Understanding 'saturation' of radar signals over forests (original) (raw)
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Remote Sensing of Environment, 2011
Understanding the spatial variability of tropical forest structure and its impact on the radar estimation of aboveground biomass (AGB) is important to assess the scale and accuracy of mapping AGB with future low frequency radar missions. We used forest inventory plots in old growth, secondary succession, and forest plantations at the La Selva Biological Station in Costa Rica to examine the spatial variability of AGB and its impact on the L-band and P-band polarimetric radar estimation of AGB at multiple spatial scales. Field estimation of AGB was determined from tree size measurements and an allometric equation developed for tropical wet forests. The field data showed very high spatial variability of forest structure with no spatial dependence at a scale above 11 m in old-growth forest. Plot sizes of greater than 0.25 ha reduced the coefficients of variation in AGB to below 20% and yielded a stationary and normal distribution of AGB over the landscape. Radar backscatter measurements at all polarization channels were strongly positively correlated with AGB at three scales of 0.25 ha, 0.5 ha, and 1.0 ha. Among these measurements, PHV and LHV showed strong sensitivity to AGB b 300 Mg ha − 1 and AGB b 150 Mg ha − 1 respectively at the 1.0 ha scale. The sensitivity varied across forest types because of differences in the effects of forest canopy and gap structure on radar attenuation and scattering. Spatial variability of structure and speckle noise in radar measurements contributed equally to degrading the sensitivity of the radar measurements to AGB at spatial scales less than 1.0 ha. By using algorithms based on polarized radar backscatter, we estimated AGB with RMSE = 22.6 Mg ha − 1 for AGB b 300 Mg ha − 1 at P-band and RMSE = 23.8 Mg ha − 1 for AGB b 150 Mg ha − 1 at L-band and with the accuracy optimized at 1-ha scale within 95% confidence interval. By adding the forest height, estimated from the C-band Interferometry data as an independent variable to the algorithm, the AGB estimation improved beyond the backscatter sensitivity by 20% at P-band and 40% at L-band. The results suggested the estimation of AGB can be improved substantially from the fusion of lidar or InSAR derived forest height with the polarimetric backscatter.
Techniques for Wide-Area Mapping of Forest Biomass Using Radar Data
Aspects of forest biomass mapping using SAR (Synthetic Aperture Radar) data were studied in study sites in northern Sweden, Germany, and south-eastern Finland. Terrain topography – via the area of a resolution cell – accounted for 61 percent of the total variation in a Seasat (L-band) SAR scene in a hilly and mountainous study site. A methodology – based on least squares adjustment of tie point and ground control point observations in a multi-temporal SAR mosaic dataset – produced a tie point RMSE (Root Mean Square Error) of 56 m and a GCP RMSE of 240 m in the African mosaic of the GRFM (Global Rain Forest Mapping) project. The mosaic consisted of 3624 JERS SAR scenes. A calibration revision methodology – also based on least squares adjustment and points in overlap areas between scenes – removed a calibration artifact of about 1 dB. A systematic search of the highest correlation between forest stem volume and backscattering amplitude was conducted over all combinations of transmit a...
Decrease of L-band SAR backscatter with biomass of dense forests
Remote Sensing of Environment, 2015
Synthetic aperture radar (SAR) is one of the most promising remote sensors to map forest carbon. The unique spaceborne and long-wavelength SAR data currently available are L-band data, but their relationship with forest biomass is still controversial, particularly for high biomass values. While many studies assume a complete loss of sensitivity above a saturation point, typically around 100 t.ha −1 , others assume a continuous positive correlation between SAR backscatter and biomass. The objective of this paper is to revisit the relationship between L-band SAR backscatter and dense tropical forest biomass for a large range of biomass values using both theoretical and experimental approaches. Both approaches revealed that after reaching a maximum value, SAR backscatter correlates negatively with forest biomass. This phenomenon is interpreted as a signal attenuation from the forest canopy as the canopy becomes denser. This result has strong implications for L-band vegetation mapping because it can lead to a greaterthan-expected under-estimation of biomass. The consequences for L-band biomass mapping are illustrated, and solutions are proposed.
Detecting and Quantifying Forest Change: The Potential of Existing C- and X-Band Radar Datasets
PLOS ONE, 2015
This paper evaluates the opportunity provided by global interferometric radar datasets for monitoring deforestation, degradation and forest regrowth in tropical and semi-arid environments. The paper describes an easy to implement method for detecting forest spatial changes and estimating their magnitude. The datasets were acquired within space-borne high spatial resolutions radar missions at near-global scales thus being significant for monitoring systems developed under the United Framework Convention on Climate Change (UNFCCC). The approach presented in this paper was tested in two areas located in Indonesia and Australia. Forest change estimation was based on differences between a reference dataset acquired in February 2000 by the Shuttle Radar Topography Mission (SRTM) and TanDEM-X mission (TDM) datasets acquired in 2011 and 2013. The synergy between SRTM and TDM datasets allowed not only identifying changes in forest extent but also estimating their magnitude with respect to the reference through variations in forest height. deforestation with adequate certainty for determining reference emissions . However, monitoring carbon stock enhancements (e.g., afforestation or forest regrowth) beyond the point of canopy closure becomes less reliable using optical sensors due to signal saturation problems. Recently, it has been shown that radar backscatter-based methods allow for a much longer monitoring of forest regrowth (i.e., signal saturation occurs when forests reach 45-50 years) when compared to optical datasets (i.e., signal saturation occurs when forest reach 15-20 years) in semi-arid or boreal environments . Furthermore, monitoring degradation using optical datasets is problematic since degraded areas are characterized by changes in forest structure rather than land cover type, i.e., forest canopy cover may not change significantly . It is likely that initial degradation stages might be difficult to pick up using optical or radar backscatter-based remote sensing methods since the remaining trees may still provide sufficient canopy cover and respectively scattering elements to saturate the signal . Quantifying the magnitude of positive forest change due to sustainable management, conservation or carbon stock enhancements would pose similar challenges when using optical or radar backscatter-based techniques.
Relationships of S-Band Radar Backscatter and Forest Aboveground Biomass in Different Forest Types
Remote Sensing, 2017
Synthetic Aperture Radar (SAR) signals respond to the interactions of microwaves with vegetation canopy scatterers that collectively characterise forest structure. The sensitivity of S-band (7.5-15 cm) backscatter to the different forest types (broadleaved, needleleaved) with varying aboveground biomass (AGB) across temperate (mixed, needleleaved) and tropical (broadleaved, woody savanna, secondary) forests is less well understood. In this study, Michigan Microwave Canopy Scattering (MIMICS-I) radiative transfer model simulations showed strong volume scattering returns from S-band SAR for broadleaved canopies caused by ground/trunk interactions. A general relationship between AirSAR S-band measurements and MIMICS-I simulated radar backscatter with forest AGB up to nearly 100 t/ha in broadleaved forest in the UK was found. Simulated S-band backscatter-biomass relationships suggest increasing backscatter sensitivity to forest biomass with a saturation level close to 100 t/ha and errors between 37 t/ha and 44 t/ha for HV and VV polarisations for tropical ecosystems. In the near future, satellite SAR-derived forest biomass from P-band BIOMASS mission and L-band ALOS-2 PALSAR-2 in combination with S-band UK NovaSAR-S and the joint NASA-ISRO NISAR sensors will provide better quantification of large-scale forest AGB at varying sensitivity levels across primary and secondary forests and woody savannas.
The Prospect of Radar Remote Sensing in Assessment of Forest above Ground Biomass
International Journal of Agricultural Science and Research
The assessment of forest Above Ground Biomass (AGB) is a major requirement in the current scenario where world faces a serious threat of climate change. The assessments of AGB or carbon stock are carried out in various ways. The allometric equations were developed which were used for the estimation of plot level stand volume. The Land Use Land Cover (LULC) observations and signal measurements by satellite have now-a-days become an important tool in estimation of AGB in forests. The remote sensing techniques were widely used in the assessment of biomass across globe, by establishing relationship between field level biomass and spectral responses/vegetation indices derived from multispectral images. However, frequent cloud coverage hinders the acquisition of all weather data in the optical sensors. The Radar Remote Sensing has an advantage of its own illumination (signal) hand hence independent of sun illumination. The active nature of radar remote sensing makes it applicable to work during day and night, penetrate clouds, canopy, snow, rain, etc. The Synthetic Aperture Radar (SAR) sensors are transmitting microwave energy from 3cm (X-band) to 100cm (P-band). The space borne SAR seems to be very useful for measuring biomass and forest carbon tracking over a large area. The longer wavelength and shorter featuring SAR data is capable to penetrate deep inside the forest and its weak backscatter coefficient from rough surface makes it crucial for the estimation of forest AGB. This review tells about the capabilities of radar remote sensing in the assessment of biomass along with the most adopted methodologies. It deals primarily the application of polarimetric SAR in forest above ground biomass mapping.
Forest biomass from combined ecosystem and radar backscatter modeling
Remote Sensing of Environment, 1997
Above-ground woody biomass is an important parameter for describing the function and productivity of forested ecosystems. Recent studies have demonstrated that synthetic aperture radar (SAR) can be used to estimate above-ground standing biomass. T o date, these studies have relied on extensive ground-truth measurements to construct
Forest Biomass Estimation at High Spatial Resolution: Radar vs. Lidar sensors
This study evaluates the biomass retrieval error in pine-dominated stands when using high spatial resolution airborne measurements from fully polarimetric L-band radar and airborne laser scanning sensors. Information on total above-ground biomass was estimated through allometric relationships from plot-level field measurements. Multiple linear regression models were developed to model relationships between biomass and radar/lidar data. Overall, lidar data provided lower estimation errors (17.2 t ha-1, 30% relative) when compared to radar data (30.3 t ha-1, 61% relative). However, for the 30-100 t ha-1 biomass range, the relative error from radar-based models was only 9% higher than that from lidar-based models. This suggests that high spatial resolution radar data could provide fundamentally similar results to lidar for some biomass intervals. This is an important finding for large scale biomass estimation that needs to rely upon satellite data, as there are no lidar satellites planned for the foreseeable future.