The world’s natural ecosystems are under pressure due to land conversion and climate change. Forests are a major part of these natural ecosystems and cover up to 30% of the earth’s surface. Trees are crucial for timber, store carbon, and provide other ecosystem functions. Assessing and predicting forest ecosystem responses based on global environmental changes is an important task for scientists.
For many decades monitoring of forest ecosystems has been implemented using various well established (conventional) methods, but in more recent decades remote sensing techniques have made steep developments and provide opportunities in this respect. Although conventional monitoring has proven its value in many cases, monitoring forest ecosystems for decision making often requires large scale monitoring. In this sense, remote sensing (RS) offers a solution and has been successfully used for monitoring forest disturbance at regional and global scales. However, remote sensing also has made advances at plot scale, for example using near-sensing terrestrial laser scanning (TLS). Despite the potential for close collaborations between the remote sensing and forest ecology communities, there is still a disconnect (e.g. spatial / temporal resolution of data) between the two fields of expertise, which means that combining data from the two fields is difficult. To better understand for example tree physiological processes using remote sensing, further synchronisation of the two fields is vital for improving the potential for satellite data.
In this thesis, I explored how to link conventional ground-based methods with remote sensing techniques, using both satellite and novel near-sensing TLS in order to investigate aspects of forest change. To do this, I looked at four representative cases with different forest types, which were selected to address different current environmental challenges with indirect (reduced tree vitality) and direct (change in forest structure) impacts.
In chapter 2, we attempted to upscale ground-based conventional forest canopy measurements at plot level to remote sensing derived indices of the canopy in the Pampa del Tamarugal aquifer, in the hyper-arid Atacama desert of Chile. We assessed the foliage loss (dry branches) of the Prosopis tamarugo Phil. (a native tree) by ground-based visual assessment and digital pictures over three groundwater depletion conditions. These pictures were segmented and classified into green and brown canopy to derive the GCF (green canopy fraction). The GCF was then related to NDVIw (NDVI in winter time) from the WorldView2 satellite data, and NDVIw was used to estimate and thus upscale the GCF to all P. tamarugo trees in the aquifer. NDVIw derived from the Landsat archive allowed us to not only assess the current status of the P. tamarugo trees, but also changes over time. In this study we could successfully link ground-based conventional forest canopy measurements to remote sensing derived indices of the canopy. This allowed us, in combination with the groundwater level grids, to assess the tree vitality of the whole aquifer and determine a critical groundwater depth of 20 m for the P. tamarugos survival.
Chapter 3 and 4 link ring width (RW) data at plot level to remote sensing derived plot level indices of the canopy. In both chapters the aim was to better understand the effect of environmental factors (e.g. water shortage) on the growth of trees both from the stem (wood) and canopy perspective. In Chapter 3 we used the GCF (current situation) and NDVI-based indices (historical situation) from satellite data derived from chapter 2, and assessed the correlation between NDVI-based satellite indices with ground-based tree-ring increments in two contrasting sites (low and high groundwater depletion). Time-series analysis (over a period of 26 years) and NDVI-derived parameters showed significant negative trends in the high-depletion site, indicating drought stress. Ring width of P. tamarugo trees was 48% lower in the high-depletion site. At the tree level, the GCF in the highly depleted site also indicated drought stress since a larger percentage of trees fell within lower GCF classes. In this case monitoring water shortage over time was straightforward since the stand was monospecific, and water shortage happened gradually. This was not the case in chapter 4 where we addressed the effect of climate on tree growth by combining tree-ring data of 25 locations in Slovenia with remote sensing derived EVI indices (enhanced vegetation index) from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data. We attempted to upscale the results at plot scale to national level for the tree species Fagus sylvatica L. (Beech) in the temperate forests of Slovenia. We were not able to find any relations of both RW and EVI based anomalies with climate parameters, nor was there a relation between RW and EVI based anomalies. Reasons might be: (i) time-series length (i.e. overlap between the data types), (ii) complexity of the environmental stress, such as the interplay of climatic conditions with other factors such as topography (also depending on the timing and duration of a climate event within the year), (iii) a satellite pixel might consist of other tree species less sensitive to drought, and (iv) empirical linkage between parameters – uncertainty about the direct relationship between stem and canopy derived parameters. We did find indications that both RW and EVI based anomalies were negatively affected by the extreme climate events in Slovenia, in particular the effect of the ice storm of 2014. Combining dendrochronology and remote sensing allowed us to understand the effects of drought stress on two different carbon pools (crown and stem, respectively), providing more insights on the physiological response of the species to drought.
In Chapter 5 we investigated forest structure in tropical forests in Ethiopia. Here we combined conventional forest inventory measurements such as biomass, tree density, and tree species, with near-sensing TLS measurements such as PAVD (plant area vegetation density) and canopy openness. Differences between four forest types (intact forest, coffee forest, silvopasture, and plantation) for both conventional and TLS measurements were assessed. Results showed that the 3D vegetation structure (i.e. PAVD) and canopy parameters could be used to differentiate between forest types. TLS as tool for monitoring forest structure showed potential as it can capture the 3D position of the vegetation volume and open spaces at all heights. To quantify changes in different forest types, consistent monitoring of 3D structure is needed and here TLS is an add-on or an alternative to conventional forest structure monitoring.
This thesis contributes to the exploration of the advantages of combining conventional ground-based data and remote sensing derived data. Combining both data types can mutually enhance the potential capabilities of each other. Remote sensing can upscale plot data to large spatial scales, while near sensing tools such as TLS can provide detailed forest structural data. Conventional ground-based data provide insight into the ecology of stands, e.g. RW or ecophysiological processes, and can help to understand remote sensing derived canopy indices. Advances in remote sensing are moving towards higher spatial, temporal, and spectral detail, but without the in-situ ecological data these advances do not reach their full potential. I, therefore, strongly advocate closer collaborations between the two research fields in the set-up of monitoring campaigns (e.g. different spatial scales). In this thesis I explored the value of combing the two fields in an empirical way. However, not all issues have been solved and more research is needed. Future research could focus on an integrated and tree-centred approach that can help to understand climate-growth interaction and the connections between stem and canopy derived indices. Future challenges also lay in improving the data operability and data processing. For remote sensing to become a conventional method, it has to become more ecologically (ecosystem) driven in an operational and cost-effective way.