Remote Sensing of Forests: Analyzing Biomass Stocks, Changes and Variability with Empirical Data and Simulations

Forests are an important component in the earth system. They cover nearly one third of the land surface, store about as much carbon as the entire atmosphere and host more than half of the planet’s biodiversity. Forests provide ecosystem services such as climate regulation and water cycling and they supply resources. However, forests are increasingly at risk worldwide, due to anthropogenic deforestation, degradation and climate change. Concepts for counteracting this development require abilities to monitor forests and predict possible future developments. Given the vast size of forest cover along with the variety of forest types, field measurements and experiments alone cannot provide the solution for this task. Remote sensing and forest modeling enable a broader and deeper understanding of the processes that shape our planet’s forests. Remote sensing from airborne and spaceborne platforms can provide detailed measurements of forest attributes ranging from landscape to global scale. The challenge is to interpret the measurements in an appropriate way and derive biophysical properties. This requires a good understanding of the interaction between radiation and the vegetation. Forest models are tools that synthesize our knowledge about processes, such as tree growth, competition, disturbances and mortality. They allow simulation experiments which go beyond the spatial and temporal scales of field experiments. In this thesis, several major challenges in forest ecology and remote sensing were addressed. The main variable of interest was forest biomass, as it is the most important variable for forest carbon mapping and for understanding the role of vegetation in the global carbon cycle. For the purpose of biomass estimation, remote sensing derived canopy height and structure measurements were combined with field data, forest simulations and remote sensing simulations. The goals were: 1) to integrate remote sensing measurements into a forest model; 2) to understand the effects of spatial scale and disturbances on biomass estimation using a variety of remote sensing metrics; 3) to develop approaches for quantifying biomass changes over time with remote sensing and 4) to overcome differences among forest types by considering several structural aspects in the biomass estimation function. In the first study, a light detection and ranging (lidar) simulator was developed and integrated in the forest model FORMIND. The model was parameterized for the tropical rainforest on Barro Colorado Island (BCI, Panama). The output of the lidar simulator was validated against real airborne lidar data from BCI. Undisturbed and disturbed forests were simulated with FORMIND to identify the most well suited lidar metric for biomass estimation. The objective hereby was to achieve a low normalized root mean squared error (nRMSE) over the entire range of forest structures caused by disturbances and succession. Results identified the mean top-of-canopy height (TCH) as the best lidar-derived predictor. The accuracy strongly depended on spatial scale and relative errors < 10% could be achieved if the spatial resolution of the produced biomass map was ≥ 100 m and the spatial resolution of the remote sensing input was ≤ 10 m. These results could provide guidance for biomass mapping efforts. In the second study, forest simulations were used to explore approaches for estimating changes in forest biomass over time based on observed changes in canopy height. In an ideal situation, remote sensing provides measurements of canopy height above ground which allows the estimation of biomass stocks and changes. However, this requires sensors which are able to detect canopy surface and terrain elevation, and some sensors can only detect the surface (e.g., X-band radar). In such cases, biomass change has to be estimated from height change using a direct relationship. Unfortunately, such a relationship is not constant for forests in different successional stages, which can lead to considerable biases in the estimates of biomass change. A solution to this problem was found, where missing information of canopy height was compensated by integrating metrics of canopy texture. Applying this improved approach enables estimations of biomass losses and gains after disturbances at 1-ha resolution. In mature forests with very small changes in height and biomass all tested approaches have limited capabilities, as was revealed by an application using TanDEM-X derived canopy height from BCI. In the third study, a general biomass estimation function, which links remote sensing-derived structure metrics to forest biomass, was developed. General in this context means that it can be applied in different forest types and different biomes. For this purpose a set of predictor metrics was explored, with each predictor representing one of the following structural aspects: mean canopy height, maximal possible canopy height, maximal possible stand density, vertical canopy structure and wood density. The derived general equation resulted in almost equally accurate biomass estimates across the five considered sites (nRMSE = 12.4%, R² = 0.74) as site-specific equations (nRMSE = 11.7%, R²= 0.77). The contributions of the predictors provide a better understanding of the variability in the height-to-biomass relationship observed across forest types. The thesis has laid foundations for a close link between remote sensing, forest modeling and forest inventories. Several ongoing projects carry this further, by 1) disentangling and quantifying the uncertainty in biomass remote sensing, 2) trying to predict forest productivity based on structure and 3) detecting single trees from lidar to be used as forest model input. These methods can in the future lead to an integrated forest monitoring and information system, which assimilates remote sensing measurements and produces predictions about forest development. Such tools are urgently needed to reduce the risks forests are facing worldwide