Use the tags below to search publications from 2016 - 2022

Kolmann, Kalacska, Lucanus, Sousa, Wainwright, Arroyo-Mora, Andrade. Hyperspectral data as a biodiversity screening tool can differentiate among diverse Neotropical fishes. 2021 Scientific Reports 11:16157

Abstract: Hyperspectral data encode information from electromagnetic radiation (i.e., color) of any object in the form of a spectral signature; these data can then be used to distinguish among materials or even map whole landscapes. Although hyperspectral data have been mostly used to study landscape ecology, floral diversity and many other applications in the natural sciences, we propose that spectral signatures can be used for rapid assessment of faunal biodiversity, akin to DNA barcoding and metabarcoding. We demonstrate that spectral signatures of individual, live fish specimens can accurately capture species and clade-level differences in fish coloration, specifically among piranhas and pacus (Family Serrasalmidae), fishes with a long history of taxonomic confusion. We analyzed 47 serrasalmid species and could distinguish spectra among different species and clades, with the method sensitive enough to document changes in fish coloration over ontogeny. Herbivorous pacu spectra were more like one another than they were to piranhas; however, our method also documented interspecific variation in pacus that corresponds to cryptic lineages. While spectra do not serve as an alternative to the collection of curated specimens, hyperspectral data of fishes in the field should help clarify which specimens might be unique or undescribed, complementing existing molecular and morphological techniques.


Lucanus O, Kalacska M, Arroyo-Mora JP, Sousa L, Nobre Carvalho L. Before and after: A multiscale remote sensing assessment of the Sinop dam, Mato Grosso, Brazil. 2021 Earth 2(2):303-330

Abstract: Hydroelectric dams are a major threat to rivers in the Amazon. They are known to decrease river connectivity, alter aquatic habitats, and emit greenhouse gases such as carbon dioxide and methane. Multiscale remotely sensed data can be used to assess and monitor hydroelectric dams over time. We analyzed the Sinop dam on the Teles Pires river from high spatial resolution satellite imagery to determine the extent of land cover inundated by its reservoir, and subsequent methane emissions from TROPOMI S-5P data. For two case study areas, we generated 3D reconstructions of important endemic fish habitats from unmanned aerial vehicle photographs. We found the reservoir flooded 189 km2 (low water) to 215 km2 (high water) beyond the extent of the Teles Pires river, with 13–30 m tall forest (131.4 Mg/ha average AGB) the predominant flooded class. We further found the reservoir to be a source of methane enhancement in the region. The 3D model showed the shallow habitat had high complexity important for ichthyofauna diversity. The distinctive habitats of rheophile fishes, and of the unique species assemblage found in the tributaries have been permanently modified following inundation. Lastly, we illustrate immersive visualization options for both the satellite imagery and 3D products.


Kalacska , Arroyo-Mora , Lucanus , Kishe-Machumu. Land cover, land use, and climate change impacts on endemic cichlid habitats in Northern Tanzania 2017. Remote Sensing 9(6):623

Abstract: Freshwater ecosystems are among the most threatened on Earth, facing environmental and anthropogenic pressures often surpassing their terrestrial counterparts. Land use and land cover change (LUCC) such as degradation and fragmentation of the terrestrial landscape negatively impacts aquatic ecosystems. Satellite imagery allows for an impartial assessment of the past to determine habitat alterations. It can also be used as a forecasting tool in the development of species conservation strategies through models based on ecological factors extracted from imagery. In this study, we analyze Landsat time sequences (1984–2015) to quantify LUCC around three freshwater ecosystems with endemic cichlids in Tanzania. In addition, we examine population growth, agricultural expansion, and climate change as stressors that impact the habitats. We found that the natural vegetation cover surrounding Lake Chala decreased from 15.5% (1984) to 3.5% (2015). At Chemka Springs, we observed a decrease from 7.4% to 3.5% over the same period. While Lake Natron had minimal LUCC, severe climate change impacts have been forecasted for the region. Subsurface water data from the Gravity Recovery and Climate Experiment (GRACE) satellite observations further show a decrease in water resources for the study areas, which could be exacerbated by increased need from a growing population and an increase in agricultural land use.


Osei Darko, Kalacska, Arroyo-Mora, Fagan. Spectral complexity of hyperspectral images: A new approach for mangrove classification. 2021 Remote Sensing 13(13):2604

Abstract: Hyperspectral remote sensing across multiple spatio-temporal scales allows for mapping and monitoring mangrove habitats to support urgent conservation efforts. The use of hyperspectral imagery for assessing mangroves is less common than for terrestrial forest ecosystems. In this study, two well-known measures in statistical physics, Mean Information Gain (MIG) and Marginal Entropy (ME), have been adapted to high spatial resolution (2.5 m) full range (Visible-Shortwave-Infrared) airborne hyperspectral imagery. These two spectral complexity metrics describe the spatial heterogeneity and the aspatial heterogeneity of the reflectance. In this study, we compare MIG and ME with surface reflectance for mapping mangrove extent and species composition in the Sierpe mangroves in Costa Rica. The highest accuracy for separating mangroves from forest was achieved with visible-near infrared (VNIR) reflectance (98.8% overall accuracy), following by shortwave infrared (SWIR) MIG and ME (98%). Our results also show that MIG and ME can discriminate dominant mangrove species with higher accuracy than surface reflectance alone (e.g., MIG–VNIR = 93.6% vs. VNIR Reflectance = 89.7%)


Kalacska, Lucanus, Sousa, Vieira, Arroyo-Mora. UAV-based 3D point clouds of freshwater fish habitats, Xingu River Basin, Brazil. 2019 Data 4(1):9

Abstract: Dense 3D point clouds were generated from Structure-from-Motion Multiview Stereo (SFM-MVS) photogrammetry for five representative freshwater fish habitats in the Xingu river basin, Brazil. The models were constructed from Unmanned Aerial Vehicle (UAV) photographs collected in 2016 and 2017. The Xingu River is one of the primary tributaries of the Amazon River. It is known for its exceptionally high aquatic biodiversity. The dense 3D point clouds were generated in the dry season when large areas of aquatic substrate are exposed due to the low water level. The point clouds were generated at ground sampling distances of 1.20–2.38 cm. These data are useful for studying the habitat characteristics and complexity of several fish species in a spatially explicit manner, such as calculation of metrics including rugosity and the Minkowski–Bouligand fractal dimension (3D complexity). From these dense 3D point clouds, substrate complexity can be determined more comprehensively than from conventional arbitrary cross sections.


Kalacska, Arroyo-Mora, Lucanus, Pereira, Vieira. Deciphering the many maps of the Xingu River Basin - an assessment of land cover classifications at multiple scales. 2020 Proceedings of the Academy of Natural Sciences of Philadelphia 166:1-55

Abstract: Remote sensing is an invaluable tool to objectively illustrate the rapid decline in habitat extents worldwide. The many operational Earth Observation platforms provide options for the generation of land cover maps, each with unique characteristics and considerable semantic differences in the definition of classes. As a result, differences in baseline estimates are inevitable. Here we compare forest cover and surface water estimates over four time periods spanning three decades (1989–2018) for 1.3 million km2 encompassing the Xingu River Basin, Brazil, from published, freely accessible remotely sensed land cover classifications. While all showed a decrease in forest extent over time, the total deforested area reported by each ranged widely for all time periods. The greatest differences ranged from 9% to 17% (116,958 to 219,778 km2) deforestation of the total area for year 2000 and 2014–2018 time period, respectively. We also show the high sensitivity of forest fragmentation metrics (entropy and foreground area density) to data quality and spatial resolution, with cloud cover and sensor artefacts resulting in errors. Surface water classifications must be chosen carefully because sources differ greatly in location and mapped area of surface water. After operationalization of the Belo Monte dam complex, the large reservoirs are notably absent from several of the classifications illustrating land cover. Freshwater ecosystem health is influenced by the land cover surrounding water bodies (e.g., riparian zones). Understanding differences between the many remotely sensed baselines is fundamentally important to avoid information misuse, and to objectively choose the most appropriate classification for ecological studies, conservation, or policy making. The differences between the classifications examined here are not a failure of the technology, but due to different interpretations of ‘forest cover’ and characteristics of the input data (e.g., spatial resolution). Our findings demonstrate the importance of transparency in the generation of remotely sensed classifications and the need for users to familiarize themselves with the characteristics and limitations of each data set.


Abstract: Submerged aquatic vegetation (SAV) is a critical component of aquatic ecosystems. It is however understudied and rapidly changing due to global climate change and anthropogenic disturbances. Remote sensing (RS) can provide the efficient, accurate and large-scale monitoring needed for proper SAV management and has been shown to produce accurate results when properly implemented. Our objective is to introduce RS to researchers in the field of aquatic ecology. Applying RS to underwater ecosystems is complicated by the water column as water, and dissolved or suspended particulate matter, interacts with the same energy that is reflected or emitted by the target. This is addressed using theoretical or empiric models to remove the water column effect, though no model is appropriate for all aquatic conditions. The suitability of various sensors and platforms to aquatic research is discussed in relation to both SAV as the subject and to project aims and resources. An overview of the required corrections, processing and analysis methods for passive optical imagery is presented and discussed. Previous applications of remote sensing to identify and detect SAV are briefly presented and notable results and lessons are discussed. The success of previous work generally depended on the variability in, and suitability of, the available training data, the data’s spatial and spectral resolutions, the quality of the water column corrections and the level to which the SAV was being investigated (i.e., community versus species.)


Abstract: We describe a new high spatial resolution surface water classification dataset generated for the Xingu river, Brazil, from its confluence with the Iriri river to the Pimental dam prior to construction of the Belo Monte hydropower complex, and after its operationalization. This river is well-known for its exceptionally high diversity and endemism in ichthyofauna. Pre-existing datasets generated from moderate resolution satellite imagery (e.g., 30 m) do not adequately capture the extent of the river. Accurate measurements of water extent are important for a range of applications utilizing surface water data, including greenhouse gas emission estimation, land cover change mapping, and habitat loss/change estimates, among others. We generated the new classifications from RapidEye imagery (5 m pixel size) for 2011 and PlanteScope imagery (3 m pixel size) for 2019 using a Geographic Object Based Image Analysis (GEOBIA) approach.


Lamboj, Lucanus, Osei Darko, Arroyo-Mora, Kalacska. Habitat loss in the restricted range of the endemic Ghanaian cichlid Limbochromis robertsi. 2020 Biotropica 52:896-912

Abstract: Remote sensing has become an integral and invaluable tool to inform biodiversity conservation and monitoring of habitat degradation and restoration over time. Despite the disproportionately high levels of biodiversity loss in freshwater ecosystems worldwide, ichthyofauna are commonly overlooked in favor of other keystone species. Freshwater fish, as indicators of overall aquatic ecosystem health, can also be indicators of larger scale problems within an ecosystem. As a case study with multi-temporal, multi-resolution satellite imagery, we examined deforestation and forest fragmentation around the Atewa Forest Reserve, Ghana. Within small creeks, Limbochromis robertsi, a unique freshwater cichlid with an extremely limited distribution range, can be found. Historically, the land cover in the area has undergone substantial deforestation for agriculture and artisanal small-scale mining. In the 1389-km2 study area, we found deforestation accelerated along with increased forest fragmentation in the 2014–2017 period (167.4 km2 of deforestation) with the majority of the forest loss along the river and creek banks due to small-scale mining operations and increased agriculture. Field visits indicated a decrease in the total L. robertsi population by approximately 90% from the early 1990s to 2018. Its distribution has been reduced to higher elevations by anthropogenic habitat barriers at low elevations and the presence of predatory species. Loss of riparian forest through land use and cover change to mining and agriculture contributes to the habitat degradation for this endemic species. Fine spatial- and temporal-scale studies are required to assess habitat characteristics are not captured by global- or continental-scale datasets.


Kalacska, Lucanus, Sousa, Arroyo-Mora. A new multi-temporal forest cover classification for the Xingu River Basin, Brazil. 2019 Data 4(3):114

Abstract: We describe a new multi-temporal classification for forest/non-forest classes for a 1.3 million square kilometer area encompassing the Xingu River basin, Brazil. This region is well known for its exceptionally high biodiversity, especially in terms of the ichthyofauna, with approximately 600 known species, 10% of which are endemic to the river basin. Global and regional scale datasets do not adequately capture the rapidly changing land cover in this region. Accurate forest cover and forest cover change data are important for understanding the anthropogenic pressures on the aquatic ecosystems. We developed the new classifications with a minimum mapping unit of 0.8 ha from cloud free mosaics of Landsat TM5 and OLI 8 imagery in Google Earth Engine using a classification and regression tree (CART) aided by field photographs for the selection of training and validation points


Kalacska, Lucanus, Sousa, Vieira, Arroyo-Mora. Freshwater fish habitat complexity mapping using above and underwater Structure-From-Motion photogrammetry. 2018 Remote Sensing 10(12):1912

Abstract: Substrate complexity is strongly related to biodiversity in aquatic habitats. We illustrate a novel framework, based on Structure-from-Motion photogrammetry (SfM) and Multi-View Stereo (MVS) photogrammetry, to quantify habitat complexity in freshwater ecosystems from Unmanned Aerial Vehicle (UAV) and underwater photography. We analysed sites in the Xingu river basin, Brazil, to reconstruct the 3D structure of the substrate and identify and map habitat classes important for maintaining fish assemblage biodiversity. From the digital models we calculated habitat complexity metrics including rugosity, slope and 3D fractal dimension. The UAV based SfM-MVS products were generated at a ground sampling distance (GSD) of 1.20–2.38 cm while the underwater photography produced a GSD of 1 mm. Our results show how these products provide spatially explicit complexity metrics, which are more comprehensive than conventional arbitrary cross sections. Shallow neural network classification of SfM-MVS products of substrate exposed in the dry season resulted in high accuracies across classes. UAV and underwater SfM-MVS is robust for quantifying freshwater habitat classes and complexity and should be chosen whenever possible over conventional methods (e.g., chain-and-tape) because of the repeatability, scalability and multi-dimensional nature of the products. The SfM-MVS products can be used to identify high priority freshwater sectors for conservation, species occurrences and diversity studies to provide a broader indication for overall fish species diversity and provide repeatability for monitoring change over time.


Sousa, Lucanus, Arroyo-Mora, Kalacska. Conservation and trade of the endangered Hypancistrus zebra (Siluriformes, Loricariidae), one of the most trafficked Brazilian fish. 2021 Global Ecology and Conservation 27:e01570

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.


Abstract: 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.


Elmer, Soffer, Arroyo-Mora, Kalacska. ASDToolkit: A Novel MATLAB Processing Toolbox for ASD Field Spectroscopy Data. 2020 Data 5(4), 96

Abstract: Over the past 30 years, the use of field spectroscopy has risen in importance in remote sensing studies for the characterization of the surface reflectance of materials in situ within a broad range of applications. Potential uses range from measurements of individual targets of interest (e.g., vegetation, soils, validation targets) to characterizing the contributions of different materials within larger spatially mixed areas as would be representative of the spatial resolution captured by a sensor pixel (UAV to satellite scale). As such, it is essential that a complete and rigorous assessment of both the data acquisition procedures and the suitability of the derived data product be carried out. The measured energy from solar-reflective range spectroradiometers is influenced by the viewing and illumination geometries and the illumination conditions, which vary due to changes in solar position and atmospheric conditions. By applying corrections, the estimated absolute reflectance (Rabs) of targets can be calculated. This property is independent of illumination intensity or conditions, and is the metric commonly suggested to be used to compare spectra even when data are collected by different sensors or acquired under different conditions. By standardizing the process of estimated Rabs, as is provided in the described toolkit, consistency and repeatability in processing are ensured and the otherwise labor-intensive and error-prone processing steps are streamlined. The resultant end data product (Rabs) represents our current best effort to generate consistent and comparable ground spectra that have been corrected for viewing and illumination geometries as well as other factors such as the individual characteristics of the reference panel used during acquisition.


Abstract: Vector based shortest path analysis in geographic information system (GIS) is well established for road networks. Even though these network algorithms can be applied to river layers, they do not generally consider the direction of flow. This paper presents a Python 3.7 program ( that was specifically developed for river networks. It implements multiple single-source (one to one) weighted Dijkstra shortest path calculations, on a list of provided source and target nodes, and returns the route geometry, the total distance between each source and target node, and the total upstream and downstream distances for each shortest path. The end result is similar to what would be obtained by an “all-pairs” weighted Dijkstra shortest path algorithm. Contrary to an “all-pairs” Dijkstra, the algorithm only operates on the source and target nodes that were specified by the user and not on all of the nodes contained within the graph. For efficiency, only the upper distance matrix is returned (e.g., distance from node A to node B), while the lower distance matrix (e.g., distance from nodes B to A) is not. The program is intended to be used in a multiprocessor environment and relies on Python’s multiprocessing package.


Girard, Schweiger, Carteron, Kalacska, Laliberté. Foliar Spectra and Traits of Bog Plants across Nitrogen Deposition Gradients. 2020 Remote Sensing 12(15), 2448

Abstract: 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. Using an integrating sphere fitted to a field spectrometer, we measured spectral properties of leaves from the four most common vascular plant species (Chamaedaphne calyculata, Kalmia angustifolia, Rhododendron groenlandicum and Eriophorum vaginatum) in three bogs in southern Québec and Ontario, 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, leaf water, and dry matter content (<10% RMSEP). However, these multi-species PLSR models were not accurate within species, where the range of values was narrow. To improve the detection of short-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-species differences due to N deposition, suggest that spectroscopy is a promising approach for assessing long-term vegetation changes in bogs subject to atmospheric pollution.


Inamdar, Kalacska, Leblanc, Arroyo-Mora. Characterizing and Mitigating Sensor Generated Spatial Correlations in Airborne Hyperspectral Imaging Data 2020 Remote Sensing 12(4), 641

Abstract: In hyperspectral imaging (HSI), the spatial contribution to each pixel is non-uniform and extends past the traditionally square spatial boundaries designated by the pixel resolution, resulting in sensor-generated blurring effects. The spatial contribution to each pixel can be characterized by the net point spread function, which is overlooked in many airborne HSI applications. The objective of this study was to characterize and mitigate sensor blurring effects in airborne HSI data with simple tools, emphasizing the importance of point spread functions. Two algorithms were developed to (1) quantify spatial correlations and (2) use a theoretically derived point spread function to perform deconvolution. Both algorithms were used to characterize and mitigate sensor blurring effects on a simulated scene with known spectral and spatial variability. The first algorithm showed that sensor blurring modified the spatial correlation structure in the simulated scene, removing 54.0%–75.4% of the known spatial variability. Sensor blurring effects were also shown to remove 31.1%–38.9% of the known spectral variability. The second algorithm mitigated sensor-generated spatial correlations. After deconvolution, the spatial variability of the image was within 23.3% of the known value. Similarly, the deconvolved image was within 6.8% of the known spectral variability. When tested on real-world HSI data, the algorithms sharpened the imagery while characterizing the spatial correlation structure of the dataset, showing the implications of sensor blurring. This study substantiates the importance of point spread functions in the assessment and application of airborne HSI data, providing simple tools that are approachable for all end-users.


Arroyo-Mora, Kalacska, Soffer, Moore, Roulet, Juutinen, Ifimov, Leblanc, Inamdar. Airborne Hyperspectral Evaluation of Maximum Gross Photosynthesis, Gravimetric Water Content, and CO2 Uptake Efficiency of the Mer Bleue Ombrotrophic Peatland. 2018 Remote Sensing 10(4), 565

Peatlands cover a large area in Canada and globally (12% and 3% of the landmass, respectively). These ecosystems play an important role in climate regulation through the sequestration of carbon dioxide from, and the release of methane to, the atmosphere. Monitoring approaches, required to understand the response of peatlands to climate change at large spatial scales, are challenged by their unique vegetation characteristics, intrinsic hydrological complexity, and rapid changes over short periods of time (e.g., seasonality). In this study, we demonstrate the use of multitemporal, high spatial resolution (1 m2) hyperspectral airborne imagery (Compact Airborne Spectrographic Imager (CASI) and Shortwave Airborne Spectrographic Imager (SASI) sensors) for assessing maximum instantaneous gross photosynthesis (PGmax) in hummocks, and gravimetric water content (GWC) and carbon uptake efficiency in hollows, at the Mer Bleue ombrotrophic bog. We applied empirical models (i.e., in situ data and spectral indices) and we derived spatial and temporal trends for the aforementioned variables. Our findings revealed the distribution of hummocks (51.2%), hollows (12.7%), and tree cover (33.6%), which is the first high spatial resolution map of this nature at Mer Bleue. For hummocks, we found growing season PGmax values between 8 μmol m−2 s−1 and 12 μmol m−2 s−1 were predominant (86.3% of the total area). For hollows, our results revealed, for the first time, the spatial heterogeneity and seasonal trends for gravimetric water content and carbon uptake efficiency for the whole bog.


Kalacska, Arroyo-Mora, Soffer, Roulet, Moore, Humphreys, Leblanc, Lucanus, Inamdar. Estimating Peatland Water Table Depth and Net Ecosystem Exchange: A Comparison between Satellite and Airborne Imagery 2018 Remote Sensing 10(5), 687

Abstract: Peatlands play a fundamental role in climate regulation through their long-term accumulation of atmospheric carbon. Despite their resilience, peatlands are vulnerable to climate change. Remote sensing offers the opportunity to better understand these ecosystems at large spatial scales through time. In this study, we estimated water table depth from a 6-year time sequence of airborne shortwave infrared (SWIR) hyperspectral imagery. We found that the narrowband index NDWI1240 is a strong predictor of water table position. However, we illustrate the importance of considering peatland anisotropy on SWIR imagery from the summer months when the vascular plants are in full foliage, as not all illumination conditions are suitable for retrieving water table position. We also model net ecosystem exchange (NEE) from 10 years of Landsat TM5 imagery and from 4 years of Landsat OLI 8 imagery. Our results show the transferability of the model between imagery from sensors with similar spectral and radiometric properties such as Landsat 8 and Sentinel-2. NEE modeled from airborne hyperspectral imagery more closely correlated to eddy covariance tower measurements than did models based on satellite images. With fine spectral, spatial and radiometric resolutions, new generation satellite imagery and airborne hyperspectral imagery allow for monitoring the response of peatlands to both allogenic and autogenic factors.


Abstract: 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.


Abstract: Spectral reflectance within the 350–2500 nm range was measured for 17 pelts of arctic mammals (polar bear, caribou, muskox, and ringed, harp and bearded seals) in relation to snow. Reflectance of all pelts was very low at the ultraviolet (UV) end of the spectrum (<10%), increased through the visual and near infrared, peaking at 40%–60% between 1100 and 1400 nm and then gradually dropped, though remaining above 20% until at least 1800 nm. In contrast, reflectance of snow was very high in the UV range (>90%), gradually dropped to near zero at 1500 nm, and then fluctuated between zero and 20% up to 2500 nm. All pelts could be distinguished from clean snow at many wavelengths. The polar bear pelts had higher and more uniform averaged reflectance from about 600–1100 nm than most other pelts, but discrimination was challenging due to variation in pelt color and intensity among individuals within each species. Results suggest promising approaches for using remote sensing tools with a broad spectral range to discriminate polar bears and other mammals from clean snow. Further data from live animals in their natural environment are needed to develop functions to discriminate among species of mammals and to determine whether other environmental elements may have similar reflectance.


Inamdar, Kalacska, Arroyo-Mora, Leblanc. The Directly-Georeferenced Hyperspectral Point Cloud: Preserving the Integrity of Hyperspectral Imaging Data 2021 Frontiers in Remote Sensing - Data Fusion 2:675323

Abstract: The raster data model has been the standard format for hyperspectral imaging (HSI) over the last four decades. Unfortunately, it misrepresents HSI data because pixels are not natively square nor uniformly distributed across imaged scenes. To generate end products as rasters with square pixels while preserving spectral data integrity, the nearest neighbor resampling methodology is typically applied. This process compromises spatial data integrity as the pixels from the original HSI data are shifted, duplicated and eliminated so that HSI data can conform to the raster data model structure. Our study presents a novel hyperspectral point cloud data representation that preserves the spatial-spectral integrity of HSI data more effectively than conventional square pixel rasters. This Directly-Georeferenced Hyperspectral Point Cloud (DHPC) is generated through a data fusion workflow that can be readily implemented into existing processing workflows used by HSI data providers. The effectiveness of the DHPC over conventional square pixel rasters is shown with four HSI datasets. These datasets were collected at three different sites with two different sensors that captured the spectral information from each site at various spatial resolutions (ranging from 1.5 cm to 2.6 m). The DHPC was assessed based on three data quality metrics (i.e., pixel loss, pixel duplication and pixel shifting), data storage requirements and various HSI applications. All of the studied raster data products were characterized by either substantial pixel loss (50–75%) or pixel duplication (35–75%), depending on the resolution of the resampling grid used in the nearest neighbor methodology. Pixel shifting in the raster end products ranged from 0.33 to 1.95 pixels. The DHPC was characterized by zero pixel loss, pixel duplication and pixel shifting. Despite containing additional surface elevation data, the DHPC was up to 13 times smaller in file size than the corresponding rasters. Furthermore, the DHPC consistently outperformed the rasters in all of the tested applications which included classification, spectra geo-location and target detection. Based on the findings from this work, the developed DHPC data representation has the potential to push the limits of HSI data distribution, analysis and application.


Ihuoma, Madramootoo, Kalacska. Integration of satellite imagery and in situ soil moisture data for estimating irrigation water requirements 2021 International Journal of Applied Earth Observation and Geoinformation 102:102396

Abstract: Remote sensing images provide a reliable approach to monitor crop physiological development and can be used to assess crop evapotranspiration (ETc) and irrigation water requirements (IWR). This study compared the suitability of multispectral images acquired from Unmanned Aerial Vehicles (UAV-MSI), PlanetScope, and Sentinel-2A & 2B satellite platforms for estimating ETc. It integrated this ETc data with in situ soil moisture data to estimate IWR of field gown tomato crops (Lycopersicum esculentum) in southeastern Canada. The experimental field was divided into three (3) blocks, and irrigation scheduling consisted of 100, 80, and 60% of soil's field capacity, corresponding to three irrigation regimes. Plants were selected from each of the three blocks, through a systematic grid sampling approach. The sampled plants were georeferenced and identified in the images. Normalized difference vegetation indices (NDVI) obtained from the remote sensing platforms were evaluated for estimating the crop consumptive coefficient and ETc. The ETc predicted from satellite images were compared with estimates of ETc obtained from the FAO 56 Penman-Monteith module of the AquaCrop model. ETc maps from Sentinel-2 were combined with soil moisture data to predict IWR. The results indicate a significant difference in average NDVI values obtained from the UAV-MSI (0.87 ± 0.03) and satellite platforms (0.71 ± 0.03 and 0.82 ± 0.05, for PlanetScope and Sentinel-2, respectively), which suggests that the UAV-MSI overestimated the field NDVI values. There was a good agreement between Kc and NDVI values extracted from satellite images, with R2 = 0.98, p < 0.001, for Sentinel-2 and R2 = 0.78, p < 0.001, for PlanetScope. ETc values estimated from Sentinel-2 satellite platform were closely corroborated with AquaCrop model (with R2 = 0.94; p < 0.01), which shows the suitability of Sentinel-2 imagery for assessing crop canopy cover and IWR at the field scale. The amount of irrigation water that the grower applied using micro-drip irrigation system (342 and 416 mm in 2017 and 2018 growing seasons, respectively) exceeded the estimated IWR (165 and 199 mm in 2017 and 2018 growing seasons, respectively), which suggests that the field was over-irrigated. This study has shown the practicality of integrating soil moisture measurements and remotely sensed crop parameters for mapping actual irrigation requirements. It indicates a significant progress towards the development of a near real-time approach for supporting precision irrigation.


Abstract: 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.


Kalacska, Lucanus, Arroyo-Mora, Laliberté, Elmer, Leblanc, Groves. Accuracy of 3D Landscape Reconstruction without Ground Control Points Using Different UAS Platforms 2020 Drones 4(2),13

Abstract: The rapid increase of low-cost consumer-grade to enterprise-level unmanned aerial systems (UASs) has resulted in the exponential use of these systems in many applications. Structure from motion with multiview stereo (SfM-MVS) photogrammetry is now the baseline for the development of orthoimages and 3D surfaces (e.g., digital elevation models). The horizontal and vertical positional accuracies (x, y and z) of these products in general, rely heavily on the use of ground control points (GCPs). However, for many applications, the use of GCPs is not possible. Here we tested 14 UASs to assess the positional and within-model accuracy of SfM-MVS reconstructions of low-relief landscapes without GCPs ranging from consumer to enterprise-grade vertical takeoff and landing (VTOL) platforms. We found that high positional accuracy is not necessarily related to the platform cost or grade, rather the most important aspect is the use of post-processing kinetic (PPK) or real-time kinetic (RTK) solutions for geotagging the photographs. SfM-MVS products generated from UAS with onboard geotagging, regardless of grade, results in greater positional accuracies and lower within-model errors. We conclude that where repeatability and adherence to a high level of accuracy are needed, only RTK and PPK systems should be used without GCPs.


Arroyo-Mora, Kalacska, Inamdar, Soffer, Lucanus, Gorman, Naprstek, Schaaf, Ifimov, Elmer, Leblanc. Implementation of a UAV–Hyperspectral Pushbroom Imager for Ecological Monitoring 2019 Drones 3(1), 12

Abstract: Hyperspectral remote sensing provides a wealth of data essential for vegetation studies encompassing a wide range of applications (e.g., species diversity, ecosystem monitoring, etc.). The development and implementation of UAV-based hyperspectral systems have gained popularity over the last few years with novel efforts to demonstrate their operability. Here we describe the design, implementation, testing, and early results of the UAV-μCASI system, which showcases a relatively new hyperspectral sensor suitable for ecological studies. The μCASI (288 spectral bands) was integrated with a custom IMU-GNSS data recorder built in-house and mounted on a commercially available hexacopter platform with a gimbal to maximize system stability and minimize image distortion. We deployed the UAV-μCASI at three sites with different ecological characteristics across Canada: The Mer Bleue peatland, an abandoned agricultural field on Ile Grosbois, and the Cowichan Garry Oak Preserve meadow. We examined the attitude data from the flight controller to better understand airframe motion and the effectiveness of the integrated Differential Real Time Kinematic (RTK) GNSS. We describe important aspects of mission planning and show the effectiveness of a bundling adjustment to reduce boresight errors as well as the integration of a digital surface model for image geocorrection to account for parallax effects at the Mer Bleue test site. Finally, we assessed the quality of the radiometrically and atmospherically corrected imagery from the UAV-μCASI and found a close agreement (<2%) between the image derived reflectance and in-situ measurements. Overall, we found that a flight speed of 2.7 m/s, careful mission planning, and the integration of the bundling adjustment were important system characteristics for optimizing the image quality at an ultra-high spatial resolution (3–5 cm). Furthermore, environmental considerations such as wind speed (<5 m/s) and solar illumination also play a critical role in determining image quality. With the growing popularity of “turnkey” UAV-hyperspectral systems on the market, we demonstrate the basic requirements and technical challenges for these systems to be fully operational.


Ngyuen, Turner, Kalacska. Challenging slopes: ethnic minority livelihoods, state visions, and land-use land cover change in Vietnam’s northern mountainous borderlands 2021 Environment, Development and Sustainability

Abstract: Sloping farmlands dominate much of Vietnam’s northern borderlands with China. Here, ethnic minority farmers have relied on their traditional ecological knowledge for centuries to fashion sustainable semi-subsistence livelihoods as best they can. With a rapidly increasing agrarian transition, these farmers must now juggle the agro-ecological limits of their farmlands with new state agricultural policies, growing market integration, and increasing extreme weather events. Despite about 60 percent of Vietnam’s landmass comprising slopes greater than 15°, there is sparse information regarding how best to support sustainable livelihood approaches in such regions. Yet, an understanding of current crop choices, agricultural limits, and farmer decision-making processes in such locales is vital for relevant, slope-related policy suggestions to be formulated. In this paper, we take a mixed methods approach, combining land-use and land cover (LULC) change mapping with qualitative interviews and observations, to investigate the interactions among sloping lands, LULC change, and local livelihoods in a remote, mountainous commune in northern Vietnam’s borderlands. We analyze LULC maps for Bản Phố commune, Lào Cai province, which contains 13 ethnic minority Hmong villages and has a fairly typical upland topography with three-quarters of the land sloped over 15°. Focusing on three main findings from our LULC analysis we then determine the drivers and livelihood consequences of an increase of ‘shrubs’ on sloped land, specific pockets of conversion to ‘bare soils’, and an increase in particular urban areas. We find that state afforestation policies, lowland demand for ‘authentic upland alcohols’, and officials keen to raise the status of a nearby town, all factor into the challenges and opportunities farmers now face.


Arroyo-Mora, Kalacska, Løke, Schläpfer, Coops, Lucanus, Leblanc. Assessing the impact of illumination on UAV pushbroom hyperspectral imagery collected under various cloud cover conditions 2021 Remote Sensing of Environment 258:112396

Abstract: 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.


Räsänen, Juutinen, Kalacska, Aurela, Heikkinen, Mäenpää, Rimali, Virtanen. Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing 2020 GIScience & Remote Sensing 57:7, 943-964

Abstract: 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.


Abstract: Calibration/validation (cal/val) practices applied to airborne hyperspectral imagery of Arctic regions were developed and assessed as an integrated up-scaling methodology that considers: (i) calibration of a laboratory reflectance reference panel; (ii) cross-calibration of multiple field panels; (iii) quality assurance checks of field spectroscopy data; and, (iv) comparison of results with airborne hyperspectral imagery. Overall errors of up to 27% were reduced to <4% using these methods. Calibration results of the laboratory panel provided an improvement of 1% in the visible, near and lower shortwave infrared regions with respect to best estimates achievable using manufacturer supplied calibration data. This improvement was transferred to field panels using an in-house cross-calibration approach that also allowed panels to be assessed for degradation that occurs during field deployment. Comparison of the field spectroscopy results of four cal/val targets with hyperspectral imagery following atmospheric correction identified discrepancies from 1% to 4% (absolute) between 450 nm and 1050 nm, with errors as high as 27% at lower wavelengths. Application of scene-based refinements using two cal/val targets reduced this error across the entire spectral range (<4%) with the most significant improvements below 500 nm. These methods also have important implications to satellite image analysis, especially in Arctic and northern regions.


Arroyo-Mora, Kalacska, Soffer, Ifimov, Leblanc, Schaaf, Lucanus Evaluation of phenospectral dynamics with Sentinel-2A using a bottom-up approach in a northern ombrotrophic peatland 2018 Remote Sensing of Environment 216 544-560

Abstract: Peatlands cover very large extents in northern regions and play a significant role in the global carbon cycle by functioning as a carbon sink. Large-scale satellite based monitoring systems, such as the Sentinel-2 Multispectral Instrument (MSI), are necessary to improve our understanding of how these ecosystems respond to climate change by providing verifiable land products. For instance, satellite-based land product validation approaches can benefit from airborne hyperspectral imagery and in-situ data, which provide higher spatial and spectral resolution baselines, ideal for measuring vegetation changes (e.g. phenology, LAI) at local scales. Here, we assessed the short-term phenospectral dynamics (spectral changes indicated by specific spectral features as a function of phenology) of five ombrotrophic peatland vegetation physiognomies over four dates at the Mer Bleue bog in Canada. We took advantage of a unique remote sensing data acquisition campaign aiming to validate Sentinel-2A land products, and analyzed three spatially and spectrally distinctive datasets (i.e. field spectra, VISNIR airborne hyperspectral imagery (HSI) and Sentinel-2A imagery) over the first half of the 2016 growing season. By implementing a bottom-up approach, first we assessed the airborne HSI's capability to detect phenological changes as compared to in-situ acquired field spectroscopy measurements in a 10 ha area at Mer Bleue and evaluated the spectral features characteristic of these phenological changes. Second, over the entire Mer Bleue area (28,000 ha), we compared a series of four Sentinel-2A images to four airborne HSI mosaics (spatially and spectrally resampled to Sentinel-2A) to assess the utility of Sentinel-2A for detecting small spectral variations due to phenological changes (i.e. greening). In addition, for this second comparison, three spectral vegetation indices were derived from the Sentinel-2A images and the airborne HSI mosaics. The spectral comparisons between the airborne HSI and the field spectroscopy data revealed clear phenological changes from the airborne HSI. For instance, a closer agreement between reflectance measured by the field spectrometer and the airborne HSI spectral response was found in the visible region (450–680 nm). A greater difference however, was consistently seen in the near-infrared region (681–866 nm) across the four dates. Narrow spectral features in three regions of the visible range (global minima, red absorption, green peak), indicating changes in vegetation colour, were consistent for both datasets and with expected phenological patterns at Mer Bleue. At the landscape level, Sentinel-2A mirrored the spectral changes depicted by the resampled HSI data. However, band level, pair-wise comparisons showed significant differences (p < 0.001) in reflectance for each band, with Sentinel-2A exhibiting higher reflectance values than the HSI for the first three dates. Only for the last date (June 23rd) did the airborne HSI have higher reflectance values or no significant difference with the Sentinel-2A data. Overall, our three datasets captured the short-term phenological changes at Mer Bleue and have provided promising results in terms of using the Sentinel-2A MSI sensor to monitor these changes at the landscape level.


Moore, Abraham, Kalacska, Murphy, Potvin. Changes from pasture to a native tree plantation affect soil organic matter in a tropical soil, Panamá 2018 Plant and Soil 425:133-143

Background and aims
We examined changes in soil organic matter arising from conversion of a 45-year old pasture to a 10 yr. old native tree plantation in Panamá, to evaluate the effect of monoculture and mixtures.

We intensively sampled the soil 0–10 cm depth in the pasture in 2001 and in 22 plantation plots in 2011, ranging from 5 monocultures to 3- and 6-species treatments; samples were also taken from an undisturbed forest site. Soil analyses included organic carbon (SOC) and δ13C.

Conversion of the pasture to tree plantation resulted in an overall loss of SOC of 0.6 kg m−2 (18%) in the top 10 cm, but neither tree species nor diversity had a significant effect. End-member δ13C values suggested that the contribution of C3 plants to SOC was increased from 26% in the pasture to 55% after 10 years of plantation and SOC turnover times were calculated to be 21–36 yr.

The magnitude of the loss in soil SOC is smaller than the increases in tree biomass (~3 kg C m−2) and litter (~0.3 kg C m−2) in the plantation, but still a significant part of the ecosystem C balance.


Kalacska, Chmura, Lucanus, Bérubé, Arroyo-Mora Structure from motion will revolutionize analyses of tidal wetland landscapes 2017 Remote Sensing of Environment 199:14-24

Abstract: b Ecosystem services of tidal wetlands depend upon hydrology and vegetation, which in turn vary with elevation differences on the order of centimeters. Variability on such a fine scale is not captured in digital elevation models prepared from conventionally acquired LiDAR data products that generally have a spatial resolution of 0.5–1.0 m and vertical uncertainties up to 15 cm. Until recently, capturing critical fine scale features required laborious, hands-on field surveys that took days to collect data and time limitations usually required surveys to be restricted to selected areas of a wetland. Using Structure-from-motion (SfM) photogrammetry and a small unmanned aerial vehicle, precise three-dimensional point clouds, digital surface models (DSM) and color orthomosaics were produced for three salt marshes in Eastern Canada. Vertical and horizontal measurements from the SfM photogrammetry compared favorably to those taken with a Differential Global Positioning System DGPS). Average horizontal displacements of 1.0–2.9 cm were found across the three salt marshes with an average elevation difference of 2.7 cm (± 1.7 cm) in comparison to DGPS. Analysis of the relationship of elevation between points taken with the DGPS and extracted from the SfM DSM gave an R2 of 0.99. With a ground sampling distance of 2.3 cm our SfM photogrammetry generated models captured variations in topography associated with geomorphic features such as creeks, ponds, channel edges, and logs not visible in the DSM prepared from LiDAR of the same sites. SfM photogrammetry enables mapping of important hydrological features, such as creeks carrying drainage from upland watersheds or connectivity of ponds on the wetland surface. The former are important for transport of contaminants or diadromous fish, and the latter is important for resident fish, water birds, and mosquito larvae. Using SfM to distinguish vegetation structure that may indicate vegetation composition will enable more informed analyses of elevational controls on plant distribution and better prediction of their fate with sea level rise.


Dalva, Moore, Kalacska, Leblanc. Nitrous oxide, methane and carbon dioxide patterns and dynamics from an experimental pig mass grave 2017 Forensic Science International 277: 229-240

Abstract: The objective of the three-year study was to examine spatial and temporal patterns of fluxes and soil pore air concentrations of methane (CH4), carbon dioxide (CO2) and nitrous oxide (N2O) from an experimental mass grave located in a temperate environment. The mass grave (5 × 10 m) contained twenty pig carcasses at a depth of approximately 1 m was compared to a plot of the same dimensions containing only disturbed soil, as well as an undisturbed plot. Soil pore air CH4 concentrations were sub-ambient (<1.8 ppm) except at 75 and 100 cm depths at the mass grave in years 1 and 2 but decreased in year 3. The consumption of CH4 within the aerobic soil resulted in small negative fluxes at the soil surface. Soil pore air CO2 concentration showed an increase with depth in all three plots, with the largest increase (>100,000 ppm at 1 m) in the mass grave, though there was a marked decrease from years 1 to 3. Surface fluxes of CO2 showed strong seasonal variations, peaking in summer. Soil pore air N2O concentration showed major increases in the mass grave, compared to the other two plots with the pattern maintained over the three years, resulting in larger surface fluxes of N2O. To establish the role of the carcasses in N2O dynamics, we incubated a soil sample containing carcass material which resulted in fast rates of N2O production and consumption. The maintenance of elevated pore air concentration and surface flux of N2O throughout the 3 years suggests that this is a long-term pattern and likely the best of the three gases to use to detect graves. Thus, we suggest that measurement of soil pore air concentrations, especially of N2O, could be a simple and effective approach to help determine the location of clandestine graves.


Kalacska, Arroyo-Mora, Soffer, Leblanc. Quality Control Assessment of the Mission Airborne Carbon 13 (MAC-13) Hyperspectral Imagery from Costa Rica 2016 Canadian Journal of Remote Sensing 42:2, 85-105

Abstract: A data quality assessment of airborne hyperspectral imagery (HSI) from Mission Airborne Carbon 2013 (MAC13) is presented. Because data quality is fundamentally important for modeling landscape biophysical characteristics from HSI, this article presents an assessment related to spectral alignment, spectroradiometric calibration, and geocorrection for 2,700 km2 of imagery acquired with the CASI-1500 and SASI-644 systems (375 nm – 2523 nm, 2.5 m resampled pixel size). MODIS, in-situ and image-based estimations of aerosol optical depth are compared for calculations of visibility for atmospheric correction. Information content (dimensionality) across the 5 ecosystems and 2 developed areas are also compared to illustrate the benefit of the extensive spectral resolution of the data.

New approaches to the offset corrections of the imagery improved the accuracy of the calibrated results (radiance and reflectance). Assessment of visibility values applied to the atmospheric correction adduced that apparent reflectance computed using in-scene modeled visibility produced the most similar results to ground spectra. Dimensionality analysis revealed increased information content for all ecosystems when both sensors were considered. While not every HSI issue can be completely compensated for, an appreciation of common artifacts allows users to make more informed decision about their impact on planned analysis.


Hedjam, Kalacska, Mignotte, Ziaei Nafchi, Cheriet. Iterative Classifiers Combination Model for Change Detection in Remote Sensing Imagery 2016 IEEE Transactions on Geoscience and Remote Sensing 54(12):6997 - 7008

Abstract: In this paper, we propose a new unsupervised change detection method designed to analyze multispectral remotely sensed image pairs. It is formulated as a segmentation problem to discriminate the changed class from the unchanged class in the difference images. The proposed method is in the category of the committee machine learning model that utilizes an ensemble of classifiers (i.e., the set of segmentation results obtained by several thresholding methods) with a dynamic structure type. More specifically, in order to obtain the final “change/no-change” output, the responses of several classifiers are combined by means of a mechanism that involves the input data (the difference image) under an iterative Bayesian-Markovian framework. The proposed method is evaluated and compared to previously published results using satellite imagery.


Inamdar, Kalacska, Leblanc, Arroyo-Mora. Implementation of the directly-georeferenced hyperspectral point cloud 2021 MethodsX 8:101429

Abstract: Before pushbroom hyperspectral imaging (HSI) data can be applied in remote sensing applications, it must typically be preprocessed through radiometric correction, atmospheric compensation, geometric correction and spatial resampling procedures. After these preprocessing procedures, HSI data are conventionally given as georeferenced raster images. The raster data model compromises the spatial-spectral integrity of HSI data, leading to suboptimal results in various applications. Inamdar et al. (2021) developed a point cloud data format, the Directly-Georeferenced Hyperspectral Point Cloud (DHPC), that preserves the spatial-spectral integrity of HSI data more effectively than rasters. The DHPC is generated through a data fusion workflow that uses conventional preprocessing protocols with a modification to the digital surface model used in the geometric correction. Even with the additional elevation information, the DHPC is still stored with file sizes up to 13 times smaller than conventional rasters, making it ideal for data distribution. Our article aims to describe the DHPC data fusion workflow from Inamdar et al. (2021), providing all the required tools for its integration in pre-existing processing workflows. This includes a MATLAB script that can be readily applied to carry out the modification that must be made to the digital surface model used in the geometric correction. The MATLAB script first derives the point spread function of the HSI data and then convolves it with the digital surface model input in the geometric correction. By breaking down the MATLAB script and describing its functions, data providers can readily develop their own implementation if necessary. The derived point spread function is also useful for characterizing HSI data, quantifying the contribution of materials to the spectrum from any given pixel as a function of distance from the pixel center. Overall, our work makes the implementation of the DHPC data fusion workflow transparent and approachable for end users and data providers.

  • Our article describes the Directly-Georeferenced Hyperspectral Point Cloud (DHPC) data fusion workflow, which can be readily implemented with existing processing protocols by modifying the input digital surface model used in the geometric correction.

  • We provide a MATLAB function that performs the modification to the digital surface model required for the DHPC workflow. This MATLAB script derives the point spread function of the hyperspectral imager and convolves it with the digital surface model so that the elevation data are more spatially consistent with the hyperspectral imaging data as collected.

  • We highlight the increased effectiveness of the DHPC over conventional raster end products in terms of spatial-spectral data integrity, data storage requirements, hyperspectral imaging application results and site exploration via virtual and augmented reality.


Rowan, Kalacska, Inamdar, Arroyo-Mora, Soffer. Multi-Scale Spectral Separability of Submerged Aquatic Vegetation Species in a Freshwater Ecosystem. 2021 Frontiers in Environmental Science - Freshwater Science 9:439

Abstract: Optical remote sensing has been suggested as a preferred method for monitoring submerged aquatic vegetation (SAV), a critical component of freshwater ecosystems that is facing increasing pressures due to climate change and human disturbance. However, due to the limited prior application of remote sensing to mapping freshwater vegetation, major foundational knowledge gaps remain, specifically in terms of the specificity of the targets and the scales at which they can be monitored. The spectral separability of SAV from the St. Lawrence River, Ontario, Canada, was therefore examined at the leaf level (i.e., spectroradiometer) as well as at coarser spectral resolutions simulating airborne and satellite sensors commonly used in the SAV mapping literature. On a Leave-one-out Nearest Neighbor criterion (LNN) scale of values from 0 (inseparable) to 1 (entirely separable), an LNN criterion value between 0.82 (separating amongst all species) and 1 (separating between vegetation and non-vegetation) was achieved for samples collected in the peak-growing season from the leaf level spectroradiometer data. In contrast, samples from the late-growing season and those resampled to coarser spectral resolutions were less separable (e.g., inter-specific LNN reduction of 0.25 in late-growing season samples as compared to the peak-growing season, and of 0.28 after resampling to the spectral response of Landsat TM5). The same SAV species were also mapped from actual airborne hyperspectral imagery using target detection analyses to illustrate how theoretical fine-scale separability translates to an in situ, moderate-spatial scale application. Novel radiometric correction, georeferencing, and water column compensation methods were applied to optimize the imagery analyzed. The SAV was generally well detected (overall recall of 88% and 94% detecting individual vegetation classes and vegetation/non-vegetation, respectively). In comparison, underwater photographs manually interpreted by a group of experts (i.e., a conventional SAV survey method) tended to be more effective than target detection at identifying individual classes, though responses varied substantially. These findings demonstrated that hyperspectral remote sensing is a viable alternative to conventional methods for identifying SAV at the leaf level and for monitoring at larger spatial scales of interest to ecosystem managers and aquatic researchers.


Elmer, Kalacska, Arroyo-Mora. 2021 Mapping the extent of invasive Phragmites australis subsp. australis from airborne hyperspectral imagery Frontiers in Environmental Science - Environmental Informatics and Remote Sensing 9:496

Abstract: Invasive species pose one of the greatest threats to global biodiversity. Early detection of invasive species is critical in order to prevent or manage their spread before they exceed the ability of land management groups to control them. Optical remote sensing has been established as a useful technology for the early detection and mapping of invasive vegetation populations. Through the use of airborne hyperspectral imagery (HSI), this study establishes a target detection methodology used to identify and map the invasive reed Phragmites australis subsp. australis within the entire extent of Îles-de-Boucherville National Park (Quebec, ON, Canada). We applied the Spectral Angle Mapper (SAM) target detection algorithm trained with a high accuracy GNSS ground truth data set to produce a park-wide map illustrating the extent of detected Phragmites. The total coverage of detected Phragmites was 26.74 ha (0.267 km2), which represents 3.28% of the total park area of 814 ha (8.14 km2). The inherent spatial uncertainty of the airborne HSI (2.25 m) was accounted for with uncertainty buffers, which, when included in the measurement of detected Phragmites, lead to a total area of 59.17 ha (0.591 km2), or 7.26% of the park. The overall accuracy of the Phragmites map was 84.28%, with a sensitivity of 76.32% and a specificity of 91.57%. Additionally, visual interpretation of the validation ground truth dataset was performed by 10 individuals, in order to compare their performance to that of the target detection algorithm. The overall accuracy of the visual interpretation was lower than the target detection (i.e., 69.18%, with a sensitivity of 59.21% and a specificity of 78.31%). Overall, this study is one of the first to utilize airborne HSI and target detection to map the extent of Phragmites over a moderately large extent. The uses and limitations of such an approach are established, and the methodology described here in detail could be adapted for future remote sensing studies of Phragmites or other vegetation species, native or invasive, at study sites around the world.


Leblanc, Kalacska, Arroyo-Mora, Luanus, Todd. A Practical Validation of Uncooled Thermal Imagers for Small RPAS 2021 Drones 5(4), 132

Abstract: Uncooled thermal imaging sensors in the LWIR (7.5 μm to 14 μm) have recently been developed for use with small RPAS. This study derives a new thermal imaging validation methodology via the use of a blackbody source (indoors) and real-world field conditions (outdoors). We have demonstrated this method with three popular LWIR cameras by DJI (Zenmuse XT-R, Zenmuse XT2 and, the M2EA) operated by three different popular DJI RPAS platforms (Matrice 600 Pro, M300 RTK and, the Mavic 2 Enterprise Advanced). Results from the blackbody work show that each camera has a highly linearized response (R2 > 0.99) in the temperature range 5–40 °C as well as a small (<2 °C) temperature bias that is less than the stated accuracy of the cameras. Field validation was accomplished by imaging vegetation and concrete targets (outdoors and at night), that were instrumented with surface temperature sensors. Environmental parameters (air temperature, humidity, pressure and, wind and gusting) were measured for several hours prior to imaging data collection and found to either not be a factor, or were constant, during the ~30 min data collection period. In-field results from imagery at five heights between 10 m and 50 m show absolute temperature retrievals of the concrete and two vegetation sites were within the specifications of the cameras. The methodology has been developed with consideration of active RPAS operational requirements.


Inamdar, Rowan, Kalacska, Arroyo-Mora. Water column compensation workflow for hyperspectral imaging data 2022 MethodsX 9, 101601

Abstract: Our article describes a data processing workflow for hyperspectral imaging data to compensate for the water column in shallow, clear to moderate optical water types. We provide a MATLAB script that can be readily used to implement the described workflow. We break down each code segment of this script so that it is more approachable for use and modification by end users and data providers. The workflow initially implements the method for water column compensation described in Lyzenga (1978) and Lyzenga (1981), generating depth invariant indices from spectral band pairs. Given the high dimensionality of hyperspectral imaging data, an overwhelming number of depth invariant indices are generated in the workflow. As such, a correlation based feature selection methodology is applied to remove redundant depth invariant indices. In a post-processing step, a principal component transformation is applied, extracting features that account for a substantial amount of the variance from the non-redundant depth invariant indices while reducing dimensionality. To fully showcase the developed methodology and its potential for extracting bottom type information, we provide an example output of the water column compensation workflow using hyperspectral imaging data collected over the coast of Philpott's Island in Long Sault Parkway provincial park, Ontario, Canada.

•Workflow calculates depth invariant indices for hyperspectral imaging data to compensate for the water column in shallow, clear to moderate optical water types.

•The applied principal component transformation generates features that account for a substantial amount of the variance from the depth invariant indices while reducing dimensionality.

•The output (both depth invariant index image and principal component image) allows for the analysis of bottom type in shallow, clear to moderate optical water types.


Coomes, Kalacska, Takasaki, Abizaid, Grupp. Smallholder agriculture results in stable forest cover in riverine Amazonia 2022 Environmental Research Letters 17, 014024

Abstract: Recent studies point to a rapid increase in small-scale deforestation in Amazonia. Where people live along the rivers of the basin, customary shifting cultivation creates a zone of secondary forest, orchards and crop fields around communities in what was once was old-growth terra firme forest. Visible from satellite imagery as a narrow but extensive band of forest disturbance along rivers, this zone is often considered as having been deforested. In this paper we assess forest disturbance and the dynamics of secondary forests around 275 communities along a 725 km transect on the Napo and Amazon Rivers in the Peruvian Amazon. We used high-resolution satellite imagery to define the 'working area' around each community, based on the spatial distribution of forest/field patches and the visible boundary between old-growth and secondary forests. Land cover change was assessed between ca. 1989 and 2015 using CLASlite™ image classification. Statistical analyses using community and household-level data from the Peruvian Amazon Rural Livelihoods and Poverty project identified the predictors of the extent of forest disturbance and the dynamics of secondary forests around communities. Although shifting cultivation is the primary driver of old-growth forest loss, we find that secondary forest cover, which replaces old-growth forests, is stable through time, and that both the area and rate of expansion into old-growth forests are modest when compared to forest conversion in Peru for colonization and plantation development. Our findings challenge the notion that smallholder agriculture along rivers is an important threat to terra firme forests in Amazonia and point to the importance of protecting forests on community lands from loggers, colonists and other outsiders.


Kalacska, Arroyo-Mora, Coomes, Takasaki, Abizaid. Multi-Temporal Surface Water Classification for Four Major Rivers from the Peruvian Amazon 2022 Data 7(1),6.

Abstract: 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 (>90%).

Publications prior to 2016

Kalacska M, Lalonde M, Moore TR. (2015) Estimation of foliar chlorophyll and nitrogen content in an ombrotrophic bog from hyperspectral data: scaling from leaf to image. Remote Sensing of Environment 169:270-279

Dalva M, Moore TR, Kalacska M, Leblanc G, Costopoulos A. (2015) Nitrous oxide, methane and carbon dioxide dynamics from experimental pig graves. Forensic Science International 247:41-47

Hedjam R., Ziaei Nafchi H., Kalacska M, Cheriet M. (2014) Influence of color-to-gray conversion on the performance of document image binarization: toward a novel optimization problem. IEEE Transactions on Image Processing 24(11):3637-3651

Arroyo-Mora JP, Svob S, Kalacska M, Chazdon R. (2014) Historical patterns of natural forest management in Costa Rica: The good, the bad and the ugly. Forests 5(7):1777-1797

Leblanc G, Kalacska M, Soffer R. (2014) Detection of single graves by airborne hyperspectral imaging. Forensic Science International 245:17-23.

Svob S, Arroyo-Mora JP, Kalacska M. (2014) The development of a forestry geodatabase for natural forest management plans in Costa Rica. Forest Ecology and Management 327:240-250.

Svob S, Arroyo-Mora JP, Kalacska M. (2014) A wood density and aboveground biomass variability assessment using prefelling inventory data in Costa Rica. Carbon Balance and Measurement. Carbon Balance and Management 9(1):9

Xing J, Sieber R, Kalacska M. (2014) The Challenges of image segmentation in big remotely sensed imagery data. Annals of GIS 20(4):233-244

Zhang J, Pham Thi Thanh H., Kalacska M, Turner S. (2014) Using Landsat Thematic Mapper records to map land cover change and the impacts of reforestation programmes in the borderlands of southeast Yunnan, China: 1990–2010. International Journal of Applied Earth Observation 31:25-36

Bouchard M, Beauregard E, Kalacska M. (2013) Journey to grow op: Linking process to outcome in criminal site selection. Journal of Research in Crime and Delinquency 50(1):33-52

Kalacska M, Arroyo-Mora JP, de Gea, J, Snirer E, Herzog C, Moore TR. (2013) Videographic analysis of Eriophorum vaginatum spatial coverage in an ombotrophic bog. Remote Sensing 5(12):6501-6512.

Dalva M, Kalacska M, Moore TR, Costopoulos A. (2012) Detecting grave with methane. Geoderma 18:18-27

Heller E, Rhemtulla J, Sharachandra L, Kalacska M, Shrinivas B, Sengupta R, Ramankutty N. (2012) Mapping irrigated areas and cropping intensities: the contribution of multi-season data in highly heterogeneous agricultural regions of southern India. Photogrammetric Engineering and Remote Sensing 78(8):815-827

Kalacska M, Bouchard M. (2011) Using police seizure data to estimate the size of an outdoor cannabis industry. Police Practice and Research: An International Journal 12(5):424-434

Sanchez-Azofeifa GA, Kalacska M, Do Espirito-Santo MM, Fernandes GW, Schnitzer SA. (2009) Tropical forest succession and the contribution of lianas to Wood Area Index (WAI) Forest Ecology and Management 258:941-948.

Sanchez-Azofeifa GA, Castro K, Wright SJ, Gamon J, Kalacska M, Rivard B, et al. (2009) Differences in leaf traits, leaf internal structure, and spectral reflectance between two communities of lianas and trees: Implications for remote sensing in tropical environments. Remote Sensing of Environment 113:2076-2088

Kalacska M, Bell LS, Sanchez-Azofeifa GA, Caelli T. (2009) The application of remote sensing for detecting mass graves: an experimental animal case study from Costa Rica. Journal of Forensic Sciences 54:154-166

Kalacska M & Bell LS. (2009) Remote Sensing as a tool for detecting clandestine graves. Canadian Society of Forensic Sciences Journal 39:1-13

Parent R, Kalacska M. (2008) Harm Reduction in Policing: Responding to Persons Under the Influence of Illicit Drugs. Thin Blue Line 6:39-40.

Kalacska M, Sanchez-Azofeifa GA, Rivard B, Quesada M. (2008) Baseline assessment for environmental services payments from satellite imagery. Journal of Environmental Management 88:348-359.

Kalacska M, Sanchez-Azofeifa GA, Rivard B, Caelli T, White HP, Calvo-Alvarado JC. (2007) Ecological fingerprinting of ecosystem succession: estimating secondary tropical dry forest structure and diversity using imaging spectroscopy. Remote Sensing of Environment 108:82-96

Kalacska M, Bohlman S, Sanchez-Azofeifa GA, Castro-Esau K, Caelli T. (2007) Hyperspectral discrimination of tropical dry forest lianas and trees: Comparative data reduction approaches at the leaf and canopy levels. Remote Sensing of Environment 109:406-415

Kalacska M, Bell LS. (2007) Remote Detection of Clandestine Graves. Thin Blue Line 3:9-10

Morisette JT, Baret F, Privette JL, Myneni RB, Nickeson JE, Garrigues S, et al. (2006) Validation of global moderate-resolution LAI products: a framework proposed within the CEOS land product validation subgroup. IEEE Transactions on Geoscience and Remote Sensing 44:1804-1817

Sanchez-Azofeifa GA, Quesada M, Rodriguez JP, Nassar JM, Stoner KE, Castillo A, et al. (2005) Research priorities for Neotropical dry forests. Biotropica 37:477-487

Sanchez-Azofeifa GA, Kalacska M, Quesada M, Calvo-Alvarado JC, Nassar JM, Rodriguez JP. (2005) Need for integrated research for a sustainable future in tropical dry forests. Conservation Biology 19:285-286

Kalacska M, Sanchez-Azofeifa GA, Caelli T, Rivard B, Boerlage B. (2005) Estimating leaf area index from satellite imagery using Bayesian Networks. IEEE Transactions on Geoscience and Remote Sensing 43:1866-1873

Kalacska M, Calvo-Alvarado JC, Sanchez-Azofeifa GA. (2005) Calibration and assessment of seasonal changes in leaf area index of a tropical dry forest in different stages of succession. Tree Physiology 25:733-744

Arroyo-Mora JP, Sanchez-Azofeifa GA, Kalacska M, Rivard B, Calvo-Alvarado JC, Janzen DH. (2005) Secondary forest detection in a Neotropical dry forest using Landsat 7 ETM+ imagery. Biotropica 37:497-507

Kalacska M, Sanchez-Azofeifa GA, Rivard B, Calvo-Alvarado JC, Journet ARP, Arroyo-Mora JP, et al. (2004) Leaf area index measurements in a tropical moist forest: A case study from Costa Rica. Remote Sensing of Environment 91:134-152

Kalacska M, Sanchez-Azofeifa GA, Calvo-Alvarado JC, Quesada M, Rivard B, Janzen DH. (2004) Species composition, similarity and diversity in three successional stages of seasonally dry tropical forest. Forest Ecology and Management 200:227-247

Kalacska M, Keresztes R, Kalacska G. (2003) Engineering polymers or engineering wood (Guaiacum sanctum)? Tribological examination of nature's and science's composites. Műanyag és Gumi 40:403-407


Kalacska M, Sanchez-Azofeifa GA (Eds.), Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests Boca Raton, FL: Taylor and Francis Group. 2008.

Book Chapters

Arroyo-Mora JP, Kalacska M. (2019) Relationships between Economic and Environmental Factors, and Labour Migration to Réunion, 1820–1860. In: Campbell G., Stanziani A. (Eds) The Palgrave Handbook of Bondage and Human Rights in Africa and Asia: Palgrave Macmillan US pp. 105-112

Sanchez-Azofeifa GA, Kalacska M, Quesada M, Stoner KE, Lobo JA, Arroyo-Mora JP. (2013) Tropical Dry Climates. In Schwartz MD (Ed.), Phenology: An Integrative Environmental Science (2nd ed.): Springer Science + Business Media B.V. pp. 157-171

Kalacska M, Arroyo-Mora JP, Snirer E, Parent R. (2011) A review of potential biological controls of Cannabis. In: Decorte T, & Potter G (Eds.), World wide weed: global trends in cannabis cultivation and its control London: Ashgate. pp. 215-238

Arroyo-Mora JP, Kalacska M, Chazdon R, Civco D, Obando G, Sanchun A. (2008) Assessing recovery following selective logging of lowland tropical rainforests based on hyperspectral imagery. In Kalacska M & Sanchez-Azofeifa GA (Eds.), Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests Boca Raton, FL: Taylor and Francis Group. pp. 193-212

Calvo-Alvarado JC, Kalacska M, Sanchez-Azofeifa GA, (2008) Effect of soil type on plant growth, leaf nutrient/chlorophyll concentration and leaf reflectance of tropical tree and grass species. In Kalacska M & Sanchez-Azofeifa GA (Eds.), Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests Boca Raton, FL: Taylor and Francis Group. pp. 87-123

Castro-Esau KL, Kalacska M. (2008) Tropical dry forest phenology and the discrimination of tropical species using hyperspectral data. In Kalacska M & Sanchez-Azofeifa GA (Eds.), Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests Boca Raton, FL.: Taylor and Francis Group. pp. 1-25

Arroyo-Mora JP, Kalacska M, Caraballo B, Trujillo J, Vargas O. (2008) Spectral expression of gender: a pilot study with two dioecious Neotropical tree species. In: Kalacska M, Sanchez-Azofeifa GA, (Eds). Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests. Boca Raton, FL.: Taylor and Francis Group. pp. 125-140

Chong MM, Dutchak K, Gamon J, Huang Y, Kalacska M, Lawrence D, et al. (2008) Remote Sensing. In: Nassar JM, Rodriguez JP, Sanchez-Azofeifa GA, Garvin T, Quesada M (Eds.), Human, ecological and biophysical dimensions of tropical dry forests Caracas, Venezuela: Instituto Venezolano de Investigaciones Científicas. pp. 47-80

Sanchez-Azofeifa GA, Kalacska M, Quesada M, Stoner KE, Lobo JA, Arroyo-Mora JP. (2003) Tropical dry climates. In: Schwartz MD, (Ed). Phenology: An Integrative Environmental Science: Kluwer Academic Press. pp. 121-137.

Sanchez-Azofeifa GA, Kachmar M, Kalacska M, Hamilton S. (2003) Issues in field data collection for land use and land cover classifications in Boreal and Tropical environments. In: Wulder M, & Franklin S, (Eds). Methods for remote sensing of forests: Concepts and case studies Kluwer Academic Press. pp. 433-446.

Applied Remote Sensing Lab

Department of Geography
McGill University
805 Sherbrooke West
Burnside Hall 705
Montreal, QC H3A 0B9


Coming soon!


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