Aerial LiDAR Data Research Articles

Building Volume Per Capita (BVPC): A Spatially Explicit Measure of Inequality Relevant to the SDGs

Building Volume Per Capita (BVPC - cubic meters of building per person) is presented as a proxy measure of economic inequality and a direct measure of housing inequality. The Sustainable Development Goal 10 (SDG 10: Reduced Inequalities) is synergic for achieving SDG 11 on Sustainable Cities and Communities. Access to safe and affordable housing, transport systems, and public spaces are some of the targets of SDG 11 that mostly link with reducing inequalities. The Habitat III New Urban Agenda sets equal access to urban spaces, infrastructures and basic services as crucial for developing sustainable cities. Earth Observation (EO) data including remotely sensed satellite data, airborne data, and model outputs, in combination with demographic, and other statistical data, have been gaining importance for monitoring progress of the SDGs. High spatial resolution building footprint data derived from aerial photographs, stereo imagery, and LIDAR data, obtained for the cities of California, between 2010-2015, were used in this study. These measures of building volume were rasterized and juxtaposed with (divided by) a variety of demographic data including vector-based census data of 2015 and LandScan raster data of population counts of 2015. The National Landcover dataset of 2011 was used to characterize the land cover variability of the cities. Using these datasets, the spatial pattern and distribution of BVPC for nine cities in California were studied. The results showed that BVPC was inversely related with intensity of development, and positively related with median household income within cities. A BV-GINI was also developed to characterize the variability of the BVPC at the census tract level and the pixel level. The results were inconclusive and require further investigation.

Topographic, Hydraulic, and Vegetative Controls on Bar and Island Development in Mixed Bedrock‐Alluvial, Multichanneled, Dryland Rivers

AbstractWe investigate processes of bedrock‐core bar and island development in a bedrock‐influenced anastomosed reach of the Sabie River, Kruger National Park, eastern South Africa. For sites subject to alluvial stripping during an extreme flood event (~4,470–5,630 m3 s−1) in 2012, preflood and postflood aerial photographs and LiDAR data, 2‐D morphodynamic simulations, and field observations reveal that the thickest surviving alluvial deposits tend to be located over bedrock topographic lows. At a simulated peak discharge (~4,500 m3 s−1), most sediment (sand, fine gravel) is mobile but localized deposition on bedrock topographic highs is possible. At lower simulated discharges (<1,000 m3 s−1), topographic highs are not submerged, and deposition occurs in lower elevation areas, particularly in areas disconnected from the main channels during falling stage. Field observations suggest that in addition to discharge, rainwash between floods may redistribute sediments from bedrock topographic highs to lower elevation areas, and also highlight the critical role of vegetation colonization in bar stability, and in trapping of additional sediment and organics. These findings challenge the assumptions of preferential deposition on topographic highs that underpin previous analyses of Kruger National Park river dynamics, and are synthesized in a new conceptual model that demonstrates how initial bedrock topographic lows become topographic highs (bedrock‐core bars and islands) in the latter stages of sediment accumulation. The model provides particular insight into the development of mixed bedrock‐alluvial anastomosing along the Kruger National Park rivers, but similar processes of bar/island development likely occur along numerous other bedrock‐influenced rivers across dryland southern Africa and farther afield.

Compatibility of Aerial and Terrestrial LiDAR for Quantifying Forest Structural Diversity

Structural diversity is a key feature of forest ecosystems that influences ecosystem functions from local to macroscales. The ability to measure structural diversity in forests with varying ecological composition and management history can improve the understanding of linkages between forest structure and ecosystem functioning. Terrestrial LiDAR has often been used to provide a detailed characterization of structural diversity at local scales, but it is largely unknown whether these same structural features are detectable using aerial LiDAR data that are available across larger spatial scales. We used univariate and multivariate analyses to quantify cross-compatibility of structural diversity metrics from terrestrial versus aerial LiDAR in seven National Ecological Observatory Network sites across the eastern USA. We found strong univariate agreement between terrestrial and aerial LiDAR metrics of canopy height, openness, internal heterogeneity, and leaf area, but found marginal agreement between metrics that described heterogeneity of the outermost layer of the canopy. Terrestrial and aerial LiDAR both demonstrated the ability to distinguish forest sites from structural diversity metrics in multivariate space, but terrestrial LiDAR was able to resolve finer-scale detail within sites. Our findings indicated that aerial LiDAR could be of use in quantifying broad-scale variation in structural diversity across macroscales.

Multitemporal Analysis of Gully Erosion in Olive Groves by Means of Digital Elevation Models Obtained with Aerial Photogrammetric and LiDAR Data

Gully erosion is one of the main processes of soil degradation, representing 50%–90% of total erosion at basin scales. Thus, its precise characterization has received growing attention in recent years. Geomatics techniques, mainly photogrammetry and LiDAR, can support the quantitative analysis of gully development. This paper deals with the application of these techniques using aerial photographs and airborne LiDAR data available from public database servers to identify and quantify gully erosion through a long period (1980–2016) in an area of 7.5 km2 in olive groves. Several historical flights (1980, 1996, 2001, 2005, 2009, 2011, 2013 and 2016) were aligned in a common coordinate reference system with the LiDAR point cloud, and then, digital surface models (DSMs) and orthophotographs were obtained. Next, the analysis of the DSM of differences (DoDs) allowed the identification of gullies, the calculation of the affected areas as well as the estimation of height differences and volumes between models. These analyses result in an average depletion of 0.50 m and volume loss of 85000 m3 in the gully area, with some periods (2009–2011 and 2011–2013) showing rates of 10,000–20,000 m3/year (20–40 t/ha*year). The manual edition of DSMs in order to obtain digital elevation models (DTMs) in a detailed sector has facilitated an analysis of the influence of this operation on the erosion calculations, finding that it is not significant except in gully areas with a very steep shape.

Using object‐based image analysis with multi‐temporal aerial imagery and LiDAR to detect change in temperate intertidal habitats

Abstract Intertidal habitat maps are needed at both fine and coarse scales to monitor change and inform conservation and management, but current methods of field survey and expert interpretation of aerial imagery can be time‐consuming and subjective. Object‐based image analysis (OBIA) of remote sensing data is increasingly employed for producing habitat or land cover maps. Users create automated workflows to segment imagery, creating ecologically meaningful objects, which are then classified based on their spectral or geometric properties, relationships to other objects and contextual data. This study evaluates the change‐detection capability of OBIA in the intertidal environment by developing and comparing two OBIA methods for quantifying change in extent and distribution of habitats from freely available multi‐temporal aerial imagery and LiDAR data. Despite considerable variability in the data, pre‐ and post‐classification change detection methods had sufficient accuracy (mean overall accuracy from 70.5 to 82.6%) to monitor deviation from a background level of natural environmental fluctuation. This insight into spatial and temporal patterns of natural cyclical change and their detectability by OBIA could inform use of remote sensing for regular, rapid coastal assessment, providing an alert system to direct survey resources to areas of ecologically relevant change.

Forest Landscape Heterogeneity Increases Shrub Diversity at the Expense of Tree Seedling Diversity in Temperate Mixedwood Forests

Partial disturbances enhance spatial heterogeneity through the diversification of forest structure, which contributes to niche partitioning and consequently to species diversity. However, this heterogeneity–diversity relationship may differ between groups of species, and is potentially modified by biotic interactions at the community level. We propose that shrub diversity will be greater in heterogeneous landscapes, while tree diversity will be lower in those same landscapes, due to the biotic interactions of shrub competition. We conducted field sampling in the balsam fir/yellow birch bioclimatic domain in western Québec, a forested ecosystem disturbed by natural and anthropogenic partial disturbances. We selected 12 forested landscapes (1 km2), four in each of three classes of landscape heterogeneity (heterogeneous, moderately heterogeneous, homogenous). Shrub and tree species regeneration abundance was measured in three different size classes of canopy gaps and the forest understory. Gap proportions were assessed in each landscape using aerial LiDAR data. Tree and shrub alpha-diversity significantly responded to landscape heterogeneity, shrubs being more diverse while tree seedlings were less diverse in heterogeneous landscapes. Heterogeneous landscapes showed highest species accumulation rates for shrubs in medium-sized gaps. For tree seedlings, species accumulation rates were highest in heterogeneous landscapes in the forest understory. Our study thus supports the heterogeneity–diversity relationship with shrubs having higher alpha and beta diversity in heterogeneous landscapes whereas local-scale tree diversity was higher in homogenous landscapes.

Detecting northern peatland vegetation patterns at ultra‐high spatial resolution

AbstractWithin northern peatlands, landscape elements such as vegetation and topography are spatially heterogenic from ultra‐high (centimeter level) to coarse scale. In addition to within‐site spatial heterogeneity, there is evident between‐site heterogeneity, but there is a lack of studies assessing whether different combinations of remotely sensed features and mapping approaches are needed in different types of landscapes. We evaluated the value of different mapping methods and remote sensing datasets and analyzed the kinds of differences present in vegetation patterns and their mappability between three northern boreal peatland landscapes in northern Finland. We utilized field‐inventoried vegetation plots together with spectral, textural, topography and vegetation height remote sensing data from 0.02‐ to 3‐m pixel size. Remote sensing data included true‐color unmanned aerial vehicle images, aerial images with four spectral bands, aerial lidar data and multiple PlanetScope satellite images. We used random forest regressions for tracking plant functional type (PFT) coverage, non‐metric multidimensional scaling ordination axes and fuzzy k‐medoid plant community clusters. PFT regressions had variable performance for different study sites (R2 −0.03 to 0.69). Spatial patterns of some spectrally or structurally distinctive PFTs could be predicted relatively well. The first ordination axis represented wetness gradient and was well predicted using remotely sensed data (R2 0.64 to 0.82), but the other three axes had a less straightforward explanation and lower mapping performance (R2 −0.09 to 0.53). Plant community clusters were predicted most accurately in the sites with clear string‐flark topography but less accurately in the flatter site (R2 0.16–0.82). The most important remote sensing features differed between dependent variables and study sites: different topographic, spectral and textural features; and coarse‐scale and fine‐scale datasets were the most important in different tasks. We suggest that multiple different mapping approaches should be tested and several remote sensing datasets used when maps of vegetation are produced.

MAPPING URBAN TREES WITHIN CADASTRAL PARCELS USING AN OBJECT-BASED CONVOLUTIONAL NEURAL NETWORK

Abstract. Urban trees offer significant benefits for improving the sustainability and liveability of cities, but its monitoring is a major challenge for urban planners. Remote-sensing based technologies can effectively detect, monitor and quantify urban tree coverage as an alternative to field-based measurements. Automatic extraction of urban land cover features with high accuracy is a challenging task and it demands artificial intelligence workflows for efficiency and thematic quality. In this context, the objective of this research is to map urban tree coverage per cadastral parcel of Sandy Bay, Hobart from very high-resolution aerial orthophoto and LiDAR data using an Object Based Convolution Neural Network (CNN) approach. Instead of manual preparation of a large number of required training samples, automatically classified Object based image analysis (OBIA) output is used as an input samples to train CNN method. Also, CNN output is further refined and segmented using OBIA to assess the accuracy. The result shows 93.2% overall accuracy for refined CNN classification. Similarly, the overlay of improved CNN output with cadastral parcel layer shows that 21.5% of the study area is covered by trees. This research demonstrates that the accuracy of image classification can be improved by using a combination of OBIA and CNN methods. Such a combined method can be used where manual preparation of training samples for CNN is not preferred. Also, our results indicate that the technique can be implemented to calculate parcel level statistics for urban tree coverage that provides meaningful metrics to guide urban planning and land management practices.

Multi-scale roof characterization from LiDAR data and aerial orthoimagery: Automatic computation of building photovoltaic capacity

Abstract Photovoltaic self-consumption in buildings requires the installation of photovoltaic (PV) systems mostly on roofs, taking the advantage of these building locations for both the available surface area and the certainty of a big amount of annual incoming solar radiation. Since the parameters required for a proper PV system design are mainly geometric: azimuth-orientation, tilt angle and effective dimensions of the different roof slopes; aerial LiDAR data and orthoimagery offered by the National Geographic Agencies became suitable data sources for this purpose, ensuring its availability for any city regardless of its location. This paper presents a novel automatic methodology that combines LiDAR and orthoimage data processing to geometrically characterize roofs at slope level and calculate their PV solar potential. The methodology developed has been validated against results obtained from a higher-resolution aerial 3D point cloud of the roofs under study. Different locations and roof types have been tested in order to confirm the performance of the methodology under different conditions, being able to accurately characterize the geometry of most types of roofs, such as flat roofs, gable or saddle roofs, single pitched roofs and pyramid roofs at city, neighbourhood and building level.

OCCLUDED AREA REMOVING FROM HANDHELD LASER SCANNER DATA DURING 3D BUILDING MODELLING

Abstract. 3D building modelling has been turned to be one of the most interesting and hottest subjects in photogrammetry in last two decades, and it seems that photogrammetry provides the only economic means to acquire truly 3D city-data. Most of the researches proposed methods for 3d building modelling in LoD2 using aerial images and LIDAR data and the produced models will be enriched by oblique images, therefore there is always a demand for a user to interpret the façade or in other manual building reconstruction process the operator should draw boundaries to represent the building model and the process will be too time-consuming for 3d modelling for a whole city. Creating building facade models for a whole city requires considerable work, therefore for decades, much research has been dedicated to the automation of this reconstruction process. Nowadays researchers attempt to recommend a new method which is flexible to model hug variety of buildings and has a solution for several challenges such as irrelevant objects (pedestrians, trees, traffic signs, etc.), occluded areas and non-homogenous data. Based on various 3d building models applications, namely navigation systems, location-based system, city planning and etc. the demand for adding semantic features (such as windows and doors) is increasing and becoming more essential, therefore simple blocks as the representation of 3d buildings aren’t sufficient anymore. Therefore 2.5 models which show the façade details using pixel values have been substituted by LoD3 models recently.The lack of automation in image based approaches can be explained by the difficulties in image interpretation. Specifically, factors like illumination and occlusion can cause considerable confusion for machine understanding and some conditions (relative orientation, feature matching, etc.) need to be accurately determined to transfer image pixels to 3D coordinates. In recent years, terrestrial laser scanning data has been proven as a valuable source for building facade reconstruction. The point density of stationary laser scanning in urban areas can be up to hundreds or thousands of points per square meter, which is high enough for documenting most details on building facades. In comparison with image-based modelling, several steps such as image matching, intersection and resection will be eliminated, while there is no need to image interpret in laser data-based reconstruction approaches, these methods face major challenges such as extracting meaningful structures from a huge amount of data.This paper presents a data-driven algorithm for façade reconstruction, using a handheld laser scanner, Zebedee. The mentioned device is consisting of 2d laser scanner and an inertial measurement unit mounted on one or two springs, it has 270-degree field of view. Its mass is 210 g which makes it ideal for low measurement and it is maximum range is 30 m. The proposed method was implemented by using the Zebedee point cloud in order to determine the challenges of zeb1 data and ensure that the introduced device can be practical for 3d reconstruction.Due to obstacle existence, operator gross errors while data capturing and façade elements arrangement, there will always be occluded area and shadows in produced data. Occluded area cause tribulation in machine understanding and problems for automatic reconstruction algorithms. The proposed method represents a new way to detect occluded area and remove the artificial objects which are produced by them. The 3d point cloud is used to cover all façade elements and details, also image matching and producing 3-dimensional data steps will be omitted from the process.The proposed workflow is indicated in figure 1. Most researches such as road, building or other objects detection and reconstruction put ground points detection in priority in order to decrease data volume and processing time, so as a pre-processing step, point cloud is classified into two separate groups (non-ground and ground points).

Mapping of arthropod alpha and beta diversity in heterogeneous arctic-alpine ecosystems

Assessing biodiversity in arctic-alpine ecosystems is a costly task. We test in the current study whether we can map the spatial patterns of spider alpha and beta diversity using remotely-sensed surface reflectance and topography in a heterogeneous alpine environment in Central Norway. This proof-of-concept study may provide a tool for an assessment of arthropod communities in remote study areas. Data on arthropod species distribution and richness were collected through pitfall trapping and subjected to a detrended correspondence analysis (DCA) to extract the main species composition gradients. The DCA axis scores as indicators of species composition as well as trap species richness were regressed against a combined data set of surface reflectance as measured by the Sentinel-2 satellite and topographical parameters extracted from a digital elevation model. The models were subsequently applied to the spatial data set to achieve a pixel-wise prediction of both species richness and position in the DCA space. The spatial variation in the modelled DCA scores was used to draw conclusions regarding spider beta-diversity. The species composition was described with two DCA axes that were characterized by post hoc-defined indicator species, which showed a typical annidation in the arctic-alpine environment under study. The fits of the regression models for the DCA axes and species richness ranged from R2 = 0.25 up to R2 = 0.62. The resulting maps show strong gradients in alpha and beta diversity across the study area. Our results indicate that the diversity patterns of spiders can at least partially be explained by means of remotely sensed data. Our approach would likely benefit from the additional use of high resolution aerial photography and LiDAR data and may help to improve conservation strategies in arctic-alpine ecosystems.

A simplified method on estimation of forest roughness by use of aerial LIDAR data

AbstractIn the last decade, satellite‐based measurements combined with local land cover information have produced datasets with a very detailed land cover description. CORINE Land Cover (CLC) dataset is owned and maintained by the European Environmental Agency (EEA) and published at the agency's website. Another remote sensing tool, developed in the same period, is the terrain LIDAR scanners with very high resolution and porosity information. In the current study, LIDAR scans of mainland Denmark with 0.4 m resolution were used to estimate the aerodynamic roughness of large forests, the borders of which were defined with the help of the CLC dataset. The results are compared with available in situ measurement results from the scientific literature. There was a generally good agreement between calculated and measured displacement height values but less so for aerodynamic roughness values due to the employed spatial averaging process. The results reveal a promising application that can be used for forest parameterization within modeling tools.

Estimation of maize above-ground biomass based on stem-leaf separation strategy integrated with LiDAR and optical remote sensing data.

Above-ground biomass (AGB) is an important indicator for effectively assessing crop growth and yield and, in addition, is an important ecological indicator for assessing the efficiency with which crops use light and store carbon in ecosystems. However, most existing methods using optical remote sensing to estimate AGB cannot observe structures below the maize canopy, which may lead to poor estimation accuracy. This paper proposes to use the stem-leaf separation strategy integrated with unmanned aerial vehicle LiDAR and multispectral image data to estimate the AGB in maize. First, the correlation matrix was used to screen optimal the LiDAR structural parameters (LSPs) and the spectral vegetation indices (SVIs). According to the screened indicators, the SVIs and the LSPs were subjected to multivariable linear regression (MLR) with the above-ground leaf biomass (AGLB) and above-ground stem biomass (AGSB), respectively. At the same time, all SVIs derived from multispectral data and all LSPs derived from LiDAR data were subjected to partial least squares regression (PLSR) with the AGLB and AGSB, respectively. Finally, the AGB was computed by adding the AGLB and the AGSB, and each was estimated by using the MLR and the PLSR methods, respectively. The results indicate a strong correlation between the estimated and field-observed AGB using the MLR method (R2 = 0.82, RMSE = 79.80 g/m2, NRMSE = 11.12%) and the PLSR method (R2 = 0.86, RMSE = 72.28 g/m2, NRMSE = 10.07%). The results indicate that PLSR more accurately estimates AGB than MLR, with R2 increasing by 0.04, root mean square error (RMSE) decreasing by 7.52 g/m2, and normalized root mean square error (NRMSE) decreasing by 1.05%. In addition, the AGB is more accurately estimated by combining LiDAR with multispectral data than LiDAR and multispectral data alone, with R2 increasing by 0.13 and 0.30, respectively, RMSE decreasing by 22.89 and 54.92 g/m2, respectively, and NRMSE decreasing by 4.46% and 7.65%, respectively. This study improves the prediction accuracy of AGB and provides a new guideline for monitoring based on the fusion of multispectral and LiDAR data.

Bringing Lunar LiDAR Back Down to Earth: Mapping Our Industrial Heritage through Deep Transfer Learning

This article presents a novel deep learning method for semi-automated detection of historic mining pits using aerial LiDAR data. The recent emergence of national scale remotely sensed datasets has created the potential to greatly increase the rate of analysis and recording of cultural heritage sites. However, the time and resources required to process these datasets in traditional desktop surveys presents a near insurmountable challenge. The use of artificial intelligence to carry out preliminary processing of vast areas could enable experts to prioritize their prospection focus; however, success so far has been hindered by the lack of large training datasets in this field. This study develops an innovative transfer learning approach, utilizing a deep convolutional neural network initially trained on Lunar LiDAR datasets and reapplied here in an archaeological context. Recall rates of 80% and 83% were obtained on the 0.5 m and 0.25 m resolution datasets respectively, with false positive rates maintained below 20%. These results are state of the art and demonstrate that this model is an efficient, effective tool for semi-automated object detection for this type of archaeological objects. Further tests indicated strong potential for detection of other types of archaeological objects when trained accordingly.

Allergenic pollen production across a large city for common ragweed (Ambrosia artemisiifolia)

Predictions of airborne allergenic pollen concentrations at fine spatial scales require information on source plant location and pollen production. Such data are lacking at the urban scale, largely because manually mapping allergenic pollen producing plants across large areas is infeasible. However, modest-sized field surveys paired with allometric equations, remote sensing, and habitat distribution models can predict where these plants occur and how much pollen they produce. In this study, common ragweed (Ambrosia artemisiifolia) was mapped in a field survey in Detroit, MI, USA. The relationship between ragweed presence and habitat-related variables derived from aerial imagery, LiDAR, and municipal data were used to create a habitat distribution model, which was then used to predict ragweed presence across the study area (392 km2). The relationship between inflorescence length and pollen production was used to predict pollen production in the city. Ragweed occurs in 1.7% of Detroit and total pollen production is 312 × 1012 pollen grains annually, but ragweed presence was highly heterogeneous across the city. Ragweed was predominantly found in in vacant lots (75%) and near demolished structures (48%), and had varying associations with land cover types (e.g., sparse vegetation, trees, pavement) detected by remote sensing. These findings also suggest several management strategies that could help reduce levels of allergenic pollen, including appropriate post-demolition management practices. Spatially-resolved predictions for pollen production will allow mechanistic modeling of airborne allergenic pollen and improved exposure estimates for use in epidemiological and other applications.

UFPR CampusMap: a laboratory for a Smart City developments

UFPR CampusMap: a laboratory for a Smart City developments

City-wide canopy cover decline due to residential property redevelopment in Christchurch, New Zealand

Urban redevelopment influences urban forests, with consequences for ecosystem service provision. Better understanding the effect of redevelopment on trees in cities can improve management and inform policy, thus having positive effects on ecosystem service provision and human wellbeing. This study quantified the effect of residential property redevelopment on canopy cover change in Christchurch, New Zealand. By applying an object-based image analysis (OBIA) technique to aerial imagery and LiDAR data, this study delineated tree canopy cover city-wide in 2011 and again in 2015 and then spatially quantified changes in city-wide canopy cover between 2011 and 2015. Changes in tree canopy cover were also determined at a finer scale, that of the meshblock, a geographic boundary used for census purposes. The results show a small absolute magnitude of city-wide tree canopy cover decline, from 10.84% to 10.28% between 2011 and 2015, but a statistically significant decrease in meshblock-scale mean tree canopy cover. Tree canopy cover losses were more likely to occur in meshblocks containing properties that underwent redevelopment, but the loss was insensitive to the density of redeveloped properties within meshblocks. These findings show that property redevelopment is an important influencer of urban forest dynamics.

Automatic building extraction from high-resolution aerial images and LiDAR data using gated residual refinement network

Automated extraction of buildings from remotely sensed data is important for a wide range of applications but challenging due to difficulties in extracting semantic features from complex scenes like urban areas. The recently developed fully convolutional neural networks (FCNs) have shown to perform well on urban object extraction because of the outstanding feature learning and end-to-end pixel labeling abilities. The commonly used feature fusion or skip-connection refine modules of FCNs often overlook the problem of feature selection and could reduce the learning efficiency of the networks. In this paper, we develop an end-to-end trainable gated residual refinement network (GRRNet) that fuses high-resolution aerial images and LiDAR point clouds for building extraction. The modified residual learning network is applied as the encoder part of GRRNet to learn multi-level features from the fusion data and a gated feature labeling (GFL) unit is introduced to reduce unnecessary feature transmission and refine classification results. The proposed model - GRRNet is tested in a publicly available dataset with urban and suburban scenes. Comparison results illustrated that GRRNet has competitive building extraction performance in comparison with other approaches. The source code of the developed GRRNet is made publicly available for studies.

Improving Ecotope Segmentation by Combining Topographic and Spectral Data

Ecotopes are the smallest ecologically distinct landscape features in a landscape mapping and classification system. Mapping ecotopes therefore enables the measurement of ecological patterns, process and change. In this study, a multi-source GEOBIA workflow is used to improve the automated delineation and descriptions of ecotopes. Aerial photographs and LIDAR data provide input for landscape segmentation based on spectral signature, height structure and topography. Each segment is then characterized based on the proportion of land cover features identified at 2 m pixel-based classification. The results show that the use of hillshade bands simultaneously with spectral bands increases the consistency of the ecotope delineation. These results are promising to further describe biotopes of high ecological conservation value, as suggested by a successful test on ravine forest biotope.

Exploiting low-cost and commonly shared aerial photographs and LiDAR data for detailed vegetation structure mapping of the Wadden Sea island of Ameland

Regular mapping of vegetation structure is important for biodiversity monitoring, and increasingly for tracking compliance with nature policy mandates. As such, the Netherlands uses vegetation structure mapping to monitor the Natura 2000 site on the Dutch Wadden Sea island of Ameland. Three decades of natural gas extraction here has caused soil subsidence, impacting vegetation and habitats on the island. In the Netherlands, vegetation structure mapping is typically done using conventional techniques, primarily field surveys combined with visual interpretation of aerial photographs. This procedure is time-consuming and often too inconsistent and inefficient for large areas. In the current study we exploited commonly shared and low-cost aerial photographs and LiDAR data for detailed vegetation structure mapping. Aerial photographs are not always easy to use in automatic classification procedures, as they often lack calibrated spectral reflectance values. Furthermore, pre-processing of aerial photographs to render them more attractive may skew the image so that it no longer accurately depicts the original scene anymore. Our aim was to determine if automatic or semi-automatic classification techniques could be applied to these readily available Dutch data to support mapping and monitoring of the vegetation structure of larger areas. We compared the effectiveness of two well-known classification methods, namely rule-based (RB) and random forest (RF). The RF algorithm was applied with its default settings, as supplied by eCognition software. Both classification methods performed well, with overall accuracies of 84.1% (RB) and 86.4% (RF). Each method, however, has its advantages and disadvantages, which are discussed. Overall, RF classification was preferred over RB classification, as it was better able to handle the complexity of the rules needed for distinguishing more classes. Provision of in situ training data, such as vegetation releves, was not really problem in the Dutch context. Nevertheless, exploitation of new digital aerial photographs produced each year in a semi-automatic process remains a challenge. Commercial high resolution satellite imagery (~0.5 m resolution) is therefore still preferred by us. This latter, unfortunately, is more costly than aerial photographs which, while not always ideal, are readily available at no additional costs for involved organisations