Forest Management Data Research Articles

Satellite-based radar mapping of British forest age and Net Ecosystem Exchange using ERS tandem coherence

A method of mapping forest age structure using satellite remote sensing data in combination with ground data is used to form an age structure map of mainland Britain’s forests. Age structure is then used to demonstrate a method of calculating Net Ecosystem Exchange (NEE) for a region of forest. Synthetic Aperture Radar (SAR) coherence data provides an indication of forest biomass, which is related to forest age using forest management data at five independent locations. Coherence data is sensitive to time varying environmental effects and hence requires extensive calibration of the function relating coherence to forest age. The calibration approach appropriate in Britain makes use of the extensive ground GIS data available. Age structure information for nearly 3 million hectares of forest is generated, of which 70% is privately owned and age information is otherwise unavailable. The resulting map has a spatial resolution that can differentiate individual forest stands and provides detailed regional age and biomass estimates. Comparison of stand age estimates with ground data is has potential to provide growth rate and felling information. The age structure map is used in combination with an exemplary function relating forest age to NEE to estimate atmospheric carbon exchange for England, Scotland and Wales. This method predicts threefold higher (10.87 M t a −1) forest carbon uptake than national inventory figures. The remote sensing data also indicates age estimates that conflict with the ground data in Wales, which is explainable by the introduction of partial felling practices in this region during the time period of the coherence acquisitions.

Monitoring Sustainability in Tropical Forests: How Changes in Canopy Spatial Pattern Can Indicate Forest Stands for Biodiversity Surveys

Sustainable management of tropical forests has been identified as one of the main objectives for global conservation and management of carbon stocks. Toward this goal, managers need tools to determine whether current management practices are sustainable. Several international initiatives have been undertaken for the development of criteria and indicators to aid managers in moving toward sustainable practices. Despite these efforts, the question of how to apply and assess indicators remains to be answered from an operational, field-based perspective. Field surveys are expensive and time-consuming when management areas are large and in the face of logistical constraints. Thus, there is a need for an approach to prioritization. We sought to determine whether satellite imagery can be used, in conjunction with standard forest management data, to identify and rank priority areas for field surveys of bioindicators. The study area in Costa Rica, in forest areas managed by the Fundacion para el Desarrollo de la Cordillera Volcanica Central (FUNDECOR), was imaged by the Landsat-5 Thematic Mapper in 1986 and 2001. Through spatial statistical analysis applied to the wide dynamic range vegetation index, we were able to quantify and rank changes in canopy spatial structure. The resulting categories can be used by forest managers to identify which areas are in need of field surveys. More generally, we show how to generate a moving baseline for change analysis and evaluate for significant deviations in spatial structure

Retrieval of leaf area index for a coniferous forest by inverting a forest reflectance model

The aim of this paper was to serve as a pilot study for running a physically based forest reflectance model through an operational forest management data base in Finnish coniferous forests. The LAI values of 250 boreal coniferous stands were retrieved with the physically based model by inversion from a SPOT HRVIR1 image. The use of three spectral vegetation indices (NDVI, RSR and MSI) in LAI estimation was tested for the same stands. Ground-truth LAI was based on an allometric model which can be applied to routine stand inventory data. Stand reflectances were computed as an average of reflectances of the pixels located within the digital stand borders. The relationships of LAI and spectral vegetation indices calculated from the SPOT data were very scattered. RSR exhibited the widest range of values (and the highest correlation with LAI), suggesting it to be more dynamic than MSI or NDVI. Inversion of the reflectance model was done twice: first using as simultaneous input three wavelength bands (red, NIR and MIR), then only the red and NIR bands. The aim was to observe whether including the MIR band in the inversion would improve the inverted LAI estimates or if using only the red and NIR bands would result in the same reliability of inverted values. The motivation for examining the influence of the MIR band resulted from several recent studies from the boreal zone which suggest that the pronounced understory effect could be minimized by the inclusion of the MIR band. The LAI values inverted by the model were slightly larger than the ground-truth LAI values. A minor improvement in LAI estimates was observed after the inclusion of the MIR band in reflectance model inversion. The errors in the ground-truth LAI were uncertain and the background understory reflectance was expected to be highly variable. Thus, the quality of the data used may be to a large extent responsible for the observed low utility of the tested channels.

The uncertainty of forest management planning data in Finnish non-industrial private forestry

Knowledge about the growing stock and the cutting potentials of stands, as well as predictions of growth and yield, are essential aspects of forest management planning. Growth predictions are obtained using complex simulation systems, whose accuracy and precision are difficult to predict. The uncertainty of growth and yield predictions, as well as the uncertainty of the stand-level inventory data behind the predictions, are not usually taken into account sufficiently in the planning process. Furthermore, the uncertainty resulting from the increasing use of updated inventory data as planning data should be studied in connection with current forest planning practices. However, the lack of suitable comprehensive re-measured study data sets with true planning data must also be noted. This dissertation provides new knowledge on the uncertainty related to forest management data. It also addresses the possibilities to use updated forest inventory data as forest management planning data and evaluates assessment methods for predicting the uncertainty of forest management data. Furthermore, four alternative simulation methods are evaluated as regards their ability to generate assessment errors in forest management planning data for further research. The usability of updated forest management planning data is evaluated also by looking at the suitability of the proposals for forest management operations as derived from the updated data. The accuracy and precision of stand-level inventory were found to be moderate, although the costs and time spent in field work are considered to be fairly high. The variation between measurers was substantial. This variation in stand-level inventory data should be noted in forest management planning. The assessment of uncertainty of updated forest management data was approached by means of two different methods, i.e. by modelling observed (past) errors and by applying the k-nearest neighbor method with multiobjective optimization. The uncertainty assessments of growth and yield predictions using these methods were found to be feasible with large stand data. The main advantage of the studied methods is in that both bias and accuracy can be assessed. However, the methods require independent contemporary data, which is their main drawback. Modelling observed (past) errors and k-nearest neighbor are quite easy to apply in forest simulation systems if only contemporary models and distance functions are estimated. The utilization of both methods does not considerably add the calculation time even when dealing with growth predictions for large areas. Stand-specific predictions of uncertainty were also found to be satisfactory. According to the results of this study, updated stand inventory data can be used as a forest management planning data with respect to the accuracy of the updated stand characteristics. Updated stand inventory data were also found feasible with respect to treatment proposals when the mean stand characteristics and regulations and recommendations of current forest management practices were considered. However, treespecific data are considered to be a slightly more suitable in this context.

Use of remote sensing for forest vegetation management: A problem analysis

Forest managers require accurate and timely data that describe vegetation conditions on cutover areas to assess vegetation development and prescribe actions necessary to achieve forest regeneration objectives. Needs for such data are increasing with current emphasis on ecosystem management, escalating silvicultural treatment costs, evolving computer-based decision support tools, and demands for greater accountability. Deficiencies associated with field survey methods of data acquisition (e.g. high costs, subjectivity, and low spatial and temporal coverage) frequently limit decision-making effectiveness. The potential for remotely sensed data to supplement field-collected forest vegetation management data was evaluated in a problem analysis consisting of a comprehensive literature review and consultation with remote sensing and vegetation management experts at a national workshop. Among curently available sensors, aerial photographs appear to offer the most suitable combination of characteristics, including high spatial resolution, stereo coverage, a range of image scales, a variety of film, lens, and camera options, capability for geometric correction, versatility, and moderate cost. A flexible strategy that employs a sequence of 1:10,000-, 1:5,000-, and 1:500-scale aerial photographs is proposed to: 1) accurately map cutover areas, 2) facilitate location-specific prescriptions for silvicultural treatments, sampling, buffer zones, wildlife areas, etc., and 3) monitor and document conditions and activities at specific points during the regeneration period. Surveys that require very detailed information on smaller plants (<0.5-m tall) and/or individual or rare plant species are not likely to be supported by current remote sensing technologies. Recommended areas for research include : 1) digital frame cameras, or other cost-effective digital imagers, as replacements for conventional cameras, 2) computer-based classification and interpretation algorithms for digital image data, 3) relationships between image measures and physical measures, such as leaf-area index and biomass, 4) imaging standards, 5) airborne video, laser altimeters, and radar as complementary sensors, and 6) remote sensing applications in partial cutting systems. Key words: forest vegetation management, regeneration, remote sensing, aerial photography

Applicability of JERS-1 SAR Data for Monitoring of Forest Function and Environment : Preliminary analysis of forest stand structure using JERS-1 SAR data for forest management

Applicability of JERS-1 SAR Data for Monitoring of Forest Function and Environment : Preliminary analysis of forest stand structure using JERS-1 SAR data for forest management

Thematic mapping of the Flemish forest cover using aerial photography

As old forest inventory methods, based on questionnaires, become obsolete, a new procedure using remote sensing techniques has been developed. The objectives are fourfold: 1. Analysis of the forest situation in Flanders 2. Forest inventory methodology development 3. Creation of a forest management data bank 4. Production of base maps with high planimetric accuracy Basic material consists of colour infrared 1/30000 transparencies. Photo interpretation and processing is carried out using a Wild Aviopret APT-1 stereoscope and a Bausch & Lomb Zoom Transfer Scope. The legend features species composition, development stage, ground cover percentage, management practice and ownership. Data from interpretation, field work and supplemental sources are presented as a series of thematic overlays on a 1/5000 colour infrared orthophotomap. Numerical processing is executed on a PDP 11/44 microcomputer. A Wild interactive graphic system (Synercom) is used for digitizing and area calculation. Automatic map production is performed on a Wild TA 2 drawing table. Availability of stand boundaries in digital form enables generation of thematic computer maps.