simple, yet robust statistical tool in the quality assessment of hyperspectral imaging (HSI) data. The sensitivity of the metric was also characterized with respect to artificially-induced errors. The CC was found to be sensitive to spectral shifts and single feature modifications in hyperspectral ground data despite the high, artificially-induced, signal-to-noise ratio (SNR) of 100:1. The study evaluated eight airborne hyperspectral images that varied in acquisition spectrometer, acquisition date and processing methodology. For each image, we identified a uniform ground target region of interest (ROI) that was comprised of a single asphalt road pixel from each column within the sensor field-of-view (FOV). A CC was calculated between the spectra from each of the pixels in the ROI and the data from the center pixel. Potential errors were located by reductions in the CCs below a designated threshold, which was derived from the results of the sensitivity tests. The spectral range associated with each error was established using a windowing technique where the CCs were recalculated after removing the spectral data within various windows. Errors were isolated in the spectral window that removed the previously-identified reductions in the CCs. Finer errors were detected by calculating the CCs across the ROI in the spectral range surrounding various atmospheric absorption features. Despite only observing deviations in the CCs from the 3rd–6th decimal places, non-trivial errors were detected in the imagery. An error was detected within a single band of the shortwave infrared imagery. Errors were also observed throughout the visible-near-infrared imagery, especially in the blue end. With this methodology, it was possible to immediately gauge the spectral consistency of the HSI data across the FOV. Consequently, the effectiveness of various processing methodologies and the spectral consistency of the imaging spectrometers themselves could be studied. Overall, the research highlights the utility of the CC as a simple, low monetary cost, analytical tool for the localization of errors in spectroscopic imaging data