Combining Lidar and Synthetic Aperture Radar Data to Estimate Forest Biomass: Status and Prospects

Kaasalainen, Sanna; Holopainen, Markus; Karjalainen, Mika; Vastaranta, Mikko; Kankare, Ville; Karila, Kirsi; Osmanoǧlu, Batuhan

doi:10.3390/f6010252

Cited by 72 publications

(54 citation statements)

References 91 publications

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“…Furthermore, optical satellite data have limited value for forest biomass estimation, since the reflectance primarily originates from the upper layer of the forest canopy, and the vertical structure is not assessed. In this situation, using Synthetic Aperture Radar (SAR) satellite data might be the most practical option for large-scale forest mapping [7] by being a cost-effective and time-efficient way to obtain regular estimates of forest resources and to provide accurate input for carbon cycle models.…”

Section: Introductionmentioning

confidence: 99%

Interferometric SAR Coherence Models for Characterization of Hemiboreal Forests Using TanDEM-X Data

et al. 2016

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Abstract:In this study, four models describing the interferometric coherence of the forest vegetation layer are proposed and compared with the TanDEM-X data. Our focus is on developing tools for hemiboreal forest height estimation from single-pol interferometric SAR measurements, suitable for wide area forest mapping with limited a priori information. The multi-temporal set of 19 TanDEM-X interferometric pairs and the 90th percentile forest height maps are derived from Airborne LiDAR Scanning (ALS), covering an area of 2211 ha of forests over Estonia. Three semi-empirical models along with the Random Volume over Ground (RVoG) model are examined for applicable parameter ranges and model performance under various conditions for over 3000 forest stands. This study shows that all four models performed well in describing the relationship between forest height and interferometric coherence. Use of an advanced model with multiple parameters is not always justified when modeling the volume decorrelation in the boreal and hemiboreal forests. The proposed set of semi-empirical models, show higher robustness compared to a more advanced RVoG model under a range of seasonal and environmental conditions during data acquisition. We also examine the dynamic range of parameters that different models can take and propose optimal conditions for forest stand height inversion for operationally-feasible scenarios.

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Section: Introductionmentioning

confidence: 99%

Interferometric SAR Coherence Models for Characterization of Hemiboreal Forests Using TanDEM-X Data

et al. 2016

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“…The fusion of SAR and LIDAR has been attempted to improve SAR-based prediction, and it can be promising for large area efforts; a review showed that the main advantage is in the use of an accurate LIDAR digital elevation model for SAR-based AGB prediction or in the upscale of local LIDAR metrics to large areas with SAR data. 31 Combining SAR and optical data has brought accurate AGB predictions in a number of cases because the structural SAR information can be complemented with canopy density, forest type, and foliage-related information collected by optical instruments. [32][33][34][35][36] Even with the increase in new data, fusion techniques, and innovative researches, AGB prediction based on SAR remains a challenging task, as saturation of the signal is common in forests, 37 where the backscatter is influenced by several factors, such as the soil moisture, especially when the vegetation coverage is low; 38 the forest type, the leaf presence with needleleaf forests possibly having the highest saturation level; 39 the seasonal and weather conditions; 40 the canopy roughness; 41 and the SAR signal polarization as well as the incidence angle.…”

Section: Introductionmentioning

confidence: 99%

Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data

Laurin

Balling

Corona³

et al. 2018

J. Appl. Rem. Sens.

123

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Abstract. The objective of this research is to test Sentinel-1 SAR multitemporal data, supported by multispectral and SAR data at other wavelengths, for fine-scale mapping of above-ground biomass (AGB) at the provincial level in a Mediterranean forested landscape. The regression results indicate good accuracy of prediction (R 2 ¼ 0.7) using integrated sensors when an upper bound of 400 Mg ha −1 is used in modeling. Multitemporal SAR information was relevant, allowing the selection of optimal Sentinel-1 data, as broadleaf forests showed a different response in backscatter throughout the year. Similar accuracy in predictions was obtained when using SAR multifrequency data or joint SAR and optical data. Predictions based on SAR data were more conservative, and in line with those from an independent sample from the National Forest Inventory, than those based on joint data types. The potential of S1 data in predicting AGB can possibly be improved if models are developed per specific groups (deciduous or evergreen species) or forest types and using a larger range of ground data. Overall, this research shows the usefulness of Sentinel-1 data to map biomass at very high resolution for local study and at considerable carbon density. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…Radar provides another non-invasive technique to study individual tree and forest structures over wide areas. Radar pulses can either penetrate or reflect from foliage, depending on the selected wavelength (Kaasalainen et al, 2015). Most radar applications occur in forestry and are being operated from satellites or airplanes, although more compact and agile systems are being developed for precision forestry above- and below-ground (Feng et al, 2016).…”

Section: Emerging Questions and Barriers In The Mathematical Analysismentioning

confidence: 99%

Morphological plant modeling: Unleashing geometric and topological potential within the plant sciences

Bucksch

Atta-Boateng

Azihou

et al. 2016

Preprint

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The geometries and topologies of leaves, flowers, roots, shoots, and their arrangements have fascinated plant biologists and mathematicians alike. As such, plant morphology is inherently mathematical in that it describes plant form and architecture with geometrical and topological techniques. Gaining an understanding of how to modify plant morphology, through molecular biology and breeding, aided by a mathematical perspective, is critical to improving agriculture, and the monitoring of ecosystems is vital to modeling a future with fewer natural resources. In this white paper, we begin with an overview in quantifying the form of plants and mathematical models of patterning in plants. We then explore the fundamental challenges that remain unanswered concerning plant morphology, from the barriers preventing the prediction of phenotype from genotype to modeling the movement of leaves in air streams. We end with a discussion concerning the education of plant morphology synthesizing biological and mathematical approaches and ways to facilitate research advances through outreach, cross-disciplinary training, and open science. Unleashing the potential of geometric and topological approaches in the plant sciences promises to transform our understanding of both plants and mathematics.

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Combining Lidar and Synthetic Aperture Radar Data to Estimate Forest Biomass: Status and Prospects

Cited by 72 publications

References 91 publications

Interferometric SAR Coherence Models for Characterization of Hemiboreal Forests Using TanDEM-X Data

Interferometric SAR Coherence Models for Characterization of Hemiboreal Forests Using TanDEM-X Data

Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data

Morphological plant modeling: Unleashing geometric and topological potential within the plant sciences

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