Comparison of three modelling approaches of potential natural forest habitats in Bavaria, Germany

In the context of the EU Habitats Directive, which contains the obligation of environmental monitoring, nature conservation authorities face a growing demand for effective and competitive methods to survey protected habitats. Therefore the presented research study compared three modelling approaches (rule-based method with applied Bavarian woodland types, multivariate technique of cluster analysis, and a fuzzy logic approach) for the purpose of detecting potential habitat types. The results can be combined with earth observation data of different geometric resolution (ASTER, SPOT5, aerial photographs or very high resolution satellite data) in order to determine actual forest habitat types. This was carried out at two test sites, situated in the pre-alpine area in Bavaria (southern Germany). The results were subsequently compared to the terrestrial mapped habitat areas of the NATURA 2000 management plans. First results show that these techniques are a valuable support in mapping and monitoring NATURA 2000 forest habitats

Using Unmanned Aerial Systems for Deriving Forest Stand Characteristics in Mixed Hardwoods of West Virginia

Forest inventory information is a principle driver for forest management decisions. Information gathered through these inventories provides a summary of the condition of forested stands. The method by which remote sensing aids land managers is changing rapidly. Imagery produced from unmanned aerial systems (UAS) offer high temporal and spatial resolutions to small-scale forest management. UAS imagery is less expensive and easier to coordinate to meet project needs compared to traditional manned aerial imagery. This study focused on producing an efficient and approachable work flow for producing forest stand board volume estimates from UAS imagery in mixed hardwood stands of West Virginia. A supplementary aim of this project was to evaluate which season was best to collect imagery for forest inventory. True color imagery was collected with a DJI Phantom 3 Professional UAS and was processed in Agisoft Photoscan Professional. Automated tree crown segmentation was performed with Trimble eCognition Developer’s multi-resolution segmentation function with manual optimization of parameters through an iterative process. Individual tree volume metrics were derived from field data relationships and volume estimates were processed in EZ CRUZ forest inventory software. The software, at best, correctly segmented 43% of the individual tree crowns. No correlation between season of imagery acquisition and quality of segmentation was shown. Volume and other stand characteristics were not accurately estimated and were faulted by poor segmentation. However, the imagery was able to capture gaps consistently and provide a visualization of forest health. Difficulties, successes and time required for these procedures were thoroughly noted

Evaluation of remote sensing methods for continuous cover forestry

The overall aim of the project was to investigate the potential and challenges in the application of high spatial and spectral resolution remote sensing to forest stands in the UK for Continuous Cover Forestry (CCF) purposes. Within the context of CCF, a relatively new forest management strategy that has been implemented in several European countries, the usefulness of digital remote sensing techniques lie in their potential ability to retrieve parameters at sub-stand level and, in particular, in the assessment of natural regeneration and light regimes. The idea behind CCF is the support of a sustainable forest management system reducing disturbance of the forest ecosystem and encouraging the use of more natural methods, e.g. natural regeneration, for which the light environment beneath the forest canopy plays a fundamental role.The study was carried out at a test area in central Scotland, situated within the Queen Elizabeth II Forest Park (lat. 56°10' N, long. 4° 23' W). Six plots containing three different species (Norway spruce, European larch and Sessile oak), characterized by their different light regimes, were established within the area for the measurement of forest variables using a forest inventory approach and hemispherical photography. The remote sensing data available for the study consisted of Landsat ETM+ imagery, a small footprint multi-return lidar dataset over the study area, Airborne Thematic Mapper (ATM) data, and aerial photography with same acquisition date as the lidar data.Landsat ETM+ imagery was used for the spectral characterisation of the species under study and the evaluation of phenological change as a factor to consider for future acquisitions of remotely sensed imagery. Three approaches were used for the discrimination between species: raw data, NDVI, and Principal Component Analysis (PCA). It can be concluded that no single date is ideal for discriminating the species studied (early summer was best) and that a combination of two or three datasets covering their phenological cycles is optimal for the differentiation. Although the approaches used helped to characterize the forest species, especially to the discrimination between spruces, larch and the deciduous oak species, further work is needed in order to define an optimum approach to discriminate between spruce species (e.g. Sitka spruce and Norway spruce) for which spectral responses are very similar. In general, the useful ranges of the indices were small, so a careful and accurate preprocessing of the imagery is highly recommended.Lidar, ATM, and aerial photographic datasets were analysed for the characterisation of vertical and horizontal forest structure. A slope-based algorithm was developed for the extraction of ground elevation and tree heights from multiple return lidar data, the production of a Digital Terrain Model (DTM) and Digital Surface Model (DSM) of the area under study, and for the comparison of the predicted lidar tree heights with the true tree heights, followed by the building of a Digital Canopy Model (DCM) for the determination of percentage canopy cover and tree crown delineation. Mean height and individual tree heights were estimated for all sample plots. The results showed that lidar underestimated tree heights by an average of 1.49 m. The standard deviation of the lidar estimates was 3.58 m and the mean standard error was 0.38 m.This study assessed the utility of an object-oriented approach for deciduous and coniferous crown delineation, based on small-footprint, multiple return lidar data, high resolution ATM imagery, and aerial photography. Special emphasis in the analysis was made in the fusion of aerial photography and lidar data for tree crown detection and classification, as it was expected that the high vertical accuracy of lidar, combined with the high spatial resolution aerial photography would render the best results and would provide the forestry sector with an affordable and accurate means for forest management and planning. Most of the field surveyed trees could be automatically and correctly detected, especially for the spruce and larch plots, but the complexity of the deciduous plots hindered the tree recognition approach, leading to poor crown extent and gap estimations. Indicators of light availability were calculated from the lidar data by calculation of laser hit penetration rates and percentage canopy cover. These results were compared to estimates of canopy openness obtained from hemispherical pictures for the same locations.Finally, the synergistic benefits of all datasets were evaluated and the forest structural variables determined from remote sensing and hemispherical photography were examined as indicators of light availability for regenerating seedlings

Object-based image analysis for forest-type mapping in New Hampshire

The use of satellite imagery to classify New England forests is inherently complicated due to high species diversity and complex spatial distributions across a landscape. The use of imagery with high spatial resolutions to classify forests has become more commonplace as new satellite technology become available. Pixel-based methods of classification have been traditionally used to identify forest cover types. However, object-based image analysis (OBIA) has been shown to provide more accurate results. This study explored the ability of OBIA to classify forest stands in New Hampshire using two methods: by identifying stands within an IKONOS satellite image, and by identifying individual trees and building them into forest stands. Forest stands were classified in the IKONOS image using OBIA. However, the spatial resolution was not high enough to distinguish individual tree crowns and therefore, individual trees could not be accurately identified to create forest stands. In addition, the accuracy of labeling forest stands using the OBIA approach was low. In the future, these results could be improved by using a modified classification approach and appropriate sampling scheme more reflective of object-based analysis

Forest Cover Classification by Optimal Segmentation of High Resolution Satellite Imagery

This study investigated whether high-resolution satellite imagery is suitable for preparing a detailed digital forest cover map that discriminates forest cover at the tree species level. First, we tried to find an optimal process for segmenting the high-resolution images using a region-growing method with the scale, color and shape factors in Definiens® Professional 5.0. The image was classified by a traditional, pixel-based, maximum likelihood classification approach using the spectral information of the pixels. The pixels in each segment were reclassified using a segment-based classification (SBC) with a majority rule. Segmentation with strongly weighted color was less sensitive to the scale parameter and led to optimal forest cover segmentation and classification. The pixel-based classification (PBC) suffered from the “salt-and-pepper effect” and performed poorly in the classification of forest cover types, whereas the SBC helped to attenuate the effect and notably improved the classification accuracy. As a whole, SBC proved to be more suitable for classifying and delineating forest cover using high-resolution satellite images

Trooppisten alkuperäismetsien monitorointi Taita Hillsin alueella digitaalisen ilmakuva-aineiston avulla

The loss and degradation of forest cover is currently a globally recognised problem. The fragmentation of forests is further affecting the biodiversity and well-being of the ecosystems also in Kenya. This study focuses on two indigenous tropical montane forests in the Taita Hills in southeastern Kenya. The study is a part of the TAITA-project within the Department of Geography in the University of Helsinki. The study forests, Ngangao and Chawia, are studied by remote sensing and GIS methods. The main data includes black and white aerial photography from 1955 and true colour digital camera data from 2004. This data is used to produce aerial mosaics from the study areas. The land cover of these study areas is studied by visual interpretation, pixel-based supervised classification and object-oriented supervised classification. The change of the forest cover is studied with GIS methods using the visual interpretations from 1955 and 2004. Furthermore, the present state of the study forests is assessed with leaf area index and canopy closure parameters retrieved from hemispherical photographs as well as with additional, previously collected forest health monitoring data. The canopy parameters are also compared with textural parameters from digital aerial mosaics. This study concludes that the classification of forest areas by using true colour data is not an easy task although the digital aerial mosaics are proved to be very accurate. The best classifications are still achieved with visual interpretation methods as the accuracies of the pixel-based and object-oriented supervised classification methods are not satisfying. According to the change detection of the land cover in the study areas, the area of indigenous woodland in both forests has decreased in 1955-2004. However in Ngangao, the overall woodland area has grown mainly because of plantations of exotic species. In general, the land cover of both study areas is more fragmented in 2004 than in 1955. Although the forest area has decreased, forests seem to have a more optimistic future than before. This is due to the increasing appreciation of the forest areas.Metsien väheneminen ja niiden laadun heikkeneminen on maailmanlaajuisesti tunnustettu ongelma. Metsien pirstoutuminen vaikuttaa biodiversiteettiin ja ekosysteemien hyvinvointiin myös Keniassa. Tämä tutkimus keskittyy kahden trooppisen alkuperäisvuoristometsän tutkimiseen Taita Hillsin alueella Kaakkois-Keniassa. Tutkimus on osa Helsingin yliopiston maantieteen laitoksen TAITA-projektia. Tutkimusmetsiä, Ngangaoa ja Chawiaa tutkitaan kaukokartoitus- ja paikkatietomenetelmien avulla. Tutkimuksen pääaineiston muodostavat mustavalkoiset ilmakuvat vuodelta 1955 ja digitaaliset oikeaväri-ilmakuvat vuodelta 2004. Näistä ilmakuvista muodostetaan ilmakuvamosaiikit tutkimusalueilta. Alueiden maanpeite luokitellaan kolmella metodilla: visuaalisella tulkinnalla, pikselipohjaisella ohjatulla luokituksella sekä objekti-orientoidulla ohjatulla luokituksella. Metsäpinta-alan muutosta vuosina 1955-2004 tutkitaan visuaalisten luokitusten perusteella käyttämällä paikkatietomenetelmiä. Tutkimusmetsien kuntoa arvioidaan lehtipinta-alaindeksin ja latvuksen sulkeituneisuuden avulla. Nämä parametrit saadaan käyttämällä hemisfäärisiä valokuvia. Lisäksi tutkimuksessa käytetään metsien kuntoa arvioivaa aiemmin kerättyä tutkimustietoa. Latvusparametreja verrataan digitaali-ilmakuvamosaiikeilta saatuihin tekstuurisiin parametreihin. Yhteenvetona voidaan sanoa, että metsäalueiden luokitus oikeaväri-ilmakuvia käyttämällä ei ole helppoa, vaikka itse digitaali-ilmakuvista tehdyt mosaiikit olisivat erittäin tarkkoja. Parhaat luokitustulokset saavutetaan edelleen visuaalisella tulkinnalla, sillä pikselipohjainen ja objekti-orientoitu ohjattu luokitus eivät saavuta tarpeeksi hyvää luotettavuutta. Tutkimusalueiden maanpeitteen muutostulkinnan mukaan alkuperäismetsän osuus on vähentynyt sekä Ngangaossa että Chawiassa 1955-2004. Ngangaossa metsän kokonaisala on kuitenkin lisääntynyt lähinnä eksoottisten puulajien istutusten vuoksi. Molempien tutkimusalueiden maanpeite on huomattavasti pirstoutuneempaa vuonna 2004 kuin vuonna 1955. Vaikka metsäala on pienentynyt, tutkimusmetsien tulevaisuus näyttää paremmalta kuin aiemmin. Tämä johtuu lähinnä kasvavasta metsien arvostuksesta

Classification of tree species classes in a hemi-boreal forest from multispectral airborne laser scanning data using a mini raster cell method

Classification of tree species or species classes is still a challenge for remote sensing-based forest inventory. Operational use of Airborne Laser Scanning (ALS) data for prediction of forest variables has this far been dominated by area-based methods where laser scanning data have been used for estimation of forest variables within raster cells. Classification of tree species has however not been achieved with sufficient accuracy with area-based methods using only ALS data. Furthermore, analysis of tree species at the level of raster cells with typical size of 15 m ? 15 m is not ideal in the case of mixed species stands. Most ALS systems for terrestrial mapping use only one wavelength of light. New multispectral ALS systems for terrestrial mapping have recently become operational, such as the Optech Titan system with wavelengths 1550 nm, 1064 nm, and 532 nm. This study presents an alternative type of area-based method for classification of tree species classes where multispectral ALS data are used in combination with small raster cells. In this ?mini raster cell method? features for classification are derived from the intensity of the different wavelengths in small raster cells using a moving window average approach to allow for a heterogeneous tree species composition. The most common tree species in the Nordic countries are Pinus sylvestris and Picea abies, constituting about 80% of the growing stock volume. The remaining 20% consists of several deciduous species, mainly Betula pendula and Betula pubescens, and often grow in mixed forest stands. Classification was done for pine (Pinus sylvestris), spruce (Picea abies), deciduous species and mixed species in middle-aged and mature stands in a study area located in hemi-boreal forest in the southwest of Sweden (N 58?27?, E 13?39?). The results were validated at plot level with the tree species composition defined as proportion of basal area of the tree species classes. The mini raster cell classification method was slightly more accurate (75% overall accuracy) than classification with a plot level area-based method (68% overall accuracy). The explanation is most likely that the mini raster cell method is successful at classifying homogenous patches of tree species classes within a field plot, while classification based on plot level analysis requires one or several heterogeneous classes of mixed species forest. The mini raster cell method also results in a high-resolution tree species map. The small raster cells can be aggregated to estimate tree species composition for arbitrary areas, for example forest stands or area units corresponding to field plots

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Large-area inventory of species composition using airborne laser scanning and hyperspectral data

5openInternationalInternational coauthor/editorTree species composition is an essential attribute in stand-level forest management inventories and remotely sensed data might be useful for its estimation. Previous studies on this topic have had several operational drawbacks, e.g., performance studied at a small scale and at a single tree-level with large fieldwork costs. The current study presents the results from a large-area inventory providing species composition following an operational area-based approach. The study utilizes a combination of airborne laser scanning and hyperspectral data and 97 field sample plots of 250 m2 collected over 350 km2 of productive forest in Norway. The results show that, with the availability of hyperspectral data, species-specific volume proportions can be provided in operational forest management inventories with acceptable results in 90% of the cases at the plot level. Dominant species were classified with an overall accuracy of 91% and a kappa-value of 0.73. Species-specific volumes were estimated with relative root mean square differences of 34%, 87%, and 102% for Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and deciduous species, respectively. A novel tree-based approach for selecting pixels improved the results compared to a traditional approach based on the normalized difference vegetation index.openØrka, Hans Ole; Hansen, Endre Hofstad; Dalponte, Michele; Gobakken, Terje; Næsset, ErikØrka, H.O.; Hansen, E.H.; Dalponte, M.; Gobakken, T.; Næsset, E