Composition versus physiognomy of vegetation as predictors of bird assemblages: The role of lidar

Whether diversity and composition of avian communities is determined primarily by responses of species to the floristic composition or to the structural characteristics of habitats has been an ongoing debate, at least since the publication of MacArthur and MacArthur (1961). This debate, however, has been hampered by two problems: 1) it is notoriously time consuming to measure the physiognomy of habitat, particularly in forests, and 2) rigorous statistical methods to predict the composition of bird assemblages from assemblages of plants have not been available. Here we use airborne laser scanning (lidar) to measure the habitat (vegetation) structure of a montane forest across large spatial extents with a very fine grain. Furthermore, we use predictive co-correspondence and canonical correspondence analyses to predict the composition of bird communities from the composition and structure of another community (i.e. plants). By using these new techniques, we show that the physiognomy of the vegetation is a significantly more powerful predictor of the composition of bird assemblages than plant species composition in the field and as well in the shrub/tree layer, both on a level of p < 0.001. Our results demonstrate that ecologists should consider remote sensing as a tool to improve the understanding of the variation of bird assemblages in space and time. Particularly in complex habitats, such as forests, lidar is a valuable and comparatively inexpensive tool to characterize the structure of the canopy even across large and rough terrain.     

Remote sensing of structural complexity indices for habitat and species distribution modeling

Spatial distribution models are increasingly used in ecological studies, but are limited by the poor accuracy of remote sensing (RS) for mapping microhabitat (< 0.1 ha) features. Mapping accuracy can be improved by combining advanced RS image-processing techniques with microhabitat data expressed as a structural complexity index (SCI). To test this idea, we used principal components analysis (PCA) and an additive SCI method developed for forest ecology (calculated by re-scaling and summing representative structural variables) to summarize 13 microhabitat-scale (0.04 ha) vegetation structure attributes describing the rare mountain bongo antelope's (Tragelaphus eurycerus isaaci) habitat in Kenya's Aberdare mountains. Microhabitat data were collected in 127 plots: 37 related to bongo habitat use, 90 from 1 km-spaced grid points representing overall habitat availability and bongo non-presence. We then assessed each SCI's effectiveness for discerning microhabitat variability and bongo habitat selection, using Wilcoxon Rank Sum tests for differences in mean SCI scores among plots divided into 4 vegetation classes, and the Area Under the Curve (AUC) of receiver operating characteristics from logistic regressions. We also examined the accuracy of predicted SCI scores resulting from regression models based on variables derived from a) ASTER imagery processed with spectral mixture and texture analysis, b) an SRTM DEM and c) rainfall data, using the 90 grid plots for model training and the bongo plots as an independent test dataset. Of the five SCIs derived, two performed best: the PCA-derived Canopy Structure Index (CSI) and an additive index summarizing 8 structural variables (AI₈). CSI and AI₈ showed significant differences between 5 of 6 vegetation class pairs, strong abilities to distinguish bongo-selected from available habitat (AUCs = 0.71 (CSI); 0.70 (AI₈)), and predicted scores 60-110% more accurate than reported by other studies using RS to quantify individual microhabitat structural attributes (CSI model R² = 0.51, RMSE = 0.19 (training) and 0.21 (test); AI₈ model R² = 0.46, RMSE = 0.17 (training) and 0.19 (test)). Repeating the Wilcoxon tests and logistic regressions with RS-predicted SCI values showed that AI₈ most effectively preserved the patterns found with the observed SCIs. These results demonstrate that SCIs effectively characterize microhabitat structure and selection, and boost microhabitat mapping accuracy when combined with enhanced RS image-processing techniques. This approach can improve distribution models and broaden their applicability, makes RS more relevant to applied ecology, and shows that processing field data to be more compatible with RS can improve RS-based habitat mapping accuracy.     

Laser remote sensing of canopy habitat heterogeneity as a predictor of bird species richness in an eastern temperate forest, USA

Habitat heterogeneity has long been recognized as a fundamental variable indicative of species diversity, in terms of both richness and abundance. Satellite remote sensing data sets can be useful for quantifying habitat heterogeneity across a range of spatial scales. Past remote sensing analyses of species diversity have largely been limited to correlative studies based on the use of vegetation indices or derived land cover maps. A relatively new form of laser remote sensing (lidar) provides another means to acquire information on habitat heterogeneity. Here we examine the efficacy of lidar metrics of canopy structural diversity as predictors of bird species richness in the temperate forests of Maryland, USA. Canopy height, topography and the vertical distribution of canopy elements were derived from lidar imagery of the Patuxent National Wildlife Refuge and compared to bird survey data collected at referenced grid locations. The canopy vertical distribution information was consistently found to be the strongest predictor of species richness, and this was predicted best when stratified into guilds dominated by forest, scrub, suburban and wetland species. Similar lidar variables were selected as primary predictors across guilds. Generalized linear and additive models, as well as binary hierarchical regression trees produced similar results. The lidar metrics were also consistently better predictors than traditional remotely sensed variables such as canopy cover, indicating that lidar provides a valuable resource for biodiversity research applications.     

The use of airborne lidar to assess avian species diversity, density, and occurrence in a pine/aspen forest

Vegetation structure is an important factor that influences wildlife-habitat selection, reproduction, and survival. However, field-based measurements of vegetation structure can be time consuming, costly, and difficult to undertake in areas that are remote and/or contain rough terrain. Light detection and ranging (lidar) is an active remote sensing technology that can quantify three-dimensional vegetation structure over large areas and thus holds promise for examining wildlife-habitat relationships. We used discrete-return airborne lidar data acquired over the Black Hills Experimental Forest in South Dakota, USA in combination with field-collected vegetation and bird data to assess the utility of lidar data in quantifying vegetation structural characteristics that relate to avian diversity, density, and occurrence. Indices of foliage height diversity calculated from lidar data were positively and significantly correlated with indices of bird species diversity, with the highest correlations observed when foliage height diversity categories contained proportionally more foliage layers near the forest floor (< 5 m). In addition, lidar-derived indices of vegetation volume were significantly correlated with bird density. Using lidar-derived vegetation height data in combination with multispectral IKONOS data, we delineated five general habitat types within the study area according to the presence of prominent vegetation layers at lower levels of the forest and predominant tree type (deciduous or conifer). Habitat type delineations were tested by examining the occurrence and relative density of two bird species common to the study area that prefer lower level vegetation for foraging and nesting. Dark-eyed Juncos were significantly associated with the 0.5–2.0 m high vegetation layer in pine-dominated stands, and Warbling Vireos were significantly associated with this same layer in aspen-dominated stands. These results demonstrate that discrete-return lidar can be an effective tool to remotely quantify vegetation structural attributes important to birds, and may be enhanced when used in combination with spectral data.     

Mapping snags and understory shrubs for a LiDAR-based assessment of wildlife habitat suitability

The lack of maps depicting forest three-dimensional structure, particularly as pertaining to snags and understory shrub species distribution, is a major limitation for managing wildlife habitat in forests. Developing new techniques to remotely map snags and understory shrubs is therefore an important need. To address this, we first evaluated the use of LiDAR data for mapping the presence/absence of understory shrub species and different snag diameter classes important for birds (i.e. ≥ 15 cm, ≥ 25 cm and ≥ 30 cm) in a 30,000 ha mixed-conifer forest in Northern Idaho (USA). We used forest inventory plots, LiDAR-derived metrics, and the Random Forest algorithm to achieve classification accuracies of 83% for the understory shrubs and 86% to 88% for the different snag diameter classes. Second, we evaluated the use of LiDAR data for mapping wildlife habitat suitability using four avian species (one flycatcher and three woodpeckers) as case studies. For this, we integrated LiDAR-derived products of forest structure with available models of habitat suitability to derive a variety of species-habitat associations (and therefore habitat suitability patterns) across the study area. We found that the value of LiDAR resided in the ability to quantify 1) ecological variables that are known to influence the distribution of understory vegetation and snags, such as canopy cover, topography, and forest succession, and 2) direct structural metrics that indicate or suggest the presence of shrubs and snags, such as the percent of vegetation returns in the lower strata of the canopy (for the shrubs) and the vertical heterogeneity of the forest canopy (for the snags). When applied to wildlife habitat assessment, these new LiDAR-based maps refined habitat predictions in ways not previously attainable using other remote sensing technologies. This study highlights new value of LiDAR in characterizing key forest structure components important for wildlife, and warrants further applications to other forested environments and wildlife species.     

Ecological fingerprinting of ecosystem succession: Estimating secondary tropical dry forest structure and diversity using imaging spectroscopy

We evaluated the use of EO-1 Hyperion hyperspectral satellite imagery for mapping structure and floristic diversity in a Neotropical tropical dry forest as a way of assessing a region's ecological fingerprint. Analysis of satellite imagery provides a means to spatially appraise the dynamics of the structure and diversity of the forest. We derived optimal models for mapping canopy height, live aboveground biomass, Shannon diversity, basal area and the Holdridge Complexity Index from a dry season image. None of the evaluated models adequately estimated stem or species density. Due to the dynamic nature of the leaf phenology we found that for the application of remote sensing in Neotropical dry forests, the spectro-temporal domain (changes in the spectral signatures over time-season) must be taken into account when choosing imagery. The analyses and results presented here provide a means for rapid spatial assessment of structure and diversity characteristics from the microscale site level to an entire region.     

Studies of land-cover, land-use, and biophysical properties of vegetation in the Large Scale Biosphere Atmosphere experiment in Amazônia

We summarize early research on land-cover, land-use, and biophysical properties of vegetation from the Large Scale Biosphere Atmosphere (LBA) experiment in Amazônia. LBA is an international research program developed to evaluate regional function and to determine how land-use and climate modify biological, chemical and physical processes there. Remote sensing has played a fundamental role in LBA in research planning, land-cover mapping and in long-term monitoring of changes in land-cover and land-use at multiple scales. This special issue includes 12 papers that cover a range in spatial scales from regional mapping to local scales that cover only a portion of a Landsat scene. Several themes dominate, including land-cover mapping with an emphasis on wetlands and second-growth forest, evaluation of pasture sustainability and forest degradation and the impact of land-cover change on stream chemistry. New techniques introduced include automated Monte Carlo unmixing (AutoMCU) and several new approaches for mapping land-cover. A diversity of sensors are utilized, including ETM+, IKONOS, SPOT-4, Airborne P-band synthetic aperture radar (SAR), and L-band SAR. Census data are fused with an existing land-cover map to generate spatially explicit estimates of land-use from historical data. Several papers include important, new field measures of species composition, forest structure and biomass in mature forest and secondary succession.     

Evaluating MODIS data for mapping wildlife habitat distribution

Habitat distribution models have a long history in ecological research. With the development of geospatial information technology, including remote sensing, these models are now applied to an ever-increasing number of species, particularly those located in areas in which it is logistically difficult to collect habitat data in the field. Many habitat studies have used data acquired by multi-spectral sensor systems such as the Landsat Thematic Mapper (TM), due mostly to their availability and relatively high spatial resolution (30 m/pixel). The use of data collected by other sensor systems with lower spatial resolutions but high frequency of acquisitions has largely been neglected, due to the perception that such low spatial resolution data are too coarse for habitat mapping. In this study we compare two models using data from different satellite sensor systems for mapping the spatial distribution of giant panda habitat in Wolong Nature Reserve, China. The first one is a four-category scheme model based on combining forest cover (derived from a digital land cover classification of Landsat TM imagery acquired in June, 2001) with information on elevation and slope (derived from a digital elevation model obtained from topographic maps of the study area). The second model is based on the Ecological Niche Factor Analysis (ENFA) of a time series of weekly composites of WDRVI (Wide Dynamic Range Vegetation Index) images derived from MODIS (Moderate Resolution Imaging Spectroradiometer – 250 m/pixel) for 2001. A series of field plots was established in the reserve during the summer–autumn months of 2001–2003. The locations of the plots with panda feces were used to calibrate the ENFA model and to validate the results of both models. Results showed that the model using the seasonal variability of MODIS-WDRVI had a similar prediction success to that using Landsat TM and digital elevation model data, albeit having a coarser spatial resolution. This suggests that the phenological characterization of the land surface provides an appropriate environmental predictor for giant panda habitat mapping. Therefore, the information contained in remotely sensed data acquired with low spatial resolution but high frequency of acquisitions has considerable potential for mapping the habitat distribution of wildlife species.     

Mapping riparian habitat using a combination ofremote-sensing techniques

Light detection and ranging (lidar) and object-oriented classification (OOC) can be used to overcome the shortcomings of the traditional pixel-based classification (PBC) of coarse spatial resolution data, such as Landsat data, for habitat mapping in riparian zones. The purposes of this study were to investigate methods to classify multispectral data and lidar for riparian habitat mapping, and to identify major habitat components for two target species. The mapping of riparian habitat based on OOC and Decision Tree Classification (DTC) was carried out by merging vertical data from lidar and spectral data of high-resolution imagery. Our results showed an overall classification accuracy of 88.2%. In particular, small and continuous habitat types, such as short and tall grasses, rock outcrop and gravel, and riffles, improved the classification accuracy compared with the pixel-based methods. The habitat patches and paths for each target species were identified by incorporating the point data from the field survey and the outcomes of image classification. Our study demonstrated that the proposed methodology can be successfully used for the identification and restoration of fragmented riparian habitats, and can offer an opportunity to obtain high classification accuracies for microhabitat components in dynamic riverine areas.     

Predictive mapping of reef fish species richness, diversity and biomass in Zanzibar using IKONOS imagery and machine-learning techniques

During the last three decades, the large spatial coverage of remote sensing data has been used in coral reef research to map dominant substrate types, geomorphologic zones, and bathymetry. During the same period, field studies have documented statistical relationships between variables quantifying aspects of the reef habitat and its fish community. Although the results of these studies are ambiguous, some habitat variables have frequently been found to correlate with one or more aspects of the fish community. Several of these habitat variables, including depth, the structural complexity of the substrate, and live coral cover, are possible to estimate with remote sensing data. In this study, we combine a set of statistical and machine-learning models with habitat variables derived from IKONOS data to produce spatially explicit predictions of the species richness, biomass, and diversity of the fish community around two reefs in Zanzibar. In the process, we assess the ability of IKONOS imagery to estimate live coral cover, structural complexity and habitat diversity, and we explore the importance of habitat variables, at a range of spatial scales, in the predictive models using a permutation-based technique. Our findings indicate that structural complexity at a fine spatial scale (∼ 5 to 10 m) is the most important habitat variable in predictive models of fish species richness and diversity, whereas other variables such as depth, habitat diversity, and structural complexity at coarser spatial scales contribute to predictions of biomass. In addition, our results demonstrate that complex model types such as tree-based ensemble techniques provide superior predictive performance compared to the more frequently used linear models, achieving a reduction of the cross-validated root-mean-squared prediction error of 3-11%. Although aerial photographs and airborne lidar instruments have recently been used to produce spatially explicit predictions of reef fish community variables, our study illustrates the possibility of doing so with satellite data. The ability to use satellite data may bring the cost of creating such maps within the reach of both spatial ecology researchers and the wide range of organizations involved in marine spatial planning.     

Classification of heathland vegetation in a hierarchical contextual framework

Heathlands in Western Europe have shown dramatic declines over the last century and therefore have been given a high conservation priority in the Habitats Directive of the European Union (EU). Accurate surveying and monitoring of heathland habitats is essential for appropriate conservation management, but the large heterogeneity of vegetation types within habitats as well as the occurrence of similar vegetation across habitat types hinders a straightforward, automated mapping based on aerial images. In such a case, a context-dependent classification algorithm is expected to be superior to traditional classification techniques. This article presents a novel approach to map the conservation status of heathland vegetation by using a hierarchical classification scheme that describes the structural dependencies in the field between the basic vegetation and the land-cover types that habitats are composed of. These dependency relationships are included as contextual information in the classification process, using a tree-structured Markov random field (TS-MRF) technique with a tree that reflects the hierarchy of the classification scheme. Results of this approach for a heathland area in Belgium were compared with results from more conventional classification approaches. Validation of the results showed that the structure of the scheme contained important spatial relationships, which were further reinforced by using the contextual classification strategy, especially for the most detailed level of the classification scheme. Accuracy increased and the classification results were more suitable for visual interpretation.     

Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments

Remotely sensed images and processing techniques are a primary tool for mapping changes in tropical forest types important to biodiversity and environmental assessment. Detailed land cover data are lacking for most wet tropical areas that present special challenges for data collection. For this study, we utilize decision tree (DT) classifiers to map 32 land cover types of varying ecological and economic importance over an 8000 km² study area and biological corridor in Costa Rica. We assess multivariate QUEST DTs with unbiased classification rules and linear discriminant node models for integrated vegetation mapping and change detection. Predictor variables essential to accurate land cover classification were selected using importance indices statistically derived with classification trees. A set of 35 variables from SRTM-DEM terrain variables, WorldClim grids, and Landsat TM bands were assessed.

Of the techniques examined, QUEST trees were most accurate by integrating a set of 12 spectral and geospatial predictor variables for image subsets with an overall cross-validation accuracy of 93% ± 3.3%. Accuracy with spectral variables alone was low (69% ± 3.3%). A random selection of training and test set pixels for the entire landscape yielded lower classification accuracy (81%) demonstrating a positive effect of image subsets on accuracy. A post-classification change comparison between 1986 and 2001 reveals that two lowland forest types of differing tree species composition are vulnerable to agricultural conversion. Tree plantations and successional vegetation added forest cover over the 15-year time period, but sometimes replaced native forest types, reducing floristic diversity. Decision tree classifiers, capable of combining data from multiple sources, are highly adaptable for mapping and monitoring land cover changes important to biodiversity and other ecosystem services in complex wet tropical environments.     

Monitoring snowmelt across the Arctic forest–tundra ecotone using Synthetic Aperture Radar

Detailed snowpack observations, meteorology, topography and landcover classification were integrated with multi‐temporal SAR data to assess its capability for landscape scale snowmelt mapping at the forest–tundra ecotone. At three sites along an approximately 8° latitudinal gradient in the Fennoscandian mountain range, 16 multi‐temporal spaceborne ERS‐2 synthetic aperture radar (SAR) were used for mapping snowmelt.

Comparison of field measurements and backscatter values demonstrates the difficulty of interpreting observed backscatter response because of complex changes in snow properties on diurnal and seasonal temporal scales. Diurnal and seasonal melt–freeze effects in the snowpack, relative to the timing of ERS‐2 SAR image acquisition, effectively reduce the temporal resolution of such data for snow mapping, even at high latitudes.

The integration of diverse data sources did reveal significant associations between vegetation, topography and snowmelt. Several problems with the application of thresholding for the automatic identification of snowmelt were encountered. These largely related to changes in backscattering from vegetation in the late stages of snowmelt. Due to the impact of environmental heterogeneity in vegetation at the forest–tundra ecotone, we suggest that the potential to map snow cover using single polarization C‐band SAR at the forest–tundra ecotone may be limited to tundra areas.     

Mapping of flooded vegetation by means of polarimetric Sentinel-1 and ALOS-2/PALSAR-2 imagery

This article presents for the first time the combination of dual-polarimetric C-band Sentinel-1 synthetic aperture radar (SAR) data and quad-polarimetric L-band ALOS-2/PALSAR-2 imagery for mapping of flooded areas with a special focus on flooded vegetation. L-band SAR data is well suited for mapping of flooded vegetation, while C-band enables an accurate extraction open water areas. Polarimetric decomposition-based unsupervised Wishart classification is combined with object-based post-classification refinement and the integration of spatial contextual information and global auxiliary data. In eight different scenarios, focusing on single datasets or fusion of classification results of several ones, respectively, different polarimetric decomposition and classification principles, including the entropy/anisotropy/alpha and the Freeman–Durden–Wishart classification, were investigated. The helix scattering component of the Yamaguchi decomposition, derived from ALOS-2 imagery, showed high suitability to refine the Sentinel-1-based detection of flooded vegetation. A test site at the Evros River (Greek/Turkish border region) was chosen, which was affected by a flooding event that occurred in spring 2015. The validation was based on high spatial resolution optical WorldView-2 imagery acquired with short temporal delay to the SAR data.     

Characterisation of surface roughness and sediment texture of intertidal flats using ERS SAR imagery

High resolution, synoptic information on sediment characteristics of intertidal flats is required for coastal management, e.g., for habitat mapping and dredging studies. This study aims to derive such information from space-borne Synthetic Aperture Radar (SAR). Estimates of the backscattering coefficient were extracted from ERS-1 SAR and ERS-2 SAR PRI imagery of four intertidal flats in the Westerschelde (southwest Netherlands). They were related to field measurements of surface roughness, moisture conditions and sediment texture. The field data were also used as input to the backscattering model IEM. The data and model predictions show that on the intertidal flats, backscattering depends mainly on vertical surface roughness, with rougher surfaces associated with more backscattering. Surface roughness, mainly determined by the ripple structure of the bed, decreased with the amount of mud in the sediment. This resulted in a significant negative correlation between backscattering and mud content, and a significant positive correlation between backscattering and median grain-size of the sediment. Sediment texture was also correlated with the volumetric moisture content of the sediment, with finer sediments being associated with higher moisture contents. However, moisture contents were generally high, and therefore the backscatter signal was not sensitive to differences in moisture content. The relationships allowed the development of regression models for the prediction of surface characteristics from SAR imagery, from which maps of, for example, mud content, have been derived.     

Quantifying livestock effects on bunchgrass vegetation with Landsat ETM+ data across a single growing season

Grassland systems provide important habitat for native biodiversity and forage for livestock, with livestock grazing playing an important role influencing sustainable ecosystem function. Traditional field techniques to monitor the effects of grazing on vegetation are costly and limited to small spatial scales. Remote sensing has the potential to provide quantitative and repeatable monitoring data across large spatial and temporal scales for more informed grazing management. To investigate the ability of vegetation metrics derived from remotely sensed imagery to detect the effect of cattle grazing on bunchgrass grassland vegetation across a growing season, we sampled 32 sites across four prescribed stocking rates on a section of Pacific Northwest bunchgrass prairie in northeastern Oregon. We collected vegetation data on vertical structure, biomass, and cover at three different time periods: June, August, and October 2012 to understand the potential to measure vegetation at different phenological stages across a growing season. We acquired remotely sensed Landsat Enhanced Thematic Mapper Plus (ETM+) data closest in date to three field sampling bouts. We correlated the field vegetation metrics to Landsat spectral bands, 14 commonly used vegetation indices, and the tasselled cap wetness, brightness, and greenness transformations. To increase the explanatory value of the satellite-derived data, full, stepwise, and best-subset multiple regression models were fit to each of the vegetation metrics at the three different times of the year. Predicted vegetation metrics were then mapped across the study area. Field-based results indicated that as the stocking rate increased, the mean vegetation amounts of vertical structure, cover, and biomass decreased. The multiple regression models using common vegetation indices had the ability to discern different levels of grazing across the study area, but different spectral indices proved to be the best predictors of vegetation metrics for differing phenological windows. Field measures of vegetation cover yielded the highest correlations to remotely sensed data across all sampling periods. Our results from this analysis can be used to improve grassland monitoring by providing multiple measures of vegetation amounts across a growing season that better align with land management decision making.