Towards Automated Forest Mapping

Abstract

The need for up-to-date and accurate information on forest resources has rapidly increased in recent years. Forest mapping is an important source of information for the assessment of woodland resources and a key issue for any National Forest Inventory (NFI). Nowadays, new perspectives for automated forest mapping are emerging through the latest developments in remote sensing data and techniques.In this chapter, an overview of current remote sensing data and techniques for mapping woodland and forests, the challenges and requirements for optimization and automation, and the need for validating final products are presented. Special attention is paid to land use -a crucial criterion for forest mapping which, in contrast to land cover, cannot be easily derived from remotely sensed data. Three different approaches for extracting woodland areas (i.e., patches of trees and shrubs) are presented, all of which involve a high degree of automation. Two additional approaches, which are based on NFI forest definitions, are presented. These require the subdivision of woodlands into the classes "used for forestry" and "other use" and implement the criteria "height", "minimum crown coverage", "minimum area" and "minimum width", and land use. Special attention is paid to connecting patches using distance criteria from national forest definitions.The main points of this chapter are: (1) forest needs an exact definition which may differ depending on the country, (2) mapping woodland can be highly automated and is indispensable prior to mapping forests, and (3) forest mapping is now feasible using remote sensing data and techniques, however, it is less automated due to the implementation of a forest definition.

Measuring Tree Height with Remote Sensing—A Comparison of Photogrammetric and LiDAR Data with Different Field Measurements

“…Within the last decades, various remote-sensing-based methods have been developed and studied. Remote sensing has the potential to provide consistent, reproducible and up-to-date information on various forest parameters in order to make inventories more efficient [6]. Previous studies mostly applied a specific remote sensing system to a single area and demonstrated how remote sensing can be used for quick assessment of forests at large spatial scales (see, e.g., [7][8][9][10][11][12][13][14][15][16]).…”

Estimating Uncertainty of Point-Cloud Based Single-Tree Segmentation with Ensemble Based Filtering

“…The choice of a logarithmic variable radius in Equations (2) and 3is based on the observed relationship between upper crown radius and tree height in the region of interest. However, this relationship may vary significantly between forest types [30] and other variable radius functions such as those proposed in [31][32][33] may be used in place of Equations (2) and 3.…”

“…Airborne Laser Scanning (ALS) is an effective technology to support forest survey, research and management [1][2][3]. ALS can provide valuable data on forest structure and composition, in the form of dense 3D point clouds which can be further processed to create a range of useful products (such as topographical relief, canopy height, canopy layering, timber volume, canopy fuel characteristics, stand/edge delineation and deciduous/coniferous proportion).…”

Semi-Supervised Segmentation Framework Based on Spot-Divergence Supervoxelization of Multi-Sensor Fusion Data for Autonomous Forest Machine Applications

“…For the foreseeable future, autonomous forest machines (AFMs) will play a central role in harvesting, tending and forest management because lots of artificial and natural forests are facing with poor forest quality on a large-scale [ 1 ]. Currently, the proportion of good quality forest resources is very small, which directly affects the economic potential and ecological value of the whole forestry industry.…”