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Dry forests and savannas have highly heterogeneous horizontal and vertical structure of woody vegetation and high temporal variability in moisture and phenology. This presents distinct challenges to SAR-based approaches to mapping canopy-height compared to tropical and temperate forests. Dense time-series of C-band (Sentinel-1A/1B) and, in future, L-band (NASA/ISRO NISAR) provide a pathway to reduce the impact of signal-noise and environmental conditions and to extract additional information more directly related to height, using repeat-pass Interferometric SAR (InSAR) techniques.  This work explores the potential information in Sentinel-1(S1) C-band InSAR temporal decorrelation and Global Ecosystem Dynamics Investigation (GEDI)-derived relative height metrics for canopy height estimation. The 12-day InSAR correlation (𝛾) and seasonal median were analyzed over a wide range of canopy heights and woody ecosystems, with a focus on dry forests and savannas in Australia, India, and South Africa as part of a NASA Carbon Monitoring System (CMS) investigation. To scale up the geographical extent, the algorithm was deployed on NASA’s Multi-Mission Algorithm and Analysis Platform (MAAP). To further enhance the computational efficiency, a tile-based approach is employed using the Military Grid Reference System. The canopy height maps were produced for  ~12.3 million km²  by processing nearly 1500 1⁰x1⁰ Sentinel-1 C-band Coherence tiles and approximately 130 million GEDI samples. In summary, this study provides new insights into the applicability and limitations of using InSAR data for canopy-height estimation in dry forest and savanna studies. Implementation of the proposed canopy-height estimation algorithm on the NASA-MAAP enables global scalability with current and future InSAR time series. The limitations with the C-band could be overcome partly, if not completely, with longer wavelengths like the L-band, such as those proposed for the upcoming NISAR, ALOS-3/PALSAR-4, and ROSE-L missions.

Figure 1. Maps of estimated canopy height for three countries using Sentinel-1 InSAR coherence and GEDI RH98. 

Test site	No. of1ᵒ x 1ᵒtiles	No. of GEDI samples x106	100m		500m		1000m
			r	RMSE	r	RMSE	r	RMSE
Australia	910	68.3	0.75	3.74	0.85	2.56	0.89	2.05
India	408	52.1	0.66	5.18	0.83	3.49	0.88	2.71
South Africa	165	9.2	0.44	3.35	0.56	2.38	0.59	2.02

Narayanarao Bhogapurapu¹ Paul Siqueira¹, John Armston², Mikhail Urbazaev²,  Xiaoxuan Li³, Konrad Wessels³, Laura Duncanson²

¹ University of Massachusetts Amherst,² University of Maryland, ³ George Mason University

Related Publications: 

• N. Bhogapurapu, P. Siqueira, J. Armston, Xiaoxuan Li, M. Urbazaev, K. Wessels, and L. Duncanson 2023 “Largescale forest stand height estimation using C-band InSAR correlation,” Geoscience and Remote Sensing Symposium(IGARSS), IEEE International.
• N. Bhogapurapu, P. Siqueira, J. Armston, Xiaoxuan Li, M. Urbazaev, K. Wessels, and L. Duncanson 2023 “Large-scale Canopy Height Estimation using C-band InSAR Correlation,” PolInSAR 2023: 11th International Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry and BIOMASS Workshop
• N. Bhogapurapu, P. Siqueira, J. Armston, Xiaoxuan Li, K. Wessels, L. Duncanson 2022 “Temporal analysis of C-band InSAR decorrelation for canopy height mapping over dry forests and tropical savannas “, AGU Fall Meeting 2022.

Biomass Harmonization is a coordinated effort of scientists to release forest-carbon estimates obtained with space data in a comparable manner. This helps identify underlying assumptions, definitions and uncertainty-estimation frameworks (Ref 1). By collaborating with scientists and policy-makers in various countries, this effort also aims to align the carbon estimates with policy guidelines, which will ease their uptake for climate reporting purposes. 

An example of this effort is research that leverages data from NASA’s GEDI and ICESat-2 missions, and ESA’s Climate Change Initiative (CCI) to produce IPCC Tier 1 default biomass values for natural forests (Ref 2). The integration of the various space-derived datasets is conducted within an open-science framework (Ref 3) aimed at enhancing the flexibility and adaptability of the estimates by countries. 

The CEOS Biomass Harmonization activity is funded by the NASA Carbon Monitoring Systems (CMS) 2022 and hosted on the NASA MAAP, where open science and a public repository of source code (Ref 4) permits transparency in a collaborative environment between science teams and national policy experts.

References: 

Ref 1 – https://iopscience.iop.org/article/10.1088/1748-9326/ad0b60

Ref 2 – https://authorea.com/users/747800/articles/720021-intergovernmental-panel-on-climate-change-ipcc-tier-1-forest-biomass-estimates-from-earth-observation

Ref 3 – https://daac.ornl.gov/CMS/guides/CMS_Global_Forest_Age.html

Ref 4 – The public Github Repository: https://github.com/CEOSBiomassHarmonization/NASA_CMS/tree/main/NASA_CMS_2023

Mapping boreal forest biomass recovery rates across gradients of vegetation structure and environmental change.

The ABoVE boreal biomass mapping project has produced on MAAP a circa 2020 boreal-wide 30-m aboveground biomass map from ICESat-2 forest structure, Landsat, and other ancillary products (Ref 1). Collaborating across multiple institutions, aboveground biomass density models were created and fit to 19 million ICESat-2 observations (Ref 2), and boreal forest structure from spaceborne remote-sensing has been validated at multiple scales (Ref 3, Ref 4).

Now, the work is continuing: assessing changes in boreal biomass over time, starting with areas affected by wildfire (Ref 5), and using a suite of time-series data from Landsat and Sentinel-1 to expand to boreal-wide annual change and mapping of biomass recovery-rates after disturbance.

References:

• Ref 1, Duncanson et al. 2023: https://doi.org/10.3334/ORNLDAAC/2186
• Ref 2, Neuenschwander et al. 2024: https://doi.org/10.1016/j.srs.2024.100150
• Ref 3, Feng et al 2023: https://doi.org/10.1016/j.rse.2023.113570
• Ref 4, Neuenshwander et al. 2020: https://doi.org/10.1016/j.rse.2020.112110
• Ref 5, Feng et al 2024, DOI: 10.1109/JSTARS.2024.3400218