Study of Shrub Cover and Height Using LIDAR Data in a Mediterranean Area

[EN] In this work we studied the height and coverage of shrub vegetation using light detection and ranging (LIDAR) data. The maximum dominant heights of vegetation were measured in the field in 83 stands of a 0.5-m radius, and the data were compared with figures for heights obtained from LIDAR data in concentric areas with different radii. The minimum root mean square error (RMSE) between the field measurements and LIDAR data was found for radii between 1.5 and 2.25 m, RMSE being 0.26 m. When the slopes are low and an accurate digital terrain model is obtained, it was shown that the radius can be reduced. Shrub heights were also studied in plots of 100 m(2). In this case, the 95th percentile of the LIDAR data included in each plot was the best predictor of height with R(2) of 0.71 and a RMSE of 0.13 m. For detecting the presence of shrub vegetation, the highest accuracy was obtained when the canopy height model and a spectral image were combined (overall accuracy of 90%). FOR. SCI. 57(3):171-179.Financial support of this study was provided by Universidad Politècnica de Valencia (PAID-06-08-3297). We thank the city hall of Chiva for their support in the field campaign.Estornell Cremades, J.; Ruiz Fernández, LÁ.; Velázquez Martí, B. (2011). Study of Shrub Cover and Height Using LIDAR Data in a Mediterranean Area. Forest Science. 57(3):171-179. http://hdl.handle.net/10251/47020S17117957

Transferability of vegetation recovery models based on remote sensing across different fire regimes

P. 441-451Aim To evaluate the transferability between fire recurrence scenarios of post‐fire vegetation cover models calibrated with satellite imagery data at different spatial resolutions within two Mediterranean pine forest sites affected by large wildfires in 2012. Location The northwest and east of the Iberian Peninsula. Methods In each study site, we defined three fire recurrence scenarios for a reference period of 35 years. We used image texture derived from the surface reflectance channels of WorldView‐2 and Sentinel‐2 (at a spatial resolution of 2 m × 2 m and 20 m × 20 m, respectively) as predictors of post‐fire vegetation cover in Random Forest regression analysies. Percentage vegetation cover was sampled in two sets of field plots with a size roughly equivalent to the spatial resolution of the imagery. The plots were distributed following a stratified design according to fire recurrence scenarios. Model transferability was assessed within each study site by applying the vegetation cover model developed for a given fire recurrence scenario to predict vegetation cover in other scenarios, iteratively. Results For both wildfires, the highest model transferability between fire recurrence scenarios was achieved for those holding the most similar vegetation community composition regarding the balance of species abundance according to their plant‐regenerative traits (root mean square error [RMSE] around or lower than 15%). Model transferability performance was highly improved by fine‐grained remote‐sensing data. Conclusions Fire recurrence is a major driver of community structure and composition so the framework proposed in this study would allow land managers to reduce efforts in the context of post‐fire decision‐making to assess vegetation recovery within large burned landscapes with fire regime variability.S

Natural Regeneration of Beech Forests in the Strict Protected Area of the Plitvice Lakes National Park

Background and Purpose: The study presents the results of an investigation of regeneration processes, growth, development and survival of young growth by field measurement and three-dimensional visualization of horizontal and vertical structure. The results are based on the ten-year investigation (1998-2009) on a permanent experimental plot in a mountain beech forest with dead nettle tree (Lamio orvale - Fagetum sylvaticae Ht. 1938) in conditions of passive protection. Materials and Methods: Basic structural indicators were measured (diameter at breast height and height), structural crown elements (size and shape, ground cover crowns) and the occurrence and survival of young growth as the basic conditions of natural regeneration. Particular emphasis in the investigation was paid to the development of crown structures and the process of natural regeneration during the 10 year period. Results and Conclusions: Investigation indicates the occurrence of young growth regeneration cores arising as a result of the die-back of one dominant beech tree with horizontal crown projections of 145 m2 which initiated the possibility of natural regeneration. The greatest change occurred in the beech seedling count, whose numbers increased fourfold from 3556 plants per hectare in 1998 to 12694 plants per hectare in 2009. The share of beech seedlings increased from 8.7% to 22.6% of all species of young growth and shrubs. Thus beech became dominant among the tree species regeneration. However, the majority of the young plants of beech are of poor quality and thus their further development in conditions of passive protection is questionable. The investigations also showed the possibility of a new approach to the study of the dynamics of crown structures and the process of natural regeneration by methods of three-dimensional visualization of horizontal and vertical structures. The methods presented offer a more graphic illustration of the development of stands and high quality presentation of the obtained results. For a long-term scientifically based plan, with the aim of reaching the most favourable decisions on the future of forest stands in protected areas, particularly in today’s conditions of climatic changes, continuous improvement and expansion of monitoring methods by means of a network of permanent experimental plots in all protected forest areas is necessary

Mapping vegetation density in a heterogeneous river floodplain ecosystem using pointable CHRIS/PROBA data

River floodplains in the Netherlands serve as water storage areas, while they also have the function of nature rehabilitation areas. Floodplain vegetation is therefore subject to natural processes of vegetation succession. At the same time, vegetation encroachment obstructs the water flow into the floodplains and increases the flood risk for the hinterland. Spaceborne pointable imaging spectroscopy has the potential to quantify vegetation density on the basis of leaf area index (LAI) from a desired view zenith angle. In this respect, hyperspectral pointable CHRIS data were linked to the ray tracing canopy reflectance model FLIGHT to retrieve vegetation density estimates over a heterogeneous river floodplain. FLIGHT enables simulating top-of-canopy reflectance of vegetated surfaces either in turbid (e.g., grasslands) or in 3D (e.g., forests) mode. By inverting FLIGHT against CHRIS data, LAI was computed for three main classified vegetation types, ‘herbaceous’, ‘shrubs’ and ‘forest’, and for the CHRIS view zenith angles in nadir, backward (-36°) and forward (+36°) scatter direction. The -36° direction showed most LAI variability within the vegetation types and was best validated, closely followed by the nadir direction. The +36° direction led to poorest LAI retrievals. The class-based inversion process has been implemented into a GUI toolbox which would enable the river manager to generate LAI maps in a semiautomatic way

Intra-Annual Variabilities of Rubus caesius L. Discrimination on Hyperspectral and LiDAR Data

The study was focused on a plant native to Poland, the European dewberry Rubus caesius L., which is a species with the ability to become excessively abundant within its original range, potentially causing significant changes in ecosystems, including biodiversity loss. Monitoring plant distributions over large areas requires mapping that is fast, reliable, and repeatable. For Rubus, different types of data were successfully used for classification, but most of the studies used data with a very high spectral resolution. The aim of this study was to indicate, using hyperspectral and Light Detection and Ranging (LiDAR) data, the main functional trait crucial for R. caesius differentiation from non-Rubus. This analysis was carried out with consideration of the seasonal variability and different percentages of R. caesius in the vegetation patches. The analysis was based on hyperspectral HySpex images and Airborne Laser Scanning (ALS) products. Data were acquired during three campaigns: early summer, summer, and autumn. Differentiation based on Linear Discriminate Analysis (LDA) and Non-Parametric Multivariate Analysis of Variance (NPMANOVA) analysis was successful for each of the analysed campaigns using optical data, but the ALS data were less useful for identification. The analysis indicated that selected spectral ranges (VIS, red-edge, and parts of the NIR and possibly SWIR ranges) can be useful for differentiating R. caesius from non-Rubus. The most useful indices were ARI1, CRI1, ARVI, GDVI, CAI, NDNI, and MRESR. The obtained results indicate that it is possible to classify R. caesius using images with lower spectral resolution than hyperspectral data

Airborne Laser Scanning - the Status and Perspectives for the Application in the South-East European Forestry

Background and Purpose: Over the last twenty years airborne laser scanning (ALS) technology, also referred to as LiDAR, has been established in a many disciplines as a fully automated and highly efficient method of collecting spatial data. In Croatia, as well as in most countries of the South-East Europe (SEE) with the exception of Slovenia, the research on the application of ALS in forestry has not yet been conducted. Also, regional scientific and professional literature dealing with ALS application is scarce. Therefore, the main goal of this review paper is to present the ALS technology to the forestry community of SEE and to provide an overview of its potential application in forest inventory. The primary focus is given to discrete return ALS systems. Conclusions and Future Research Streams: Results presented in this paper show that the ALS technology has a significant potential for application in forest inventory. Moreover, the two-phase forest inventory based on the combination of ALS and field measurements has become a quite common operational method. Due to the expected advancement of the ALS technology, it may be presumed that ALS will have an even more important role in forestry in the future. Therefore, researches on application of ALS technology in SEE forestry are needed, primarily focusing to question of “if” and “to what extent” the ALS technology can improve the existing terrestrial method of forest inventory. Besides the application in the classical forest inventory, the option to apply it for estimation of the biomass, carbon stock, combustible matter, etc, should also be further investigated

Mapping of aggregated floodplain plant communities using image fusion of CASI and LiDAR data

Combined optical and laser altimeter data offer the potential to map and monitor plant communities based on their spectral and structural characteristics. A problem unresolved is, however, that narrowly defined plant communities, i.e. plant communities at a low hierarchical level of classification in the Braun-Blanquet system, often cannot be linked directly to remote sensing data for vegetation mapping. We studied whether and how a floristic dataset can be aggregated into a few major discrete, mappable classes without substantial loss of ecological meaning. Multi-source airborne data (CASI and LiDAR) and floristic field data were collected for a floodplain along the river Waal in the Netherlands. Mapping results based on floristic similarity alone did not achieve highest levels of accuracy. Ordination of floristic data showed that terrain elevation and soil moisture were the main underlying environmental drivers shaping the floodplain vegetation, but grouping of plant communities based on their position in the ordination space is not always obvious. Combined ordination-based grouping with floristic similarity clustering led to syntaxonomically relevant aggregated plant assemblages and yielded highest mapping accuracie

Estimación y caracterización estructural del bosque de ribera y de las plantaciones de chopo de los ríos Martín y Guadalope mediante datos LIDAR de baja densidad y ortofotografías PNOA.

El objetivo ha sido evaluar la aplicación de ortofotografías PNOA-RGB junto con datos PNOA-LiDAR para estimar la extensión del bosque de ribera de dos sectores de las cuencas del río Mar-tín y del río Guadalope, así como la caracterización de las masas de chopos de plantación presentes en dichas zonas<br /