Spatial Modelling in Forest Ecology and Management: A Case Study

ISBN-13: 9783642628047 / Angielski / Miękka / 2012 / 227 str.

Martin Jansen; Michael Judas; Joachim Saborowski

Opis

At the end of the 1970s, when signs of destabilization of forests became visible in Eu rope on a large scale, it soon became obvious that the syndrome called "forest de cline" was caused by a network of interrelated factors of abiotic and biotic origin. All attempts to explain the wide-spread syndrome by a single cause, and there were many of them, failed or can only be regarded as a single mosaic stone in the network of caus es behind the phenomenon. Forest ecosystems are highly complex natural or quasi natural systems, which exhibit different structures and functions and as a conse quence different resilience to internal or external stresses. Moreover, forest ecosys tems have a long history, which means that former impacts may act as predisposing factors for other stresses. The complexity and the different history of forest ecosys tems are two reasons that make it difficult to assess the actual state and future devel opment of forests. But there are two other reasons: one is the large time scale in which forests react, the other is the idiosyncrasy of the reactions on different sites. Due to the slow reaction and the regional complexity of the abiotic environment of forest ecosys tems, a profound analysis of each site and region is necessary to identify the underly ing causes and driving forces when attempting to overcome the destruction of forest ecosystems."

Recenzje

From the reviews:

"This book focuses on the integration of spatial statistics, GIS-technology, ecosystem studies, and scenario modelling. Its main aim is to extend the information gained at the stand level to larger spatial scales, i.e. to forest districts, forest landscapes or to the total area of Lower Saxony. ... This book is an indispensable tool for scientists and those involved in forest management." (Annals of Forest Science, Issue 1, 2003)

Spis treści

1 Abstract.- 2 Introduction.- 3 Study areas and basic data.- 3.1 Study areas.- 3.2 Basic data.- 3.2.1 Digital Elevation Models.- 3.2.2 Primary Forest Layers.- 3.2.2.1 Forest Inventory.- 3.2.2.2 Forest Site Evaluation.- 4 Statistical methods for regionalization of ecological state variables.- 4.1 Ordinary Kriging.- 4.2 Trend surface prediction.- 4.3 Kriging with trend.- 4.4 Crossvalidation.- 5 Spatial prediction of climate, soil, and macrofauna.- 5.1 Geomorphological and topoclimatic predictors derived from a DEM ..- Schulz, Mues, Jansen, Judas, Saborowski.- 5.1.1 Location dependent variables.- 5.1.2 First and second order derivatives of a DEM.- 5.1.3 Lee indices.- 5.1.4 Catchment size and related variables.- 5.1.5 Length of upward slopes.- 5.1.6 Relative exposure.- 5.1.7 Shape-Position Indices.- 5.1.8 Insolation indices.- 5.1.9 Quantification of landuse.- 5.2 Regionalization of climatic elements in Lower Saxony.- Mues, Jansen, Sloboda, Radler, Saborowski.- 5.2.1 Introduction.- 5.2.2 Sites and measurements.- 5.2.2.1 German Meteorological Service (DWD).- 5.2.2.2 Harz Mountains Waterworks (HWW).- 5.2.2.3 TRANSECT data.- 5.2.3 Localization of measurement stations.- 5.2.3.1 Accuracy of position.- 5.2.3.2 Shifting of the measurement stations.- 5.2.4 Models.- 5.2.4.1 Stratified models for Mountainous Region and Plains.- 5.2.4.2 Precipitation.- 5.2.4.2.1 Precipitation Plains.- 5.2.4.2.2 Precipitation Mountainous Region.- 5.2.4.3 Air temperature.- 5.2.4.3.1 Air temperature Plains.- 5.2.4.3.2 Air temperature Mountainous Region.- 5.2.5 Spatial representation of statistical models.- 5.2.6 Summary.- 5.3 Regionalization of soil chemical variables in the Harz mountains.- Jansen, Eberl, Beese.- 5.3.1 Introduction.- 5.3.2 Fundamentals of forest site evaluation: theory and models.- 5.3.3 Materials.- 5.3.4 Statistical analyses.- 5.3.5 Results and discussion.- 5.3.5.1 Geological substrates as a predictor of soil chemical variables.- 5.3.5.2 Nutrient index as a predictor of soil chemical variables.- 5.3.6 Multivariate models.- 5.3.7 Outlook.- 5.3.8 Summary.- 5.4 Regionalization of macrofauna populations.- Judas, Schaefer.- 5.4.1 Introduction.- 5.4.2 Area data.- 5.4.3 Point data.- 5.4.3.1 Sampling.- 5.4.3.2 Local habitat.- 5.4.4 Distribution patterns.- 5.4.4.1 General patterns.- 5.4.4.2 Species - habitat relations.- 5.4.4.3 Sampling stratification.- 5.4.5 Case study - distribution models for Pterostichus madidus.- 5.4.5.1 Initial Anova models.- 5.4.5.2 Multiple regression models.- 5.4.6 Multiple regression models for carabid beetle species.- 5.4.6.1 Optimization.- 5.4.6.2 Prediction.- 5.4.6.3 Habitat factors.- 5.4.6.4 Conclusions.- 5.4.7 Summary.- 6 Spatial models for site evaluation and forest planning.- 6.1 Forecast classification for the mapping of forest site properties.- Schulz, Judas.- 6.1.1 Principles of site mapping.- 6.1.2 Topoclimatic layers as support tools for mapping.- 6.1.2.1 Relative exposure in an unlimited surrounding.- 6.1.2.2 Insolation.- 6.1.2.3 Exposure to wind.- 6.1.3 Statistical approach to the modelling of relief units.- 6.1.3.1 Conceptual framework.- 6.1.3.2 Model quality indices.- 6.1.3.3 Implementation.- 6.1.3.4 Limitations to the modelling approach.- 6.1.3.5 Model selection.- 6.1.4 Statistical classification of moisture variants.- 6.1.4.1 Database.- 6.1.4.2 Modelling.- 6.1.4.3 Classification probabilities.- 6.1.5 Rule-based modelling of relief units.- 6.1.5.1 Shortcomings of the statistical approach.- 6.1.5.2 Implementation of classification rules.- 6.1.6 Conclusions.- 6.2 Modelling of forest growth areas in Lower Saxony.- Jansen, StUber, Wachter, Schulz, Schmidt, Saborowski, Mues, Eberl, Sloboda.- 6.2.1 Introduction.- 6.2.2 Analysis of current growth areas of Lower Saxony.- 6.2.3 Modelling of growth areas.- 6.2.3.1 Atlanticity-Continentality.- 6.2.3.2 Altitude belts.- 6.2.3.2.1 Influence of elevation and catchment size.- 6.2.3.2.2 Solar radiation.- 6.2.3.2.3 Cold air on level planes.- 6.2.3.2.4 Delimitation of altitude belts.- 6.2.3.3 Connection of atlanticity and altitude belts.- 6.2.4 Consequences of predicted air temperature increase on regional growth districts.- 6.2.5 General assessment of the model.- 6.3 Modelling of natural woodland communities in the Harz mountains.- Jansen, Schmidt, StUber, Wachter, Naeder, Weckesser, Knauft.- 6.3.1 Introduction.- 6.3.2 Zonal woodland communities.- 6.3.3 Azonal woodland communities.- 6.3.4 Key for labeling woodland communities.- 6.3.5 Cartographic visualization of natural woodland communities in the Harz mountains.- 6.3.6 Computation and cartographic representation of the naturalness of the actual forest cover.- 7 GIS based investigations of effects of the LÖWE program in the Harz mountains.- 7.1 Scenarios of long-term forest stand development in the Harz mountains.- Jansen, Schulz, Konitzer, Sloboda.- 7.1.1 Introduction.- 7.1.2 The LÖWE model.- 7.1.3 Simulation of the LÖWE model for the Harz mountains.- 7.1.4 Development of tree species composition.- 7.1.5 Area of tree species according to alternatives al-a3.- 7.1.6 Consequences of the LÖWE model.- 7.1.6.1 Naturalness.- 7.1.6.2 Economic yield.- 7.1.7 Discussion.- 7.1.8 Outlook.- 7.1.9 Summary.- 7.2 Economic effects of the LÖWE program in the Harz mountains.- Konitzer, Bitter, jansen.- 7.2.1 Introduction and objectives.- 7.2.2 GIS-based selection of forest stands.- 7.2.3 Stratification of the stands.- 7.2.4 Silvicultural strategies.- 7.2.5 Economic indicators.- 7.2.5.1 Stumpage values.- 7.2.5.2. Contribution margin.- 7.2.5.3 Total stand value.- 7.2.6 Results of simulations.- 7.2.6.1 Comparison of growth and yield.- 7.2.6.2 Comparison of stumpage values.- 7.2.6.3 Comparison of contribution margins.- 7.2.6.4 Comparison of total stand values.- 7.2.7 Regionalization of simulation results.- 7.2.8 Discussion.- 7.2.9 Summary.- List of Figures.- List of Tables.

Blurb

In this book, GIS-based geomorphological, statistical, and geostatistical methods are used to improve the ecological site description of forest areas. Possibilities and limitations of the regionalization of climatic, soil, and macrofauna point measurements are presented. The resulting maps of indicators provide a broad and precise basis for forest planning and are essential for an integration of ecological and economic aspects of forest management.The applicability for forest practice is exemplified by the delimitation of forest site classes and of forest growth districts as well as the modelling of potential natural woodland communities. A scenario technique was used to simulate the forest stand development in the Harz mountains and to reveal the economic and ecological results of new management guidelines that are favoured by the forest administration. Spatial modelling and GIS-based information and planning systems provide improved data bases that are valuable instruments to describe the consequences of different management guidelines. Thus, they make a major contribution to a fruitful discussion about sustainable forest management.