Improved methods for measuring forest landscape structure: LiDAR complements field-based habitat assessment

Conservation and monitoring of forest biodiversity requires reliable information about forest structure and composition at multiple spatial scales. However, detailed data about forest habitat characteristics across large areas are often incomplete due to difficulties associated with field sampling methods. To overcome this limitation we employed a nationally available light detection and ranging (LiDAR) remote sensing dataset to develop variables describing forest landscape structure across a large environmental gradient in Switzerland. Using a model species indicative of structurally rich mountain forests (hazel grouse Bonasa bonasia), we tested the potential of such variables to predict species occurrence and evaluated the additional benefit of LiDAR data when used in combination with traditional, sample plot-based field variables. We calibrated boosted regression trees (BRT) models for both variable sets separately and in combination, and compared the models’ accuracies. While both field-based and LiDAR models performed well, combining the two data sources improved the accuracy of the species’ habitat model. The variables retained from the two datasets held different types of information: field variables mostly quantified food resources and cover in the field and shrub layer, LiDAR variables characterized heterogeneity of vegetation structure which correlated with field variables describing the understory and ground vegetation. When combined with data on forest vegetation composition from field surveys, LiDAR provides valuable complementary information for encompassing species niches more comprehensively. Thus, LiDAR bridges the gap between precise, locally restricted field-data and coarse digital land cover information by reliably identifying habitat structure and quality across large areas.     

Associations between wasp communities and forest structure: Do strong local patterns hold across landscapes?

Abstract Forest structure and habitat complexity have been used extensively to predict the distribution and abundance of insect assemblages in forest ecosystems. We tested empirically derived predictions of strong, consistent relationships between wasp assemblages and habitat complexity, using both field assessments and vegetation indices from remote sensing as measures of habitat complexity. Wasp samples from 26 paired ‘high and low’ complexity sites in two forests approximately 70 km apart, were compared with normalized difference vegetation indices (NDVIs) derived from multispectral videography of the survey sites. We describe a strong unequivocal link between habitat complexity and wasp communities, the patterns holding over coarse and fine landscape scales. NDVIs were also excellent predictors of habitat complexity and hence wasp community patterns. Sites with greater NDVIs consistently supported a greater abundance and species richness, and a different composition of wasps to sites with low NDVIs. Using vegetation indices from remote sensing to gauge habitat complexity has significant potential for ecosystem modelling and rapid biodiversity assessment.     

Use of Sentinel 2 imagery to estimate vegetation height in fragments of Atlantic Forest

Information on vegetation height can be used in a variety of applications, but the high cost to obtain it in large areas using field sampling and the latest remote sensing technologies is still a barrier for low-income countries and organizations. In an attempt to overcome these limitations, we explored the possibility to estimate vegetation height in fragments of Atlantic Forest (São Paulo - Brazil) based on Sentinel 2 imagery, using LiDAR (Light Detection And Ranging) and field data as reference. The initial results showed that only wet season images appear to be related to the vegetation height, especially band 5 (red-edge) and related vegetation indices (VIs). Predictions made with Sentinel 2 and LiDAR data showed that vegetation height can be estimated with a root mean square error (RMSE) close to 3 m, with simple linear models outperforming random forest algorithms. It's also shown in a variety of validation tests, that although better results are obtained if the models are applied to the same images they were trained in, they are still able to reasonably predict vegetation height when applied to other images and locations if the right predictive variables are used. The results agree with recent studies made in other biomes and show that Sentinel 2 imagery can be used to estimate vegetation height in the Atlantic Forest as well. We conclude that vegetation height estimates with linear models can be used as a simple low cost alternative for future applications in this environment.     

Using Satellite and Airborne LiDAR to Model Woodpecker Habitat Occupancy at the Landscape Scale

Incorporating vertical vegetation structure into models of animal distributions can improve understanding of the patterns and processes governing habitat selection. LiDAR can provide such structural information, but these data are typically collected via aircraft and thus are limited in spatial extent. Our objective was to explore the utility of satellite-based LiDAR data from the Geoscience Laser Altimeter System (GLAS) relative to airborne-based LiDAR to model the north Idaho breeding distribution of a forest-dependent ecosystem engineer, the Red-naped sapsucker (Sphyrapicus nuchalis). GLAS data occurred within ca. 64 m diameter ellipses spaced a minimum of 172 m apart, and all occupancy analyses were confined to this grain scale. Using a hierarchical approach, we modeled Red-naped sapsucker occupancy as a function of LiDAR metrics derived from both platforms. Occupancy models based on satellite data were weak, possibly because the data within the GLAS ellipse did not fully represent habitat characteristics important for this species. The most important structural variables influencing Red-naped Sapsucker breeding site selection based on airborne LiDAR data included foliage height diversity, the distance between major strata in the canopy vertical profile, and the vegetation density near the ground. These characteristics are consistent with the diversity of foraging activities exhibited by this species. To our knowledge, this study represents the first to examine the utility of satellite-based LiDAR to model animal distributions. The large area of each GLAS ellipse and the non-contiguous nature of GLAS data may pose significant challenges for wildlife distribution modeling; nevertheless these data can provide useful information on ecosystem vertical structure, particularly in areas of gentle terrain. Additional work is thus warranted to utilize LiDAR datasets collected from both airborne and past and future satellite platforms (e.g. GLAS, and the planned IceSAT2 mission) with the goal of improving wildlife modeling for more locations across the globe.     

Willow Warbler Phylloscopus trochilus habitat in woods with different structure and management in southern England

Capsule Use of Light Detection and Ranging (LiDAR) data identified suitable Willow Warbler habitat based on mean vegetation height. This habitat model provided maps of distribution and occupation of suitable habitat.

Aims To identify habitat associations in woods with different vegetation structure and management systems during a period of low Willow Warbler populations.

Methods Locations of all Willow Warblers were mapped during the breeding season in three woods of contrasting management; recent low intervention, actively coppiced woodland and high forest with clear‐fells. Height profile models of each wood were derived from airborne LiDAR. The mean vegetation height at locations with Willow Warblers and a sample from the rest of the wood were used to produce models of optimum habitat and breadth of habitat occupied in each wood. The habitat model was then used to produce maps of suitable habitat.

Results The habitat models did not differ between woods, with highest probability of Willow Warbler occurrence in mean vegetation heights of 3.7–5.3 m. Habitat of heights 6–11 m appeared less suitable, being only partly occupied. Habitat maps showed that habitat of suitable height was only occupied when it occurred as large patches; smaller patches (mostly <0.5 ha) and edges along rides and fields were not used.

Conclusion The use of LiDAR derived measures of vegetation height identified areas of suitable habitat for Willow Warblers. Willow Warblers occupied areas of low mean vegetation height either as early successional or open canopy woodland in all woods. Height‐based habitat maps can identify areas of suitable habitat within larger expanses of heterogeneous woodland and are a potentially useful tool in assessing changes in extent of what are often temporary patches of habitat.     

Remote sensing‐based landscape indicators for the evaluation of threatened‐bird habitats in a tropical forest

Avian species persistence in a forest patch is strongly related to the degree of isolation and size of a forest patch and the vegetation structure within a patch and its matrix are important predictors of bird habitat suitability. A combination of space‐borne optical (Landsat), ALOS‐PALSAR (radar), and airborne Light Detection and Ranging (LiDAR) data was used for assessing variation in forest structure across forest patches that had undergone different levels of forest degradation in a logged forest—agricultural landscape in Southern Laos. The efficacy of different remote sensing (RS) data sources in distinguishing forest patches that had different seizes, configurations, and vegetation structure was examined. These data were found to be sensitive to the varying levels of degradation of the different patch categories. Additionally, the role of local scale forest structure variables (characterized using the different RS data and patch area) and landscape variables (characterized by distance from different forest patches) in influencing habitat preferences of International Union for Conservation of Nature (IUCN) Red listed birds found in the study area was examined. A machine learning algorithm, MaxEnt, was used in conjunction with these data and field collected geographical locations of the avian species to identify the factors influencing habitat preference of the different bird species and their suitable habitats. Results show that distance from different forest patches played a more important role in influencing habitat suitability for the different avian species than local scale factors related to vegetation structure and health. In addition to distance from forest patches, LiDAR‐derived forest structure and Landsat‐derived spectral variables were important determinants of avian habitat preference. The models derived using MaxEnt were used to create an overall habitat suitability map (HSM) which mapped the most suitable habitat patches for sustaining all the avian species. This work also provides insight that retention of forest patches, including degraded and isolated forest patches in addition to large contiguous forest patches, can facilitate bird species retention within tropical agricultural landscapes. It also demonstrates the effective use of RS data in distinguishing between forests that have undergone varying levels of degradation and identifying the habitat preferences of different bird species. Practical conservation management planning endeavors can use such data for both landscape scale monitoring and habitat mapping.     

Assessing ecological habitat structure from local to landscape scales using synthetic aperture radar

Ecological studies need accurate environmental data such as vegetation characterization, landscape structure and organization, to predict and explain the spatial distribution of biodiversity. Few ecological studies use remote sensing data to assess the biophysical or structural properties of vegetation to understand species distribution. To date, synthetic aperture radar (SAR) data have seldom been used for ecological applications. However, these sensors provide data allowing access to the inner structure of vegetation which is a key information in ecology. The objective of this article is to compare the predictive power of ecological habitat structure variables derived from a TerraSAR-X image, an aerial photograph and a SPOT-5 image for species distribution. The test was run with a hedgerow network in Brittany and assessed the spatial distribution of the forest ground carabid beetles which inhabit these hedgerows. The results confirmed that radar and optical images can be indifferently used to extract hedgerow network and derived landscape metrics (hedgerow density, network grain) useful to explain the spatial distribution of forest carabid beetles. In comparison with passive optical remotely sensed data, VHSR SAR images provide new data to characterize vegetation structure and more particularly hedgerow canopy cover, a variable known to explain the spatial distribution of carabid beetles in an agricultural landscape, but not yet quantified at a fine scale. The hedgerow canopy cover derived from the SAR image is a strong predictor of the abundance of forest carabid beetles at two scales i.e., a local scale and a landscape scale.     

Characterization of moorland vegetation and the prediction of bird abundance using remote sensing

Aims To characterize and identify upland vegetation composition and height from a satellite image, and assess whether the resulting vegetation maps are accurate enough for predictions of bird abundance. Location South‐east Scotland, UK. Methods Fine‐taxa vegetation data collected using point samples were used for a supervised classification of a Landsat 7 image, while linear regression was used to model vegetation height over the same image. Generalized linear models describing bird abundance were developed using field‐collected bird and vegetation data. The satellite‐derived vegetation data were substituted into these models and efficacy was examined. Results The accuracy of the classification was tested over both the training and a set of test plots, and showed that more common vegetation types could be predicted accurately. Attempts to estimate the heights of both dwarf shrub and graminoid vegetation from satellite data produced significant, but weak, correlations between observed and predicted height. When these outputs were used in bird abundance–habitat models, bird abundance predicted using satellite‐derived vegetation data was very similar to that obtained when the field‐collected data were used for one bird species, but poor estimates of vegetation height produced from the satellite data resulted in a poor abundance prediction for another. Conclusions This pilot study suggests that it is possible to identify moorland vegetation to a fine‐taxa level using point samples, and that it may be possible to derive information on vegetation height, although more appropriate field‐collected data are needed to examine this further. While remote sensing may have limitations compared with relatively fine‐scale fieldwork, when used at relatively large scales and in conjunction with robust bird abundance–habitat association models, it may facilitate the mapping of moorland bird abundance across large areas.     

生物多样性近地面遥感监测: 应用现状与前景展望

近年来中国生物多样性监测与研究网络(Sino BON)建设得到了快速发展, 为我国生物多样性长期监测和研究提供了良好的平台条件。其中, 以激光雷达技术为核心的近地面遥感平台, 作为Sino BON综合监测与管理中心的重要组成部分, 已研发形成了较为成熟的软、硬件技术体系, 可以提供林下地形建模, 林分高度、林分表面结构, 林窗或内部分界线, 郁闭度动态, 植被群落划分、群落内部精细空间结构, 单木高度与胸径, 冠层形态、周长和盖度, 物种识别, 亚米级三维景观图等数字产品, 从而能够为国家相关部门和研究单位开展多种时空尺度的生物多样性监测、评价和保护工作提供精准、高效的技术支持。本文首先介绍了遥感技术在生物多样性研究中的应用发展历史及最新趋势。然后在生物多样性遥感监测直接和间接两种方法研究进展基础之上, 总结了从遥感数据中可提取的重要生物多样性指标, 以及选择不同类型遥感数据源时需要考虑的时空尺度信息。在详细阐述NEON、CEOS、GEO BON等国际合作组织推动遥感技术开展生物多样性监测的过程中指明: 以无人机为代表的近地面遥感平台具有机动灵活、高效低廉和高分辨率的特点, 可在卫星平台、载人航空平台和地面常规调查平台之间架构起生物多样性信息尺度推绎不可或缺的中间桥梁, 将是未来生物多样性监测的一个重要手段。最后, 文章指出: Sino BON近地面遥感平台的逐步建设完善将为我国生物多样性监测提供全方位的立体定量化信息, 在促进我国生物多样性监测网络向跨尺度等级动态系统监测、多源信息集成、智能决策与服务的平台方向发展意义重大。     

Vegetation survey in the former brown coal mining area of eastern Germany by integrating remote sensing and ground-based methods

Abstract. In the former brown coal mining area of eastern Germany, now scheduled as a nature conservation area, an analysis of the spatial distribution of vegetation was considered as an important tool in landscape planning. Therefore a comprehensive vegetation survey by means of satellite imagery (Landsat-TM), airborne imagery (CASI), and ground-based methods, notably habitat mapping and vegetation sampling was carried out. With respect to the scales of resolution the classification results of the four methods are, to a certain degree, comparable. Differences in the outcome can be ascribed to the fact that methods of low resolution result in a discrete array of polygons whereas methods of high resolution depict a mosaic structure with an underlying, continuously changing gradient. Provided that the biological meaning of the remote sensing classification is known, a shift from single vegetation patterns to the landscape scale will be possible. Neither satellite nor airborne imagery is restricted to the purpose of mapping but may also serve for vegetation classification itself.     

Airborne hyperspectral data predict Ellenberg indicator values for nutrient and moisture availability in dry grazed grasslands within a local agricultural landscape

Species-based ecological indices, such as Ellenberg indicators, reflect plant habitat preferences and can be used to describe local environment conditions. One disadvantage of using vegetation data as a substitute for environmental data is the fact that extensive floristic sampling can usually only be carried out at a plot scale within limited geographical areas. Remotely sensed data have the potential to provide information on fine-scale vegetation properties over large areas. In the present study, we examine whether airborne hyperspectral remote sensing can be used to predict Ellenberg nutrient (N) and moisture (M) values in plots in dry grazed grasslands within a local agricultural landscape in southern Sweden. We compare the prediction accuracy of three categories of model: (I) models based on predefined vegetation indices (VIs), (II) models based on waveband-selected VIs, and (III) models based on the full set of hyperspectral wavebands. We also identify the optimal combination of wavebands for the prediction of Ellenberg values. The floristic composition of 104 (4 m × 4 m grassland) plots on the Baltic island of Öland was surveyed in the field, and the vascular plant species recorded in the plots were assigned Ellenberg indicator values for N and M. A community-weighted mean value was calculated for N (mN) and M (mM) within each plot. Hyperspectral data were extracted from an 8 m × 8 m pixel window centred on each plot. The relationship between field-observed and predicted mean Ellenberg values was significant for all three categories of prediction models. The performance of the category II and III models was comparable, and they gave lower prediction errors and higher R² values than the category I models for both mN and mM. Visible and near-infrared wavebands were important for the prediction of both mN and mM, and shortwave infrared wavebands were also important for the prediction of mM. We conclude that airborne hyperspectral remote sensing can detect spectral differences in vegetation between grassland plots characterised by different mean Ellenberg N and M values, and that remote sensing technology can potentially be used to survey fine-scale variation in environmental conditions within a local agricultural landscape.     

Laserfarm – A high-throughput workflow for generating geospatial data products of ecosystem structure from airborne laser scanning point clouds

Quantifying ecosystem structure is of key importance for ecology, conservation, restoration, and biodiversity monitoring because the diversity, geographic distribution and abundance of animals, plants and other organisms is tightly linked to the physical structure of vegetation and associated microclimates. Light Detection And Ranging (LiDAR) — an active remote sensing technique — can provide detailed and high resolution information on ecosystem structure because the laser pulse emitted from the sensor and its subsequent return signal from the vegetation (leaves, branches, stems) delivers three-dimensional point clouds from which metrics of vegetation structure (e.g. ecosystem height, cover, and structural complexity) can be derived. However, processing 3D LiDAR point clouds into geospatial data products of ecosystem structure remains challenging across broad spatial extents due to the large volume of national or regional point cloud datasets (typically multiple terabytes consisting of hundreds of billions of points). Here, we present a high-throughput workflow called ‘Laserfarm’ enabling the efficient, scalable and distributed processing of multi-terabyte LiDAR point clouds from national and regional airborne laser scanning (ALS) surveys into geospatial data products of ecosystem structure. Laserfarm is a free and open-source, end-to-end workflow which contains modular pipelines for the re-tiling, normalization, feature extraction and rasterization of point cloud information from ALS and other LiDAR surveys. The workflow is designed with horizontal scalability and can be deployed with distributed computing on different infrastructures, e.g. a cluster of virtual machines. We demonstrate the Laserfarm workflow by processing a country-wide multi-terabyte ALS dataset of the Netherlands (covering ∼34,000 km² with ∼700 billion points and ∼ 16 TB uncompressed LiDAR point clouds) into 25 raster layers at 10 m resolution capturing ecosystem height, cover and structural complexity at a national extent. The Laserfarm workflow, implemented in Python and available as Jupyter Notebooks, is applicable to other LiDAR datasets and enables users to execute automated pipelines for generating consistent and reproducible geospatial data products of ecosystems structure from massive amounts of LiDAR point clouds on distributed computing infrastructures, including cloud computing environments. We provide information on workflow performance (including total CPU times, total wall-time estimates and average CPU times for single files and LiDAR metrics) and discuss how the Laserfarm workflow can be scaled to other LiDAR datasets and computing environments, including remote cloud infrastructures. The Laserfarm workflow allows a broad user community to process massive amounts of LiDAR point clouds for mapping vegetation structure, e.g. for applications in ecology, biodiversity monitoring and ecosystem restoration.     

Crop 3D—a LiDAR based platform for 3D high-throughput crop phenotyping

With the growing population and the reducing arable land, breeding has been considered as an effective way to solve the food crisis.As an important part in breeding, high-throughput phenotyping can accelerate the breeding process effectively. Light detection and ranging(LiDAR) is an active remote sensing technology that is capable of acquiring three-dimensional(3 D) data accurately,and has a great potential in crop phenotyping. Given that crop phenotyping based on LiDAR technology is not common in China,we developed a high-throughput crop phenotyping platform, named Crop 3 D, which integrated LiDAR sensor, high-resolution camera, thermal camera and hyperspectral imager. Compared with traditional crop phenotyping techniques, Crop 3 D can acquire multi-source phenotypic data in the whole crop growing period and extract plant height, plant width, leaf length, leaf width, leaf area, leaf inclination angle and other parameters for plant biology and genomics analysis. In this paper, we described the designs,functions and testing results of the Crop 3 D platform, and briefly discussed the potential applications and future development of the platform in phenotyping. We concluded that platforms integrating LiDAR and traditional remote sensing techniques might be the future trend of crop high-throughput phenotyping.     

Quantifying Tropical Dry Forest Type and Succession: Substantial Improvement with LiDAR

Improved technologies are needed to advance our knowledge of the biophysical and human factors influencing tropical dry forests, one of the world's most threatened ecosystems. We evaluated the use of light detection and ranging (LiDAR) data to address two major needs in remote sensing of tropical dry forests, i.e., classification of forest types and delineation of forest successional status. We evaluated LiDAR‐derived measures of three‐dimensional canopy structure and subcanopy topography using classification‐tree techniques to separate different dry forest types and successional stages in the Guánica Biosphere Reserve in Puerto Rico. We compared the LiDAR‐based results with classifications made from commonly used remote sensing data, including Landsat satellite imagery and radar‐based topographic data. The accuracy of the LiDAR‐based forest type classification (including native‐ and exotic‐dominated forest classes) was substantially higher than those from previously available data (kappa = 0.90 and 0.63, respectively). The best result was obtained when combining LiDAR‐derived metrics of canopy structure and topography, and adding Landsat spectral data did not improve the classification. For the second objective, we observed that LiDAR‐derived variables of vegetation structure were better predictors of forest successional status (i.e., mid‐secondary, late‐secondary, and primary forests) than was spectral information from Landsat. Importantly, the key LiDAR predictors identified within each classification‐tree model agreed with previous ecological knowledge of these forests. Our study highlights the value of LiDAR remote sensing for assessing tropical dry forests, reinforcing the potential for this novel technology to advance research and management of tropical forests in general.     

Parid foraging choices in urban habitat and their consequences for fitness

Urban environments are habitat mosaics, often with an abundance of exotic flora, and represent complex problems for foraging arboreal birds. In this study, we used compositional analysis to assess how Blue Tits Cyanistes caeruleus and Great Tits Parus major use heterogeneous urban habitat, with the aim of establishing whether breeding birds were selective in the habitat they used when foraging and how they responded to non‐native trees and shrubs. We also assessed whether they showed foraging preferences for certain plant taxa, such as oak Quercus, that are important to their breeding performance in native woodland. Additionally, we used mixed models to assess the impact of these different habitat types on breeding success (expressed as mean nestling mass). Blue Tits foraged significantly more in native than non‐native deciduous trees during incubation and when feeding fledglings, and significantly more in deciduous than evergreen plants throughout the breeding season. Great Tits used deciduous trees more than expected by chance when feeding nestlings, and a positive relationship was found between the availability of deciduous trees and mean nestling mass. Overall, the breeding performance of both species was poor and highly variable. Positive relationships were found between mean nestling mass and the abundance of Quercus for Great Tits, but not for Blue Tits. Our study shows the importance of native vegetation in the complex habitat matrix found in urban environments. The capacity of some, but not all, species to locate and benefit from isolated patches of native trees suggests that species vary in their response to urbanization and this has implications for urban ecosystem function.     

Estimation of wetland vegetation height and leaf area index using airborne laser scanning data

Wetland vegetation is a core component of wetland ecosystems. Wetland vegetation structural parameters, such as height and leaf area index (LAI) are important variables required by earth-system and ecosystem models. Therefore, rapid, accurate, objective and quantitative estimations of wetland vegetation structural parameters are essential. The airborne laser scanning (also called LiDAR) is an active remote sensing technology and can provide accurate vertical vegetation structural parameters, but its accuracy is limited by short, dense vegetation canopies that are typical of wetland environments. The objective of this research is to explore the potential of estimating height and LAI for short wetland vegetation using airborne discrete-return LiDAR data.The accuracies of raw laser points and LiDAR-derived digital elevation models (DEM) data were assessed using field GPS measured ground elevations. The results demonstrated very high accuracy of 0.09 m in raw laser points and the root mean squared error (RMSE) of the LiDAR-derived DEM was 0.15 m.Vegetation canopy height was estimated from LiDAR data using a canopy height model (CHM) and regression analysis between field-measured vegetation heights and the standard deviation (σ) of detrended LiDAR heights. The results showed that the actual height of short wetland vegetation could not be accurately estimated using the raster CHM vegetation height. However, a strong relationship was observed between the σ and the field-measured height of short wetland vegetation and the highest correlation occurred (R² = 0.85, RMSE = 0.14 m) when sample radius was 1.50 m. The accuracy assessment of the best-constructed vegetation height prediction model was conducted using 25 samples that were not used in the regression analysis and the results indicated that the model was reliable and accurate (R² = 0.84, RMSE = 0.14 m).Wetland vegetation LAI was estimated using laser penetration index (LPI) and LiDAR-predicted vegetation height. The results showed that the vegetation height-based predictive model (R² = 0.79) was more accurate than the LPI-based model (the highest R² was 0.70). Moreover, the LAI predictive model based on vegetation height was validated using the leave-one-out cross-validation method and the results showed that the LAI predictive model had a good generalization capability. Overall, the results from this study indicate that LiDAR has a great potential to estimate plant height and LAI for short wetland vegetation.     

LiDAR as a rapid tool to predict forest habitat types in Natura 2000 networks

Management strategies for the conservation of biodiversity can be developed only with precise information on the spatial distribution of organisms on relevant, mostly regional, spatial scales. Current surrogates for approximating the distribution of biodiversity are habitats mapped within a number of national and international frameworks (e.g., Natura 2000), even though conventional habitat mapping is time consuming and requires well-trained personnel. Here we evaluated the use of light detection and ranging (LiDAR) to map forest habitat types to simplify the process. We used available data of habitat types for the Bavarian Forest National Park as a basis to predict habitat types with LiDAR-derived variables. Furthermore, we compared these results with predictions based on extensive ground-based climate, soil and vegetation data. Using linear and flexible discriminant analyses, we found that LiDAR is able to predict forest habitat types with the same overall accuracy as the extensive ground data for climate, soil and vegetation composition. Subtle differences in the vegetation structure between habitat types, particularly in the vertical and horizontal vegetation profiles, were captured by LiDAR. These differences in the physiognomy were in part caused by changes in altitude, which also influence tree species composition. We propose that the most-efficient way to identify forest habitat types according Natura 2000 is to combine remote-sensing LiDAR data with well-directed field surveys.     

Mapping migratory bird prevalence using remote sensing data fusion

Background

Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA.

Methodology and Principal Findings

A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy (“fusion”) models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species.

Conclusion and Significance

Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level.     

Integration of ground survey and remote sensing derived data: Producing robust indicators of habitat extent and condition

The availability of suitable habitat is a key predictor of the changing status of biodiversity. Quantifying habitat availability over large spatial scales is, however, challenging. Although remote sensing techniques have high spatial coverage, there is uncertainty associated with these estimates due to errors in classification. Alternatively, the extent of habitats can be estimated from ground‐based field survey. Financial and logistical constraints mean that on‐the‐ground surveys have much lower coverage, but they can produce much higher quality estimates of habitat extent in the areas that are surveyed. Here, we demonstrate a new combined model which uses both types of data to produce unified national estimates of the extent of four key habitats across Great Britain based on Countryside Survey and Land Cover Map. This approach considers that the true proportion of habitat per km² (Z_i) is unobserved, but both ground survey and remote sensing can be used to estimate Z_i. The model allows the relationship between remote sensing data and Z_i to be spatially biased while ground survey is assumed to be unbiased. Taking a statistical model‐based approach to integrating field survey and remote sensing data allows for information on bias and precision to be captured and propagated such that estimates produced and parameters estimated are robust and interpretable. A simulation study shows that the combined model should perform best when error in the ground survey data is low. We use repeat surveys to parameterize the variance of ground survey data and demonstrate that error in this data source is small. The model produced revised national estimates of broadleaved woodland, arable land, bog, and fen, marsh and swamp extent across Britain in 2007.