Using Remote Sensing to Estimate a Renewable Resource: Forest Residual Biomass

García-Martín, Alberto; Fernández, Juan Ramón de la Riva; Pérez‐Cabello, Fernando; Montorio, Raquel

doi:10.5772/17353

Cited by 5 publications

(4 citation statements)

References 55 publications

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“…[7][8][9] Moreover, extending this method to map forest biomass across a large area is extremely challenging when factors such as ecological differences, variations in inventory systems, and scattered sources of biomass data are considered. 15 Additionally, remote sensing is the best approach to estimate biomass at a regional level where field data are scarce or difficult to collect. 10 There have been efforts in developing generalized regional and national tree biomass equations that could be applied to a larger geographic footprint than most existing allometric equations.…”

Section: Introductionmentioning

confidence: 99%

Review of the use of remote sensing for biomass estimation to support renewable energy generation

Kumar¹,

Sinha²,

Taylor³

et al. 2015

J. Appl. Remote Sens

154

119

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The quantification, mapping and monitoring of biomass are now central issues due to the importance of biomass as a renewable energy source in many countries of the world. The estimation of biomass is a challenging task, especially in areas with complex stands and varying environmental conditions, and requires accurate and consistent measurement methods. To efficiently and effectively use biomass as a renewable energy source, it is important to have detailed knowledge of its distribution, abundance, and quality. Remote sensing offers the technology to enable rapid assessment of biomass over large areas relatively quickly and at a low cost. This paper provides a comprehensive review of biomass assessment techniques using remote sensing in different environments and using different sensing techniques. It covers forests, savannah, and grasslands/rangelands, and for each of these environments, reviews key work that has been undertaken and compares the techniques that have been the most successful. © 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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Section: Introductionmentioning

confidence: 99%

Review of the use of remote sensing for biomass estimation to support renewable energy generation

Kumar¹,

Sinha²,

Taylor³

et al. 2015

J. Appl. Remote Sens

154

119

View full text Add to dashboard Cite

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“…There is a limitation to using remotely sensed data in the estimation of forest residual biomass are relating to; only feasible in monoculture forest and field operation has to be conducted, inaccuracies related to heterogeneity. However, despite all these limitations, the size of the scenes and cheap distribution, make remote sensing the most suitable for estimation of forest residual biomass (FRB) [80]. The below-ground biomass is larger than above-ground biomass, but it is impossible to estimate their magnitude directly with the existing remote sensing system.…”

Section: Discussionmentioning

confidence: 99%

Exploring Renewable Energy Resources Using Remote Sensing and GIS—A Review

et al. 2019

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Renewable energy has received noteworthy attention during the last few decades. This is partly due to the fact that fossil fuels are depleting and the need for energy is soaring because of the growing population of the world. This paper attempts to provide an idea of what is being done by researchers in remote sensing and geographical information system (GIS) field for exploring the renewable energy resources in order to get to a more sustainable future. Several studies related to renewable energy resources viz. geothermal energy, wind energy, hydropower, biomass, and solar energy, have been considered in this paper. The focus of this review paper is on exploring how remote sensing and GIS-based techniques have been beneficial in exploring optimal locations for renewable energy resources. Several case studies from different parts of the world which use such techniques in exploring renewable energy resource sites of different kinds have also been included in this paper. Though each of the remote sensing and GIS techniques used for exploration of renewable energy resources seems to efficiently sell itself in being the most effective among others, it is important to keep in mind that in actuality, a combination of different techniques is more efficient for the task. Throughout the paper, many issues relating to the use of remote sensing and GIS for renewable energy are examined from both current and future perspectives and potential solutions are suggested. The authors believe that the conclusions and recommendations drawn from the case studies and the literature reviewed in the present study will be valuable to renewable energy scientists and policymakers.

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“…Mediterranean forests have received little attention compared to other forest types [52], partly because the complexity of the terrain, environmental conditions, and heterogeneity (in terms of species and structure) of these forests presents major difficulties for consistent estimation of AGB over large areas [53]. Most studies have focused on modeled relationships between optical data field measurements of AGB.…”

Section: Mediterranean Forests Woodlands and Scrubmentioning

confidence: 99%

“…For Cistus scrub in southeastern Portugal, [54] exploited seasonal differences in the Landsat NDVI to map AGB and 74 % of estimates were within 50 % of the reference measurements (R 2 =0.75). For Mediterranean pine forests in northeastern Spain, [53] generated maps of forest residual biomass (FRB; comprising branches, foliage, and unmerchantable stem tops) using a multiple linear regression between Landsat-5 TM data and forest inventory data (relative RMSE of 26.7 % or 4.8 Mg ha [56] estimated AGB fractions using small footprint discrete return LIDAR intensity data; species-specific models outperformed generic models (with R 2 values >0.7). RMSE values for AGB ranged between 9.7 and 18.5 Mg ha −1 for stands dominated by Holm oak (Quercus ilex) and black pine (Pinus nigra), respectively.…”

Section: Mediterranean Forests Woodlands and Scrubmentioning

confidence: 99%

Measurement of Forest Above-Ground Biomass Using Active and Passive Remote Sensing at Large (Subnational to Global) Scales

2015

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Within the global forest area, a diverse range of forest types exist with each supporting varying amounts of biomass and allocations to different plant components. At country to continental scales, remote sensing techniques have been progressively developed to quantify the above-ground biomass (AGB) of these forests, with these based on optical, radar, and/or light detection and ranging (LiDAR) (airborne and spaceborne) data. However, none have been found to be globally applicable at high (≤30 m) resolution, largely because of different forest structures (e.g., heights, covers, allocations of AGB) and varying environmental conditions (e.g., frozen, inundated). For this reason, techniques have varied between the major forest biomes. However, when combined, these estimates provide some insight into the distribution of AGB at country to global levels with associated levels of uncertainty. Comparisons of data and derived products have, in some cases, also contributed to our understanding of changes in carbon stocks across large areas. Further improvements in estimates are anticipated with the launch of new spaceborne LiDAR and SAR that have been specifically designed for better retrieval of forest structure and AGB.

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Using Remote Sensing to Estimate a Renewable Resource: Forest Residual Biomass

Cited by 5 publications

References 55 publications

Review of the use of remote sensing for biomass estimation to support renewable energy generation

Review of the use of remote sensing for biomass estimation to support renewable energy generation

Exploring Renewable Energy Resources Using Remote Sensing and GIS—A Review

Measurement of Forest Above-Ground Biomass Using Active and Passive Remote Sensing at Large (Subnational to Global) Scales

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