Sensitivity of SAR data to post-fire forest regrowth in Mediterranean and boreal forests

Disturbed forests may need decades to reach a mature stage and optically-based vegetation indices are usually poorly suited for monitoring purposes due to the rapid saturation of the signal with increasing canopy cover. Spaceborne synthetic aperture radar (SAR) data provide an alternate monitoring approach since the backscattered microwave energy is sensitive to the vegetation structure. Images from two regions in Spain and Alaska were used to analyze SAR metrics (cross-polarized backscatter and co-polarized interferometric coherence) from regrowing forests previously affected by fire. TerraSAR-X X-band backscatter showed the lowest sensitivity to forest regrowth, with the average backscatter increasing by 1-2 dB between the most recent fire scar and the unburned forest. Increased sensitivity (around 3-4 dB) was observed for C-band Envisat Advanced Synthetic Aperture (ASAR) backscatter. The Advanced Land Observing Satellite (ALOS) Phased Array-type L-band Synthetic Aperture Radar (PALSAR) L-band backscatter presented the highest dynamic range from unburned to recently burned forests (approximately 8 dB). The interferometric coherence showed low sensitivity to forest regrowth at all SAR frequencies. For Mediterranean forests, five phases of forest regrowth were discerned whereas for boreal forest, up to four different regrowth phases could be discerned with L-band SAR data. In comparison, the Normalized Difference Vegetation Index (NDVI) provided reliable differentiation only for the most recent development stages. The results obtained were consistent in both environments.     

Aboveground biomass retrieval in tropical forests — The potential of combined X- and L-band SAR data use

In the context of reducing emissions from deforestation and forest degradation (REDD) and the international effort to reduce anthropogenic greenhouse gas emissions, a reliable assessment of aboveground forest biomass is a major requirement. Especially in tropical forests which store huge amounts of carbon, a precise quantification of aboveground biomass is of high relevance for REDD activities. This study investigates the potential of X- and L-band SAR data to estimate aboveground biomass (AGB) in intact and degraded tropical forests in Central Kalimantan, Borneo, Indonesia. Based on forest inventory data, aboveground biomass was first estimated using LiDAR data. These results were then used to calibrate SAR backscatter images and to upscale the biomass estimates across large areas and ecosystems. This upscaling approach not only provided aboveground biomass estimates over the whole biomass range from woody regrowth to mature pristine forest but also revealed a spatial variation due to varying growth condition within specific forest types. Single and combined frequencies, as well as mono- and multi-temporal TerraSAR-X and ALOS PALSAR biomass estimation models were analyzed for the development of accurate biomass estimations. Regarding the single frequency analysis overall ALOS PALSAR backscatter is more sensitive to AGB than TerraSAR-X, especially in the higher biomass range (> 100 t/ha). However, ALOS PALSAR results were less accurate in low biomass ranges due to a higher variance. The multi-temporal L- and X-band combined model achieved the best result and was therefore tested for its temporal and spatial transferability. The achieved accuracy for this model using nearly 400 independent validation points was r² = 0.53 with an RMSE of 79 t/ha. The model is valid up to 307 t/ha with an accuracy requirement of 50 t/ha and up to 614 t/ha with an accuracy requirement of 100 t/ha in flat terrain. The results demonstrate that direct biomass measurements based on the synergistic use of L- and X-band SAR can provide large-scale AGB estimations for tropical forests. In the context of REDD monitoring the results can be used for the assessment of the spatial distribution of the biomass, also indicating trends in high biomass ranges and the characterization of the spatial patterns in different forest types.     

Mapping canopy damage from understory fires in Amazon forests using annual time series of Landsat and MODIS data

Understory fires in Amazon forests alter forest structure, species composition, and the likelihood of future disturbance. The annual extent of fire-damaged forest in Amazonia remains uncertain due to difficulties in separating burning from other types of forest damage in satellite data. We developed a new approach, the Burn Damage and Recovery (BDR) algorithm, to identify fire-related canopy damages using spatial and spectral information from multi-year time series of satellite data. The BDR approach identifies understory fires in intact and logged Amazon forests based on the reduction and recovery of live canopy cover in the years following fire damages and the size and shape of individual understory burn scars. The BDR algorithm was applied to time series of Landsat (1997-2004) and MODIS (2000-2005) data covering one Landsat scene (path/row 226/068) in southern Amazonia and the results were compared to field observations, image-derived burn scars, and independent data on selective logging and deforestation. Landsat resolution was essential for detection of burn scars < 50 ha, yet these small burns contributed only 12% of all burned forest detected during 1997-2002. MODIS data were suitable for mapping medium (50-500 ha) and large (> 500 ha) burn scars that accounted for the majority of all fire-damaged forests in this study. Therefore, moderate resolution satellite data may be suitable to provide estimates of the extent of fire-damaged Amazon forest at a regional scale. In the study region, Landsat-based understory fire damages in 1999 (1508 km²) were an order of magnitude higher than during the 1997-1998 El Niño event (124 km² and 39 km², respectively), suggesting a different link between climate and understory fires than previously reported for other Amazon regions. The results in this study illustrate the potential to address critical questions concerning climate and fire risk in Amazon forests by applying the BDR algorithm over larger areas and longer image time series.     

Estimating the quantity and quality of coarse woody debris in Yellowstone post-fire forest ecosystem from fusion of SAR and optical data

The Coarse Woody Debris (CWD) quantity, defined as biomass per unit area (t/ha), and the quality, defined as the proportion of standing dead logs to the total CWD quantity, greatly contribute to many ecological processes such as forest nutrient cycling, tree regeneration, wildlife habitat, fire dynamics, and carbon dynamics. However, a cost-effective and time-saving method to determine CWD is not available. Very limited literature could be found that applies remote sensing technique to CWD inventory. In this paper, we fused the wall-to-wall multi-frequency and multi-polarization Airborne Synthetic Aperture Radar (AirSAR) and optical Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) to estimate the quantity and quality of CWD in Yellowstone post-fire forest ecosystem, where the severe 1988 fire event resulted in high spatial heterogeneity of dead logs. To relate backscatter values to CWD metrics, we first reduced the terrain effect to remove the interference of topography on AirSAR backscatter. Secondly, we removed the influence of regenerating sapling by quadratic polynomial fitting between AVIRIS Enhanced Vegetation Index (EVI) and different channels backscatters. The quantity of CWD was derived from P_hh and P_hv, and the quality of CWD was derived from P_hh aided by the ratio of L_hv and P_hh. Two maps of Yellowstone post-fire CWD quantity and quality were produced. The calculated CWD quantity and quality were validated by extensive field surveys. Regarding CWD quantity, the correlation coefficient between measured and predicted CWD is only 0.54 with mean absolute error up to 29.1 t/ha. However, if the CWD quantity was discretely classified into three categories of “≤ 60”, “60-120”, and “≥ 120”, the overall accuracy is 65.6%; if classified into two categories of “≤ 90” and “≥ 90”, the overall accuracy is 73.1%; if classified into two categories of “≤ 60” and “≥ 60”, the overall accuracy is 84.9%. This indicates our attempt to map CWD quantity spatially and continuously achieved partial success; however, the general and discrete categories are reasonable. Regarding CWD quality, the overall accuracy of 5 types (Type 1—standing CWD ratio ≥ 40%; Type 2—15% ≤ standing CWD ratio < 40%; Type 3—7% ≤ standing CWD ratio< 15%; Type 4—3% ≤ standing CWD ratio < 7%; Type 5—standing CWD ratio < 3%) is only 40.32%. However, when type 1, 2, 3 are combined into one category and type 4 and 5 are combined into one category, the overall accuracy is 67.74%. This indicates the partial success of our initial results to map CWD quality into detailed categories, but the result is acceptable if solely very coarse CWD quality is considered. Bias can be attributed to the complex influence of many factors, such as field survey error, sapling compensation, terrain effect reduction, surface properties, and backscatter mechanism understanding.     

BRDF measurement of understory vegetation in pine forests: dwarf shrubs, lichen, and moss

The spectral and directional reflection properties of pine forest understory in Suonenjoki, Finland were measured using a newly developed transportable field goniospectrometer under direct sunlight or plant lamp. The samples represent the most typical types in Finnish forests. Large differences between species were found. Wax-leaved shrubs such as lingonberry and blueberry proved to be strong forward scatterers, whereas lichen and soft-leaved dwarf shrubs such as heather were strong backscatterers. The measured moss showed both forward and backscattering features. There were variations among the samples of the same species, but many typical features appeared consistent and reproducible. Both “pure” and mixed samples were measured, the latter showing smoother behavior than the former, that is, the strongest forward and backward features are downscaled. The results provide a starting point for an empirical understory model and a basis for development and validation of a theoretical model.