Remote Sensing Measurements of Forest Structure Types for Ecosystem Service Mapping
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Forests represent an important pool in the global carbon cycle. However, biomass stocks and carbon fluxes are variable due to the fact that forest dynamics are driven by processes that act on different spatial and temporal scales. Estimating forest biomass and productivity for larger regions is therefore a major challenge. In this study, horizontal and vertical forest structure is used to improve forest ecosystem service mapping by remote sensing. By linking remote sensing techniques with vegetation modelling (here FORMIND) and forest inventories, forest structure maps were derived for Germany (resolution 4 km). Using these maps, the role of forest structure for selected ecosystem services of forests has been investigated. For forest state estimations (like biomass) horizontal forest structure plays a key role while for productivity estimations both horizontal and vertical structures are relevant. This concept of forest structure classification in combination with forest modelling and remote sensing has high potential for applications at continental scales as future remote sensing missions will provide information on forest structure.
KeywordsForest structure Biomass Productivity Remote sensing Forest model Germany
Which ecosystem services are addressed? Ecosystem services provided by forests: forest biomass and forest productivity. Knowledge about these ecosystem services can be used to deliver further forest ecosystem services related to climate regulation, soil protection, biodiversity protection, water regulation, disturbance regulation, and bioenergy
What is the research question addressed? How can we estimate forest structure from remote sensing, and what is the role of forest structure for forest biomass and productivity estimations?
Which method has been applied? Linking forest inventory data, forest model simulations, and remote sensing
What is the main result? Forest structure can be estimated from remote sensing by using structural indices. Additionally, we show that over a broad range of forest stands, forest structure is the important driver for estimating forest biomass and forest productivity
What is concluded, recommended? Future remote sensing missions will provide information on forest structure with a high degree of detail. This will lead to more accurate estimations of forest biomass and productivity
Twenty-five percent of Earth’s land surface is covered by forests, and they are habitat for more than 70% of all terrestrial species [1, 2, 3]. Forests represent an important pool in the global carbon cycle as they bind huge amounts of carbon in their living biomass [4, 5, 6]. They are also able to regulate the water cycle through processes of evapotranspiration, which is important for stabilizing the global climate . Additionally, forest management is an important economic sector in Europe.
Global forests are characterized by complex patterns and structures. Forest dynamics are driven by processes that act on different spatial and temporal scales. Consequently, biomass stocks and carbon fluxes are variable in space and time. Therefore, estimating forest properties such as biomass or productivity for larger regions is a major challenge. The IPCC (Intergovernmental Panel on Climate Change) reported that missing knowledge on biomass distribution is one large source for uncertainty in the global carbon cycle [6, 8].
11.2.1 Study Site
For this study we used ground-inventory data from the new forest megaplot Traunstein (https://forestgeo.si.edu/sites/europe/traunstein). It is a large permanent research plot, established as a new super test site (25 ha, 30,000 measured trees) in a highly diverse structured forest district in the German alpine upland—including even- and uneven-aged forest stands and 26 tree species. In 2016, a full tree survey was performed (stem diameter and position for each tree) and an airborne lidar campaign was conducted by the German Aerospace Center (DLR). Since 2017 this unique research plot is part of the global Smithsonian Tropical Forest Institute Network (ForestGEO).
To apply our local findings from Traunstein to Germany, we used the German national forest inventory data (‘BWI’; ), which consists of 48,562 field plots distributed across Germany.
11.2.2 Describing Forest Structure by Structural Indices
11.2.3 Estimating Structural Indices from Lidar Remote Sensing
To find relationships between field-based forest structure and lidar, we used ground-inventory data from the Traunstein megaplot (see Sect. 11.2.1). Additionally, we analysed the airborne lidar campaign conducted for this forest (see Sect. 11.2.1). We explored relationships between field-based forest structure (here, basal area and tree height heterogeneity) and lidar on different scales (e.g., 20 m, 100 m). Depending on the spatial scale, we found good relationships between field-based and lidar-based structure index for horizontal forest structure (e.g., r2 = 0.77 for field basal area vs. lidar top-of-canopy height at the scale of 20 m). Relations for the vertical index are more challenging (e.g., r2 = 0.41 for tree height heterogeneity vs. lidar 90% height quantile at the scale of 20 m).
11.2.4 Classifying Forest Stands into Structure Types
Basal area and tree height heterogeneity can be used to classify forest stands into different structure types. For this we divided both indices into four classes: basal area (m2/ha) as horizontal structural descriptor: H1: 0–15, H2: 15–25, H3: 25–35, H4: >35, and tree height heterogeneity (m) as vertical structural descriptor: V1: 0–1, V2: 1–2, V3: 2–3, V4: >3. With this classification scheme we assign each forest stand to a vertical and horizontal structure class. Both indices can be estimated from lidar due to the derived relationships between field-based metrics and lidar.
This classification scheme was applied to the German national forest inventory (BWI) data . As no wall-to-wall lidar data for Germany was available, we generated lidar data for the BWI plots using a lidar simulation model . At the end we developed a Germany-wide forest structure map estimated from lidar remote sensing. The same type of maps can be derived also from radar (e.g., L-Band) as radar measurements can be also used to quantify forest structure. Similar maps can be generated using other structural indices (like SDI, not shown).
11.2.5 Forest Biomass and Productivity
In a second step, 300,000 virtual forest stands were analysed to identify the importance of forest structure for biomass and productivity estimations (“forest factory approach,” ). The virtual stands were generated with the individual-based forest model FORMIND . We calculate for each forest stand (400 m2) biomass and productivity (here, aboveground woody productivity AWP) and relate them to the structural indices: here, basal area BA and standard deviation of tree heights σheight.
11.3 Results and Discussion
11.3.1 Classifying Forest Stands into Structural Classes Using Field Data and Lidar
According to this analysis, most forest stands in Germany have a high basal area >35 m2/ha (see Fig. 11.3). The amount of forest area with heterogeneous vertical structure (σheight > 2 m) is as high as the forest area with homogenous vertical structure (σheight < 2 m).
11.3.2 The Importance of Forest Structure for Biomass and Productivity Estimates
Forest productivity increases with basal area for stands with a low vertical heterogeneity (Fig. 11.4b). However, with increasing vertical heterogeneity, the positive effect of basal area on productivity diminishes. The reason is that large trees shade smaller trees, which reduces the productivity of smaller trees. To sum up, for forest state estimations (like biomass) the horizontal forest structure plays a key role (here, basal area; Fig. 11.4). For productivity estimations, however, the horizontal and vertical structures are relevant.
Using the derived forest structure maps for Germany, we can estimate forest biomass and forest productivity distributions for forests and explore relationships between forest structure and other forest properties . We show that over a broad range of forest stands, forest structures are the important drivers for estimating forest biomass and forest productivity. Knowledge about ecosystem functions like biomass and productivity is crucial for evaluating timber volume for forestry, estimating disturbance state of forests, and understanding the effects of climate change. The concept of forest structure types in combination with forest modelling and remote sensing has high potential for applications at larger scales . Future remote sensing missions (like BIOMASS, GEDI, Tandem-L) will provide information on forest structure with a high degree of detail.
We thank the Thünen Institute for providing the German national forest inventory (BWI) data. We also want to thank Hans Pretzsch, Peter Biber, and Michael Heym (TUM) for their input on forest structure and structure metrics. Kostas Papathanassiou, Victor Cazcarra-Bes, Matteo Pardini and Marivi Tello Alonso (DLR) gave useful insights into linking forest structure and remote sensing. This study was part of the Helmholtz-Alliance Remote Sensing and Earth System Dynamics. NK was funded by the German Federal Ministry for Economic Affairs and Energy (BMWi) under the funding reference 50EE1416.
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