Multi-temporal L-band Synthetic Aperture Radar Research Articles

Assessment of Aboveground Woody Biomass Dynamics Using Terrestrial Laser Scanner and L-Band ALOS PALSAR Data in South African Savanna

The use of optical remote sensing data for savanna vegetation structure mapping is hindered by sparse and heterogeneous distribution of vegetation canopy, leading to near-similar spectral signatures among lifeforms. An additional challenge to optical sensors is the high cloud cover and unpredictable weather conditions. Longwave microwave data, with its low sensitivity to clouds addresses some of these problems, but many space borne studies are still limited by low quality structural reference data. Terrestrial laser scanning (TLS) derived canopy cover and height metrics can improve aboveground biomass (AGB) prediction at both plot and landscape level. To date, few studies have explored the strength of TLS for vegetation structural mapping, and particularly few focusing on savannas. In this study, we evaluate the potential of high resolution TLS-derived canopy cover and height metrics to estimate plot-level aboveground biomass, and to extrapolate to a landscape-wide biomass estimation using multi-temporal L-band Synthetic Aperture Radar (SAR) within a 9 km2 area savanna in Kruger National Park (KNP). We inventoried 42 field plots in the wet season and computed AGB for each plot using site-specific allometry. Canopy cover, canopy height, and their product were regressed with plot-level AGB over the TLS-footprint, while SAR backscatter was used to model dry season biomass for the years 2007, 2008, 2009, and 2010 for the study area. The results from model validation showed a significant linear relationship between TLS-derived predictors with field biomass, p < 0.05 and adjusted R2 ranging between 0.56 for SAR to 0.93 for the TLS-derived canopy cover and height. Log-transformed AGB yielded lower errors with TLS metrics compared with non-transformed AGB. An assessment of the backscatter based on root mean square error (RMSE) showed better AGB prediction with cross-polarized (RMSE = 6.6 t/ha) as opposed to co-polarized data (RMSE = 6.7 t/ha), attributed to volume scattering of woody vegetation along river valleys and streams. The AGB change analysis showed 32 ha (3.5%) of the 900 ha experienced AGB loses above an average of 5 t/ha per annum, which can mainly be attributed to the falling of trees by mega herbivores such as elephants. The study concludes that SAR data, especially L-band SAR, can be used in the detection of small changes in savanna vegetation over time.

Eco-Hydrological Characterization of Inland Wetlands in Africa Using L-Band SAR

Maps describing the eco-hydrology of inland wetland systems in Africa are needed to identify and implement appro- priate adaptive management plans related to land use and land cover. Many African countries lack regional baseline informa- tion on the temporal extent, distribution and characteristics of wetlands. This information is provided here in the form of maps which characterize two wetland sites of international importance in Malawi and Mozambique. Multi-temporal L-band Synthetic Aperture Radar (SAR) datasets are combined with Landsat The- matic Mapper and ASTER images, digital elevation models, and vegetation species data to provide information on wetland ecology and hydrology. These data were used as input to a hybrid, Decision Tree classifier and a Principal Components Analysis classification approach to produce maps depicting the spatial distribution of vegetation species and characterizing the wetland dynamics. The maps exhibit classification accuracies of 89% and 84% for the two sites respectively. The L-band SAR datasets have proved to be an essential information source in the production of these maps due to i) frequent cloud cover/smoke which reduces the temporal coverage of optical data, and ii) a systematic observation strategy and frequent image acquisition which enables characterization of the flood dynamics at a high temporal resolution. Index Terms—ALOS PALSAR, ecology, hydrology, Kyoto and carbon initiative, Malawi, Mozambique, wetlands.

Soil moisture retrieval through a merging of multi-temporal L-band SAR data and hydrologic modelling

Abstract. The objective of the study is to investigate the potential of retrieving superficial soil moisture content (mv) from multi-temporal L-band synthetic aperture radar (SAR) data and hydrologic modelling. The study focuses on assessing the performances of an L-band SAR retrieval algorithm intended for agricultural areas and for watershed spatial scales (e.g. from 100 to 10 000 km2). The algorithm transforms temporal series of L-band SAR data into soil moisture contents by using a constrained minimization technique integrating a priori information on soil parameters. The rationale of the approach consists of exploiting soil moisture predictions, obtained at coarse spatial resolution (e.g. 15–30 km2) by point scale hydrologic models (or by simplified estimators), as a priori information for the SAR retrieval algorithm that provides soil moisture maps at high spatial resolution (e.g. 0.01 km2). In the present form, the retrieval algorithm applies to cereal fields and has been assessed on simulated and experimental data. The latter were acquired by the airborne E-SAR system during the AgriSAR campaign carried out over the Demmin site (Northern Germany) in 2006. Results indicate that the retrieval algorithm always improves the a priori information on soil moisture content though the improvement may be marginal when the accuracy of prior mv estimates is better than 5%.

Evaluation and Perspectives of Using Multitemporal L-Band SAR Data to Monitor Deforestation in the Brazilian AmazÔnia

Japanese Earth Resources Satellite 1 (JERS-1) synthetic aperture radar (SAR) data were evaluated to map areas of deforestation in a Brazilian Amazo/spl circ/nia test-site. The results were compared with information derived from a Landsat TM multitemporal series. Unambiguous detection of deforested areas was observed only when the entire deforestation process (slash, burning, and terrain clearing) had already occurred. This result recommends further investigations on the effectiveness of horizontal polarization SAR data to map deforestation in a consistent basis. The cross-polarized (horizontal-vertical) channel designed to be in the ALOS/PALSAR system is expected to improve the distinction between forested and recently deforested areas.

Estimation of tree growth in a conifer plantation over 19 years from multi-satellite L-band SAR

The general capability of synthetic aperture radar (SAR) for monitoring forest ecosystems is well documented. However, the majority of SAR studies of forest dynamics use only imagery acquired by one SAR system and are thus limited to the lifecycle of a particular satellite. The synergistic analysis of SAR data from one of the earliest spaceborne SAR missions, the SEASAT mission, with the Japanese JERS-1 satellite-borne SAR is presented. Biophysical parameters frequently retrieved from SAR are tree biomass using backscatter and tree height from the interferometric phase. One potential application that has not been thoroughly examined is mapping of incremental tree growth from SAR backscatter changes. Tree growth measures biomass changes over time, and is correlated to the amount of carbon sequestered by the trees. This paper examines the retrieval of tree growth from multitemporal spaceborne L-band SAR. A SEASAT SAR image from 1978 and a JERS-1 SAR image from 1997 over Thetford forest, UK are used to retrieve tree growth of Corsican Pine stands. Incremental growth was estimated from the changes in backscatter coefficient, and compared to the expected tree growth from general yield class models used by the UK Forestry Commission. The accuracy of the retrieval algorithm depends on the minimum forest stand size included in the analysis. For managed forest plantations, multitemporal L-band SAR has some potential for detecting incremental biomass to support sustainable forest management.