Remotely sensed data controlled forest inventory concept

Abstract

Nowadays, the image of the forest in Germany is changing from monoculture areas to very mixed forests, where individual stands are no longer clearly visible. The objective of this study was to examine the use of remotely sensed data at enterprise level for pre-stratification and sample plot allocation in the planning stage of forest inventories in a very heterogeneous forest. On the basis of RapidEye satellite data and object-based image analysis, a stratified segment-based non-permanent sampling design was developed and evaluated against the results of a permanent systematic sampling design. The relative efficiency (RE) was calculated based on variance estimators for simple random sampling and stratified random sampling for the variable timber volume [m 3 /ha]. By stratification of the sample designs, we achieved an RE of 1.25 for the systematic sampling and 1.34 with the segment-based sampling design. Based on a targeted standard error of 4.6%, the sampling designs were compared with respect to the required sample size. The stratified segment-based sampling design reduced the number of sample plots compared to the systematic sampling design by 28%. Furthermore, it was shown that the possible reduction of sampling plots leads to a cost saving of 21%.

A remote sensing-guided forest inventory concept using multispectral 3D and height information from ZiYuan-3 satellite data

“…The classification of the forest-type classes based on MS and angular information derived from the ZY-3 dataset is in line with the results of the classification approach of Wallner et al (2018). The latter study obtained an OA of 84 per cent for coniferous and broadleaved forest types in Southeast Germany, using RapidEye data with a similar spatial resolution of 5 m. Also based on RapidEye data, Schneider et al (2013) obtained an OA range between 66 per cent and 77 per cent for classifying a forest in southern Bavaria into coniferous, broadleaved and mixed forest types using a multi-temporal data stack.…”

“…Thus, a lower number of sample plots may suffice to achieve a targeted precision. Grafström et al (2014) and Wallner et al (2018) provide examples of this approach. Further, the approach of twophase sampling can be used to support terrestrial sampling in combination with wall-to-wall maps, which are generated with observations from auxiliary data correlated to the response variable, such as growing-stock volume.…”

Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review

“…In central European conditions most of the managed forests are already stratified by previous spatial division into so called stand divisions, stands and compartments, based on different conditions including ownership, geographic relief, and other environmental factors. Though, as pointed out by Wallner et al [19], the current trends in society push towards abandonment of monocultures and area based harvesting (clear cuts) what requires more precise and effective ways for inventory and monitoring.…”

“…The authors described 11 weeks difference in aerial imagery processing in comparison to satellite imagery processing (in favor of satellites). From more recent works [19] utilized RapidEye data for examination of remotely sensed data at enterprise level for prestratification sample plot allocation in the planning stage of forest inventories in heterogeneous forest. The work was motivated with ongoing change from monoculture forestry towards mixed forests where inventory is becoming more difficult, but none the less important.…”

Earth observation for the implementation of Sustainable Development Goals: the role of the European Journal of Remote Sensing

“…This is the area in which EO technologies have already demonstrated their potential in an operational way. More or less, all of the indicators set up for this goal can be quantified through the use of spatial data, for example, forest mapping and inventorying (Wallner et al, 2018), sustainable forest management assessment (Corona et al, 2012), desertification (Wang, 2020), protected sites (Gil et al, 2013), wildlife (Kuželka & Surový, 2018) and biodiversity monitoring (Bochenek et al, 2018).…”