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Remote Sensing of Biodiversity

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (1 December 2015) | Viewed by 175080

Special Issue Editor


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Guest Editor
Distinguished Professor of Environmental and Resource Science, University of California Davis, Davis, CA 95616, USA
Interests: remote sensing of environmental properties and landscape analysis; spectroscopy (wetlands, rangeland and forests); radiation interactions in plant canopies; detection of ecophysiological properties; vegetation stress; application to hydrological and ecological problems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biodiversity is increasingly recognized as a key factor in the maintenance of ecosystem function, processes, and services, and a significant amount of conservation efforts have been focused around sustaining biodiversity. Biodiversity is defined by the number and diversity of species in an ecosystem, their interactions with each other, and their environment. Biodiversity has come to broadly include the diversity of taxonomic/systematic/genetic attributes, morphological/structural attributes, and their ecological/functional traits. Losses of biodiversity have a major impact on the stability and resilience of ecosystems, possibly by loss of functional traits associated with resource capture and decomposition.  Human actions now drive climate and land use changes throughout the globe, which are causing major losses of biodiversity. Human actions impact the climate and seasonality, and the magnitude and timing of disturbance events (e.g., drought and wildfires); such actions create pollution and contamination in the water, air, and soil, and have many other impacts that accelerate species losses, so as to alter ecosystem processes, functions, and their services. The pace of global change requires a rapid increase in knowledge about species numbers, compositions, conditions, and interactions with themselves and the environments they utilize. Remote sensing provides the only feasible way to measure and monitor these changes at the necessary scales. Today’s satellite and aircraft instruments provide a wide range of observational capability, in terms of spatial, temporal, and spectral resolutions, especially when combined with “big data” computational capacity and in situ monitoring systems.

We would like to invite you to submit articles about your recent research with respect to the following topics.

  • Direct mapping of biodiversity and changes in biodiversity using Remote Sensing data (species that are directly measured): Methods and evaluations.
  • Indirect mapping of biodiversity using Remote Sensing data (species and interactions that are not directly measured): Methods and evaluations
  • Remote Sensing of changing composition, density or habitats: Methods and evaluations.
  • Remote Sensing of biodiversity and climate change: Methods and evaluations.
  • Remote Sensing of ecosystem functions: Methods and evaluations.
  • Remote Sensing of tipping points and biodiversity change: Methods and evaluations.
  • Remote Sensing of ecosystem services: Methods and evaluations.
  • Comparison and evaluation of different remote sensing methods for monitoring biodiversity conservation.
  • Improvement and evaluation of input data needed for modeling biodiversity, and for predicting the efficacy of conservation efforts.
  • Review articles covering one or more of these topics are also welcome.

Professor Susan L. Ustin
Guest Editors

Published Papers (19 papers)

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Editorial

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158 KiB  
Editorial
Preface: Remote Sensing of Biodiversity
by Susan L. Ustin
Remote Sens. 2016, 8(6), 508; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8060508 - 16 Jun 2016
Cited by 2 | Viewed by 4379
Abstract
Since the 1992 Earth Summit in Rio de Janeiro, the importance of biological diversity insupporting and maintaining ecosystem functions and processes has become increasingly understood [1]. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)

Research

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1853 KiB  
Article
Drivers of Productivity Trends in Cork Oak Woodlands over the Last 15 Years
by Maria João Santos, Matthias Baumann and Catarina Esgalhado
Remote Sens. 2016, 8(6), 486; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8060486 - 08 Jun 2016
Cited by 9 | Viewed by 5527
Abstract
Higher biodiversity leads to more productive ecosystems which, in turn, supports more biodiversity. Ongoing global changes affect ecosystem productivity and, therefore, are expected to affect productivity-biodiversity relationships. However, the magnitude of these relationships may be affected by baseline biodiversity and its lifeforms. Cork [...] Read more.
Higher biodiversity leads to more productive ecosystems which, in turn, supports more biodiversity. Ongoing global changes affect ecosystem productivity and, therefore, are expected to affect productivity-biodiversity relationships. However, the magnitude of these relationships may be affected by baseline biodiversity and its lifeforms. Cork oak (Quercus suber) woodlands are a highly biodiverse Mediterranean ecosystem managed for cork extraction; as a result of this management cork oak woodlands may have both tree and shrub canopies, just tree and just shrub canopies, and just grasslands. Trees, shrubs, and grasses may respond differently to climatic variables and their combination may, therefore, affect measurements of productivity and the resulting productivity-biodiversity relationships. Here, we asked whether the relationship between productivity and climate is affected by the responses of trees, shrubs, and grasses in cork oak woodlands in Southern Portugal. To answer this question, we linked a 15-year time series of Enhanced Vegetation Index (EVI) derived from Landsat satellites to micrometeorological data to assess the relationship between trends in EVI and climate. Between 2000 and 2013 we observed an overall decrease in EVI. However, EVI increased over cork oaks and decreased over shrublands. EVI trends were strongly positively related to changes in relative humidity and negatively related to temperature. The intra-annual EVI cycle of grasslands and sparse cork oak woodland without understorey (savannah-like ecosystem) had higher variation than the other land-cover types. These results suggest that oaks and shrubs have different responses to changes in water availability, which can be either related to oak physiology, to oaks being either more resilient or having lagged responses to changes in climate, or to the fact that shrublands start senesce earlier than oaks. Our results also suggest that in the future EVI could improve because the rate of increase in minimum EVI is greater than the rate of decrease in maximum EVI, and that this is contingent on management of the shrub understorey as it affects the rate of decrease in maximum EVI. This will be the challenge for the persistence of cork oak woodlands, their associated biodiversity and social-ecological system. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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Article
Landsat ETM+ and SRTM Data Provide Near Real-Time Monitoring of Chimpanzee (Pan troglodytes) Habitats in Africa
by Samuel M. Jantz, Lilian Pintea, Janet Nackoney and Matthew C. Hansen
Remote Sens. 2016, 8(5), 427; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8050427 - 20 May 2016
Cited by 24 | Viewed by 10663
Abstract
All four chimpanzee sub-species populations are declining due to multiple factors including human-caused habitat loss. Effective conservation efforts are therefore needed to ensure their long-term survival. Habitat suitability models serve as useful tools for conservation planning by depicting relative environmental suitability in geographic [...] Read more.
All four chimpanzee sub-species populations are declining due to multiple factors including human-caused habitat loss. Effective conservation efforts are therefore needed to ensure their long-term survival. Habitat suitability models serve as useful tools for conservation planning by depicting relative environmental suitability in geographic space over time. Previous studies mapping chimpanzee habitat suitability have been limited to small regions or coarse spatial and temporal resolutions. Here, we used Random Forests regression to downscale a coarse resolution habitat suitability calibration dataset to estimate habitat suitability over the entire chimpanzee range at 30-m resolution. Our model predicted habitat suitability well with an r2 of 0.82 (±0.002) based on 50-fold cross validation where 75% of the data was used for model calibration and 25% for model testing; however, there was considerable variation in the predictive capability among the four sub-species modeled individually. We tested the influence of several variables derived from Landsat Enhanced Thematic Mapper Plus (ETM+) that included metrics of forest canopy and structure for four three-year time periods between 2000 and 2012. Elevation, Landsat ETM+ band 5 and Landsat derived canopy cover were the strongest predictors; highly suitable areas were associated with dense tree canopy cover for all but the Nigeria-Cameroon and Central Chimpanzee sub-species. Because the models were sensitive to such temporally based predictors, our results are the first to highlight the value of integrating continuously updated variables derived from satellite remote sensing into temporally dynamic habitat suitability models to support near real-time monitoring of habitat status and decision support systems. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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Article
Using NDVI and EVI to Map Spatiotemporal Variation in the Biomass and Quality of Forage for Migratory Elk in the Greater Yellowstone Ecosystem
by Erica L. Garroutte, Andrew J. Hansen and Rick L. Lawrence
Remote Sens. 2016, 8(5), 404; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8050404 - 11 May 2016
Cited by 106 | Viewed by 16014
Abstract
The Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) have gained considerable attention in ecological research and management as proxies for landscape-scale vegetation quantity and quality. In the Greater Yellowstone Ecosystem (GYE), these indices are especially important for mapping spatiotemporal [...] Read more.
The Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) have gained considerable attention in ecological research and management as proxies for landscape-scale vegetation quantity and quality. In the Greater Yellowstone Ecosystem (GYE), these indices are especially important for mapping spatiotemporal variation in the forage available to migratory elk (Cervus elaphus). Here, we examined how the accuracy of using MODIS-derived NDVI and EVI as proxies for forage biomass and quality differed across elevation-related phenology and land use gradients, determined if polynomial NDVI/EVI, site, and season effects improved these models, and then mapped spatiotemporal variation in the abundance of high quality forage available to elk across the Upper Yellowstone River Basin (UYRB) of the GYE. Models with a polynomial NDVI effect explained 19%–55% more variation in biomass than the linear NDVI and EVI models. Models with linear season effect explained 14%–20% more variation in chlorophyll, 37%–69% more variation in crude protein, and 26%–50% more variation in in vitro dry matter digestibility (IVDMD) than the linear NDVI and EVI models. Linear NDVI models explained more variation in biomass and quality across the UYRB than the linear EVI models. The accuracy of these models was lowest in grasslands with late onset of growth, in irrigated agriculture, and after the peak in biomass. Forage biomass and quality varied across the elevation-related phenology and land use gradients in the UYRB throughout the season. At their seasonal peak, the abundance of high quality forage for elk was 50% greater in grasslands with late onset of growth and 200% greater in irrigated agriculture than in all other grasslands, suggesting that these grasslands play an especially important role in the movement and fitness of migratory elk. These results provide novel information on the utility of NDVI and EVI for mapping spatiotemporal patterns of forage biomass and quality. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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Article
Linking Land Surface Phenology and Vegetation-Plot Databases to Model Terrestrial Plant α-Diversity of the Okavango Basin
by Rasmus Revermann, Manfred Finckh, Marion Stellmes, Ben J. Strohbach, David Frantz and Jens Oldeland
Remote Sens. 2016, 8(5), 370; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8050370 - 29 Apr 2016
Cited by 20 | Viewed by 7783
Abstract
In many parts of Africa, spatially-explicit information on plant α-diversity, i.e., the number of species in a given area, is missing as baseline information for spatial planning. We present an approach on how to combine vegetation-plot databases and remotely-sensed land surface phenology [...] Read more.
In many parts of Africa, spatially-explicit information on plant α-diversity, i.e., the number of species in a given area, is missing as baseline information for spatial planning. We present an approach on how to combine vegetation-plot databases and remotely-sensed land surface phenology (LSP) metrics to predict plant α-diversity on a regional scale. We gathered data on plant α-diversity, measured as species density, from 999 vegetation plots sized 20 m × 50 m covering all major vegetation units of the Okavango basin in the countries of Angola, Namibia and Botswana. As predictor variables, we used MODIS LSP metrics averaged over 12 years (250-m spatial resolution) and three topographic attributes calculated from the SRTM digital elevation model. Furthermore, we tested whether additional climatic data could improve predictions. We tested three predictor subsets: (1) remote sensing variables; (2) climatic variables; and (3) all variables combined. We used two statistical modeling approaches, random forests and boosted regression trees, to predict vascular plant α-diversity. The resulting maps showed that the Miombo woodlands of the Angolan Central Plateau featured the highest diversity, and the lowest values were predicted for the thornbush savanna in the Okavango Delta area. Models built on the entire dataset exhibited the best performance followed by climate-only models and remote sensing-only models. However, models including climate data showed artifacts. In spite of lower model performance, models based only on LSP metrics produced the most realistic maps. Furthermore, they revealed local differences in plant diversity of the landscape mosaic that were blurred by homogenous belts as predicted by climate-based models. This study pinpoints the high potential of LSP metrics used in conjunction with biodiversity data derived from vegetation-plot databases to produce spatial information on a regional scale that is urgently needed for basic natural resource management applications. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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Article
Spectral Reflectance of Polar Bear and Other Large Arctic Mammal Pelts; Potential Applications to Remote Sensing Surveys
by George Leblanc, Charles M. Francis, Raymond Soffer, Margaret Kalacska and Julie De Gea
Remote Sens. 2016, 8(4), 273; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8040273 - 25 Mar 2016
Cited by 22 | Viewed by 12244
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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Article
Spatial-Temporal Dynamics of China’s Terrestrial Biodiversity: A Dynamic Habitat Index Diagnostic
by Chunyan Zhang, Danlu Cai, Shan Guo, Yanning Guan, Klaus Fraedrich, Yueping Nie, Xuying Liu and Xiaolin Bian
Remote Sens. 2016, 8(3), 227; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8030227 - 11 Mar 2016
Cited by 17 | Viewed by 6891
Abstract
Biodiversity in China is analyzed based on the components of the Dynamic Habitat Index (DHI). First, observed field survey based spatial patterns of species richness including threatened species are presented to test their linear relationship with remote sensing based DHI (2001–2010 MODIS). Areas [...] Read more.
Biodiversity in China is analyzed based on the components of the Dynamic Habitat Index (DHI). First, observed field survey based spatial patterns of species richness including threatened species are presented to test their linear relationship with remote sensing based DHI (2001–2010 MODIS). Areas with a high cumulative DHI component are associated with relatively high species richness, and threatened species richness increases in regions with frequently varying levels of the cumulative DHI component. The analysis of geographical and statistical distributions yields the following results on interdependence, polarization and change detection: (1) The decadal mean Cumulative Annual Productivity (DHI-\(\overline{cum}\) < 4) in Northwest China and (DHI-\(\overline{cum}\) > 4) in Southeast China are in a stable (positive) relation to the Minimum Annual Apparent Cover (DHI-\(\overline{min}\)) and is positively (negatively) related to the Seasonal Variation of Greenness (DHI-\(\overline{sea}\)); (2) The decadal tendencies show bimodal frequency distributions aligned near DHI-\(\overline{min}\)~0.05 and DHI-\(\overline{sea}\)~0.5 which separated by zero slopes; that is, regions with both small DHI-min and DHI-sea are becoming smaller and vice versa; (3) The decadal tendencies identify regions of land-cover change (as revealed in previous research). That is, the relation of strong and significant tendencies of the three DHI components with climatic or anthropogenic induced changes provides useful information for conservation planning. These results suggest that the spatial-temporal dynamics of China’s terrestrial species and threatened species richness needs to be monitored by first and second moments of remote sensing based information of the DHI. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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3837 KiB  
Article
Associations of Leaf Spectra with Genetic and Phylogenetic Variation in Oaks: Prospects for Remote Detection of Biodiversity
by Jeannine Cavender-Bares, Jose Eduardo Meireles, John J. Couture, Matthew A Kaproth, Clayton C. Kingdon, Aditya Singh, Shawn P. Serbin, Alyson Center, Esau Zuniga, George Pilz and Philip A. Townsend
Remote Sens. 2016, 8(3), 221; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8030221 - 09 Mar 2016
Cited by 99 | Viewed by 12890
Abstract
Species and phylogenetic lineages have evolved to differ in the way that they acquire and deploy resources, with consequences for their physiological, chemical and structural attributes, many of which can be detected using spectral reflectance form leaves. Recent technological advances for assessing optical [...] Read more.
Species and phylogenetic lineages have evolved to differ in the way that they acquire and deploy resources, with consequences for their physiological, chemical and structural attributes, many of which can be detected using spectral reflectance form leaves. Recent technological advances for assessing optical properties of plants offer opportunities to detect functional traits of organisms and differentiate levels of biological organization across the tree of life. Here, we connect leaf-level full range spectral data (400–2400 nm) of leaves to the hierarchical organization of plant diversity within the oak genus (Quercus) using field and greenhouse experiments in which environmental factors and plant age are controlled. We show that spectral data significantly differentiate populations within a species and that spectral similarity is significantly associated with phylogenetic similarity among species. We further show that hyperspectral information allows more accurate classification of taxa than spectrally-derived traits, which by definition are of lower dimensionality. Finally, model accuracy increases at higher levels in the hierarchical organization of plant diversity, such that we are able to better distinguish clades than species or populations. This pattern supports an evolutionary explanation for the degree of optical differentiation among plants and demonstrates potential for remote detection of genetic and phylogenetic diversity. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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5182 KiB  
Article
The Optimal Leaf Biochemical Selection for Mapping Species Diversity Based on Imaging Spectroscopy
by Yujin Zhao, Yuan Zeng, Dan Zhao, Bingfang Wu and Qianjun Zhao
Remote Sens. 2016, 8(3), 216; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8030216 - 08 Mar 2016
Cited by 18 | Viewed by 6817
Abstract
Remote sensing provides a consistent form of observation for biodiversity monitoring across space and time. However, the regional mapping of forest species diversity is still difficult because of the complexity of species distribution and overlapping tree crowns. A new method called “spectranomics” that [...] Read more.
Remote sensing provides a consistent form of observation for biodiversity monitoring across space and time. However, the regional mapping of forest species diversity is still difficult because of the complexity of species distribution and overlapping tree crowns. A new method called “spectranomics” that maps forest species richness based on leaf chemical and spectroscopic traits using imaging spectroscopy was developed by Asner and Martin. In this paper, we use this method to detect the relationships among the spectral, biochemical and taxonomic diversity of tree species, based on 20 dominant canopy species collected in a subtropical forest study site in China. Eight biochemical components (chlorophyll, carotenoid, specific leaf area, equivalent water thickness, nitrogen, phosphorus, cellulose and lignin) are quantified by spectral signatures (R2 = 0.57–0.85, p < 0.01). We also find that the simulated maximum species number based on the eight optimal biochemical components is approximately 15, which is suitable for most 30 m × 30 m forest sites within this study area. This research may support future work on regional species diversity mapping using airborne imaging spectroscopy. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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3141 KiB  
Article
Integrated Analysis of Productivity and Biodiversity in a Southern Alberta Prairie
by Ran Wang, John A. Gamon, Craig A. Emmerton, Haitao Li, Enrica Nestola, Gilberto Z. Pastorello and Olaf Menzer
Remote Sens. 2016, 8(3), 214; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8030214 - 08 Mar 2016
Cited by 40 | Viewed by 9078
Abstract
Grasslands play important roles in ecosystem production and support a large farming and grazing industry. An accurate and efficient way is needed to estimate grassland health and production for monitoring and adjusting management to get sustainable products and other ecosystem services. Previous studies [...] Read more.
Grasslands play important roles in ecosystem production and support a large farming and grazing industry. An accurate and efficient way is needed to estimate grassland health and production for monitoring and adjusting management to get sustainable products and other ecosystem services. Previous studies of grasslands have shown varying relationships between productivity and biodiversity, with most showing either a positive or a hump-shaped relationship where productivity peaks at intermediate diversity. In this study, we used airborne imaging spectrometry combined with ground sampling and eddy covariance measurements to estimate the spatial pattern of production and biodiversity for two sites of contrasting productivity in a southern Alberta prairie ecosystem. Resulting patterns revealed that more diverse sites generally had greater productivity, supporting the hypothesis of a positive relationship between production and biodiversity for this site. We showed that the addition of evenness to richness (using the Shannon Index of dominant species instead of the number of dominant species alone) improved the correlation with optical diversity, an optically derived metric of biodiversity based on the coefficient of variation in spectral reflectance across space. Similarly, the Shannon Index was better correlated with productivity (estimated via NDVI (Normalized Difference Vegetation Index)) than the number of dominant species alone. Optical diversity provided a potent proxy for other more traditional biodiversity metrics (richness and Shannon index). Coupling field measurements and imaging spectrometry provides a method for assessing grassland productivity and biodiversity at a larger scale than can be sampled from the ground, and allows the integrated analysis of the productivity–biodiversity relationship over large areas. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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Article
Phylogenetic Structure of Foliar Spectral Traits in Tropical Forest Canopies
by Kelly M. McManus, Gregory P. Asner, Roberta E. Martin, Kyle G. Dexter, W. John Kress and Christopher B. Field
Remote Sens. 2016, 8(3), 196; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8030196 - 27 Feb 2016
Cited by 39 | Viewed by 8512
Abstract
The Spectranomics approach to tropical forest remote sensing has established a link between foliar reflectance spectra and the phylogenetic composition of tropical canopy tree communities vis-à-vis the taxonomic organization of biochemical trait variation. However, a direct relationship between phylogenetic affiliation and foliar reflectance [...] Read more.
The Spectranomics approach to tropical forest remote sensing has established a link between foliar reflectance spectra and the phylogenetic composition of tropical canopy tree communities vis-à-vis the taxonomic organization of biochemical trait variation. However, a direct relationship between phylogenetic affiliation and foliar reflectance spectra of species has not been established. We sought to develop this relationship by quantifying the extent to which underlying patterns of phylogenetic structure drive interspecific variation among foliar reflectance spectra within three Neotropical canopy tree communities with varying levels of soil fertility. We interpreted the resulting spectral patterns of phylogenetic signal in the context of foliar biochemical traits that may contribute to the spectral-phylogenetic link. We utilized a multi-model ensemble to elucidate trait-spectral relationships, and quantified phylogenetic signal for spectral wavelengths and traits using Pagel’s lambda statistic. Foliar reflectance spectra showed evidence of phylogenetic influence primarily within the visible and shortwave infrared spectral regions. These regions were also selected by the multi-model ensemble as those most important to the quantitative prediction of several foliar biochemical traits. Patterns of phylogenetic organization of spectra and traits varied across sites and with soil fertility, indicative of the complex interactions between the environmental and phylogenetic controls underlying patterns of biodiversity. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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Article
Automated Detection of Forest Gaps in Spruce Dominated Stands Using Canopy Height Models Derived from Stereo Aerial Imagery
by Katarzyna Zielewska-Büttner, Petra Adler, Michaela Ehmann and Veronika Braunisch
Remote Sens. 2016, 8(3), 175; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8030175 - 25 Feb 2016
Cited by 43 | Viewed by 7377
Abstract
Forest gaps are important structural elements in forest ecology to which various conservation-relevant, photophilic species are associated. To automatically map forest gaps and detect their changes over time, we developed a method based on Digital Surface Models (DSM) derived from stereoscopic aerial imagery [...] Read more.
Forest gaps are important structural elements in forest ecology to which various conservation-relevant, photophilic species are associated. To automatically map forest gaps and detect their changes over time, we developed a method based on Digital Surface Models (DSM) derived from stereoscopic aerial imagery and a LiDAR-based Digital Elevation Model (LiDAR DEM). Gaps were detected and delineated in relation to height and cover of the surrounding forest comparing data from two public flight campaigns (2009 and 2012) in a 1023-ha model region in the Northern Black Forest, Southwest Germany. The method was evaluated using an independent validation dataset obtained by visual stereo-interpretation. Gaps were automatically detected with an overall accuracy of 0.90 (2009) and 0.82 (2012). However, a very high users’ accuracy of more than 0.95 (both years) was counterbalanced by a producer’s accuracy of 0.84 (2009) and 0.73 (2012) as some gaps were not automatically detected. Accuracy was mainly dependent on the shadow occurrence and height of the surrounding forest with user’s accuracies dropping to 0.70 (2009) and 0.52 (2012) in high stands (>8 m tree height). As one important step in the workflow, the class of open forest, an important feature for many forest species, was delineated with a very good overall accuracy of 0.92 (both years) with uncertainties occurring mostly in areas with intermediate canopy cover. Presence of complete or partial shadow and geometric limitations of stereo image matching were identified as the main sources of errors in the method performance, suggesting that images with a higher overlap and resolution and ameliorated image-matching algorithms provide the greatest potential for improvement. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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Article
Tree Species Abundance Predictions in a Tropical Agricultural Landscape with a Supervised Classification Model and Imbalanced Data
by Sarah J. Graves, Gregory P. Asner, Roberta E. Martin, Christopher B. Anderson, Matthew S. Colgan, Leila Kalantari and Stephanie A. Bohlman
Remote Sens. 2016, 8(2), 161; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8020161 - 19 Feb 2016
Cited by 63 | Viewed by 8578
Abstract
Mapping species through classification of imaging spectroscopy data is facilitating research to understand tree species distributions at increasingly greater spatial scales. Classification requires a dataset of field observations matched to the image, which will often reflect natural species distributions, resulting in an imbalanced [...] Read more.
Mapping species through classification of imaging spectroscopy data is facilitating research to understand tree species distributions at increasingly greater spatial scales. Classification requires a dataset of field observations matched to the image, which will often reflect natural species distributions, resulting in an imbalanced dataset with many samples for common species and few samples for less common species. Despite the high prevalence of imbalanced datasets in multiclass species predictions, the effect on species prediction accuracy and landscape species abundance has not yet been quantified. First, we trained and assessed the accuracy of a support vector machine (SVM) model with a highly imbalanced dataset of 20 tropical species and one mixed-species class of 24 species identified in a hyperspectral image mosaic (350–2500 nm) of Panamanian farmland and secondary forest fragments. The model, with an overall accuracy of 62% ± 2.3% and F-score of 59% ± 2.7%, was applied to the full image mosaic (23,000 ha at a 2-m resolution) to produce a species prediction map, which suggested that this tropical agricultural landscape is more diverse than what has been presented in field-based studies. Second, we quantified the effect of class imbalance on model accuracy. Model assessment showed a trend where species with more samples were consistently over predicted while species with fewer samples were under predicted. Standardizing sample size reduced model accuracy, but also reduced the level of species over- and under-prediction. This study advances operational species mapping of diverse tropical landscapes by detailing the effect of imbalanced data on classification accuracy and providing estimates of tree species abundance in an agricultural landscape. Species maps using data and methods presented here can be used in landscape analyses of species distributions to understand human or environmental effects, in addition to focusing conservation efforts in areas with high tree cover and diversity. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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Article
Moving Towards Dynamic Ocean Management: How Well Do Modeled Ocean Products Predict Species Distributions?
by Elizabeth A. Becker, Karin A. Forney, Paul C. Fiedler, Jay Barlow, Susan J. Chivers, Christopher A. Edwards, Andrew M. Moore and Jessica V. Redfern
Remote Sens. 2016, 8(2), 149; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8020149 - 16 Feb 2016
Cited by 73 | Viewed by 12452
Abstract
Species distribution models are now widely used in conservation and management to predict suitable habitat for protected marine species. The primary sources of dynamic habitat data have been in situ and remotely sensed oceanic variables (both are considered “measured data”), but now ocean [...] Read more.
Species distribution models are now widely used in conservation and management to predict suitable habitat for protected marine species. The primary sources of dynamic habitat data have been in situ and remotely sensed oceanic variables (both are considered “measured data”), but now ocean models can provide historical estimates and forecast predictions of relevant habitat variables such as temperature, salinity, and mixed layer depth. To assess the performance of modeled ocean data in species distribution models, we present a case study for cetaceans that compares models based on output from a data assimilative implementation of the Regional Ocean Modeling System (ROMS) to those based on measured data. Specifically, we used seven years of cetacean line-transect survey data collected between 1991 and 2009 to develop predictive habitat-based models of cetacean density for 11 species in the California Current Ecosystem. Two different generalized additive models were compared: one built with a full suite of ROMS output and another built with a full suite of measured data. Model performance was assessed using the percentage of explained deviance, root mean squared error (RMSE), observed to predicted density ratios, and visual inspection of predicted and observed distributions. Predicted distribution patterns were similar for models using ROMS output and measured data, and showed good concordance between observed sightings and model predictions. Quantitative measures of predictive ability were also similar between model types, and RMSE values were almost identical. The overall demonstrated success of the ROMS-based models opens new opportunities for dynamic species management and biodiversity monitoring because ROMS output is available in near real time and can be forecast. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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Article
Airborne Hyperspectral Data Predict Fine-Scale Plant Species Diversity in Grazed Dry Grasslands
by Thomas Möckel, Jonas Dalmayne, Barbara C. Schmid, Honor C. Prentice and Karin Hall
Remote Sens. 2016, 8(2), 133; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8020133 - 08 Feb 2016
Cited by 42 | Viewed by 6914
Abstract
Semi-natural grasslands with grazing management are characterized by high fine-scale species richness and have a high conservation value. The fact that fine-scale surveys of grassland plant communities are time-consuming may limit the spatial extent of ground-based diversity surveys. Remote sensing tools have the [...] Read more.
Semi-natural grasslands with grazing management are characterized by high fine-scale species richness and have a high conservation value. The fact that fine-scale surveys of grassland plant communities are time-consuming may limit the spatial extent of ground-based diversity surveys. Remote sensing tools have the potential to support field-based sampling and, if remote sensing data are able to identify grassland sites that are likely to support relatively higher or lower levels of species diversity, then field sampling efforts could be directed towards sites that are of potential conservation interest. In the present study, we examined whether aerial hyperspectral (414–2501 nm) remote sensing can be used to predict fine-scale plant species diversity (characterized as species richness and Simpson’s diversity) in dry grazed grasslands. Vascular plant species were recorded within 104 (4 m × 4 m) plots on the island of Öland (Sweden) and each plot was characterized by a 245-waveband hyperspectral data set. We used two different modeling approaches to evaluate the ability of the airborne spectral measurements to predict within-plot species diversity: (1) a spectral response approach, based on reflectance information from (i) all wavebands, and (ii) a subset of wavebands, analyzed with a partial least squares regression model, and (2) a spectral heterogeneity approach, based on the mean distance to the spectral centroid in an ordinary least squares regression model. Species diversity was successfully predicted by the spectral response approach (with an error of ca. 20%) but not by the spectral heterogeneity approach. When using the spectral response approach, iterative selection of important wavebands for the prediction of the diversity measures simplified the model but did not improve its predictive quality (prediction error). Wavebands sensitive to plant pigment content (400–700 nm) and to vegetation structural properties, such as above-ground biomass (700–1300 nm), were identified as being the most important predictors of plant species diversity. We conclude that hyperspectral remote sensing technology is able to identify fine-scale variation in grassland diversity and has a potential use as a tool in surveys of grassland plant diversity. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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2168 KiB  
Article
Seasonal Variation in the NDVI–Species Richness Relationship in a Prairie Grassland Experiment (Cedar Creek)
by Ran Wang, John A. Gamon, Rebecca A. Montgomery, Philip A. Townsend, Arthur I. Zygielbaum, Keren Bitan, David Tilman and Jeannine Cavender-Bares
Remote Sens. 2016, 8(2), 128; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8020128 - 05 Feb 2016
Cited by 66 | Viewed by 13462
Abstract
Species richness generally promotes ecosystem productivity, although the shape of the relationship varies and remains the subject of debate. One reason for this uncertainty lies in the multitude of methodological approaches to sampling biodiversity and productivity, some of which can be subjective. Remote [...] Read more.
Species richness generally promotes ecosystem productivity, although the shape of the relationship varies and remains the subject of debate. One reason for this uncertainty lies in the multitude of methodological approaches to sampling biodiversity and productivity, some of which can be subjective. Remote sensing offers new, objective ways of assessing productivity and biodiversity. In this study, we tested the species richness–productivity relationship using a common remote sensing index, the Normalized Difference Vegetation Index (NDVI), as a measure of productivity in experimental prairie grassland plots (Cedar Creek). Our study spanned a growing season (May to October, 2014) to evaluate dynamic changes in the NDVI–species richness relationship through time and in relation to environmental variables and phenology. We show that NDVI, which is strongly associated with vegetation percent cover and biomass, is related to biodiversity for this prairie site, but it is also strongly influenced by other factors, including canopy growth stage, short-term water stress and shifting flowering patterns. Remarkably, the NDVI-biodiversity correlation peaked at mid-season, a period of warm, dry conditions and anthesis, when NDVI reached a local minimum. These findings confirm a positive, but dynamic, productivity–diversity relationship and highlight the benefit of optical remote sensing as an objective and non-invasive tool for assessing diversity–productivity relationships. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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5255 KiB  
Article
Organismic-Scale Remote Sensing of Canopy Foliar Traits in Lowland Tropical Forests
by K. Dana Chadwick and Gregory P. Asner
Remote Sens. 2016, 8(2), 87; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8020087 - 23 Jan 2016
Cited by 64 | Viewed by 8730
Abstract
Airborne high fidelity imaging spectroscopy (HiFIS) holds great promise for bridging the gap between field studies of functional diversity, which are spatially limited, and satellite detection of ecosystem properties, which lacks resolution to understand within landscape dynamics. We use Carnegie Airborne Observatory HiFIS [...] Read more.
Airborne high fidelity imaging spectroscopy (HiFIS) holds great promise for bridging the gap between field studies of functional diversity, which are spatially limited, and satellite detection of ecosystem properties, which lacks resolution to understand within landscape dynamics. We use Carnegie Airborne Observatory HiFIS data combined with field collected foliar trait data to develop quantitative prediction models of foliar traits at the tree-crown level across over 1000 ha of humid tropical forest. We predicted foliar leaf mass per area (LMA) as well as foliar concentrations of nitrogen, phosphorus, calcium, magnesium and potassium for canopy emergent trees (R2: 0.45–0.67, relative RMSE: 11%–14%). Correlations between remotely sensed model coefficients for these foliar traits are similar to those found in laboratory studies, suggesting that the detection of these mineral nutrients is possible through their biochemical stoichiometry. Maps derived from HiFIS provide quantitative foliar trait information across a tropical forest landscape at fine spatial resolution, and along environmental gradients. Multi-nutrient maps implemented at the fine organismic scale will subsequently provide new insight to the functional biogeography and biological diversity of tropical forest ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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Article
Using Remotely-Sensed Land Cover and Distribution Modeling to Estimate Tree Species Migration in the Pacific Northwest Region of North America
by Nicholas C. Coops, Richard H. Waring, Andrew Plowright, Joanna Lee and Thomas E. Dilts
Remote Sens. 2016, 8(1), 65; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8010065 - 15 Jan 2016
Cited by 19 | Viewed by 7326
Abstract
Understanding future tree species migration is challenging due to the unprecedented rate of climate change combined with the presence of human barriers that may limit or impede species movement. Projected changes in climatic conditions outpace migration rates, and more realistic rates of range [...] Read more.
Understanding future tree species migration is challenging due to the unprecedented rate of climate change combined with the presence of human barriers that may limit or impede species movement. Projected changes in climatic conditions outpace migration rates, and more realistic rates of range expansion are needed to make sound environmental policies. In this paper, we develop a modeling approach that takes into account both the geographic changes in the area suitable for the growth and reproduction of tree species, as well as limits imposed geographically on their potential migration using remotely-sensed land cover information. To do so, we combined a physiologically-based decision tree model with a remotely-sensed-derived diffusion-dispersal model to identify the most likely direction of future migration for 15 native tree species in the Pacific Northwest Region of North America, as well as the degree that landscape fragmentation might limit movement. Although projected changes in climate through to 2080 are likely to create favorable environments for range expansion of the 15 tree species by 65% on average, by limiting the potential movement by previously published migration rates and landscape fragmentation, range expansion will likely be 50%–90% of the potential. The hybrid modeling approach using distribution modeling and remotely-sensed data fills a gap between naïve and more complex approaches to take into account major impediments on the potential migration of native tree species. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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6334 KiB  
Article
Determining Subcanopy Psidium cattleianum Invasion in Hawaiian Forests Using Imaging Spectroscopy
by Jomar M. Barbosa, Gregory P. Asner, Roberta E. Martin, Claire A. Baldeck, Flint Hughes and Tracy Johnson
Remote Sens. 2016, 8(1), 33; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8010033 - 05 Jan 2016
Cited by 28 | Viewed by 6426
Abstract
High-resolution airborne imaging spectroscopy represents a promising avenue for mapping the spread of invasive tree species through native forests, but for this technology to be useful to forest managers there are two main technical challenges that must be addressed: (1) mapping a single [...] Read more.
High-resolution airborne imaging spectroscopy represents a promising avenue for mapping the spread of invasive tree species through native forests, but for this technology to be useful to forest managers there are two main technical challenges that must be addressed: (1) mapping a single focal species amongst a diverse array of other tree species; and (2) detecting early outbreaks of invasive plant species that are often hidden beneath the forest canopy. To address these challenges, we investigated the performance of two single-class classification frameworks—Biased Support Vector Machine (BSVM) and Mixture Tuned Matched Filtering (MTMF)—to estimate the degree of Psidium cattleianum incidence over a range of forest vertical strata (relative canopy density). We demonstrate that both BSVM and MTMF have the ability to detect relative canopy density of a single focal plant species in a vertically stratified forest, but they differ in the degree of user input required. Our results suggest BSVM as a promising method to disentangle spectrally-mixed classifications, as this approach generates decision values from a similarity function (kernel), which optimizes complex comparisons between classes using a dynamic machine learning process. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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