# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## 1 Introduction
### State-of-the-Art Forest Disturbance Monitoring with Optical Data  
The opening of the U.S. Geological Survey (USGS) Landsat data archive in 2008 [9] and the launch of new satellite missions with an open data policy, such as ESA's Sentinel Missions [10], hasformed the basis for time series analysis of optical satellite data at high spatial and high temporal resolution [9; 11; 12; 13]. Although the Sentinel-2 Multi Spectral Instrument (MSI) and the Landsat 8 Operational Land Imager (OLI) bands are not fully compatible in terms of radiometry, recent investigations on radiometric consistency between the two sensors revealed a high correlation between corresponding bands. Dense optical image time series allow for developing new Land Use and Land Cover (LULC) mapping approaches and novel methods for detecting dynamic and gradual, as well as long-term, change processes. Satellite systems that operate at daily acquisition [14] rates, such as MODIS, can achieve consistent temporal coverage, even in tropical regions. But small disturbances are largely omitted due to the coarse spatial resolution of 250-500 m [15]. Many different algorithms have been proposed to detect forest changes while using time series of medium spatial resolution optical satellite imagery [16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. In the last decade, yearly deforestation mapping and the derivation of deforestation rates have become operational at global [1] and national level e.g., [2; 16; 17], but there is still only fragmented information available on the extent and magnitude of small forest disturbance and forest degradation [18]. As stated in the GOFC-GOLD REDD sourcebook [19], measuring forest degradation or forest regrowth and related forest carbon stock changes is more challenging than measuring deforestation alone, since degradation monitoring requires more frequent and better imagery and processing. Cloud cover strongly influences tropical forest disturbance detection accuracies in both space and time [4]. When there are temporal gaps in the time series, rapid vegetation recovery can obscure the signs of disturbance events [20]. Previous studies using multiple years of cloud-free Landsat data in less cloudy regions showed that the regrowth of trees rapidly masks the spectral signature, even of forest clear cuts [21; 22]. Thus, a higher percentage of forest disturbances can only be detected with dense time series of optical data. Dense time series are essential for the continued development of near real-time monitoring systems, such as the Global Forest Watch Humid Tropical Forest Alerts [8], and can provide much needed alert information on location and exact timing of illegal logging activities [23]. In terms of methodology, we can divide the time series analysis methods that were used for forest change detection into four broad categories: (1) threshold based change detection; (2) curve fitting; (3) trajectory fitting; and, (4) trajectory segmentation. Existing algorithms often use a combination of these categories. A more detailed description and comparison of these categories can be found in [24]. Thresholding procedures that separate forest from non-forest or intact from degraded forest in a time series are used in the Vegetation Change Tracker (VCT) and Global Forest Watch algorithm [8; 25]. Curve fitting approaches for monitoring forest dynamics have been applied in several studies [26; 27]. A large number of forest monitoring approaches today are based on trajectory fitting and trajectory segmentation. Most of the disturbance types show a distinct temporal behavior before and after a degradation event, resulting in a \"characteristic spectro-temporal signature\" that can be exploited to detect and classify forest changes [22]. Trajectory fitting and segmentation algorithms include LandTrendr, Breaks For Additive Season, and Trend (BFAST) and the Continuous Change Detection and Classification (CCDC) algorithm [28; 29; 30; 31]. The dense time series of satellite data also allow to detect forest disturbances from harmonic regression models.