FORZA — Multi-scale remote sensing and dendro-chronological methods for an improved understanding of forest decline in the Zagros forest area

Funded by DAAD / 2018 - 2022

Keeping track of forest change processes to support environmental monitoring and a sustainable use of forests can be challenging, especially if large areas have to be addressed. The Zagros area in Iran contains more than 42 percent of the country's forests and provides many ecosystem services. Over the last years, a notable decrease in forest area and a decline in vegetation cover have been observed in Zagros. The reasons for this decline are manifold and include unsustainable use but also an increased number of insect infestations. In this realm, identifying and mapping declining and infested trees is an important step to manage and prevent the spread of infestations. Due to the high workload associated with mapping declining areas in the field, the application of remote sensing data could be a suitable supplement or even an alternative for identifying declining and dead trees.

While several earlier studies have demonstrated the potential of remote sensing data for such purposes, there are still open questions concerning why certain declining areas can be detected with remote sensing techniques and others not. Having a sound understanding of when remote sensing data reach their limitations is important for developing efficient monitoring systems. Furthermore, mapping declined areas is just a first step towards understanding the reasons for forest decline. Once the affected areas have been identified, it is necessary to also understand the underlying drivers that have caused the declines in the identified areas. In the suggested project, we will address these issues by combining very high-resolution drone imagery, satellite data from drone surveys, multispectral Landsat and Sentinel-2 satellite data as well as dendro-chronological information. Initially, we will identify the minimum size of declined or dead forest patches that are detectable with current multispectral satellites. We will also assess the influence of co-occurring land-cover classes on these results by applying a multiscale approach using drone imagery as a reference for the multispectral satellite data.

Then, the derived spatial patterns of declined forest areas will be analyzed in relation to a range of environmental parameters to understand whether forest decline is especially severe under certain environmental conditions. Ultimately, a retrospective analysis of stress events will be performed during which dendro-chronological time-series of stress events will be compared to spectral information available from historic Landsat datasets. In summary, the suggested project aims to improve the understanding of the severe forest decline in the Zagros forest area using innovative remote sensing approaches. Besides providing knowledge on the locally important issue in the Zagros area, the methodical approaches developed in the project could also be beneficial for other cases of forest decline in other parts of the world.