The effect of changing environmental conditions on microwave signatures of forest ecosystems: preliminary results of the March 1988 Alaskan aircraft SAR experiment

Way, J.; Paris, J. F.; Kasischke, Eric S.; Slaughter, Charles W.; Viereck, Leslie A.; Christensen, Norman L.; Dobson, M.C.; Ulaby, F. T.; Richards, John A.; Milne, A.K.; Sieber, A.J.; Ahern, Francis J.; Simonett, D. S.; Hoffer, R. M.; Imhoff, Marc L.; Weber, J.A.

doi:10.1080/01431169008955084

Cited by 109 publications

(36 citation statements)

References 9 publications

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“…It has also been demonstrated that AGB changes could be mapped and quantified based on changes in InSAR height, with results corresponding fairly well to changes measured by field inventory and airborne LiDAR, although a certain bias remained apparently due to errors in the input InSAR DEMs [37]. A second requirement is that the relationship between AGB and InSAR height needs to be the same at the points of time of the InSAR acquisitions, and although a certain temporal stability has been found in TanDEM-X height [38,39], one should avoid severe differences in weather conditions and effects of leaf-on versus leaf-off in deciduous forests, if possible [40,41].…”

Section: Introductionmentioning

confidence: 99%

Interferometric SAR DEMs for Forest Change in Uganda 2000–2012

et al. 2018

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Abstract:Monitoring changes in forest height, biomass and carbon stock is important for understanding the drivers of forest change, clarifying the geography and magnitude of the fluxes of the global carbon budget and for providing input data to REDD+. The objective of this study was to investigate the feasibility of covering these monitoring needs using InSAR DEM changes over time and associated estimates of forest biomass change and corresponding net CO 2 emissions. A wall-to-wall map of net forest change for Uganda with its tropical forests was derived from two Digital Elevation Model (DEM) datasets, namely the SRTM acquired in 2000 and TanDEM-X acquired around 2012 based on Interferometric SAR (InSAR) and based on the height of the phase center. Errors in the form of bias, as well as parallel lines and belts having a certain height shift in the SRTM DEM were removed, and the penetration difference between X-and C-band SAR into the forest canopy was corrected. On average, we estimated X-band InSAR height to decrease by 7 cm during the period 2000-2012, corresponding to an estimated annual CO 2 emission of 5 Mt for the entirety of Uganda. The uncertainty of this estimate given as a 95% confidence interval was 2.9-7.1 Mt. The presented method has a number of issues that require further research, including the particular SRTM biases and artifact errors; the penetration difference between the X-and C-band; the final height adjustment; and the validity of a linear conversion from InSAR height change to AGB change. However, the results corresponded well to other datasets on forest change and AGB stocks, concerning both their geographical variation and their aggregated values.

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Section: Introductionmentioning

confidence: 99%

Interferometric SAR DEMs for Forest Change in Uganda 2000–2012

et al. 2018

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“…In the case where a high quality DTM is available it has been demonstrated that forest height and biomass can be estimated either based on the height of the phase centre above the ground [9] or based on a more sophisticated approach utilizing the ground-corrected complex coherence [10]. Although the height of the phase centre in a tropical forest appears to be fairly stable between acquisitions [11], weather conditions, phenology [12][13][14], terrain steepness and ascending or descending mode acquisitions can cause differences in height [15]. One particular approach to overcome these limitations is to monitor height changes rather than heights, which would not require a DTM, and to combine several acquisitions.…”

Section: Introductionmentioning

confidence: 99%

Modelling above Ground Biomass in Tanzanian Miombo Woodlands Using TanDEM-X WorldDEM and Field Data

et al. 2017

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Abstract:The use of Interferometric Synthetic Aperture Radar (InSAR) data has great potential for monitoring large scale forest above ground biomass (AGB) in the tropics due to the increased ability to retrieve 3D information even under cloud cover. To date; results in tropical forests have been inconsistent and further knowledge on the accuracy of models linking AGB and InSAR height data is crucial for the development of large scale forest monitoring programs. This study provides an example of the use of TanDEM-X WorldDEM data to model AGB in Tanzanian woodlands. The primary objective was to assess the accuracy of a model linking AGB with InSAR height from WorldDEM after the subtraction of ground heights. The secondary objective was to assess the possibility of obtaining InSAR height for field plots when the terrain heights were derived from global navigation satellite systems (GNSS); i.e., as an alternative to using airborne laser scanning (ALS). The results revealed that the AGB model using InSAR height had a predictive accuracy of RMSE = 24.1 t·ha −1 ; or 38.8% of the mean AGB when terrain heights were derived from ALS. The results were similar when using terrain heights from GNSS. The accuracy of the predicted AGB was improved when compared to a previous study using TanDEM-X for a sub-area of the area of interest and was of similar magnitude to what was achieved in the same sub-area using ALS data. Overall; this study sheds new light on the opportunities that arise from the use of InSAR data for large scale AGB modelling in tropical woodlands.

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“…Basically, the different scatterers' type and structure within the forested and non-forested resolution cells may result in different decorrelation processes when seasonal or sudden variations in surface parameters and meteorological conditions occur. During a frozen season, a decreased dielectric constant leads to reduced attenuation and a deeper penetration of electromagnetic waves into the forest canopy [34][35][36]. Consequently, this will cause a decrease in backscatter and influence the polarimetric signature and InSAR coherence [34][35][36][37][38].…”

Section: The Effect Of Seasonality On Temporal Coherencementioning

confidence: 99%

“…During a frozen season, a decreased dielectric constant leads to reduced attenuation and a deeper penetration of electromagnetic waves into the forest canopy [34][35][36]. Consequently, this will cause a decrease in backscatter and influence the polarimetric signature and InSAR coherence [34][35][36][37][38]. In terms of coherence, between two winter images under stable frozen conditions, water content (soil moisture) changes do not occur, leading to low temporal decorrelation for open areas [34].…”

Section: The Effect Of Seasonality On Temporal Coherencementioning

confidence: 99%

L-Band Temporal Coherence Assessment and Modeling Using Amplitude and Snow Depth over Interior Alaska

et al. 2018

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Abstract:Interferometric synthetic aperture radar (InSAR) provides the capability to detect surface deformation. Numerous processing approaches have been developed to improve InSAR results and overcome its limitations. Regardless of the processing methodology, however, temporal decorrelation is a major obstacle for all InSAR applications, especially over vegetated areas and dynamic environments, such as Interior Alaska. Temporal coherence is usually modeled as a univariate exponential function of temporal baseline. It has been, however, documented that temporal variations in surface backscattering due to the change in surface parameters, i.e., dielectric constant, roughness, and the geometry of scatterers, can result in gradual, seasonal, or sudden decorrelations and loss of InSAR coherence. The coherence models introduced so far have largely neglected the effect of the temporal change in backscattering on InSAR coherence. Here, we introduce a new temporal decorrelation model that considers changes in surface backscattering by utilizing the relative change in SAR intensity between two images as a proxy for the change in surface scattering parameters. The model also takes into account the decorrelation due to the change in snow depth between two images. Using the L-band Advanced Land Observation Satellite (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) data, the model has been assessed over forested and shrub landscapes in Delta Junction, Interior Alaska. The model decreases the RMS error of temporal coherence estimation from 0.18 to 0.09 on average. The improvements made by the model have been statistically proved to be significant at the 99% confidence level. Additionally, the model shows that the coherence of forested areas are more prone to changes in backscattering than shrub landscape. The model is based on L-band data and may not be expanded to C-band or X-band InSAR observations.

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The effect of changing environmental conditions on microwave signatures of forest ecosystems: preliminary results of the March 1988 Alaskan aircraft SAR experiment

Cited by 109 publications

References 9 publications

Interferometric SAR DEMs for Forest Change in Uganda 2000–2012

Interferometric SAR DEMs for Forest Change in Uganda 2000–2012

Modelling above Ground Biomass in Tanzanian Miombo Woodlands Using TanDEM-X WorldDEM and Field Data

L-Band Temporal Coherence Assessment and Modeling Using Amplitude and Snow Depth over Interior Alaska

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