A Review of Remote Sensing of Forest Biomass and Biofuel: Options for Small-Area Applications

Gleason, C. J.; Im, Jungho

doi:10.2747/1548-1603.48.2.141

Cited by 103 publications

(55 citation statements)

References 119 publications

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“…As no remote sensing instrument can directly measure AGB, field inventory measurements are mandatory for both calibrating and validating spatial estimations of AGB [18]. The use of LiDAR (light detection and ranging) measurements has rapidly evolved in the last years due to the ability to precisely quantify the vertical structure of the vegetation and forest attributes such as canopy height distribution, tree height, and crown diameter [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Dynamics in Tropical Peat Swamp Forest Biomass with Multi-Temporal LiDAR Datasets

2013

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Tropical peat swamp forests in Indonesia store huge amounts of carbon and are responsible for enormous carbon emissions every year due to forest degradation and deforestation. These forest areas are in the focus of REDD+ (reducing emissions from deforestation, forest degradation, and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks) projects, which require an accurate monitoring of their carbon stocks or aboveground biomass (AGB). Our study objective was to evaluate multi-temporal LiDAR measurements of a tropical forested peatland area in Central Kalimantan on Borneo. Canopy height and AGB dynamics were quantified with a special focus on unaffected, selective logged and burned forests. More than 11,000 ha were surveyed with airborne LiDAR in 2007 and 2011. In a first step, the comparability of these datasets was examined and canopy height models were created. Novel AGB regression models were developed on the basis of field inventory measurements and LiDAR derived height

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Section: Introductionmentioning

confidence: 99%

Quantifying Dynamics in Tropical Peat Swamp Forest Biomass with Multi-Temporal LiDAR Datasets

2013

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“…Globally, carbon stocks are decreasing each year by about 0.3 Gt due to deforestation caused, e.g., by illegal logging in many tropical countries. Curbing deforestation and evaluating how forests affect the carbon cycle and climate change [27] requires efficient, continuous and worldwide monitoring of forest biomass. Remote sensing imagery can provide continuous coverage with high temporal and spatial resolutions , especially since the opening of the Landsat archive [28] and the first Sentinel satellites of the Copernicus programme.…”

Section: Introductionmentioning

confidence: 99%

Relasphone—Mobile and Participative In Situ Forest Biomass Measurements Supporting Satellite Image Mapping

et al. 2016

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Due to the high cost of traditional forest plot measurements, the availability of up-to-date in situ forest inventory data has been a bottleneck for remote sensing image analysis in support of the important global forest biomass mapping. Capitalizing on the proliferation of smartphones, citizen science is a promising approach to increase spatial and temporal coverages of in situ forest observations in a cost-effective way. Digital cameras can be used as a relascope device to measure basal area, a forest density variable that is closely related to biomass. In this paper, we present the Relasphone mobile application with extensive accuracy assessment in two mixed forest sites from different biomes. Basal area measurements in Finland (boreal zone) were in good agreement with reference forest inventory plot data on pine (R 2 = 0.75, RMSE = 5.33 m 2 /ha), spruce (R 2 = 0.75, RMSE = 6.73 m 2 /ha) and birch (R 2 = 0.71, RMSE = 4.98 m 2 /ha), with total relative RMSE(%) = 29.66%. In Durango, Mexico (temperate zone), Relasphone stem volume measurements were best for pine (R 2 = 0.88, RMSE = 32.46 m 3 /ha) and total stem volume (R 2 = 0.87, RMSE = 35.21 m 3 /ha). Relasphone data were then successfully utilized as the only reference data in combination with optical satellite images to produce biomass maps. The Relasphone concept has been validated for future use by citizens in other locations.

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“…The potential for regionalizing forest inventory data was demonstrated in a study from South Germany, presenting a relatively small estimation error for the mapping of timber volume using k-NN, but moderate accuracy for the classification of six main tree species [29]. A comprehensive summary of related studies with sensors and methods used for the estimation of aboveground biomass or timber volume is given in Lu [30] and Gleason [31].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Suitability of Future Multi- and Hyperspectral Satellite Systems for Mapping the Spatial Distribution of Norway Spruce Timber Volume

et al. 2015

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Abstract:The availability of accurate and timely information on timber volume is important for supporting operational forest management. One option is to combine statistical concepts (e.g., small area estimates) with specifically designed terrestrial sampling strategies to provide estimations also on the level of administrative units such as forest districts. This may suffice for economic assessments, but still fails to provide spatially explicit information on the distribution of timber volume within these management units. This type of information, however, is needed for decision-makers to design and implement appropriate management operations. The German federal state of Rhineland-Palatinate is currently implementing an object-oriented database that will also allow the direct integration of Earth observation data products. This work analyzes the suitability of forthcoming multi-and hyperspectral satellite imaging systems for producing local distribution maps for timber volume of Norway spruce, one of the most economically important tree species. In combination with site-specific inventory data, fully processed hyperspectral data sets (HyMap) were used to simulate datasets of the forthcoming EnMAP and Sentinel-2 systems to establish adequate models for estimating timber volume OPEN ACCESSRemote Sens. 2015, 1 12010 maps. The analysis included PLS regression and the k-NN method. Root Mean Square Errors between 21.6% and 26.5% were obtained, where k-NN performed slightly better than PLSR. It was concluded that the datasets of both simulated sensor systems fulfill accuracy requirements to support local forest management operations and could be used in synergy. Sentinel-2 can provide meaningful volume distribution maps in higher geometric resolution, while EnMAP, due to its hyperspectral coverage, can contribute complementary information, e.g., on biophysical conditions.

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A Review of Remote Sensing of Forest Biomass and Biofuel: Options for Small-Area Applications

Cited by 103 publications

References 119 publications

Quantifying Dynamics in Tropical Peat Swamp Forest Biomass with Multi-Temporal LiDAR Datasets

Quantifying Dynamics in Tropical Peat Swamp Forest Biomass with Multi-Temporal LiDAR Datasets

Relasphone—Mobile and Participative In Situ Forest Biomass Measurements Supporting Satellite Image Mapping

Assessing the Suitability of Future Multi- and Hyperspectral Satellite Systems for Mapping the Spatial Distribution of Norway Spruce Timber Volume

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