# A SAR-Based Index for Landscape Changes in African Savannas
## 1 Introduction
Studies investigating landscape sensitivity, the severity of disturbances and human impact based on SAR data are rare: [PERSON] and [PERSON] proposed a statistical indicator for the intensity of flooding for the Roanoke River floodplain (United States) based on eleven RADARSAT-1 scenes acquired over a period of seventeen months [52]. They used the distinctive signature of water in SAR images expressed in usually very low backscatter values. They achieved overall model accuracies of 87.8%, however they only looked at two classes (flooded and non-flooded). [PERSON] et al. followed a similar approach for the estimation of landscape sensitivity towards floods based on two JERS-1 mosaics in Western Arizona [53]. Their study targeted four LULC classes and five levels of flood hazards but only compared two images (pre- and post-flooding). [PERSON] et al. derived forest fire damage in East Kalimantan, Indonesia, based on 56 scenes of ERS-2 from 1997 [54]. They discriminated three different classes of burn severity and additionally derived a LULC classification. This study is one of the few making full use of large SAR archives for the assessment of impacts of disturbances on landscapes. As well, human impacts upon forest systems deforestation of the Amazon rainforests is of special interest in remote sensing studies. [PERSON] et al. made use of the polarimetric covariance between channels of SIR-C [55], comparing an intra-annual pair of images--interferometric signatures and coherence [56]. L-band data have been found to be of special importance due to its pronounced interaction with vegetation volumes such as canopy structures [57; 58; 59]. Most of the studies, however, do not make use of large sets of SAR images provided by the JAXA archives. For the African continent, [PERSON] et al. have found a robust relationship for the estimation of biomass changes in savanna ecosystems [60; 61]. They derived change maps based on 7 biomass classes for an image pair from 1997 (JERS-1) and 2007 (ALOS PALSAR). [PERSON] et al. used polarimetric RADARSAT-2 data to derive landscape metrics from eight LULC classes in the Nanjishan Wetland Nature Reserve, PR China [62]. Although this approach aims at a deeper understanding of the classified landscape it doesn't make allowance to investigate temporal changes. Based on the prior developments and findings we identified the following research deficits. While indices of spatial change based on optical data are numerous, there is almost no SAR-based methodology for long-term analysis of landscape developments at a class level. Pixel-based approaches developed for optical data are not applicable for SAR data as they do not allow for smaller misclassifications or isolated pixels arising from speckle effects. Additionally, most studies strongly focus on a stepwise comparison of image pairs without considering the total variation along the full time-series [63]. This is especially a problem in landscapes with high dynamics due to wild-fires, land degradation, vegetation encroachment or strong human impact. For those situations, distinct indices have to be developed, especially when dealing with SAR or very high resolution (VHR) optical data [64]. Other indices are highly elaborate but hard to read for people from outside the field of research. Accordingly, [PERSON] and [PERSON] argue that findings of multi-temporal remote sensing products are often complicated to read or even misleading and susceptible to making false conclusions [65]. In order to perform long-term analyses of landscape change independently from cloud cover, a robust and transparent approach based on radar data has to be developed. As a result, an integrative index should explain both the location and intensity of changes, as well as their implications for environmental integrity and human well-being. This index must be reproducible and adjustable to different needs of the users according to the significance of the targeted classes.