Using hyperspectral satellite imagery for regional inventories: a test with tropical emergent trees in the Amazon Basin

Questions: Understanding distributions of tree species at landscape scales in tropical forests is a difficult task that could benefit from the recent development of satellite imaging spectroscopy. We tested an application of the EO‐1 Hyperion satellite sensor to spectrally detect the location of five important tree taxa in the lowland humid tropical forests of southeastern Peru. Location: Peru, Departamento de Madre de Díos. Methods: We used linear discriminant analysis with a stepwise selection procedure to analyze two Hyperion datasets (July and December 2006) to choose the most informative narrow bands for classifying trees. Results: Optimal channels selected were different between the two seasons. Classification was 100% successful for the five taxa when using 25 narrow bands and pixels that represented >40% of tree crowns. We applied the discriminant functions developed separately for the two seasons to the entire study area, and found significantly nonrandom overlap in the anticipated distributions of the five taxa between seasons. Conclusions: Despite known issues, such as signal‐to‐noise ratio and spatial resolution, Hyperion imaging spectroscopy has potential for developing regional mapping of large‐crowned tropical trees.     

Estimating the relative abundance of emperor penguins at inaccessible colonies using satellite imagery

Emperor penguin (Aptenodytes forsteri) populations are useful environmental indicators due to the bird’s extreme reliance on sea ice. We used remote sensing technology to estimate relative adult bird abundance at two inaccessible emperor penguin colonies in the Ross Sea, Antarctica. We performed supervised classification of 12 panchromatic satellite images of the seven known Ross Sea colonies. We used regression to predict adult bird counts at the inaccessible colonies by relating the number of pixels classified as “penguin” in the satellite images of the accessible colonies to corresponding known adult bird counts from aerial photographs or ground counts. While our analysis was hampered by excessive guano and shadows, we used satellite imagery to differentiate between relatively small (<3,000 adult birds) and larger colonies (>5,000 adult birds). Remote sensing technology is logistically less intense and less costly than aerial or ground censuses when the objective is to document penguin presence and/or large emperor penguin population changes (e.g., catastrophic changes). Improvements expected soon in the resolution of the satellite images should allow for more accurate abundance estimates.     

Landscape-scale detection and mapping of invasive African Olive (Olea europaea L. ssp. cuspidata Wall ex G. Don Ciferri) in SW Sydney,Australia using satellite remote sensing

Question: Is satellite imagery an effective tool for mapping and examining the distribution of the invasive species Olea europaea L. ssp. cuspidata at a regional landscape scale? Location: Southwest Sydney, Australia. Methods: Remote sensing software was used to classify pixels of Olea europaea L. ssp. cuspidata (African Olive) and major vegetation types from satellite imagery, using a “supervised classification” technique across a 721 km² study area in the Cumberland Plain region of western Sydney. A map of African Olive distribution was produced from the image analysis and checked for accuracy at 337 random locations using ground observation and comparison with existing vegetation maps. The African Olive distribution data were then used in a GIS analysis with additional spatial datasets to investigate the relationship between the distribution of African Olive and environmental factors, and to quantify the conservation threat to endangered native vegetation. Results: A total area of 1907 ha of dense African Olive infestation was identified, with an omission error of 7.5% and a commission error of 5.4%. African Olive was found to occur on the steepest slopes (mean slope 14.3°) of the vegetation classes examined, with aspect analysis identifying a high prevalence on south- and southwest-facing slopes. The analysis also quantified the level of African Olive infestation in endangered ecological communities, with Western Sydney Dry Rainforest (25% affected) and Moist Shale Woodland (28% affected) identified as most vulnerable to African Olive invasion. Conclusion: The distribution of African Olive can be efficiently mapped at a landscape scale. This technique, used in association with additional spatial datasets, identified African Olive as a significant environmental weed in SW Sydney, occupying a greater area than previously recognised and threatening several endangered native vegetation communities.     

Mapping species density of trees,shrubs and vines in a tropical forest,using field measurements,satellite multiespectral imagery and spatial interpolation

We estimated the number of species in a tropical forest landscape in Quintana Roo, Mexico, based on the relationship between reflectance values of satellite imagery and field measurements of plant species density (mean number of species per plot). Total species density as well as that of tree, shrub and vine species were identified from 141 sampling quadrats (16543 individuals sampled). Spatial prediction of plant diversity was performed using universal kriging. This approach considered the linear relationship between plant species density and reflectance values of Thematic Mapper™, as well as the spatial dependence of the observations. We explored the linear relationships between spectral properties of TM bands and the species density of trees, shrubs and vines, using regression analysis. We employed Akaike Information Criterion (AIC) to select a set of candidate models. Based on Akaike weights, we calculated model-averaged parameters. Linear regression between number of species and reflectance values of TM bands yielded regression residuals. We used variogram analysis to analyze the spatial structure of these residuals. Results show that accounting for spatial autocorrelation in the residual variation improved model R² from 0.57 to 0.66 for number of all species, from 0.58 to 0.65 for number of tree species, from 0.26 to 0.41 for number of shrub species and from 0.13 to 0.17 for species density of vines. The empirical models we developed can be used to predict landscape-level species density in the Yucatan Peninsula, helping to guide and evaluate management and conservation strategies.     

Remote sensing of upland vegetation: the potential of high spatial resolution satellite sensors

Aim Traditional methodologies of mapping vegetation, as carried out by ecologists, consist primarily of field surveying or mapping from aerial photography. Previous applications of satellite imagery for this task (e.g. Landsat TM and SPOT HRV) have been unsuccessful, as such imagery proved to have insufficient spatial resolution for mapping vegetation. This paper reports on a study to assess the capabilities of the recently launched remote sensing satellite sensor Ikonos, with improved capabilities, for mapping and monitoring upland vegetation using traditional image classification methods. Location The location is Northumberland National Park, UK. Methods Traditional remote sensing classification methodologies were applied to the Ikonos data and the outputs compared to ground data sets. This enabled an assessment of the value of the improved spatial resolution of satellite imagery for mapping upland vegetation. Post‐classification methods were applied to remove noise and misclassified pixels and to create maps that were more in keeping with the information requirements of the NNPA for current management processes. Results The approach adopted herein for quick and inexpensive land cover mapping was found to be capable of higher accuracy than achieved with previous approaches, highlighting the benefits of remote sensing for providing land cover maps. Main conclusions Ikonos imagery proved to be a useful tool for mapping upland vegetation across large areas and at fine spatial resolution, providing accuracies comparable to traditional mapping methods of ground surveys and aerial photography.     

Satellite observations of main oceanographic processes to identify ecological associations in the Northern Arabian Sea for fishery resources exploration

Ecological associations are the inter-relationship between the species and their environment. Oceanographic processes like upwelling events and formation of eddies, rings, and fronts have been monitored using National Oceanic and Atmospheric Administration Advanced Very High Resolution (NOAA AVHRR) and Indian Remote Sensing Satellite-P4-Ocean Colour Monitor (IRS-OCM) data. Sea Surface Temperature (SST) and chlorophyll concentration (CC) images were derived from AVHRR and OCM, respectively. Upwelling event was monitored using AVHRR-SST by detecting the differences in surface water temperature. The formation of eddies, rings, cyclonic eddies, and anti-cyclonic eddies and their biological responses were studied using CC. Eddies and rings were found with high phytoplankton production in the form of bloom, which provide grazing ground for fishes. The anti-cyclonic eddies were found with very low CC, indicating the biological deserts in the ocean. The impacts of these processes on fish catch were studied using fishing operations data procured from Fishery Survey of India. In this paper, the occurrence of different oceanographic processes, their persistence, and relevance with catch statistics of fishery resources in the study area are discussed. The study explains the potentials of satellite remote sensing to establish the habitat linkage between oceanographic processes and fishery resources. Guest editor: V. D. Valavanis Essential Fish Habitat Mapping in the Mediterranean     

Climate,satellite imagery and the seasonal abundance of the tick Rhipicephalus appendiculatus in southern Africa: a new perspective

Abstract. Recent predictive models for the distribution of the African tick Rhipicephalus appendiculatus Neumann, based on the computer packages CLIMEX and BIOCLIM and data derived from meteorological satellites, and for the seasonal dynamics of the same tick using the computer simulation models ECFXPERT and T3HOST, all have their limitations. Statistical analysis of the relationships between the seasonal abundance of all three life stages of this tick and climatic and satellite-derived data from five sites in southern Africa, taken from the literature, supports a new perspective that it is the timing of the questing activity of the desiccation-vulnerable larvae that determines the pattern of the tick's seasonal dynamics. The timing of the activity of nymphs and adults is determined by temperature-dependent development rates plus the delaying phenomenon of photoperiod-sensitive diapause, the timing and duration of which have evolved to achieve maximum generation survival by ensuring the occurrence of eggs and larvae during periods of optimal climatic conditions. The most important environmental factor appears to be night-time minimum temperature, determining condensation and saturation deficit and thus the tick's ability to replenish moisture lost during the daytime and so to survive while questing for hosts. It is the larvae whose numbers are correlated most closely with these factors, consistent with earlier experimental results showing larvae to be most susceptible to desiccating conditions. There is a statistical linkage between larval tick numbers and satellite imagery, arising from the correlation between larval numbers and minimum temperature and saturation deficit conditions, and in turn the relationship between these climatic conditions and the subsequent vegetational changes monitored by the satellites. Moisture availability to larvae is likely to be the critical factor throughout the geographical range of R.appendiculatus, but the precise combination of climatic conditions that optimize moisture availability and questing tick survival can be expected to vary geographically. The relationships between ticks, temperatures and satellite data in parts of equatorial Africa have yet to be established. These correlative patterns highlight both the critical life stage and environmental factors when trying to understand temporal, and ultimately spatial, variations in tick abundance.     

BioFTF: An R package for biodiversity assessment with the functional data analysis approach

This paper presents the R package BioFTF, which is a tool for statistical biodiversity assessment in the functional data analysis framework. Diversity is a key topic in many research fields; however, in the literature, it is demonstrated that the existing indices do not capture the different aspects of this concept. Thus, a main drawback is that different indicators may lead to different orderings among communities according to their biodiversity. A possible method to evaluate biodiversity consists in using diversity profiles that are curves depending on a specific parameter. In this setting, it is possible to adopt some functional instruments proposed in the literature, such as the first and second derivatives, the curvature, the radius of curvature and the arc length. Specifically, the derivatives and the curvature (or the radius of curvature) highlight any peculiar behaviour of the profiles, whereas the arc length helps in ranking curves, given the richness. Because these instruments do not solve the issue of ranking communities with different numbers of species, we propose an important methodological contribution that introduces the surface area. Indeed, this tool is a scalar measure that reflects the information provided by the biodiversity profile and allows for ordering communities with different richness. However, this approach requires mathematical skills that the average user may not have; thus, our idea is to provide a user-friendly tool for both non-statistician and statistician practitioners to measure biodiversity in a functional context.     

Microclimate of daytime den sites in a tropical possum: implications for the conservation of tropical arboreal marsupials

Tree cavities are an important shelter site for a variety of vertebrate species, including birds, reptiles and mammals. Studies indicate that in most taxa favoured tree hollows are those in larger trees and higher from the ground, generally thought to be related to decreased predation risk and a more optimal thermal environment. However, neither of these ideas has been tested definitively. Here, we investigate the microclimate of daytime den sites in tree hollows of common brushtail possums in tropical northern Australia. We compare tree and hollow characteristics of dens known to be used by possums, and those not known to be used, to determine whether possums choose trees with microhabitats with a more favourable daytime microclimate. Possums chose to den in tree hollows which were on average 1.6 °C cooler during the day, and were more buffered from temperature extremes, than other potential den locations. Important factors explaining daytime temperatures between hollows included height of the hollow, entrance width and tree diameter. Tropical arboreal marsupials have been identified as being particularly vulnerable to climate change and there are calls to identify and preserve natural refuges, such as tree hollows, which could buffer them from extreme temperatures. Our results highlight the value of older, larger hollow-bearing trees as refuges from extreme temperature, the importance of which may become critical for some temperature-sensitive species under the combined effects of continuing habitat loss and climate change.     

Monitoring Karenia brevis blooms in the Gulf of Mexico using satellite ocean color imagery and other data

Harmful algal blooms (HABs) of Karenia brevis are a recurrent problem in the Gulf of Mexico, with nearly annual occurrences on the Florida southwest coast, and fewer occurrences on the northwest Florida and Texas coasts. Beginning in 1999, the National Oceanic and Atmospheric Administration has issued the Gulf of Mexico HAB Bulletins to support state monitoring and management efforts. These bulletins involve analysis of satellite imagery with field and meteorological station data. The effort involves several components or models: (a) monitoring the movement of an algal bloom that has previously been identified as a HAB (type 1 forecast); (b) detecting new blooms as HAB or non-HAB (type 2); (c) predicting the movement of an identified HAB (type 3); (d) predicting conditions favorable for a HAB to occur where blooms have not yet been observed (type 4). The types 1 and 2 involve methods of bloom detection requiring routine remote sensing, especially satellite ocean color imagery and in situ data. Prediction (types 3 and 4) builds on the monitoring capability by using interpretative and numerical modeling. Successful forecasts cover more than 1000 km of coast and require routine input of remotely sensed and in situ data.The data sources used in this effort include ocean color imagery from the Sea-Viewing Wide Field-of-View Sensor/OrbView-2 satellite and processed using coastal-specific algorithms, wind data from coastal and offshore buoys, field observations of bloom location and intensity provided by state agencies, and forecasts from the National Weather Service. The HAB Bulletins began in coordination with the state of Florida in autumn of 1999 and included K. brevis bloom monitoring (type 1), with limited advisories on transport (type 3) and the detection of blooms in new areas (type 2). In autumn 2000, we improved both the transport forecasts and detection capabilities and began prediction of conditions favorable for bloom development (type 4). The HAB Bulletins have had several successes. The state of Florida was advised of the potential for a bloom to occur at the end of September 2000 (type 4), and the state was alerted to the position of blooms in January 2000 and October 2001 in areas that had not been previously sampled (type 3). These successful communications of HAB activity allowed Florida agencies responsible for shellfish management and public health to respond to a rapidly developing event in a timely, efficient manner.     

The devil is in the detail: Nonadditive and context‐dependent plant population responses to increasing temperature and precipitation

In climate change ecology, simplistic research approaches may yield unrealistically simplistic answers to often more complicated problems. In particular, the complexity of vegetation responses to global climate change begs a better understanding of the impacts of concomitant changes in several climatic drivers, how these impacts vary across different climatic contexts, and of the demographic processes underlying population changes. Using a replicated, factorial, whole‐community transplant experiment, we investigated regional variation in demographic responses of plant populations to increased temperature and/or precipitation. Across four perennial forb species and 12 sites, we found strong responses to both temperature and precipitation change. Changes in population growth rates were mainly due to changes in survival and clonality. In three of the four study species, the combined increase in temperature and precipitation reflected nonadditive, antagonistic interactions of the single climatic changes for population growth rate and survival, while the interactions were additive and synergistic for clonality. This disparity affects the persistence of genotypes, but also suggests that the mechanisms behind the responses of the vital rates differ. In addition, survival effects varied systematically with climatic context, with wetter and warmer + wetter transplants showing less positive or more negative responses at warmer sites. The detailed demographic approach yields important mechanistic insights into how concomitant changes in temperature and precipitation affect plants, which makes our results generalizable beyond the four study species. Our comprehensive study design illustrates the power of replicated field experiments in disentangling the complex relationships and patterns that govern climate change impacts across real‐world species and landscapes.     

Mapping the spatial distribution of plant diversity indices in a tropical forest using multi-spectral satellite image classification and field measurements

The relationships among alpha and beta diversity indices, computed from 141 randomly sampled quadrats, and the vegetation classes obtained by multi-spectral satellite image classification, were used as a strategy for mapping plant diversity in a tropical landscape mosaic. A relatively high accuracy of the land cover map was revealed by the overall accuracy assessment and the Cohen's Kappa statistic. Species accumulation models were used to evaluate how representative the sample size was the different vegetation types. A standard one-way, between-subjects ANOVA confirmed a significant reduction of the within-class variance of plant diversity with respect to their total variance across the landscape. Computed uniformity indices, to assess the internal uniformity of vegetation classes on the diversity indices, confirmed the goodness of the mapped classes in stratifying variability of plant diversity. This allowed for the use of the mapped classes as spatial interpolators of plant diversity values for estimation and up-scaling purposes. Finally, it was revealed that the plant diversity of the landscape depends, to a large extent, on the diversity contained in the most mature forest class, which is also the most diverse community in the studied area. High and moderate beta diversity values between mature forests and both the secondary associations and the first stages of succession, respectively, indicated that there is a significant contribution to the diversity of the landscape by those vegetation classes.     

Detecting Karenia brevis blooms and algal resuspension in the western Gulf of Mexico with satellite ocean color imagery

Blooms of the toxic dinoflagellate, Karenia brevis, have had detrimental impacts on the coastal Gulf of Mexico for decades. Detection of Karenia brevis blooms uses an ecological approach based on anomalies derived from ocean color imagery. The same anomaly product used in Florida produces frequent false positives on the Texas coast. These failures occurred during wind-driven resuspension events. During these events resuspension of benthic algae significantly increases chlorophyll concentrations in the water, resulting in confusion with normal water column phytoplankton, such as Karenia. A method was developed to separate the resuspended chlorophyll from the water column chlorophyll, decreasing the false positives used with the detection method.     

Forecasting sagebrush ecosystem components and greater sage-grouse habitat for 2050: Learning from past climate patterns and Landsat imagery to predict the future

Sagebrush (Artemisia spp.) ecosystems constitute the largest single North American shrub ecosystem and provide vital ecological, hydrological, biological, agricultural, and recreational ecosystem services. Disturbances have altered and reduced this ecosystem historically, but climate change may ultimately represent the greatest future risk. Improved ways to quantify, monitor, and predict climate-driven gradual change in this ecosystem is vital to its future management. We examined the annual change of Daymet precipitation (daily gridded climate data) and five remote sensing ecosystem sagebrush vegetation and soil components (bare ground, herbaceous, litter, sagebrush, and shrub) from 1984 to 2011 in southwestern Wyoming. Bare ground displayed an increasing trend in abundance over time, and herbaceous, litter, shrub, and sagebrush showed a decreasing trend. Total precipitation amounts show a downward trend during the same period. We established statistically significant correlations between each sagebrush component and historical precipitation records using a simple least squares linear regression. Using the historical relationship between sagebrush component abundance and precipitation in a linear model, we forecasted the abundance of the sagebrush components in 2050 using Intergovernmental Panel on Climate Change (IPCC) precipitation scenarios A1B and A2. Bare ground was the only component that increased under both future scenarios, with a net increase of 48.98 km² (1.1%) across the study area under the A1B scenario and 41.15 km² (0.9%) under the A2 scenario. The remaining components decreased under both future scenarios: litter had the highest net reductions with 49.82 km² (4.1%) under A1B and 50.8 km² (4.2%) under A2, and herbaceous had the smallest net reductions with 39.95 km² (3.8%) under A1B and 40.59 km² (3.3%) under A2. We applied the 2050 forecast sagebrush component values to contemporary (circa 2006) greater sage-grouse (Centrocercus urophasianus) habitat models to evaluate the effects of potential climate-induced habitat change. Under the 2050 IPCC A1B scenario, 11.6% of currently identified nesting habitat was lost, and 0.002% of new potential habitat was gained, with 4% of summer habitat lost and 0.039% gained. Our results demonstrate the successful ability of remote sensing based sagebrush components, when coupled with precipitation, to forecast future component response using IPCC precipitation scenarios. Our approach also enables future quantification of greater sage-grouse habitat under different precipitation scenarios, and provides additional capability to identify regional precipitation influence on sagebrush component response.     

Coral spawning in the western Pacific Ocean is related to solar insolation: evidence of multiple spawning events in Palau

Coral reproductive schedules have long been related to concomitant increases in regional sea surface temperature (SST). Yet, corals also mass spawn in the tropics, where SSTs vary little throughout the year. Here we show that the rise toward and fall from solar insolation maxima, which is the electromagnetic energy incident on the surface of the earth, coincide with coral spawning events in tropical Palau. Two insolation maxima (a consequence of the vernal and autumnal equinoxes) in the tropics lead to multiple spawning events per year. SSTs are poor predictors of coral spawning in the tropics. Multiple spawning events increase the rate of genetic recombination, which in turn may facilitate the likelihood of speciation and explain, in part, the high coral diversity in the tropics. We also show that mass spawning coincides with near-maximum solar insolation (5.0–7.2 kWh/m²/day) events from 33°N to 30°S in the western Pacific Ocean, and should be considered as one of the primary variables driving coral reproductive cycles.     

Species distribution modelling for the people: unclassified landsat TM imagery predicts bird occurrence at fine resolutions

Aim

Assessing the influence of land cover in species distribution modelling is limited by the availability of fine‐resolution land‐cover data appropriate for most species responses. Remote‐sensing technology offers great potential for predicting species distributions at large scales, but the cost and required expertise are prohibitive for many applications. We test the usefulness of freely available raw remote‐sensing reflectance data in predicting species distributions of 40 commonly occurring bird species in western Oregon.

Location

Central Coast Range, Cascade and Klamath Mountains Oregon, USA.

Methods

Information on bird observations was collected from 4598 fixed‐radius point counts. Reflectance data were obtained using 30‐m resolution Landsat imagery summarized at scales of 150, 500, 1000 and 2000 m. We used boosted regression tree (BRT) models to analyse relationships between distributions of birds and reflectance values and evaluated prediction performance of the models using area under the receiver operating characteristic curve (AUC) values.

Results

Prediction success of models using all reflectance values was high (mean AUC = 0.79 ± 0.10 SD). Further, model performance using individual reflectance bands exceeded those that used only Normalized Difference Vegetation Index (NDVI). The relative influence of band 4 predictors was highest, indicating the importance of variables associated with vegetation biomass and photosynthetic activity. Across spatial scales, the average influence of predictors at the 2000 m scale was greatest.

Main Conclusions

We demonstrate that unclassified remote‐sensing imagery can be used to produce species distribution models with high prediction success. Our study is the first to identify general patterns in the usefulness of spectral reflectances for species distribution modelling of multiple species. We conclude that raw Landsat Thematic Mapper data will be particularly useful in species distribution models when high‐resolution predictions are required, including habitat change detection studies, identification of fine‐scale biodiversity hotspots and reserve design.