Biomass and InSAR height relationship in a dense tropical forest

Solberg, Svein; Hansen, Endre Hofstad; Gobakken, Terje; Næssset, Erik; Zahabu, Eliakimu

doi:10.1016/j.rse.2017.02.010

Cited by 54 publications

(49 citation statements)

References 48 publications

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“…These factors, as well as information on the elevation of the ground, need to be considered when estimating biomass from the InSAR height. Several research papers demonstrated that simple linear relationships could predict biomass from estimates of InSAR height of single-pass datasets [35][36][37][38][39][40][41][42]. Nevertheless, it is unclear whether such linear models that were validated at a number of test sites in boreal and savannah forest apply in other forest ecosystems as well [42].…”

Section: Retrieval Of Biomass Using Insar Observationsmentioning

confidence: 99%

“…Several research papers demonstrated that simple linear relationships could predict biomass from estimates of InSAR height of single-pass datasets [35][36][37][38][39][40][41][42]. Nevertheless, it is unclear whether such linear models that were validated at a number of test sites in boreal and savannah forest apply in other forest ecosystems as well [42]. An advanced solution having more potential for generalization is given by physically-based models, such as Equation (3), which take into account the sensitivity of the InSAR elevation to baseline and by a combination of single-images estimates of biomass with a multi-temporal combination [40].…”

Section: Retrieval Of Biomass Using Insar Observationsmentioning

confidence: 99%

“…This appears to be a promising approach to be further evaluated. It is worth noting that the predictors and approaches that are proposed in [38,42] are favored by the high spatial resolution of the SAR data used in the experiments, pointing at the importance of scales in the context of biomass retrieval with interferometric data.…”

Section: Retrieval Of Biomass Using Insar Observationsmentioning

confidence: 99%

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Research Pathways of Forest Above-Ground Biomass Estimation Based on SAR Backscatter and Interferometric SAR Observations

Santoro

Cartus

2018

Remote Sensing

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Estimation of forest biomass with synthetic aperture radar (SAR) and interferometric SAR (InSAR) observables has been surveyed in 186 peer-reviewed papers to identify major research pathways in terms of data used and retrieval models. Research evaluated primarily (i) L-band observations of SAR backscatter; and, (ii) single-image or multi-polarized retrieval schemes. The use of multi-temporal or multi-frequency data improved the biomass estimates when compared to single-image retrieval. Low frequency SAR backscatter contributed the most to the biomass estimates. Single-pass InSAR height was reported to be a more reliable predictor of biomass, overcoming the loss of sensitivity of SAR backscatter and coherence in high biomass forest. A variety of empirical and semi-empirical regression models relating biomass to the SAR observables were proposed. Semi-empirical models were mostly used for large-scale mapping because of the simple formulation and the robustness of the model parameters estimates to forest structure and environmental conditions. Non-parametric models were appraised for their capability to ingest multiple observations and perform accurate retrievals having a large number of training samples available. Some studies argued that estimating compartment biomass (in stems, branches, foliage) with different types of SAR observations would lead to an improved estimate of total biomass. Although promising, scientific evidence for such an assumption is still weak. The increased availability of free and open SAR observations from currently orbiting and forthcoming spaceborne SAR missions will foster studies on forest biomass retrieval. Approaches attempting to maximize the information content on biomass of individual data streams shall be pursued.

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Section: Retrieval Of Biomass Using Insar Observationsmentioning

confidence: 99%

Section: Retrieval Of Biomass Using Insar Observationsmentioning

confidence: 99%

Section: Retrieval Of Biomass Using Insar Observationsmentioning

confidence: 99%

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Research Pathways of Forest Above-Ground Biomass Estimation Based on SAR Backscatter and Interferometric SAR Observations

Santoro

Cartus

2018

Remote Sensing

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“…Neeff et al [20] obtained a similar relationship for a virgin rainforest in Brazil. Gama et al [21] obtained a curvilinear relationship, while for African tropical forests Solberg et al [15,17] obtained noisier results having RMSE values of 40 t·ha −1 (78% of the mean AGB) and 203 t·ha −1 (44% of the mean AGB) in Tanzanian miombo woodlands and dense tropical forests, respectively. The latter cases suffered from small and few field inventory plots, not taking into account the large size of single trees in those forests, thus the applicability of models like Equation (1) remains uncertain.…”

Section: Introductionmentioning

confidence: 98%

“…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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Modelling Green Volume Using Sentinel-1, -2, PALSAR-2 Satellite Data and Machine Learning for Urban and Semi-Urban Areas in Germany

Lehmler

Förster

Frick

2023

Environmental Management

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Urban Green Infrastructure (UGI) provides ecosystem services such as cooling of temperatures and is majorly important for climate change adaptation. Green Volume (GV) describes the 3-D space occupied by vegetation and is highly useful for the assessment of UGI. This research uses Sentinel-2 (S-2) optical data; vegetation indices (VIs); Sentinel-1 (S-1) and PALSAR-2 (P-2) radar data to build machine learning models for yearly GV estimation on large scales. Our study compares random and strati ed sampling of reference data, assesses the performance of different machine learning algorithms and tests model transferability by independent validation. The results indicate that strati ed sampling of training data leads to improved accuracies when compared to random sampling. While the Gradient Tree Boost (GTB) and Random Forest (RF) algorithms show generally similar performance, Support Vector Machine (SVM) exhibits considerably greater model error. The results suggest RF to be the most robust classi er overall, achieving highest accuracies for independent and inter-annual validation. Furthermore, modelling GV based on S-2 features considerably outperforms using only S-1 or P-2 based features. Moreover, the study nds that underestimation of large GV magnitudes in urban forests constitutes the biggest source of model error. Overall, modelled GV explains around 79% of the variability in reference GV at 10m resolution and over 90% when aggregated to 100m resolution. The research shows that accurately modelling GV is possible using openly available satellite data. Resulting GV predictions can be useful for environmental management by providing valuable information for climate change adaptation, environmental monitoring and change detection. 1 Introduction Urbanization and climate change are considered global megatrends that will continue to affect life on this planet (Retief et al. 2016). The United Nations suggest that already today, 55% of the world's population live in urban areas and that this number is estimated to rise to 68% by 2050 (United Nations, Department of Economic and Social Affairs, Population Division 2018). Human induced climate change leads to continuously rising average temperatures and poses risks through increased climate andweather extremes including oods, heatwaves and droughts (IPCC 2021). Both phenomena further increase pressures on the natural environment, including biodiversity and ecosystem resilience. Thus, in urban and environmental planning, these megatrends and their interconnected effects need to be considered (Retief et al. 2016;Gill et al. 2007;Mathey et al. 2011).Looking at climate change adaptation in urban contexts, green spaces function as urban green infrastructure (UGI) that provide a variety of ecosystem services (Gill et al. 2007;Mathey et al. 2011; Frick et al. 2020;Palliwoda et al. 2020;Matzarakis 2001). Studies show that greater abundance of UGI, including increased green volume and number of green roofs, has strong positive effects on reducing peak summer temperatures in citi...

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Biomass and InSAR height relationship in a dense tropical forest

Cited by 54 publications

References 48 publications

Research Pathways of Forest Above-Ground Biomass Estimation Based on SAR Backscatter and Interferometric SAR Observations

Research Pathways of Forest Above-Ground Biomass Estimation Based on SAR Backscatter and Interferometric SAR Observations

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

Modelling Green Volume Using Sentinel-1, -2, PALSAR-2 Satellite Data and Machine Learning for Urban and Semi-Urban Areas in Germany

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